Long-Range OASDI Projection

Methodology

Intermediate Assumptions of the 2014 Trustees 

Report

June 2014

Office of the Chief Actuary

Social Security Administration
A. Flow Charts
Overview of Long-Range OASDI Projection Methodology

Process 2:

Economics

Process 1:

Demography

Process 4:

Trust Fund Operations and Actuarial Status

Process 3:

Beneficiaries

Chart 1:

Social Security Administration

Office of the Chief Actuary

August 2014
Trustees ultimate assumptions

Fertility

Mortality

Legal Immigration

Other Immigration

1.2 Mortality

Inputs: Historical number of U.S. deaths by cause and 

U.S. resident population; medicare deaths and enrollments

Outputs: Historical and projected death probabilities 

1.4 Historical Population

Inputs: Historical U.S. population, undercounts, 

marital status data, other immigrant population 

data, and estimates of population in other 

components of Social Security area. 

Outputs: Historical Social Security area 

population by age, sex, and marital status 

(including starting population)

Historical other immigrant population by age 

and sex 

1.6 Marriage

Inputs: Historical 

number of marriages, 

remarriage data, 

and consistent 

population (detailed data 

for a subset of the U.S. 

population) 

Outputs: Historical and 

projected central 

marriage rates 

1.7 Divorce

Inputs: Historical number

of divorces and consistent 

population (detailed 

data for a subset of the U.S. 

population) 

Outputs: Historical and 

projected central divorce 

rates 

1.8 Projected Population

Inputs: Historical U.S. family data 

Outputs: Projected data – Social Security area population 

by age, sex, and marital status; other immigrant population

by age, sex and marital status; children by age of parent and 

average family size; projected deferred action for 

childhood arrivals (DACA) population by age and sex

Economics, Beneficiaries, and Trust Fund Operation and Actuarial Status 

Note: 1.2, 1.3, 1.4, and 1.5 also provide outputs used by these processes

1.1 Fertility 

Inputs: Historical U.S. births and female 

 resident population 

Outputs: Historical and projected central birth rates

1.3 Legal Immigration

Inputs: Historical legal U.S. immigration 

levels 

Outputs: Historical and projected annual

legal immigration and emigration levels 

Demography – Process 1

Social Security Administration

Office of the Chief Actuary

August 2014

Chart 2:

1.5 Other Immigration

Inputs: Historical levels of the

foreign-born population

Outputs: Projected annual other 

immigration and emigration levels 
Trustees Ultimate Economic Assumptions

Average real wage

Productivity

Average hours worked

Inflation

Full-employment unemployment rate

 2.2 Class of Worker

Inputs: Historical BLS data, 

population (Demography), & 

economic factors

Outputs: Wage workers & selfemployed by age and sex 

2.3 AWI Series

Inputs: Historical AWI 

2.1 Real & Potential GDP, Nominal GDP

Inputs: Historical NIPA (BEA) data

Outputs: Real, potential, and nominal GDP 

 2.3 Covered Employment

Inputs: Historical U.S. & covered employment

data, population including other immigrant 

population and DACA immigrants (Demography) 

Outputs: Covered worker rates, covered 

worker levels, & total employed (all by age, 

sex, type of worker) 

2.2 U. S. Wages & Income

Inputs: Historical NIPA (BEA) data & employment

(BLS) by sector 

Outputs: Wages & SE income by sector 

2.3 Average Covered Earnings

Outputs: Projected average 

covered earnings by type of worker 

2.4 Taxable Earnings

Inputs: Historical distribution of earnings 

Outputs: Taxable wages, taxable SE income &

 multi-employer refunds 

2.4 Average Taxable Earnings

Outputs: Average taxable earnings 

2.4 Effective taxable payroll

Outputs: Effective taxable payroll 

Chart 3: Economics – Process 2

Social Security Administration

Office of the Chief Actuary

August 2014

 2.1 U. S. Employment

Inputs: Historical BLS data, population 

(Demography), disability prevalence 

rates (from prior TR), life expectancies (from 

Demography), & economic factors 

Outputs: Historical & projected labor force 

participation & unemployment rates by age and sex

2.3 Covered Earnings

Inputs: Historical NIPA & covered earnings

data by sector 

Outputs: Historical & projected covered 

earnings by type of worker 

Beneficiaries, 

Trust Fund Operations and Actuarial Status

2.3 Taxable Maximum

Inputs: Historical taxable maximum 
 3.1 Fully-insured (FI) population

Inputs: Covered workers, median earnings,

and AWI (Economics); SS area population 

(Demography); DACA population (Demography) 

earnings distribution, historical FI population 

(historical and short-range projections).

Outputs: FI population (historical and projected)

3.1 Disability-insured (DII) population

Inputs: Historical DII population 

Outputs: DII population (historical and projected)

3.2 Disabled-worker beneficiary (DIB) population

Inputs: Base period incidence, recovery, and 

mortality rates. 

Projected Data -- Assumed ultimate changes in 

incidence rates, recovery rates, and mortality 

rates (Demography) from base period 

Outputs: DIB population and conversions to OAB 

(projected) 

3.3 Old-Age beneficiary (OAB) population

Inputs: Historical OAB population, labor force 

participation rates (Economics), and scheduled 

reductions for early retirement and increases for

delayed retirement 

Outputs: OAB population (projected) 

3.3 Auxiliary Beneficiaries of Retired and Deceased Workers

Inputs: Historical auxiliary beneficiaries of retired and deceased

workers, SS area population by marital status (Demography), 

number of children with at least one parent aged 62+ 

(Demography), number of children with at least one deceased 

Parent (Demography), average number of children per family 

(Demography) and other relationships 

Outputs: Auxiliary beneficiaries of retired and deceased workers 

by type of benefit (projected) 

Trust Fund Operations and Actuarial Status

 3.3 Widow beneficiary population

Inputs: Historical widow beneficiary population,

SS area population by marital status (Demography)

and other relationships

Outputs: Insured and uninsured widow beneficiary

population (projected)

Chart 4: Beneficiaries – Process 3

Social Security Administration

Office of the Chief Actuary

August 2014

 3.2 DIB Auxiliary Beneficiaries

Inputs: Historical beneficiaries by type of 

benefit, Population (Demography), other 

relationships 

Outputs: Auxiliary beneficiaries of disabled 

workers by type of benefit (projected) 

Note: Insured widow refers to widow beneficiaries who are insured for OAIB benefits, but not receiving those benefits

Trustees ultimate assumptions

 Disability incidence rates 

 Disability recovery rates 

Disability mortality rates 
Chart 5: Trust Fund Operations and Actuarial Status – Process 4

 4.3 Payroll taxes

Inputs: Payroll tax rate, effective taxable 

payroll (Economics), incurred-to-cash lag factor,

short-range estimates 

Outputs: Payroll taxes 

4.1 Fraction benefits taxable

Inputs: Historical and projected short-range data on 

income taxation of benefits from Office of Tax Analysis

in the Department of the Treasury and data from 

current population survey 

Outputs: TOB as of a percent of benefits (projected) 

 4. 3 Taxation of benefits (TOB)

Outputs: Taxes on benefits 

 4.3 Administrative expenses

Inputs: Short-range administrative expenses; 

total beneficiary population and AWI, 

assumed increase in productivity (Economics),

Outputs: Administrative expenses 

 4.3 Benefit payments

Inputs: Starting average benefits, OAB population, DIB population,

married and divorced aged population (Demography), 

Cost-of-Living Adjustments (Economics), post-entitlement factors, 

assumed benefit relationships between workers and auxiliaries, shortrange estimates of benefit payments 

Outputs: Scheduled benefit payments during year, average scheduled

benefits 

 4. 3 Railroad interchange

Inputs: Data from Railroad Retirement Board and 

AWI, covered workers, taxable payroll (Economics), 

short range estimates of railroad interchange 

Outputs: Net payments to RRB 

 4. 3 Interest income

Inputs: Short-range estimates

of interest income

Outputs: Interest income, annual 

yield rate on the OASI, DI, and 

combined funds

Trust Fund Operations and Actuarial Status

Output: Summarized income and cost rates and actuarial balances; 

open Group unfunded obligations; annual income rate, cost rate and 

Social Security Administration Balance: Dollar trust fund operations and trust fund ratios 

Office of the Chief Actuary

August 2014

Trustees ultimate assumptions

Real interest rate, CPI

4.2 AIME Levels for newly-entitled OABs and DIBs

Inputs: Sample of newly-entitled OABs and DIBs; sample

of earnings from CWHS; SS area population (Demography);

covered workers, average taxable earnings (Economics); 

and National Average Wage Index (AWI), Tax Maximum; 

historical and projected fully insured rates by sex and age, 

historical and projected (Beneficiaries)

Outputs: Projected AIME distributions for new 

entitlements 
8

B. Process Descriptions

The long-range programs used to make projections for the annual Trustees Report are grouped into 

four major processes. These include Demography, Economics, Beneficiaries, and Trust Fund 

Operations and Actuarial Status. Each major process consists of a number of subprocesses. Each 

subprocess is described in terms of three elements: 

 This overview attempts to provide a general description of the purpose of each subprocess. 

Key projected variables used in the subprocess are introduced. Some variables are 

represented as being dependent in an equation, where the dependent variable is defined in 

terms of one or more independent variables. Independent variables may include previously 

calculated dependent variables or data provided from outside the subprocess. Other key 

variables are referenced by “(·)” following the variable name. This indicates that the 

calculation of this variable can not easily be communicated by an equation and, thus, requires 

a more complex discussion. 

 Input Data – Data used in the subprocess are described. These data include those from other 

subprocesses, ultimate long-range assumptions provided by the Board of Trustees of the 

OASDI Trust Funds, data from other offices of the Social Security Administration, and data 

from outside the Social Security Administration (e.g., estimates of the U.S. population). Data 

description includes data source and data detail (e.g., define age detail of data). In addition, 

an indication of how often additional data are expected to be received is included. 

 Development of Output – The key variables are described in greater detail, including the 

level of disaggregation of the data. 
9

Process 1:

Demography
10

1. Demography

The primary purpose of the Demography Process is to provide estimates of the projected Social 

Security area population1for each year of the 75-year projection period in the Trustees Report. 

For the 2014 report, the projection period covers the years 2014 through 2088. The 

Demography Process receives input data mainly from other government agencies, and provides 

output data to the Economics, Beneficiaries, and Trust Fund Operations and Actuarial Status 

processes.

The Demography Process is composed of eight subprocesses: FERTILITY, MORTALITY, LEGAL 

IMMIGRATION, HISTORICAL POPULATION, OTHER IMMIGRATION, MARRIAGE, DIVORCE, and 

PROJECTED POPULATION. The following chart displays the key outputs of each subprocess:

Subprocess Key Outputs

FERTILITY  Birth rates, by age of mother

MORTALITY  Probabilities of death, by age and sex

LEGAL 

IMMIGRATION

 Legal immigrants, by age and sex

 Legal emigrants, by age and sex

 Adjustments of status from other than legal (OTL) status to legal status, 

by age and sex

HISTORICAL 

POPULATION

 Historical estimates of the Social Security area total population, by single 

year of age, sex, and marital status

 Historical estimates of the OTL population, by single year of age and sex

OTHER 

IMMIGRATION

 OTL immigrants, by age and sex

 OTL emigrants, by age and sex

 Projected OTL populations, by single year of age, sex, and OTL type 

(never-authorizeds, nonimmigrants, visa-overstayers)

 Historical estimates of the OTL population, by single year of age, sex, and 

OTL type 

MARRIAGE  Marriage rates, by age-of-husband crossed with age-of-wife

DIVORCE  Divorce rates, by age-of-husband crossed with age-of-wife

PROJECTED 

POPULATION

 Projected total populations, by age, sex, and marital status

 

1 The Social Security area population consists of all persons who are potentially eligible to receive benefits under the 

Social Security program or who have the potential to work in covered employment. This population consists of 

residents of the U.S. and its territories, citizens living abroad, and beneficiaries living abroad.
11

1.1. FERTILITY

1.1.a. Overview

The National Center for Health Statistics (NCHS) collects data on annual numbers of births by 

single year of age of mother and the U.S. Census Bureau produces estimates of the resident 

population by single year of age for females. Age-specific birth rates (

z

x b

) for a given year z are 

defined as the ratio of (1) births (

z Bx

) during the year to mothers at the specified age x to (2)

the midyear female population (

z Px

) at that age. The total fertility rate

z

TFR

summarizes the 

age-specific fertility rates for a given year z. The total fertility rate for a given year z equals the 

sum of the age-specific birth rates for all ages x during the year. One can also interpret the 

total fertility rate as the number of children born to a woman if she were to survive her 

childbearing years and experience the age-specific fertility rates of year z throughout her 

childbearing years. 

The FERTILITY subprocess combines the historical values of 

z

x b

and 

z

TFR

and assumed future 

values of the TFR to develop projections of 

z

x b . The primary equations of this subprocess are 

given below:

z

x b

= 

z

x b

(

.

) (1.1.1)

 

x

z

x

z

TFR b

(1.1.2) 

1.1.b. Input Data

Trustees Assumptions -

Each year the Board of Trustees of the OASDI Trust Funds sets the ultimate assumed values 

for the TFR. The TFR reaches its ultimate value in the 25th year of the projection period. 

Under the intermediate assumptions underlying the 2014 Trustees Report, the ultimate TFR

is 2.0 and it is assumed to be reached in 2038.

Other input data - 
12

 From the NCHS, annual numbers of births by age of mother2(10-14, 15, 16, 17, ..., 48, 

49-54) for years 1980-2011. In general, the NCHS provides an annual update 

including one additional year of final birth data and the previous historical years are 

only updated if the NCHS makes a historical revision to their data. 

 From the NCHS, preliminary TFR for 2012. In general, NCHS provides a preliminary 

TFR only for the year after the last final data year. 

 From the U.S. Census Bureau, estimates of the July 1st female resident population by 

single year of age for ages 14-49 for 1980-2011. In general, each year, Census 

provides updated data for years after the most recent decennial census.

 From the NCHS, historical birth rates, by single year of age of mother (14-49) for the 

period 1917-1979. No updates of these data are needed.

1.1.c. Development of Output

Equation 1.1.1 - Age-specific birth rates

The FERTILITY subprocess produces the age-specific birth rates, by childbearing ages 14 through 49, for years 1941 

through the end of the 75-year projection period. For historical years prior to 1980, age-specific birth rates come 

from the NCHS. For years 1980 through the remaining historical period, age-specific birth rates are calculated 

as:

z

x

z

z x

x

P

B

b  , using birth data from the NCHS and estimates of the July 1st female resident population from the 

U.S. Census Bureau. The age-specific birth rates are projected using a process that is consistent with both the 

observed trends in recent data and the ultimate assumed total fertility rate. This process consists of the following 

steps:

1. Averaged birth rates by age group,3designated as 

z

x b5 , are calculated from the age-specific birth rates 

z

x b

for each year during the period 1980-2011.

2. To calculate the starting values of the projection process, the 

z

x b5

values from the last five years of 

historical data are averaged using weights of 5, 4, 3, 2, and 1 for years 2011, 2010, 2009, 2008, and 2007, 

respectively.

3. For each 

z

x b5

age group series, the slope of the least squares line is calculated based on a regression over 

the period 1986-2011.

4. For 2012, each of the seven starting values of 

z

x b5

(from Step 2) is projected forward by adding 100 

percent of their respective slope (from Step 3).

 

2 The ages provided include 10-14, 15, 16, 17, ..., 48, 49-54. Births at ages less than 14 are treated as having 

occurred at age 14 and ages reported to mothers older than 49 are treated as having occurred at age 49.

3 The average is calculated by giving each age in the group equal weight without regard to population. The age 

groups calculated are: 14-19, 20-24, 25-29, 30-34, 35-39, 40-44, and 45-49. 
13

5. Then, a preliminary total fertility rate for 2012, 

2012 TFRp

, is calculated such that, in general, it is equal to 

5 times the sum of each 

2012

5 bx

. For the age group 14-19, 6 times the sum is used since this age group 

actually contains one additional age.

6. For 2012, the preliminary TFR released by NCHS was 1.8805 and thus the Trustees set 

2012 TFR

equal to 

1.8805. 

7. To ensure the assumed total fertility rate is achieved for 2012, each value of 

2012

5 bx

(from Step 4) is now 

multiplied by the ratio of the assumed

2012 TFR

(from Step 6) and the respective value of 

2012 TFRp

(from 

Step 5).

8. For 2013, each final 

z

x b5

for 2012 is projected forward by adding 96 percent of the respective slope (from 

Step 3). For subsequent projection years (2014-2038), an arithmetically decreasing portion of the slopes4

is added to the previous year’s final values of

z

x b5

to get preliminary values of 

z

x b5

. 

9. For years 2013 and later, a preliminary total fertility rate, 

z

TFRp

, is calculated from the preliminary 

values of 

z

x b5

in Step 8 and is calculated in the same manner as in Step 5.

10. Then, for each year, an adjustment is made so that the annual 

z

TFR

is consistent with the Trustees’ 

assumed TFRs. As mentioned in Step 6, 

2012 TFR

is assumed to be 1.8805. From 2013 – 2021, 

z

TFR

is 

assumed to increase consistent with the economic recovery where greater increases in the TFR occur in 

the middle of the period. For years after 2021, 

z

TFR

is assumed to decrease linearly from 

2021 TFR

until reaching the ultimate value in 2038.

11. To ensure the assumed total fertility rate is achieved, each value of 

z

x b5

(Step 8) is multiplied by the ratio 

of the assumed

z

TFR

(Step 10) and the respective value of 

z

TFRp

(Step 9).

12. The final step of the projection method disaggregates the adjusted

z

x b5

 into single age birth rates by 

multiplying the final 

z

x b5

values for each year (Steps 7 or 11) by the ratio of the single year 

z

x b

to the 

z

x b5

for each of the respective ages and age groups as calculated in the last year of complete historical 

data (Step 1). Then, minor adjustments correct rounding issues. 

 

4

For each year of the projection, the slope is reduced by four percentage points.
14

1.2 MORTALITY

1.2.a. Overview

The National Center for Health Statistics (NCHS) collects data on annual numbers of deaths and 

the U.S. Census Bureau produces estimates of the U.S. resident population. Central death rates 

(yMx) are defined as the ratio of (1) the number of deaths occurring during the year to persons 

between exact ages x and x+y to (2) the midyear population between exact ages x and x+y. For 

historical years prior to 1968, yMx are calculated from NCHS and Census data by sex. For 

historical years beginning in 1968, the same data are used in the calculations for ages under 65, 

but data from the Centers for Medicare and Medicaid Services (CMS) are used for ages 65 and 

over. Based on death by cause data from the NCHS, the yMx are distributed by cause of death 

for years 1979 and later.5

Over the last century, death rates have decreased substantially. The historical improvement in 

mortality is quantified by calculating the average annual percentage reduction (yAAx) in the 

central death rate. In order to project future yMx, the Board of Trustees of the OASDI Trust 

Funds determines the ultimate average annual percentage reduction that will be realized 

during the projection period (

u

y AAx

) for each sex and cause of death.

The basic mortality outputs of the MORTALITY subprocess that are used in projecting the 

population are probabilities of death by age and sex (qx). The probability that a person age x

will die within one year (qx) is calculated from the central death rates (the series of yMx). 

Period life expectancy ( ) is defined as the average number of years of life remaining for 

people who are age x and are assumed to experience the assumed probabilities of death 

throughout their lifetime. It is generated from the probabilities of death for a given year and is 

a summary statistic of overall mortality for that year. 

Age-adjusted death rates (ADR) are also used to summarize the mortality experience of a single 

year, making different years comparable to each other. Age-adjusted death rates are a 

weighted average of the yMx, where the weights used are the numbers of people in the 

corresponding age groups of the standard population, the 2010 U.S. Census resident population 

(

y SPx

). Thus, if the age-adjusted death rate for a particular year and sex is multiplied by the 

 

5 Data needed in order to project central death rates by cause of death were obtained from Vital Statistics 

tabulations for years since 1979. For the years 1979-1998, adjustments were made to the distribution of the numbers 

of deaths by cause. The adjustments were needed in order to reflect the revision in the cause of death coding that 

occurred in 1999, making the data for the years 1979-1998 more comparable with the coding used for the years 1999 

and later. The adjustments were based on comparability ratios published by the National Center for Health 

Statistics.
15

total 2010 U.S. Census resident population, the result gives the number of deaths that would 

have occurred in the 2010 U.S. Census resident population if the yMx for that particular year and 

sex had been experienced. Age-sex-adjusted death rates (ASDR) are calculated to summarize 

death rates for both sexes combined and are calculated as a weighted average of the yMx, 

where each weight is the number of people in the corresponding age and sex group of the 2010 

U.S. Census resident population.

MORTALITY projects annual yMx, which are then used to calculate the program’s additional 

outputs. The equations for this subprocess, 1.2.1 through 1.2.6, are given below:

 yMx = yMx (·) (1.2.1)

 

 yAAx = yAAx (·) (1.2.2)

qx = qx (·) (1.2.3)

 

 = (·) (1.2.4)



 



x

y x

z

y x s

x

y x

z

s

SP

SP M

ADR

,

(1.2.5)



 



s x

y x s

s

z

y x s

x

y x s

z

SP

SP M

ASDR

,

, ,

(1.2.6)

where

z

y Mx,s

refers to the central death rate between exact age x and x+y, by sex, in year z; 

ySPx denotes the number of people in the standard population (male and female combined) 

who are between exact age x and x+y; and ySPx,s denotes the number of people, by sex, in the 

standard population who are between exact age x and x+y.

1.2.b. Input Data

Trustees Assumptions -

Each year the Board of Trustees of the OASDI Trust Funds sets the ultimate assumed 

values for the yAAx by sex, age group,6and cause of death.7 The average annual 

percentage reductions reach their ultimate values in the 25th year of the 75-year 

 

6 Age groups are: less than 15, 15-49, 50-64, 65-84, 85+

7 The five causes of death are: Cardiovascular Disease, Cancer, Violence, Respiratory Disease, and Other
16

projection period. The ultimate rates of reduction by sex, age group, and cause of death 

can be found in Appendix 1.2-1.

NCHS Data -

 Annual numbers of registered deaths by sex and age group for the period 1900-1978. 

These data are not updated. Registered deaths refer to deaths in the Death 

Registration area. Since 1933, the Death Registration area has included all of the U.S.

 Annual numbers of deaths by sex, age group, and cause for the period 1979-2010. 

Generally, a new year of data is received each year. In addition, revised data are 

often available for years beginning with 1999. (1999 was the starting year of the 

latest international classification of diseases – ICD10.)

 The monthly number of births, by sex, for years 1938-2010. These data are updated 

annually, when the NCHS provides an additional year of data. 

 The number of infant deaths, by age, sex, and age group,8for years 1938-2010. These 

data are updated annually, when the NCHS provides an additional year of data. 

 Deaths for 1995 and 1996 by sex, 4 marital statuses, and 21 age groups. The age 

groups are generally 5-year age groups and are as follows: 0, 1-4, 5-9, 10-14, ..., 95+). 

These data are updated as resources are available.

 The population of states in the Death Registration area by age group9and sex, for 

years 1900-1939. These data are not updated.

 The number of registered deaths, by sex and age groups (85-89, 90-94, and 95+), for 

the years 1900-1967. These data are not updated.

U.S. Census Bureau Data -

 Estimates of the July 1 resident population by single year of age (0 through 100+) for 

years 1980-2010. Each year, Census provides an additional year of data and updated 

data for years after the most recent decennial census.

 From the Current Population Survey (CPS), the population by sex, marital status, and 

 

8

 Age groups are: under 24 hours, 1-2 days, 3-6 days, 7 -27 days, 28 days-1 month, 2 months, 3 months,..., 11 

months, 1 year, 2 years, 3 years, 4 years

9 Age groups are: 0, 1-4, 5-14, 15-24, ..., 75-84, 85+
17

age group10 for the years 1995 and 1996. These data are updated as resources are 

available.

 The resident population by sex, marital status, and age group,11 as of as of July 1, 

1995 and 1996. These data are updated when new NCHS death data by marital 

status are incorporated.

 The resident population at ages 75-79 and 80-84, by sex, for years 1900-1940 (at ten 

year intervals). These data are not updated.

 The resident population, by sex and age group,12 for 1940-2000. These data are not 

updated. 

CMS Data -

 Annual numbers of deaths, by sex and single year of age (ages 65 and over), for the 

period 1968-2011. These data are updated annually, when the CMS provides an 

additional year of preliminary data and replaces the prior year’s preliminary with final 

data.

 Annual numbers of Medicare enrollments (who are insured for Social Security 

benefits), by sex and single year of age (ages 65 and over), for the period 1968-2012. 

These data are updated annually, when the CMS provides an additional year of 

preliminary data and replaces the prior year’s preliminary with final data.

Other input data -

 From a previous year’s Trustees Report, the July 1, 1995 and 1996, Social Security 

area population by sex, marital status, and single year of age (5 through 100+). These 

data are updated when new NCHS death data by marital status are incorporated.

 

10 Age groups are: 15-17, 18-19, 20-24, 25-29,..., 40-44, 45-54, ..., 65-74, 85+

11 Age groups are: 15-19, 20-24, 25-29, ..., 90-94, 95+

12 Age groups for years prior to 1980 are: 0, 1-4, 5-9, ..., 80-84, 85+. For years 1980 and later, the age groups are: 0, 

1-4, 5-9, ..., 90-94, 95+.
18

1.2.c. Development of Output

Equation 1.2.2 - Average Annual Percentage Reduction in the Central Death Rates (

y AAx

)

The 

y AAx

, by sex and cause, are calculated based on the decline in the yMx for the period 1997 

through 2010, and distributed by 21 age groups,13 2 sexes, and 5 causes of death.14 The values 

are calculated as the complement of the exponential of the slope of the least-squares line 

through the logarithms of the yMx.

The ultimate assumed values for the central death rates (

u

y AAx

), as set by the Board of Trustees 

of the OASI and DI Trust Funds, are assumed to be reached in the 25th year of the 75-year 

projection period. The assumed ultimate values are specified by five causes of death for the 

following five age groups: under age 15, 15-49, 50-64, and 65-84, and 85 and older. Male and 

female values are set equal.

The values of 

y AAx

, by the 21 age groups, sex, and cause, for 2011 through 2013, are assumed 

to equal the average 

y AAx

based on the decline in the yMx for the period 1997-2010. Available 

Medicare preliminary data is used for overall levels with the last available NCHS data year cause 

of death percentages carrying forward. For years after 2013, a method of graduation is used 

that causes the absolute difference between the current 

y AAx

and the ultimate 

u

y AAx

to 

decrease rapidly until it reaches the Trustees’ ultimate assumed value,

u

y AAx

. This is 

accomplished by repeating the following steps for each of the first 25 years of the projection:

1. The absolute value of the distance between the prior year’s calculated 

y AAx

and the 

ultimate assumed 

u

y AAx

is calculated.

2. If the ultimate assumed 

u

y AAx

is greater than the prior year’s 

y AAx , then 80 percent 

of the difference is subtracted from the ultimate assumed 

u

y AAx

. If the ultimate 

assumed 

u

y AAx

is less than the prior year’s

y AAx

, then 80 percent of the difference is 

added to the ultimate assumed

u

y AAx

.

3. These steps are repeated until the 25th year at which time the

y AAx

are set equal to 

their ultimate assumed values, 

u

y AAx

. 

 

13

 Age groups are: 0, 1-4, 5-9, 10-14, ..., 90-94, and 95+

14

 The five causes of death are: Cardiovascular Disease, Cancer, Violence, Respiratory Disease, and Other.
19

Equation 1.2.1 – Central Death Rates (yMx)

Values of yMx are determined for each historical and projected year by the 21 age groups, 2 

sexes, and 5 causes of death. The starting year for the projections of the yMx is 2010, and is the 

most recent data year in the historical period. However, instead of using the historical data for 

yMx in this year as the starting point for mortality projections, starting yMx values are calculated 

to be consistent with the trend inherent in the last 12 years of available data. Each starting value 

for the yMx, by sex and cause of death, is computed as the value for the most recent year falling 

on a weighted least square line, where yMx is regressed on year, over the last 12 years. The 

weights are 0.2, 0.4, 0.6, and 0.8 for the earliest four years of the 12 years and are 1.0 for all 

other years.

For years after 2010, yMx are projected, by sex and cause of death, by applying the respective 

y AAx

to the prior year yMx. 

Equations 1.2.3 – Probabilities of death (qx)

In order to project population by age and sex, probabilities of death are applied to determine 

the projected number of deaths that will occur in the population. These probabilities, denoted 

as qx, reflect the probability a person age x will die within one year, where x refers to age last 

birthday as of the beginning of each year. For each year in the historical and projection period, 

separate qx series are estimated by sex. 

Different methods of projecting qx are used for age 0, for ages 1 through 4, for ages 5 through 

94, and for ages 95 and above. The following descriptions provide a brief discussion of these 

different methods. Additional detail is provided in Actuarial Study number 120. This study, 

titled Life Tables for the United States Social Security area 1900-2100, can be accessed at the 

following internet site:

http://www.socialsecurity.gov/OACT/NOTES/s2000s.html. (Choose study number 120.)

 Values for qx at Age 0: During the first year of life, mortality starts at an extremely high 

level, which becomes progressively lower. This is unlike mortality at other ages, which 

does not change very much within a single year of age. Thus, it is particularly important at 

age 0 to estimate accurately the pattern of mortality throughout the year of age, as described 

above, for the calculation of q0. For the period 1940 through the last historical year, q0 is 

calculated directly from tabulations of births by month and from tabulations of deaths at 

ages 0, 1-2, 3-6, 7-28 days, 1 month, 2 months, ..., and 11 months. After the last historical 

year, q0 is calculated from 1M0, assuming that the ratio of q0 to 1M0 measured for the last 

historical year would remain constant thereafter. 

 Values for qx at Ages 1 – 4: For the period 1940 through the last year of historical data, 

probabilities of death at each age 1 through 4 (qx, x = 1, 2, 3, 4) are calculated from 

tabulations of births by year and from tabulations of deaths at ages 1, 2, 3, and 4 years. 

After the last historical year, each qx (where x = 1, 2, 3, 4) is calculated from 4M1 assuming 

that the ratio of qx to 4M1 measured for the last historical year would remain constant 

thereafter.
20

 Values for qx at Ages 5 – 94: Probabilities of death for these ages are calculated from the 

projected central death rates, 5Mx. As mentioned above, the calculations are discussed in 

detail in Actuarial Study number 120.

 Values for qx at Ages 95+: It has been observed that the mortality rates of women, though 

lower than those of men, tend to increase faster with advancing age than those of men. An 

analysis of Social Security charter Old-Age Insurance beneficiaries has shown that at the 

very old ages mortality increases about five percent per year of age for men and about six 

percent per year for women. For men, probabilities of death at each ages 95 and older are 

calculated as follows: 

For women, the same formulas are used, except that 1.06 is substituted for 1.05. The 

larger rate of growth in female mortality would eventually, at a very high age, cause 

female mortality to be higher than male mortality. At the point where this crossover 

would occur, female mortality is set equal to male mortality.

The values of qx used in projecting the population are based on age last birthday and are 

calculated by sex for 1⁄2q0 (neonatal) and for qx, where x represents age last birthday for ages 0

through 100 (with 100 representing the age group 100 and older). Because life table values of 

probabilities of death are based on exact ages, values for qx representing age last birthday are 

derived as follows:

1⁄2q0 = 1 – L0 / l0 for neonatal

qx = 1 – Lx+1 / Lx for ages 0 to 99

q100 = 1 – T101 / T100 for age group 100 and older

See Actuarial Study number 120 for the definitions of the life table terms. This study can be 

accessed at the following internet site: 

http://www.socialsecurity.gov/OACT/NOTES/s2000s.html. (Choose study number 120; then 

section IV.A in the table of contents.) 

In addition, probabilities of death are broken down further into marital status. Historical data 

indicate differential in mortality by marital status is significant. To reflect this, projected 

relative differences in death rates by marital status are projected to be the same as observed 

during calendar years 1995 and 1996.
21

Equation 1.2.4 –Life expectancy 

Actuarial Study number 120 presents background information on the calculation of life 

expectancy, , from the probabilities of death (qx,). This study can be accessed at the 

following internet site: http://www.socialsecurity.gov/OACT/NOTES/s2000s.html. (Choose 

study number 120; then IV.A in the table of contents.)
22

Appendix: 1.2-1

The Board of Trustees of the OASDI Trust Funds sets the ultimate rates of mortality reduction by age 

group and cause of death. For comparison purposes, rates are also presented for two historical periods. 

Note that although the ultimate rates are the same for males and females, the historical rates differ.

Table 2.3: Average Annual Rates of Reduction in Central Death Rates by Age Group, Sex, and Cause

Historical Alternative II* Historical Alternative II*

2013 TR 2014 TR 2013 TR 2014 TR 

1979 to 2010 2000 to 2010 2037-2087 2038-2088 1979 to 2010 2000 to 2010 2037-2087 2038-2088

Under Age 15 Male Female

Cardiovascular Disease 2.70 1.83 2.3 2.3 2.58 2.37 2.3 2.3

Cancer 2.52 2.15 1.5 1.5 2.02 1.47 1.5 1.5

Violence 2.89 2.47 1.0 1.0 2.37 2.35 1.0 1.0

Respiratory Disease 2.74 0.43 2.0 2.0 2.80 1.04 2.0 2.0

Other 2.38 1.35 1.7 1.7 2.27 1.08 1.7 1.7

Resulting Total ** 2.51 1.57 1.56 1.56 2.30 1.33 1.58 1.58

Ages 15 - 49 Male Female

Cardiovascular Disease 2.06 1.33 1.5 1.5 1.23 1.11 1.5 1.5

Cancer 1.81 2.24 1.5 1.5 1.63 1.80 1.5 1.5

Violence 0.88 -0.03 0.7 0.7 0.15 -1.12 0.7 0.7

Respiratory Disease 0.82 0.54 0.5 0.5 -0.25 -0.78 0.5 0.5

Other 0.13 3.08 0.8 0.8 -0.53 1.22 0.8 0.8

Resulting Total ** 1.10 1.22 0.87 0.88 0.57 .62 0.94 0.94

Ages 50 - 64 Male Female

Cardiovascular Disease 3.17 2.78 2.2 2.2 2.66 3.22 2.2 2.2

Cancer 1.61 1.91 1.5 1.5 1.32 2.11 1.5 1.5

Violence 0.41 -2.68 0.5 0.5 -0.21 -3.43 0.5 0.5

Respiratory Disease 1.19 0.66 0.7 0.7 -0.57 0.70 0.7 0.7
23

Other -0.37 -0.68 0.6 0.6 -0.43 0.05 0.6 0.6

Resulting Total ** 1.74 1.16 1.06 1.06 1.17 1.50 1.06 1.06

Ages 65 - 84 Male Female

Cardiovascular Disease 3.18 4.63 2.2 2.2 2.79 4.48 2.2 2.2

Cancer 0.73 1.89 0.9 0.9 -0.19 1.11 0.9 0.9

Violence 0.70 0.11 0.5 0.5 0.00 -0.23 0.5 0.5

Respiratory Disease 0.51 1.94 0.3 0.3 -2.28 0.76 0.3 0.3

Other -0.79 -0.35 0.3 0.3 -1.57 -0.63 0.3 0.3

Resulting Total ** 1.54 2.40 0.78 0.77 0.69 1.79 0.73 0.72

Ages 85 and older Male Female

Cardiovascular Disease 1.57 3.43 1.2 1.2 1.73 3.79 1.2 1.2

Cancer -0.43 1.29 0.5 0.5 -0.62 0.55 0.5 0.5

Violence -0.65 -0.18 0.3 0.3 -1.07 -1.56 0.3 0.3

Respiratory Disease -0.87 2.78 0.2 0.2 -2.10 1.92 0.2 0.2

Other -2.35 -2.12 0.2 0.2 -3.27 -2.60 0.2 0.2

Resulting Total ** 0.20 1.57 0.51 0.51 0.03 1.26 0.49 0.48

Total Male Female

Cardiovascular Disease 2.60 3.76 2.28 3.94

Cancer 0.81 1.79 0.36 1.34

Violence 0.70 -0.44 0.04 -1.24

Respiratory Disease 0.15 2.09 -1.87 1.10

Other -0.86 -0.58 -1.61 -1.08

Resulting Total ** 1.19 1.83 0.75 0.74 0.58 1.48 0.70 0.69

* Alternative 1 is 1/2 times Alternative 2; Alternative 3 is 5/3 times Alternative 2.

**Resulting total represents average annual percent reduction in age-adjusted death rates for the last 50 years of the 75 year projection 

period.
24

1.3. LEGAL IMMIGRATION

1.3.a. Overview

Legal immigration is defined as those persons who have been admitted into the United States 

and been granted legal permanent resident (LPR) status. Legal emigration consists of legal 

permanent residents and U.S. Citizens who depart the Social Security area population to reside 

elsewhere. 

For each year z of the projection period, the LEGAL IMMIGRATION subprocess produces 

estimates of legal immigration (L

z

) and legal emigration (E

z

), by age and sex, based on 

assumptions set by the Trustees for each category. In addition, the LEGAL IMMIGRATION 

subprocess disaggregates the estimates of L

z

into those who have been admitted into the 

United States during the year (NEWz) and those who adjusted from the other-immigrant 

population to LPR status (AOSz).

Each fiscal year,15 the Department of Homeland Security (DHS) collects data on the number of 

persons granted LPR status by age, sex, and class of admission. The U.S Census Bureau 

provided OCACT with an unpublished estimate of the annual number of legal emigrants, by sex 

and age, based on the change between the 1980 and 1990 censuses. These historical data are 

used as a basis for developing age-sex distributions that are applied to the Trustees’ aggregate 

immigration assumptions to produce annual legal immigration and emigration estimates by age 

and sex. 

The primary equations of LEGAL IMMIGRATION, by age (x) and sex (s), for each year (z) of the 

75-year projection period are summarized below:

( )

, ,

 

z

x s

z NEWx s NEW

(1.3.1)

( )

, ,

 

z

x s

z AOSx s AOS

(1.3.2)

z

x s

z

x s

z

Lx,s NEW

,

 AOS

,

(1.3.3)

( )

, ,

 

z

x s

z Ex s E

(1.3.4)

z

x s

z

x s

z NLx,s

 L

,  E,

(1.3.5)

1.3.b. Input Data

 

15

 The federal fiscal year begins on October 1 of the previous calendar year and ends on September 30 of the 

specified calendar year.
25

Trustees Assumptions -

Each year the Board of Trustees of the OASDI Trust Funds specifies the total annual assumed 

values for legal immigration and legal emigration. For the 2014 Trustees Report, the ultimate 

values for legal immigration and emigration are 1,050,000 and 262,500, respectively (both 

reached in 2014). 

Office of Immigration Statistics –

 Historical legal immigration by fiscal year (1941-1995), 5-year age group (0-4, 5-9, ..., 80-

84), and sex. These data will not be updated.

 Legalizations due to IRCA by 5-year age group and sex for the years 1989-1996. These 

data will not be updated.

Department of Homeland Security –

 Historical legal immigration by fiscal year (1996-2012), single year of age (0 through 99), 

sex, and class of admission (New Arrival, Adjustment of Status, Refugee, and Asylee). 

These data are updated annually, with the DHS providing an additional year of data each 

year.

 Total adjustments of status for the years 1966 to 1995 (OCACT further estimates total 

adjustments of status for 1963-1965). These data will not be updated.

U.S. Census Bureau –

 Unpublished estimates of annual legal emigration by five-year age groups (0-4, 5-9, ..., 

80-84) and sex for 1990 based on the change between the 1980 and 1990 censuses. 

These data are updated occasionally (based on having new data from an outside source 

and on OCACT resource time constraints).

Other input data -

 Legal emigration conversion factors. These estimates were developed internally by fiveyear age groups (0-4, 5-9, ..., 80-84) and sex to reflect the fact that the estimated 

number of people leaving the United States is not equivalent to the number of people 

leaving the Social Security area. These data are updated when annual legal emigration 

estimates are updated (see above).
26

1.3.c. Development of Output

Equations 1.3.1 and 1.3.2 – Legal Immigration

The Trustees specify the aggregate amount of legal immigration for each year of the 75-year 

projection period. In order to incorporate the numbers of new immigrants into the Social 

Security area population projections, the total level of new immigrants is disaggregated by age 

and sex.

There are two ways for an immigrant to be admitted into the U.S. for lawful permanent 

residence: 

(1) New arrivals, such as persons living abroad who are granted an LPR visa and then enter 

the U.S. through a port of entry. Refugees and asylees that are granted LPR status are 

also treated as new arrivals in the OCACT model. 

(2) Adjustments of status, who are people already residing in the U.S. as other immigrants 

and have an application for adjustment to LPR status approved by the DHS.

The DHS provides data on legal immigrants by sex, single year of age, classification of 

admission, and fiscal year of entry. The 10 most recent years of data are used to calculate 

separate age-sex distributions for both new arrivals and adjustments of status by taking the 

following steps: 

1. Refugee and Asylee LPR admissions are subtracted from the adjustment of status data 

and added into the new arrival category.

2. The data are converted from fiscal year data to calendar year data.

3. For each class of admission, new arrival and adjustment of status, the historical data for 

the last 10 years (from 2003-2012) are combined into an average age-sex distribution.

z NEWx,s

, the expected number of new arrival legal immigrants by age (x) and sex (s), is 

calculated by applying the age-sex distribution for new arrivals to the Trustees assumed level of 

new arrivals. The Trustees’ assumed number of adjustments of status is multiplied by the agesex distribution of adjustments of status to calculate

z AOSx,s

.

Equation 1.3.4 – Legal Emigration

The Trustees specify the aggregate amount of legal emigration for each year of the projection 

period. This is done by setting the ratio of emigration to legal immigration. For the 2014 

Trustees Report, the ratio is set at 25 percent.
27

In order to produce the number of emigrants from the Social Security area population, the total 

level of emigrants is disaggregated by age and sex. The disaggregation is based on a 

distribution of emigrants, by sex and five-year age groups, provided to OCACT in unpublished 

estimates by Census that are based on changes between the 1980 and 1990 censuses. Since 

the emigration numbers estimated by Census are for all people leaving the United States, they 

are adjusted downward by a series of conversion factors so the data correspond to the number 

of people leaving the Social Security area population.

For each sex (s), the Beers formula is used to interpolate and distribute each five-year age 

group into a single year of age (x) distribution, EDISTx,s. For each projection year, this 

distribution is used to distribute the assumed level of total legal emigrants by age and sex using 

the following equation:

 



f

s m x

x s

z

x s

z Ex s L EDIST

84

0

, , * ,

.25( )
28

1.4. HISTORICAL POPULATION

1.4.a. Overview

For each historical year, the HISTORICAL subprocess provides estimates of the Social Security 

area population for the period December 31, 1940 through December 31, 2011. The Social 

Security area population consists of:

 U.S. resident population and armed forces overseas plus

 Net census undercount plus

 Civilian residents of Puerto Rico, the Virgin Islands, Guam, the Northern Mariana

Islands, and American Samoa plus

 Federal civilian employees overseas plus

 Dependents of armed forces and federal civilian employees overseas plus

 Residual beneficiaries living abroad plus

 Other citizens overseas

The U.S. Census Bureau collects population data and tabulates it by age, sex, and marital status 

every ten years for the decennial census. Generally, each subsequent year, the Census Bureau 

publishes an estimate of the post-censal population. This subprocess combines these census 

and post-censal estimates, along with the estimates of the other components of the Social 

Security area population listed above, and components of change described in sections 1.1 to 

1.3 to develop historical estimates of the total Social Security area population (

z Px,s

) and other 

than legal population (

z Ox,s

). Combining the total populations by single year of age and sex with 

an estimated marital status matrix provides the total Social Security area historical population 

by single year of age, sex, and marital status (

z Px,s,m

). These estimates are then used as the 

basis for the PROJECTED POPULATION subprocess described in section 1.8. The primary 

equations for this subprocess, 1.4.1, 1.4.2, and 1.4.3, are given below:

z Px,s

= 

z Px,s

(·) (1.4.1)

z Px,s,m

= 

z Px,s,m

(·) (1.4.2)

z Ox,s

= 

z Ox,s

(·) (1.4.3)
29

1.4.b. Input Data

Long-Range OASDI Projection Data -

Demography

 Probabilities of death from MORTALITY, by age last birthday and sex, for years 

1941-2012. These data are updated every year. 

 The number of new legal immigrants by age and sex for years 1941-2011. These 

data are from the LEGAL IMMIGRATION subprocess and are updated each year.

 The number of legal emigrants by age and sex for years 1941-2011. These data are 

from the LEGAL IMMIGRATION subprocess and are updated each year.

 The number of adjustments of status by age and sex for years 1941-2011. These 

data are from the LEGAL IMMIGRATION subprocess and are updated each year.

 The number of “other immigrants” legalized under the Immigration Reform and 

Control Act of 1986 (IRCA) from LEGAL IMMIGRATION. These data are reproduced 

each year.

 Birth rates by single year of age of mother (14-49) for the years 1941-2012 from 

the FERTILITY subprocess. These data are updated each year.

U.S. Census Bureau Data -

 Estimates of U.S resident population and Armed Forces population overseas as of 

each July 1 (1940-1979) by sex and single-year of age through 84, and for the group 

aged 85 and older. These data are generally not updated.

 Estimates of the U.S. resident population for each Census (April 1) 1970 – 2010 by 

sex and single year of age 0 through 85+. New decennial Census estimates come 

out about every ten years.

 Estimates of total U.S. residential population and total residential population plus 

Armed Forces overseas population for each January of each Census year from 1990 

and 2010. New decennial Census estimates come out about every ten years.

 Estimates of U.S resident population, and U.S. resident plus Armed Forces 

population overseas as of each July 1 (1980-2012) by sex and single-year of age 0 

through 99, and ages 100 and older. Generally, the U.S. Census Bureau restates 

the data back to the most recent decennial census and includes one additional year 
30

of data.

 Estimates of the population by age group,16 sex, and marital status for years 1940-

2010 from the CPS. These data are not updated.

 Estimates of the population by marital status and sex, which have more age groups 

than the CPS, and sex for years 1982-1989 and 1992-2000. These data are not 

updated.

 Estimates of the population by marital status, sex, and age from the American 

Community Survey (ACS) public use microdata samples (PUMS) for years 2000 –

2011. An additional year of data is available each year.

 Undercount factors by single year of age (0-85+) and sex, estimated using postcensal survey data. These data are updated after each decennial census.

 The total annual population estimates for Puerto Rico, Virgin Islands, Guam, 

Northern Marianas, and American Samoa for years 1951-2012. For each Trustees 

Report, an additional data year is downloaded from the U.S. Census Bureau’s 

international database. Historical data back to 1951 is also obtained if any changes 

have occurred.

 Decennial Census population estimates, by varying degree of age detail and sex, for 

Censuses from 1950 – 2000 for territories and components outside the 50 states, 

D.C., and armed forces overseas. Most data is aggregated into 18 age groups for 

each sex though single year of age data is available for young ages in the territories 

for 1960 and 1970 and all ages starting in 1980. New estimates are added as they 

become available.

 July populations of the territories by single year of age and sex from 2000 – 2012. 

An additional year of data is available each year.

 From American Community Survey (ACS) public use microdata samples (PUMS), 

number of existing marriages from 2000 – 2011 by age group of husband crossed 

with age group of wife. An additional year of data is available each year.

 From decennial Census public use microdata samples (PUMS) via the University of 

Minnesota’s IPUMS website, number of existing marriages for Census years 1940 –

2000 by age group of husband crossed with age group of wife. New estimates are 

added as they become available.

 Estimates of net immigration by age and sex of the U.S. residential plus armed

 

16 Age groups are: 15-17, 18-19, 20-24, 25-29, ..., 55-64, 65-74, 75-84, 85+
31

forces overseas (USAF) population from April 1, 2000 through July 1, 2012. 

 Total Americans overseas estimate based on international data sources and 

estimates of federal employees and military in Iraq and Afghanistan. The data 

from the various international sources are derived from different years but center 

around the year 2003. Additional data will be updated as they become available. 

Other input data -

 From the Centers for Medicare and Medicaid (CMS), Medicare enrollments by 

single year of age (85-100+) and sex for January 1, 1968 – 2012. The last year of 

data is provisional. Each year, the CMS provides a final year of data to replace the 

prior year’s provisional data, and a new provisional year of data.

 From the Department of State, old historical total estimates of outside area 

populations (federal employees overseas, overseas dependents of federal 

employees and military, and other Americans overseas.

 The SSA Annual Statistical Supplement provides estimates of the total number of 

OASDI Beneficiaries living abroad for most years December 31, 1953 – 2011. Age 

group data is also available. For each Trustees Report, an additional year of data is 

available. Age group data is updated for each decennial Census year.

 From the National Center for Health Statistics (NCHS), the sex ratio (number of 

males born per female) for years 1941 – 2011. Each year, NCHS provides another 

year of data. For 2012, the sex ratio is assumed to be 1.05, the same as the 

assumed projected sex ratio.

 From the Department of Homeland Security (DHS), the total number of other 

immigrants (unauthorized immigrants plus nonimmigrants) from January 1, 2005 –

2011.

 From the Office of Personnel Management (OPM), total estimates of the number 

of federal employees overseas from July 1, 1998 – 2012. These estimates are 

updated as they become available on the OPM website.

 From the OPM, the number of federal employees overseas by single year of age 

and sex from a subset of the OPM data source above. Years 1980 – 2012 are 

available. These estimates are updated as they become available.

 From the Department of Defense, total numbers of armed forces in Puerto Rico, 

Virgin Islands, Guam, and American Samoa each Census year starting in 1990. 

These data are updated as they become available.
32

1.4.c. Development of Output

Equation 1.4.1 – Historical Population by age and sex (

z Px,s

)

The Census Bureau's estimate of the residents of the 50 States, D.C., and U.S. Armed Forces 

overseas is used as a basis for calculating

z Px,s

. The base estimate is adjusted for net census 

undercount and increased for other U.S. citizens living abroad (including residents of US 

territories) and for non-citizens living abroad who are insured for Social Security benefits. 

The estimates of the number of residents of the fifty States and D.C. and Armed Forces 

overseas, as of July 1 of each year, by sex for single years of age through 84, and for the group 

aged 85 or older, are obtained from the Census Bureau. Adjustments for net census 

undercount are estimated using post-censal survey data from the Census Bureau. Population 

counts over age 65 after the last Census year are modified to be consistent with OCACT 

mortality and Census USAF net immigration data. The numbers of persons in the other 

components of the Social Security area as of July 1 are estimated by sex for single years of age 

through 84, and for the group aged 85 or older, from data of varying detail. Numbers of people 

residing in Puerto Rico, the Virgin Islands, Guam, American Samoa, and the Northern Mariana 

Islands are estimated from data obtained from the Census Bureau. Numbers of Federal civilian 

employees overseas are based on estimates from the Office of Personnel Management (OPM). 

Dependents of Federal civilian employees and Armed Forces overseas are based on the stock of 

Federal civilian employees from OPM and the stock of armed forces overseas from the Census 

Bureau. Other citizens overseas covered by Social Security are also based on estimates 

compiled by the Census Bureau. The overlap among the components, believed to be small, is 

ignored.

The first step of the process is to estimate

z Px,s

as of January 1st for certain “tab years” (1941, 

1951, 1957, 1961, each decennial Census year [1970 through 2000], and the last year of 

historical data [2012 for the 2014 Trustees Report]). For ages 0-84, 

z Px,s

for each tab year is set 

equal to the averaged surrounding July 1 U.S. population and armed forces overseas counts 

from the Census Bureau (modified by OCACT mortality rates and Census USAF net immigration 

for ages over 65 and years past 2010) plus an undercount adjustment plus other component 

populations. For ages 85 and over,

z Px,s

for each tab year is set equal to [Built Up Pops Age x, 

Sex s] * [Total 85+ for Sex s]/[Total Built Up 85+ for Sex s], where the built up estimates are 

created by taking into account deaths and immigration data from the previous tab year and 

[Total 85+ for Sex s] is the sum of the averaged surrounding July 1 U.S. population and armed 

forces overseas counts from the Census Bureau (modified by OCACT mortality rates and Census 

USAF net immigration for years past 2010) plus an undercount adjustment plus other 

component populations for ages 85 and over for each sex. 
33

For years between the tab years, populations are estimated taking into account the 

components of changes due to births, deaths, legal emigration, adjustments of status, and net 

legal immigration (or net immigration, if known) during that time period. These estimates are 

then multiplied by the appropriate age-sex-specific ratios so that the error of closure at the tab 

years is eliminated. 

Equation 1.4.2 – Historical Population by age, sex, and marital status (

z Px,s,m

)

Since eligibility for auxiliary benefits is dependent on marital status, the Social Security area 

population is disaggregated by marital status. The four marital states are defined as single 

(having never been married), married, widowed, and divorced.

The distribution of the number of existing marriages are available for Census years 1940 – 2000 

from Census public use microsample data (PUMS) samples. These data are aggregated by age 

group of husband crossed with age group of wife. Additional tabulations from the American 

Community Survey from 2000 – 2011 are incorporated to adjust these marital prevalence grids 

for changes since 2000. The grids are transformed from age grouped numbers to single year of 

age figures from ages 14 to 100+ for husband and wife using the two dimensional H.S. Beers 

method of interpolation.

Percentages of single, married, widowed, and divorced persons are calculated by taking the 

estimate for each marital status category and dividing them by the total number of people for 

each age group and sex based on either the CPS, more detailed Census numbers, if available, or 

ACS. Then, for each sex, if one age group has a higher or lower percentage than the surrounding 

age groups, an average of the surrounding groups replaces the original value. After verifying the 

percentages are close to the original data (and adjusted if needed), these percentages are 

multiplied by the total populations calculated in Equation 1.4.1 for each age, sex, and year to get 

a preliminary population for each age, sex, and marital status. 

To keep the marriage prevalence grids and the marital status percentages smooth and consistent, 

several algorithms are used. First, the married population is adjusted so that the number of 

married males equals the number of married females. Then, the number of married persons for 

each age and sex is set equal to the marginal total of the associated year’s marital prevalence 

grid. Finally, the other marital statuses population totals are adjusted to keep the total number of 

people in all marital statuses the same as calculated before splitting into marital statuses. 

Equation 1.4.3 – Historical Other Than Legal (OTL) Population by age and sex (

z Ox,s

)

This subprocess also estimates historical levels of “other immigrants” in the population, by age 

and sex. For each year, an initial net residual estimate by single year of age and sex is backed 

out from estimates of beginning and end of year populations, births, deaths, legal immigrants, 

adjustments of status, and legal emigrants. This net residual equals the implied initial other in 

minus other out. These residuals are then modified to ensure reasonableness. Next, using 

these modified net residuals, along with adjustments of status and OTL deaths (using the same 

death rates as for the total population), an initial OTL stock is built. These stocks are then 
34

modified to ensure reasonableness. After 2000, one further adjustment is done to the stocks. 

From January 2001 through January 2004, the total OTL populations are set equal to the values 

that linearly grade from the final OCACT January 2000 total OTL population to a DHS-based 

January 2005 total OTL population. From January 2005 through January 2011, the total OTL 

population is forced to match DHS-based total OTL population estimates. The total OTL 

population from the DHS is equal to the sum of their estimates for unauthorized immigrants 

and nonimmigrants. Nonimmigrants include categories such as students, temporary workers, 

and exchange visitors.
35

1.5. OTHER IMMIGRATION

1.5.a. Overview

The term “other immigration” refers to persons entering the U.S. in a manner other than being 

lawfully admitted for permanent residence and who reside in the U.S. for at least 6 months. 

This includes temporary immigrants (persons legally admitted for a limited period of time, such 

as temporary workers and foreign students), also called nonimmigrants, in addition to 

undocumented immigrants living in the U.S. These undocumented immigrants can be split into 

those that were never authorized or those that were nonimmigrants but overstayed their visas 

(visa-overstayers). 

For each year z of the projection period, the OTHER IMMIGRATION subprocess produces 

estimates of other-than-legal (OTL) immigration (

z OI x,s,t

), by age (x), sex, and OTL type (t) based 

on assumptions set by the Trustees. Estimates of projected other emigration (those leaving the 

Social Security area, denoted as 

z OEx,s,t

), by age, sex and OTL type are also developed in this 

subprocess. 

The Department of Homeland Security (DHS) estimated the stock of nonimmigrants by age 

group and sex for April 2008 and for the end of 2010. The HISTORICAL POPULATION program 

already produces historical estimates of other-than-legal immigrants. These historical data are 

used to develop recent estimates of the OTL stock by age, sex, and immigration type where 

immigration type is never-authorizeds, nonimmigrants, and visa-overstayers.

The primary equations of OTHER IMMIGRATION, by age (x), sex (s), and OTL type (t) for each 

year (z) of the 75-year projection period are summarized below:

( )

, , , ,

 

z

x s t

z OI x s t OI

(1.5.1)

( )

, , , ,

 

z

x s t

z OEx s t OE (1.5.2)

z

x s t

z

x s t

z

x s t

z NOx,s,t

 OI

, , OE, ,  AOS, ,

(1.5.3)

where 

z NOx,s,t

are the number of net other immigrants, by age (x), sex (s), and OTL type (t) for 

year z, and 

z AOSx,s,t

are the number of adjustments to legal status by age (x), sex (s), and OTL 

type(t) for year z;

z

x s t

z

x s t

z

x s t

z

x s t

z

x s t

z OPx s t OP OI 1, ,

OE 1, ,AOS 1, ,OD 1, ,

1

, , 1, ,    



      (1.5.4)
36

where, 

z OPx,s,t

is equal to the other immigrant population, by age (x), sex (s), and OTL type (t) 

as of December 31st of year z, 

z ODx,s,t

are the number of deaths in the other immigrant 

population by age (x), sex (s), and OTL type (t) for year z, and 

z AOSx,s,t

are the number of 

adjustments to legal status by age (x), sex (s), and OTL type (t) for year z.

1.5.b. Input Data

Trustees Assumptions -

Each year the Board of Trustees of the OASDI Trust Funds specifies the total annual assumed 

values for other immigration. The Trustees set the ultimate annual level at 1,400,000 persons 

per year for each year beginning in 2022. Due to the recent recession, they assume that the 

level of other immigration was 1,000,000 in 2011, and will gradually increase above the 

ultimate level to 1,600,000 by 2018, followed by a gradual decrease until 2022 when the 

ultimate is reached.

Long-Range OASDI Projection Data -

Demography

 Historical and projected probabilities of death by age last birthday (including a neonatal 

mortality factor, single year of age for ages 0-99, and age group 100+) and sex, for years 

1941-2101. These data are updated each year from the MORTALITY program.

 Historical net other immigration by single year of age (0-100+) and sex for years 1961-

2011. These data are updated each year from the HISTORICAL program. 

 Historical December 31 OTLs by single year of age (0-100+) and sex for years 1963-2011. 

These data are updated each year from the HISTORICAL program. 

 Historical July 1 OTLs by single year of age (0-100+) and sex for years 1964-2011. These 

data are updated each year from the HISTORICAL program. 

 Historical new arrivals by single year of age (0-83 and 84+) and sex for years 1941-2011. 

These data are updated each year from the LEGAL IMMIGRATION program.

 Historical and projected adjustments of status by single year of age (0-83 and 84+) and 

sex for years 1941-2101. These data are updated each year from the LEGAL 

IMMIGRATION program.

Department of Homeland Security –

 Components of the Unauthorized Immigrant Population by year for 2005-2011, used to 

calculate undercount factors. These data are updated as new data become available.

 Nonimmigrant stock in April 2008 and December 2010 by age-group and sex. These 

data are updated as new data become available and internal resources are sufficient to 

examine and interpret such new data.

 Nonimmigrant admissions by class of admission for various fiscal years 1981 – 2012. 
37

These data are updated each year.

U.S. Census Bureau –

 From the American Community Survey (ACS), foreign-born new arrivals by ACS year 

(2000-2011), entry year (1900-2011), age (0-100) and sex. These data are updated as 

new data become available.

Other input data -

 The number of those potentially eligible under the deferred action for childhood arrivals 

(DACAs) initiative by age group and an overall gender split, from the Migration Policy 

Institute. These data will not be updated.

 Internally developed factors of potential DACA stock attaining DACA status by sex and 

ages 5-30 for the initial and ultimate DACA years. These data are updated as new data 

become available and internal resources are sufficient to examine and interpret such 

new data.

 Internally developed factors to apply to other immigrants that enter as nonimmigrants. 

These data will not be updated.

 Overstay percentages by age. These data are based off a RAND Corporation document 

using data from the 1980s, and are adjusted based on insights from the DHS. These 

data are updated as new data become available and internal resources are sufficient to 

examine and interpret such new data.

1.5.c. Development of Output

The ACS provides the number of foreign-born new arrivals, which is then used to separate the 

historical net other than legal immigration into those entering and those leaving. There are 

several other key inputs that go into this calculation, including an estimated undercount factor. 

This factor accounts for (1) differences between the foreign-born data from the ACS and the 

component pieces obtained from DHS, (2) differences between the ACS (Public Use Microdata 

Sample) and Census’ total population, and (3) the foreign-born residing in Puerto Rico. The 

estimated other than legal immigration is calculated by taking the Beers’d foreign born from 

the ACS (after applying the undercount factors) and subtracting the legal new arrivals. The 

estimated historical other than legal emigration is then calculated as the difference between 

the net other than legal immigration (calculated in the HISTORICAL subprocess) and the 

estimated historical other than legal immigration. A series of steps are then taken to smooth 

the two categories. Assumptions split the historical other immigrants into those who arrive or 
38

depart the Social Security area as a never-authorized immigrant, nonimmigrant, and visaoverstayer immigrant.

Equation 1.5.1 – Other Immigration

The Trustees specify the aggregate amount of other immigrants for each year of the projection 

period. For each projection year, an age-sex-type distribution is used to distribute this 

assumption by age, sex, and OTL type. This age-sex distribution is denoted as ODISTx,s,t and is 

developed by using average historical estimates of other immigrants entering the country from 

2000 through 2007. 

The assumed total level of other immigration is denoted by 

z

TO

. Thus, for each year (z) other 

immigration is defined by the following equation:

x s t

z z OI x,s,t

 TO ODIST

, ,

Equation 1.5.2 – Other Immigrant Deaths

Deaths for the other immigrant population use the same death probabilities as the total 

population:

z

x s t

z

x s

z ODx,s,t

 q ,OP

, ,

Equation 1.5.3 – Other Emigration

z OEx,s,t

is equal to the annual number of other immigrants who depart the Social Security area 

by age (x), sex (s), and OTL type (t) they left from. These estimates are based on a build-up of 

stocks from 2008 through 2010 including other immigration discussed above, deaths, 

adjustments of status (from the LEGAL IMMIGRATION subprocess), and assumptions about how 

many departures come from each OTL type. Then, rates are calculated by dividing 

z OEx,s,t

by 

z OPx,s,t

for this 2008 – 2010 period at each age, sex, and OTL type. After smoothing, these rates 

are used to calculate 

z OEx,s,t

in projected years by being applied to the OTL stock populations 

1

, ,

z OPx s t

.

This subprocess also projects the Deferred Action for Childhood Arrivals (DACA) population, a 

subset of the other immigrant population, by age (x) and sex (s). The DACA population consists 

of other than legal immigrants who meet the criteria for the DACA initiative and will be granted 

authorization to work. These populations further divide the never-authorized and visaoverstayer populations.
39

Finally, this subprocess also splits historical OTLs into the various categories. It is assumed that 

all OTL immigrants were nonimmigrants as of December 31, 1963. Between December 31, 

1963, and December 31, 2010, the percentage of total OTL by age and sex in each OTL category 

is linearly interpolated from the percentages at those two points in time. A final adjustment 

ensures the total nonimmigrants are appropriate, based on DHS nonimmigrant admissions.
40

1.6. MARRIAGE

1.6.a Overview

The National Center for Health Statistics (NCHS) collected detailed data on the annual number 

of new marriages in the Marriage Registration Area (MRA), by age of husband crossed with age 

of wife, for the period 1978 through 1988 (excluding 1980). In 1988, the MRA consisted of 42 

States and D.C. and accounted for 80 percent of all marriages in the U.S. Estimates of the 

unmarried population in the MRA, by single year of age (or age group if single year of age was 

not available) and sex, were obtained from the NCHS. Marriage rates for this period are 

calculated from these data. The age-of-husband crossed with age-of-wife marriage grid rates

are transformed from age grouped numbers to single year of age figures from ages 14 to 100+ 

for husband and wife using the two dimensional H.S. Beers method of interpolation. 

The NCHS stopped collecting data on the annual number of new marriages in the MRA in 1989. 

Less detailed data on new marriages from a subset of the MRA were obtained for the years 

1989-1995. The American Community Survey (ACS) public use microdata samples (PUMS) 

started asking if a person was married in the last 12 months in 2008. Using this question along 

with ages of spouses, new marriages grids by age-group-of-husband crossed with age-group-ofwife were developed. For the years in between the NCHS and ACS data, the marriage grids 

were linearly interpolated. These NCHS and ACS data are used to adjust the more detailed ageof-husband crossed with age-of-wife rates from the earlier years.

Age-specific marriage rates (

z mx, y

ˆ

) for a given year z are defined as the ratio of (1) number of 

marriages for a given age-of-husband (x) crossed with age-of-wife (y) to (2) a theoretical 

midyear unmarried population at those ages (

z Px, y

). The theoretical midyear population is 

defined as the geometric mean17 of the midyear unmarried males and unmarried females.

An age-adjusted central marriage rate (

z AM

ˆ

R

) summarizes the 

z mx, y

ˆ

for a given year. The 

standard population chosen for age adjusting is the unmarried males and unmarried females in 

the MRA as of July 1, 1982. The first step in calculating the total age-adjusted central marriage 

rate for a particular year is to determine an expected number of marriages by applying the ageof-husband-age-of-wife specific central marriage rates for that year to the geometric mean of the 

corresponding age groups in the standard population. 

The 

z AM

ˆ

R

is then obtained by dividing:

 

17 The geometric mean, as used in this document, is the square root of the product of two numbers.
41

 The expected number of marriages by 

 The geometric mean of (a) the number of unmarried males, ages 15 and older, and (b) 

the unmarried females, ages 15 and older, in the standard population.

The MARRIAGE subprocess projects annual 

z mx, y

ˆ

by age-of-husband crossed with age-of-wife. 

The equations for this subprocess, 1.6.1 and 1.6.2, are given below:

z

x y

z mx, y m

,

ˆ



ˆ

(·) (1.6.1)



 



x y

S

x y

z

x y

x y

S

x y

z

P

P m

AMR

,

,

,

,

,

ˆ

ˆ

(1.6.2)

where and x and y refer to the age of males and females, respectively, and

S Px, y

is the theoretical 

unmarried population in the MRA as of July 1, 1982 (the geometric mean of the corresponding 

age groups in the standard population).

1.6.b. Input Data

Long-Range OASDI Projection Data -

Demography

 Estimates of the Social Security area population as of January 1, by age, sex, and marital 

status for years 1978-2011, excluding 1980. These data are updated each year based on 

output of the HISTORICAL POPULATION subprocess.

Assumptions -

For each Trustees Report, ultimate values for the

z AM

ˆ

R

are assumed. The 

z AM

ˆ

R

reaches 

its ultimate value in the 25th year of the 75-year projection period. For the 2014 report, the 

intermediate ultimate 

z AM

ˆ

R

assumption is 4,000 per 100,000 unmarried couples.

NCHS Data -

 Number of new marriages in the MRA, by age-of-husband crossed with age-of-wife, for 

calendar years 1978 through 1988, excluding 1980. These data are no longer available 

for years after 1988. The data vary in detail by year. They are broken out by single year 

age-of-husband crossed with single year age-of-wife for many ages (particularly younger 

ages).
42

 Number of unmarried males and females in the MRA for calendar years 1978 through 

1988, excluding 1980. These data are no longer available for years after 1988. The data 

are generally broken out by single year age for ages under 40 and by age groups 40-44, 

45-49, 50-54, 55-59, 60-64, 65-74, and 75+. 

 Number of new marriages, in a subset of the MRA, by age-group-of-husband crossed 

with age-group-of-wife (age groups include 15-19, 20-24, 25-29, 30-34, 35-44, 45-54, 55-

64, and 65+), for calendar years 1989-1995. These data are updated as new data 

become available and internal resources are sufficient to examine and interpret such 

new data.

 The total number of new marriages in the MRA less marriages in those states not 

included in the MRA unmarried population for the period 1957-1988. These data are 

not updated.

 The total number of new marriages in the United States for the period 1989-2011. 

Normally, each year, the NCHS publishes the total number of marriages for one more 

year.

 Number of new marriages in the MRA for years 1979 and 1981-1988 by age group (age 

groups include 14-19, 20-24, 25-29, 30-34, 35-44, 45-54, 55-64, and 65+), sex, and prior 

marital status (single, widowed, and divorced). These data are no longer available for 

years after 1988.

 Number of unmarried people in the MRA for years 1979 and 1981-1988 by age group 

(age groups include 14-19, 20-24, 25-29, 30-34, 35-44, 45-54, 55-64, and 65+), sex, and 

prior marital status (single, widowed, and divorced). These data are no longer available 

for years after 1988.

U.S. Census Bureau Data -

 Estimates of new marriages by age-group-of-husband crossed with age-group-of-wife 

from the American Community Survey (ACS) public use microdata samples (PUMS) 

occurring, on average, end of years 2007 – 2010. An additional year of data is available 

each year.

Other Input Data -

 From the vital statistics offices in various states, number of same-sex marriages from 

2004 – 2011. These data are updated as they become available.
43

1.6.c. Development of Output

Equation 1.6.1 -

Age-specific marriage rates are determined for a given age-of-husband crossed with age-of-wife, where ages range 

from 14 through 100+. The historical period includes years of complete NCHS data on the number of marriages 

and the unmarried population in the MRA for the period 1978 through 1988, excluding 1980. Data for a subset of 

the MRA, available by age group only, are used for the period 1989 through 1995 and ACS new married grids by 

age group are used for the period 2008 through 2010. The marriage grids by age group for the years 1996 through 

2007 are linearly interpolated. The total number of marriages from NCHS are also used in the age-specific 

marriage rate calculations for the provisional period 1989 – 2011. The projection period of the MARRIAGE 

subprocess begins one year after the last historical data year (2012).

The historical age-specific marriage rates are calculated for each year in the historical period based on NCHS data

of the number of new marriages by age-of-husband crossed with age-of-wife and the number of unmarried 

persons by age and sex. The formula used in the calculations is given below:

z

x y

z

z x y

x y

P

M

m

,

,

,

ˆ

ˆ 

, where

 x refers to the age of males and y refers to the age of females;



z M x, y

ˆ

is the number of marriages in year z; and

 (

z Px, y

) is the geometric mean of the midyear unmarried males and unmarried females in year z.

The rates for the period 1978 through 198818 are then averaged, graduated, and loaded into an 87 by 87 matrix 

(age-of-husband crossed with age-of-wife for ages 14 through 100+), denoted as MarGrid. This matrix is used in 

the calculation of the age-specific marriage rates for all later provisional years and the years in the projection 

period.

For the provisional period, 1989-2010, the NCHS and ACS provided data on the number of 

marriages by age-group-of-husband crossed with age-group-of-wife (age groups include 15-19, 

20-24, 25-29, 30-34, 35-44, 45-54, 55-64, and 65+). These data are used to change the 

distribution of MarGrid by these age groups. For each age-group-of-husband crossed with agegroup-of-wife, the more detailed marriage rates in MarGrid that are contained within this 

group are adjusted so that the number of marriages obtained by using the rates in MarGrid 

match the number implied in the subset. For the last provisional year, 2011, ACS data was not 

available, so the previous year’s MarGrid is used.

For each year of the entire provisional period (1989 – 2011), an expected total number of 

marriages is calculated by multiplying the rates in the MarGrid (or the adjusted MarGrid for 

years 1989-2011) by the corresponding geometric mean of the unmarried males and unmarried 

females in the Social Security area population. All rates in MarGrid (or the adjusted MarGrid for 

years 1989-2011) are then proportionally adjusted to correspond to the total number of 

marriages estimated in the year for the Social Security area population. This estimate is 

obtained by increasing the number of marriages reported in the U.S. to reflect the difference 

between the Social Security area population and the U.S. population. In addition, we also 

 

18 Data for 1980 is not available and is excluded from the calculations.
44

subtract out same-sex marriages from the NCHS data, as we handle those in a later step. The 

provisional period age-specific rates are then graduated using the Whittaker-Henderson 

method and are used to calculate the age-adjusted rates for each year. 

The age-adjusted marriage rates are expected to reach their ultimate value in the 25th year of 

the 75-year projection period. Rather than use the last year of provisional data to calculate the 

starting rate, we calculate the weighted average of the rates for the past five historical data 

years to derive the starting value. The annual rate of change in the age-adjusted marriage rate 

is calculated by taking the 27th root of the ratio of the ultimate value and the starting value. 

Thus, to calculate the rate for a projected year, the rate of change is applied to the prior year’s 

rate (or the starting value for the first year of the projection period).

To obtain the age-of-husband-age-of-wife-specific rates for a particular year from the ageadjusted rate projected for that year, the age-of-husband-age-of-wife-specific rates in MarGrid 

are proportionally scaled so as to produce the age-adjusted rate for the particular year. The 

MarGrid rates are then increased to include projected same-sex marriages.

A complete projection of age-of-husband-age-of-wife-specific marriage rates was not done 

separately for each previous marital status. However, data indicate that the differential in 

marriage rates by prior marital status is significant. Thus, future relative differences in marriage 

rates by prior marital status are assumed to be the same as the average of those experienced 

during 1979 and 1981-1988. 
45

1.7. DIVORCE

1.7.a. Overview

For the period 1979 through 1988, the National Center for Health Statistics (NCHS) collected 

data on the annual number of divorces in the Divorce Registration Area (DRA), by age-group-ofhusband crossed with age-group-of-wife. In 1988, the DRA consisted of 31 States and 

accounted for about 48 percent of all divorces in the U.S. These data are then inflated to 

represent an estimate of the total number of divorces in the Social Security area. This estimate 

for the Social Security area is based on the total number of divorces in the 50 States, the District 

of Columbia, Puerto Rico, and the Virgin Islands. Divorce rates for this period are calculated 

using this adjusted data on number of divorces and estimates of the married population by age 

and sex in the Social Security area. 

An age-of-husband (x) crossed with age-of-wife (y) specific divorce rate (

z

x y d,

ˆ

) for a given year z

is defined as the ratio of (1) the number of divorces in the Social Security area for the given age 

of husband and wife (

z Dx, y

ˆ

) to (2) the corresponding number of married couples in the Social 

Security area (

z Px, y

) with the given age of husband and wife. An age-adjusted central divorce 

rate (

z ADRx, y

ˆ

) summarizes the 

z

x y d,

ˆ

for a given year. 

The 

z AD

ˆ

R

is calculated by determining the expected number of divorces by applying:

 The age-of-husband crossed with age-of-wife specific divorce rates to 

 The July 1, 2010, population of married couples in the Social Security area by 

corresponding age-of-husband and age-of-wife. 

The DIVORCE subprocess projects annual 

z

x y d,

ˆ

by age-of-husband crossed with age-of-wife. The 

primary equations, 1.7.1 and 1.7.2, are given below:

z

x y

z

dx, y d

,

ˆ ˆ



(·) (1.7.1)



 



x y

S

x y

z

x y

x y

S

x y

z

P

P d

ADR

,

,

,

,

,

ˆ

ˆ

(1.7.2)

where x and y refer to the age of husband and age of wife, respectively, and

S Px, y

is the number 

of married couples in the Social Security area population as of July 1, 2010.
46

1.7.b. Input Data

Long-Range OASDI Projection Data -

Demography

 Social Security area population of married couples by age-of-husband crossed with ageof-wife as of January 1 for years 1979-2012. These data are updated each year from the 

HISTORICAL POPULATION subprocess.

 The total population in the Social Security area for years 1979-2011. An additional year 

of data is added for each additional year of divorce data from the NCHS.

 The total population in the residential population plus armed forces overseas for years 

1979-2011. An additional year of data is added for each additional year of divorce data 

from the NCHS.

 The total population in Puerto Rico and the Virgin Islands for years 1979-2011. An 

additional year of data is added for each additional year of divorce data from the NCHS.

Assumptions -

Each year, the ultimate assumed value for the age-adjusted divorce rate is established. The 

rate reaches its ultimate value in the 25th year of the 75-year projection period. For the 

2014 report, the ultimate assumed 

z AD

ˆ

R

is 1,700 per 100,000 married couples. 

NCHS Data -

 The number of divorces in the DRA, by age-of-husband crossed with age-of-wife, for 

calendar years 1979 through 1988. These data are no longer available for years after 

1988. The data are broken out by single year age-of-husband crossed with single year 

age-of-wife for many ages (particularly younger ages).

 The total number of divorces in the United States for the period for 1989-2011. Data is 

updated when it becomes available. 

 The total number of divorces in Puerto Rico and the Virgin Islands for years 1989-2011. 

The most recent year of data was obtained in 2000; the 2000 figures are used as a proxy 

for 2001-2011.19 New data are incorporated as they become available and resources 

are sufficient to validate their use. 

 

19 Data for 1988 was used to estimate the number of Puerto Rico and Virgin Island divorces for 1989-1997. 
47

State Divorce Data -

Since NCHS stopped collecting state specific divorce data by age of husband crossed with 

age of wife, we went directly to the state health departments for their most recent data. 

We were able to get this data from 18 states. The years and age groups available vary by 

state. In general, the years were from 2009 – 2012. States that had these data available 

online or that sent us the data via email are Alaska, Alabama, Idaho, Kansas, Kentucky, 

Michigan, Missouri, Montana, Nebraska, New Hampshire, Utah, Tennessee, Texas, Virginia, 

Vermont, Washington, West Virginia, and Wyoming.

1.7.c. Development of Output

Equation 1.7.1 -

Age-specific divorce rates are defined for ages 14 through 100+. Detailed NCHS data on the number of divorces by 

age-group-of-husband crossed with age-group-of-wife are available for the period 1979 through 1988. Provisional 

data on the total number of divorces in the United States are used for the period 1989 through 2011. With the 

data from the various states, we developed a grid age-group-of-husband crossed with age-group-of-wife for 2011. 

First, the detailed NCHS data on divorces by age group is disaggregated into single year of age of husband (x) and 

age of wife (y), for ages 14-100+, using the H.S. Beers method of interpolation. Then, the age-specific divorce rates 

(

z

x y d,

ˆ

), for each year, z, are calculated for the period 1979-1988 by taking the number of divorces (inflated to 

represent the Social Security area, 

z Dx, y

) and dividing by the married population in the Social Security area at that 

age-of-husband and age-of-wife (

z Px, y

). The formula for this calculation is given below:

z

x y

z

z x y

x y

P

D

d

,

,

,

ˆ

ˆ 

(1.7.3)

These rates are then averaged, graduated,20 and loaded into an 87 by 87 matrix (age-of-husband crossed with ageof-wife for ages 14 through 100+), denoted as DivGrid. DivGrid is then adjusted using the state data grid 

developed for 2011. DivGrid after 1988 will be equal to a weighted average of the 1988 DivGrid and the state data 

single year grid. This state data single year of age grid is derived by ratioing the 1988 DivGrid cells using the 

original state age-group data. DivGrid will be used in the calculation of the age-specific divorce rates for all later 

years including the projection period. 

For each year in the provisional period (1989-2011), an expected number of total divorces in the Social Security 

area is obtained by applying the age-of-husband crossed with age-of-wife rates in DivGrid to the corresponding 

married population in the Social Security area. The rates in DivGrid are then proportionally adjusted so that they 

would yield an estimate of the total number of divorces in the Social Security area. The estimate of total divorces 

is obtained by adjusting the reported number of divorces in the U.S. for (1) the differences between the total 

divorces in the U.S. and in the combined U.S., Puerto Rico, and Virgin Islands area, and (2) the difference between 

the population in the combined U.S., Puerto Rico, and Virgin Islands area and in the Social Security area.

The starting age-adjusted divorce rate is set to a weighted average of the past five years of 

data. This age-adjusted rate is assumed to reach its ultimate value in the 25th year of the 75-

year projection period. The annual rate of change in the age-adjusted divorce rate is calculated 

by taking the 27th root of the ratio of the ultimate value and the starting value. Thus, to 

calculate the age-adjusted rate for a projected year, the rate of change is applied to the prior 

year’s rate (or to the starting value for the first year of the projection period).

 

20 Using the Whittaker-Henderson method of graduation.
48

To obtain age-specific rates for use in the projections, the age-of-husband-age-of-wife-specific 

rates in DivGrid are adjusted proportionally so as to produce the age-adjusted rate assumed for 

that particular year.
49

1.8. PROJECTED POPULATION

1.8.a. Overview

For the 2014 Trustees Report, the starting population for the population projections is the 

January 1, 2012, Social Security area population, by age, sex, and marital status, produced by 

the HISTORICAL POPULATION subprocess. (For this section, section 1.8, the term “starting 

year” refers to the year 2012.) The Social Security area population is then projected using a 

component method. The components of change include births, deaths, net legal immigration, 

and net other immigration. The components of change are applied to the starting population 

by age and sex to prepare an estimated population as of January 1, 2013, and to project the 

population through the 75-year projection period (years 2014-88). There is a separate equation 

for each of the components of change as follows:

 ()

z

s

z Bs B (1.8.1)

where 

z Bs

is the number of births of each sex (s) born in year z;

( )

, ,

 

z

x s

z Dx s D

(1.8.2)

where 

z Dx,s

is the number of deaths at age (x) and sex (s) that occurs in year z; and

z

x s

z

x s

z NI x,s

 NL

,

 NO

,

(1.8.3)

where 

z NI x,s

is the net total immigration (both legal and other), 

z NLx,s

is the net number of legal 

immigrants, and 

z NOx,s

is the net number of other immigrants.

Once the components of change are calculated, the following equation is used to calculate the 

Social Security area population by age and sex:

z Px,s

= 

1

1,

1

1,

1

1,











  

z

x s

z

x s

z Px s D NI (1.8.4)

where 

z Px,s

is the population, by age (x) and sex (s), as of January 1st of year z. Note that for age 

0, 

1

1,





z D s

represents neonatal deaths.

The population is further disaggregated into the following four marital statuses: single (never 

married), married, widowed, and divorced. The following equation shows the population by 

age (x), sex (s), and marital status (m) for each year z:

( )

, , , ,

 

z

x s m

z Px s m P

(1.8.5)

The children (ages 0-18) population is further disaggregated into the following four parent 

statuses (i.e., fates): both parents survive, only father survives, only mother survives, and both 

parents deceased. The following equation shows the children population by age of child (x), sex 

of parent (s), age group of parent (g), and fate of parent (f) for each year z:

( )

, , , , , ,

 

z

x s g f

z Cx s g f C

(1.8.6)
50

1.8.b. Input Data

Long-Range OASDI Projection Data -

Demography

 FERTILITY

 Historical birth rates by single year of age of mother (14-49) for the years 

beginning with 1941 and ending with the year prior to the starting year. These

data are updated each year.

 Projected birth rates by single year of age of mother (14-49) for the years 

beginning with the starting year and ending with 2101. These data are updated 

each year.

 MORTALITY

 Historical probabilities of death by age last birthday (including neonatal mortality 

factor, single year of age for ages 0-99, and age group 100+) and sex for years 

beginning with 1941 and ending with the year prior to the starting year. These 

data are updated each year.

 Projected probabilities of death by age last birthday (including neonatal 

mortality factor, single year of age for ages 0-99, and age group 100+) and sex 

for the years beginning with the starting year and ending with 2101. These data 

are updated each year.

 Factors to distribute probabilities of death by marital status. They are 

dimensioned by sex, single year of age (ages 14-100+), and marital status. These 

data are updated each year.

 LEGAL IMMIGRATION

 Projected numbers of legal immigrants who are new arrivals, by single year of 

age (0-84) and sex for years beginning with the starting year and ending with 

2101. These data are updated each year.

 Projected numbers of legal emigrants by single year of age (0-84) and sex for 

years beginning with the starting year and ending with 2101. These data are 

updated each year.

 Projected numbers of legal immigrants who are adjustments of status, by single 

year of age (0-84) and sex for years beginning with the starting year and ending 

with 2101. These data are updated each year.

HISTORICAL POPULATION

 Social Security area population by single year of age (0-99 and 100+), sex, and 

marital status for the years beginning with 1941 and ending with the starting 

year. These data are updated each year. 
51

 Married couples by single year of age of husband (ages 14-100+) crossed with 

single year of age of wife (ages 14-100+) for the years beginning with 1941 and 

ending with the starting year. These data are updated each year. 

 Other than legal population by age and sex for the years beginning with 1964 

and ending with the starting year. These data are updated each year.

 OTHER IMMIGRATION

 Projected numbers of other immigrants entering the country by age (0-85) and 

sex for years beginning with the starting year and ending with 2101. These data 

are updated each year.

 Projected numbers of other immigrants leaving the country by age (0-85) and sex 

for years beginning with the starting year and ending with 2101. These data are 

updated each year.

 Other than legal population by age and sex for the years beginning with the 

starting year and ending with 2101. These data are updated each year.

MARRIAGE

 Projected central marriage rates by single year of age of husband (ages 14-100+)

crossed with single year of age of wife (ages 14-100+) for each year of the 

projection period. These data are updated each year.

 Averaged and graduated marriage rates for the period 1979 and 1981-1988 by 

single year of age (ages 14-100+), sex, and prior marital status (single, divorced, 

and widowed). These data are updated each year.

 Total number of marriages for the years beginning with 1989 and ending the 

year prior to the starting date. These data are updated each year.

 DIVORCE

 Projected central divorce rates by single year of age of husband (14-100+) 

crossed with single year of age of wife (14-100+) for each year of the projection 

period. These data are updated each year.

U.S. Census Bureau Data -

 CPS data on the average number of children per married couple with children by age 

group of householder (age groups 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 

and 55-64) for 1960-2012. (Note that the program splits the last age group, which is 

a 10-year age group, into two 5-year age groups.) An additional year of data is 

added each year.
52

Other Input Data -

 The initial existing same-sex marriage grid as of 2013 is based on the number of 

same-sex marriages from 2004 – 2011 from the vital statistics offices in various 

states, and the same-sex marriage grid by age-group-of-husband crossed with agegroup-of-wife from the American Community Survey (ACS) public use microdata 

samples (PUMS) occurring, on average, at the end of years 2007 – 2010. 

1.8.c. Development of Output

Equation 1.8.1 - Births

The number of births in the Social Security area, 

z Bx

, is computed for each year, z, of the 

projection period by applying the age-specific birth rate to the midyear female population aged 

14 to 49 as follows:















 





2

z 1

x

z

z x

x

z

x

FP FP

B b

where,

 

z Bx

= number of births to mothers age x in year z;

 

z

x b

= birth rate of mothers age x in year z; and

 

z FPx

= female population age x at the beginning of year z.

The total number of births in a given year is the sum of the number of births to mothers at each 

age. This total number of births is disaggregated by sex by assuming a gender ratio of 105 male 

births for every 100 female births.

Equation 1.8.2 – Deaths

The number of deaths for the Social Security area by age (x) and sex (s),

z Dx,s

, is computed for 

each projection year by applying the death probabilities for each age and sex, 

z

x s q ,

, to the 

exposed population at the beginning of the year.

z

x s

z

x s

z Dx,s

 q ,P

,

Equation 1.8.5 – Disaggregating the population by marital status
53

Once the population is projected by single year of age and sex, it is then disaggregated into the 

following four marital states; single, married, widowed, and divorced. Estimates of the Social 

Security area population by single year of age (0-99 and 100+), sex, and marital status as of the 

starting date of the population projection are obtained from the HISTORICAL POPULATION 

subprocess. In addition, the HISTORICAL POPULATION subprocess provides the number of 

married couples by single year of age of husband crossed with single year of age of wife as of 

the starting date. 

At the end of 2013, existing same-sex married couples, who were previously not recognized as 

married, are moved from the single marital status category to the married category.

All births are assigned to the single category. For a given age and sex, deaths are assigned by 

marital status according to the relative differences in death rates by marital status observed for 

that age and sex during the calendar years 1995 and 1996, as determined in the MORTALITY 

subprocess. For a given age and sex, immigrants are assigned by marital status according to the 

beginning of year marital distribution of the Social Security area population for that age and 

sex.

Once the number of marriages, divorces, and widowings during a year are determined, the 

population by age, sex, and marital status is updated to represent end of year. The unmarried 

population at the end of the year is estimated from the population at the beginning of the year 

by subtracting deaths and marriages and adding new immigrants, widows (or widowers), and 

divorces during the year. The married population at the end of the year is estimated from the 

population at the beginning of the year by reducing the population for divorces, widows (or 

widowers), dissolutions of marriages when both husband and wife dies, and by increasing the 

population for new immigrants and marriages during the year.

Numbers of new marriages are determined for each projection year. The annual number of 

marriages occurring at each age of husband crossed with each age of wife is obtained by 

multiplying the age-of-husband and-age-of-wife-specific marriage rates with the geometric mean 

of the midyear unmarried male population and the midyear unmarried female population. 

The age-specific midyear unmarried male population21 is estimated from the beginning of the 

year unmarried populations. It is calculated by adjusting the number of unmarried males at the 

beginning of the year to represent midyear using the relationship between the prior beginning of 

year and the current beginning of year unmarried male populations. The midyear female 

unmarried population is approximated similarly.

The numbers of marriages are then distributed by previous marital status (single, widowed, 

divorced) in the same proportions as would have been produced by applying the previous 

marital-status-specific marriage rates from the MARRIAGE subprocess to the population by 

marital status at the beginning of the year. 

 

21 The midyear populations exposed to marriage are the unmarried populations (sum of those single, widowed, and 

divorced).
54

Numbers of new divorces are determined for each projection year. The number of divorces 

during a year, occurring at each age of husband crossed with each age of wife, is obtained by 

multiplying the age-of-husband by age-of-wife divorce rates for that year with the midyear 

number of married couples in that age crossing. 

The number of age-of-husband by age-of-wife midyear married couples is estimated from the 

beginning of the year married couples. It is calculated by adjusting the number of married 

couples at the beginning of the year to represent midyear using the relationship between the 

number of married couples at the beginning of the prior year and the beginning of the current 

year.

Widowings are computed by applying general population probabilities of death to the marriage 

prevalence at the beginning of the year. Widowings and deaths by marital status are then 

reconciled for internal consistency.

Equation 1.8.6 – Disaggregating the children by parent survival status

Once the population is projected by single year of age, sex, and marital status, the number of 

children are then categorized by age of father, age of mother, and orphan status. The 

HISTORICAL POPULATION subprocess provides the historical number of children (ages 0-18), 

number of women (ages 14-49), and the number of married couples by single year of age of 

husband crossed with single year of age of wife. The projected number of children (ages 0-18), 

number of women (ages 14-49), and marriage grid age of husband crossed with age of wife is 

calculated in the projected population.

For women aged 14-49, births are calculated by multiplying the age-specific birth rate, from the 

FERTILITY subprocess, with the average number of women at the corresponding age. The births 

are then distributed to the age of husband in the same proportions as the age of husband 

crossed with age of wife married couples grid.

Each year the number of children is then rolled forward a year to the next age of husband, age 

of wife, and child age. Parent survival is calculated based on the deaths rates from the 

MORTALITY subprocess. The number of orphans consists of children with at least one parent 

deceased. The calculated number of children by age of father and age of mother must match 

the number of children in the historical or projected population. To accomplish this, the 

calculated number of children is multiplied by the ratio of the number of children in the 

historical or projected population to the number of children by age of father and age of mother 

that was calculated using the fertility rates. For any remaining difference, an adjustment of one 

is made for each age of husband crossed with age of wife until the total number of children 

match.

Once the population is projected by single year of age, sex, marital status, and children, the 

mean number of children per married couple with children is determined by year and age of 

householder. The historical mean number of children by year and age of householder in the 

population program is calculated from the number of children categorized by age of father, age 
55

of mother, and the number of married men by age group from the HISTORICAL POPULATION 

subprocess. Linear regression is used to model the relationship between the mean number of 

children in the population program to the mean number of children from the U.S. Census 

Bureau. The regression model is then used to project the mean number of children by age of 

householder in the population program. 
56

Process 2:

Economics
57

2. Economic

The Office of the Chief Actuary uses the Economic process to project OASDI employment and 

earnings-related variables, such as the average wage for indexing and the effective taxable 

payroll. The Economic process receives input data from the Demography process and provides 

output data to the Beneficiaries and the Trust Fund Operations & Actuarial Status processes.

The Economic Process is composed of four subprocesses, U.S. EMPLOYMENT, U.S. 

EARNINGS, COVERED EMPLOYMENT AND EARNINGS, and TAXABLE PAYROLL. As 

a rough overview, U.S. EMPLOYMENT and U.S. EARNINGS project U.S. employment and 

earnings data, respectively, while COVERED EMPLOYMENT AND EARNINGS converts 

these employment and earnings variables to OASDI covered concepts. TAXABLE PAYROLL, 

in turn, converts OASDI covered earnings to taxable concepts, which are eventually used to 

estimate future payroll tax income and future benefit payments. 

U.S. EMPLOYMENT and U.S. EARNINGS produce quarterly output, while the output from 

COVERED EMPLOYMENT AND EARNINGS is annual. TAXABLE PAYROLL produces 

both.

Two appendices are at the end of this documentation. The first appendix, 2-1, provides details for 

most of the equations given in the following descriptions of the Economic process. The second 

appendix, 2-2, provides a listing with explanations of acronyms used in this documentation.
58

2.1. U.S. EMPLOYMENT (USEMP)

2.1.a. Overview

The Bureau of Labor Statistics (BLS) publishes historical monthly estimates for civilian U.S. 

employment-related concepts from the Current Population Survey (CPS). The principal measures 

include the civilian labor force (LC) and its two components – employment (E) and 

unemployment (U), along with the civilian non-institutional population (N). The BLS also 

publishes values for the civilian labor force participation rate (LFPR) and the civilian 

unemployment rate (RU). The LFPR is defined as the ratio of LC to N, while the RU is the ratio 

of U to LC, expressed to a base of 100. For many of these concepts, the BLS publishes historical 

data disaggregated by age, gender, marital status, and presence of children.

For various disaggregated groups22, USEMP projects quarterly and annual values for these 

principal measures of U.S. employment and population. Equations 2.1.1 through 2.1.6 outline the 

subprocess’ overall structure and solution sequence for the total economy. We assume that the 

military population (M) will remain constant over the first ten years of the projection horizon 

then grow at the same rate as E. We also assume that the sum of N and M will grow at the same 

annual rate projected for the Social Security area population (P) (see Demography Process 

input).

 

M t = M

2013 for t < 2023

M t-1* (E t/ E t-1) for t > 2023 (2.1.1)

N t= [(N t-1+ M t-1)*( P t/ P t-1)] – M t(2.1.2)

RU = RU(·) (2.1.3)

LFPR = LFPR(·) (2.1.4)

LC = LFPR * N (2.1.5)

E = LC * (1 -RU / 100) (2.1.6)

Note: the superscript t represents the projection year.

The Demography Process estimates historical values for the total Social Security area population 

(P) and an important component, the other immigrant population (OP). OP is further 

disaggregated into components by visa status: those temporarily authorized to reside or work in 

the US (OP_A), those who have overstayed their authorization (OP_NA), and those who were 

never authorized to reside or work in the US (OP_NO). Similarly, USEMP projects annual 

values for E and employed OP (EO), including its visa-status components (EO_A, EO_NA, 

EO_NO). USEMP also separates EO to those whose earnings are reported and posted to the 

 

22 Group disaggregation includes age and gender. Some groups are additionally disaggregated by marital status, 

presence of children.
59

Master Earnings File (EO_MEF), those whose earnings are reported posted to the Earnings 

Suspense File (EO_ESF), those in the underground economy (EO_UND). A further subgroup of 

EO_MEF is also calculated: those who are OASDI covered (EO_MEFC). Equations 2.1.7 

through 2.1.14 outline the overall structure of the subprocess used to estimate EO and its 

sub-components.

EO_A = EO_A(·) (2.1.7)

EO_NA = E * OP_NA / N (2.1.8)

EO_NO = E * OP_NO / N (2.1.9)

EO = EO_A + EO_NA + EO_NO (2.1.10)

EO_MEF = EO_MEF(·) (2.1.11)

EO_MEFC = EO_MEFC(·) (2.1.12)

EO_ESF = EO_ESF(·) (2.1.13)

EO_UND = EO – EO_MEF – EO_ESF (2.1.14)

Finally, for each age/gender group, USEMP projects total “at-any-time” employed other 

immigrant population (TEO). EO represents the average weekly employment of the other 

immigrant population during a calendar year. TEO represents the total number of individuals in 

the other immigrant population who had any employment during the calendar year. (EO can be 

roughly viewed as the average number of jobs worked by OP during a calendar year, while TEO 

represents the total number of individuals who worked those jobs.) Effectively, Equations 2.1.15 

through 2.1.19 convert every EO age-gender sub-component to an at-any-time TEO age-gender 

sub-component counterpart. 

TEO_MEF = TEO_MEF(·) (2.1.15)

TEO_MEFC = TEO_MEFC(·) (2.1.16)

TEO_ESF = TEO_ESF(·) (2.1.17)

TEO_UND = TEO_UND(·) (2.1.18)

TEO = TEO_MEF + TEO_ESF + TEO_UND (2.1.19)
60

2.1.b. Input Data

Long-Range OASDI Projection Data

These data are updated each year.

Demography

 Social Security area population as of year-end (1941 – 2099) by age, marital status 

(single, married, widowed, divorced) and gender (M, F)

 “Other immigrant” population as of year-end (1964 – 2099) by age, gender (M, F), 

and visa status (OP_A, OP_NA, and OP_NO)

 Number of children by age of child and age of mother (1960-2099)

 Life expectancy by age and gender (1950-2099)

 Exit rates (probability of leaving the “other immigrant” population by other than 

death) by age and gender.

 Mortality rates by age and gender (1941-2099)

Trust Fund Operations and Actuarial Status – The Trust Fund Operations and Actuarial 

Status Process provides no input to the Economic Process sections. However, the LFPRs use 

input from the Outgo Process from the prior year’s Trustees Report. That is, the projected 

LFPRs for the 2014 Trustees Report use input from the 2013 Trustees Report that includes 

projections for the disability prevalence rates by age and gender (originally from the 

Beneficiaries subprocess), and the primary insurance amount (PIA) replacement rates by age 

and gender. The disability prevalence rate is defined as the ratio of the number of disabled 

worker beneficiaries to the disability-insured population. The PIA replacement rate is defined 

as the ratio of a hypothetical medium-scaled worker’s PIA to his/her career-average indexed 

earnings level. 

Trustees’ Assumptions

Each year the Board of Trustees of the OASDI Trust Funds sets the ultimate average annual 

growth rate values for key economic variables:

 Real wage

 Total economy productivity

 Average hours worked

 Ratio of wages to compensation (RWSD)

 Ratio of compensation to GDP (RWSSY)

 GDP deflator (PGDP)

 Consumer Price Index (CPI)

The Board also sets ultimate values for:

 Annual trust fund real interest rate

 Unemployment rate

These ultimate values are typically reached during the last half of the short-range (first 10 

years) of the projection horizon. Earlier projected values are set to provide a smooth 
61

transition from the latest actual historical values to the assumed long-range ultimate ones. As 

a by-product of this process, values for real GDP, and potential GDP are set. The ratio (RTP) 

of real to potential GDP is an important summary measure of the economic cycle.

The Trustees also agree on the assumed short-range values for the listed variables.

Addfactors

Addfactors are adjustments that move an estimate closer to an expected value. They may be 

used for a variety of reasons associated with data availability, structural changes in the data 

and/or model, and perceived temporary aberrations in recent historical data. Addfactors were 

included on male and female LFPRs starting around age 40 to reflect projected changes in 

life expectancy.

Other input data 

 U.S. armed forces (EDMIL) by age and gender, estimated by the Department of Defense 

and published by the Census Bureau on a monthly basis (1948-2000) by single year of age 

(17 to 64) and gender. These data are no longer produced by Census.

 EDMIL by age and gender, estimated by the Economic Process as the difference in 

monthly resident plus Armed Forces overseas population and the monthly civilian 

population. These two populations are available from the Census Bureau on a monthly basis 

(April 2000 to October 2013) by single year of age (16 to 69) and gender. These data are 

updated several times a year.

 Data for the mobilized military reservist population, by branch of service (September 

2001-September 2013) are reported by the US Department of Defense weekly. This 

subprocess updates the data several times a year.

 Data from the March Supplement of the Joint BLS/Census Current Population Survey 

(CPS) by year (1968-2012), for levels of the civilian noninstitutional population, labor 

force, military, and unemployment. These data are available from the U.S. Census Bureau, 

via DataFerrett, by single year of age (16 to 85+), gender, marital status (never married, 

married with a spouse present, and married with no spouse present), and presence of 

children. These data are updated by the U.S. Census Bureau for the BLS annually. This 

subprocess updates the data every other year (or more often, based on time availability). 

Data from the March Supplement of the CPS by year (1992-2012), for levels of the civilian 

noninstitutional population. These data are available from the U.S. Census Bureau, via 

DataFerrett, by single year of age (16 to 85+), gender, and educational attainment level. 

These data are updated by the U.S. Census Bureau for the BLS annually. This subprocess 

updates the data every year, if time availability allows.

 Data from the CPS (1948-2012) for levels of civilian employment, civilian labor force, 

civilian unemployment, and civilian noninstitutional population. These data are available 
62

from the BLS by age group and gender. These data are updated by the BLS monthly. This 

subprocess updates the data several times a year.

Data from the CPS by year (1994-2012), for the civilian noninstitutional population. These 

data are available from the BLS by single year of age (16 to 85+), gender, marital status, 

labor force employment status, and (for those not in the labor force) reason for not being in 

the labor force. These data are updated by the BLS monthly. Monthly data are used to 

calculate annual averages. This subprocess updates the data every year, if time availability 

allows.

 Data from the Current Employment Statistics survey (CES) (1964 (varies) to 2012) for 

establishment employment, average hourly earnings, average weekly earnings, and average 

weekly hours. These data are available from the BLS by sector. These data are updated by 

the BLS monthly. This subprocess updates the data several times a year.

Unpublished data from the CPS (1965 - October 2013) for male and female civilian labor 

force participation rates for older workers. These data are available from the BLS by single 

year of age (ages 55-79) and by group (75 and over, and 80 and over). These data are 

updated by the BLS monthly. This subprocess updates the data several times a year. 

 The historical resident population is published annually by the Census (1980-

2012). These data are available by gender and age group. This subprocess uses data 

for age groups under 16 and 60 and over. This subprocess updates the data 

annually.
63

2.1.c. Development of Output

Equation 2.1.3 - Unemployment Rate (RU)

The RU is disaggregated by age and gender. The age groups include 16-17, 18-19, 20-24, 25-

29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, and 75 and over. Thus, 

USEMP contains 28 RU equations, 14 for males and 14 for females. Each disaggregated RU 

is specified using a first-difference model that depends on the distributed lag in the change in 

the ratio of real to potential GDP (RTP) and an adjustment to ensure that values converge to 

its estimated trend level. Coefficients are estimated by regression and constrained to an 

expected aggregate behavior whereby a 2.0 percentage point increase in the RTP elicits a 1.0 

percentage point decrease in the RU. Furthermore, projections are constrained to the ultimate 

age-gender-adjusted RU set by the Trustees. The aggregate RU is dependent on the projected 

distribution of the labor force by age and gender. See Appendix 2-1 for details on the 

equations.

Equation 2.1.4 - Labor Force Participation Rate (LFPR)

The LFPR is disaggregated by age and gender. Age groups include 16 to 17 (i.e., 16-17), 18-

19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55, 56, ... 99, 100 and over. For age 

groups between 20 and 54, male and female LFPRs are further disaggregated by marital 

status, categories of which include never married, ever married with spouse present, and ever 

married with spouse absent (which includes separated, widowed, and divorced). Female 

LFPRs disaggregated by age (between 20 and 44) and by marital status are further 

disaggregated by presence of own child. The groups for presence of own child include 

females with at least one child under the age of six and females without a child under the age 

of six. Thus, USEMP contains 153 LFPR equations, 69 for males and 84 for females. See 

Appendix 2-1 for details on the equations.

Given the level of demographic disaggregation, the aggregate LFPR is dependent on the 

projected distribution of the population by age, gender, marital status, and presence of own 

child. Each disaggregated LFPR, however, is dependent on the input variables that are most 

relevant to the demographic group. For example, only the LFPRs for relevant older workers 

are dependent on changes to the normal retirement age (NRA). Specific examples of the 

impact of input data on the disaggregated LFPRs are presented below.

 Disability prevalence ratio (RD) is defined as the ratio of disabled worker beneficiaries to 

the disability-insured population. An increase in RD lowers the LFPR. RD is adjusted 

with a multiplicative factor defined as the group’s average historical LFPR over a 15-year 

period. This adjustment implicitly assumes that disability can strike any person in the 

population with equal probability. For ages 62 to NRA, RDs are not “pure” RDs in that 

they are subject to the confounding effect of the availability of retirement benefits. For 

example, at age 62, a marginally disabled individual may opt to begin receiving retirement 

benefits rather than go through an uncertain disability application/appeals process. For 

ages NRA and above, RDs are unavailable because at the NRA all disabled-worker 

beneficiaries become retired-worker beneficiaries. To avoid these problems, RDs for ages 
64

62-74 are set to their cohort RD at age 61. For example, the RD for males age 62 in year 

(t) is set to the RD for males age 61 in year (t-1).

 The unemployment rate (RU) is a measure of the economic cycle. An increase in the 

lagged and current unemployment rate leads to a decrease in the LFPR. The RU affects 

most LFPRs. 

 The normal retirement age (NRA) is assumed to affect the LFPRs for those age 62 

through 69 through an earnings test and replacement rate. The replacement rate is defined 

as the ratio of a hypothetical worker’s PIA to career-average wage level. This value is 

projected for hypothetical workers with medium-scaled earnings patterns23 who retire at 

ages 62 through 69. The replacement rate is adjusted to include the reduction for early 

retirement and the delayed retirement credit. An increase in the NRA decreases the 

adjusted replacement rates, which, in turn, leads to increases in the LFPRs for those 

between the ages of 62 and 69. The potential earnings test tax rate (POT_ET_TXRT) is 

used in LFPRs between 62 and 69. It is defined as a tax rate on monthly retirement 

benefits faced by an individual who opts to collect Social Security benefits before 

reaching NRA while continuing to work and earn income. An increase in the NRA from 

66 to 67 leads to an increase in the potential tax rate for those age 66, which, in turn, leads 

to a decrease in their LFPR.

 The education distribution of the workforce increases the LFPRs if the level of educational 

attainment increases.

 The index of females with children is approximately equal to the ratio of the number of 

children under age 6 to mothers age 20 to 44. For females aged 20 to 44 with at least one 

own child, an increase in the index lowers the LFPR. 

 A LFPR increases with its lagged cohort. Lagged cohort variables affect female LFPRs 

age 55 and over, and male LFPRs age 75 and over.

 The LFPRs for males age 62 through 74 increase with spousal LFPRs.

 For those approximately age 40 and over, an increase in life expectancy leads to an 

increase in LFPRs .

Equation 2.1.7 to 2.1.20 – Employed Other Immigrant Population (EO) and At-Any-Time 

Employed Other Immigrant Population (TEO)

EO is estimated by gender and single-year of age from 16 to 100 based on OP and estimated 

employment-to-population ratios by visa-status component (OP_A, OP_NA, OP_NO). For 

this purpose, OP_A is further disaggregated into subgroups by visa type that differ in 

employment patterns or OASDI coverage status. The other two components are assumed to 

have equal employment-to-population ratio as the legal permanent resident population of the 

same age and gender. This portion of USEMP contains 4,250 equations, for 85 ages, 2 

genders, and 25 components and subgroups. We separate EO_NO into those who worked in 

2001 and earlier and those who began working in 2002 and later, since we believe that those 

who worked in 2001 and earlier are more likely to have OASDI covered wages. Each 

component is then further separated into EO_MEF, EO_MEFC, EO_ESF, and EO_UND. 

 

23 More details on the hypothetical scaled workers are provided in Actuarial Note #2005.3, located at the following 

internet address: www.socialsecurity.gov/OACT/NOTES/ran3/index.html.
65

Every EO sub-component by age, gender, and visa status is converted to its age-gender TEO 

sub-component counterpart using an age-gender conversion weight. For example, if the 

sub-component of EO is for never authorized males age 20 to 24, the conversion weight is 

defined as the ratio of total economy-wide at-any-time employed males age 20 to 24 

(TEM2024) to the sum of military and CPS civilian male employment age 20 to 24. For 

authorized workers and students on temporary visas, conversion weights take into account 

their partial presence in the year of arrival and the year of departure.
66

2.2. U.S. EARNINGS (USEAR)

2.2.a. Overview

In the CPS data, E is separated by class of worker. The broad categories include wage and salary 

workers (EW), the self-employed (ES), and unpaid family workers (EU). For the nonagricultural 

sector, a self-employed participation rate (SEPR) is defined as the ratio of ES to E, the 

proportion of employed persons who are self-employed. For the agricultural sector, the SEPR is 

defined as the ratio of ES to the civilian noninstitutional population. 

USEAR projects quarterly values for these principal classes of employment. Equations 2.2.1 

through 2.2.4 outline the subprocess’ overall structure and solution sequence.

SEPR = SEPR(·) (2.2.1)

ES = SEPR * E (2.2.2)

EU = EU(·) (2.2.3)

EW = E - ES - EU (2.2.4)

In the National Income and Product Accounts (NIPA), the Bureau of Economic Analysis (BEA) 

publishes historical quarterly estimates for gross domestic product (GDP), real GDP, and the 

GDP price deflator (PGDP). PGDP is equal to the ratio of nominal to real GDP. Potential (or 

full-employment) GDP is a related concept defined as the level of real GDP that is consistent 

with a full-employment aggregate RU.

USEAR projects quarterly values for these output measures. Potential GDP is based on the 

change in full-employment values for: (1) E (including U.S. armed forces), (2) average hours 

worked per week, and (3) productivity. Full-employment values for E are derived by solving 

USEMP under full-employment conditions, while the full-employment values for the other 

variables (average hours worked and productivity) are set by assumption. Projected real GDP is 

set equal to the product of potential GDP and RTP. RTP reaches 1.0 in the short-range period 

and remains at 1.0 thereafter. Nominal GDP is the product of real GDP and PGDP. The growth 

rate in PGDP is set by assumptions.

The BEA also publishes quarterly values for the principal components of U.S. earnings, 

including total wage worker compensation (WSS), total wage and salary disbursements (WSD), 

and total proprietor income (Y). These concepts can be aggregated and rearranged. Total 

compensation (WSSY) is defined as the sum of WSS and Y. The total compensation ratio 

(RWSSY) is defined as the ratio of WSSY to the GDP. The income ratio (RY) is defined as the 

ratio of Y to WSSY. The earnings ratio (RWSD) is defined as the ratio of WSD to WSS. 
67

USEAR projects quarterly values for these principle components of U.S. earnings using 

Equations 2.2.5 through 2.2.11.

RWSSY = RWSSY(·) (2.2.5)

WSSY = RWSSY * GDP (2.2.6)

RY = RY(·) (2.2.7)

Y = RY * WSSY (2.2.8)

WSS = WSSY - Y (2.2.9)

RWSD = RWSD(·) (2.2.10)

WSD = RWSD * WSS (2.2.11)

2.2.b. Input Data

Long-Range OASDI Projection Data

Demography- (See Section 2.1.b.)

Economics - Data from Section 2.1 include the total employed (E), E by age and gender, 

LFPRs by age and gender, the aggregate unemployment rate (RU), and the full-employment 

concepts for LC, RU, and E.

Trustees Assumptions - (See Section 2.1.b.)

Addfactors

Addfactors were included on some employment and output variables to smooth the transition 

between the latest historical data and the projected values. The need for addfactors is 

reviewed each year and they are implemented if necessary.

Other input data

 Data from the NIPA (1929 (varies) to 2013) for GDP, income, wages, compensation, 

personal consumption expenditures, investment, employer contributions for employee 

pension and insurance funds, and employer contributions for government social insurance. 

They are published by the BEA quarterly and/or annually. This subprocess updates the 

data several times a year.

 Ratio of OASDI covered to NIPA wages, and ratio of OASDI taxable to covered wages. 

NIPA wages by sector are available quarterly and annually from 1947 to 2013. They are 

published by the BEA and updated several times during the year. OASDI covered and 
68

taxable wages (1971 to 2011) are updated annually by the Economic process. Covered and 

taxable data for more recent historical years are estimated from preliminary tabulations of 

Form 941 and W-2 data. Projected values for covered ratios are set to the latest historical 

year.

 OASDI employee, employer, and self-employed tax rates from 1937 to 2080. These 

contribution rates are set according to the Social Security Act of 1935 as amended through 

2013. The rates are updated when legislation mandates a change.

 The October ratio of the number of teenagers enrolled in school to the civilian 

noninstitutional population by gender and age group (16-17 and 18-19) for the period 

1947 to 2013. An additional new year of data from the Census Bureau is usually available 

for including in preparation of the next annual Trustees Report. Projected values are set to 

levels from the latest historical year.

 The historical Consumer Price Index (CPI) is published monthly by the BLS. This 

subprocess updates the data several times a year.

 The historical CPI for medical services is published monthly by the BLS. Quarterly values 

are projected based on the projected growth in the aggregate CPI and an additional 

amount defined as the growth rate differential in the two price measures that was assumed 

in the latest President’s Fiscal Year Budget. The series is updated annually.

 U.S. armed forces (EDMIL) by age and gender were estimated by the Department of 

Defense and published by the Census Bureau on a monthly basis (1948-2000) by single 

year of age (17 to 64) and gender. These data are no longer produced by Census.

 EDMIL by age and gender are estimated by the Economic process as the difference in the 

monthly resident plus Armed Forces overseas population and the monthly civilian 

population. These two populations are available from the Census Bureau on a monthly 

basis (April 2000 to June 2013) by single year of age (16 to 69) and gender. These data 

are updated several times a year.

 Wages for railroad workers are wages covered by the Railroad Retirement Act. The 

annual data are for the period 1971 to 2012. An additional year of data from the Railroad 

Retirement Board is usually available for including in preparation of the next annual 

Trustees Report.

 Unpublished data from the CPS (1988-2013) on employment by class of worker (i.e., 

agricultural, non-agricultural, unpaid family, private industry, government, wage and 

salary, self-employed). These data are available from the BLS by age group and gender. 

These data are updated by the BLS annually. This subprocess updates the data annually.

 Data from the NIPA (1947-2013) for wages and compensation of households and 

institutions are published by the BEA quarterly. This subprocess updates the data several 

times a year.
69

 Other program-related parameters, including the average indexing wage, the benefit 

increase, the taxable maximum, and the annual retirement earnings test exempt amounts, 

are obtained annually from the Short-Range section of OCACT. This subprocess updates 

the data annually.

 Ratios of OASDI covered wages to NIPA wages are used from the prior year’s Trustees 

Report as input to the current year’s calculations, for the following sectors: Federal 

civilian, Federal military, state and local, and private industry.

 Recent historical values (2000-2013) for quarterly real potential GDP are obtained from 

the Congressional Budget Office. This subprocess updates the data annually and adjusts 

the series to create a series usable for the current Trustees Report.

 Unpublished data from the CES & CPS for total hours worked in the economy. These data 

are available from the BLS. These data are updated by the BLS quarterly (1948-2013) and 

annually (1948-2012). This subprocess updates the data several times a year.

 The Federal minimum hourly wage is based on the Fair Labor Standards Act from the 

Department of Labor for 1938 to 2013. The wage is updated when there is legislation 

mandating a change.

 Time trends (set by Economic process) are used in the agriculture sector for employment, 

real output, and compensation in the short-range period. These short-range trends are 

extended for each year’s Trustees Report, reflecting a new short-range period.

2.2.c. Development of Output

Equation 2.2.1 - Self-Employed Participation Rate (SEPR)

The SEPR is disaggregated by age, gender, and industry. The age groups include 16-17, 18-

19, 20-24, 25-34, 35-44, 45-54, 55-64, and 65 and over. The industry groups include 

agriculture and non-agriculture.

For the non-agriculture sector, the SEPRs by age and gender are defined as the ratio of the 

non-agriculture self-employment to total employment. Thus, the aggregate non-agriculture 

SEPR is dependent on the projected distribution of employment by age and gender. All nonagriculture SEPRs by age and gender are dependent on the RTP. Increases in the RTP lead to 

decreases in the SEPRs. For female age groups between 20 and 64, the non-agriculture 

SEPRs are also dependent on the groups LFPRs. Increases in the LFPRs lead to increases in 

the SEPRs.

For the agriculture sector, the male SEPRs by age are defined as the ratio of agriculture selfemployment to the civilian noninstitutional population. Thus, the aggregate agriculture SEPR 

for males is dependent on the projected distribution of the population by age. The agriculture 

SEPRs for males by age are dependent on the ratio of total agriculture employment (EA) to 
70

the total civilian population aged 16 and over. (EA is projected in a farm sub-program. Real 

farm output is projected to increase with the population, while farm productivity, defined as 

output per worker, is projected to continue to follow its historical trend. EA is projected as 

the ratio of farm output to farm productivity.) An increase in the ratio of EA to the total 

civilian population aged 16 and over leads to an increase in the agriculture SEPRs for males.

The female SEPRs by age for the agriculture sector are defined as the ratio of the female to 

male agriculture self-employment. Thus, the aggregate agriculture SEPR for females is 

dependent on the projected distribution of male agriculture employment by age. For female 

age groups between 18 and 64, the SEPRs are dependent on the RTP and the corresponding 

ratio of total female to male employment. Generally, an increase in the RTP leads to 

increases in the SEPRs. An increase in the total employment ratio also leads to an increase in 

the SEPR. 

Equation 2.2.3 - Unpaid Family Workers (EU)

EU is disaggregated by age, gender, and industry. The age groups include 16-17, 18-19, 20-

24, 25-34, 35-44, 45-54, 55-64, and 65 and over. The industry groups include agriculture and 

non-agriculture.

From 1970 to 2012, the level of EU fell from about 0.5 to 0.03 million in the agriculture 

sector and from about 0.5 to 0.08 million in the nonagriculture sector. For projections, the 

levels of EU by age and gender in the agriculture sector are assumed constant and about five 

thousand or less. The EUs by age and gender in the nonagriculture sector are projected as a 

constant ratio to ES. 

Equation 2.2.5 - Total Compensation Ratio (RWSSY)

The Trustees set the ultimate annual growth rate for RWSSY. For the short-range period, 

total WSS, WSD, and Y are aggregated from sector components. Total GDP, WSS, and 

WSD are divided into the farm and nonfarm sectors. The nonfarm sector is further separated 

into the government and government enterprises, households, non-profit institutions, and 

residual (private nonfarm business excluding government enterprises (PBNFXGE)) sectors. 

Total Y is divided into the farm and residual (i.e., PBNFXGE) sectors.

The methodology used to estimate GDP, WSS, WSD, and Y differs by sector.

Farm - Nominal GDP is the product of real GDP and the farm price deflator. Real farm GDP 

is projected from estimates of real farm per capita output. EA is projected from estimates of 

farm productivity. EAW is projected to continue its historical increase relative to EA. Farm 

compensation (WSSPF) is the product of estimates for average farm compensation 

(AWSSPF) and EAW, while farm proprietor income (YF) is the product of estimates of 

average farm proprietor income (AYF) and EAS. AYF is projected based, in part, on the 

growth in average compensation in the private sector.
71

Government and Government Enterprises - This sector is further disaggregated to Federal 

Civilian, Federal Military, and State and Local. In each sector, WSD is the product of 

estimates for average wages and employment. WSS is the sum of WSD and estimates for 

non-wage components of compensation. GDP is the sum of WSS and estimates of 

consumption of fixed capital.

Household - WSS is the product of estimates for average compensation and employment. 

WSD is WSS less employer contributions for the OASDHI tax. GDP is the sum of WSS and 

the gross value added of owner-occupied housing.

Nonprofit Institutions - The Nonprofit Institutions sector is further disaggregated to Health, 

Education, and Social Services sectors. In each sector, WSS is the product of estimates for 

average compensation and employment. WSD is WSS less the estimates for non-wage 

components of compensation. GDP is WSS plus a residual component of output.

Private Nonfarm Business Excluding Government Enterprises (PBNFXGE) - GDP in the 

PBNFXGE sector is total economy-wide GDP less the sum of the other sector GDPs. WSS is 

projected as a ratio to GDP less Y. The ratio is projected to be mostly stable, varying only 

temporarily with changes in RTP. Y is projected to grow with GDP and the ratio of EAS to 

total employment in the sector.

Thus, total labor compensation (WSSY) is summed from sector components, while the total 

compensation ratio (RWSSY) is the ratio of total WSSY to total GDP. It is important to note 

that the pure program-generated estimate for the total RWSSY is adjusted to ensure a smooth 

transition between the latest historical data and the Trustees’ ultimate assumptions.

Equation 2.2.7 - Income Ratio (RY)

Y is disaggregated to the farm and PBNFXGE sectors. In the PBNFXGE sector, the Y is 

projected to grow with GDP and the ratio of EAS to total employment in the sector. In the 

farm sector, Y is projected based in part on the growth in average compensation in the 

private sector.

Equation 2.2.10 - Earnings Ratio (RWSD)

In the NIPA, the difference between WSS and WSD is defined as employer contributions for 

employee pension and insurance funds (OLI) and employer contributions for government 

social insurance (SOC). OLI is mostly health and life insurance, and pension and profit 

sharing. SOC is composed of employer contributions to Federal and State & Local 

government social insurance funds. Federal government funds include OASDI, HI, UI, and 

other small groups. State and Local government funds mostly include workers’ 

compensation.

RWSD is defined as the ratio of WSD to WSS. RWSD is projected to mostly decline on a 

year-by-year basis over the entire 75-year projection horizon due to projected increases in 

employer contributions to employee group health insurance premiums (ECEGHIP) and 
72

pensions. ECEGHIP is projected by the Center for Medicare and Medicaid Services (CMS) 

and is consistent with new health care legislation enacted in 2010. Employer contributions to 

employee pension funds are assumed to increase as life expectancy increases.
73

2.3. OASDI COVERED EMPLOYMENT AND EARNINGS (COV)

2.3.a. Overview 

Total at-any-time employment (TE) is defined as the sum of total OASDI covered employment 

(TCE) and total noncovered employment (NCE). TCE can be decomposed to workers who only 

report OASDI covered self-employed earnings (SEO) and to wage and salary workers who 

report some OASDI covered wages (WSW). Combination workers (CMB_TOT) are those who 

have both OASDI covered wages and self-employed income. Workers with some selfemployment income (CSW) are the sum of SEO and CMB_TOT.

Some of these concepts can be rearranged. The total employed ratio (RTE) is defined as the ratio 

of TE to the sum of EW, ES, and EDMIL, while the combination employment ratio (RCMB) is 

defined as the ratio of CMB_TOT to WSW.

COV projects annual values for TE and the principle measures of OASDI covered employment. 

Equations 2.3.1 through 2.3.9 outline the overall structure and solution sequence used to project 

these concepts.

(Equation 2.3.1 not used in this version.) (2.3.1)

TE = TE(·) (2.3.2)

NCE = NCE(·) (2.3.3)

TCE = TE - NCE (2.3.4)

SEO = SEO(·) (2.3.5)

WSW = TCE - SEO (2.3.6)

RCMB = RCMB(·) (2.3.7)

CMB_TOT = RCMB * WSW (2.3.8)

CSW = SEO + CMB_TOT (2.3.9)
74

Total OASDI covered earnings is defined as the sum of OASDI covered wages (WSC) and total 

covered self-employed income (CSE_TOT). Both components can be expressed as ratios to their 

U.S. earnings counterparts. The covered wage ratio (RWSC) is defined as the ratio of WSC to 

WSD, while the covered self-employed ratio (RCSE) is the ratio of CSE_TOT to Y.

COV projects annual values for the principal measures of OASDI covered earnings using 

Equations 2.3.10 through 2.3.13.

RWSC = RWSC(·) (2.3.10)

WSC = RWSC * WSD (2.3.11)

RCSE = RCSE(·) (2.3.12)

CSE_TOT = RCSE * Y (2.3.13)

COV projects various annual measures of average OASDI covered earnings, including the 

average covered wage (ACW), average covered self-employed income (ACSE), and average 

covered earnings (ACE). 

ACW = WSC / WSW (2.3.14)

ACSE = CSE_TOT / CSW (2.3.15)

ACE = (WSC + CSE_TOT) / TCE (2.3.16)

The average wage index (AWI) is based on the average wage of all workers with wages from 

Forms W-2 posted to the Master Earnings File (MEF). By law, it is used to set the OASDI 

contribution and benefit base (TAXMAX).

COV projects annual values for the AWI and TAXMAX.

AWI = AWI(·) (2.3.17)

TAXMAX = TAXMAX(·) (2.3.18)

2.3.b. Input Data

Long-Range OASDI Projection Data

Demography - (See Section 2.1.b.)

Economics- Employment and earnings-related data from Sections 1.1 and 1.2.
75

Trustees Assumptions - (See Section 2.1.b.)

Addfactors

Addfactors were included on some employment variables to smooth the transition from the 

latest historical data to program estimates. The need for addfactors is reviewed each year and 

they are implemented if necessary. 

Other input data

 Ratios of OASDI covered to NIPA wages by sector. NIPA wages by sector are 

available quarterly and annually from 1947 to 2011. They are published by the BEA 

and updated several times during the year. OASDI covered wages (1971 to 2011) are 

updated annually by the Economic process. Covered data for the latest historical year 

are estimated from tabulations of Form 941 and W-2 data.

 U.S. armed forces (EDMIL) by age and gender were estimated by the Department of 

Defense and published by the Census Bureau on a monthly basis (1948-2000) by 

single year of age (17 to 64) and gender. These data are no longer produced by 

Census.

 EDMIL by age and gender are estimated by the Economic process as the difference in 

the monthly resident plus Armed Forces overseas population and the monthly civilian 

population. These two populations are available from the Census Bureau on a 

monthly basis (April 2000 to October 2013) by single year of age (16 to 69) and 

gender. These data are updated several times a year.

 Railroad employment is covered by the Railroad Retirement Act. The annual 

historical data are for the period 1971 to 2012. An additional new year of historical 

data from the Railroad Retirement Board is usually available for inclusion in 

preparation of the next annual Trustees Report.

 Data obtained from Office of Research, Evaluation, and Statistics (ORES) are 

tabulations of quarterly Form 941 data. Data currently used are the OASDI, HI, and 

income taxable wages by sector for the most recent five years. The data represent 

changes in reported wages since the prior quarterly report. The most recent data are 

appended to previously reported data. Annual totals are computed and used to derive 

estimates of OASDI covered wages by sector for the latest historical years.

 Data obtained from the most recently available 1.0% CWHS active file, maintained 

on Social Security’s mainframe and made available by ORES. The years of data are 

1951 to the third year prior to the current Trustees Report year. The data are used for 

comparison of OASDI covered earnings from other sources.
76

 Data obtained from extracting information from the 1.0% Employee-Employer Files, 

maintained on Social Security’s mainframe and made available by ORES. Each year 

two files are created: a Version 1 file for the third year prior to the current Trustees 

Report and a Version 3 file for the fifth year prior to the current Trustees Report. Data 

currently being used are government and farm sector OASDI, HI, and total wages and 

employment. Data from the latest files are used to estimate OASDI covered wages for 

the years available on each file.

 Data obtained from quarterly IRS Form 941 files, provided by Office of Systems 

(OS). Data currently used are the OASDI and HI taxable wages for 1978 to the most 

recent year available. The data represent changes in reported wages since the prior 

quarterly report. The most recent data are appended to previously reported data. 

Annual totals are computed and used to derive estimates of HI taxable wages, which 

are then used to develop OASDI covered wages for the most recent historical years.

 Data for the most recent ten years from the Quarterly EPOXY Report, received in 

hard-copy and, more recently, electronic formats obtained from OS. The data 

currently used are the number of workers with OASDI taxable earnings, number of 

workers with HI taxable earnings, distribution of number of HI workers by wage 

intervals, distribution of number of OASDI workers by wage intervals, number of 

persons with OASDI taxable wages, number of persons with HI taxable wages, 

number of persons with OASDI taxable self-employment earnings, and number of 

persons with HI taxable self-employment earnings. 

 Data obtained from the Quarterly Trust Fund Letter, received from Office of 

Financial Policy and Operations (OFPO). Data currently used are OASDI and HI 

taxable wages accumulated from all Forms 941 and W-2 to date, and changes in selfemployment earnings and self-reported tips since the prior Letter. These data are for 

years 1978 to the most recent year available.

 Data obtained from OS on amounts of OASDI taxable wages on the Earnings 

Suspense File for 1937 through the second year prior to the current Trustees Report 

year. The data are used in estimating total OASDI covered employment.

2.3.c. Development of Output

Equation 2.3.2 - Total Employment (TE)

Based on the CPS, BLS estimates the total number of persons with any work experience 

(WE) during a calendar year. Average weeks worked (AWW) during a calendar year is 

defined as AWW = E * 52 / WE. Based on a 100 percent count of earnings reports (i.e., 

Form W-2 and Schedule SE) tabulated by SSA, OCACT estimates the total number of 

persons employed at any time during a calendar year (TE). Compared to WE, TE is a broader 

measure of employment. WE is an estimate of the number of workers in the civilian non-
77

institutional US population age 16 and over. TE is an estimate of employment in the broader 

Social Security area population, which includes U.S. territories, the military, and institutions. 

TE also includes employment of workers who age 15 and younger.

AWW is disaggregated by gender and age and is projected as a function of a time trend and 

unemployment rate. WE is projected as (E * 52) / AWW. TE is projected as the product of 

its lagged value and the growth rate for WE. TE is adjusted by two multiplicative factors due 

to differences between E and TE over the recent historical period. The first factor accounts 

for the difference in growth between N and P between the last historical value for TE and the 

last historical value for E. The second factor accounts for the effect of the 2010 decennial 

census.

Equation 2.3.3 - Non-Covered Employment (NCE)

NCE is disaggregated by age and gender. Age groups include 14-15, 16-17, 18-19, 20-24, 

25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, and 70 and over. 

Employment may not be OASDI covered for a variety of reasons mostly related to the type 

of work. Consequently, NCE is further disaggregated to the type-of-work components listed 

below.

Federal Civilian Government - All Federal civilian employees are HI (i.e., Medicare) 

covered. All Federal Civilian employees hired in January 1984 and later are covered under 

the Federal Employees Retirement System (FERS) and are OASDI covered. Employees hired 

before January 1984 are covered under the Civil Service Retirement System (CSRS) and are 

not OASDI covered. This “closed group” of relatively older CSRS employees is projected to 

fall to near zero by 2030.

State and Local Government - In 1983, about 70 percent of State and Local Government 

(S&L) employment and wages were covered under OASDI and HI. Beginning April 1986, 

all newly hired S&L employees were covered under HI. Beginning January 1990, all S&L 

employees not under an S&L retirement system were covered under OASDHI.

By 2011, about 28 and 3 percent of S&L employment (and wages) are still not covered under 

OASDI and HI respectively. The closed group of relatively older S&L employees not 

covered under HI is projected to fall to near zero by 2020. S&L employment not covered 

under OASDI is projected to grow at about the same rate as the labor force.

Students at Public Schools - Prior to 2000, students working at S&L public schools were 

covered under OASDI and HI if the other school employees were covered. In 2000, 

legislation offered an “open season” allowing schools to remove their students from 

coverage. Virtually all major schools opted for removal. Hence, almost no students working 

at their public schools are covered under OASDI or HI. Students at public schools are 

projected to grow at about the same rate as the population aged 18 to 24.

Election Workers - Most S&L election workers are subject to an earnings test and are not 

covered under OASDHI. The earnings test was raised from $100 to $1,000 beginning 
78

January 1995 and indexed beginning in 2000. Election workers are projected to grow at 

about the same rate as LC.

Private Household - The threshold for coverage of domestic employees’ earnings was raised 

from $50 per calendar quarter to $1,000 per calendar year (CY) per employee. Domestic 

workers are no longer covered if under age 18. Private household employment is projected to 

grow at about the same rate as E and vary with RTP. 

Students at Private Schools - All students working in private schools are not covered under 

OASDHI. Students at private schools are projected to grow at about the same rate as the 

population aged 18 to 24.

Railroad - Employers do not submit payments for payroll taxes to the IRS for railroad 

employees. Railroad employees are projected by the Railroad Retirement Board.

Underground Economy Workers - Set to the at-any-time employed in the other immigrant 

population who have no reported earnings and therefore are part of the underground 

economy (i.e., TEO_UND). 

Foreign Students and Exchange Visitors

Equation 2.3.5 - Self-Employed Only (SEO)

SEO is projected to grow at the same rate as ES.

Equation 2.3.7 - Ratio of Combination Workers (RCMB)

Total CMB_TOT can be separated into two groups depending on whether they have OASDI 

covered wages under or over the TAXMAX. CMB_TOT with covered wages under the 

TAXMAX have taxable wages and self-employed income. CMB_TOT with covered wages 

over the TAXMAX have taxable wages only. CMB_TOT with covered wages over the 

TAXMAX would have paid taxes on their self-employed income if the TAXMAX had been 

eliminated. 

Total CMB_TOT is projected as a ratio to WSW. This ratio is dependent on the RTP. If RTP 

rises, then the CMB_TOT increases.

Equation 2.3.10 - Ratio of Covered Wages (RWSC)

RWSC is disaggregated by the following sectors: Federal Civilian government, Federal 

Military, S&L government, and Private.

Federal Civilian government - Total Federal civilian employment and wages are split by 

retirement system. Those under FERS are OASDI covered, while those under CSRS are not. 

Employment and wages are projected for workers under each retirement system. 
79

Employment under CSRS is a closed group that is expected to fall to zero by about 2030. 

Employment under FERS is defined as total Federal employment less employment under 

CSRS. Total Federal civilian employment is projected to grow at an average annual rate of 

about 0 percent over the short-range period, and about equal to the growth in the LC 

thereafter. The growth rates in the average wage for those under CSRS and FERS are 

projected based on, for the first five years, pay raises assumed under the most recent OMB 

FY Budget and on the growth rate in the CPI. Hence, the RWSC for the Federal civilian 

employment is defined as the ratio of wages for employment under FERS to total Federal 

civilian wages.

Federal Military - The RWSC for the Federal military sector is projected to remain constant 

at its latest actual historical level.

S&L government - The RWSC for the S&L government sector is projected to remain 

constant at its latest actual historical level.

Private - The private sector is separated into sub-sectors including private households, farm, 

railroad, tips, and a residual private “base”. The RWSCs for the private household and farm 

sub-sectors are projected to remain constant at their latest actual historical levels. By

definition, the RWSCs for the railroad and tips sub-sectors are projected to remain constant 

at 0.0 and 1.0, respectively. The projected RWSC for the private base sub-sector is dependent 

on the ratio of EO wage workers in the private base sub-sector who are covered under the 

OASDI program to all EO wage workers in the private base sub-sector. We assume that all of 

EO will be wage workers employed in the private residual base sub-sector of the economy 

and that the proportion of EO that is covered under the OASDI program will decrease. 

Therefore, we assume that the RWSC for the private residual base sector will also decrease.

Equation 2.3.12 - Ratio of Covered Self-Employed Earnings (RCSE)

The RCSE is projected to remain constant at its latest actual historical level.

Equation 2.3.17 - Average Wage Index (AWI)

The growth in the AWI is projected to be equal to the growth in the average wage for 

employees with any wages (covered and noncovered) posted to the MEF (AWS_MEF). Total 

wages posted to the MEF (WS_MEF) is equal to WSC less wages posted to the ESF plus any 

non-OASDI covered wages posted to the MEF. Similarly, the total number of employees 

with any wages posted to the MEF (WSW_MEF) is equal to WSW less employees posted 

only to the ESF plus any employees with no OASDI covered wages posted to the MEF.

Equation 2.3.18 - OASDI Taxable Maximum (TAXMAX)

By law, the growth in the AWI is used to increase the TAXMAX.
80

2.4. Effective TAXABLE PAYROLL (TAXPAY)

2.4.a. Overview

TAXPAY estimates historical annual taxable earnings data including total employee OASDI 

taxable wages (WTEE), total employer taxable wages (WTER), and total self-employed taxable 

income (SET). By law, each employee is required to pay the OASDI tax on wages from all 

covered jobs up to the TAXMAX, while each employer is required to pay the OASDI tax on the 

wages of each worker up to the TAXMAX. If an employee works more than one covered wage 

job and the sum of all covered wages exceeds the TAXMAX, the employee but not the employer 

is due a refund. Hence, WTER is greater than WTEE. The difference (i.e., WTER less WTEE) is 

defined as multi-employer refund wages (MER).

TAXPAY also estimates the historical annual OASDI effective taxable payroll (ETP). ETP is the 

amount of earnings in a year which, when multiplied by the combined employee-employer tax 

rate, yields the total amount of taxes due from wages and self-employed income in the year. ETP 

is used in estimating OASDI income and in determining income and cost rates and the actuarial 

balance. ETP is defined as WTER plus SET less one-half of MER.

TAXPAY projects annual values for ETP after first estimating its components. The components 

in turn are estimated by a collection of ratios. The employee taxable ratio (RWTEE) is defined as 

the ratio of WTEE to WSC. The multi-employer refund wage ratio (RMER) is defined as the 

ratio of MER to WSC. The self-employed net income taxable ratio (RSET) is defined as the ratio 

of SET to CSE_TOT. Equations 2.4.1 through 2.4.8 outline the projection methodology.

RWTEE = RWTEE(·) (2.4.1)

WTEE = RWTEE * WSC (2.4.2)

RMER = RMER(·) (2.4.3)

MER = RMER * WSC (2.4.4)

WTER = WTEE + MER (2.4.5)

RSET = RSET(·) (2.4.6)

SET = RSET * CSE_TOT (2.4.7)

ETP = WTER + SET - 0.5 * MER (2.4.8)

In order to conform to the Trustees’ assumption for an ETP of 0.825 in the final short range year 

(see below), TAXPAY solves equations 2.4.1 through 2.4.8 iteratively, altering the trend 

adjustment on RWTEE until the assumed ratio is reached.

Over the short-range projection horizon (i.e., first 10 years), TAXPAY also projects annual 
81

OASDI wage tax liabilities (WTL) and self-employment tax liabilities (SEL). In Equation 2.4.9, 

WTL is the product of the effective taxable wages, defined as WTER less one-half of MER, and 

the combined OASDI employee-employer tax rate (TRW). In Equation 2.4.10, SEL is the 

product of SET and the OASDI self-employed tax rate (TRSE).

WTL = WTER * TRW (2.4.9)

SEL = SET * TRSE (2.4.10)

Also over the short-range horizon, TAXPAY decomposes WTL into quarterly wage tax 

liabilities (WTLQ), then to quarterly wage tax collections (WTLQC). TAXPAY also 

decomposes SEL into quarterly self-employed net income tax collections (SELQC).

WTLQ = WTLQ(·) (2.4.11)

WTLQC = WTLQC(·) (2.4.12)

SELQC = SELQC(·) (2.4.13)

Finally, over the first two projected quarters, TAXPAY estimates of WTLQC and SELQC are 

replaced with ones from the most recent OMB FY Budget. And, over the first four projected 

quarters, TAXPAY includes estimates for appropriation adjustments (AA).

AA = AA(·) (2.4.14)

2.4.b. Input Data

Trustees Assumptions

The Board of Trustees of the OASDI Trust Funds assumes that the ratio of effective OASDI 

taxable payroll to covered earnings for the final calendar year of the short range period (2023) is 

0.825 and remains approximately constant in subsequent years.

Data used to obtain values input directly to model

 Data obtained from ORES by email for the amounts of single and multi-employer 

refunds for the latest 5 years. Each year, data are updated.

 Data obtained from ORES are tabulations of quarterly Form 941 data. Data currently 

used are the OASDI, HI, and income taxable wages by sector for the most recent five 

years. The data represent changes in reported wages since the prior quarterly report. 

The most recent data are appended to previously reported data. Annual totals are 

computed and used to derive estimates of OASDI taxable wages by sector for the 

latest historical years.
82

 Data obtained from the most recently available 1.0% CWHS active file, maintained 

on Social Security’s mainframe and made available by ORES. The years of data are 

1951 to the third year prior to the current Trustees Report year. The data are used for 

comparison of OASDI taxable earnings from other sources.

 Data obtained from quarterly IRS Form 941 files, provided by OS. Data currently 

used are the OASDI and HI taxable wages for 1978 to the most recent year available. 

The data represent changes in reported wages since the prior quarterly report. The 

most recent data are appended to previously reported data. Annual totals are 

computed and used to derive estimates of OASDI taxable wages for the most recent 

historical years.

 Data for the most recent ten years from the quarterly EPOXY Report, received in 

hard-copy and, more recently, electronic formats obtained from OS. The data 

currently used are the total number of workers with OASDI taxable earnings, total 

number of workers with OASDI self-employed taxable earnings, distribution of 

number of HI workers by wage intervals, distribution of number of OASDI workers 

by wage intervals, number of persons with OASDI taxable wages, number of persons 

with HI taxable wages, number of persons with OASDI taxable self-employment, 

number of persons with HI taxable self-employment, number of workers with singleemployer excess wages, and number of workers with multi-employer excess wages. 

 Data obtained from the Quarterly Trust Fund Letter, received from OFPO. Data 

currently used are OASDI and HI taxable wages accumulated from all Forms 941 and 

W-2 to date, and changes in self-employment earnings and self-reported tips since the 

prior Letter. These data are for years 1978 to the most recent year available.

Long-Range OASDI Projection Data

Historical and projected data from Sections 2.1, 2.2, and 2.3 are used as input. Data for the 

following variables have final year of 2099. Each variable is shown with starting year.

AIW Average wage for indexing ($), 1971

AWSCFM Average covered wage for farm workers ($), 1971

AWSCML Average covered wage for military ($), 1971

DMWCHI Deemed military wage credits for HI ($ millions), 1978

DMWCOD Deemed military wage credits for OASDI ($ millions), 1978

ECFCHO Number of HI-only covered Federal Civilian workers 

(millions), 1983

ECFCOD Number of OASDI covered Federal Civilian workers 

(millions), 1983

ECHITOT Number of HI covered workers (millions), 1971

ECSEHI Number of HI covered self-employed workers (millions), 

1994

ECSENOMAX Number of covered self-employed workers if no taxable 

maximum (millions), 1983

ECSEO Number of OASDI covered self-employed only workers 
83

(millions), 1971

ECSEOD Number of OASDI covered self-employed workers 

(millions), 1971

ECSLNOIS Number of non-OASDI covered State and Local workers 

including students (millions), 1983

ECSLNRP Number of OASDI covered State and Local workers with no 

retirement plan (millions), 1983

ECSLOD Number of OASDI covered State and Local workers 

(millions), 1983

ECSLP91 Number of State and Local workers covered under OASDI 

under pre-1991 law (millions), 1983

ECWSHI Number of HI covered wage workers (millions), 1983

ECWSOD_MEF Number of OASDI covered wage workers on the Master 

Earnings File (MEF) in millions, 1990

ECWSODFU16_MEF Number of OASDI covered wage workers on the MEF, 

female under 16 years old (millions), 1990

ECWSODF1619_MEF Number of OASDI covered wage workers on the MEF, 

female 16 to 19 years old (millions), 1990

ECWSODF2024_MEF Number of OASDI covered wage workers on the MEF, 

female 20 to 24 years old (millions), 1990

ECWSODF2529_MEF Number of OASDI covered wage workers on the MEF, 

female 25 to 29 years old (millions), 1990

ECWSODF3034_MEF Number of OASDI covered wage workers on the MEF, 

female 30 to 34 years old (millions), 1990

ECWSODF3539_MEF Number of OASDI covered wage workers on the MEF, 

female 35 to 39 years old (millions), 1990

ECWSODF4044_MEF Number of OASDI covered wage workers on the MEF, 

female 40 to 44 years old (millions), 1990

ECWSODF4549_MEF Number of OASDI covered wage workers on the MEF, 

female 45 to 49 years old (millions), 1990

ECWSODF5054_MEF Number of OASDI covered wage workers on the MEF, 

female 50 to 54 years old (millions), 1990

ECWSODF5559_MEF Number of OASDI covered wage workers on the MEF, 

female 55 to 59 years old (millions), 1990

ECWSODF6064_MEF Number of OASDI covered wage workers on the MEF, 

female 60 to 64 years old (millions), 1990

ECWSODF6569_MEF Number of OASDI covered wage workers on the MEF, 

female 65 to 69 years old (millions), 1990

ECWSODF70O_MEF Number of OASDI covered wage workers on the MEF, 

female 70 years old and older (millions), 1990

ECWSODMU16_MEF Number of OASDI covered wage workers on the MEF, male 

under 16 years old (millions), 1990

ECWSODM1619_MEF Number of OASDI covered wage workers on the MEF, male 

16 to 19 years old (millions), 1990

ECWSODM2024_MEF Number of OASDI covered wage workers on the MEF, male 

20 to 24 years old (millions), 1990
84

ECWSODM2529_MEF Number of OASDI covered wage workers on the MEF, male 

25 to 29 years old (millions), 1990

ECWSODM3034_MEF Number of OASDI covered wage workers on the MEF, male 

30 to 34 years old (millions), 1990

ECWSODM3539_MEF Number of OASDI covered wage workers on the MEF, male 

35 to 39 years old (millions), 1990

ECWSODM4044_MEF Number of OASDI covered wage workers on the MEF, male 

40 to 44 years old (millions), 1990

ECWSODM4549_MEF Number of OASDI covered wage workers on the MEF, male 

45 to 49 years old (millions), 1990

ECWSODM5054_MEF Number of OASDI covered wage workers on the MEF, male 

50 to 54 years old (millions), 1990

ECWSODM5559_MEF Number of OASDI covered wage workers on the MEF, male 

55 to 59 years old (millions), 1990

ECWSODM6064_MEF Number of OASDI covered wage workers on the MEF, male 

60 to 64 years old (millions), 1990

ECWSODM6569_MEF Number of OASDI covered wage workers on the MEF, male 

65 to 69 years old (millions), 1990

ECWSODM70O_MEF Number of OASDI covered wage workers on the MEF, male 

70 years old and older (millions), 1990

ESLCG Number of State and Local workers not covered under HI 

(millions), 1983

ESLSTUD Number of noncovered students at public schools employed 

by their school (millions), 1983

GAPLAG Ratio of real to potential GDP lagged (units), 1971

RTP Ratio of real to potential GDP (units), 1971

RU Civilian unemployment rate (percent), 1971

SEECCMB Self-employed earnings of all SE workers who also earned 

wages in same year ($ millions), 1991

SEECHI HI covered self-employed earnings ($ millions), 1991

SEECNOMAX Covered self-employed earnings if no taxable maximum ($ 

millions), 1991

SEECOD OASDI covered self-employed earnings ($ millions), 1991

SEECOD_OLD OASDI covered self-employed earnings excluding selfemployed earnings of workers with covered wages greater 

than or equal to the OASDI taxable maximum ($ millions), 

1971

TAXMAXHI HI taxable maximum ($) – 0 indicates no maximum, 1971

TAXMAXOD OASDI taxable maximum ($), 1971

TCFCD Proportion of annual Federal Civilian wages earned in each 

quarter (units), 1971

TCMD Proportion of annual military wages earned in each quarter 

(units), 1971

TCPD Proportion of annual private sector wages earned in each 

quarter (units), 1971

TCSLD Proportion of annual State and Local wages earned in each 
85

quarter (units), 1971

WSCCMB Wages earned in same year by all SE workers with both types 

of earnings ($ millions), 1991

WSCFCHO HI Covered wages of Federal Civilian HI-only workers ($ 

millions), 1983

WSCFCOD OASDI Covered wages of Federal Civilian workers ($ 

millions), 1971

WSCFM Covered wages of farm workers ($ millions), 1971

WSCHI HI covered wages ($ millions), 1983

WSCML Covered wages of members of the Armed Forces ($ 

millions), 1971

WSCOD OASDI covered wages ($ millions), 1971

WSCOD_SF OASDI covered wages on the Suspense File ($ millions), 

1971

WSCPHH Covered wages of private household workers ($ millions), 

1971

WSCPNF Covered wages of private nonfarm workers ($ millions), 

1971

WSCSLHI HI covered State and Local wages ($ millions), 1983

WSCSLNRP Covered wages of State and Local workers with no 

retirement plan ($ millions), 1971

WSCSLOD OASDI covered State and Local wages ($ millions), 1971

WSCSLP91 Wages of State and Local workers covered under OASDI 

under pre-1991 law ($ millions), 1971

WSD Total NIPA wages ($ millions), 1971

WSP Total NIPA private sector wages ($ millions), 1971

WSS Total NIPA compensation ($ millions), 1971

WSSLCG Wages of State and Local workers not covered under HI ($ 

millions), 1983

WSSLNOIS Wages of non-OASDI covered State and Local workers 

including students ($ millions), 1983

WSSLSTUD Wages of noncovered students at public schools employed by 

their school ($ millions), 1983

WSTTIPSSR Taxable tips reported by tip earner instead of employer ($ 

millions), 1978

WTWPO Proportion of annual Postal Service wages earned in each 

quarter (units), 1971

Other direct input data

 FICA, SECA, and Federal Employer tax transfers by month from the Department of 

the Treasury for years 1984 to 2013.

 FICA, SECA, and Federal Employer tax transfers by month from the Department of 

the Treasury for January 2014 through March 2014 and estimated transfers for April 

2014 through June 2014.
86

 FICA and SECA tax transfers by month split by liability period from the Department 

of the Treasury for 1984 to 2013.

 Monthly OASI, DI, and HI deposits by States, received periodically throughout the 

year from the Department of the Treasury for 1984 to 2008. (This is an obsolete type 

of revenue which has had no valid non-zero amount since 2002.)

 Historical annual HI taxable self-employment earnings for 1983 to 1993. (Values 

from 1994 on are equal to HI covered earnings and are obtained from subprocess 

COV.).

 Historical annual OASDI taxable self-employment earnings for 1971 to 2012.

 Historical annual OASDI multi-employer refund wages for 1971 to 2012.

 Historical annual HI taxable wages for 1983 to 1993. (Values from 1994 on are equal 

to HI covered wages and are obtained from subprocess COV.).

 Historical annual OASDI taxable wages for 1971 to 2013.

 Historical annual HI-only taxable Federal Civilian wages for 1987 to 1993. (Values 

from 1994 on are equal to HI-only covered wages and are obtained from subprocess 

COV.).

 Historical annual OASDI taxable Federal Civilian wages for 1971 to 2012.

 Historical annual HI taxable Federal Civilian wages for 1987 to 1993. (Values from 

1994 on are equal to HI covered wages and are obtained from subprocess COV.).

 Historical annual OASDI taxable farm sector wages for 1971 to 2012.

 Historical annual HI taxable farm sector wages for 1991 to 1993. (Values from 1994 

on are equal to HI covered wages and are obtained from subprocess COV.).

 Historical annual OASDI taxable military sector wages for 1971 to 2012.

 Historical annual HI taxable military sector wages for 1991 to 1993. (Values from 

1994 on are equal to HI covered wages and are obtained from subprocess COV.).

 Historical annual OASDI taxable State and Local government sector wages for 1971 

to 2012.
87

 Historical annual HI taxable State and Local government sector wages for 1987 to 

1993. (Values from 1994 on are equal to HI covered wages and are obtained from 

subprocess COV.).

 Historical and projected annual OASDI taxable tips for employees as reported by 

employers for 1971 to 2023.

 Historical and projected annual OASDI taxable tips for employers as reported by 

employers for 1971 to 2023.

 Annual and quarterly OASDI and HI taxable wages for calendar year 2013.

 Estimated annual HI taxable payroll for railroad workers for 1971 to 2099.

 Historical FICA and SECA appropriation adjustments for OASI, DI, and HI by month 

for 1968 to 2014Q1.

 Historical FICA revenues for OASI, DI, and HI by quarter for 1984 to 2013.

 Historical SECA revenues for OASI, DI, and HI by quarter for 1984 to 2013.

 Historical Federal Employer revenues for OASI, DI, and HI by quarter for 1984 to 

2013.

 Historical Deposits by States for OASI, DI, and HI by quarter for 1984 to 2013.

 Historical single-employer refunds of excess taxes for OASI, DI, and HI by quarter 

for 1984 to 2012.

 Historical FICA credits for OASI and DI by quarter for 1984 to 2013.

 Historical SECA credits for OASI, DI, and HI by quarter for 1984 to 2013.

 Historical multi-employer refunds of excess taxes for OASI, DI, and HI by month for 

1968 to 2013.

 Data used to estimate future FICA appropriation adjustments for 2014Q2 to 2015Q2 

– These adjustments are used to make sure that transfers to the Trust Funds reflect 

taxable wage amounts reported to SSA. The data input includes estimated quarterly 

tax liabilities ultimately owed the Trust Funds from President’s Budget model 

solutions, quarterly tax liabilities for 2013Q1 from IRS-941 tabulations, estimates of 

tax liabilities for all prior years for 2013Q1 to 2014Q2, and estimated ratios of 

quarterly liability amounts reported to SSA over ultimate values for 2012Q2 to 

2014Q2. Values are estimated for the last three quarters of the Trustees Report year 

and for the first two quarters of the following year.
88

 Miscellaneous historical covered employment and earnings data:

o HI Covered self-employed workers for 1986 to 1993.

o Number of OASDI covered wage workers by age group and gender for 1996.

o HI covered self-employment earnings for 1971 to 1993.

o Covered self-employment earnings if there were no taxable maximum for 

1971 to 1993.

o OASDI covered self-employment earnings for 1971 to 1993.

 Miscellaneous historical and fixed projected data:

o Quarterly distribution of annual OASDI taxable farm wages for 1971 to 2023.

o Quarterly OASDI covered private nonfarm sector wages for 1971 to 1977.

o Quarterly OASDI covered State and Local government sector wages for 1971 

to 1977.

o Quarterly OASDI covered military sector wages for 1971 to 1977.

o Quarterly OASDI covered Federal Civilian sector wages for 1971 to 1977.

o Quarterly OASDI taxable private nonfarm sector wages for 1971 to 1977.

o Quarterly OASDI taxable State and Local government sector wages for 1971 

to 1977.

o Quarterly OASDI taxable military sector wages for 1971 to 1977.

o Quarterly OASDI taxable Federal Civilian sector wages for 1971 to 1977.

o Quarterly OASDI taxable farm sector wages for 1971 to 1977.

o OASDI employee, employer, and self-employment tax rates from 1937 to 

2100. These contribution rates are set according to the Social Security Act of 

1935 and amendments to the Act through 2004. The rates are updated when 

legislation mandates a change (which hasn’t occurred since 2000).

o Annual OASDI employee credit tax rate for 1984.

o Annual OASDI self-employment credit tax rates for 1984 to 1989.
89

o Annual reductions in OASDI employee and self-employment tax rates due to 

the payroll tax holiday for 2011 and 2012. 

o Annual trend variable for taxable to covered wage ratio calculation for 1971 to 

2100 (no longer used)

o Annual trend variable for taxable to covered self-employment earnings ratio 

calculation for 1971 to 2100 (no longer used)

 Proportions of OASDI tax liabilities for self-employment earnings for the current and 

prior calendar year estimated to be collected in each quarter for 1971 to 2100. (In any 

particular quarter, some self-employed individuals are paying taxes on earnings from 

the prior year and some are paying from the current year’s earnings.) Values are 

derived from historical data from the Office of Tax Analysis (OTA) in the 

Department of the Treasury for the amount of self-employment taxes transferred to 

the OASDI Trust Funds in each month split by the calendar year (either the current or 

the prior) in which the self-employment income was earned. The data are updated 

every year after historical information for a complete new year is received (usually in 

March).

 Average OASDI covered wages by age groups and gender for 1996.

 Ratio of OASDI taxable to covered wages by age groups and gender for 1996.

 Corrections to prior FICA appropriation adjustments made in March 2000.

 Projected single-employer refund wages by calendar year for 2013 through 2023.

 An adjustment factor to trend variable for ratio of OASDI taxable to covered wages 

for 1971 to 2100. A factor of 0.4 is input for years 2002 to 2100 and is applied to 

model-computed time trend adjustments to RWTEE. Historical analysis suggests a 

“trend” rate of decline in RWTEE of about 0.0016 per year through 2001. This trend 

reflects a movement toward greater amounts of wages being paid to workers earning 

above the taxable maximum. We expect this trend to continue to the end of the shortrange projection horizon, but at a slower rate. Consequently, we assume an 

adjustment factor of 0.4 such that the trend annual rate of decline from 2002 to the 

end of the short-range projection horizon is about 0.0006 per year (i.e., 0.0016 * 0.4). 

The adjustment factor has not been updated since first decided upon. It will be 

changed if and when it is deemed appropriate.

 Value of ratio of OASDI effective taxable payroll to covered earnings assumed for 

calendar year 2023.
90

2.4.c. Development of Output

Equation 2.4.1 - Employee Taxable Ratio (RWTEE)

Over the short-range projection horizon, the projected value for RWTEE is the sum of the 

model’s “raw” estimate and an addfactor consisting of four components. The raw estimate 

for RWTEE is dependent on the distribution of workers by wage interval, the RELMAX, 

RTP, the age-gender distribution of wage workers, a time trend adjustment, and a base-year 

error adjustment. The projected distribution of workers by wage interval is an average (or 

amalgam) distribution over the 1993 through 2011 period. Holding other factors constant, a 

distribution with relatively more workers with wages over the TAXMAX leads to a lower 

RWTEE. The RELMAX is defined as the ratio of the TAXMAX to the ACW. A higher 

RELMAX leads to a higher RWTEE.

An increase in the RTP leads to a lower RWTEE. The change in the projected RWTEE due 

to the change in the age-gender distribution of wage workers is calculated by allowing 

employment by age and gender to change while holding taxable ratios (and average covered 

wages) by age and gender constant to levels in 1996. The time trend adjustment reduces the 

level of RWTEE by about 0.6 percentage point over the short-range projection horizon. The 

base-year error adjustment starts with the value obtained by subtracting the estimated value 

of RWTEE for the latest historical (or base) year from the actual value and phases this 

amount out linearly over the ten years of the short-range projection period.

RWTEE is assumed to remain constant over the long-range projection horizon.

RWTEEs are also projected for various sub-aggregates including Federal Civilian employees 

under FERS and CSRS, Federal Civilian employees under CSRS only, S&L employees 

covered under OASDI, S&L employees covered under HI only, U.S. armed forces, and 

agriculture. The RWTEE for each sub-aggregate is dependent only on its sub-aggregate 

RELMAX, that is, the ratio of the TAXMAX to the sub-aggregate’s average covered wage.

Equation 2.4.2 - Employee Taxable Wages (WTEE)

WTEE is computed by multiplying the ratio of taxable employee wages to covered wages by 

the level of covered wages.

Equation 2.4.3 - Multi-Employer Refund Wage Ratio (RMER)

The RMER is functionally related to the RWTEE. As RWTEE approaches one, then RMER 

approaches zero. In between the limit values, RMER is positive. Given the present position 

of RWTEE and RMER on the function, a projected decline in RWTEE leads to an increase in 

RMER.

The projected RMER is also dependent on RU. An increase in RU leads to a decrease in 

RMER.
91

RMER is assumed to remain constant over the long-range projection horizon.

Equation 2.4.4 - Multi-Employer Refund Wages (MER)

MER is computed by multiplying the ratio of multi-employer refund wages to covered wages 

by the level of covered wages.

Equation 2.4.5 - Employer Taxable Wages (WTER)

WTER is computed by adding employer taxable wages to multi-employer refund wages.

Equation 2.4.6 - Self-Employed Net Income Taxable Ratio (RSET)

The RSET is disaggregated by type of self-employed worker, SEO and CMB_TOT.

SEO - The RSET is dependent on the distribution of self-employed workers by income 

interval and a RELMAX. The projected distribution of self-employed workers by income 

interval is set to the 1996 distribution. The RELMAX is defined here as the ratio of the 

TAXMAX to the average income for SEO. A higher RELMAX leads to a higher RSET. 

CMB_TOT - Taxable self-employed net income for CMB_TOT is projected in two steps. 

First, a taxable earnings (wages and self-employed income) ratio for CMB_TOT is projected 

based on the 1996 distribution and a RELMAX defined as the ratio of the TAXMAX to the 

average covered earnings. The projected level of taxable earnings for CMB_TOT is the 

product of the estimated taxable earnings ratio for CMB_TOT and their covered earnings. 

Second, a taxable wage ratio for CMB_TOT is projected based on a RELMAX defined as the 

ratio of the TAXMAX to the average covered wage for CMB_TOT. The projected level of 

taxable wages for CMB_TOT is the product of the estimated taxable wage ratio for 

CMB_TOT and their covered wages.

Taxable self-employed net income for CMB_TOT is obtained by subtracting taxable wages 

from taxable earnings for CMB_TOT.

A “combined” RSET is calculated as the ratio of taxable self-employed net income for SEO 

and CMB_TOT to CSE_TOT. As with the RWTEE, the combined RSET is adjusted over the 

short-range period due to other factors (i.e., RTP, the age-gender distribution of workers, and 

a trend). The effect of the other factors are taken from RWTEE and “scaled.” That is, RSET 

is adjusted by a percent effect (as opposed to percentage point) that is equal to the percent 

change in RWTEE due to changes in these other factors.

It is important to note that while the RWTEE is held constant after the short-range period, the 

RSETs for self-employed workers are not. After the short-range period, the projected RSETs 

for SEO and CMB_TOT continue to be dependent on their respective RELMAXs. Since by 

law the TAXMAX grows at the rate of the AWI and since ACSE is assumed to grow faster 

than the ACW (since only ACW declines with the growth in fringe benefits), the RELMAXs 

for self-employed workers decline over the long-range period while the RELMAX for wage 
92

workers is approximately constant. Hence, the RSETs for SEO and CMB_TOT are projected 

to decline over the long-range period while the RWTEE is held constant.

Equation 2.4.7 - Taxable Self-Employed Net Income (SET)

SER is computed by multiplying the self-employed net income taxable ratio by covered self 

by the level of covered self-employed income.

Equation 2.4.8 – Effective Taxable Payroll (ETP)

ETP is computed by adding employer taxable wages and taxable self-employed net income 

to one-half of multi-employer refund wages. (Only employees can obtain refunds of excess 

taxes withheld in multi-employer refund wage cases.)

As noted above, in order to meet the Trustees’ assumption that the ratio of ETP to total 

covered earnings is 0.825 in the last year of the short-range period, equations 2.4.1 through 

2.4.8 are solved repeatedly with changes to the time-trend adjustment in equation 2.4.1 until 

the ratio is obtained.

Equation 2.4.11 - Quarterly Wage Tax Liabilities (WTLQ)

Total WTLQ is summed from sector components that include Federal Civilian, Federal 

Military, S&L, Private Household, Farm, Self-reported Tips, and residual Private Nonfarm. 

Sector WTLQs are determined by computing ratios of quarterly to annual liabilities for each 

quarter. These are calculated for the Private Nonfarm, S&L, Federal Civilian and Military 

sectors. Each is dependent on the quarterly distribution of WSD and the RWTEE for the 

relevant sector, and on a payday adjustment that takes into account the actual number of 

paydays that fall into a particular calendar quarter. WTLQ ratios are also calculated for each 

quarter for the other sectors. However, these are expected to be constants over the projection 

horizon.

Equation 2.4.12 - Quarterly Wage Tax Collections (WTLQC)

Employers incur tax liabilities when they pay wages to their employees. These liabilities are 

required to be deposited with the U.S. Treasury by employers based on the amount of total 

payroll tax liability (income taxes plus Social Security and Medicare taxes withheld) 

accumulated. Some very large employers must deposit their tax liabilities the next banking 

day after paying their employees. Other levels of accumulated tax liabilities require 

depositing within three days, by the middle of the following month, or by the end of the 

month following the quarter. If employers follow these deposit requirements, the result is that 

all tax liability for a particular quarter is deposited by the last day of the month following the 

end of the quarter. Thus, the WTLQC for any particular quarter are the sum of the tax 

liabilities deposited for wages paid in the same quarter and the liabilities deposited for wages 

paid in the prior quarter.

WTLQC are summed from sector components that include the Federal Civilian, Federal 
93

Military, Farm, S&L, and residual Private Nonfarm (including Private Household and SelfReported Tips). For the Federal Civilian and Military sectors, the WTLQC are set equal to 

their respective WTLQ since tax liabilities for the two sectors are collected immediately. The 

WTLQC for Farm is also set equal to its WTLQ, due in part to the fact that farms report tax 

liabilities annually. For the S&L and Private Nonfarm sectors, WTLQC are the product of 

WTLQ and the proportion of WTLQ that should be deposited in the month following the end 

of each quarter. This proportion is based on the deposit requirements and estimates of 

accumulated tax liabilities, which in turn are based on firm size (or total wages paid).

Equation 2.4.13 - Quarterly Self-Employed Net Income Tax Collections (SELQC)

For wage workers, annual liabilities (WTL) are distributed to quarterly liabilities (WTLQ), 

which in turn are distributed to quarterly collections (WTLQC). However, for self-employed 

workers, annual liabilities (SEL) are distributed directly to SELQC, since the SSA only 

receives self-employed liability amounts on an annual basis (from tabulations of Form 1040 

Schedule SE provided by IRS).

SEL for a particular calendar year are distributed as collections to the four quarters of that 

year and to the first three quarters of the next year. This distribution uses quarterly 

proportions that are based on an historical pattern of the amount of SEL collected in each 

month, as estimated by the OTA. The OTA estimates reflect IRS regulations that require selfemployed workers to deposit estimated tax liabilities four times a year (January, April, June, 

and September).

Equation 2.4.14 - Appropriation Adjustments (AA)

We estimate WTLQC and SELQC for the next two quarters based on projected levels of 

WSD and Y in the various OMB FY Budgets. The Treasury uses these estimates to make 

initial appropriations to the OASDI Trust Funds over the period. Roughly one year after an 

initial appropriation has been made for a quarter, the initial appropriation is adjusted based 

on certified taxable wages reported on Forms 941 and W-2. Similarly, the initial SECA 

appropriation for each calendar year is adjusted at the end of the second year following the 

calendar year, based on certified taxable self-employment income reported on Forms 1040 

Schedule SE. This appropriation adjustment occurs quarterly and includes amounts due to 

newly-reported wages and self-employment income for all earlier periods.

FICA appropriation adjustments for wages are projected for all quarters for which we have 

provided the Treasury with transfer estimates. This generally means that we make estimates 

for adjustments to occur through the second quarter of the year following the Trustees Report 

year. The projected adjustment for each quarter is composed of two parts. The first part 

estimates the error in the initial appropriation for the fourth earlier quarter. This error is 

defined as the difference between the Treasury’s initial appropriation and that portion of 

TAXPAY’s latest estimate for that quarter which is expected to be certified at the point when 

the adjustment is made. The second part is an estimate of the tax liability from additional 

certified wages for all previous periods and is based on an average of additional amounts 

over a recent historical period. These estimates are made in TAXPAY.
94

Appropriation adjustments for self-employment income are projected over the entire 

projection horizon based on the historical pattern of reporting. These estimates are made in 

TAXPAY.
95

Appendix 2-1

Equations

2.1 U.S. Employment (USEMP)

UNEMPLOYMENT RATES, PRELIMINARY

MALES

RM1617_P = RM1617_P.1 + (-36.2076 * DIFF(RTP) - 14.2816 * DIFF(RTP.1) - 26.6756 * DIFF(RTP.2) - 16.9202 * DIFF(RTP.3)) * 

50.00/44.72

RM1617

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009

diff(rm1617)

 = - 36.2076 * diff(rtp) - 14.2816 * diff(rtp)[-1]

 (2.29349) (0.87160)

 - 26.6756 * diff(rtp)[-2] - 16.9202 * diff(rtp)[-3]

 (1.63426) (1.06303)

 + 1.64214 * minw - 0.90365 * minw[-1] + 0.06020 * minw[-2]

 (1.76311) (0.70381) (0.04365)

 - 0.77627 * minw[-3] - 0.12616

 (0.72653) (0.26611)

Sum Sq 198.967 Std Err 1.2719 LHS Mean 0.0553

R Sq 0.1483 R Bar Sq 0.0929 F 8,123 2.6769

D.W.( 1) 2.5142 D.W.( 4) 2.3291

RM1819_P = RM1819_P.1 + (-48.4227 * DIFF(RTP) - 25.8766 * DIFF(RTP.1) - 21.7466 * DIFF(RTP.2) + 1.1551 * DIFF(RTP.3)) * 

50.00/44.72

RM1819

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009

diff(rm1819)

 = - 48.4227 * diff(rtp) - 25.8766 * diff(rtp)[-1]

 (3.45103) (1.77685)

 - 21.7466 * diff(rtp)[-2] + 1.15512 * diff(rtp)[-3]

 (1.49900) (0.08165)

 + 0.62723 * minw - 0.48738 * minw[-1] + 0.67739 * minw[-2]

 (0.75770) (0.42710) (0.55270)

 - 0.79385 * minw[-3] - 0.11294

 (0.83595) (0.26803)

Sum Sq 157.172 Std Err 1.1304 LHS Mean 0.0235

R Sq 0.1890 R Bar Sq 0.1362 F 8,123 3.5828

D.W.( 1) 2.6908 D.W.( 4) 2.4565

RM2024_P = RM2024_P.1 + (-51.6518 * DIFF(RTP) - 16.6465 * DIFF(RTP.1) - 13.1350 * DIFF(RTP.2) - 10.9309 * DIFF(RTP.3)) * 

50.00/44.72

RM2024

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009
96

diff(rm2024)

 = - 51.6518 * diff(rtp) - 16.6465 * diff(rtp)[-1]

 (7.75482) (2.35721)

 - 13.1350 * diff(rtp)[-2] - 10.9309 * diff(rtp)[-3] + 0.00093

 (1.86731) (1.59404) (0.01922)

Sum Sq 38.7297 Std Err 0.5522 LHS Mean -0.0048

R Sq 0.4356 R Bar Sq 0.4178 F 4,127 24.5002

D.W.( 1) 2.4679 D.W.( 4) 2.2856

RM2529_P = RM2529_P.1 + (-37.9533 * DIFF(RTP) - 17.3941 * DIFF(RTP.1) - 14.9170 * DIFF(RTP.2) - 7.0513 * DIFF(RTP.3)) * 

50.00/44.72

RM2529

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm2529)

 = - 37.9533 * diff(rtp) - 17.3941 * diff(rtp)[-1]

 (7.06307) (3.05222)

 - 14.9170 * diff(rtp)[-2] - 7.05126 * diff(rtp)[-3] + 0.00609

 (2.61734) (1.28703) (0.15745)

Sum Sq 23.9433 Std Err 0.4394 LHS Mean 0.0095

R Sq 0.4417 R Bar Sq 0.4237 F 4,124 24.5278

D.W.( 1) 2.1341 D.W.( 4) 2.4166

RM3034_P = RM3034_P.1 + (-23.6417 * DIFF(RTP) - 14.1284 * DIFF(RTP.1) - 7.5008 * DIFF(RTP.2) - 9.7232 * DIFF(RTP.3)) * 50.00/44.72

RM3034

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm3034)

 = - 23.6417 * diff(rtp) - 14.1286 * diff(rtp)[-1]

 (6.21241) (3.50067)

 - 7.50079 * diff(rtp)[-2] - 9.7232 * diff(rtp)[-3] + 0.01058

 (1.85832) (2.50593) (0.38580)

Sum Sq 12.0091 Std Err 0.3112 LHS Mean 0.0119

R Sq 0.4221 R Bar Sq 0.4034 F 4,124 22.6397

D.W.( 1) 2.1876 D.W.( 4) 1.8816

RM3539_P = RM3539_P.1 + (-27.6828 * DIFF(RTP) - 5.4850 * DIFF(RTP.1) - 10.8974 * DIFF(RTP.2) - 9.8932 * DIFF(RTP.3)) * 50.00/44.72

RM3539

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm3539)

 = - 27.6828 * diff(rtp) - 5.48498 * diff(rtp)[-1]

 (6.57840) (1.22901)

 - 10.8974 * diff(rtp)[-2] - 9.8932 * diff(rtp)[-3] + 0.01127

 (2.44154) (2.30580) (0.37184)

Sum Sq 14.6843 Std Err 0.3441 LHS Mean 0.0130

R Sq 0.3795 R Bar Sq 0.3595 F 4,124 18.9589

D.W.( 1) 2.3381 D.W.( 4) 1.9092

RM4044_P = RM4044_P.1 + (-14.6558 * DIFF(RTP) - 14.9735 * DIFF(RTP.1) - 8.2594 * DIFF(RTP.2) - 5.5023 * DIFF(RTP.3)) * 50.00/44.72

RM4044

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4
97

Date: 23 OCT 2009

diff(rm4044)

 = - 14.6558 * diff(rtp) - 14.9735 * diff(rtp)[-1]

 (3.48851) (3.36064)

 - 8.25944 * diff(rtp)[-2] - 5.50233 * diff(rtp)[-3] + 0.00570

 (1.85359) (1.28455) (0.18829)

Sum Sq 14.6357 Std Err 0.3436 LHS Mean 0.0064

R Sq 0.2692 R Bar Sq 0.2456 F 4,124 11.4206

D.W.( 1) 2.6138 D.W.( 4) 2.0655

RM4549_P = RM4549_P.1 + (-20.7806 * DIFF(RTP) - 11.5121 * DIFF(RTP.1) - 9.9409 * DIFF(RTP.2) + 1.5480 * DIFF(RTP.3)) * 

50.00/44.72

RM4549

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm4549)

 = - 20.7806 * diff(rtp) - 11.5121 * diff(rtp)[-1]

 (5.31669) (2.77721)

 - 9.9409 * diff(rtp)[-2] + 1.54797 * diff(rtp)[-3] + 0.00874

 (2.39795) (0.38844) (0.31046)

Sum Sq 12.6680 Std Err 0.3196 LHS Mean 0.0114

R Sq 0.3249 R Bar Sq 0.3031 F 4,124 14.9185

D.W.( 1) 2.2355 D.W.( 4) 1.7877

RM5054_P = RM5054_P.1 + (-19.3341 * DIFF(RTP) - 9.5336 * DIFF(RTP.1) - 8.8784 * DIFF(RTP.2) - 7.6218 * DIFF(RTP.3)) * 50.00/44.72

RM5054

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm5054)

 = - 19.3341 * diff(rtp) - 9.5336 * diff(rtp)[-1]

 (4.72314) (2.19601)

 - 8.87840 * diff(rtp)[-2] - 7.62180 * diff(rtp)[-3] + 0.01083

 (2.04491) (1.82617) (0.36742)

Sum Sq 13.8950 Std Err 0.3347 LHS Mean 0.0118

R Sq 0.2957 R Bar Sq 0.2730 F 4,124 13.0163

D.W.( 1) 2.1290 D.W.( 4) 1.7836

RM5559_P = RM5559_P.1 + (-25.9031 * DIFF(RTP) - 11.4442 * DIFF(RTP.1) - 4.5421 * DIFF(RTP.2) + 0.55815 * DIFF(RTP.3)) * 

50.00/44.72

RM5559

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm5559)

 = - 25.9031 * diff(rtp) - 11.4442 * diff(rtp)[-1]

 (5.21572) (2.17280)

 - 4.54211 * diff(rtp)[-2] + 0.55815 * diff(rtp)[-3] + 0.00326

 (0.86229) (0.11023) (0.09111)

Sum Sq 20.4526 Std Err 0.4061 LHS Mean 0.0068

R Sq 0.2605 R Bar Sq 0.2366 F 4,124 10.9177

D.W.( 1) 2.2469 D.W.( 4) 1.7904

RM6064_P = RM6064_P.1 + (1.3133 * DIFF(RTP) - 12.9625 * DIFF(RTP.1) - 2.4816 * DIFF(RTP.2) - 14.4797 * DIFF(RTP.3)) * 50.00/44.72
98

RM6064

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm6064)

 = 1.31332 * diff(rtp) - 12.9625 * diff(rtp)[-1]

 (0.25208) (2.34596)

 - 2.48164 * diff(rtp)[-2] - 14.4797 * diff(rtp)[-3] + 0.00491

 (0.44909) (2.72583) (0.13091)

Sum Sq 22.5085 Std Err 0.4261 LHS Mean 0.0021

R Sq 0.1187 R Bar Sq 0.0903 F 4,124 4.1768

D.W.( 1) 2.3485 D.W.( 4) 1.9007

RM6569_P = RM6569_P.1 + (-19.5151 * DIFF(RTP) + 4.9785 * DIFF(RTP.1) -13.3449 * DIFF(RTP.2) + 2.4706 * DIFF(RTP.3)) * 

50.00/44.72

RM6569

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rm6569)

 = - 19.5151 * diff(rtp) + 4.97852 * diff(rtp)[-1]

 (2.18595) (0.52582)

 - 13.3449 * diff(rtp)[-2] + 2.47056 * diff(rtp)[-3] - 0.01208

 (1.40935) (0.27142) (0.18783)

Sum Sq 66.0895 Std Err 0.7301 LHS Mean -0.0096

R Sq 0.0551 R Bar Sq 0.0246 F 4,124 1.8065

D.W.( 1) 2.6235 D.W.( 4) 1.5080

RM7074_P = RM7074_P.1 + (4.1938 * DIFF(RTP) - 5.9012 * DIFF(RTP.1) - 27.0406 * DIFF(RTP.2) + 7.0400 * DIFF(RTP.3)) * 50.00/44.72

RM7074

Ordinary Least Squares

QUARTERLY data for 111 periods from 1981Q2 to 2008Q4

Date: 23 OCT 2009

diff(rm7074)

 = 4.19384 * diff(rtp) - 5.90117 * diff(rtp)[-1]

 (0.25776) (0.34403)

 - 27.0406 * diff(rtp)[-2] + 7.03995 * diff(rtp)[-3] + 0.02434

 (1.59539) (0.43075) (0.24587)

Sum Sq 114.781 Std Err 1.0406 LHS Mean 0.0185

R Sq 0.0282 R Bar Sq -0.0085 F 4,106 0.7684

D.W.( 1) 2.8303 D.W.( 4) 1.7482

RM75O_P = RM75O_P.1 + (-12.1042 * DIFF(RTP) - 15.6142 * DIFF(RTP.1) + 7.06185 * DIFF(RTP.2) - 2.5738 * DIFF(RTP.3)) * 50.00/44.72

RM75O

Ordinary Least Squares

QUARTERLY data for 111 periods from 1981Q2 to 2008Q4

Date: 23 OCT 2009

diff(rm75o)

 = - 12.1042 * diff(rtp) - 15.6142 * diff(rtp)[-1]

 (0.80507) (0.98509)

 + 7.06185 * diff(rtp)[-2] - 2.57381 * diff(rtp)[-3] + 0.00860

 (0.45088) (0.17042) (0.09395)

Sum Sq 98.0128 Std Err 0.9616 LHS Mean 0.0133

R Sq 0.0212 R Bar Sq -0.0157 F 4,106 0.5749

D.W.( 1) 2.6726 D.W.( 4) 1.8788
99

FEMALES

RF1617_P = RF1617_P.1 + (-27.3243 * DIFF(RTP) + 13.4173 * DIFF(RTP.1) - 50.4583 * DIFF(RTP.2) - 0.3678 * DIFF(RTP.3))*50.00/44.72

RF1617

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009

diff(rf1617)

 = - 27.3243 * diff(rtp) + 13.4173 * diff(rtp)[-1]

 (1.81297) (0.85773)

 - 50.4583 * diff(rtp)[-2] - 0.36782 * diff(rtp)[-3]

 (3.23806) (0.02421)

 + 0.33050 * minw + 0.19356 * minw[-1] + 0.18090 * minw[-2]

 (0.37169) (0.15791) (0.13742)

 - 0.68394 * minw[-3] - 0.13675

 (0.67051) (0.30213)

Sum Sq 181.339 Std Err 1.2142 LHS Mean -0.0136

R Sq 0.1227 R Bar Sq 0.0656 F 8,123 2.1501

D.W.( 1) 2.9150 D.W.( 4) 2.4862

RF1819_P = RF1819_P.1 + (-42.6358 * DIFF(RTP) - 13.6261 * DIFF(RTP.1) + 9.5650 * DIFF(RTP.2) - 31.4798 * DIFF(RTP.3))*50.00/44.72

RF1819

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009

diff(rf1819)

 = - 42.6358 * diff(rtp) - 13.6261 * diff(rtp)[-1]

 (3.54124) (1.09043)

 + 9.5650 * diff(rtp)[-2] - 31.4798 * diff(rtp)[-3]

 (0.76838) (2.59333)

 + 0.27394 * minw - 0.95221 * minw[-1] + 1.01588 * minw[-2]

 (0.38566) (0.97247) (0.96600)

 - 0.32609 * minw[-3] - 0.05888

 (0.40019) (0.16286)

Sum Sq 115.721 Std Err 0.9700 LHS Mean -0.0217

R Sq 0.1706 R Bar Sq 0.1167 F 8,123 3.1631

D.W.( 1) 2.7048 D.W.( 4) 2.3991

RF2024_P = RF2024_P.1 + (-16.9400 * DIFF(RTP) - 13.2669 * DIFF(RTP.1) - 7.8323 * DIFF(RTP.2) - 8.6887 * DIFF(RTP.3))*50.00/44.72

RF2024

Ordinary Least Squares

QUARTERLY data for 132 periods from 1976Q1 to 2008Q4

Date: 23 OCT 2009

diff(rf2024)

 = - 16.9400 * diff(rtp) - 13.2669 * diff(rtp)[-1]

 (3.09139) (2.28348)

 - 7.83232 * diff(rtp)[-2] - 8.68870 * diff(rtp)[-3] - 0.02226

 (1.35342) (1.54010) (0.56228)

Sum Sq 26.2142 Std Err 0.4543 LHS Mean -0.0275

R Sq 0.1917 R Bar Sq 0.1663 F 4,127 7.5310

D.W.( 1) 2.5252 D.W.( 4) 2.1988

RF2529_P = RF2529_P.1 + (-15.5798 * DIFF(RTP) - 11.9097 * DIFF(RTP.1) - 9.8424 * DIFF(RTP.2) - 2.7555 * DIFF(RTP.3))*50.00/44.72
100

RF2529

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf2529)

 = - 15.5798 * diff(rtp) - 11.9097 * diff(rtp)[-1]

 (3.32423) (2.39607)

 - 9.8424 * diff(rtp)[-2] - 2.75548 * diff(rtp)[-3] - 0.01837

 (1.97999) (0.57663) (0.54398)

Sum Sq 18.2145 Std Err 0.3833 LHS Mean -0.0172

R Sq 0.2094 R Bar Sq 0.1839 F 4,124 8.2130

D.W.( 1) 2.3764 D.W.( 4) 2.0455

RF3034_P = RF3034_P.1 + (-12.5396 * DIFF(RTP) - 1.6601 * DIFF(RTP.1) - 21.0289 * DIFF(RTP.2) + 0.0881 * DIFF(RTP.3))*50.00/44.72

RF3034

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf3034)

 = - 12.5396 * diff(rtp) - 1.66005 * diff(rtp)[-1]

 (2.58233) (0.32234)

 - 21.0289 * diff(rtp)[-2] + 0.08813 * diff(rtp)[-3] - 0.01851

 (4.08297) (0.01780) (0.52910)

Sum Sq 19.5533 Std Err 0.3971 LHS Mean -0.0179

R Sq 0.1929 R Bar Sq 0.1669 F 4,124 7.4100

D.W.( 1) 2.5217 D.W.( 4) 2.0509

RF3539_P = RF3539_P.1 + (-21.9314 * DIFF(RTP) - 3.0139 * DIFF(RTP.1) - 7.8723 * DIFF(RTP.2) - 6.4785 * DIFF(RTP.3))*50.00/44.72;

RF3539

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf3539)

 = - 21.9314 * diff(rtp) - 3.01391 * diff(rtp)[-1]

 (5.04991) (0.65436)

 - 7.87232 * diff(rtp)[-2] - 6.47846 * diff(rtp)[-3] - 0.00217

 (1.70904) (1.46307) (0.06942)

Sum Sq 15.6401 Std Err 0.3551 LHS Mean -0.0005

R Sq 0.2453 R Bar Sq 0.2210 F 4,124 10.0778

D.W.( 1) 2.3835 D.W.( 4) 1.7499

RF4044_P = RF4044_P.1 + (-7.7893 * DIFF(RTP) - 7.7152 * DIFF(RTP.1) - 5.7849 * DIFF(RTP.2) - 2.7298 * DIFF(RTP.3))*50.00/44.72

RF4044

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf4044)

 = - 7.78933 * diff(rtp) - 7.71518 * diff(rtp)[-1]

 (1.61580) (1.50905)

 - 5.78494 * diff(rtp)[-2] - 2.72977 * diff(rtp)[-3] - 0.00986

 (1.13141) (0.55538) (0.28389)

Sum Sq 19.2707 Std Err 0.3942 LHS Mean -0.0095

R Sq 0.0780 R Bar Sq 0.0483 F 4,124 2.6232

D.W.( 1) 2.2897 D.W.( 4) 2.2106
101

RF4549_P = RF4549_P.1 + (-7.8747 * DIFF(RTP) -12.5212 * DIFF(RTP.1) + 3.56675 * DIFF(RTP.2) - 5.4812 * DIFF(RTP.3))*50.00/44.72

RF4549

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf4549)

 = - 7.87468 * diff(rtp) - 12.5212 * diff(rtp)[-1]

 (1.73362) (2.59919)

 + 3.56675 * diff(rtp)[-2] - 5.48119 * diff(rtp)[-3] - 0.00968

 (0.74033) (1.18351) (0.29587)

Sum Sq 17.1092 Std Err 0.3715 LHS Mean -0.0093

R Sq 0.1055 R Bar Sq 0.0767 F 4,124 3.6575

D.W.( 1) 2.5032 D.W.( 4) 2.1339

RF5054_P = RF5054_P.1 + (-9.7818 * DIFF(RTP) - 3.1242 * DIFF(RTP.1) - 14.0327 * DIFF(RTP.2) - 4.0364 * DIFF(RTP.3))*50.00/44.72

RF5054

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf5054)

 = - 9.7818 * diff(rtp) - 3.12420 * diff(rtp)[-1]

 (2.25978) (0.68055)

 - 14.0327 * diff(rtp)[-2] - 4.03638 * diff(rtp)[-3] - 0.00195

 (3.05649) (0.91457) (0.06247)

Sum Sq 15.5373 Std Err 0.3540 LHS Mean -0.0021

R Sq 0.1556 R Bar Sq 0.1283 F 4,124 5.7111

D.W.( 1) 2.3211 D.W.( 4) 1.9891

RF5559_P = RF5559_P.1 + (-2.4665 * DIFF(RTP) - 4.8191 * DIFF(RTP.1) - 11.4418 * DIFF(RTP.2) - 3.5854 * DIFF(RTP.3))*50.00/44.72

RF5559

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf5559)

 = - 2.46651 * diff(rtp) - 4.81906 * diff(rtp)[-1]

 (0.39811) (0.73342)

 - 11.4418 * diff(rtp)[-2] - 3.58538 * diff(rtp)[-3] - 0.00784

 (1.74120) (0.56759) (0.17561)

Sum Sq 31.8294 Std Err 0.5066 LHS Mean -0.0088

R Sq 0.0474 R Bar Sq 0.0166 F 4,124 1.5416

D.W.( 1) 2.8260 D.W.( 4) 1.3743

RF6064_P = RF6064_P.1 + (-22.1139 * DIFF(RTP) + 4.5539 * DIFF(RTP.1) - 6.2406 * DIFF(RTP.2) - 7.0337 * DIFF(RTP.3))*50.00/44.72

RF6064

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf6064)

 = - 22.1139 * diff(rtp) + 4.55389 * diff(rtp)[-1]

 (2.75662) (0.53526)

 - 6.24055 * diff(rtp)[-2] - 7.03372 * diff(rtp)[-3] - 0.00247

 (0.73345) (0.85995) (0.04266)

Sum Sq 53.3640 Std Err 0.6560 LHS Mean -0.0008

R Sq 0.0732 R Bar Sq 0.0433 F 4,124 2.4497
102

D.W.( 1) 3.0445 D.W.( 4) 1.3297

RF6569_P = RF6569_P.1 + (9.2541 * DIFF(RTP) + 7.6281 * DIFF(RTP.1) - 22.5230 * DIFF(RTP.2) + 0.2738 * DIFF(RTP.3))*50.00/44.72

RF6569

Ordinary Least Squares

QUARTERLY data for 129 periods from 1976Q4 to 2008Q4

Date: 23 OCT 2009

diff(rf6569)

 = 9.25410 * diff(rtp) + 7.62811 * diff(rtp)[-1]

 (0.80440) (0.62521)

 - 22.5230 * diff(rtp)[-2] + 0.27380 * diff(rtp)[-3] - 0.01724

 (1.84584) (0.02334) (0.20808)

Sum Sq 109.749 Std Err 0.9408 LHS Mean -0.0199

R Sq 0.0329 R Bar Sq 0.0017 F 4,124 1.0547

D.W.( 1) 3.0050 D.W.( 4) 1.0241

RF7074_P = RF7074_P.1 + (24.2237 * DIFF(RTP) + 8.3386 * DIFF(RTP.1) - 13.5317 * DIFF(RTP.2) - 8.1855 * DIFF(RTP.3))*50.00/44.72

RF7074

Ordinary Least Squares

QUARTERLY data for 111 periods from 1981Q2 to 2008Q4

Date: 23 OCT 2009

diff(rf7074)

 = 24.2237 * diff(rtp) + 8.33858 * diff(rtp)[-1]

 (1.28864) (0.42077)

 - 13.5317 * diff(rtp)[-2] - 8.18546 * diff(rtp)[-3] + 0.01891

 (0.69102) (0.43349) (0.16535)

Sum Sq 153.213 Std Err 1.2023 LHS Mean 0.0075

R Sq 0.0218 R Bar Sq -0.0151 F 4,106 0.5903

D.W.( 1) 2.6506 D.W.( 4) 1.6645

RF75O_P = RF75O_P.1 + (-28.8294 * DIFF(RTP) + 55.5911 * DIFF(RTP.1) - 31.0676 * DIFF(RTP.2) - 15.8580 * DIFF(RTP.3))*50.00/44.72

RF75O

Ordinary Least Squares

QUARTERLY data for 111 periods from 1981Q2 to 2008Q4

Date: 23 OCT 2009

diff(rf75o)

 = - 28.8294 * diff(rtp) + 55.5911 * diff(rtp)[-1]

 (1.09028) (1.99419)

 - 31.0676 * diff(rtp)[-2] - 15.8580 * diff(rtp)[-3] + 0.03090

 (1.12786) (0.59703) (0.19206)

Sum Sq 303.162 Std Err 1.6912 LHS Mean 0.0324

R Sq 0.0508 R Bar Sq 0.0149 F 4,106 1.4170

D.W.( 1) 2.6248 D.W.( 4) 1.5572

UNEMPLOYMENT RATES, AGE-GENDER ADJUSTED, PRELIMINARY

RUM_ASA_P = (RM1617_P * LM1617_BY + RM1819_P * LM1819_BY + RM2024_P * LM2024_BY + RM2529_P * LM2529_BY +

 RM3034_P * LM3034_BY + RM3539_P * LM3539_BY + RM4044_P * LM4044_BY + RM4549_P * LM4549_BY + 

 RM5054_P * LM5054_BY + RM5559_P * LM5559_BY + RM6064_P * LM6064_BY + RM6569_P * LM6569_BY + 

 RM7074_P * LM7074_BY + RM75O_P * LM75O_BY)/ LCM_BY

RUF_ASA_P = (RF1617_P * LF1617_BY + RF1819_P * LF1819_BY + RF2024_P * LF2024_BY + RF2529_P * LF2529_BY + 

 RF3034_P * LF3034_BY + RF3539_P * LF3539_BY + RF4044_P * LF4044_BY + RF4549_P * LF4549_BY +

 RF5054_P * LF5054_BY + RF5559_P * LF5559_BY + RF6064_P * LF6064_BY + RF6569_P * LF6569_BY + 
103

 RF7074_P * LF7074_BY + RF75O_P * LF75O_BY)/ LCF_BY

RU_ASA_P = (RUM_ASA_P * LCM_BY + RUF_ASA_P * LCF_BY) / LC_BY

UNEMPLOYMENT RATES

MALES

RM1617= RM1617_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM1819= RM1819_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM2024= RM2024_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM2529= RM2529_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM3034= RM3034_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM3539= RM3539_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM4044= RM4044_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM4549= RM4549_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM5054= RM5054_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM5559= RM5559_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM6064= RM6064_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM6569= RM6569_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM7074= RM7074_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RM75O = RM75O_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

FEMALES

RF1617= RF1617_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF1819= RF1819_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF2024= RF2024_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF2529= RF2529_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF3034= RF3034_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF3539= RF3539_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF4044= RF4044_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF4549= RF4549_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF5054= RF5054_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF5559= RF5559_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF6064= RF6064_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF6569= RF6569_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF7074= RF7074_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

RF75O = RF75O_P * (1 + RU_ASA_ADJ / RU_ASA_P) 

UNEMPLOYMENT RATES, AGE-GENDER ADJUSTED

RUM_ASA = (RM1617 * LM1617_BY+ RM1819 * LM1819_BY + RM2024 * LM2024_BY + RM2529 * LM2529_BY + 

 RM3034 * LM3034_BY + RM3539 * LM3539_BY + RM4044 * LM4044_BY + RM4549 * LM4549_BY + 

 RM5054 * LM5054_BY + RM5559 * LM5559_BY + RM6064 * LM6064_BY + RM6569 * LM6569_BY + 

 RM7074 * LM7074_BY + RM75O * LM75O_BY)/ LCM_BY

RUF_ASA = (RF1617 * LF1617_BY + RF1819 * LF1819_BY + RF2024 * LF2024_BY + RF2529 * LF2529_BY + 

 RF3034 * LF3034_BY + RF3539 * LF3539_BY + RF4044 * LF4044_BY + RF4549 * LF4549_BY + 

 RF5054 * LF5054_BY + RF5559 * LF5559_BY + RF6064 * LF6064_BY + RF6569 * LF6569_BY + 

 RF7074 * LF7074_BY + RF75O * LF75O_BY)/ LCF_BY

RU_ASA = (RUM_ASA * LCM_BY + RUF_ASA * LCF_BY) / LC_BY 

UNEMPLOYMENT RATES, FULL EMPLOYMENT DIFFERENTIALS

MALES

DRM1617_FE = (-36.2076 * (1 - RTP) - 14.2816 * (1 - RTP.1) - 26.6756 * (1 - RTP.2) - 16.9202 * (1 - RTP.3))*50.00/44.72

DRM1819_FE = (-48.4227 * (1 - RTP) - 25.8766 * (1 - RTP.1) - 21.7466 * (1 - RTP.2) + 1.1551 * (1 - RTP.3))*50.00/44.72

DRM2024_FE = (-51.6518 * (1 - RTP) - 16.6465 * (1 - RTP.1) - 13.1350 * (1 - RTP.2) - 10.9309 * (1 - RTP.3))*50.00/44.72

DRM2529_FE = (-37.9533 * (1 - RTP) - 17.3941 * (1 - RTP.1) - 14.9170 * (1 - RTP.2) - 7.0513 * (1 - RTP.3))*50.00/44.72

DRM3034_FE = (-23.6417 * (1 - RTP) - 14.1284 * (1 - RTP.1) - 7.5008 * (1 - RTP.2) - 9.7232 * (1 - RTP.3))*50.00/44.72

DRM3539_FE = (-27.6828 * (1 - RTP) - 5.4850 * (1 - RTP.1) - 10.8974 * (1 - RTP.2) - 9.8932 * (1 - RTP.3))*50.00/44.72

DRM4044_FE = (-14.6558 * (1 - RTP) - 14.9735 * (1 - RTP.1) - 8.2594 * (1 - RTP.2) - 5.5023 * (1 - RTP.3))*50.00/44.72
104

DRM4549_FE = (-20.7806 * (1 - RTP) - 11.5121 * (1 - RTP.1) - 9.9409 * (1 - RTP.2) + 1.5480 * (1 - RTP.3))*50.00/44.72

DRM5054_FE = (-19.3341 * (1 - RTP) - 9.5336 * (1 - RTP.1) - 8.8784 * (1 - RTP.2) - 7.6218 * (1 - RTP.3))*50.00/44.72

DRM5559_FE = (-25.9031 * (1 - RTP) - 11.4442 * (1 - RTP.1) - 4.5421 * (1 - RTP.2) + 0.55815 * (1 - RTP.3))*50.00/44.72

DRM6064_FE = (1.3133 * (1 - RTP) - 12.9625 * (1 - RTP.1) - 2.4816 * (1 - RTP.2) - 14.4797 * (1 - RTP.3))*50.00/44.72

DRM6569_FE = (-19.5151 * (1 - RTP) + 4.9785 * (1 - RTP.1) - 13.3449 * (1 - RTP.2) + 2.4706 * (1 - RTP.3))*50.00/44.72

DRM7074_FE = 4.1938 * (1 - RTP) - 5.9012 * (1 - RTP.1) - 27.0406 * (1 - RTP.2) + 7.0400 * (1 - RTP.3))*50.00/44.72

DRM75O_FE = (-12.1042 * (1 - RTP) - 15.6142 * (1 - RTP.1) + 7.06185 * (1 - RTP.2) - 2.5738 * (1 - RTP.3))*50.00/44.72

FEMALES

DRF1617_FE = (-27.3243 * (1 - RTP) + 13.4173 * (1 - RTP.1) - 50.4583 * (1 - RTP.2) - 0.3678 * (1 - RTP.3))*50.00/44.72

DRF1819_FE = (-42.6358 * (1 - RTP) - 13.6261 * (1 - RTP.1) + 9.5650 * (1 - RTP.2) - 31.4798 * (1 - RTP.3))*50.00/44.72

DRF2024_FE = (-16.9400 * (1 - RTP) - 13.2669 * (1 - RTP.1) - 7.8323 * (1 - RTP.2) - 8.6887 * (1 - RTP.3))*50.00/44.72

DRF2529_FE = (-15.5798 * (1 - RTP) - 11.9097 * (1 - RTP.1) - 9.8424 * (1 - RTP.2) - 2.7555 * (1 - RTP.3))*50.00/44.72

DRF3034_FE = (-12.5396 * (1 - RTP) - 1.6601 * (1 - RTP.1) - 21.0289 * (1 - RTP.2) + 0.0881 * (1 - RTP.3))*50.00/44.72

DRF3539_FE = (-21.9314 * (1 - RTP) - 3.0139 * (1 - RTP.1) - 7.8723 * (1 - RTP.2) - 6.4785 * (1 - RTP.3))*50.00/44.72

DRF4044_FE = (- 7.7893 * (1 - RTP) - 7.7152 * (1 - RTP.1) - 5.7849 * (1 - RTP.2) - 2.7298 * (1 - RTP.3))*50.00/44.72

DRF4549_FE = (- 7.8747 * (1 - RTP) - 12.5212 * (1 - RTP.1) + 3.56675 * (1 - RTP.2) - 5.4812 * (1 - RTP.3))*50.00/44.72

DRF5054_FE = (- 9.7818 * (1 - RTP) - 3.1242 * (1 - RTP.1) - 14.0327 * (1 - RTP.2) - 4.0364 * (1 - RTP.3))*50.00/44.72

DRF5559_FE = (- 2.4665 * (1 - RTP) - 4.8191 * (1 - RTP.1) - 11.4418 * (1 - RTP.2) - 3.5854 * (1 - RTP.3))*50.00/44.72

DRF6064_FE = (-22.1139 * (1 - RTP) + 4.5539 * (1 - RTP.1) - 6.2406 * (1 - RTP.2) - 7.0337 * (1 - RTP.3))*50.00/44.72

DRF6569_FE = (9.2541 * (1 - RTP) + 7.6281 * (1 - RTP.1) - 22.5230 * (1 - RTP.2) + 0.2738 * (1 - RTP.3))*50.00/44.72

DRF7074_FE = (24.2237 * (1 - RTP) + 8.3386 * (1 - RTP.1) - 13.5317 * (1 - RTP.2) - 8.1855 * (1 - RTP.3))*50.00/44.72

DRF75O_FE = (-28.8294 * (1 - RTP) + 55.5911 * (1 - RTP.1) - 31.0676 * (1 - RTP.2) - 15.8580 * (1 - RTP.3))*50.00/44.72

UNEMPLOYMENT RATES, FULL EMPLOYMENT DIFFERENTIALS

TOTALS

MALES

RM1617_FE = RM1617 + DRM1617_FE

RM1819_FE = RM1819 + DRM1819_FE

RM2024_FE = RM2024 + DRM2024_FE

RM2529_FE = RM2529 + DRM2529_FE

RM3034_FE = RM3034 + DRM3034_FE

RM3539_FE = RM3539 + DRM3539_FE

RM4044_FE = RM4044 + DRM4044_FE

RM4549_FE = RM4549 + DRM4549_FE

RM5054_FE = RM5054 + DRM5054_FE

RM5559_FE = RM5559 + DRM5559_FE

RM6064_FE = RM6064 + DRM6064_FE

RM6569_FE = RM6569 + DRM6569_FE

RM7074_FE = RM7074 + DRM7074_FE

RM75O_FE = RM75O + DRM75O_FE

RF1617_FE = RF1617 + DRF1617_FE

RF1819_FE = RF1819 + DRF1819_FE

RF2024_FE = RF2024 + DRF2024_FE

RF2529_FE = RF2529 + DRF2529_FE

RF3034_FE = RF3034 + DRF3034_FE

RF3539_FE = RF3539 + DRF3539_FE

RF4044_FE = RF4044 + DRF4044_FE

RF4549_FE = RF4549 + DRF4549_FE

RF5054_FE = RF5054 + DRF5054_FE

RF5559_FE = RF5559 + DRF5559_FE

RF6064_FE = RF6064 + DRF6064_FE

RF6569_FE = RF6569 + DRF6569_FE

RF7074_FE = RF7074 + DRF7074_FE

RF75O_FE = RF75O + DRF75O_FE
105

LABOR FORCE PARTICIPATION RATES (LFPR)

MALE LFPR EQUATIONS

AGE 16 TO 19

PM1617_P = 0.98298 - 0.000026

 - 0.368046 * RM1617DI 

 - (- 0.78720 + 0.01330 * TR_PM1617 + 0.00158 * RM1617 + 0.00180 * RM1617.1 + 0.00115 * RM1617.2 + 0.00014 * RM1617.3 -

0.00072 * RM1617.4 - 0.00094 * RM1617.5);

PM1819_P = 0.97979 - 0.000070

 - 0.614469 * RM1819DI 

 - (- 0.50476 + 0.00764 * TR_PM1819 + 0.00626 * MOVAVG(5,RM1819));

PM1617 = PM1617_P;

PM1819 = PM1819_P;

AGE 20 TO 54

NEVER MARRIED

PM2024NM_P = 1.04005 - 0.00523 - 0.000143 - 0.00225 * TR_PM2024 

 - 0.00063 * RM2024 - 0.00077 * RM2024.1 - 0.00059 * RM2024.2 - 0.00027 * RM2024.3 + 0.00005 * RM2024.4 + 0.00020 * 

RM2024.5

 - 0.788895 * RM2024DI;

PM2529NM_P = 0.97919 - 0.00809 + 0.000424 - 0.00070 * TR_PM2529 

 - 0.00028 * RM2529 - 0.00044 * RM2529.1 - 0.00050 * RM2529.2 - 0.00047 * RM2529.3 - 0.00037 * RM2529.4 - 0.00021 * 

RM2529.5

 - 0.887239 * RM2529DI;

PM3034NM_P = 0.90427 + 0.000567 

 - 0.00046 * RM3034 - 0.00061 * RM3034.1 - 0.00054 * RM3034.2 - 0.00036 * RM3034.3 - 0.00014 * RM3034.4 + 0.00001 * 

RM3034.5

 - 0.883076 * RM3034DI;

PM3539NM_P = 0.86825 - 0.000105 

 - 0.00004 * RM3539 - 0.00010 * RM3539.1 - 0.00016 * RM3539.2 - 0.00021 * RM3539.3 - 0.00021 * RM3539.4 - 0.00015 * 

RM3539.5

 - 0.846107 * RM3539DI;

PM4044NM_P = 0.83977 - 0.001481 

 - 0.00057 * RM4044 - 0.00066 * RM4044.1 - 0.00044 * RM4044.2 - 0.00009 * RM4044.3 + 0.00022 * RM4044.4 + 0.00031 * 

RM4044.5

 - 0.807518 * RM4044DI;

PM4549NM_P = 0.81116 - 0.004005

 - 0.00002 * RM4549 - 0.00016 * RM4549.1 - 0.00034 * RM4549.2 - 0.00049 * RM4549.3 - 0.00054 * RM4549.4 - 0.00040 * 

RM4549.5

 - 0.765856 * RM4549DI;

 

PM5054NM_P = 0.77540 - 0.010316

 + 0.00112 * RM5054 + 0.00103 * RM5054.1 + 0.00023 * RM5054.2 - 0.00078 * RM5054.3 - 0.00149 * RM5054.4 - 0.00139 * 

RM5054.5

 - 0.715161 * RM5054DI;

AGE 20 TO 54

MARRIED, SPOUSE PRESENT

PM2024MS_P = 1.03184 - 0.00733 + 0.000543 - 0.00069 * TR_PM2024 
106

 - 0.00063 * RM2024 - 0.00077 * RM2024.1 - 0.00059 * RM2024.2 - 0.00027 * RM2024.3 + 0.00005 * RM2024.4 + 0.00020 * 

RM2024.5 

 - 0.937270 * RM2024DI;

PM2529MS_P = 1.00110 - 0.00498 + 0.001239 - 0.00025 * TR_PM2529 

 - 0.00028 * RM2529 - 0.00044 * RM2529.1 - 0.00050 * RM2529.2 - 0.00047 * RM2529.3 - 0.00037 * RM2529.4 - 0.00021 * 

RM2529.5

 - 0.956095 * RM2529DI;

PM3034MS_P = 0.97120 + 0.001964 + 0.16457 * 1/(TR_PM3034 - 85) 

 - 0.00046 * RM3034 - 0.00061 * RM3034.1 - 0.00054 * RM3034.2 - 0.00036 * RM3034.3 - 0.00014 * RM3034.4 + 0.00001 * 

RM3034.5

 - 0.961197 * RM3034DI;

PM3539MS_P = 0.98068 + 0.002566

 - 0.00004 * RM3539 - 0.00010 * RM3539.1 - 0.00016 * RM3539.2 - 0.00021 * RM3539.3 - 0.00021 * RM3539.4 - 0.00015 * 

RM3539.5

 - 0.958567 * RM3539DI;

PM4044MS_P = 0.98250 + 0.003434

 - 0.00057 * RM4044 - 0.00066 * RM4044.1 - 0.00044 * RM4044.2 - 0.00009 * RM4044.3 + 0.00022 * RM4044.4 + 0.00031 * 

RM4044.5

 - 0.950227 * RM4044DI;

PM4549MS_P = 0.98115 + 0.004829

 - 0.00002 * RM4549 - 0.00016 * RM4549.1 - 0.00034 * RM4549.2 - 0.00049 * RM4549.3 - 0.00054 * RM4549.4 - 0.00040 * 

RM4549.5

 - 0.935691 * RM4549DI;

PM5054MS_P = 0.94484 + 0.004732

 + 0.00112 * RM5054 + 0.00103 * RM5054.1 + 0.00023 * RM5054.2 - 0.00078 * RM5054.3 - 0.00149 * RM5054.4 - 0.00139 * 

RM5054.5

 + 0.09796 * (RF5054CU6+RF5054C6O) - 0.901430 * RM5054DI;

AGE 20 TO 54

MARRIED, SPOUSE ABSENT

PM2024MA_P = 1.14087 - 0.01412 + 0.000270 - 0.00232 * TR_PM2024 

 - 0.00063 * RM2024 - 0.00077 * RM2024.1 - 0.00059 * RM2024.2 - 0.00027 * RM2024.3 + 0.00005 * RM2024.4 + 0.00020 * 

RM2024.5 

 - 0.878382 * RM2024DI;

PM2529MA_P = 0.98602 - 0.00788 + 0.000733 - 0.00051 * TR_PM2529 

 - 0.00028 * RM2529 - 0.00044 * RM2529.1 - 0.00050 * RM2529.2 - 0.00047 * RM2529.3 - 0.00037 * RM2529.4 - 0.00021 * 

RM2529.5

 - 0.913332 * RM2529DI;

PM3034MA_P = 0.93933 + 0.001197

 - 0.00046 * RM3034 - 0.00061 * RM3034.1 - 0.00054 * RM3034.2 - 0.00036 * RM3034.3 - 0.00014 * RM3034.4 + 0.00001 * 

RM3034.5

 - 0.918267 * RM3034DI;

PM3539MA_P = 0.92354 + 0.001209

 - 0.00004 * RM3539 - 0.00010 * RM3539.1 - 0.00016 * RM3539.2 - 0.00021 * RM3539.3 - 0.00021 * RM3539.4 - 0.00015 * 

RM3539.5

 - 0.901443 * RM3539DI;

PM4044MA_P = 0.91512 + 0.001111

 - 0.00057 * RM4044 - 0.00066 * RM4044.1 - 0.00044 * RM4044.2 - 0.00009 * RM4044.3 + 0.00022 * RM4044.4 + 0.00031 * 

RM4044.5

 - 0.882777 * RM4044DI;

PM4549MA_P = 0.89473 + 0.000334

 - 0.00002 * RM4549 - 0.00016 * RM4549.1 - 0.00034 * RM4549.2 - 0.00049 * RM4549.3 - 0.00054 * RM4549.4 - 0.00040 * 

RM4549.5 

 - 0.849283 * RM4549DI;
107

 PM5054MA_P = 0.84912 - 0.004345

 + 0.00112 * RM5054 + 0.00103 * RM5054.1 + 0.00023 * RM5054.2 - 0.00078 * RM5054.3 - 0.00149 * RM5054.4 - 0.00139 * 

RM5054.5

 - 0.789075 * RM5054DI;

AGE 20 TO 54

PM2024_P = (PM2024NM_P * NM2024NM + PM2024MS_P * NM2024MS + PM2024MA_P * NM2024MA) / NM2024;

PM2529_P = (PM2529NM_P * NM2529NM + PM2529MS_P * NM2529MS + PM2529MA_P * NM2529MA) / NM2529;

PM3034_P = (PM3034NM_P * NM3034NM + PM3034MS_P * NM3034MS + PM3034MA_P * NM3034MA) / NM3034;

PM3539_P = (PM3539NM_P * NM3539NM + PM3539MS_P * NM3539MS + PM3539MA_P * NM3539MA) / NM3539;

PM4044_P = (PM4044NM_P * NM4044NM + PM4044MS_P * NM4044MS + PM4044MA_P * NM4044MA) / NM4044;

PM4549_P = (PM4549NM_P * NM4549NM + PM4549MS_P * NM4549MS + PM4549MA_P * NM4549MA) / NM4549;

PM5054_P = (PM5054NM_P * NM5054NM + PM5054MS_P * NM5054MS + PM5054MA_P * NM5054MA) / NM5054;

PM2024 = PM2024_P;

PM2529 = PM2529_P;

PM3034 = PM3034_P;

PM3539 = PM3539_P;

PM4044 = PM4044_P;

PM4549 = PM4549_P;

PM5054 = PM5054_P;

PM2024NM = PM2024NM_P * PM2024 / PM2024_P;

PM2529NM = PM2529NM_P * PM2529 / PM2529_P;

PM3034NM = PM3034NM_P * PM3034 / PM3034_P;

PM3539NM = PM3539NM_P * PM3539 / PM3539_P;

PM4044NM = PM4044NM_P * PM4044 / PM4044_P;

PM4549NM = PM4549NM_P * PM4549 / PM4549_P;

PM5054NM = PM5054NM_P * PM5054 / PM5054_P;

PM2024MS = PM2024MS_P * PM2024 / PM2024_P;

PM2529MS = PM2529MS_P * PM2529 / PM2529_P;

PM3034MS = PM3034MS_P * PM3034 / PM3034_P;

PM3539MS = PM3539MS_P * PM3539 / PM3539_P;

PM4044MS = PM4044MS_P * PM4044 / PM4044_P;

PM4549MS = PM4549MS_P * PM4549 / PM4549_P;

PM5054MS = PM5054MS_P * PM5054 / PM5054_P;

PM2024MA = PM2024MA_P * PM2024 / PM2024_P;

PM2529MA = PM2529MA_P * PM2529 / PM2529_P;

PM3034MA = PM3034MA_P * PM3034 / PM3034_P;

PM3539MA = PM3539MA_P * PM3539 / PM3539_P;

PM4044MA = PM4044MA_P * PM4044 / PM4044_P;

PM4549MA = PM4549MA_P * PM4549 / PM4549_P;

PM5054MA = PM5054MA_P * PM5054 / PM5054_P;

AGE 55 TO 61

PM55_P = - 0.76902 + 0.000142 - 0.818694 * RM55DI + PM55E_DE + PM55_DM

 + 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 - 0.00147 * RM5559.4 - 0.00126 * 

RM5559.5;

PM56_P = - 0.76098 - 0.002143 - 0.798138 * RM56DI + PM56E_DE + PM56_DM

 + 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 - 0.00147 * RM5559.4 - 0.00126 * 

RM5559.5;

PM57_P = - 0.71065 - 0.004979 - 0.776220 * RM57DI + PM57E_DE + PM57_DM

 + 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 - 0.00147 * RM5559.4 - 0.00126 * 

RM5559.5; 

PM58_P = - 0.69412 - 0.007970 - 0.755977 * RM58DI + PM58E_DE + PM58_DM

 + 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 - 0.00147 * RM5559.4 - 0.00126 * 

RM5559.5; 
108

PM59_P = - 0.60153 - 0.012123 - 0.730720 * RM59DI + PM59E_DE + PM59_DM

 + 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 - 0.00147 * RM5559.4 - 0.00126 * 

RM5559.5; 

PM60_P = - 0.58858 - 0.011072 + 0.000362 - 0.682175 * RM60DI + PM60E_DE + PM60_DM

 + 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 - 0.00374 * RM6064.4 - 0.00331 * 

RM6064.5

 + 0.01566 * PF58;

PM61_P = - 0.55646 - 0.019672 + 0.002517 - 0.647667 * RM61DI + PM61E_DE + PM61_DM

 + 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 - 0.00374 * RM6064.4 - 0.00331 * 

RM6064.5

 + 0.08544 * PF59;

AGE 62 TO 74

PM62_P = 0.26329 * PF60 - 0.29161 - 0.544582 * RM61DI.4 + PM62E_DE + PM62_DM - 0.056836 + 0.008814

 + 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 - 0.00374 * RM6064.4 - 0.00331 * 

RM6064.5

 - 0.60 * RRADJ_M62 - 0.02 * POT_ET_TXRT_62; 

PM63_P = 0.40940 * PF61 - 0.29495 - 0.480592 * RM61DI.8 + PM63E_DE + PM63_DM - 0.085295 + 0.017121

 + 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 - 0.00374 * RM6064.4 - 0.00331 * 

RM6064.5

 - 0.55 * RRADJ_M63 - 0.02 * POT_ET_TXRT_63; 

PM64_P = 0.47933 * PF62 - 0.22665 - 0.435189 * RM61DI.12 + PM64E_DE + PM64_DM - 0.113466 + 0.030849

 + 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 - 0.00374 * RM6064.4 - 0.00331 * 

RM6064.5

 - 0.50 * RRADJ_M64 - 0.02 * POT_ET_TXRT_64; 

PM65_P = 0.72722 * PF63 - 0.35819 - 0.368105 * RM61DI.16 + PM65E_DE + PM65_DM - 0.016932 + 0.059764

 + 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 - 0.00178 * RM6569.4 - 0.00151 * 

RM6569.5

 - 0.45 * RRADJ_M65 - 0.02 * POT_ET_TXRT_65;

PM66_P = 0.38684 * PF64 - 0.20883 - 0.330352 * RM61DI.20 + PM66E_DE + PM66_DM + 0.000194 + 0.036539

 + 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 - 0.00178 * RM6569.4 - 0.00151 * 

RM6569.5

 - 0.40 * RRADJ_M66 - 0.02 * POT_ET_TXRT_66;

PM67_P = 0.35012 * PF65 - 0.15975 - 0.294277 * RM61DI.24 + PM67E_DE + PM67_DM + 0.042362 - 0.012475

 + 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 - 0.00178 * RM6569.4 - 0.00151 * 

RM6569.5

 - 0.35 * RRADJ_M67 - 0.02 * POT_ET_TXRT_67; 

PM68_P = 0.95984 * PF66 - 0.26305 - 0.269452 * RM61DI.28 + PM68E_DE + PM68_DM + 0.057835 - 0.027492

 + 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 - 0.00178 * RM6569.4 - 0.00151 * 

RM6569.5

 - 0.30 * RRADJ_M68 - 0.02 * POT_ET_TXRT_68; 

PM69_P = 0.74113 * PF67 - 0.22589 - 0.246975 * RM61DI.32 + PM69E_DE + PM69_DM + 0.047260 - 0.018867

 + 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 - 0.00178 * RM6569.4 - 0.00151 * 

RM6569.5

 - 0.30 * RRADJ_M69 - 0.02 * POT_ET_TXRT_69;

PM70_P = 0.46445 * PF68 - 0.23451 - 0.220464 * RM61DI.36 + PM70E_DE + PM70_DM + 0.037323 - 0.010528

 - 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 + 0.00000 * RM7074.4 + 0.00003 *

RM7074.5;

PM71_P = 0.27684 * PF69 - 0.20679 - 0.202537 * RM61DI.40 + PM71E_DE + PM71_DM + 0.030867 - 0.005653

 - 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 + 0.00000 * RM7074.4 + 0.00003 * 

RM7074.5;

PM72_P = 0.77240 * PF70 - 0.25289 - 0.186071 * RM61DI.44 + PM72E_DE + PM72_DM + 0.040290 - 0.012330

 - 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 + 0.00000 * RM7074.4 + 0.00003 * 

RM7074.5;
109

PM73_P = 0.65971 * PF71 - 0.19394 - 0.167545 * RM61DI.48 + PM73E_DE + PM73_DM + 0.036021 - 0.009631

 - 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 + 0.00000 * RM7074.4 + 0.00003 * 

RM7074.5;

PM74_P = 0.78464 * PF72 - 0.17649 - 0.147134 * RM61DI.52 + PM74E_DE + PM74_DM + 0.033451 - 0.010839

 - 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 + 0.00000 * RM7074.4 + 0.00003 * 

RM7074.5;

AGE 75 TO 79

PM75_P = PM74.4 * 0.920 + DPM75O_FE;

PM76_P = PM75.4 * 0.920 + DPM75O_FE;

PM77_P = PM76.4 * 0.920 + DPM75O_FE;

PM78_P = PM77.4 * 0.920 + DPM75O_FE;

PM79_P = PM78.4 * 0.920 + DPM75O_FE;

AGE 55 TO 79

PM55 = PM55_P;

PM56 = PM56_P;

PM57 = PM57_P;

PM58 = PM58_P;

PM59 = PM59_P;

PM60 = PM60_P;

PM61 = PM61_P;

PM62 = PM62_P;

PM63 = PM63_P;

PM64 = PM64_P;

PM65 = PM65_P;

PM66 = PM66_P;

PM67 = PM67_P;

PM68 = PM68_P;

PM69 = PM69_P;

PM70 = PM70_P;

PM71 = PM71_P;

PM72 = PM72_P;

PM73 = PM73_P;

PM74 = PM74_P;

PM75 = PM75_P;

PM76 = PM76_P;

PM77 = PM77_P;

PM78 = PM78_P;

PM79 = PM79_P;

AGE 80 AND OVER

PM80_P = PM79.4 * 0.965**( 1) + DPM75O_FE;

PM81_P = PM79.8 * 0.965**( 2) + DPM75O_FE;

PM82_P = PM79.12 * 0.965**( 3) + DPM75O_FE;

PM83_P = PM79.16 * 0.965**( 4) + DPM75O_FE;

PM84_P = PM79.20 * 0.965**( 5) + DPM75O_FE;

PM85_P = MOVAVG(8,PM79.24) * 0.965**( 6) + DPM75O_FE;

PM86_P = MOVAVG(8,PM79.28) * 0.965**( 7) + DPM75O_FE;

PM87_P = MOVAVG(8,PM79.32) * 0.965**( 8) + DPM75O_FE;

PM88_P = MOVAVG(8,PM79.36) * 0.965**( 9) + DPM75O_FE;

PM89_P = MOVAVG(8,PM79.40) * 0.965**(10) + DPM75O_FE;

PM90_P = MOVAVG(8,PM79.44) * 0.965**(11) + DPM75O_FE;

PM91_P = MOVAVG(8,PM79.48) * 0.965**(12) + DPM75O_FE;

PM92_P = MOVAVG(8,PM79.52) * 0.965**(13) + DPM75O_FE;

PM93_P = MOVAVG(8,PM79.56) * 0.965**(14) + DPM75O_FE;

PM94_P = MOVAVG(8,PM79.60) * 0.965**(15) + DPM75O_FE;
110

 

PM95_P = PM94_P * 0.965 + DPM75O_FE;

PM96_P = PM95_P * 0.965 + DPM75O_FE;

PM97_P = PM96_P * 0.965 + DPM75O_FE;

PM98_P = PM97_P * 0.965 + DPM75O_FE;

PM99_P = PM98_P * 0.965 + DPM75O_FE;

PM100_P = PM99_P * 0.965 + DPM75O_FE;

PM80O_P = (PM80_P*NM80 + PM81_P*NM81 + PM82_P*NM82 + PM83_P*NM83 + PM84_P*NM84 + PM85_P*NM85 + 

PM86_P*NM86 + PM87_P*NM87 + PM88_P*NM88 + PM89_P*NM89 +

 PM90_P*NM90 + PM91_P*NM91 + PM92_P*NM92 + PM93_P*NM93 + PM94_P*NM94 + PM95_P*NM95 + PM96_P*NM96 + 

PM97_P*NM97 + PM98_P*NM98 + PM99_P*NM99 +

 PM100_P*NM100 ) / NM80O;

PM80O = PM80O_P;

PM80 = PM80_P * PM80O / PM80O_P;

PM81 = PM81_P * PM80O / PM80O_P;

PM82 = PM82_P * PM80O / PM80O_P;

PM83 = PM83_P * PM80O / PM80O_P;

PM84 = PM84_P * PM80O / PM80O_P;

PM85 = PM85_P * PM80O / PM80O_P;

PM86 = PM86_P * PM80O / PM80O_P;

PM87 = PM87_P * PM80O / PM80O_P;

PM88 = PM88_P * PM80O / PM80O_P;

PM89 = PM89_P * PM80O / PM80O_P;

PM90 = PM90_P * PM80O / PM80O_P;

PM91 = PM91_P * PM80O / PM80O_P;

PM92 = PM92_P * PM80O / PM80O_P;

PM93 = PM93_P * PM80O / PM80O_P;

PM94 = PM94_P * PM80O / PM80O_P;

PM95 = PM95_P * PM80O / PM80O_P;

PM96 = PM96_P * PM80O / PM80O_P;

PM97 = PM97_P * PM80O / PM80O_P;

PM98 = PM98_P * PM80O / PM80O_P;

PM99 = PM99_P * PM80O / PM80O_P;

PM100 = PM100_P * PM80O / PM80O_P; 

FEMALE LFPR EQUATIONS

AGE 16 TO 19

PF1617_P = 0.98681 - 0.000047 - (0.377204 * RF1617DI) 

 - (- 0.00741 + 0.23393 * RF1617CU6 + 0.00051 * MOVAVG(5,RF1617))

 - (- 0.69608 + 0.01166 * TR_PF1617 + 0.00616 * MOVAVG(5,RF1617));

 

PF1819_P = 0.98200 - 0.000069

 - (0.584930 * RF1819DI) 

 - (-0.00080 + 0.22814 * RF1819CU6 + 0.00318 * MOVAVG(5,RF1819))

 - (- 0.53433+ 0.00764 * TR_PF1819 + 0.00667 * MOVAVG(5,RF1819));

PF1617 = PF1617_P;

PF1819 = PF1819_P;

AGE 20 TO 44

FEMALES - NEVER MARRIED WITH AT LEAST 1 OWN CHILD UNDER AGE 6

 

PF2024NMC6U_P = ( 0.70868 - 0.000477

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.661872 * RF2024DI)
111

 * 1.0160;

PF2529NMC6U_P = ( 0.74861 - 0.000966

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.700572 * RF2529DI)

 * 0.9981;

PF3034NMC6U_P = ( 0.73944 - 0.001998

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.690741 * RF3034DI)

 * 0.9980;

PF3539NMC6U_P = ( 0.75363 - 0.003230

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 * 

RF3539.5

 - 0.701148 * RF3539DI)

 * 0.9989;

PF4044NMC6U_P = ( 0.73920 - 0.005589

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.677955 * RF4044DI)

 * 0.9989;

 

 

FEMALES - NEVER MARRIED NO OWN CHILDREN UNDER AGE 6

PF2024NMNC6_P = ( 1.13654 - 0.000206 - 0.00366 * TR_PF2024NMNC6

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.742995 * RF2024DI)

 * 1.0160;

PF2529NMNC6_P = ( 0.98148 + 0.000457 - 0.00111 * TR_PF2529NMNC6

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.853721 * RF2529DI)

 * 0.9981;

PF3034NMNC6_P = ( 0.84901 + 0.000598 

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.838634 * RF3034DI)

 * 0.9980;

PF3539NMNC6_P = ( 0.84953 + 0.000015 

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 * 

RF3539.5

 - 0.822121 * RF3539DI)

 * 0.9989;

PF4044NMNC6_P = ( 0.83790 - 0.000878 

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.799021 * RF4044DI)

 * 0.9989;

FEMALES - MARRIED SPOUSE PRESENT WITH AT LEAST 1 OWN CHILD UNDER AGE 6

PF2024MSC6U_P = ( 0.69043 - 0.000935 + 0.000015

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.524765 * RF2024DI - 0.1*IF2024MSC6U)
112

 * 1.0160;

PF2529MSC6U_P = ( 0.76218 - 0.001821 - 0.000004

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.608428 * RF2529DI - 0.1*IF2529MSC6U)

 * 0.9981;

PF3034MSC6U_P = ( 0.78186 - 0.002891 - 0.000006

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.639819 * RF3034DI - 0.1*IF3034MSC6U)

 * 0.9980;

PF3539MSC6U_P = ( 0.79072 - 0.004987 - 0.000005

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 *

RF3539.5

 - 0.635646 * RF3539DI - 0.1*IF3539MSC6U)

 * 0.9989;

PF4044MSC6U_P = ( 0.79356 - 0.007041 - 0.000008

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.640644 * RF4044DI - 0.1*IF4044MSC6U)

 * 0.9989;

FEMALES - MARRIED SPOUSE PRESENT NO OWN CHILDREN UNDER AGE 6

PF2024MSNC6_P = ( 0.79421 - 0.000075

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.781956 * RF2024DI)

 * 1.0160;

PF2529MSNC6_P = ( 0.83502 + 0.000186

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.824520 * RF2529DI)

 * 0.9981;

PF3034MSNC6_P = ( 0.80379 - 0.000056

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.801357 * RF3034DI)

 * 0.9980;

PF3539MSNC6_P = ( 0.80906 - 0.000984

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 * 

RF3539.5

 - 0.784868 * RF3539DI)

 * 0.9989; 

PF4044MSNC6_P = ( 0.82602 - 0.001249

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.789470 * RF4044DI)

 * 0.9989;

FEMALES - MARRIED SPOUSE ABSENT WITH AT LEAST 1 OWN CHILD UNDER AGE 6

PF2024MAC6U_P = ( 0.95787 - 0.000403 + 0.000007

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.683999 * RF2024DI - 0.16722 * IF2024MAC6U)
113

 * 1.0160; 

PF2529MAC6U_P = ( 0.90653 - 0.000676

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.731798 * RF2529DI - 0.10000 * IF2529MAC6U)

 * 0.9981;

PF3034MAC6U_P = ( 0.88071 - 0.001042

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.745172 * RF3034DI - 0.10000 * IF3034MAC6U)

 * 0.9980;

PF3539MAC6U_P = ( 0.90258 - 0.002058

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 * 

RF3539.5

 - 0.744846 * RF3539DI - 0.10000 * IF3539MAC6U)

 * 0.9989;

PF4044MAC6U_P = ( 0.89876 - 0.003975 - 0.000005

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.719419 * RF4044DI - 0.10000 * IF4044MAC6U)

 * 0.9989;

FEMALES - MARRIED SPOUSE ABSENT NO OWN CHILDREN UNDER AGE 6

PF2024MANC6_P = ( 0.75174 - 0.000258 + 0.000005

 - 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 * RF2024.4 + 0.00052 * 

RF2024.5

 - 0.727427 * RF2024DI)

 * 1.0160;

PF2529MANC6_P = ( 0.82060 - 0.000015

 - 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * RF2529.4 + 0.00013 * 

RF2529.5

 - 0.802962 * RF2529DI)

 * 0.9981;

PF3034MANC6_P = ( 0.83806 + 0.000442

 - 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 * RF3034.4 + 0.00128 * 

RF3034.5

 - 0.829717 * RF3034DI)

 * 0.9980;

PF3539MANC6_P = ( 0.86613 + 0.000458

 - 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 * RF3539.4 + 0.00132 * 

RF3539.5

 - 0.838637 * RF3539DI)

 * 0.9989;

PF4044MANC6_P = ( 0.85937 + 0.000112

 - 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * RF4044.4 - 0.00046 * 

RF4044.5

 - 0.824438 * RF4044DI)

 * 0.9989;

PF2024NM_P = (PF2024NMC6U_P * NF2024NMC6U + PF2024NMNC6_P * NF2024NMNC6) / NF2024NM;

PF2024MS_P = (PF2024MSC6U_P * NF2024MSC6U + PF2024MSNC6_P * NF2024MSNC6) / NF2024MS;

PF2024MA_P = (PF2024MAC6U_P * NF2024MAC6U + PF2024MANC6_P * NF2024MANC6) / NF2024MA;

PF2529NM_P = (PF2529NMC6U_P * NF2529NMC6U + PF2529NMNC6_P * NF2529NMNC6) / NF2529NM;

PF2529MS_P = (PF2529MSC6U_P * NF2529MSC6U + PF2529MSNC6_P * NF2529MSNC6) / NF2529MS;

PF2529MA_P = (PF2529MAC6U_P * NF2529MAC6U + PF2529MANC6_P * NF2529MANC6) / NF2529MA;
114

PF3034NM_P = (PF3034NMC6U_P * NF3034NMC6U + PF3034NMNC6_P * NF3034NMNC6) / NF3034NM;

PF3034MS_P = (PF3034MSC6U_P * NF3034MSC6U + PF3034MSNC6_P * NF3034MSNC6) / NF3034MS;

PF3034MA_P = (PF3034MAC6U_P * NF3034MAC6U + PF3034MANC6_P * NF3034MANC6) / NF3034MA;

PF3539NM_P = (PF3539NMC6U_P * NF3539NMC6U + PF3539NMNC6_P * NF3539NMNC6) / NF3539NM;

PF3539MS_P = (PF3539MSC6U_P * NF3539MSC6U + PF3539MSNC6_P * NF3539MSNC6) / NF3539MS;

PF3539MA_P = (PF3539MAC6U_P * NF3539MAC6U + PF3539MANC6_P * NF3539MANC6) / NF3539MA;

PF4044NM_P = (PF4044NMC6U_P * NF4044NMC6U + PF4044NMNC6_P * NF4044NMNC6) / NF4044NM;

PF4044MS_P = (PF4044MSC6U_P * NF4044MSC6U + PF4044MSNC6_P * NF4044MSNC6) / NF4044MS;

PF4044MA_P = (PF4044MAC6U_P * NF4044MAC6U + PF4044MANC6_P * NF4044MANC6) / NF4044MA;

AGE 45 TO 54

FEMALES - NEVER MARRIED 45 TO 54

PF4549NM_P = 0.03650 - 0.003349 + PF4549E_DE

 - 0.00076 * RF4549 - 0.00070 * RF4549.1 - 0.00018 * RF4549.2 + 0.00049 * RF4549.3 + 0.00096 * RF4549.4 + 0.00091 * RF4549.5

 - 0.774819 * RF4549DI;

PF5054NM_P = 0.05788 - 0.008250 + PF5054E_DE 

 + 0.00003 * RF5054 - 0.00011 * RF5054.1 - 0.00032 * RF5054.2 - 0.00051 * RF5054.3 - 0.00059 * RF5054.4 - 0.00045 * RF5054.5

 - 0.732696 * RF5054DI;

FEMALES - MARRIED SPOUSE PRESENT 45 TO 54

PF4549MS_P = 0.03842 - 0.003545 + PF4549E_DE 

 - 0.00076 * RF4549 - 0.00070 * RF4549.1 - 0.00018 * RF4549.2 + 0.00049 * RF4549.3 + 0.00096 * RF4549.4 + 0.00091 * 

RF4549.5

 - 0.771341 * RF4549DI - 0.15 * RF4549MSCU6;

PF5054MS_P = - 0.40180 - 0.008735 + PF5054E_DE

 + 0.00003 * RF5054 - 0.00011 * RF5054.1 - 0.00032 * RF5054.2 - 0.00051 * RF5054.3 - 0.00059 * RF5054.4 - 0.00045 * RF5054.5

 + 0.00454 * TR_PF5054MS - 0.726715 * RF5054DI - 0.12 * RF5054MSCU6;

FEMALES - MARRIED SPOUSE ABSENT 45 TO 54

PF4549MA_P = 0.06830 - 0.001541 + PF4549E_DE 

 - 0.00076 * RF4549 - 0.00070 * RF4549.1 - 0.00018 * RF4549.2 + 0.00049 * RF4549.3 + 0.00096 * RF4549.4 + 0.00091 * 

RF4549.5

 - 0.806961 * RF4549DI - 0.1 * RF4549MACU6; 

PF5054MA_P = 0.08983 - 0.006021 + PF5054E_DE 

 + 0.00003 * RF5054 - 0.00011 * RF5054.1 - 0.00032 * RF5054.2 - 0.00051 * RF5054.3 - 0.00059 * RF5054.4 - 0.00045 * RF5054.5

 - 0.760157 * RF5054DI - 0.2 * RF5054MACU6; 

AGE 20 TO 54

PF2024_P = (PF2024NM_P * NF2024NM + PF2024MS_P * NF2024MS + PF2024MA_P * NF2024MA) / NF2024;

PF2529_P = (PF2529NM_P * NF2529NM + PF2529MS_P * NF2529MS + PF2529MA_P * NF2529MA) / NF2529;

PF3034_P = (PF3034NM_P * NF3034NM + PF3034MS_P * NF3034MS + PF3034MA_P * NF3034MA) / NF3034;

PF3539_P = (PF3539NM_P * NF3539NM + PF3539MS_P * NF3539MS + PF3539MA_P * NF3539MA) / NF3539;

PF4044_P = (PF4044NM_P * NF4044NM + PF4044MS_P * NF4044MS + PF4044MA_P * NF4044MA) / NF4044;

PF4549_P = (PF4549NM_P * NF4549NM + PF4549MS_P * NF4549MS + PF4549MA_P * NF4549MA) / NF4549;

PF5054_P = (PF5054NM_P * NF5054NM + PF5054MS_P * NF5054MS + PF5054MA_P * NF5054MA) / NF5054;

PF2024 = PF2024_P;

PF2529 = PF2529_P;
115

PF3034 = PF3034_P;

PF3539 = PF3539_P;

PF4044 = PF4044_P;

PF4549 = PF4549_P;

PF5054 = PF5054_P;

PF2024NM = PF2024NM_P * PF2024 / PF2024_P;

PF2529NM = PF2529NM_P * PF2529 / PF2529_P;

PF3034NM = PF3034NM_P * PF3034 / PF3034_P;

PF3539NM = PF3539NM_P * PF3539 / PF3539_P;

PF4044NM = PF4044NM_P * PF4044 / PF4044_P;

PF4549NM = PF4549NM_P * PF4549 / PF4549_P;

PF5054NM = PF5054NM_P * PF5054 / PF5054_P;

PF2024MS = PF2024MS_P * PF2024 / PF2024_P;

PF2529MS = PF2529MS_P * PF2529 / PF2529_P;

PF3034MS = PF3034MS_P * PF3034 / PF3034_P;

PF3539MS = PF3539MS_P * PF3539 / PF3539_P;

PF4044MS = PF4044MS_P * PF4044 / PF4044_P;

PF4549MS = PF4549MS_P * PF4549 / PF4549_P;

PF5054MS = PF5054MS_P * PF5054 / PF5054_P;

PF2024MA = PF2024MA_P * PF2024 / PF2024_P;

PF2529MA = PF2529MA_P * PF2529 / PF2529_P;

PF3034MA = PF3034MA_P * PF3034 / PF3034_P;

PF3539MA = PF3539MA_P * PF3539 / PF3539_P;

PF4044MA = PF4044MA_P * PF4044 / PF4044_P;

PF4549MA = PF4549MA_P * PF4549 / PF4549_P;

PF5054MA = PF5054MA_P * PF5054 / PF5054_P;

PF2024NMC6U = PF2024NMC6U_P * PF2024 / PF2024_P; 

PF2529NMC6U = PF2529NMC6U_P * PF2529 / PF2529_P; 

PF3034NMC6U = PF3034NMC6U_P * PF3034 / PF3034_P; 

PF3539NMC6U = PF3539NMC6U_P * PF3539 / PF3539_P; 

PF4044NMC6U = PF4044NMC6U_P * PF4044 / PF4044_P; 

PF2024NMNC6 = PF2024NMNC6_P * PF2024 / PF2024_P; 

PF2529NMNC6 = PF2529NMNC6_P * PF2529 / PF2529_P; 

PF3034NMNC6 = PF3034NMNC6_P * PF3034 / PF3034_P; 

PF3539NMNC6 = PF3539NMNC6_P * PF3539 / PF3539_P; 

PF4044NMNC6 = PF4044NMNC6_P * PF4044 / PF4044_P; 

PF2024MSC6U = PF2024MSC6U_P * PF2024 / PF2024_P; 

PF2529MSC6U = PF2529MSC6U_P * PF2529 / PF2529_P;

PF3034MSC6U = PF3034MSC6U_P * PF3034 / PF3034_P; 

PF3539MSC6U = PF3539MSC6U_P * PF3539 / PF3539_P; 

PF4044MSC6U = PF4044MSC6U_P * PF4044 / PF4044_P; 

PF2024MSNC6 = PF2024MSNC6_P * PF2024 / PF2024_P; 

PF2529MSNC6 = PF2529MSNC6_P * PF2529 / PF2529_P; 

PF3034MSNC6 = PF3034MSNC6_P * PF3034 / PF3034_P; 

PF3539MSNC6 = PF3539MSNC6_P * PF3539 / PF3539_P; 

PF4044MSNC6 = PF4044MSNC6_P * PF4044 / PF4044_P; 

PF2024MAC6U = PF2024MAC6U_P * PF2024 / PF2024_P; 

PF2529MAC6U = PF2529MAC6U_P * PF2529 / PF2529_P; 

PF3034MAC6U = PF3034MAC6U_P * PF3034 / PF3034_P; 

PF3539MAC6U = PF3539MAC6U_P * PF3539 / PF3539_P; 

PF4044MAC6U = PF4044MAC6U_P * PF4044 / PF4044_P; 

PF2024MANC6 = PF2024MANC6_P * PF2024 / PF2024_P; 

PF2529MANC6 = PF2529MANC6_P * PF2529 / PF2529_P; 

PF3034MANC6 = PF3034MANC6_P * PF3034 / PF3034_P; 

PF3539MANC6 = PF3539MANC6_P * PF3539 / PF3539_P; 

PF4044MANC6 = PF4044MANC6_P * PF4044 / PF4044_P; 

AGE 55 TO 61
116

PF55_P = - 0.678427 * RF55DI - 0.011198

 + 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * RF5559.4 - 0.00132 * RF5559.5

 + PF55E_DE + PF55_DM + 0.00368 * PF55COH48 - 0.90941; 

PF56_P = - 0.651951 * RF56DI - 0.014942

 + 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * RF5559.4 - 0.00132 * RF5559.5

 + PF56E_DE + PF56_DM + 0.00486 * PF56COH48 - 0.96865; 

PF57_P = - 0.634496 * RF57DI - 0.018113

 + 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * RF5559.4 - 0.00132 * RF5559.5

 + PF57E_DE + PF57_DM + 0.00344 * PF57COH48 - 0.85033; 

PF58_P = - 0.604503 * RF58DI - 0.023096

 + 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * RF5559.4 - 0.00132 * RF5559.5

 + PF58E_DE + PF58_DM + 0.00362 * PF58COH48 - 0.83081; 

PF59_P = - 0.571801 * RF59DI - 0.029463

 + 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * RF5559.4 - 0.00132 * RF5559.5

 + PF59E_DE + PF59_DM + 0.00470 * PF59COH48 - 0.85665; 

PF60_P = - 0.526008 * RF60DI - 0.033474

 + 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * RF6064.4 - 0.00066 * RF6064.5

 + PF60E_DE + PF60_DM + 0.00819 * PF60COH48 - 1.18744; 

PF61_P = - 0.487750 * RF61DI - 0.041036

 + 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * RF6064.4 - 0.00066 * RF6064.5

 + PF61E_DE + PF61_DM + 0.00520 * PF61COH48 - 0.87850; 

AGE 62 TO 74

PF62_P = - 0.412411 * RF61DI.4 - 0.063463 

 + 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * RF6064.4 - 0.00066 * RF6064.5

 + PF62E_DE + PF62_DM + 0.00523 * PF62COH48 - 0.56287 - 0.5100 * RRADJ_F62 - 0.02 * POT_ET_TXRT_62; 

PF63_P = - 0.359185 * RF61DI.8 - 0.082104 

 + 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * RF6064.4 - 0.00066 * RF6064.5

 + PF63E_DE + PF63_DM + 0.00659 * PF63COH48 - 0.68289 - 0.4675 * RRADJ_F63 - 0.02 * POT_ET_TXRT_63; 

PF64_P = - 0.323866 * RF61DI.12 - 0.094477 

 + 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * RF6064.4 - 0.00066 * RF6064.5

 + PF64E_DE + PF64_DM + 0.00745 * PF64COH48 - 0.72813 - 0.4250 * RRADJ_F64 - 0.02 * POT_ET_TXRT_64;

PF65_P = - 0.256938 * RF61DI.16 + 0.035143

 + 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * RF6569.4 - 0.00072 * RF6569.5

 + PF65E_DE + PF65_DM + 0.00348 * PF65COH48 - 0.37490 - 0.3825 * RRADJ_F65 - 0.02 * POT_ET_TXRT_65;

PF66_P = - 0.226911 * RF61DI.20 + 0.028402

 + 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * RF6569.4 - 0.00072 * RF6569.5

 + PF66E_DE + PF66_DM + 0.00512 * PF66COH48 - 0.52851 - 0.3400 * RRADJ_F66 - 0.02 * POT_ET_TXRT_66;

PF67_P = 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * RF6569.4 - 0.00072 * RF6569.5

 - 0.206835 * RF61DI.24 + 0.025203 + PF67E_DE + PF67_DM + 0.00518 * PF67COH48 - 0.53735 - 0.2975 * RRADJ_F67 - 0.02 * 

POT_ET_TXRT_67;

PF68_P = 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * RF6569.4 - 0.00072 * RF6569.5

 - 0.180608 * RF61DI.28 + 0.020367 + PF68E_DE + PF68_DM + 0.00400 * PF68COH48 - 0.41046 - 0.2550 * RRADJ_F68 - 0.02 * 

POT_ET_TXRT_68;

PF69_P = 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * RF6569.4 - 0.00072 * RF6569.5

 - 0.162386 * RF61DI.32 + 0.017869 + PF69E_DE + PF69_DM + 0.00400 * PF69COH48 - 0.36706 - 0.2550 * RRADJ_F69 - 0.02 * 

POT_ET_TXRT_69;

PF70_P = - 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * RF7074.4 - 0.00046 * RF7074.5

 - 0.136965 * RF61DI.36 + 0.015491 + PF70E_DE + PF70_DM + 0.00424 * PF70COH48 - 0.49282;

PF71_P = - 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * RF7074.4 - 0.00046 * RF7074.5

 - 0.123245 * RF61DI.40 + 0.014381 + PF71E_DE + PF71_DM + 0.00300 * PF71COH48 - 0.37390;
117

PF72_P = - 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * RF7074.4 - 0.00046 * RF7074.5

 - 0.107978 * RF61DI.44 + 0.013644 + PF72E_DE + PF72_DM + 0.00286 * PF72COH48 - 0.34670;

PF73_P = - 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * RF7074.4 - 0.00046 * RF7074.5

 - 0.094426 * RF61DI.48 + 0.012196 + PF73E_DE + PF73_DM + 0.00370 * PF73COH48 - 0.41058;

PF74_P = - 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * RF7074.4 - 0.00046 * RF7074.5

 - 0.082404 * RF61DI.52 + 0.010762 + PF74E_DE + PF74_DM + 0.00304 * PF74COH48 - 0.35113;

AGE 75 TO 79

PF75_P = PF74.4 * 0.900 + DPF75O_FE;

PF76_P = PF75.4 * 0.900 + DPF75O_FE;

PF77_P = PF76.4 * 0.900 + DPF75O_FE;

PF78_P = PF77.4 * 0.900 + DPF75O_FE;

PF79_P = PF78.4 * 0.900 + DPF75O_FE;

AGE 55 TO 79

PF55 = PF55_P;

PF56 = PF56_P;

PF57 = PF57_P;

PF58 = PF58_P;

PF59 = PF59_P;

PF60 = PF60_P;

PF61 = PF61_P;

PF62 = PF62_P;

PF63 = PF63_P;

PF64 = PF64_P;

PF65 = PF65_P;

PF66 = PF66_P;

PF67 = PF67_P;

PF68 = PF68_P;

PF69 = PF69_P;

PF70 = PF70_P;

PF71 = PF71_P;

PF72 = PF72_P;

PF73 = PF73_P;

PF74 = PF74_P;

PF75 = PF75_P;

PF76 = PF76_P;

PF77 = PF77_P;

PF78 = PF78_P;

PF79 = PF79_P;

AGE 80 AND OVER

PF80_P = PF79.4 * 0.965**( 1) + DPF75O_FE;

PF81_P = PF79.8 * 0.965**( 2) + DPF75O_FE;

PF82_P = PF79.12 * 0.965**( 3) + DPF75O_FE;

PF83_P = PF79.16 * 0.965**( 4) + DPF75O_FE;

PF84_P = PF79.20 * 0.965**( 5) + DPF75O_FE;

PF85_P = MOVAVG(8,PF79.24) * 0.965**( 6) + DPF75O_FE;

PF86_P = MOVAVG(8,PF79.28) * 0.965**( 7) + DPF75O_FE;

PF87_P = MOVAVG(8,PF79.32) * 0.965**( 8) + DPF75O_FE;

PF88_P = MOVAVG(8,PF79.36) * 0.965**( 9) + DPF75O_FE;

PF89_P = MOVAVG(8,PF79.40) * 0.965**(10) + DPF75O_FE;

PF90_P = MOVAVG(8,PF79.44) * 0.965**(11) + DPF75O_FE;

PF91_P = MOVAVG(8,PF79.48) * 0.965**(12) + DPF75O_FE;

PF92_P = MOVAVG(8,PF79.52) * 0.965**(13) + DPF75O_FE;

PF93_P = MOVAVG(8,PF79.56) * 0.965**(14) + DPF75O_FE;
118

PF94_P = MOVAVG(8,PF79.60) * 0.965**(15) + DPF75O_FE;

PF95_P = PF94_P * 0.965 + DPF75O_FE;

PF96_P = PF95_P * 0.965 + DPF75O_FE;

PF97_P = PF96_P * 0.965 + DPF75O_FE;

PF98_P = PF97_P * 0.965 + DPF75O_FE;

PF99_P = PF98_P * 0.965 + DPF75O_FE;

PF100_P = PF99_P * 0.965 + DPF75O_FE;

PF80O_P = (PF80_P*NF80 + PF81_P*NF81 + PF82_P*NF82 + PF83_P*NF83 + PF84_P*NF84 + PF85_P*NF85 + PF86_P*NF86 + 

PF87_P*NF87 + PF88_P*NF88 + PF89_P*NF89 +

 PF90_P*NF90 + PF91_P*NF91 + PF92_P*NF92 + PF93_P*NF93 + PF94_P*NF94 + PF95_P*NF95 + PF96_P*NF96 + 

PF97_P*NF97 + PF98_P*NF98 + PF99_P*NF99 +

 PF100_P*NF100 ) / NF80O;

PF80O = PF80O_P;

PF80 = PF80_P * PF80O / PF80O_P;

PF81 = PF81_P * PF80O / PF80O_P;

PF82 = PF82_P * PF80O / PF80O_P;

PF83 = PF83_P * PF80O / PF80O_P;

PF84 = PF84_P * PF80O / PF80O_P;

PF85 = PF85_P * PF80O / PF80O_P;

PF86 = PF86_P * PF80O / PF80O_P;

PF87 = PF87_P * PF80O / PF80O_P;

PF88 = PF88_P * PF80O / PF80O_P;

PF89 = PF89_P * PF80O / PF80O_P;

PF90 = PF90_P * PF80O / PF80O_P;

PF91 = PF91_P * PF80O / PF80O_P;

PF92 = PF92_P * PF80O / PF80O_P;

PF93 = PF93_P * PF80O / PF80O_P;

PF94 = PF94_P * PF80O / PF80O_P;

PF95 = PF95_P * PF80O / PF80O_P;

PF96 = PF96_P * PF80O / PF80O_P;

PF97 = PF97_P * PF80O / PF80O_P;

PF98 = PF98_P * PF80O / PF80O_P;

PF99 = PF99_P * PF80O / PF80O_P;

PF100 = PF100_P * PF80O / PF80O_P; 

LFPR EQUATIONS, AGE 16 AND OVER

PM16O_P= (

PM1617_P * NM1617 + 

PM1819_P * NM1819 + 

PM2024_P * NM2024 + 

PM2529_P * NM2529 + 

PM3034_P * NM3034 + 

PM3539_P * NM3539 + 

PM4044_P * NM4044 + 

PM4549_P * NM4549 + 

PM5054_P * NM5054 + 

PM55_P * NM55 +

PM56_P * NM56 +

PM57_P * NM57 +

PM58_P * NM58 +

PM59_P * NM59 +

PM60_P * NM60 +

PM61_P * NM61 +

PM62_P * NM62 +

PM63_P * NM63 +

PM64_P * NM64 +

PM65_P * NM65 +

PM66_P * NM66 +

PM67_P * NM67 +

PM68_P * NM68 +

PM69_P * NM69 +

PM70_P * NM70 +
119

PM71_P * NM71 +

PM72_P * NM72 +

PM73_P * NM73 +

PM74_P * NM74 +

PM75_P * NM75 +

PM76_P * NM76 +

PM77_P * NM77 +

PM78_P * NM78 +

PM79_P * NM79 +

PM80O_P * NM80O ) / NM16O;

PF16O_P= (

PF1617_P * NF1617 + 

PF1819_P * NF1819 + 

PF2024_P * NF2024 + 

PF2529_P * NF2529 + 

PF3034_P * NF3034 + 

PF3539_P * NF3539 +

PF4044_P * NF4044 + 

PF4549_P * NF4549 + 

PF5054_P * NF5054 + 

PF55_P * NF55 +

PF56_P * NF56 +

PF57_P * NF57 +

PF58_P * NF58 +

PF59_P * NF59 +

PF60_P * NF60 +

PF61_P * NF61 +

PF62_P * NF62 +

PF63_P * NF63 +

PF64_P * NF64 +

PF65_P * NF65 +

PF66_P * NF66 +

PF67_P * NF67 +

PF68_P * NF68 +

PF69_P * NF69 +

PF70_P * NF70 +

PF71_P * NF71 +

PF72_P * NF72 +

PF73_P * NF73 +

PF74_P * NF74 +

PF75_P * NF75 +

PF76_P * NF76 +

PF77_P * NF77 +

PF78_P * NF78 +

PF79_P * NF79 +

PF80O_P * NF80O ) / NF16O;

P16O_P = (PM16O_P * NM16O + PF16O_P * NF16O) / (NM16O + NF16O);

LCM_P = PM16O_P * NM16O;

LCF_P = PF16O_P * NF16O;

LC_P = LCM_P + LCF_P;

LABOR FORCE PARTICIPATION RATES, FULL EMPLOYMENT

MALE LFPR EQUATIONS, FULL EMPLOYMENT

DPM1617_FE = (- 0.00158 * RM1617_FE - 0.00180 * RM1617_FE.1 - 0.00115 * RM1617_FE.2 - 0.00014 * RM1617_FE.3 + 0.00072 * 

RM1617_FE.4 + 0.00094 * RM1617_FE.5) - (- 0.00158 * RM1617 - 0.00180 * RM1617.1 - 0.00115 * RM1617.2 - 0.00014 * RM1617.3 +

0.00072 * RM1617.4 + 0.00094 * RM1617.5)

Restricted Ordinary Least Squares
120

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pm1617_dpk

 = - 0.00158 * rm1617_dpk - 0.00180 * rm1617_dpk[-1]

 (5.56270) (6.13183)

 - 0.00115 * rm1617_dpk[-2] - 0.00014 * rm1617_dpk[-3]

 (7.49261) (0.94429)

 + 0.00072 * rm1617_dpk[-4] + 0.00094 * rm1617_dpk[-5]

 (2.47378) (3.30450)

Polynomial lags:

 rm1617_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0343 Std Err 0.0149 LHS Mean -0.0011 Res Mean 0.0037

R Sq 0.2826 R Bar Sq 0.2777 F 2,146 28.7598 %RMSE 46265.0

D.W.( 1) 0.3942 D.W.( 4) 0.9128

DPM1819_FE = (- 0.00108 * RM1819_FE - 0.00127 * RM1819_FE.1 - 0.00088 * RM1819_FE.2 - 0.00023 * RM1819_FE.3 + 0.00034 * 

RM1819_FE.4 + 0.00053 * RM1819_FE.5) - (- 0.00108 * RM1819 - 0.00127 * RM1819.1 - 0.00088 * RM1819.2 - 0.00023 * RM1819.3 +

0.00034 * RM1819.4 + 0.00053 * RM1819.5)

Restricted Ordinary Least Squares

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pm1819_dpk

 = - 0.00108 * rm1819_dpk - 0.00127 * rm1819_dpk[-1]

 (5.22267) (5.94236)

 - 0.00088 * rm1819_dpk[-2] - 0.00023 * rm1819_dpk[-3]

 (7.96365) (2.16526)

 + 0.00034 * rm1819_dpk[-4] + 0.00053 * rm1819_dpk[-5]

 (1.61951) (2.56002)

Polynomial lags:

 rm1819_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0192 Std Err 0.0114 LHS Mean -0.0040 Res Mean 0.0008

R Sq 0.2729 R Bar Sq 0.2679 F 2,146 27.3976 %RMSE 17596.7

D.W.( 1) 0.7981 D.W.( 4) 1.2825

DPM2024_FE = (- 0.00063 * RM2024_FE - 0.00077 * RM2024_FE.1 - 0.00059 * RM2024_FE.2 - 0.00027 * RM2024_FE.3 + 0.00005 * 

RM2024_FE.4 + 0.00020 * RM2024_FE.5) - (- 0.00063 * RM2024 - 0.00077 * RM2024.1 - 0.00059 * RM2024.2 - 0.00027 * RM2024.3 +

0.00005 * RM2024.4 + 0.00020 * RM2024.5)

Restricted Ordinary Least Squares

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pm2024_dpk

 = - 0.00063 * rm2024_dpk - 0.00077 * rm2024_dpk[-1]

 (4.32837) (5.14659)

 - 0.00059 * rm2024_dpk[-2] - 0.00027 * rm2024_dpk[-3]

 (7.47597) (3.39844)

 + 0.00005 * rm2024_dpk[-4] + 0.00020 * rm2024_dpk[-5]
121

 (0.35674) (1.36468)

Polynomial lags:

 rm2024_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0067 Std Err 0.0068 LHS Mean -0.0028 Res Mean -0.0002

R Sq 0.2336 R Bar Sq 0.2284 F 2,146 22.2560 %RMSE 32980.5

D.W.( 1) 0.5915 D.W.( 4) 1.0423

DPM2529_FE = (- 0.00028 * RM2529_FE - 0.00044 * RM2529_FE.1 - 0.00050 * RM2529_FE.2 - 0.00047 * RM2529_FE.3 - 0.00037 * 

RM2529_FE.4 - 0.00021 * RM2529_FE.5) - (- 0.00028 * RM2529 - 0.00044 * RM2529.1 - 0.00050 * RM2529.2 - 0.00047 * RM2529.3 -

0.00037 * RM2529.4 - 0.00021 * RM2529.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm2529_dpk

 = - 0.00028 * rm2529_dpk - 0.00044 * rm2529_dpk[-1]

 (1.40609) (2.14232)

 - 0.00050 * rm2529_dpk[-2] - 0.00047 * rm2529_dpk[-3]

 (4.60340) (4.70117)

 - 0.00037 * rm2529_dpk[-4] - 0.00021 * rm2529_dpk[-5]

 (1.87119) (1.06920)

Polynomial lags:

 rm2529_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0026 Std Err 0.0049 LHS Mean -0.0033 Res Mean -0.0016

R Sq 0.0492 R Bar Sq 0.0396 F 2, 99 2.5598 %RMSE 48872.7

D.W.( 1) 0.7362 D.W.( 4) 1.0116

DPM3034_FE = (- 0.00046 * RM3034_FE - 0.00061 * RM3034_FE.1 - 0.00054 * RM3034_FE.2 - 0.00036 * RM3034_FE.3 - 0.00014 * 

RM3034_FE.4 + 0.00001 * RM3034_FE.5) - (- 0.00046 * RM3034 - 0.00061 * RM3034.1 - 0.00054 * RM3034.2 - 0.00036 * RM3034.3 -

0.00014 * RM3034.4 + 0.00001 * RM3034.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm3034_dpk

 = - 0.00046 * rm3034_dpk - 0.00061 * rm3034_dpk[-1]

 (1.81713) (2.34240)

 - 0.00054 * rm3034_dpk[-2] - 0.00036 * rm3034_dpk[-3]

 (4.05252) (2.76527)

 - 0.00014 * rm3034_dpk[-4] + 0.00001 * rm3034_dpk[-5]

 (0.56353) (0.02760)

Polynomial lags:

 rm3034_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0024 Std Err 0.0049 LHS Mean -0.0015 Res Mean -0.0001

R Sq 0.1497 R Bar Sq 0.1411 F 2, 99 8.7134 %RMSE 50015.8

D.W.( 1) 0.6227 D.W.( 4) 0.8678
122

DPM3539_FE = (- 0.00004 * RM3539_FE - 0.00010 * RM3539_FE.1 - 0.00016 * RM3539_FE.2 - 0.00021 * RM3539_FE.3 - 0.00021 * 

RM3539_FE.4 - 0.00015 * RM3539_FE.5) - (- 0.00004 * RM3539 - 0.00010 * RM3539.1 - 0.00016 * RM3539.2 - 0.00021 * RM3539.3 -

0.00021 * RM3539.4 - 0.00015 * RM3539.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm3539_dpk

 = - 0.00004 * rm3539_dpk - 0.00010 * rm3539_dpk[-1]

 (0.12650) (0.32986)

 - 0.00016 * rm3539_dpk[-2] - 0.00021 * rm3539_dpk[-3]

 (1.10007) (1.48792)

 - 0.00021 * rm3539_dpk[-4] - 0.00015 * rm3539_dpk[-5]

 (0.72412) (0.51868)

Polynomial lags:

 rm3539_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0028 Std Err 0.0052 LHS Mean -0.0017 Res Mean -0.0010

R Sq -0.0664 R Bar Sq -0.0772 F 2, 99 NC %RMSE 1817.43

D.W.( 1) 0.4246 D.W.( 4) 0.5372

DPM4044_FE = (- 0.00057 * RM4044_FE - 0.00066 * RM4044_FE.1 - 0.00044 * RM4044_FE.2 - 0.00009 * RM4044_FE.3 + 0.00022 * 

RM4044_FE.4 + 0.00031 * RM4044_FE.5) - (- 0.00057 * RM4044 - 0.00066 * RM4044.1 - 0.00044 * RM4044.2 - 0.00009 * RM4044.3 +

0.00022 * RM4044.4 + 0.00031 * RM4044.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm4044_dpk

 = - 0.00057 * rm4044_dpk - 0.00066 * rm4044_dpk[-1]

 (1.75853) (1.97180)

 - 0.00044 * rm4044_dpk[-2] - 0.00009 * rm4044_dpk[-3]

 (2.56550) (0.54915)

 + 0.00022 * rm4044_dpk[-4] + 0.00031 * rm4044_dpk[-5]

 (0.66426) (0.95499)

Polynomial lags:

 rm4044_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0029 Std Err 0.0054 LHS Mean -0.0012 Res Mean -0.0004

R Sq 0.0268 R Bar Sq 0.0170 F 2, 99 1.3647 %RMSE 310.335

D.W.( 1) 0.5028 D.W.( 4) 0.7333

DPM4549_FE = (- 0.00002 * RM4549_FE - 0.00016 * RM4549_FE.1 - 0.00034 * RM4549_FE.2 - 0.00049 * RM4549_FE.3 - 0.00054 * 

RM4549_FE.4 - 0.00040 * RM4549_FE.5) - (- 0.00002 * RM4549 - 0.00016 * RM4549.1 - 0.00034 * RM4549.2 - 0.00049 * RM4549.3 -

0.00054 * RM4549.4 - 0.00040 * RM4549.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm4549_dpk

 = - 0.00002 * rm4549_dpk - 0.00016 * rm4549_dpk[-1]

 (0.06650) (0.55897)

 - 0.00034 * rm4549_dpk[-2] - 0.00049 * rm4549_dpk[-3]
123

 (2.31789) (3.39192)

 - 0.00054 * rm4549_dpk[-4] - 0.00040 * rm4549_dpk[-5]

 (1.92566) (1.46813)

Polynomial lags:

 rm4549_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0015 Std Err 0.0039 LHS Mean -0.0013 Res Mean -0.0003

R Sq 0.0719 R Bar Sq 0.0625 F 2, 99 3.8350 %RMSE 4821.60

D.W.( 1) 0.7819 D.W.( 4) 1.0968

DPM5054_FE = ( 0.00112 * RM5054_FE + 0.00103 * RM5054_FE.1 + 0.00023 * RM5054_FE.2 - 0.00078 * RM5054_FE.3 - 0.00149 * 

RM5054_FE.4 - 0.00139 * RM5054_FE.5) - ( 0.00112 * RM5054 + 0.00103 * RM5054.1 + 0.00023 * RM5054.2 - 0.00078 * RM5054.3 -

0.00149 * RM5054.4 - 0.00139 * RM5054.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm5054_dpk

 = 0.00112 * rm5054_dpk + 0.00103 * rm5054_dpk[-1]

 (3.16181) (2.84569)

 + 0.00023 * rm5054_dpk[-2] - 0.00078 * rm5054_dpk[-3]

 (1.30991) (4.52791)

 - 0.00149 * rm5054_dpk[-4] - 0.00139 * rm5054_dpk[-5]

 (4.15166) (3.96094)

Polynomial lags:

 rm5054_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0037 Std Err 0.0061 LHS Mean -0.0012 Res Mean 0.0001

R Sq 0.1417 R Bar Sq 0.1330 F 2, 99 8.1732 %RMSE 42597.0

D.W.( 1) 1.0445 D.W.( 4) 1.5173

DPM5559_FE = ( 0.00062 * RM5559_FE + 0.00041 * RM5559_FE.1 - 0.00026 * RM5559_FE.2 - 0.00101 * RM5559_FE.3 - 0.00147 * 

RM5559_FE.4 - 0.00126 * RM5559_FE.5) - ( 0.00062 * RM5559 + 0.00041 * RM5559.1 - 0.00026 * RM5559.2 - 0.00101 * RM5559.3 -

0.00147 * RM5559.4 - 0.00126 * RM5559.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm5559_dpk

 = 0.00062 * rm5559_dpk + 0.00041 * rm5559_dpk[-1]

 (1.23508) (0.79452)

 - 0.00026 * rm5559_dpk[-2] - 0.00101 * rm5559_dpk[-3]

 (1.02810) (4.16786)

 - 0.00147 * rm5559_dpk[-4] - 0.00126 * rm5559_dpk[-5]

 (2.91432) (2.53301)

Polynomial lags:

 rm5559_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0066 Std Err 0.0080 LHS Mean -0.0037 Res Mean -0.0014

R Sq 0.0164 R Bar Sq 0.0065 F 2, 99 0.8277 %RMSE 256947
124

D.W.( 1) 0.8793 D.W.( 4) 1.2992

DPM6064_FE = ( 0.00203 * RM6064_FE + 0.00160 * RM6064_FE.1 - 0.00021 * RM6064_FE.2 - 0.00235 * RM6064_FE.3 - 0.00374 * 

RM6064_FE.4 - 0.00331 * RM6064_FE.5) - ( 0.00203 * RM6064 + 0.00160 * RM6064.1 - 0.00021 * RM6064.2 - 0.00235 * RM6064.3 -

0.00374 * RM6064.4 - 0.00331 * RM6064.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm6064_dpk

 = 0.00203 * rm6064_dpk + 0.00160 * rm6064_dpk[-1]

 (2.66008) (2.03611)

 - 0.00021 * rm6064_dpk[-2] - 0.00235 * rm6064_dpk[-3]

 (0.51861) (5.71949)

 - 0.00374 * rm6064_dpk[-4] - 0.00331 * rm6064_dpk[-5]

 (4.77362) (4.35298)

Polynomial lags:

 rm6064_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0165 Std Err 0.0110 LHS Mean -0.0095 Res Mean -0.0067

R Sq -0.2740 R Bar Sq -0.2869 F 2, 99 NC %RMSE 21316.0

D.W.( 1) 0.5297 D.W.( 4) 0.7821

DPM6569_FE = ( 0.00067 * RM6569_FE + 0.00040 * RM6569_FE.1 - 0.00040 * RM6569_FE.2 - 0.00127 * RM6569_FE.3 - 0.00178 * 

RM6569_FE.4 - 0.00151 * RM6569_FE.5) - ( 0.00067 * RM6569 + 0.00040 * RM6569.1 - 0.00040 * RM6569.2 - 0.00127 * RM6569.3 -

0.00178 * RM6569.4 - 0.00151 * RM6569.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm6569_dpk

 = 0.00067 * rm6569_dpk + 0.00040 * rm6569_dpk[-1]

 (0.93838) (0.52443)

 - 0.00040 * rm6569_dpk[-2] - 0.00127 * rm6569_dpk[-3]

 (0.89477) (2.96224)

 - 0.00178 * rm6569_dpk[-4] - 0.00151 * rm6569_dpk[-5]

 (2.41737) (2.13109)

Polynomial lags:

 rm6569_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0210 Std Err 0.0138 LHS Mean -0.0059 Res Mean -0.0047

R Sq -0.0832 R Bar Sq -0.0942 F 2, 99 NC %RMSE 36465.0

D.W.( 1) 0.5122 D.W.( 4) 0.7034

DPM7074_FE = (- 0.00013 * RM7074_FE - 0.00016 * RM7074_FE.1 - 0.00013 * RM7074_FE.2 - 0.00006 * RM7074_FE.3 + 0.00000 * 

RM7074_FE.4 + 0.00003 * RM7074_FE.5) - (- 0.00013 * RM7074 - 0.00016 * RM7074.1 - 0.00013 * RM7074.2 - 0.00006 * RM7074.3 +

0.00000 * RM7074.4 + 0.00003 * RM7074.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm7074_dpk

 = - 0.00013 * rm7074_dpk - 0.00016 * rm7074_dpk[-1]
125

 (0.30689) (0.35702)

 - 0.00013 * rm7074_dpk[-2] - 0.00006 * rm7074_dpk[-3]

 (0.44247) (0.21289)

 + 0.00000 * rm7074_dpk[-4] + 0.00003 * rm7074_dpk[-5]

 (0.00874) (0.08371)

Polynomial lags:

 rm7074_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0114 Std Err 0.0080 LHS Mean -0.0073 Res Mean -0.0071

R Sq -0.8795 R Bar Sq -0.8984 F 2, 99 NC %RMSE 1540.73

D.W.( 1) 0.5068 D.W.( 4) 0.7160

DPM75O_FE = (- 0.00043 * RM75O_FE - 0.00051 * RM75O_FE.1 - 0.00036 * RM75O_FE.2 - 0.00010 * RM75O_FE.3 + 0.00013 * 

RM75O_FE.4 + 0.00021 * RM75O_FE.5) - (- 0.00043 * RM75O - 0.00051 * RM75O.1 - 0.00036 * RM75O.2 - 0.00010 * RM75O.3 + 0.00013 

* RM75O.4 + 0.00021 * RM75O.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pm75o_dpk

 = - 0.00043 * rm75o_dpk - 0.00051 * rm75o_dpk[-1]

 (1.82379) (2.03996)

 - 0.00036 * rm75o_dpk[-2] - 0.00010 * rm75o_dpk[-3]

 (2.39851) (0.66247)

 + 0.00013 * rm75o_dpk[-4] + 0.00021 * rm75o_dpk[-5]

 (0.51659) (0.86124)

Polynomial lags:

 rm75o_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0039 Std Err 0.0058 LHS Mean -0.0031 Res Mean -0.0025

R Sq -0.2424 R Bar Sq -0.2549 F 2, 99 NC %RMSE 19877.0

D.W.( 1) 0.8382 D.W.( 4) 1.0542

FEMALE LFPR EQUATIONS, FULL EMPLOYMENT DIFFERENTIALS

DPF1617_FE = (- 0.00224 * RF1617_FE - 0.00239 * RF1617_FE.1 - 0.00126 * RF1617_FE.2 + 0.00035 * RF1617_FE.3 + 0.00163 * 

RF1617_FE.4 + 0.00178 * RF1617_FE.5) - (- 0.00224 * RF1617 - 0.00239 * RF1617.1 - 0.00126 * RF1617.2 + 0.00035 * RF1617.3 + 0.00163 

* RF1617.4 + 0.00178 * RF1617.5)

Restricted Ordinary Least Squares

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pf1617_dpk

 = - 0.00224 * rf1617_dpk - 0.00239 * rf1617_dpk[-1]

 (5.90261) (6.13505)

 - 0.00126 * rf1617_dpk[-2] + 0.00035 * rf1617_dpk[-3]

 (6.26010) (1.72973)
126

 + 0.00163 * rf1617_dpk[-4] + 0.00178 * rf1617_dpk[-5]

 (4.17464) (4.69274)

Polynomial lags:

 rf1617_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0354 Std Err 0.0153 LHS Mean 0.0006 Res Mean 0.0030

R Sq 0.2161 R Bar Sq 0.2107 F 2,146 20.1227 %RMSE 28883.0

D.W.( 1) 0.4141 D.W.( 4) 0.9322

DPF1819_FE = (- 0.00124 * RF1819_FE - 0.00147 * RF1819_FE.1 - 0.00106 * RF1819_FE.2 - 0.00035 * RF1819_FE.3 + 0.00030 * 

RF1819_FE.4 + 0.00053 * RF1819_FE.5) - (- 0.00124 * RF1819 - 0.00147 * RF1819.1 - 0.00106 * RF1819.2 - 0.00035 * RF1819.3 + 0.00030 * 

RF1819.4 + 0.00053 * RF1819.5)

Restricted Ordinary Least Squares

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pf1819_dpk

 = - 0.00124 * rf1819_dpk - 0.00147 * rf1819_dpk[-1]

 (4.35316) (5.03839)

 - 0.00106 * rf1819_dpk[-2] - 0.00035 * rf1819_dpk[-3]

 (7.08785) (2.38461)

 + 0.00030 * rf1819_dpk[-4] + 0.00053 * rf1819_dpk[-5]

 (1.03892) (1.89331)

Polynomial lags:

 rf1819_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0217 Std Err 0.0120 LHS Mean -0.0031 Res Mean 0.0021

R Sq 0.2645 R Bar Sq 0.2594 F 2,146 26.2493 %RMSE 53411.9

D.W.( 1) 0.8392 D.W.( 4) 1.3288

DPF2024_FE = (- 0.00087 * RF2024_FE - 0.00099 * RF2024_FE.1 - 0.00063 * RF2024_FE.2 - 0.00007 * RF2024_FE.3 + 0.00041 * 

RF2024_FE.4 + 0.00052 * RF2024_FE.5) - (- 0.00087 * RF2024 - 0.00099 * RF2024.1 - 0.00063 * RF2024.2 - 0.00007 * RF2024.3 + 0.00041 

* RF2024.4 + 0.00052 * RF2024.5)

Restricted Ordinary Least Squares

QUARTERLY data for 148 periods from 1971Q1 to 2007Q4

Date: 2 SEP 2009

pf2024_dpk

 = - 0.00087 * rf2024_dpk - 0.00099 * rf2024_dpk[-1]

 (1.95040) (2.16369)

 - 0.00063 * rf2024_dpk[-2] - 0.00007 * rf2024_dpk[-3]

 (2.80524) (0.30336)

 + 0.00041 * rf2024_dpk[-4] + 0.00052 * rf2024_dpk[-5]

 (0.90125) (1.17782)

Polynomial lags:

 rf2024_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0212 Std Err 0.0120 LHS Mean -0.0017 Res Mean 0.0002

R Sq 0.0408 R Bar Sq 0.0343 F 2,146 3.1076 %RMSE 51970.8

D.W.( 1) 0.2424 D.W.( 4) 0.5144
127

DPF2529_FE = (- 0.00056 * RF2529_FE - 0.00070 * RF2529_FE.1 - 0.00057 * RF2529_FE.2 - 0.00029 * RF2529_FE.3 - 0.00002 * 

RF2529_FE.4 + 0.00013 * RF2529_FE.5) - (- 0.00056 * RF2529 - 0.00070 * RF2529.1 - 0.00057 * RF2529.2 - 0.00029 * RF2529.3 - 0.00002 * 

RF2529.4 + 0.00013 * RF2529.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf2529_dpk

 = - 0.00056 * rf2529_dpk - 0.00070 * rf2529_dpk[-1]

 (0.83571) (1.02414)

 - 0.00057 * rf2529_dpk[-2] - 0.00029 * rf2529_dpk[-3]

 (1.64524) (0.87576)

 - 0.00002 * rf2529_dpk[-4] + 0.00013 * rf2529_dpk[-5]

 (0.02505) (0.19165)

Polynomial lags:

 rf2529_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0129 Std Err 0.0110 LHS Mean 0.0014 Res Mean 0.0030

R Sq 0.0265 R Bar Sq 0.0167 F 2, 99 1.3467 %RMSE 1908.57

D.W.( 1) 0.2611 D.W.( 4) 0.6593

DPF3034_FE = (- 0.00081 * RF3034_FE - 0.00065 * RF3034_FE.1 + 0.00005 * RF3034_FE.2 + 0.00089 * RF3034_FE.3 + 0.00143 * 

RF3034_FE.4 + 0.00128 * RF3034_FE.5) - (- 0.00081 * RF3034 - 0.00065 * RF3034.1 + 0.00005 * RF3034.2 + 0.00089 * RF3034.3 + 0.00143 

* RF3034.4 + 0.00128 * RF3034.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf3034_dpk

 = - 0.00081 * rf3034_dpk - 0.00065 * rf3034_dpk[-1]

 (1.30466) (1.02194)

 + 0.00005 * rf3034_dpk[-2] + 0.00089 * rf3034_dpk[-3]

 (0.16121) (2.80112)

 + 0.00143 * rf3034_dpk[-4] + 0.00128 * rf3034_dpk[-5]

 (2.29055) (2.08640)

Polynomial lags:

 rf3034_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0083 Std Err 0.0088 LHS Mean 0.0040 Res Mean 0.0026

R Sq -0.1326 R Bar Sq -0.1440 F 2, 99 NC %RMSE 29049.2

D.W.( 1) 0.3221 D.W.( 4) 0.8675

DPF3539_FE = (- 0.00195 * RF3539_FE - 0.00216 * RF3539_FE.1 - 0.00128 * RF3539_FE.2 + 0.00002 * RF3539_FE.3 + 0.00111 * 

RF3539_FE.4 + 0.00132 * RF3539_FE.5) - (- 0.00195 * RF3539 - 0.00216 * RF3539.1 - 0.00128 * RF3539.2 + 0.00002 * RF3539.3 + 0.00111 

* RF3539.4 + 0.00132 * RF3539.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf3539_dpk

 = - 0.00195 * rf3539_dpk - 0.00216 * rf3539_dpk[-1]

 (2.96355) (3.19201)

 - 0.00128 * rf3539_dpk[-2] + 0.00002 * rf3539_dpk[-3]
128

 (3.74105) (0.06643)

 + 0.00111 * rf3539_dpk[-4] + 0.00132 * rf3539_dpk[-5]

 (1.67998) (2.03681)

Polynomial lags:

 rf3539_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0079 Std Err 0.0089 LHS Mean -0.0021 Res Mean -0.0007

R Sq 0.0801 R Bar Sq 0.0708 F 2, 99 4.3116 %RMSE 6918.68

D.W.( 1) 0.3914 D.W.( 4) 0.5620

DPF4044_FE = (- 0.00026 * RF4044_FE - 0.00050 * RF4044_FE.1 - 0.00068 * RF4044_FE.2 - 0.00076 * RF4044_FE.3 - 0.00070 * 

RF4044_FE.4 - 0.00046 * RF4044_FE.5) - (- 0.00026 * RF4044 - 0.00050 * RF4044.1 - 0.00068 * RF4044.2 - 0.00076 * RF4044.3 - 0.00070 * 

RF4044.4 - 0.00046 * RF4044.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf4044_dpk

 = - 0.00026 * rf4044_dpk - 0.00050 * rf4044_dpk[-1]

 (0.53052) (0.99291)

 - 0.00068 * rf4044_dpk[-2] - 0.00076 * rf4044_dpk[-3]

 (2.55091) (2.90760)

 - 0.00070 * rf4044_dpk[-4] - 0.00046 * rf4044_dpk[-5]

 (1.41817) (0.96194)

Polynomial lags:

 rf4044_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0041 Std Err 0.0061 LHS Mean 0.0005 Res Mean 0.0021

R Sq 0.1306 R Bar Sq 0.1218 F 2, 99 7.4378 %RMSE 4872.89

D.W.( 1) 0.5681 D.W.( 4) 0.8422

DPF4549_FE = (- 0.00076 * RF4549_FE - 0.00070 * RF4549_FE.1 - 0.00018 * RF4549_FE.2 + 0.00049 * RF4549_FE.3 + 0.00096 * 

RF4549_FE.4 + 0.00091 * RF4549_FE.5) - (- 0.00076 * RF4549 - 0.00070 * RF4549.1 - 0.00018 * RF4549.2 + 0.00049 * RF4549.3 + 0.00096 

* RF4549.4 + 0.00091 * RF4549.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf4549_dpk

 = - 0.00076 * rf4549_dpk - 0.00070 * rf4549_dpk[-1]

 (1.20345) (1.08117)

 - 0.00018 * rf4549_dpk[-2] + 0.00049 * rf4549_dpk[-3]

 (0.50034) (1.36204)

 + 0.00096 * rf4549_dpk[-4] + 0.00091 * rf4549_dpk[-5]

 (1.47572) (1.44905)

Polynomial lags:

 rf4549_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0046 Std Err 0.0063 LHS Mean 0.0028 Res Mean 0.0027

R Sq -0.1835 R Bar Sq -0.1954 F 2, 99 NC %RMSE 316691
129

D.W.( 1) 0.7137 D.W.( 4) 0.9879

DPF5054_FE = ( 0.00003 * RF5054_FE - 0.00011 * RF5054_FE.1 - 0.00032 * RF5054_FE.2 - 0.00051 * RF5054_FE.3 - 0.00059 * 

RF5054_FE.4 - 0.00045 * RF5054_FE.5) - ( 0.00003 * RF5054 - 0.00011 * RF5054.1 - 0.00032 * RF5054.2 - 0.00051 * RF5054.3 - 0.00059 * 

RF5054.4 - 0.00045 * RF5054.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf5054_dpk

 = 0.00003 * rf5054_dpk - 0.00011 * rf5054_dpk[-1]

 (0.05929) (0.18046)

 - 0.00032 * rf5054_dpk[-2] - 0.00051 * rf5054_dpk[-3]

 (1.13804) (1.96823)

 - 0.00059 * rf5054_dpk[-4] - 0.00045 * rf5054_dpk[-5]

 (1.04732) (0.80816)

Polynomial lags:

 rf5054_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0054 Std Err 0.0073 LHS Mean -0.0004 Res Mean 0.0010

R Sq 0.0704 R Bar Sq 0.0611 F 2, 99 3.7511 %RMSE 66278.7

D.W.( 1) 0.8578 D.W.( 4) 1.1467

DPF5559_FE = ( 0.00064 * RF5559_FE + 0.00041 * RF5559_FE.1 - 0.00029 * RF5559_FE.2 - 0.00107 * RF5559_FE.3 - 0.00155 * 

RF5559_FE.4 - 0.00132 * RF5559_FE.5) - ( 0.00064 * RF5559 + 0.00041 * RF5559.1 - 0.00029 * RF5559.2 - 0.00107 * RF5559.3 - 0.00155 * 

RF5559.4 - 0.00132 * RF5559.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf5559_dpk

 = 0.00064 * rf5559_dpk + 0.00041 * rf5559_dpk[-1]

 (0.89335) (0.55546)

 - 0.00029 * rf5559_dpk[-2] - 0.00107 * rf5559_dpk[-3]

 (0.75840) (2.90227)

 - 0.00155 * rf5559_dpk[-4] - 0.00132 * rf5559_dpk[-5]

 (2.15075) (1.87882)

Polynomial lags:

 rf5559_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0102 Std Err 0.0102 LHS Mean -0.0011 Res Mean 0.0005

R Sq 0.0769 R Bar Sq 0.0676 F 2, 99 4.1224 %RMSE 4599.06

D.W.( 1) 0.8791 D.W.( 4) 0.8944

DPF6064_FE = ( 0.00141 * RF6064_FE + 0.00166 * RF6064_FE.1 + 0.00116 * RF6064_FE.2 + 0.00033 * RF6064_FE.3 - 0.00041 * 

RF6064_FE.4 - 0.00066 * RF6064_FE.5) - ( 0.00141 * RF6064 + 0.00166 * RF6064.1 + 0.00116 * RF6064.2 + 0.00033 * RF6064.3 - 0.00041 * 

RF6064.4 - 0.00066 * RF6064.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf6064_dpk

 = 0.00141 * rf6064_dpk + 0.00166 * rf6064_dpk[-1]
130

 (1.84124) (2.11172)

 + 0.00116 * rf6064_dpk[-2] + 0.00033 * rf6064_dpk[-3]

 (2.99557) (0.93319)

 - 0.00041 * rf6064_dpk[-4] - 0.00066 * rf6064_dpk[-5]

 (0.54220) (0.88278)

Polynomial lags:

 rf6064_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0113 Std Err 0.0105 LHS Mean 0.0007 Res Mean -0.0017

R Sq 0.1074 R Bar Sq 0.0984 F 2, 99 5.9541 %RMSE 8386.58

D.W.( 1) 0.6636 D.W.( 4) 0.8453

DPF6569_FE = ( 0.00029 * RF6569_FE + 0.00014 * RF6569_FE.1 - 0.00023 * RF6569_FE.2 - 0.00063 * RF6569_FE.3 - 0.00086 * 

RF6569_FE.4 - 0.00072 * RF6569_FE.5) - ( 0.00029 * RF6569 + 0.00014 * RF6569.1 - 0.00023 * RF6569.2 - 0.00063 * RF6569.3 - 0.00086 * 

RF6569.4 - 0.00072 * RF6569.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf6569_dpk

 = 0.00029 * rf6569_dpk + 0.00014 * rf6569_dpk[-1]

 (0.39369) (0.18572)

 - 0.00023 * rf6569_dpk[-2] - 0.00063 * rf6569_dpk[-3]

 (0.46891) (1.29792)

 - 0.00086 * rf6569_dpk[-4] - 0.00072 * rf6569_dpk[-5]

 (1.10980) (0.98371)

Polynomial lags:

 rf6569_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0161 Std Err 0.0097 LHS Mean -0.0088 Res Mean -0.0083

R Sq -0.8729 R Bar Sq -0.8918 F 2, 99 NC %RMSE 5935.52

D.W.( 1) 0.3864 D.W.( 4) 0.3951

DPF7074_FE = (- 0.00009 * RF7074_FE - 0.00028 * RF7074_FE.1 - 0.00048 * RF7074_FE.2 - 0.00063 * RF7074_FE.3 - 0.00064 * 

RF7074_FE.4 - 0.00046 * RF7074_FE.5) - (- 0.00009 * RF7074 - 0.00028 * RF7074.1 - 0.00048 * RF7074.2 - 0.00063 * RF7074.3 - 0.00064 * 

RF7074.4 - 0.00046 * RF7074.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf7074_dpk

 = - 0.00009 * rf7074_dpk - 0.00028 * rf7074_dpk[-1]

 (0.41200) (1.18982)

 - 0.00048 * rf7074_dpk[-2] - 0.00063 * rf7074_dpk[-3]

 (3.71663) (4.89240)

 - 0.00064 * rf7074_dpk[-4] - 0.00046 * rf7074_dpk[-5]

 (2.78954) (2.06896)

Polynomial lags:

 rf7074_dpk

 from 0 to 5 degree 3 near far
131

Sum Sq 0.0038 Std Err 0.0059 LHS Mean -0.0043 Res Mean -0.0018

R Sq -0.1504 R Bar Sq -0.1620 F 2, 99 NC %RMSE 14566.3

D.W.( 1) 0.7462 D.W.( 4) 1.0245

DPF75O_FE = (- 0.00007 * RF75O_FE - 0.00012 * RF75O_FE.1 - 0.00015 * RF75O_FE.2 - 0.00014 * RF75O_FE.3 - 0.00012 * RF75O_FE.4 -

0.00007 * RF75O_FE.5) - (- 0.00007 * RF75O - 0.00012 * RF75O.1 - 0.00015 * RF75O.2 - 0.00014 * RF75O.3 - 0.00012 * RF75O.4 - 0.00007 

* RF75O.5)

Restricted Ordinary Least Squares

QUARTERLY data for 101 periods from 1982Q4 to 2007Q4

Date: 2 SEP 2009

pf75o_dpk

 = - 0.00007 * rf75o_dpk - 0.00012 * rf75o_dpk[-1]

 (0.83244) (1.28137)

 - 0.00015 * rf75o_dpk[-2] - 0.00014 * rf75o_dpk[-3]

 (2.41595) (2.48652)

 - 0.00012 * rf75o_dpk[-4] - 0.00007 * rf75o_dpk[-5]

 (1.27553) (0.79882)

Polynomial lags:

 rf75o_dpk

 from 0 to 5 degree 3 near far

Sum Sq 0.0008 Std Err 0.0028 LHS Mean 0.0001 Res Mean 0.0004

R Sq 0.0818 R Bar Sq 0.0725 F 2, 99 4.4079 %RMSE 43067.0

D.W.( 1) 0.7531 D.W.( 4) 1.3841
132

2.2 U.S. Earnings (USEAR)

Annual Employment Equations

NonagriculturalWage Workers, Private Household Workers

EF1617NAWPH = MAX (0.001, -0.20802 * RTP - 0.40988 * RTP.1 + 0.01015 + 61.2465 * 1/YEAR - 0.00965 * MINW/CPIW_U + 0.01561 

* NU10/NF1617 - 0.13398) * EF1617

EF1819NAWPH = MAX (0.001, -0.03363 * RTP - 0.12989 * RTP.1 - 0.00661 + 8.44701 * 1/YEAR - 0.00539 * MINW/CPIW_U + 0.00345 

* NU10/NF1819 + 0.07597) * EF1819

EF2024NAWPH = MAX (0.001, -0.18707 * MOVAVG (5, RTP) - 0.00223 + 2.12060 * 1/YEAR + 0.00820 * NU10/NF2024 + 0.14537) * 

EF2024

EF2534NAWPH = MAX (0.001, 0.01874 * RTP - 0.04167 * MOVAVG (5, RTP.1) - 0.00090 + 1.55167 * 1/YEAR + 0.01021 * 

NU10/NF2534 - 0.00170) * EF2534

EF3544NAWPH = (0.00622 * RTP - 0.06062 * MOVAVG (5, RTP.1) + 0.00008 + 0.29372 * MOVAVG (3, EF2534NAWPH.9/EF2534.9) + 

0.06187) * EF3544

EF4554NAWPH = (0.02788 * RTP - 0.10996 * MOVAVG (5, RTP.1) - 0.00349 + 0.53068 * MOVAVG (3, EF3544NAWPH.9/EF3544.9) + 

0.08883) * EF4554

EF5564NAWPH = (0.05939 * RTP - 0.10618 * MOVAVG (2, RTP.1) - 0.00579 + 0.66195 * MOVAVG (3, EF4554NAWPH.9/EF4554.9) + 

0.05966) * EF5564

EF65ONAWPH = (0.22642 * RTP - 0.02069 + 0.33505 * MOVAVG (3, EF5564NAWPH.9) - 0.19707)

EM1617NAWPH = MAX (0.001, -0.05284 * RTP - 0.17833 * RTP.1 - 0.00768 + 9.19738 * 1/YEAR - 0.00588 * MINW/CPIW_U + 0.16862) 

* EM1617

EM1819NAWPH = MAX (0.001, -0.07122 * RTP - 0.03737 * RTP.1 - 0.00282 + 3.76796 * 1/YEAR - 0.00499 * MINW/CPIW_U + 0.08727) 

* EM1819

EM2024NAWPH = MAX (0.001, -0.00450 * RTP - 0.02345 * RTP.1 - 0.00113 - 0.00057 * MINW/CPIW_U + 0.03265) * EM2024

EM2534NAWPH = MAX (0.001, -0.00490 * RTP.1 - 0.00054 - 0.00051 * MINW/CPIW_U + 0.00789) * EM2534

EM3544NAWPH = (-0.00446 * RTP.1 - 0.00041 - 0.00053 * MINW/CPIW_U + 0.00726) * EM3544

EM4554NAWPH = (-0.00039 + 0.00129) * EM4554

EM5564NAWPH = (-0.00015 + 0.00200) * EM5564

EM65ONAWPH = (-0.00679 + 0.64405 * MOVAVG (3, EM5564NAWPH.9) + 0.00231)

Nonagricultural Self-employed workers

EF1617NAS = (0.12015 * RTP.1 - 0.10551) * EF1617

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef1617nas/ef1617 = 0.12015 * rtp.1 - 0.10551

 (1.96868) (1.73441)

Sum Sq 0.0000 

Std Error 0.0030 

LHS Mean 0.0142

R-Squared 0.5637 

R Bar Squared 0.4182 

F-stat 1, 3 3.8757

D.W. (1) 1.5620 

D.W. (2) 2.3626

EF1819NAS = (0.11184 * RTP.1 - 0.10241) * EF1819

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef1819nas/ef1819 = 0.11184 * rtp.1 - 0.10241

 (2.99537) (2.75170)

Sum Sq 0.0000 

Std Error 0.0018 

LHS Mean 0.0090

R-Squared 0.7494 

R Bar Squared 0.6659 

F-STAT 1, 3 8.9722

D.W. (1) 3.2586 

D.W. (2) 0.9766

EF2024NAS = (0.08908 * RTP.1 - 0.07176) * EF2024
133

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef2024nas/ef2024 = 0.08908 * rtp.1 - 0.07176

 (2.54605) (2.05763)

Sum Sq 0.0000 

Std Error 0.0017 

LHS Mean 0.0170

R-Squared 0.6836 

R Bar Squared 0.5782 

F-STAT 1, 3 6.4824

D.W. (1) 2.6600 

D.W. (2) 1.5247

EF2534NAS = (0.00906 * RTP.1 + 0.03539) * EF2534

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef2534nas/ef2534 = 0.00906 * rtp.1 + 0.03539

 (0.34277) (1.34366)

Sum Sq 0.0000 

Std Error 0.0013 

LHS Mean 0.0444

R-Squared 0.0377 

R Bar Squared 0.2831 

F-STAT 1, 3 0.1175

D.W. (1) 3.0818 

D.W. (2) 1.1094

EF3544NAS = (-0.01869 * RTP.1 + 0.08087) * EF3544

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef3544nas/ef3544 = -0.01869 * rtp.1 + 0.08087

 (0.70565) (3.06320)

Sum Sq 0.0000 

Std Error 0.0013 

LHS Mean 0.0622

R-Squared 0.1424 

R Bar Squared 0.1435 

F-STAT 1, 3 0.4979

D.W. (1) 2.2440 

D.W. (2) 2.1852

EF4554NAS = (0.07232 * RTP.1 - 0.00701) * EF4554

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef4554nas/ef4554 = 0.07232 * rtp.1 - 0.00701

 (2.86756) (0.27876)

Sum Sq 0.0000 

Std Error 0.0012 

LHS Mean 0.0651

R-Squared 0.7327 

R Bar Squared 0.6436 

F-STAT 1, 3 8.2229

D.W. (1) 1.7821 

D.W. (2) 2.7029

EF5564NAS = (0.07872 * RTP.1 + 0.00466) * EF5564

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004
134

Date: 9 NOV 2005

ef5564nas/ef5564 = 0.07872 * rtp.1 + 0.00466

 (1.38159) (0.08196)

Sum Sq 0.0000 

Std Error 0.0028 

LHS Mean 0.0831

R-Squared 0.3889 

R Bar Squared 0.1851 

F-STAT 1, 3 1.9088

D.W. (1) 2.6092 

D.W. (2) 2.2686

EF65ONAS = (0.10940 * EF6569 + 0.12265 * EF7074 + 0.14137 * EF75O)

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef6569nas/ef6569 = 0.10940

 (37.7493)

Sum Sq 0.0002 

Std Error 0.0065 

LHS Mean 0.1094

R-Squared 0.0000 

R Bar Squared 0.0000 

F 0, 4 NC

D.W. (1) 3.0431 

D.W. (2) 1.2204

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef7074nas/ef7074 = 0.12265

 (16.4939)

Sum Sq 0.0011 

Std Error 0.0166 

LHS Mean 0.1226

R-Squared 0.0000 

R Bar Squared 0.0000 

F 0, 4 NC

D.W. (1) 1.0289 

D.W. (2) 1.7188

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

ef75onas/ef75o = 0.14137

 (17.7500)

Sum Sq 0.0013 

Std Error 0.0178 

LHS Mean 0.1414

R-Squared 0.0000 

R Bar Squared 0.0000 

F 0, 4 NC

D.W. (1) 1.6889 

D.W. (2) 1.2345

EM1617NAS = (-0.23035 * RTP.1 + 0.24985) * EM1617

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em1617nas/em1617 = -0.23035 * rtp.1 + 0.24985

 (5.08538) (5.53372)
135

Sum Sq 0.0000 

Std Error 0.0022 

LHS Mean 0.0203

R-Squared 0.8961 

R Bar Squared 0.8614 

F-STAT 1, 3 25.8611

D.W. (1) 2.4658 

D.W. (2) 1.6839

EM1819NAS = (-0.05782 * RTP.1 + 0.07265) * EM1819

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em1819nas/em1819 = -0.05782 * rtp.1 + 0.07265

 (3.43044) (4.32458)

Sum Sq 0.0000 

Std Error 0.0008 

LHS Mean 0.0150

R-Squared 0.7969 

R Bar Squared 0.7291 

F-STAT 1, 3 11.7679

D.W. (1) 3.3262 

D.W. (2) 1.0399

EM2024NAS = (-0.09206 * RTP.1 + 0.11567) * EM2024

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em2024nas/em2024 = -0.09206 * rtp.1 + 0.11567

 (2.44839) (3.08618)

Sum Sq 0.0000 

Std Error 0.0018 

LHS Mean 0.0239

R-Squared 0.6665 

R Bar Squared 0.5553 

F-STAT 1, 3 5.9946

D.W. (1) 2.1493 

D.W. (2) 1.7046

EM2534NAS = (-0.09661 * RTP.1 + 0.14843) * EM2534

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em2534nas/em2534 = -0.09661 * rtp.1 + 0.14843

 (2.81478) (4.33847)

Sum Sq 0.0000 

Std Error 0.0017 

LHS Mean 0.0522

R-Squared 0.7254 

R Bar Squared 0.6338 

F-STAT 1, 3 7.9230

D.W. (1) 1.8300 

D.W. (2) 2.9632

EM3544NAS = (0.02739 * RTP.1 + 0.05236) * EM3544

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em3544nas/em3544 = 0.02739 * rtp.1 + 0.05236

 (0.61129) (1.17241)

Sum Sq 0.0000 
136

Std Error 0.0022 

LHS Mean 0.0797

R-Squared 0.1108 

R Bar Squared 0.1857 

F-STAT 1, 3 0.3737

D.W. (1) 2.5508 

D.W. (2) 2.2676

EM4554NAS = (0.06217 * RTP.1 + 0.03411) * EM4554

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em4554nas/em4554 = 0.06217 * rtp.1 + 0.03411

 (1.91738) (1.05544)

Sum Sq 0.0000 

Std Error 0.0016 

LHS Mean 0.0961

R-Squared 0.5507 

R Bar Squared 0.4009 

F-STAT 1, 3 3.6764

D.W. (1) 2.5497 

D.W. (2) 1.5554

EM5564NAS = (-0.04776 * RTP.1 + 0.16626) * EM5564

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em5564nas/em5564 = -0.04776 * rtp.1 + 0.16626

 (0.60480) (2.11226)

Sum Sq 0.0000 

Std Error 0.0039 

LHS Mean 0.1187

R-Squared 0.1087 

R Bar Squared 0.1884 

F-STAT 1, 3 0.3658

D.W. (1) 2.9234 

D.W. (2) 1.9432

EM65ONAS = (0.16527 * EM6569 + 0.17798 * EM7074 + 0.19058 * EM75O)

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em6569nas/em6569 = 0.16527

 (53.9126)

Sum Sq 0.0002 

Std Error 0.0069 

LHS Mean 0.1653

R-Squared 0.0000 

R Bar Squared 0.0000 

F-stat 0, 4 NC

D.W. (1) 1.7716 

D.W. (2) 2.9645

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em7074nas/em7074 = 0.17798

 (22.9265)

Sum Sq 0.0012 

Std Error 0.0174 

LHS Mean 0.1780

R-Squared 0.0000 

R Bar Squared 0.0000 
137

F-stat 0, 4 NC

D.W. (1) 1.7116 

D.W. (2) 2.1991

Ordinary Least Squares

ANNUAL data for 5 periods from 2000 to 2004

Date: 9 NOV 2005

em75onas/em75o = 0.19058

 (20.1892)

Sum Sq 0.0018 

Std Error 0.0211 

LHS Mean 0.1906

R-Squared 0.0000 

R Bar Squared 0.0000 

F-stat 0, 4 NC

D.W. (1) 2.7330 

D.W. (2) 0.9992

Nonagricultural Unpaid Family Workers

EF1617NAU = 0.00012 * ENAS

EF1819NAU = 0.00025 * ENAS

EF2024NAU = 0.00024 * ENAS

EF2534NAU = 0.00117 * ENAS

EF3544NAU = 0.00218 * ENAS

EF4554NAU = 0.00226 * ENAS

EF5564NAU = 0.00083 * ENAS

EF65ONAU = (0.00027 + 0.00021 + 0.00008) * ENAS

EM1617NAU = 0.00028 * ENAS

EM1819NAU = 0.00033 * ENAS

EM2024NAU = 0.00050 * ENAS

EM2534NAU = 0.00044 * ENAS

EM3544NAU = 0.00043 * ENAS

EM4554NAU = 0.00052 * ENAS

EM5564NAU = 0.00037 * ENAS

EM65ONAU = (0.00023 + 0.00010 + 0.00011) * ENAS

Agricultural Wage Workers

EM1617AW = EAW * (-0.00594 - 0.09353 * RTP + 5.28754 * EM1617/E + 0.08116)

EM1819AW = EAW * (-0.00131 - 0.18120 * RTP + 3.87151 * EM1819/E + 0.16636)

EM2024AW = EAW * (-0.00664 + 0.10493 * RTP + 2.00153 * EM2024/E - 0.08191)

EM2534AW = EAW * (-0.02065 + 0.38358 * RTP - 0.98380 * EM2534/E + 0.00751)

EM3544AW = EAW * (0.00402 - 0.15663 * RTP + 1.72119 * EM3544/E + 0.05679)

EM4554AW = EAW * (0.00834 + 0.03746 * RTP + 0.46522 * EM4554/E + 0.00144)

EM5564AW = EAW * (-0.00655 + 0.03521 * RTP + 0.46852 * EM5564/E - 0.00037)

EM65OAW = EAW * (-0.00114 + 0.07640 * RTP + 3.25911 * EM65O/E - 0.10058)

EF1617AW = EAW * (-0.00055 - 0.05470 * RTP + 1.41760 * EF1617/E + 0.04979)

EF1819AW = EAW * (0.00102 - 0.07375 * RTP + 0.78394 * EF1819/E + 0.07226)

EF2024AW = EAW * (0.00112 - 0.05971 * RTP + 0.57256 * EF2024/E + 0.05907)

EF2534AW = EAW * (0.00623 + 0.08868 * RTP + 1.00897 * EF2534/E - 0.15142)

EF3544AW = EAW * (0.00687 - 0.00259 * RTP + 0.51319 * EF3544/E - 0.00937)

EF4554AW = EAW * (0.00185 + 0.08747 * RTP + 0.28022 * EF4554/E - 0.08053)

EF5564AW = EAW * (-0.00140 - 0.03001 * RTP - 0.59383 * EF5564/E + 0.07088)

EF65OAW = EAW * (0.00096 + 0.06768 * RTP + 1.04213 * EF65O/E - 0.07359)

Agricultural Self-employed Workers

EM1617AS = NM1617 * (0.00528 + 0.00404)

EM1819AS = NM1819 * (0.00309 + 0.28448 * EA / (NM16O+ NF16O) - 0.00165)

EM2024AS = NM2024 * (-0.00181 + 0.97958 * EA / (NM16O+ NF16O) - 0.01093)

EM2534AS = NM2534 * (-0.00263 + 1.23186 * EA / (NM16O+ NF16O) - 0.01021)

EM3544AS = NM3544 * (-0.00151 + 1.66765 * EA / (NM16O+ NF16O) - 0.01450)
138

EM4554AS = NM4554 * (-0.00381 + 2.86654 * EA / (NM16O+ NF16O) - 0.03175)

EM5564AS = NM5564 * (-0.00460 + 2.78817 * EA / (NM16O+ NF16O) - 0.02398)

EM65OAS = NM65O * (0.00079 + 1.76904 * EA / (NM16O+ NF16O) - 0.01437)

EF1617AS = NF1617 * (0.00181 + 0.00030)

EF1819AS = EM1819AS * (-0.02393 + 0.63672 * RTP + 0.98791 * EF1819/EM1819 - 1.43926)

EF2024AS = EM2024AS * (0.07353 - 0.40207 * RTP + 0.57572 * EF2024/EM2024 - 0.01117)

EF2534AS = EM2534AS * (0.16575 + 0.16967 * RTP + 0.55503 * EF2534/EM2534 - 0.43412)

EF3544AS = EM3544AS * (0.15848 + 0.37839 * RTP + 0.37764 * EF3544/EM3544 - 0.45362)

EF4554AS = EM4554AS * (0.21947 + 0.29497 * RTP + 0.58974 * EF4554/EM4554 - 0.51966)

EF5564AS = EM5564AS * (0.20892 + 0.36294 * RTP + 0.65320 * EF5564/EM5564 - 0.66626)

EF65OAS = EM65OAS * (0.16242 + 0.54916 * RTP + 0.06199 * EF65O/EM65O - 0.47556)

Agricultural Unpaid Family Workers

EF1617AU = 0.002

EF1819AU = 0.001

EF2024AU = 0.001

EF2534AU = 0.003

EF3544AU = 0.004

EF4554AU = 0.005

EF5564AU = 0.003

EF65OAU = 0.001

EM1617AU = 0.006

EM1819AU = 0.005

EM2024AU = 0.005

EM2534AU = 0.002

EM3544AU = 0.002

EM4554AU = 0.001

EM5564AU = 0.001

EM65OAU = 0.002

Quarterly Employment Equations

ENA = E - EA

Nonagricultural Wage Workers, Private Household Workers:

EF1617NAWPH_R = MAX (0.001, -0.20802 * MOVAVG (4, RTP.1) - 0.40988 * MOVAVG (4, RTP.5) + 0.01015 + 61.2465 * 1/YEAR -

0.00965 * MINW/CPIW_U + 0.01561 * NU10/NF1617 - 0.13398) * EF1617 + EF1617NAWPH.ADJ

EF1819NAWPH_R = MAX (0.001, -0.03363 * MOVAVG (4, RTP.1) - 0.12989 * MOVAVG (4, RTP.5) - 0.00661 + 8.44701 * 1/YEAR -

0.00539 * MINW/CPIW_U + 0.00345 * NU10/NF1819 + 0.07597) * EF1819 + EF1819NAWPH.ADJ

EF2024NAWPH_R = MAX (0.001, -0.18707 * MOVAVG (20, RTP.1) - 0.00223 + 2.12060 * 1/YEAR + 0.00820 * NU10/NF2024 + 

0.14537) * EF2024 + EF2024NAWPH.ADJ

EF2534NAWPH_R = MAX (0.001, 0.01874 * MOVAVG (4, RTP.1) - 0.04167 * MOVAVG (20, RTP.5) - 0.00090 + 1.55167 * 1/YEAR + 

0.01021 * NU10/NF2534 - 0.00170) * EF2534 + EF2534NAWPH.ADJ

EF3544NAWPH_R = (0.00622 * MOVAVG (4, RTP.1) - 0.06062 * MOVAVG (20, RTP.5) + 0.00008 + 0.29372 * MOVAVG (12, 

EF2534NAWPH.36/EF2534.36) + 0.06187) * EF3544 + EF3544NAWPH.ADJ

EF4554NAWPH_R = (0.02788 * MOVAVG (4, RTP.1) - 0.10996 * MOVAVG (20, RTP.5) - 0.00349 + 0.53068 * MOVAVG (12, 

EF3544NAWPH.36/EF3544.36) + 0.08883) * EF4554 + EF4554NAWPH.ADJ

EF5564NAWPH_R = (0.05939 * MOVAVG (4, RTP.1) - 0.10618 * MOVAVG (8, RTP.5) - 0.00579 + 0.66195 * MOVAVG (12, 

EF4554NAWPH.36/EF4554.36) + 0.05966) * EF5564 + EF5564NAWPH.ADJ

EF65ONAWPH_R = (0.22642 * MOVAVG (4, RTP.1) - 0.02069 + 0.33505 * MOVAVG (12, EF5564NAWPH.36) - 0.19707) + 

EF65ONAWPH.ADJ

EM1617NAWPH_R = MAX (0.001, -0.05284 * MOVAVG (4, RTP.1) - 0.17833 * MOVAVG (4, RTP.5) - 0.00768 + 9.19738 * 1/YEAR -

0.00588 * MINW/CPIW_U + 0.16862) * EM1617+ EM1617NAWPH.ADJ

EM1819NAWPH_R = MAX (0.001, -0.07122 * MOVAVG (4, RTP.1) - 0.03737 * MOVAVG (4, RTP.5) - 0.00282 + 3.76796 * 1/YEAR -

0.00499 * MINW/CPIW_U + 0.08727) * EM1819+ EM1819NAWPH.ADJ

EM2024NAWPH_R = MAX (0.001, -0.00450 * MOVAVG (4, RTP.1) - 0.02345 * MOVAVG (4, RTP.5) - 0.00113 - 0.00057 * 

MINW/CPIW_U + 0.03265) * EM2024 + EM2024NAWPH.ADJ

EM2534NAWPH_R = MAX (0.001, - 0.00490 * MOVAVG (4, RTP.5) - 0.00054 - 0.00051 * MINW/CPIW_U + 0.00789) * EM2534 + 

EM2534NAWPH.ADJ

EM3544NAWPH_R = (-0.00446 * MOVAVG (4, RTP.5) - 0.00041 - 0.00053 * MINW/CPIW_U + 0.00726) * EM3544 + 

EM3544NAWPH.ADJ

EM4554NAWPH_R = (-0.00039 + 0.00129) * EM4554 + EM4554NAWPH.ADJ

EM5564NAWPH_R = (-0.00015 + 0.00200) * EM5564 + EM5564NAWPH.ADJ

EM65ONAWPH_R = (-0.00679 + 0.64405 * MOVAVG (12, EM5564NAWPH.36) + 0.00231) + EM65ONAWPH.ADJ

ENAWPH_R = EF1617NAWPH_R + EF1819NAWPH_R + EF2024NAWPH_R + EF2534NAWPH_R + EF3544NAWPH_R + 

EF4554NAWPH_R + EF5564NAWPH_R + EF65ONAWPH_R + EM1617NAWPH_R + EM1819NAWPH_R + 
139

EM2024NAWPH_R + EM2534NAWPH_R + EM3544NAWPH_R + EM4554NAWPH_R 

+ EM5564NAWPH_R + EM65ONAWPH_R

ENAWPH = IF LONGRANGE = 0 

THEN ENAWPH_R 

ELSE ENAWPH.1 * (E_FE/E_FE.1)

EM1617NAWPH = EM1617NAWPH_R * (ENAWPH/ENAWPH_R)

EM1819NAWPH = EM1819NAWPH_R * (ENAWPH/ENAWPH_R)

EM2024NAWPH = EM2024NAWPH_R * (ENAWPH/ENAWPH_R)

EM2534NAWPH = EM2534NAWPH_R * (ENAWPH/ENAWPH_R)

EM3544NAWPH = EM3544NAWPH_R * (ENAWPH/ENAWPH_R)

EM4554NAWPH = EM4554NAWPH_R * (ENAWPH/ENAWPH_R)

EM5564NAWPH = EM5564NAWPH_R * (ENAWPH/ENAWPH_R)

EM65ONAWPH = EM65ONAWPH_R * (ENAWPH/ENAWPH_R)

EF1617NAWPH = EF1617NAWPH_R * (ENAWPH/ENAWPH_R)

EF1819NAWPH = EF1819NAWPH_R * (ENAWPH/ENAWPH_R)

EF2024NAWPH = EF2024NAWPH_R * (ENAWPH/ENAWPH_R)

EF2534NAWPH = EF2534NAWPH_R * (ENAWPH/ENAWPH_R)

EF3544NAWPH = EF3544NAWPH_R * (ENAWPH/ENAWPH_R)

EF4554NAWPH = EF4554NAWPH_R * (ENAWPH/ENAWPH_R)

EF5564NAWPH = EF5564NAWPH_R * (ENAWPH/ENAWPH_R)

EF65ONAWPH = EF65ONAWPH_R * (ENAWPH/ENAWPH_R)

EFNAWPH = EF1617NAWPH + EF1819NAWPH + EF2024NAWPH + EF2534NAWPH + EF3544NAWPH + EF4554NAWPH + 

EF5564NAWPH + EF65ONAWPH

EMNAWPH = EM1617NAWPH + EM1819NAWPH + EM2024NAWPH + EM2534NAWPH + EM3544NAWPH + EM4554NAWPH + 

EM5564NAWPH + EM65ONAWPH

Nonagricultural Self-employed Workers:

EF1617NAS_R = (0.12015 * RTP.1 - 0.10551) * EF1617 + EF1617NAS.ADJ

EF1819NAS_R = (0.11184 * RTP.1 - 0.10241) * EF1819 + EF1819NAS.ADJ

EF2024NAS_R = (0.08908 * RTP.1 - 0.07176) * EF2024 + EF2024NAS.ADJ

EF2534NAS_R = (0.00906 * RTP.1 + 0.03539) * EF2534 + EF2534NAS.ADJ

EF3544NAS_R = (-0.01869 * RTP.1 + 0.08087) * EF3544 + EF3544NAS.ADJ

EF4554NAS_R = (0.07232 * RTP.1 - 0.00701) * EF4554 + EF4554NAS.ADJ

EF5564NAS_R = (0.07872 * RTP.1 + 0.00466) * EF5564 + EF5564NAS.ADJ

EF65ONAS_R = (0.10940 * EF6569 + 0.12265 * EF7074 + 0.14137 * EF75O) + EF65ONAS.ADJ

EM1617NAS_R = (-0.23035 * RTP.1 + 0.24985) * EM1617 + EM1617NAS.ADJ

EM1819NAS_R = (-0.05782 * RTP.1 + 0.07265) * EM1819 + EM1819NAS.ADJ

EM2024NAS_R = (-0.09206 * RTP.1 + 0.11567) * EM2024 + EM2024NAS.ADJ

EM2534NAS_R = (-0.09661 * RTP.1 + 0.14843) * EM2534 + EM2534NAS.ADJ

EM3544NAS_R = (0.02739 * RTP.1 + 0.05236) * EM3544 + EM3544NAS.ADJ

EM4554NAS_R = (0.06217 * RTP.1 + 0.03411) * EM4554 + EM4554NAS.ADJ

EM5564NAS_R = (-0.04776 * RTP.1 + 0.16626) * EM5564 + EM5564NAS.ADJ

EM65ONAS_R = (0.16527 * EM6569 + 0.17798 * EM7074 + 0.19058 * EM75O) + EM65ONAS.ADJ

ENAS_R = EF1617NAS_R + EF1819NAS_R + EF2024NAS_R + EF2534NAS_R + EF3544NAS_R + EF4554NAS_R + EF5564NAS_R + 

EF65ONAS_R + EM1617NAS_R + EM1819NAS_R + EM2024NAS_R + EM2534NAS_R + EM3544NAS_R + 

EM4554NAS_R + EM5564NAS_R + EM65ONAS_R

ENAS = IF LONGRANGE = 0 

THEN ENAS_R 

ELSE ENA * (ENAS.1/ENA.1)

EM1617NAS = EM1617NAS_R * (ENAS/ENAS_R)

EM1819NAS = EM1819NAS_R * (ENAS/ENAS_R)

EM2024NAS = EM2024NAS_R * (ENAS/ENAS_R)

EM2534NAS = EM2534NAS_R * (ENAS/ENAS_R)

EM3544NAS = EM3544NAS_R * (ENAS/ENAS_R)

EM4554NAS = EM4554NAS_R * (ENAS/ENAS_R)

EM5564NAS = EM5564NAS_R * (ENAS/ENAS_R)

EM65ONAS = EM65ONAS_R * (ENAS/ENAS_R)

EF1617NAS = EF1617NAS_R * (ENAS/ENAS_R)
140

EF1819NAS = EF1819NAS_R * (ENAS/ENAS_R)

EF2024NAS = EF2024NAS_R * (ENAS/ENAS_R)

EF2534NAS = EF2534NAS_R * (ENAS/ENAS_R)

EF3544NAS = EF3544NAS_R * (ENAS/ENAS_R)

EF4554NAS = EF4554NAS_R * (ENAS/ENAS_R)

EF5564NAS = EF5564NAS_R * (ENAS/ENAS_R)

EF65ONAS = EF65ONAS_R * (ENAS/ENAS_R)

EFNAS = EF1617NAS + EF1819NAS + EF2024NAS + EF2534NAS + EF3544NAS + EF4554NAS + EF5564NAS + EF65ONAS

EMNAS = EM1617NAS + EM1819NAS + EM2024NAS + EM2534NAS + EM3544NAS + EM4554NAS + EM5564NAS + 

EM65ONAS

Nonagricultural Unpaid Family Workers

EF1617NAU_R = 0.00012 * ENAS + EF1617NAU.ADJ

EF1819NAU_R = 0.00025 * ENAS + EF1819NAU.ADJ

EF2024NAU_R = 0.00024 * ENAS + EF2024NAU.ADJ

EF2534NAU_R = 0.00117 * ENAS + EF2534NAU.ADJ

EF3544NAU_R = 0.00218 * ENAS + EF3544NAU.ADJ

EF4554NAU_R = 0.00226 * ENAS + EF4554NAU.ADJ

EF5564NAU_R = 0.00083 * ENAS + EF5564NAU.ADJ

EF65ONAU_R = (0.00027 + 0.00021 + 0.00008) * ENAS + EF65ONAU.ADJ

EM1617NAU_R = 0.00028 * ENAS + EM1617NAU.ADJ

EM1819NAU_R = 0.00033 * ENAS + EM1819NAU.ADJ

EM2024NAU_R = 0.00050 * ENAS + EM2024NAU.ADJ

EM2534NAU_R = 0.00044 * ENAS + EM2534NAU.ADJ

EM3544NAU_R = 0.00043 * ENAS + EM3544NAU.ADJ

EM4554NAU_R = 0.00052 * ENAS + EM4554NAU.ADJ

EM5564NAU_R = 0.00037 * ENAS + EM5564NAU.ADJ

EM65ONAU_R = (0.00023 + 0.00010 + 0.00011) * ENAS + EM65ONAU.ADJ

ENAU_R = EF1617NAU_R + EF1819NAU_R + EF2024NAU_R + EF2534NAU_R + EF3544NAU_R + EF4554NAU_R + 

EF5564NAU_R + EF65ONAU_R + EM1617NAU_R + EM1819NAU_R + EM2024NAU_R + EM2534NAU_R + 

EM3544NAU_R + EM4554NAU_R + EM5564NAU_R + EM65ONAU_R

ENAU = ENAU_R

EM1617NAU = EM1617NAU_R * (ENAU/ENAU_R)

EM1819NAU = EM1819NAU_R * (ENAU/ENAU_R)

EM2024NAU = EM2024NAU_R * (ENAU/ENAU_R)

EM2534NAU = EM2534NAU_R * (ENAU/ENAU_R)

EM3544NAU = EM3544NAU_R * (ENAU/ENAU_R)

EM4554NAU = EM4554NAU_R * (ENAU/ENAU_R)

EM5564NAU = EM5564NAU_R * (ENAU/ENAU_R)

EM65ONAU = EM65ONAU_R * (ENAU/ENAU_R)

EF1617NAU = EF1617NAU_R * (ENAU/ENAU_R)

EF1819NAU = EF1819NAU_R * (ENAU/ENAU_R)

EF2024NAU = EF2024NAU_R * (ENAU/ENAU_R)

EF2534NAU = EF2534NAU_R * (ENAU/ENAU_R)

EF3544NAU = EF3544NAU_R * (ENAU/ENAU_R)

EF4554NAU = EF4554NAU_R * (ENAU/ENAU_R)

EF5564NAU = EF5564NAU_R * (ENAU/ENAU_R)

EF65ONAU = EF65ONAU_R * (ENAU/ENAU_R)

EFNAU = EF1617NAU + EF1819NAU + EF2024NAU + EF2534NAU + EF3544NAU + EF4554NAU + EF5564NAU + EF65ONAU

EMNAU = EM1617NAU + EM1819NAU + EM2024NAU + EM2534NAU + EM3544NAU + EM4554NAU + EM5564NAU + 

EM65ONAU

Agricultural Workers

EA = IF LONGRANGE = 0 

THEN GDPPF00 / (EXP (-0.20541 + 0.03254 * YEAR - 0.07829 + 0.37854)) 

ELSE E * EA.1/E.1

Agricultural Wage Workers

EAW = IF LONGRANGE = 0 
141

THEN EA * (0.00893 * YEAR + 0.33159 * RTP - 0.67943) 

ELSE EA * (EAW.1/EA.1)

EM1617AW_R = MAX (0, EAW * (-0.00594 - 0.09353 * MOVAVG (2, RTP.1) + 5.28754 * EM1617/E + 0.08116) + EM1617AW.ADJ)

EM1819AW_R = MAX (0, EAW * (-0.00131 - 0.18120 * MOVAVG (2, RTP.1) + 3.87151 * EM1819/E + 0.16636) + EM1819AW.ADJ)

EM2024AW_R = MAX (0, EAW * (-0.00664 + 0.10493 * MOVAVG (2, RTP.1) + 2.00153 * EM2024/E - 0.08191) + EM2024AW.ADJ)

EM2534AW_R = MAX (0, EAW * (-0.02065 + 0.38358 * MOVAVG (2, RTP.1) - 0.98380 * EM2534/E + 0.00751) + EM2534AW.ADJ)

EM3544AW_R = MAX (0, EAW * (0.00402 - 0.15663 * MOVAVG (2, RTP.1) + 1.72119 * EM3544/E + 0.05679) + EM3544AW.ADJ)

EM4554AW_R = MAX (0, EAW * (0.00834 + 0.03746 * MOVAVG (2, RTP.1) + 0.46522 * EM4554/E + 0.00144) + EM4554AW.ADJ)

EM5564AW_R = MAX (0, EAW * (-0.00655 + 0.03521 * MOVAVG (2, RTP.1) + 0.46852 * EM5564/E - 0.00037) + EM5564AW.ADJ)

EM65OAW_R = MAX (0, EAW * (-0.00114 + 0.07640 * MOVAVG (2, RTP.1) + 3.25911 * EM65O/E - 0.10058) + EM65OAW.ADJ)

EF1617AW_R = MAX (0, EAW * (-0.00055 - 0.05470 * MOVAVG (2, RTP.1) + 1.41760 * EF1617/E + 0.04979) + EF1617AW.ADJ)

EF1819AW_R = MAX (0, EAW * (0.00102 - 0.07375 * MOVAVG (2, RTP.1) + 0.78394 * EF1819/E + 0.07226) + EF1819AW.ADJ)

EF2024AW_R = MAX (0, EAW * (0.00112 - 0.05971 * MOVAVG (2, RTP.1) + 0.57256 * EF2024/E + 0.05907) + EF2024AW.ADJ)

EF2534AW_R = MAX (0, EAW * (0.00623 + 0.08868 * MOVAVG (2, RTP.1) + 1.00897 * EF2534/E - 0.15142) + EF2534AW.ADJ)

EF3544AW_R = MAX (0, EAW * (0.00687 - 0.00259 * MOVAVG (2, RTP.1) + 0.51319 * EF3544/E - 0.00937) + EF3544AW.ADJ)

EF4554AW_R = MAX (0, EAW * (0.00185 + 0.08747 * MOVAVG (2, RTP.1) + 0.28022 * EF4554/E - 0.08053) + EF4554AW.ADJ)

EF5564AW_R = MAX (0, EAW * (-0.00140 - 0.03001 * MOVAVG (2, RTP.1) - 0.59383 * EF5564/E + 0.07088) + EF5564AW.ADJ)

EF65OAW_R = MAX (0, EAW * (0.00096 + 0.06768 * MOVAVG (2, RTP.1) + 1.04213 * EF65O/E - 0.07359) + EF65OAW.ADJ)

EAW_R = EF1617AW_R + EF1819AW_R + EF2024AW_R + EF2534AW_R + EF3544AW_R + EF4554AW_R + EF5564AW_R + 

EF65OAW_R + EM1617AW_R + EM1819AW_R+ EM2024AW_R + EM2534AW_R + EM3544AW_R + 

EM4554AW_R + EM5564AW_R + EM65OAW_R

EM1617AW = EM1617AW_R * (EAW/EAW_R)

EM1819AW = EM1819AW_R * (EAW/EAW_R)

EM2024AW = EM2024AW_R * (EAW/EAW_R)

EM2534AW = EM2534AW_R * (EAW/EAW_R)

EM3544AW = EM3544AW_R * (EAW/EAW_R)

EM4554AW = EM4554AW_R * (EAW/EAW_R)

EM5564AW = EM5564AW_R * (EAW/EAW_R)

EM65OAW = EM65OAW_R * (EAW/EAW_R)

EF1617AW = EF1617AW_R * (EAW/EAW_R)

EF1819AW = EF1819AW_R * (EAW/EAW_R)

EF2024AW = EF2024AW_R * (EAW/EAW_R)

EF2534AW = EF2534AW_R * (EAW/EAW_R)

EF3544AW = EF3544AW_R * (EAW/EAW_R)

EF4554AW = EF4554AW_R * (EAW/EAW_R)

EF5564AW = EF5564AW_R * (EAW/EAW_R)

EF65OAW = EF65OAW_R * (EAW/EAW_R)

EFAW = EF1617AW+ EF1819AW+ EF2024AW+ EF2534AW+ EF3544AW+ EF4554AW+ EF5564AW+ EF65OAW

EMAW = EM1617AW+ EM1819AW+ EM2024AW+ EM2534AW+ EM3544AW+ EM4554AW+ EM5564AW+ EM65OAW

Self-employed Agricultural Workers

EAS = EA - EAU - EAW

EM1617AS_R = MAX (0, NM1617 * (0.00528 + 0.00404) + EM1617AS.ADJ)

EM1819AS_R = MAX (0, NM1819 * (0.00309 + 0.28448 * EA / (NM16O+ NF16O) - 0.00165) + EM1819AS.ADJ)

EM2024AS_R = MAX (0, NM2024 * (-0.00181 + 0.97958 * EA / (NM16O+ NF16O) - 0.01093) + EM2024AS.ADJ)

EM2534AS_R = MAX (0, NM2534 * (-0.00263 + 1.23186 * EA / (NM16O+ NF16O) - 0.01021) + EM2534AS.ADJ)

EM3544AS_R = MAX (0, NM3544 * (-0.00151 + 1.66765 * EA / (NM16O+ NF16O) - 0.01450) + EM3544AS.ADJ)

EM4554AS_R = MAX (0, NM4554 * (-0.00381 + 2.86654 * EA / (NM16O+ NF16O) - 0.03175) + EM4554AS.ADJ)

EM5564AS_R = MAX (0, NM5564 * (-0.00460 + 2.78817 * EA / (NM16O+ NF16O) - 0.02398) + EM5564AS.ADJ)

EM65OAS_R = MAX (0, NM65O * (0.00079 + 1.76904 * EA / (NM16O+ NF16O) - 0.01437) + EM65OAS.ADJ)

EF1617AS_R = MAX (0, NF1617 * (0.00181 + 0.00030) + EF1617AS.ADJ)

EF1819AS_R = MAX (0, EM1819AS.1 * (-0.02393 + 0.63672 * MOVAVG (2, RTP.1) + 0.98791 * EF1819/EM1819 - 4.43926) + 

EF1819AS.ADJ)

EF2024AS_R = MAX (0, EM2024AS.1 * (0.07353 - 0.40207 * MOVAVG (2, RTP.1) + 0.57572 * EF2024/EM2024 - 0.01117) + 

EF2024AS.ADJ)

EF2534AS_R = MAX (0, EM2534AS.1 * (0.16575 + 0.16967 * MOVAVG (2, RTP.1) + 0.55503 * EF2534/EM2534 - 0.43412) + 

EF2534AS.ADJ)

EF3544AS_R = MAX (0, EM3544AS.1 * (0.15848 + 0.37839 * MOVAVG (2, RTP.1) + 0.37764 * EF3544/EM3544 - 0.45362) + 

EF3544AS.ADJ)

EF4554AS_R = MAX (0, EM4554AS.1 * (0.21947 + 0.29497 * MOVAVG (2, RTP.1) + 0.58974 * EF4554/EM4554 - 0.51966) + 

EF4554AS.ADJ)

EF5564AS_R = MAX (0, EM5564AS.1 * (0.20892 + 0.36294 * MOVAVG (2, RTP.1) + 0.65320 * EF5564/EM5564 - 0.66626) + 
142

EF5564AS.ADJ)

EF65OAS_R = MAX (0, EM65OAS.1 * (0.16242 + 0.54916 * MOVAVG (2, RTP.1) + 0.06199 * EF65O/EM65O- 0.47556) + 

EF65OAS.ADJ)

EAS_R = EF1617AS_R + EF1819AS_R + EF2024AS_R + EF2534AS_R + EF3544AS_R + EF4554AS_R + EF5564AS_R + 

EF65OAS_R + EM1617AS_R + EM1819AS_R+ EM2024AS_R + EM2534AS_R + EM3544AS_R + EM4554AS_R + 

EM5564AS_R + EM65OAS_R

EM1617AS = EM1617AS_R * (EAS/EAS_R)

EM1819AS = EM1819AS_R * (EAS/EAS_R)

EM2024AS = EM2024AS_R * (EAS/EAS_R)

EM2534AS = EM2534AS_R * (EAS/EAS_R)

EM3544AS = EM3544AS_R * (EAS/EAS_R)

EM4554AS = EM4554AS_R * (EAS/EAS_R)

EM5564AS = EM5564AS_R * (EAS/EAS_R)

EM65OAS = EM65OAS_R * (EAS/EAS_R)

EF1617AS = EF1617AS_R * (EAS/EAS_R)

EF1819AS = EF1819AS_R * (EAS/EAS_R)

EF2024AS = EF2024AS_R * (EAS/EAS_R)

EF2534AS = EF2534AS_R * (EAS/EAS_R)

EF3544AS = EF3544AS_R * (EAS/EAS_R)

EF4554AS = EF4554AS_R * (EAS/EAS_R)

EF5564AS = EF5564AS_R * (EAS/EAS_R)

EF65OAS = EF65OAS_R * (EAS/EAS_R)

EFAS = EF1617AS + EF1819AS + EF2024AS + EF2534AS + EF3544AS + EF4554AS + EF5564AS + EF65OAS

EMAS = EM1617AS + EM1819AS + EM2024AS + EM2534AS + EM3544AS + EM4554AS + EM5564AS + EM65OAS

Unpaid Agricultural Family Workers

EM1617AU_R = MAX (0, 0.002 + EM1617AU.ADJ)

EM1819AU_R = MAX (0, 0.001 + EM1819AU.ADJ)

EM2024AU_R = MAX (0, 0.001 + EM2024AU.ADJ)

EM2534AU_R = MAX (0, 0.003 + EM2534AU.ADJ)

EM3544AU_R = MAX (0, 0.004 + EM3544AU.ADJ)

EM4554AU_R = MAX (0, 0.005 + EM4554AU.ADJ)

EM5564AU_R = MAX (0, 0.003 + EM5564AU.ADJ)

EM65OAU_R = MAX (0, 0.001 + EM65OAU.ADJ)

EF1617AU_R = MAX (0, 0.006 + EM1617AU.ADJ)

EF1819AU_R = MAX (0, 0.005 + EF1819AU.ADJ)

EF2024AU_R = MAX (0, 0.005 + EF2024AU.ADJ)

EF2534AU_R = MAX (0, 0.002 + EF2534AU.ADJ)

EF3544AU_R = MAX (0, 0.002 + EF3544AU.ADJ)

EF4554AU_R = MAX (0, 0.001 + EF4554AU.ADJ)

EF5564AU_R = MAX (0, 0.001 + EF5564AU.ADJ)

EF65OAU_R = MAX (0, 0.002 + EF65OAU.ADJ)

EAU_R = EF1617AU_R + EF1819AU_R + EF2024AU_R + EF2534AU_R + EF3544AU_R + EF4554AU_R + EF5564AU_R + 

EF65OAU_R + EM1617AU_R + EM1819AU_R + EM2024AU_R + EM2534AU_R + EM3544AU_R + EM4554AU_R + 

EM5564AU_R + EM65OAU_R

EAU = IF LONGRANGE = 0 

THEN EAU_R 

ELSE EAU.1/EA.1 * EA

EM1617AU = EM1617AU_R * (EAU/EAU_R)

EM1819AU = EM1819AU_R * (EAU/EAU_R)

EM2024AU = EM2024AU_R * (EAU/EAU_R)

EM2534AU = EM2534AU_R * (EAU/EAU_R)

EM3544AU = EM3544AU_R * (EAU/EAU_R)

EM4554AU = EM4554AU_R * (EAU/EAU_R)

EM5564AU = EM5564AU_R * (EAU/EAU_R)

EM65OAU = EM65OAU_R * (EAU/EAU_R)

EF1617AU = EF1617AU_R * (EAU/EAU_R)

EF1819AU = EF1819AU_R * (EAU/EAU_R)

EF2024AU = EF2024AU_R * (EAU/EAU_R)
143

EF2534AU = EF2534AU_R * (EAU/EAU_R)

EF3544AU = EF3544AU_R * (EAU/EAU_R)

EF4554AU = EF4554AU_R * (EAU/EAU_R)

EF5564AU = EF5564AU_R * (EAU/EAU_R)

EF65OAU = EF65OAU_R * (EAU/EAU_R)

EFAU = EF1617AU + EF1819AU + EF2024AU + EF2534AU + EF3544AU + EF4554AU + EF5564AU + EF65OAU

EMAU = EM1617AU + EM1819AU + EM2024AU + EM2534AU + EM3544AU + EM4554AU + EM5564AU + EM65OAU

ANNUAL "AVERAGE HOURS WORKED" EQUATIONS

Total Nonagricultural Wage Workers

Males

AHWM1617NAW = 34.4953 * RTP + 1.03247 * MINW/CPIW_U - 31.4229 * RNM1617S- 0.12369 * 1 + 12.1981

AHWM1819NAW = 32.4361 * RTP + 12.8742 * RTP.1 + 1.28624 * MINW/CPIW_U- 16.0989 * RNM1819S - 0.27834 * 1 - 8.58664

AHWM2024NAW = 20.1161 * RTP - 10.2292 * RNM2024S - 2.28628 * (PM2024NM * NM2024NM / (PM2024NM * NM2024NM + 

PM2024MS * NM2024MS + PM2024MA * NM2024MA)) + 23.2252

AHWM2534NAW = 17.0559 * RTP - 0.29076 * 1 + 0.04542 *YEAR + 22.5121

AHWM3544NAW = 18.3314 * RTP - 0.30475 * 1 + 0.04409 *YEAR + 22.3275

AHWM4554NAW = 16.0678 * RTP - 0.12289 * 1 + 0.07446 *YEAR + 21.4366

AHWM5564NAW = 10.8277 * RTP + 0.23715 * 1 + 30.8975

AHWM65ONAW = -0.28038 * 1 + 5.40682 * RTP + 25.4797

Females

AHWF1617NAW = 17.2598 * RTP + 7.31262 * RTP.1 - 24.9241 * RNF1617S + 16.9218

AHWF1819NAW = 18.7922 * RTP + 13.1066 * RTP.1 - 18.9417 * RNF1819S- 16.2182 * PF1819 + 0.81920 * 1 + 16.7826

AHWF2024NAW = -219.154 * PF2024 + 12.4226 * RTP - 0.98470 * 1 + 158.354 * (PF2024)2+ 98.6034

AHWF2534NAW = -39.2904 * PF2534 + 39.6515 * (PF2534)2+ 9.10839 * RTP- 0.33503 * 1 + 35.8525

AHWF3544NAW = -39.2904 * PF3544 + 39.6515 * (PF3544)2+ 7.40115 * RTP- 0.38214 * 1 + 36.9133

AHWF4554NAW = 8.64511 * RTP - 63.8042 * PF4554 + 59.9107 * (PF4554)2- 0.58355 * 1 + 44.0723

AHWF5564NAW = 6.61506 * RTP - 0.28969 * 1 + 8.36882 * PF5564 + 24.8288

AHWF65ONAW = -0.43916 * 1 + 26.5228 + (8.36882/2) * (PF65O- 0.086)

Nonagricultural Self-employed Workers

AHWNAS = 24.9592 * RTP - 17.2194 * EFNAS/ENAS + 22.0120

Nonagricultural Unpaid Family Workers

AHWNAU = 25.5124 * (EF2534NAU + EF3544NAU + EF4554NAU)/ENAU + 19.2730

Agricultural Wage Workers

AHWAW = 6.58073 * RTP + 14.9130 * RTP.1 + 19.7800

Agricultural Self-employed Workers

AHWAS = 2.45830 * PGDPAF/PGDP - 3.61107 * 1 + 44.5318

Agricultural Unpaid Family Workers

AHWAU = 39.3563

QUARTERLY "AVERAGE HOURS WORKED" EQUATIONS

Total

AHW = (AHWNA * ENA + AHWA * EA) / E

Nonagricultural sector

AHWNA = (AHWNAW * (E-ENAS-ENAU-EA) + AHWNAS * ENAS + AHWNAU * ENAU) / ENA

Nonagricultural Wage Workers

AHWNAW = (AHWMNAW * (EM – EMNAS - EMNAU – EMAW - EMAS - EMAU) + AHWFNAW * (EF - EFNAS - EFNAU –

EFAW - EFAS – EFAU))/ (E – ENAS – ENAU – EA)
144

Males

AHWMNAW1 = AHWM1617NAW * (EM1617 - EM1617NAS - EM1617NAU - EM1617AW - EM1617AS - EM1617AU)

AHWMNAW2 = AHWM1819NAW * (EM1819 - EM1819NAS - EM1819NAU - EM1819AW - EM1819AS - EM1819AU)

AHWMNAW3 = AHWM2024NAW * (EM2024 - EM2024NAS - EM2024NAU - EM2024AW - EM2024AS - EM2024AU)

AHWMNAW4 = AHWM2534NAW * (EM2534 - EM2534NAS - EM2534NAU - EM2534AW - EM2534AS - EM2534AU)

AHWMNAW5 = AHWM3544NAW * (EM3544 - EM3544NAS - EM3544NAU - EM3544AW - EM3544AS - EM3544AU)

AHWMNAW6 = AHWM4554NAW * (EM4554 - EM4554NAS - EM4554NAU - EM4554AW - EM4554AS - EM4554AU)

AHWMNAW7 = AHWM5564NAW * (EM5564 - EM5564NAS - EM5564NAU - EM5564AW - EM5564AS - EM5564AU)

AHWMNAW8 = AHWM65ONAW * (EM65O - EM65ONAS - EM65ONAU - EM65OAW - EM65OAS - EM65OAU)

AHWMNAW = (AHWMNAW1 + AHWMNAW2 + AHWMNAW3 + AHWMNAW4 + AHWMNAW5 + AHWMNAW6 + 

AHWMNAW7 + AHWMNAW8) / (EM - EMNAS - EMNAU- EMAW - EMAS - EMAU)

AHWM1617NAW = 34.4953 * MOVAVG (2, RTP.1) + 1.03247 * MINW/CPIW_U - 31.4229 * RNM1617S - 0.12369 * 1 + 12.1981 + 

AHWM1617NAW.ADJ

AHWM1819NAW = 32.4361 * MOVAVG (2, RTP.1) + 12.8742 * MOVAVG (2, RTP.5) + 1.28624 * MINW/CPIW_U - 16.0989 * 

RNM1819S - 0.27834 * 1 - 8.58664 + AHWM1819NAW.ADJ

AHWM2024NAW = 20.1161 * MOVAVG (2, RTP.1) - 10.2292 * RNM2024S - 2.28628 * (PM2024NM * NM2024NM/ (PM2024NM * 

NM2024NM + PM2024MS * NM2024MS + PM2024MA * NM2024MA)) + 23.2252 + AHWM2024NAW.ADJ

AHWM2534NAW = MIN (45, 17.0559 * MOVAVG (2, RTP.1) - 0.29076 * 1 + 0.04542 *YEAR + 22.5121) + AHWM2534NAW.ADJ

AHWM3544NAW = MIN (45, 18.3314 * MOVAVG (2, RTP.1) - 0.30475 * 1 + 0.04409 *YEAR + 22.3275) + AHWM3544NAW.ADJ

AHWM4554NAW = MIN (45, 16.0678 * MOVAVG (2, RTP.1) - 0.12289 * 1 + 0.07446 *YEAR + 21.4366) + AHWM4554NAW.ADJ

AHWM5564NAW = 10.8277 * MOVAVG (2, RTP.1) + 0.23715 * 1 + 30.8975 + AHWM5564NAW.ADJ

AHWM65ONAW = -0.28038 * 1 + 5.40682 * MOVAVG (2, RTP.1) + 25.4797 + AHWM65ONAW.ADJ

Females

AHWFNAW1 = AHWF1617NAW* (EF1617 -EF1617NAS-EF1617NAU-EF1617AW-EF1617AS-EF1617AU)

AHWFNAW2 = AHWF1819NAW* (EF1819 -EF1819NAS-EF1819NAU-EF1819AW-EF1819AS-EF1819AU)

AHWFNAW3 = AHWF2024NAW* (EF2024 -EF2024NAS-EF2024NAU-EF2024AW-EF2024AS-EF2024AU)

AHWFNAW4 = AHWF2534NAW* (EF2534 -EF2534NAS-EF2534NAU-EF2534AW-EF2534AS-EF2534AU)

AHWFNAW5 = AHWF3544NAW* (EF3544 -EF3544NAS-EF3544NAU-EF3544AW-EF3544AS-EF3544AU)

AHWFNAW6 = AHWF4554NAW* (EF4554 -EF4554NAS-EF4554NAU-EF4554AW-EF4554AS-EF4554AU)

AHWFNAW7 = AHWF5564NAW* (EF5564 -EF5564NAS-EF5564NAU-EF5564AW-EF5564AS-EF5564AU)

AHWFNAW8 = AHWF65ONAW * (EF65O -EF65ONAS-EF65ONAU-EF65OAW-EF65OAS-EF65OAU)

AHWFNAW = (AHWFNAW1 + AHWFNAW2 + AHWFNAW3 + AHWFNAW4 + AHWFNAW5 + AHWFNAW6 + AHWFNAW7 

+ AHWFNAW8) / (EF – EFNAS – EFNAU – EFAW – EFAS – EFAU)

AHWF1617NAW = 17.2598 * MOVAVG (2, RTP.1) + 7.31262 * MOVAVG (2, RTP.5) - 24.9241 * RNF1617S + 16.9218 + 

AHWF1617NAW.ADJ

AHWF1819NAW = 18.7922 * MOVAVG (2, RTP.1) + 13.1066 * MOVAVG (2, RTP.5) - 18.9417 * RNF1819S - 16.2182 * PF1819 + 

0.81920 * 1 + 16.7826 + AHWF1819NAW.ADJ

AHWF2024NAW = -219.154 * PF2024 + 12.4226 * MOVAVG (2, RTP.1) - 0.98470 * 1 + 158.354 * (PF2024)2+ 98.6034 + 

AHWF2024NAW.ADJ

AHWF2534NAW = -39.2904 * PF2534 + 39.6515 * (PF2534)2+ 9.10839 * MOVAVG (2, RTP.1) - 0.33503 * 1 + 35.8525 + 

AHWF2534NAW.ADJ

AHWF3544NAW = -39.2904 * PF3544 + 39.6515 * (PF3544)2+ 7.40115 * MOVAVG (2, RTP.1) - 0.38214 * 1 + 36.9133 + 

AHWF3544NAW.ADJ

AHWF4554NAW = 8.64511 * MOVAVG (2, RTP.1) - 63.8042 * PF4554 + 59.9107 * (PF4554)2- 0.58355 * 1 + 44.0723 + 

AHWF4554NAW.ADJ

AHWF5564NAW = 6.61506 * MOVAVG (2, RTP.1) - 0.28969 * 1 + 8.36882 * PF5564 + 24.8288 + AHWF5564NAW.ADJ

AHWF65ONAW = -0.43916 * 1 + 26.5228 + (8.36882/2) * (PF65O- 0.086) + AHWF65ONAW.ADJ

Nonagricultural Self-employed

AHWNAS = 24.9592 * MOVAVG (2, RTP.1) - 17.2194 * EFNAS/ENAS + 22.0120 + AHWNAS.ADJ

Nonagricultural Unpaid Family Workers

AHWNAU = 25.5124 * (EF2534NAU + EF3544NAU + EF4554NAU)/ ENAU + 19.2730 + AHWNAU.ADJ

Agricultural sector

AHWA = (AHWAW * EAW + AHWAS * EAS + AHWAU * EAU) / EA

Agricultural Wage Workers

AHWAW = 6.58073 * MOVAVG (2, RTP.1) + 14.9130 * MOVAVG (2, RTP.5) + 19.7800 + AHWAW.ADJ

Agricultural Self-employed

AHWAS = 2.45830 * PGDPAF/PGDP - 3.61107 * 1 + 44.5318 + AHWAS.ADJ

Agricultural Unpaid Family Workers

AHWAU = 39.3563 + AHWAU.ADJ
145

OTHER EMPLOYMENT MEASURES

Federal Civilian Government and Government Enterprises

EGFC = IF LONGRANGE = 0 

THEN (EGFC.1 * 1.00940.25) 

ELSE (EGFC.1 * (E_FE/E_FE.1))

EGEFCPS = IF LONGRANGE = 0 

THEN (EGEFCPS.1 * 1.0075

0.25) 

ELSE (EGEFCPS.1 * (E_FE/E_FE.1))

EGGEFC = EGFC + EGEFCPS

State and Local Government and Government Enterprises

EGGESL = IF LONGRANGE = 0 

THEN EGGESL.1 * (LC_FE/LC_FE.4)0.25

ELSE EGGESL.1 * (E_FE/E_FE.1)

Military

DNEDMIL = IF (EDMIL-EDMIL.4) < 0 

THEN (EDMIL-EDMIL.4) 

ELSE 0

EP = E-EGGESL - EGGEFC – EAS - ENAS

Compensation and Output Sectors

Price Deflator for Medical Services

CPIWMS = CPIWMS.1 * (1 + ((CPIW_U/CPIW_U.4)0.25- 1) * CPIWMSWT)

Unemployment Insurance and Workers Compensation Effective Tax Rates

TMAXUI_SL = TMAXUI_SL.1 *AWSUI.1/AWSUI.2

RELMAX_UI = TMAXUI_SL/AWSUI.1/1000

CR_UI = 0.775

TRATIO_UI = 0.96996 * RELMAX_UI-0.13744 * MOVAVG (4, RTP.1) + 0.10368 * MOVAVG (4, RTP.5) + 0.04887

TRATE_UI = 0.00143 * MOVAVG (4, RU.5) + 0.00128 * MOVAVG (4, RU.9) + 0.00057 * MOVAVG (4, RU.13) + 0.00356

RUIWS1 = CR_UI * TRATIO_UI * TRATE_UI

RUIWS2 = 0.32476 * MOVAVG (4, RUIWS1.8 * (WSP.8 -WSPRRB.8 +WSGGESL.8)) / (WSP.1 -WSPRR2.1 +WSGGESL.1)

Workers' Compensation

RWCWS = RWCWS.1 - (RWCWS.1 - 0.0144)/12

State and Local Government and Government Enterprises TAXMAXQ = IF QTR = 1 

THEN 300.* ROUND (MOVAVG (4, AWSE.5)/MOVAVG (4, AWSE.9) * 1000.* TAXMAX.1/300 +0.5)/1000 

ELSE TAXMAX.1

Wages

AWSPL = MOVAVG (8, AWSP.1)

AWSSPL = MOVAVG (8, AWSSP.1)

AWSGGESL = IF LONGRANGE = 0 

THEN AWSGGESL.1 * AWSPL/AWSPL.1 

ELSE AWSGGESL.1 * AVG_GDP/AVG_GDP.1 * (1 + WS_TO_WSS_D/100)0.25

WSGGESL = AWSGGESL * EGGESL

Employer Contribution for Government Social Insurance

OASDISL_L = (EMPTROASI + EMPTRDI) * 0.978 * CSLA * WSGGESL

HISL_L = EMPTRHI * 1.0 * CSLHI * WSGGESL

SOC_UISL = (-0.02821 * MOVAVG (4, RTP.2) + 0.03145) * WSGGESL

RSOCSL_WC = RSOCSL_WC.1 - (RSOCSL_WC.1 - 0.176)/12

SOC_WCSL = RSOCSL_WC * RWCWS * WSGGESL

SOC_SL = (OASDISL_L + HISL_L + SOC_UISL + SOC_WCSL)

Employer Contributions for Employee Pension and Insurance funds

Workers' Compensation - employees and annuitants
146

OLI_WCSL = (1 - RSOCSL_WC) * RWCWS * WSGGESL

Pensions

OLI_RETSL = WSGGESL * (OLI_RETSL.1/WSGGESL.1)

Life Insurance - employees and annuitants

OLI_GLI_SL = 2.0 * EGGESL * ((WSGGESL/EGGESL) +2.0) * 0.075 * 26/1000

Health Insurance - employees and annuitants

OLI_GHI_SL = (OLI_GHI_SL.1 / EGGESL.1) * CPIWMS/CPIWMS.1 * EGGESL * RGR_GHI

Total

OLI_SL = (OLI_GLI_SL + OLI_GHI_SL + OLI_WCSL + OLI_RETSL)

RCWSSL = (1 + (SOC_SL + OLI_SL)/WSGGESL)

WSSGGESL = IF LONGRANGE = 0 

THEN RCWSSL*WSGGESL 

ELSE (WSSGGESL.1/EGGESL.1) * AVG_GDP/AVG_GDP.1 * EGGESL

WSSGSL = WSSGGESL * WSSGSL.1/WSSGGESL.1

WSSGESL = WSSGGESL - WSSGSL

CFCGSL = IF LONGRANGE = 0 

THEN WSSGSL * RCFCGSL 

ELSE CFCGSL.1 * WSSGGESL/WSSGGESL.1

GDPGSL = WSSGSL + CFCGSL

CFCGESL = IF LONGRANGE = 0 

THEN WSSGESL * RCFCGESL 

ELSE CFCGESL.1 * WSSGGESL/WSSGGESL.1

GDPGESL = WSSGESL + CFCGESL

GDPGGESL = GDPGSL + GDPGESL

Federal Civilian General Government and Government Enterprises

Wages

General Government and Government Enterprises

Civilian pay raise

CRAZ1 = IF LONGRANGE = 0 

THEN ((IF QTR = 1 THEN (0.82429 * (AWSP.6/AWSP.10 - 1) -0.005) ELSE 0)) 

ELSE (IF QTR = 1 THEN (AWSP.6/AWSP.10 - 1) ELSE 0)

Military pay raise 

MRAZ = IF LONGRANGE = 0 

THEN ((IF QTR = 1 THEN (0.82429 * (AWSP.6/AWSP.10 - 1) -0.005) ELSE 0)) 

ELSE (IF QTR = 1 THEN (AWSP.6/AWSP.10 - 1) ELSE 0)

AWSGGEFC = IF LONGRANGE = 0 

THEN (AWSGGEFC.1 * (1 +1.0 *CRAZ1 +0.0015)) 

ELSE AWSGGEFC.1 * AVG_GDP/AVG_GDP.1 * (1 +WS_TO_WSS_D/100)0.25

WSGGEFC = AWSGGEFC * EGGEFC

CSRS workers

AWEFC_N = IF LONGRANGE = 0 

THEN (AWEFC_N.1 * (1 +1.0 *CRAZ1 +0.00082)) 

ELSE AWEFC_N.1 * AVG_GDP/AVG_GDP.1 * (1 +WS_TO_WSS_D/100)0.25

WEFC_N = AWEFC_N * TEFC_N

Government Enterprises (Mostly U.S. Postal Service)

AWSGEFC = IF LONGRANGE = 0 

THEN (AWSGEFC.1 * (1 +1.0 *CRAZ1 +0.0015))

ELSE AWSGEFC.1 * AVG_GDP/AVG_GDP.1 * (1 +WS_TO_WSS_D/100) 0.25

WSGEFC = AWSGEFC * EGEFCPS

General Government

WSGFC = WSGGEFC -WSGEFC

AWSGFC = WSGFC/EGFC

Employer Contribution for Government Social Insurance
147

General Government and Government Enterprises

OASDIFC_L = (EMPTROASI + EMPTRDI) * 1.04 * (WSGGEFC - WEFC_N) * ADJ_FSA_FC

HIFC_L = EMPTRHI* 1.055 * WSGGEFC * ADJ_FSA_FC

SOCF_UIFC = (-0.05934 * RTP + 0.06165) * WSGGEFC

SOCF_WC = 0.0159 * WSGGEFC

SOC_FC = (SOCF_UIFC + SOCF_WC + OASDIFC_L + HIFC_L)

Employer Contributions for Employee Pension and Insurance funds

General Government and Government Enterprises

Pensions

OLI_CSRS1 = ((0.174 * WSGEFC + 0.07 * WSGFC) / WSGGEFC) * WEFC_N

OLI_FERS1 = 0.107 * (WSGGEFC * 0.9 -WEFC_N) 

OLI_FERSFC = 0.048 * (WSGGEFC * 0.9 -WEFC_N)

OLI_RETFC = OLI_CSRS1 + OLI_FERS1 + OLI_FERSFC + OLIF_RETFCO

Life Insurance - employees and annuitants

OLI_GLI_FC = 2.0 * EGGEFC * ((WSGGEFC/EGGEFC) + 2.0) * 0.075 * 26/1000

Health Insurance - employees and annuitants

OLI_GHI_FC = (OLI_GHI_FC.1 / EGGEFC.1) * CPIWMS/CPIWMS.1 * EGGEFC * RGR_GHI

OLI_FC = (OLI_GHI_FC + OLI_GLI_FC + OLI_RETFC)

Compensation

General Government and Government Enterprises

RCWSF = (1 + (SOC_FC + OLI_FC)/WSGGEFC)

WSSGGEFC = IF LONGRANGE = 0 

THEN RCWSF * WSGGEFC 

ELSE (WSSGGEFC.1/EGGEFC.1) * AVG_GDP/AVG_GDP.1 * EGGEFC

WSSGFC = IF LONGRANGE = 0 

THEN RCWSF * WSGFC 

ELSE (WSSGFC.1 / (EGGEFC.1 - EGEFCPS.1)) * AVG_GDP/AVG_GDP.1 * (EGGEFC - EGEFCPS)

WSSGEFC = IF LONGRANGE = 0 

THEN RCWSF * WSGEFC 

ELSE (WSSGEFC.1/EGEFCPS.1) * AVG_GDP/AVG_GDP.1 * EGEFCPS

Consumption of Fixed Capital

General Government and Government Enterprises

CFCGFC = IF LONGRANGE = 0 

THEN WSSGFC * RCFCGFC 

ELSE CFCGFC.1 * WSSGGEFC / WSSGGEFC.1

CFCGEFC = IF LONGRANGE = 0 

THEN WSSGEFC * RCFCGEFC 

ELSE CFCGEFC.1 * WSSGGEFC / WSSGGEFC.1

Gross Domestic Product

General Government and Government Enterprises

GDPGFC = WSSGFC + CFCGFC

GDPGEFC = WSSGEFC + CFCGEFC

GDPGGEFC = GDPGFC + GDPGEFC

Federal Government Military

Wages

AWSGFM = IF LONGRANGE = 0 

THEN (AWSGFM.1 * (1.0027 +1.0 * MRAZ)) 

ELSE AWSGFM.1 * AVG_GDP/AVG_GDP.1 * (1 + WS_TO_WSS_D/100) 0.25

WSGFM = AWSGFM * (EDMIL+ EDMIL_R)
148

Employer Contribution for Government Social Insurance

OASDIFM_L = (EMPTROASI + EMPTRDI) * 0.9975 * CML * WSGFM

HIFM_L = EMPTRHI* 1.0 *CML * WSGFM

SOCF_UIFM = MAX (0.001, (-0.05263 * DIFF (EDMIL + EDMIL_R) - 0.03079 * RTP + 0.03310)) * WSGFM

SOCF_MIFM = 0.30 * CPIWMS * (EDMIL + EDMIL_R)

SOC_FM = (SOCF_UIFM + SOCF_MIFM + OASDIFM_L + HIFM_L)

Employer Contributions for Employee Pension and Insurance funds

OLI_RETFM = (OLI_RETFM.1/WSGFM.1 - (OLI_RETFM.1/WSGFM.1 - 0.472)/12) * WSGFM

Compensation

RCWSM = (1 + (OLI_RETFM + SOC_FM)/WSGFM)

WSSGFM = IF LONGRANGE = 0 

THEN RCWSM * WSGFM 

ELSE (WSSGFM.1/EDMIL.1) * AVG_GDP/AVG_GDP.1 * EDMIL

Consumption of Fixed Capital

CFCGFM = IF LONGRANGE = 0 

THEN WSSGFM * RCFCGFM 

ELSE CFCGFM.1 * WSSGFM/WSSGFM.1

Gross Domestic Product

GDPGFM = WSSGFM + CFCGFM

GDPGF = GDPGFC + GDPGFM

GDPGGE = GDPGGEFC + GDPGGESL + GDPGFM

Total (Civilian and Military) Federal General Government and Government Enterprises

WSSGF = WSSGFC + WSSGFM

WSSGE = WSSGEFC + WSSGESL

WSSG = WSSGF + WSSGSL

GDPGE = GDPGEFC + GDPGESL

GDPG = GDPGF + GDPGSL

NIPA Farm Output and Earnings

GDPPF00 = IF LONGRANGE = 0 

THEN EXP (- 3.52340 + 0.02055 * YEAR) * N_SSA 

ELSE GDPPF00.1 * GDP00/GDP00.1

PGDPAF = IF LONGRANGE = 0 

THEN PGDPAF.1 * ((PGDP/PGDP.1)4- 0.01) 0.25

ELSE PGDPAF.1 * ((PGDP/PGDP.1)4)

0.25

GDPPF = GDPPF00 * PGDPAF

AYF_K = ((YF.1 /EAS.1) / (WSSPF.1/EAW.1) - 1.594) * 0.8 + 1.594

WSSPF = IF LONGRANGE = 0 

THEN EAW * MOVAVG (4, WSSP.2/EP.2) * (3.15749 / (YEAR- 65) - 0.43419 * RTP + 0.68725)

ELSE (WSSPF.1/EAW.1) * AVG_GDP/AVG_GDP.1 * EAW

WSPF = IF LONGRANGE = 0 

THEN WSSPF * (MOVAVG (12, (WSP.1/WSSP.1)) + 0.015) 

ELSE (WSPF.1 /WSSPF.1) * (WSP.1/WSSP.1) / (WSP.2/WSSP.2) * WSSPF

AWSPF = WSPF/EAW

YF = AYF_K * (WSSPF/EAW) * EAS

GDP, WSS and WS, Private Households & Nonprofit Institutions

Private Households

Compensation & Wages

WSSPH = IF LONGRANGE = 0 

THEN (((WSSPH.1/ENAWPH.1)/MOVAVG (4, WSSP.3/EP.3) - 0.41) * 0.875 + 0.41) * MOVAVG (4, WSSP.2/EP.2) * 

ENAWPH 

ELSE (AVG_GDP/AVG_GDP.1) * ENAWPH * (WSSPH.1/ENAWPH.1)

WSPH = IF LONGRANGE = 0 

THEN WSSPH / (1 +CPH* 1 * (EMPTROASI + EMPTRDI + EMPTRHI)) 
149

ELSE (AWSPH.1 * ENAWPH.1/WSSPH.1) * (1 + WS_TO_WSS_D/100) 0.25 * WSSPH 

AWSPH = WSPH / ENAWPH

Owner Occupied Housing

OOH = OOH.1 * (KGDP00 * PGDP) / (KGDP00.1 * PGDP.1)

Gross Value Added

GDPPH = IF LONGRANGE = 0 

THEN WSSPH + OOH 

ELSE (AVG_GDP/AVG_GDP.1) * ENAWPH * (GDPPH.1/ENAWPH.1)

Nonprofit Institutions

Health Services

EPHS_EST = IF LONGRANGE = 0 

THEN EPHS_EST.1 + 0.275/4 

ELSE EPHS_EST.1 * (E_FE/E_FE.1)

AWSSPHS = IF LONGRANGE = 0 

THEN AWSSPHS.1 * AWSSPL/AWSSPL.1 

ELSE AWSSPHS.1 * AVG_GDP/AVG_GDP.1

WSSPHS = AWSSPHS* EPHS_EST

Educational Services

EPES_EST = IF LONGRANGE = 0 

THEN EPES_EST.1 + 0.075/4 

ELSE EPES_EST.1 * (E_FE/E_FE.1)

AWSSPES = IF LONGRANGE = 0 

THEN AWSSPES.1 * AWSSPL/AWSSPL.1 

ELSE AWSSPES.1 * AVG_GDP/AVG_GDP.1

WSSPES = AWSSPES* EPES_EST

Social Services

EPSS_EST = IF LONGRANGE = 0 

THEN EPSS_EST.1 + 0.075/4 

ELSE EPSS_EST.1 * (E_FE/E_FE.1)

AWSSPSS = IF LONGRANGE = 0 

THEN AWSSPSS.1 * AWSSPL/AWSSPL.1 

ELSE AWSSPSS.1 * AVG_GDP/AVG_GDP.1

WSSPSS = AWSSPSS* EPSS_EST

Gross Value Added

WSSPNI = WSSPNI.1 * (WSSPHS + WSSPES + WSSPSS) / (WSSPHS.1 + WSSPES.1 + WSSPSS.1)

WSPNI = IF LONGRANGE = 0 

THEN WSSPNI * (WSPNI.1/WSSPNI.1) * ((WSP.1/WSSP.1) / (WSP.9/WSSP.9)) (1/8)

ELSE WSSPNI * (WSPNI.1/WSSPNI.1) * (1 +WS_TO_WSS_D/100) 0.25

GDPPNI = IF LONGRANGE = 0 

THEN WSSPNI / ((WSSPNI.1/GDPPNI.1 - 0.866) * 0.8 + 0.866) 

ELSE WSSPNI /0.866

Private Output and Compensation

ROASDIP_L = (EMPTROASI + EMPTRDI) * TXRP * CP

RHIP_L = EMPTRHI * 1.0 * CP

RSOC_UIP = 0.00109 * MOVAVG (4, RU.2) + 0.00045 * MOVAVG (4, RU.10) + 0.00048 * MOVAVG (4, RU.18) - 0.00331

RSOC_WCP = RWCWS * RSOCSL_WC

RSOCF_PBG = 0.00022

OLI

ROLI_WCP = RWCWS * (1 - RSOCSL_WC)

ROLI_SU = 0.0005

OLI_GLI_P = 0.0025 * EP * AWSP.1

OLI_GHI_P = (OLI_GHI_P.1 / EP.1) * CPIWMS/CPIWMS.1 * EP * RGR_GHI

ROLI_PPPS = MAX (ROLI_PPPS.1, 0.00031 * YEAR + 0.00866)

Employee Compensation and Non-Farm Proprietor Income (WSS and YNF)
150

WSSGGE = (WSSGGESL + WSSGGEFC + WSSGFM)

GDPPBNFXGE = (GDP – GDPGGE – GDPPF – GDPPH – GDPPNI)

RWSSPBNFXGE = (-0.35334 * DRTPP - 0.16925 * DRTPP.1 + 0.20074 * DRTPN.1 + 0.16114 * DRTPN.2 + 0.59442)

ENAW = ENA-ENAS-ENAU

ENAWPBXGE = ENAW - (ENAWPH + EGGEFC + EGGESL + WSSPNI / (WSSPHS + WSSPES + WSSPSS) * (EPHS_EST + 

EPES_EST + EPSS_EST))

ENAWSPBXGE = ENAWPBXGE + ENAS

AYNF_K = ((YNF.1/ENAS.1 / (WSSPBNFXGE.1/ENAWPBXGE.1)) - 1.65) * 0.9 + 1.65

AYF = YF/EAS

AWSSPF = WSSPF/EAW

AYNF = YNF/ENAS

AWSSPBNFXGE = WSSPBNFXGE/ENAWPBXGE

YNF = YNF.1 * (GDPPBNFXGE / GDPPBNFXGE.1) * (ENAS / (ENAS + ENAWPBXGE)) / (ENAS.1 / (ENAS.1 + 

ENAWPBXGE.1))

WSSPBNFXGE = (GDPPBNFXGE - YNF) * ((WSSPBNFXGE.1 / (GDPPBNFXGE.1 - YNF.1) - 0.621) * (0.8) + 0.621)

WSSP = WSSPBNFXGE+WSSPF+WSSPH +WSSPNI

RCWSP = WSSP / (WSSP - SOCF_RETRR - OLI_GLI_P - OLI_GHI_P) * (1 + ROASDIP_L + RHIP_L + RSOC_UIP + 

RSOC_WCP + RSOCF_PBG + ROLI_WCP + ROLI_SU+ ROLI_PPPS)

WS = IF WS_TO_WSS_DYR = 0 

THEN (WSGGESL + WSGGEFC + WSGFM + WSSP/RCWSP) 

ELSE WSS * WS.1/WSS.1 * (1 + WS_TO_WSS_D/100) 0.25

WSP = (WS - WSGGESL - WSGGEFC - WSGFM)

AWSP = WSP/EP

AWSSP = WSSP/EP

Other Variables

WSD = WS

WSDP = (WSD –WSGGESL – WSGGEFC – WSGFM)

AWSE = WS / (E + EDMIL – EAS – ENAS)

AWSUI = (WS -WSGGEFC -WSGFM) / (E – EGGEFC – EAS – ENAS)

WSS = (WSSP + WSSGGE)

OLI = OLI_GGE + OLI_P

SOC = SOC_GGE + SOC_P

OLI_GGE = OLI_FC + OLI_SL + OLI_RETFM

SOC_GGE = SOC_FC + SOC_FM + SOC_SL

SOC_UIP = RSOC_UIP * WSP

SOC_WCP = RSOC_WCP * WSP

OASDIP_L = ROASDIP_L * WSP

HIP_L = RHIP_L * WSP

SOCF_PBG = RSOCF_PBG * WSP

SOCF_RETRR = 0.20 * WSPRRB

SOC_P = SOC_UIP + SOC_WCP + OASDIP_L + HIP_L + SOCF_PBG + SOCF_RETRR

OLI_WCP = ROLI_WCP * WSP

OLI_SU = ROLI_SU * WSP

OLI_PPPS = ROLI_PPPS * WSP

OLI_P = OLI_WCP + OLI_SU + OLI_GHI_P + OLI_GLI_P + OLI_PPPS

OLI_PPS = OLI_PPPS + OLI_RETFC + OLI_RETFM + OLI_RETSL

OLI_GHI = OLI_GHI_P + OLI_GHI_FC + OLI_GHI_SL

OLI_GLI = OLI_GLI_P + OLI_GLI_FC + OLI_GLI_SL

OLI_WC = OLI_WCP + OLI_WCSL

SOCSL_WC = SOC_WCSL + SOC_WCP

SOCF_UIFED = SOCF_UIFC + SOCF_UIFM

SOCF_UIS = (SOC_UIP + SOC_UISL) * RUIWS1 / (RUIWS1 + RUIWS2)

SOCF_UIF = (SOC_UIP + SOC_UISL) - SOCF_UIS

SOCF_OASDI = OASDIP_L + OASDISL_L + OASDIFC_L + OASDIFM_L

SOCF_HI = HIP_L + HISL_L + HIFC_L + HIFM_L
151

2.3 OASDI Covered Employment and Earnings (COV)

Total At-Any-Time Employment

Males

Aged 1 through 9

CEM1 = 0.00026 * NM1 

CEM2 = 0.00031 * NM2 

CEM3 = 0.00025 * NM3 

CEM4 = 0.00019 * NM4 

CEM5 = 0.00036 * NM5 

CEM6 = 0.00042 * NM6 

CEM7 = 0.00165 * NM7 

CEM8 = 0.00239 * NM8 

CEM9 = 0.00279 * NM9 

Aged 10 through 15

TEM1013 = 0.01969 * (NM10 + NM11 + NM12 + NM13 + NM10[+1] + NM11[+1] + NM12[+1] + NM13[+1])/2

TEM1415 = (0.42767 * RTP - 1.02624 * NM1415 / (NM1415 + NM1617) + 0.23102) * ((NM14 + NM15 + NM14[+1] + NM15[+1])/2)

Females

Aged 1 through 9

CEF1 = 0.00028 * NF1 

CEF2 = 0.00023 * NF2 

CEF3 = 0.00020 * NF3 

CEF4 = 0.00031 * NF4 

CEF5 = 0.00041 * NF5 

CEF6 = 0.00055 * NF6 

CEF7 = 0.00129 * NF7 

CEF8 = 0.00156 * NF8 

CEF9 = 0.00212 * NF9 

Aged 10 through 15

TEF1013 = IF YEAR < = 110 

THEN (0.04615 * RTP - 0.00014 * YEAR - 0.02268) * (NF10 + NF11 + NF12 + NF13 + NF10[+1] + NF11[+1] + NF12[+1] + 

NF13[+1])/2 

ELSE (0.04615 * RTP - 0.00014 * 110 - 0.02268) * (NF10 + NF11 + NF12 + NF13 + NF10[+1] + NF11[+1] + NF12[+1] 

+ NF13[+1])/2

TEF1415 = (0.48500 * RTP - 0.79002 * NF1415 / (NF1415 + NF1617) + 0.02205) * ((NF14 + NF15 + NF14[+1] + NF15[+1])/2)

Total Males, Total Females and Combined Total

TEM = CEM1 + CEM2 + CEM3 + CEM4 + CEM5 + CEM6 + CEM7 + CEM8 + CEM9 + TEM1013 + TEM1415 + TEM16O

TEF = CEF1 + CEF2 + CEF3 + CEF4 + CEF5 + CEF6 + CEF7 + CEF8 + CEF9 + TEF1013 + TEF1415 + TEF16O

TE = TEM + TEF

Male Disaggregates Aged 16 and Over

Preliminary

Average Weeks Worked

AWWM1617_P = 0.35417 * TREND_TE - 0.17568 * RM1617 + 8.80229;

AWWM1819_P = 0.28021 * TREND_TE - 0.16633 * RM1819 + 15.4522;

AWWM2024_P = 0.17338 * TREND_TE - 0.32843 * RM2024 + 31.4779;

AWWM2529_P = 0.04353 * TREND_TE - 0.32898 * RM2529 + 46.8304; 

AWWM3034_P = 0.04353 * TREND_TE - 0.32898 * RM3034 + 46.8304;

AWWM3539_P = 0.03256 * TREND_TE - 0.26573 * RM3539 + 47.9512;

AWWM4044_P = 0.03256 * TREND_TE - 0.26573 * RM4044 + 47.9512; 

AWWM4549_P = 0.03923 * TREND_TE - 0.23146 * RM4549 + 46.8025; 

AWWM5054_P = 0.03923 * TREND_TE - 0.23146 * RM5054 + 46.8025; 

AWWM5559_P = 0.04481 * TREND_TE - 0.28537 * RM5559 + 45.0196;
152

AWWM6064_P = 0.14488 * TREND_TE + 0.01910 * RM6064 + 29.5797;

AWWM6569_P = 0.21483 * TREND_TE - 0.23366 * RM6569 + 21.1343;

AWWM70O_P = 0.09069 * TREND_TE + 0.43206 * RM70O + 28.6903;

AWWM1617_PL = 0.35417 * TREND_TE.1 - 0.17568 * RM1617.1 + 8.80229;

AWWM1819_PL = 0.28021 * TREND_TE.1 - 0.16633 * RM1819.1 + 15.4522;

AWWM2024_PL = 0.17338 * TREND_TE.1 - 0.32843 * RM2024.1 + 31.4779;

AWWM2529_PL = 0.04353 * TREND_TE.1 - 0.32898 * RM2529.1 + 46.8304; 

AWWM3034_PL = 0.04353 * TREND_TE.1 - 0.32898 * RM3034.1 + 46.8304;

AWWM3539_PL = 0.03256 * TREND_TE.1 - 0.26573 * RM3539.1 + 47.9512;

AWWM4044_PL = 0.03256 * TREND_TE.1 - 0.26573 * RM4044.1 + 47.9512; 

AWWM4549_PL = 0.03923 * TREND_TE.1 - 0.23146 * RM4549.1 + 46.8025; 

AWWM5054_PL = 0.03923 * TREND_TE.1 - 0.23146 * RM5054.1 + 46.8025; 

AWWM5559_PL = 0.04481 * TREND_TE.1 - 0.28537 * RM5559.1 + 45.0196;

AWWM6064_PL = 0.14488 * TREND_TE.1 + 0.01910 * RM6064.1 + 29.5797;

AWWM6569_PL = 0.21483 * TREND_TE.1 - 0.23366 * RM6569.1 + 21.1343;

AWWM70O_PL = 0.09069 * TREND_TE.1 + 0.43206 * RM70O.1 + 28.6903;

Work Experience

WEM1617_3_P = EM1617 * 52 / AWWM1617_P;

WEM1819_3_P = EM1819 * 52 / AWWM1819_P;

WEM2024_3_P = EM2024 * 52 / AWWM2024_P;

WEM2529_3_P = EM2529 * 52 / AWWM2529_P;

WEM3034_3_P = EM3034 * 52 / AWWM3034_P;

WEM3539_3_P = EM3539 * 52 / AWWM3539_P;

WEM4044_3_P = EM4044 * 52 / AWWM4044_P;

WEM4549_3_P = EM4549 * 52 / AWWM4549_P;

WEM5054_3_P = EM5054 * 52 / AWWM5054_P;

WEM5559_3_P = EM5559 * 52 / AWWM5559_P;

WEM6064_3_P = EM6064 * 52 / AWWM6064_P;

WEM6569_3_P = EM6569 * 52 / AWWM6569_P;

WEM70O_3_P = EM70O * 52 / AWWM70O_P ;

WEM1617_3_PL = EM1617.1 * 52 / AWWM1617_PL;

WEM1819_3_PL = EM1819.1 * 52 / AWWM1819_PL;

WEM2024_3_PL = EM2024.1 * 52 / AWWM2024_PL;

WEM2529_3_PL = EM2529.1 * 52 / AWWM2529_PL;

WEM3034_3_PL = EM3034.1 * 52 / AWWM3034_PL;

WEM3539_3_PL = EM3539.1 * 52 / AWWM3539_PL;

WEM4044_3_PL = EM4044.1 * 52 / AWWM4044_PL;

WEM4549_3_PL = EM4549.1 * 52 / AWWM4549_PL;

WEM5054_3_PL = EM5054.1 * 52 / AWWM5054_PL;

WEM5559_3_PL = EM5559.1 * 52 / AWWM5559_PL;

WEM6064_3_PL = EM6064.1 * 52 / AWWM6064_PL;

WEM6569_3_PL = EM6569.1 * 52 / AWWM6569_PL;

WEM70O_3_PL = EM70O.1 * 52 / AWWM70O_PL ;

Total Employed

TEM1617_P = ((WEM1617_3_P / WEM1617_3_PL) * (TEM1617.1 - NM1617M.1) + NM1617M) * MULT1_TEM1617 * MULT2_TEM1617;

TEM1819_P = ((WEM1819_3_P / WEM1819_3_PL) * (TEM1819.1 - NM1819M.1) + NM1819M) * MULT1_TEM1819 * MULT2_TEM1819;

TEM2024_P = ((WEM2024_3_P / WEM2024_3_PL) * (TEM2024.1 - NM2024M.1) + NM2024M) * MULT1_TEM2024 * MULT2_TEM2024;

TEM2529_P = ((WEM2529_3_P / WEM2529_3_PL) * (TEM2529.1 - NM2529M.1) + NM2529M) * MULT1_TEM2529 * MULT2_TEM2529;

TEM3034_P = ((WEM3034_3_P / WEM3034_3_PL) * (TEM3034.1 - NM3034M.1) + NM3034M) * MULT1_TEM3034 * MULT2_TEM3034;

TEM3539_P = ((WEM3539_3_P / WEM3539_3_PL) * (TEM3539.1 - NM3539M.1) + NM3539M) * MULT1_TEM3539 * MULT2_TEM3539;

TEM4044_P = ((WEM4044_3_P / WEM4044_3_PL) * (TEM4044.1 - NM4044M.1) + NM4044M) * MULT1_TEM4044 * MULT2_TEM4044;

TEM4549_P = ((WEM4549_3_P / WEM4549_3_PL) * (TEM4549.1 - NM4549M.1) + NM4549M) * MULT1_TEM4549 * MULT2_TEM4549;

TEM5054_P = ((WEM5054_3_P / WEM5054_3_PL) * (TEM5054.1 - NM5054M.1) + NM5054M) * MULT1_TEM5054 * MULT2_TEM5054;
153

TEM5559_P = ((WEM5559_3_P / WEM5559_3_PL) * (TEM5559.1 - NM5559M.1) + NM5559M) * MULT1_TEM5559 * MULT2_TEM5559;

TEM6064_P = ((WEM6064_3_P / WEM6064_3_PL) * (TEM6064.1)) * MULT1_TEM6064 * MULT2_TEM6064;

TEM6569_P = ((WEM6569_3_P / WEM6569_3_PL) * (TEM6569.1)) * MULT1_TEM6569 * MULT2_TEM6569;

TEM70O_P = ((WEM70O_3_P / WEM70O_3_PL ) * (TEM70O.1 )) * MULT1_TEM70O * MULT2_TEM70O;

WEM16O_3_P = WEM1617_3_P + WEM1819_3_P + WEM2024_3_P + WEM2529_3_P + WEM3034_3_P + WEM3539_3_P + 

WEM4044_3_P + WEM4549_3_P 

 + WEM5054_3_P + WEM5559_3_P + WEM6064_3_P + WEM6569_3_P + WEM70O_3_P; 

AWWM16O_P = EM16O * 52 / WEM16O_3_P;

TEM16O_P = TEM1617_P + TEM1819_P + TEM2024_P + TEM2529_P + TEM3034_P + TEM3539_P + TEM4044_P + TEM4549_P + 

TEM5054_P + TEM5559_P + TEM6064_P + TEM6569_P + TEM70O_P;

Final (Pre-TE.ADD)

Average Weeks Worked

AWWM1617 = AWWM1617_P;

AWWM1819 = AWWM1819_P;

AWWM2024 = AWWM2024_P;

AWWM2529 = AWWM2529_P; 

AWWM3034 = AWWM3034_P;

AWWM3539 = AWWM3539_P;

AWWM4044 = AWWM4044_P; 

AWWM4549 = AWWM4549_P; 

AWWM5054 = AWWM5054_P; 

AWWM5559 = AWWM5559_P;

AWWM6064 = AWWM6064_P;

AWWM6569 = AWWM6569_P;

AWWM70O = AWWM70O_P ;

Work Experience

WEM1617_3 = WEM1617_3_P;

WEM1819_3 = WEM1819_3_P;

WEM2024_3 = WEM2024_3_P;

WEM2529_3 = WEM2529_3_P;

WEM3034_3 = WEM3034_3_P;

WEM3539_3 = WEM3539_3_P;

WEM4044_3 = WEM4044_3_P;

WEM4549_3 = WEM4549_3_P;

WEM5054_3 = WEM5054_3_P;

WEM5559_3 = WEM5559_3_P;

WEM6064_3 = WEM6064_3_P;

WEM6569_3 = WEM6569_3_P;

WEM70O_3 = WEM70O_3_P ;

Total Employed

TEM1617 = TEM1617_P;

TEM1819 = TEM1819_P;

TEM2024 = TEM2024_P;

TEM2529 = TEM2529_P;

TEM3034 = TEM3034_P;

TEM3539 = TEM3539_P;

TEM4044 = TEM4044_P;

TEM4549 = TEM4549_P;

TEM5054 = TEM5054_P;
154

TEM5559 = TEM5559_P;

TEM6064 = TEM6064_P;

TEM6569 = TEM6569_P;

TEM70O = TEM70O_P ;

WEM16O_3 = WEM16O_3_P;

AWWM16O = AWWM16O_P ;

TEM16O = TEM16O_P ; 

Female Disaggregates Aged 16 and Over

Preliminary

Average Weeks Worked

AWWF1617_P = 0.44829 * TREND_TE - 0.06786 * RF1617 + 0.37331;

AWWF1819_P = 0.24262 * TREND_TE - 0.00950 * RF1819 + 17.0791;

AWWF2024_P = 0.18291 * TREND_TE - 0.06453 * RF2024 + 26.6823;

AWWF2529_P = 0.07674 * TREND_TE - 0.10131 * RF2529 + 40.0210; 

AWWF3034_P = 0.07674 * TREND_TE - 0.10131 * RF3034 + 40.0210;

AWWF3539_P = 0.05004 * TREND_TE - 0.10322 * RF3539 + 43.9974;

AWWF4044_P = 0.05004 * TREND_TE - 0.10322 * RF4044 + 43.9974; 

AWWF4549_P = 0.08209 * TREND_TE - 0.17307 * RF4549 + 41.1649; 

AWWF5054_P = 0.08209 * TREND_TE - 0.17307 * RF5054 + 41.1649; 

AWWF5559_P = 0.04868 * TREND_TE + 0.17072 * RF5559 + 41.9580;

AWWF6064_P = 0.14339 * TREND_TE + 0.01918 * RF6064 + 29.1567;

AWWF6569_P = 0.01857 * TREND_TE + 0.64199 * RF6569 + 36.1193;

AWWF70O_P = 0.20193 * TREND_TE + 0.92866 * RF70O + 14.2412;

AWWF1617_PL = 0.44829 * TREND_TE.1 - 0.06786 * RF1617.1 + 0.37331;

AWWF1819_PL = 0.24262 * TREND_TE.1 - 0.00950 * RF1819.1 + 17.0791;

AWWF2024_PL = 0.18291 * TREND_TE.1 - 0.06453 * RF2024.1 + 26.6823;

AWWF2529_PL = 0.07674 * TREND_TE.1 - 0.10131 * RF2529.1 + 40.0210; 

AWWF3034_PL = 0.07674 * TREND_TE.1 - 0.10131 * RF3034.1 + 40.0210;

AWWF3539_PL = 0.05004 * TREND_TE.1 - 0.10322 * RF3539.1 + 43.9974;

AWWF4044_PL = 0.05004 * TREND_TE.1 - 0.10322 * RF4044.1 + 43.9974; 

AWWF4549_PL = 0.08209 * TREND_TE.1 - 0.17307 * RF4549.1 + 41.1649; 

AWWF5054_PL = 0.08209 * TREND_TE.1 - 0.17307 * RF5054.1 + 41.1649; 

AWWF5559_PL = 0.04868 * TREND_TE.1 + 0.17072 * RF5559.1 + 41.9580;

AWWF6064_PL = 0.14339 * TREND_TE.1 + 0.01918 * RF6064.1 + 29.1567;

AWWF6569_PL = 0.01857 * TREND_TE.1 + 0.64199 * RF6569.1 + 36.1193;

AWWF70O_PL = 0.20193 * TREND_TE.1 + 0.92866 * RF70O.1 + 14.2412;

Work Experience

WEF1617_3_P = EF1617 * 52 / AWWF1617_P;

WEF1819_3_P = EF1819 * 52 / AWWF1819_P;

WEF2024_3_P = EF2024 * 52 / AWWF2024_P;

WEF2529_3_P = EF2529 * 52 / AWWF2529_P;

WEF3034_3_P = EF3034 * 52 / AWWF3034_P;

WEF3539_3_P = EF3539 * 52 / AWWF3539_P;

WEF4044_3_P = EF4044 * 52 / AWWF4044_P;

WEF4549_3_P = EF4549 * 52 / AWWF4549_P;

WEF5054_3_P = EF5054 * 52 / AWWF5054_P;

WEF5559_3_P = EF5559 * 52 / AWWF5559_P;

WEF6064_3_P = EF6064 * 52 / AWWF6064_P;

WEF6569_3_P = EF6569 * 52 / AWWF6569_P;

WEF70O_3_P = EF70O * 52 / AWWF70O_P ;
155

WEF1617_3_PL = EF1617.1 * 52 / AWWF1617_PL;

WEF1819_3_PL = EF1819.1 * 52 / AWWF1819_PL;

WEF2024_3_PL = EF2024.1 * 52 / AWWF2024_PL;

WEF2529_3_PL = EF2529.1 * 52 / AWWF2529_PL;

WEF3034_3_PL = EF3034.1 * 52 / AWWF3034_PL;

WEF3539_3_PL = EF3539.1 * 52 / AWWF3539_PL;

WEF4044_3_PL = EF4044.1 * 52 / AWWF4044_PL;

WEF4549_3_PL = EF4549.1 * 52 / AWWF4549_PL;

WEF5054_3_PL = EF5054.1 * 52 / AWWF5054_PL;

WEF5559_3_PL = EF5559.1 * 52 / AWWF5559_PL;

WEF6064_3_PL = EF6064.1 * 52 / AWWF6064_PL;

WEF6569_3_PL = EF6569.1 * 52 / AWWF6569_PL;

WEF70O_3_PL = EF70O.1 * 52 / AWWF70O_PL ;

Total Employed

TEF1617_P = ((WEF1617_3_P / WEF1617_3_PL) * (TEF1617.1 - NF1617M.1) + NF1617M) * MULT1_TEF1617 * MULT2_TEF1617;

TEF1819_P = ((WEF1819_3_P / WEF1819_3_PL) * (TEF1819.1 - NF1819M.1) + NF1819M) * MULT1_TEF1819 * MULT2_TEF1819;

TEF2024_P = ((WEF2024_3_P / WEF2024_3_PL) * (TEF2024.1 - NF2024M.1) + NF2024M) * MULT1_TEF2024 * MULT2_TEF2024;

TEF2529_P = ((WEF2529_3_P / WEF2529_3_PL) * (TEF2529.1 - NF2529M.1) + NF2529M) * MULT1_TEF2529 * MULT2_TEF2529;

TEF3034_P = ((WEF3034_3_P / WEF3034_3_PL) * (TEF3034.1 - NF3034M.1) + NF3034M) * MULT1_TEF3034 * MULT2_TEF3034;

TEF3539_P = ((WEF3539_3_P / WEF3539_3_PL) * (TEF3539.1 - NF3539M.1) + NF3539M) * MULT1_TEF3539 * MULT2_TEF3539;

TEF4044_P = ((WEF4044_3_P / WEF4044_3_PL) * (TEF4044.1 - NF4044M.1) + NF4044M) * MULT1_TEF4044 * MULT2_TEF4044;

TEF4549_P = ((WEF4549_3_P / WEF4549_3_PL) * (TEF4549.1 - NF4549M.1) + NF4549M) * MULT1_TEF4549 * MULT2_TEF4549;

TEF5054_P = ((WEF5054_3_P / WEF5054_3_PL) * (TEF5054.1 - NF5054M.1) + NF5054M) * MULT1_TEF5054 * MULT2_TEF5054;

TEF5559_P = ((WEF5559_3_P / WEF5559_3_PL) * (TEF5559.1 - NF5559M.1) + NF5559M) * MULT1_TEF5559 * MULT2_TEF5559;

TEF6064_P = ((WEF6064_3_P / WEF6064_3_PL) * (TEF6064.1)) * MULT1_TEF6064 * MULT2_TEF6064;

TEF6569_P = ((WEF6569_3_P / WEF6569_3_PL) * (TEF6569.1)) * MULT1_TEF6569 * MULT2_TEF6569;

TEF70O_P = ((WEF70O_3_P / WEF70O_3_PL ) * (TEF70O.1 )) * MULT1_TEF70O * MULT2_TEF70O;

WEF16O_3_P = WEF1617_3_P + WEF1819_3_P + WEF2024_3_P + WEF2529_3_P + WEF3034_3_P + WEF3539_3_P + WEF4044_3_P + 

WEF4549_3_P 

 + WEF5054_3_P + WEF5559_3_P + WEF6064_3_P + WEF6569_3_P + WEF70O_3_P; 

AWWF16O_P = EF16O * 52 / WEF16O_3_P;

TEF16O_P = TEF1617_P + TEF1819_P + TEF2024_P + TEF2529_P + TEF3034_P + TEF3539_P + TEF4044_P + TEF4549_P + TEF5054_P 

+ TEF5559_P + TEF6064_P + TEF6569_P + TEF70O_P;

Final (Pre-TE.ADD)

Average Weeks Worked

AWWF1617 = AWWF1617_P;

AWWF1819 = AWWF1819_P;

AWWF2024 = AWWF2024_P;

AWWF2529 = AWWF2529_P; 

AWWF3034 = AWWF3034_P;

AWWF3539 = AWWF3539_P;

AWWF4044 = AWWF4044_P; 

AWWF4549 = AWWF4549_P; 

AWWF5054 = AWWF5054_P; 

AWWF5559 = AWWF5559_P;

AWWF6064 = AWWF6064_P;

AWWF6569 = AWWF6569_P;

AWWF70O = AWWF70O_P ;

Work Experience

WEF1617_3 = WEF1617_3_P;
156

WEF1819_3 = WEF1819_3_P;

WEF2024_3 = WEF2024_3_P;

WEF2529_3 = WEF2529_3_P;

WEF3034_3 = WEF3034_3_P;

WEF3539_3 = WEF3539_3_P;

WEF4044_3 = WEF4044_3_P;

WEF4549_3 = WEF4549_3_P;

WEF5054_3 = WEF5054_3_P;

WEF5559_3 = WEF5559_3_P;

WEF6064_3 = WEF6064_3_P;

WEF6569_3 = WEF6569_3_P;

WEF70O_3 = WEF70O_3_P ;

Total Employed

TEF1617 = TEF1617_P;

TEF1819 = TEF1819_P;

TEF2024 = TEF2024_P;

TEF2529 = TEF2529_P;

TEF3034 = TEF3034_P;

TEF3539 = TEF3539_P;

TEF4044 = TEF4044_P;

TEF4549 = TEF4549_P;

TEF5054 = TEF5054_P;

TEF5559 = TEF5559_P;

TEF6064 = TEF6064_P;

TEF6569 = TEF6569_P;

TEF70O = TEF70O_P ;

WEF16O_3 = WEF16O_3_P;

AWWF16O = AWWF16O_P ;

TEF16O = TEF16O_P ; 

Combined, Age 16 and Over

WE16O_3_P = WEM16O_3_P + WEF16O_3_P;

AWW16O_P = E16O * 52 / WE16O_3_P;

WE16O_3 = WE16O_3_P ;

AWW16O = AWW16O_P ;

Self-Employed Only

SEOCMB = 0.039 * (TEFC_N_N + TESL_N_N + EPRRB)

SEOCMBL1 = 0.039 * (TEFC_N_N.1 + TESL_N_N.1 + EPRRB.1)

SEO = (SEO.1 * (EAS + ENAS) / (EAS.1 + ENAS.1) + (SEOCMB - SEOCMBL1)) * MULTSEO

Combination Workers

CMB_TOT = ((-0.01468 + 0.06227 * RTP.1 -0.0008) * WSWA - SEOCMB) * MULTCMB

CSW_TOT = SEO + CMB_TOT

AW_CMBTOT = 1.4953 * ACWA

W_CMBTOT = AW_CMBTOT * CMB_TOT

CMB_WRELMAX = TAXMAX/AW_CMBTOT

CMB Wage Andover Curve

CMB_WAO1 = IF (CMB_WRELMAX < 0.0543009) 

THEN 1 - 0.722659 * CMB_WRELMAX0.65- 0.461913 * CMB_WRELMAX0.8

ELSE IF (CMB_WRELMAX<0.1086018) 

THEN -1.02884 * CMB_WRELMAX0.6 + 0.324761 * CMB_WRELMAX1.6 + 1.02015 

ELSE IF (CMB_WRELMAX < 0.1629027) 
157

THEN -0.906607 * CMB_WRELMAX0.7 + 0.947662 

ELSE IF (CMB_WRELMAX < 0.2172037) 

THEN -0.813951 * CMB_WRELMAX0.55 + 0.991722 

ELSE IF (CMB_WRELMAX < 0.3258055) 

THEN -0.755135 * CMB_WRELMAX0.55 + 0.964593 

ELSE 0

CMB_WAO2 = IF (CMB_WRELMAX < 0.5430091) 

THEN -0.649755 * CMB_WRELMAX0.6 + 0.886467 

ELSE IF (CMB_WRELMAX < 0.7059119) 

THEN -0.573205 * CMB_WRELMAX0.7 + 0.810122 

ELSE IF (CMB_WRELMAX < 0.9231155) 

THEN - 5.22264 * CMB_WRELMAX0.06 + 5.47514 

ELSE IF (CMB_WRELMAX < 1.0860183) 

THEN - 2.02619 * CMB_WRELMAX0.15 + 2.27963 

ELSE IF (CMB_WRELMAX < 1.5204256) 

THEN 0.605192 * EXP (-0.2 * CMB_WRELMAX) - 0.827158 * EXP (-0.8 * CMB_WRELMAX) 

+ 1.52918 * EXP (- 1.5 * CMB_WRELMAX) - 0.212269 

ELSE 0

CMB_WAO3 = IF (CMB_WRELMAX < 1.8462311) 

THEN 0.19139 * EXP (-0.6 * CMB_WRELMAX) + 0.764408 * EXP (- 1.8 * CMB_WRELMAX) + 0.0194903 

ELSE IF (CMB_WRELMAX < 2.3077888) 

THEN 0.12964 * EXP (-0.5 * CMB_WRELMAX) + 0.644861 * EXP (- 1.5 * CMB_WRELMAX) + 0.0183343 

ELSE IF (CMB_WRELMAX < 2.9865502) 

THEN 0.361318 * EXP (-0.8 * CMB_WRELMAX) + 0.0219491 

ELSE IF (CMB_WRELMAX < 4.3440731) 

THEN 0.193202 * EXP (-0.45 * CMB_WRELMAX) + 0.00425171 

ELSE IF (CMB_WRELMAX<5.4300913) 

THEN 0.0560412 * EXP (-0.25 * CMB_WRELMAX) +0.311286 * EXP (-0.8 * CMB_WRELMAX) + 

0.00297316 

ELSE 0

CMB_WAO4 = IF (CMB_WRELMAX < 13.5752283) 

THEN 0.0995677 * EXP (-0.32 * CMB_WRELMAX) + 0.00355234 

ELSE IF (CMB_WRELMAX < 21.7203653) 

THEN 0.041159 * EXP (-0.19 * CMB_WRELMAX) + 0.00156765 

ELSE IF (CMB_WRELMAX < 678.7614168) 

THEN 0.265022 * CMB_WRELMAX (-1.555)

ELSE 0

CMB_WAO = IF (CMB_WRELMAX < 0.3258055) 

THEN CMB_WAO1 

ELSE IF (CMB_WRELMAX < 1.5204256) 

THEN CMB_WAO2 

ELSE IF (CMB_WRELMAX < 5.4300913) 

THEN CMB_WAO3 

ELSE CMB_WAO4

CMB = (1 - (CMB_WAO - 0.019)) * CMB_TOT

CSW = SEO + CMB

SEOCMB_HI = 0.039 * (TEFC_N_N + TESL_N_N_HI)

SEO_HI = SEO - SEOCMB_HI

CMB_HI = CMB_TOT + SEOCMB_HI

CSW_HI = SEO_HI + CMB_HI

NIPA Wages

Private Residual Sector

WSDPB = WSDP – WSPH – WSPF – WSPRRB – TIPS_SR

TIPS_SR = (0.000508328 * RTP - 0.000481700) * GDP * 1.26393

OASDI Wages

Covered Employment and Wages – Federal Civilian Government
158

TEFC = (TEFC.1 / EGGEFC.1) * EGGEFC

TEFC_N = IF (CSRS.1 > 0) THEN TEFC_N.1/CSRS.1 * CSRS - TEFC_N_SW ELSE 0

TEFC_N_N = TEFC_N * 0.86

TEFC_N_O = (TEFC_N - TEFC_N_N)

TEFC_O = (TEFC - TEFC_N)

WEFC = (WEFC.1 / WSGGEFC.1) * WSGGEFC

WEFC_O = (WEFC -WEFC_N) * ADJ_FSA_FC

Covered Employment and Wages - State and Local Govt. 

TESL = (TESL.1/EGGESLMAX.1) * EGGESLMAX

TESL_O = (TESL_O.1/TESL.1) * TESL

TESL_N = (TESL-TESL_O)

TESL_N_O = (TESL_N_O_HI + TESL_N_O_NHI)

TESL_N_O_HI = (TESL_N - TESL_N_O_NHI - TESL_N_N_NHI) * CER_MQGE_O

TESL_N_O_NHI = (TESL_N_O_NHI_S + TESL_N_O_NHI_E + TESL_N_O_NHI_NS)

TESL_N_S = TESL_N_S.1 * (NF1819 + NF2024 + NM1819 + NM2024) / (NF1819.1 + NF2024.1 + NM1819.1 + 

NM2024.1)

TESL_N_E = TESL_N_E.1 * (TESL / TESL.1)

TESL_N_O_NHI_S = TESL_N_S * (TESL_N_O_NHI_S.1/TESL_N_S.1)

TESL_N_O_NHI_E = TESL_N_E * 0.6

TESL_N_O_NHI_NS = TESL_N_O_NHI_NS.1 * ESR_NS

TESL_N_N = (TESL_N - TESL_N_O)

TESL_N_N_HI = (TESL_N_N - TESL_N_N_NHI)

TESL_N_N_NHI = (TESL_N_N_NHI_S + TESL_N_N_NHI_E + TESL_N_N_NHI_NS)

TESL_N_N_NHI_S = (TESL_N_S - TESL_N_O_NHI_S)

TESL_N_N_NHI_E = (TESL_N_E - TESL_N_O_NHI_E)

TESL_N_N_NHI_NS = TESL_N_N_NHI_NS.1 * ESR_NS

WESL = (WESL.1/WSGGESL.1) * WSGGESL

WESL_O = (WESL_O.1/WSGGESL.1) * WSGGESL

WESL_N = (WESL - WESL_O)

WESL_N_HI = (WESL_N - WESL_N_NHI)

WESL_N_NHI = (WESL_N_NHI_S + WESL_N_NHI_E + WESL_N_NHI_NS)

WESL_N_NHI_S = WESL_N_NHI_S.1 * (TESL_N_S/TESL_N_S.1) * (AWSGGESL/AWSGGESL.1)

WESL_N_NHI_E = WESL_N_NHI_E.1 * (TESL_N_E/TESL_N_E.1) * (AWSGGESL/AWSGGESL.1)

RAWR_NS = IF (AWR_NS = 0) THEN 0 ELSE AWR_NS/AWR_NS.1

WESL_N_NHI_NS = IF (ESR_NS = 0) THEN 0 ELSE WESL_N_NHI_NS.1 * (TESL_N_O_NHI_NS + TESL_N_N_NHI_NS) / 

(TESL_N_O_NHI_NS.1 + TESL_N_N_NHI_NS.1) * (AWSGGESL/AWSGGESL.1) * RAWR_NS

Self-Employed Earnings Sector

Covered SENE

CSE_TOT = (YF +YNF) / (YF.1 + YNF.1) * CSE_TOT.1

CSE_CMB_N = (CSE_TOT / (CMB_TOT + SEO)) / (CSE_TOT.1 / (CMB_TOT.1 + SEO.1)) * (CSE_CMB_N.1 / (CMB_TOT.1 -

CMB.1)) * (CMB_TOT - CMB)

CSE = CSE_TOT - CSE_CMB_N

ACSE_SEO = (CSE_TOT / (SEO + 0.416488 * CMB_TOT))

ACSE_CMB_TOT = 0.416488 * ACSE_SEO

CSE_SEO = ACSE_SEO * SEO

CSE_CMB_TOT = ACSE_CMB_TOT * CMB_TOT

CSE_CMB = CSE_CMB_TOT - CSE_CMB_N

ACSE_CMB = CSE_CMB/CMB

Present Law OASDI and HI Covered Wages and Earnings

WSGMLC = CML * WSGFM

WSGFCA = WEFC_O

CFCA = WSGFCA/WSGGEFC

CSLHI = (WESL_O+WESL_N_HI)/WSGGESL

WSGSLCA = WESL_O

WSPH_O = CPH * WSPH

WSPF_O = WSPF_O.1 * WSPF/WSPF.1

CPF = WSPF_O/WSPF

WSPRR_O = CPRR * WSPRRB

CPB_ILL = (CPB) * (WSWA* 0.86 - LOST_MF) / (WSWA * 0.86 + LOST_MF) + (1.5 * 0.5) * (2.0 * LOST_MF) / (WSWA * 0.86 + 

LOST_MF)

CPB_ADJ = IF REAL_ACW_DYR = 0 

THEN CPB_ILL 

ELSE (((REAL_ACW_D + PCH (CPIW_U))/100 *ACWA.1 + ACWA.1) * WSWA - WSPH_O - WSPF_O - WSPRR_O 
159

-TIPS_SR - WSGSLCA – WSGFCA - WSGMLC)/WSDPB

WSPB_O = CPB_ADJ * WSDPB

WSPC = WSPH_O + WSPF_O + WSPRR_O + TIPS_SR + WSPB_O

CP = WSPC/WSDP

WSCA = (WSPC + WSGSLCA + WSGFCA + WSGMLC)

COVERNA = (WSCA + CSE)

ACWA = WSCA/WSWA

ASE = CSE/CSW

ASEHI = CSE_TOT/CSW_HI

ACEA = COVERNA/TCEA

ACSLW = WESL_O/TESL_O * MULTACSLW

ACMW = ACMW.1 * AWSGFM/AWSGFM.1 * CML/CML.1

ACFCW = WEFC_O/TEFC_O

ACFMW = ACFMW.1 * (AIW.1/AIW.3)0.5

TEPH_N = ENAWPH * (1 -CPH)

TEP_N_N_S = TEP_N_N_S.1 * (NF1819 + NF2024 + NM1819 + NM2024) / (NF1819.1 + NF2024.1 + NM1819.1 + NM2024.1)

TCEA = (TE - TEPH_N – EPRRB - TEP_N_N_S - TEPO_N - TESL_N_N_NHI_S - TESL_N_N_NHI_E - TESL_N_N_NHI_NS -

TESL_N_N_HI - TEFC_N_N)

WSWA = (TCEA-SEO)

Present Law HI Covered Wages and Earnings

WSCAHI_ADD = WSCA * WSCAHI_ADD.1/WSCA.1 

TCEAHI = (TCEA + TEFC_N_N + TESL_N_N_HI)

WSWAHI = TCEAHI - SEO_HI

WSCAHI = WSCA + WEFC_N + WESL_N_HI + WSCAHI_ADD

ACWAHI = WSCAHI/WSWAHI

COVERNHI = WSCAHI + CSE_TOT

ACEAHI = COVERNHI/TCEAHI

Complete Coverage concepts 

WSWC = (WSWAHI + TEPH_N + EPRRB + TEP_N_N_S + TEPO_N + TESL_N_N_NHI) + LOST_MF

ACWC = WSD/WSWC

AIW = IF AIW_GR_YR = 0 

THEN AIW.1 *ACWC/ACWC.1 * MULTAIW 

ELSE AIW.1 * (1 +AIW_GR/100)

Taxable Maximums

RAIW = AIW.2/AIWBASE

TAXMAXB1 = RAIW * TMAXBASE * 1000/300

TAXMAXB2 = IF TAXMAXB1 - ROUND (TAXMAXB1) >= 0.5 

THEN ROUND (TAXMAXB1) +1 

ELSE ROUND (TAXMAXB1)

TAXMAXB3 = IF TAXMAXB2 < TAXMAX.1 

THEN TAXMAX.1 * 1000/300 

ELSE TAXMAXB2

TAXMAX = IF BENINC.1 <= 0.001 

THEN TAXMAX.1 

ELSE 300 * TAXMAXB3/1000

Deemed Military Wage Credits

EDMILAF = EDMIL * 1.1

EDMILT = (2.00303 - 50.7517/YEAR) * EDMILAF

EDMILR = EDMILT – EDMILAF

MWC_ED_O = 1.2 * EDMILAF * 0.997

MWC_ED_HI = 1.2 * EDMILAF

AMWC_GO2 = MIN (1.2, AWSGFM * (2/52) * (1/3)) 

MWC_EDR_O = AMWC_GO2 * EDMILR * (1 - 0.017)

MWC_EDR_HI= MWC_EDR_O + ((1.2 +AMWC_GO2) * 0.5) * EDMILR * 0.017

MWC_O = MWC_ED_O + MWC_EDR_O

MWC_HI = MWC_ED_HI+ MWC_EDR_HI
160

2-4 Effective Taxable Payroll (TAXPAY)

2-4.1 Ratio of taxable employee to total covered OASDI wages (RWTEE)

IF(RM.LT.0.439103091)THEN

 RWTEE=RM-(0.26651/1.5)*RM**1.5-(0.388274/1.9)*RM**1.9

ELSE IF(RM.LT.1.313896269)THEN

 RWTEE=(1.17877/1.5)*RM**1.5-(2.9291/0.68)*DEXP(-0.68*RM)-2.31401*RM+4.327630386

ELSE IF(RM.LT.2.570433485)THEN

 RWTEE=-(0.0202216/0.25)*DEXP(-0.25*RM)-(1.329/1.4)*DEXP(-1.4*RM)+0.00944271*RM+0.904661863

ELSE IF(RM.LT.4.418783378)THEN

 RWTEE=-(0.0654817/0.35)*DEXP(-0.35*RM)-(1.415/1.51)*DEXP(-1.51*RM)+0.000676576*RM+0.954098019

ELSE IF(RM.LT.14.73231225)THEN

 RWTEE=-(0.0110626/0.15)*DEXP(-0.15*RM)-(0.0472319/0.45)*DEXP(-0.45*RM)-(0.257195/0.95)*DEXP(-

0.95*RM)+0.000367645*RM+0.970880621

ELSE

 RWTEE=-(0.179935/0.75)*RM**(-0.75)+0.999970738

END IF

Where

RM = OASDI taxable maximum / average covered OASDI wage

RWTEE = Ratio if OASDI taxable employee to covered wages 

2-4.2Taxable employee OASDI wages (WTEE)

WTEE = RWTEE * WSC

Where

RWTEE = Ratio of OASDI taxable employee to covered wages 

WSC = OASDI total covered wages

WTEE = OASDI taxable employee wages

2-4.3 Ratio of multi-employer refund wages to total OASDI covered wages (RMER)

RMER = ( MER(-1) / WSC(-1) ) - 0.03217 * ( RWTEE – RWTEE(-1) ) - 0.00024*( RU - RU(-1) )

Where

MER(-1) = Multi-employer refund wages in prior year

RMER = Ratio of multi-employer refund wages to total OASDI covered wages

RU = Annual average civilian unemployment rate

RWTEE = Ratio of OASDI taxable employee to covered wages

WSC(-1) = OASDI total covered wages in prior year

2-4.4 Multi-employer refund wages (MER)

MER = RMER * WSC

Where

MER = OASDI multi-employer refund wages

RMER = Ratio of multi-employer refund wages to total OASDI covered wages

WSC = OASDI total covered wages

2-4.5Taxable employer OASDI wages (WTER)

WTER = WTEE + MER

Where

MER = OASDI multi-employer refund wages

WTEE = OASDI taxable employee wages

WTER = OASDI taxable employer wages

2-4.6 Ratio of taxable to covered self-employment earnings (RSET)

Preliminary

BASECT = 47831.98

BASECW = 36831.79

BASEO = 23448.56
161

Self-employed only

SECSEO = CSE - SECCMB

ASESEO = SECSEO / SEO

ASEO96 = ASESEO(1996)

ASESEO = ASESEO * BASEO / ASEO96

O = TAXMAX / ASESEO

IF(O.LT.0.021323273)THEN

 OTR=O-(401.8/3.8)*O**3.8

ELSE IF(O.LT.0.170586184)THEN

 OTR=(12.6861/1.1)*O**1.1-(10.7855/1.15)*O**1.15-(.208585/1.9)*O**1.9-1.58102*O-.0000212479

ELSE IF(O.LT.0.938224014)THEN

 OTR=-(2.03924/2.5)*DEXP(-2.5*O)+(2.52113/1.6)*O**1.6-(1.11979/2.2)*O**2.2-1.33547*O+.825500132

ELSE IF(O.LT.1.705861843)THEN

 OTR=-(.375957/.3)*DEXP(-.3*O)-(.642976/2.3)*DEXP(-2.3*O)-.108863*O+1.555087525

ELSE IF(O.LT.2.302913488)THEN

 OTR=-(.248596/.25)*DEXP(-.25*O)-(1.003/2.2)*DEXP(-2.2*O)-.0565126*O+1.368881905

ELSE IF(O.LT.3.624956417)THEN

 OTR=-(.207896/.15)*DEXP(-.15*O)-(3.99112/2.5)*DEXP(-2.5*O)-.0699402*O+1.823982598

ELSE IF(O.LT.5.970516451)THEN

 OTR=-(.110423/.2)*DEXP(-.2*O)-(.571353/1.25)*DEXP(-1.25*O)-.00842494*O+1.068478626

ELSE IF(O.LT.12.79396382)THEN

 OTR=-(.0631844/.2)*DEXP(-.2*O)-(.242063/.65)*DEXP(-.65*O)+.0011163*O+.947373543

ELSE IF(O.LT.21.32327304)THEN

 OTR=-(.0218297/.13)*DEXP(-.13*O)-(.0876571/.32)*DEXP(-.32*O)+.000522742*O+.966817185

ELSE IF(O.LT.213.2327304)THEN

 OTR=-(2.86725/1.36)*O**(-1.36)+1.00002612

ELSE

 OTR=1D0

END IF

SETSEO=OTR*SECSEO

OASDI taxable wages of workers with both wages and self-employment earnings

AWSCMB=WSCCMB/CMBNT

AWSCMB96=AWSCMB(1996)

AWSCMB=AWSCMB*BASECW/AWSCMB96

CW=TAXMAX/AWSCMB

IF(CW.LT.0.0543009)THEN

 CWTR=CW-(.722659/1.65)*CW**1.65-(.461913/1.8)*CW**1.8

ELSE IF(CW.LT.0.1086018)THEN

 CWTR=-(1.02884/1.6)*CW**1.6+(.324761/2.6)*CW**2.6+1.02015*CW-.0000130669

ELSE IF(CW.LT.0.1629027)THEN

 CWTR=-(.906607/1.7)*CW**1.7+.947662*CW+.002059472

ELSE IF(CW.LT.0.2172037)THEN

 CWTR=-(.813951/1.55)*CW**1.55+.991722*CW+.002022215

ELSE IF(CW.LT.0.3258055)THEN

 CWTR=-(.755135/1.55)*CW**1.55+.964593*CW+.004355898

ELSE IF(CW.LT.0.5430091)THEN

 CWTR=-(.649755/1.6)*CW**1.6+.886467*CW+.011658928

ELSE IF(CW.LT.0.7059119)THEN

 CWTR=-(.573205/1.7)*CW**1.7+.810122*CW+.019653316

ELSE IF(CW.LT.0.9231155)THEN

 CWTR=-(5.22264/1.06)*CW**1.06+5.47514*CW-.053844798

ELSE IF(CW.LT.1.0860183)THEN

 CWTR=-(2.02619/1.15)*CW**1.15+2.27963*CW-.023407805

ELSE IF(CW.LT.1.5204256)THEN

 CWTR=-(.605192/.2)*DEXP(-.2*CW)+(.827158/.8)*DEXP(-.8*CW)-(1.52918/1.5)*DEXP(-1.5*CW)-.212269*CW+2.946985922

ELSE IF(CW.LT.1.8462311)THEN

 CWTR=-(.191389/.6)*DEXP(-.6*CW)-(.764408/1.8)*DEXP(-1.8*CW)+.0194903*CW+.719848486

ELSE IF(CW.LT.2.3077888)THEN

 CWTR=-(.12964/.5)*DEXP(-.5*CW)-(.644861/1.5)*DEXP(-1.5*CW)+.0183343*CW+.731280763

ELSE IF(CW.LT.2.9865502)THEN

 CWTR=-(.361318/.8)*DEXP(-.8*CW)+.0219491*CW+.698954858

ELSE IF(CW.LT.4.3440731)THEN

 CWTR=-(.193202/.45)*DEXP(-.45*CW)+.00425171*CW+.82237055

ELSE IF(CW.LT.5.4300913)THEN

 CWTR=-(.0560412/.25)*DEXP(-.25*CW)-(.311286/.8)*DEXP(-.8*CW)+.00297316*CW+.854848493

ELSE IF(CW.LT.13.5752283)THEN
162

CWTR=-(.0995677/.32)*DEXP(-.32*CW)+.00355234*CW+.843717127

ELSE IF(CW.LT.21.7203653)THEN

 CWTR=-(.041159/.19)*DEXP(-.19*CW)+.00156765*CW+.883046178

ELSE IF(CW.LT.678.7614168)THEN

 CWTR=-(.265022/.555)*CW**(-.555)+1.000103517

ELSE

 CWTR=1D0

END IF

WSTCMB=CWTR*WSCCMB

OASDI taxable earnings of workers with both wages and self-employment earnings

TECCMB=SECCMB+WSCCMB

ATECMB=TECCMB/CMBNT

ATECMB96=ATECMB(1996)

ATECMB=ATECMB*BASECT/ATECMB96

CT=TAXMAX/ATECMB

IF(CT.LT.0.0209065)THEN

 CTTR=CT-(58.6063/3.5)*CT**3.5

ELSE IF(CT.LT.0.1254391)THEN

 CTTR=(.320825/1.4)*CT**1.4-(1.90732/2)*CT**2+.967979*CT+.000050526-.00000622

ELSE IF(CT.LT.0.3345042)THEN

 CTTR=-(1.77251/1.9)*CT**1.9+(1.18333/2.8)*CT**2.8+1.11299*CT-.003813108-.00000622

ELSE IF(CT.LT.0.7108215)THEN

 CTTR=-(.246427/1.35)*CT**1.35-(1.24155/1.8)*CT**1.8+(.55025/2.3)*CT**2.3+1.16953*CT-.001078375-.00000622

ELSE IF(CT.LT.0.7944476)THEN

 CTTR=(3.15997/1.4)*CT**1.4-(4.21829/1D0)*DEXP(-1*CT)-4.46998*CT+4.300993015-.00000622

ELSE IF(CT.LT.1.0453257)THEN

 CTTR=(.55343/1.6)*CT**1.6-(1.76791/1.2)*DEXP(-1.2*CT)-.845578*CT+1.479639989-.00000622

ELSE IF(CT.LT.1.8815863)THEN

 CTTR=-(.360108/.3)*DEXP(-.3*CT)-(1.24485/2.7)*DEXP(-2.7*CT)-.110244*CT+1.566702582-.00000622

ELSE IF(CT.LT.3.7631726)THEN

 CTTR=-(.262138/.6)*DEXP(-.6*CT)+(.208323/1.9)*DEXP(-1.9*CT)-(2.27562/2.7)*DEXP(-2.7*CT)+.00933849*CT+

 .799685739-.00000622

ELSE IF(CT.LT.4.1813029)THEN

 CTTR=-(.26942/.6)*DEXP(-.6*CT)+(.266252/1.7)*DEXP(-1.7*CT)+.00905196*CT+.80182568-.00000622

ELSE IF(CT.LT.10.4532574)THEN

 CTTR=-(.0592625/.25)*EXP(-.25*CT)-(.541514/1)*DEXP(-1*CT)+.00174723*CT+.88757547-.00000622

ELSE IF(CT.LT.18.8158632)THEN

 CTTR=-(.0610012/.25)*DEXP(-.25*CT)+.00174344*CT+.888109189-.00000622

ELSE IF(CT.LT.522.6628676)THEN

 CTTR=-(.21993/.5455)*CT**(-.5455)+1.000030393-.00000622

ELSE

 CTTR=1D0

END IF

TETCMB=CTTR*TECCMB

SETCMB=TETCMB-WSTCMB

Ratio OASDI taxable to covered self-employment earnings

RSET = (SETCMB+SETSEO)/CSE

Where

ASEO96 = Average self-employment earnings of workers with no OASDI taxable wages in 1996

ATECMB = Average OASDI covered earnings of workers with both OASDI covered wages and self-employment earnings

ATECMB96 = Average OASDI covered earnings of workers with both OASDI covered wages and self-employment earnings in 

1996

AWSCMB = Average OASDI covered wage of workers with both wages and self-employment earnings

AWSCMB96 = Average OASDI covered wage of workers with both wages and self-employment earnings in 1996

ASESEO = Average self-employment earnings of workers with no OASDI taxable wages

AWSCMB = Average OASDI covered wage of workers with both wages and self-employment earnings

BASECT = Average total earnings of workers with both self-employment earnings and wages in 1% sample data for 1996 used to

produce equations

BASECW = Average OASDI covered wages of workers with both self-employment earnings and wages in 1% sample data for 

1996 used to produce equations

BASEO = Average self-employment earnings of workers with no OASDI taxable wages in 1% sample data for 1996 used to

produce equations

CMBNT = Number or workers with both OASDI taxable wages and self-employment earnings

CSE = OASDI covered self-employment earnings

CT = Ratio OASDI taxable maximum to average earnings of workers with both self-employment earnings and OASDI 
163

taxable wages

CW = Ratio OASDI taxable maximum to average self-employment earnings of workers with both self-employment earnings

and OASDI taxable wages

CTTR = Ratio of OASDI taxable to covered earnings for workers with both wages and self-employment earnings

CWTR = Ratio of OASDI taxable to covered wages for workers with both wages and self-employment earnings

O = Ratio OASDI taxable maximum to average self-employment earnings of workers with no OASDI taxable wages

OTR = Ratio of OASDI taxable self-employment to covered earnings for workers with no OASDI taxable wages

SECCMB = OASDI covered self-employment earnings of workers with both self-employment earnings and OASDI taxable 

wages

SECSEO = OASDI covered self-employment earnings of workers with no OASDI taxable wages

SEO = Number or workers with OASDI covered self-employment earnings and no OASDI taxable wages

SETCMB = OASDI taxable self-employment earnings of workers with both OASDI taxable wages and self-employment earnings

SETSEO = OASDI taxable self-employment earnings of workers with no OASDI taxable wages

TAXMAX = OASDI taxable maximum

TECCMB = OASDI covered earnings of workers with both wages and self-employed earnings

TETCMB = OASDI taxable earnings of workers with both wages and self-employed earnings

WSCCMB = OASDI covered wages of workers with both wages and self-employed earnings

WSTCMB = OASDI taxable wages of workers with both wages and self-employed earnings

2-4.7 OASDI taxable self-employment earnings (SET)

SET = SETR * CSE

Where

CSE = OASDI covered self-employment earnings

SET = OASDI taxable self-employment earnings

SETR = Ratio of OASDI taxable to covered self-employment earnings

2-4.8 OASDI effective taxable payroll (ETP)

ETP=WTER+SET-0.5*MER

Where

ETP = OASDI effective taxable payroll

MER = OASDI multi-employer refund wages

SET = OASDI taxable self-employment earnings

WTER = Annual OASDI taxable employer wages

2-4.9 OASDI taxable wage liability (WTL)

WTL = WTER * TRW

Where

TRW = OASDI combined employee-employer tax rate

WTL = Annual OASDI taxable wage liabilities

WTER = Annual OASDI taxable employer wages

2-4.10 OASDI taxable self-employment liability (SEL)

SEL = SET * TRSE

Where

SEL = OASDI taxable self-employment earnings liabilities

SET = OASDI taxable self-employment earnings

TRSE = OASDI self-employment tax rate

2-4.11 OASDI quarterly taxable wage liability (WTLQ)

Federal Civilian

Annual total wages (OASDI + MQGE)

BAFCW = 34198.84

AWCFC = WCFC / ECFC * BAFCW / AWCFCTOT97

T=MAX/AWCFC

IF(T.LT.0.014620379)THEN

 FCTR=T-(1.04262/1.73)*T**1.73

ELSE IF(T.LT.0.292407578)THEN

 FCTR=-(1.22471/1.6)*T**1.6+(.826746/1.8)*DEXP(-1.8*T)+1.8535*T-.459368449
164

ELSE IF(T.LT.0.760259704)THEN

 FCTR=-(.635082/2D0)*T**2+(.604884/2.9)*T**2.9-(.403213/4.6)*T**4.6+.910343*T+.002291358

ELSE IF(T.LT.1.228111829)THEN

 FCTR=-(.162181/1.7)*T**1.7+(.143632/2.7)*T**2.7-(.312012/3.4)*T**3.4+.841165*T+.011332647

ELSE IF(T.LT.1.520519407)THEN

 FCTR=-(1.34084/3.5)*T**3.5+(1.09868/5D0)*T**5-(.404253/5.8)*T**5.8+1.17397*T-.222555715

ELSE IF(T.LT.2.339260627)THEN

 FCTR=(.671304/.5)*DEXP(-.5*T)-(3.27076/1.4)*DEXP(-1.4*T)+.126626*T+.353367869

ELSE IF(T.LT.3.50889094)THEN

 FCTR=(.0571643/.95)*DEXP(-.95*T)-(3.17633/1.8)*DEXP(-1.8*T)+.000623031*T+.996284293

ELSE IF(T.LT.4.970928832)THEN

 FCTR=-(12.3148/2.25)*DEXP(-2.25*T)+.0000698013*T+.999222265

ELSE

 FCTR=-(.0285502/2D0)*T**(-2D0)+1.00007094

END IF

WTFCTOT=FCTR*WCFC

Where

AWCFC = Average covered Federal Civilian wages (OASDI plus MQGE) 

AWCFCTOT97 = Average covered Federal Civilian wages (OASDI plus MQGE) for 1997

BAFCW = Average Federal Civilian wages (OASDI plus MQGE) in 1% sample data for 1997 used to produce equations

ECFC = Covered Federal Civilian employment (OASDI plus MQGE)

FCTR = Ratio of taxable to covered Federal Civilian wages (OASDI plus MQGE)

MAX = OASDI taxable maximum

T = Ratio of the OASDI taxable maximum to average covered Federal Civilian wages (OASDI plus MQGE)

WCFC = Covered Federal Civilian wages (OASDI plus MQGE) 

WTFCTOT = Taxable Federal Civilian wages (OASDI plus MQGE)

Annual MQGE wages

BAFCW = 50147.72

AWCFC = WCFC / ECFC * BAFCW / AWCFCHO97

T = MAX / AWCFC

IF(T.LT.0.019941085)THEN

 FCTR=T-(0.0450661/1.47)*T**1.47

ELSE IF(T.LT.0.099705424)THEN

 FCTR=-(.0518044/1.9)*T**1.9-(.0368056/2.3)*T**2.3+.99479*T+.0000248091

ELSE IF(T.LT.0.358939528)THEN

 FCTR=-(.05907/1.25)*T**1.25-(.0746657/2.9)*T**2.9+1.02092*T-.00032173

ELSE IF(T.LT.0.558350377)THEN

 FCTR=-(2.4664/1.4)*T**1.4+(4.82919/2.3)*T**2.3-(3.97473/3)*T**3+1.83998*T-.026694932

LSE IF(T.LT.0.797643395)THEN

 FCTR=(.609091/2.1)*T**2.1-(1.16086/4)*T**4+.788373*T+.043208139

ELSE IF(T.LT.1.196465093)THEN

 FCTR=(2.35647/.4)*DEXP(-.4*T)-(3.87811/1.2)*DEXP(-1.2*T)-(1.1179/2.5)*DEXP(-2.5*T)+.738296*T-2.83402534

ELSE IF(T.LT.1.694992215)THEN

 FCTR=-(.422884/1.3)*DEXP(-1.3*T)-(6.90241/3D0)*DEXP(-3*T)-.0229917*T+1.068147457

ELSE IF(T.LT.2.592341034)THEN

 FCTR=(.557032/1.2)*DEXP(-1.2*T)-(5.40739/2.2)*DEXP(-2.2*T)+.0102014*T+.960037325

ELSE

 FCTR=-(32.3187/3.5)*DEXP(-3.5*T)+1.000030482

END IF

WTFCHO=FCTR*WCFC

Where

AWCFC = Average covered Federal Civilian MQGE wages

AWCFCHO97 = Average covered Federal Civilian MQGE wages for 1997

BAFCW = Average Federal Civilian MQGE wages in 1% sample data for 1997 used to produce equations

ECFC = Covered Federal Civilian MQGE employment

FCTR = Ratio of taxable to covered Federal Civilian MQGE wages

MAX = OASDI taxable maximum

T = Ratio of the OASDI taxable maximum to average covered Federal Civilian MQGE wages

WCFC = Covered Federal Civilian MQGE wages 

WTFCHO = Taxable Federal Civilian MQGE wages

Annual OASDI taxable wages
165

WTFC = WTFCTOT – WTFCHO

Where

WTFC = Annual OASDI taxable Federal Civilian wages

WTFCHO = Taxable Federal Civilian MQGE wages

WTFCTOT = Taxable Federal Civilian wages (OASDI plus MQGE)

Quarterly OASDI covered wages

CFCQD(1) = .98357 * TCFCD(I,1) + FCPD(I,1)

CFCQD(2) = .98909 * TCFCD(I,2) + FCPD(I,2)

CFCQD(3) = 1.01833 * TCFCD(I,3) + FCPD(I,3)

CFCQD(4) = 1.00814 * TCFCD(I,4) + FCPD(I,4)

QWCFCOD(J) = CFCQD(J) * WTFC

Where

CFCQD = Proportion of annual OASDI covered Federal Civilian wages paid in each quarter

FCPD = Payday variable for Federal Civilian wages based on calendar

I = Calendar year

J = Quarter

TCFCD = Proportion of annual NIPA Federal Civilian wages paid in each quarter

QWCFCOD = Quarterly OASDI covered Federal Civilian wages

WTFC = Annual OASDI taxable Federal Civilian wages

Quarterly OASDI taxable wages

IF(FCTR.LE.0.928)FCQD(2)=CFCQD(2)+.27522*(1.-FCTR)-.15127*(1.-FCTR)**2+.35146*(1.-FCTR)**3

IF(FCTR.LE.0.993)THEN

 FCQD(3)=CFCQD(3)+.28047*(1.-FCTR)-4.73021*(1.-FCTR)**2+25.3606*(1.-FCTR)**3-58.1741*(1.-FCTR)**4+45.1465*(1.-FCTR)**5

 FCQD(4)=CFCQD(4)-.75095*(1.-FCTR)+3.65109*(1.-FCTR)**2-16.9355*(1.-FCTR)**3+23.9578*(1.-FCTR)**4

END IF

First quarter is always 100 percent taxable.

QWTFC(I,1)=QWCFC(I,1)

IF(FCTR.LE.0.928)THEN

Compute taxable for 2nd-4th quarter.

 FCQ=FCQD(2)+FCQD(3)+FCQD(4)

 WTFC2=WTFC-QWTFC(I,1)

 FCQD(2:4)=FCQD(2:4)/FCQ

 QWTFC(I,2:4)=FCQD(2:4)*WTFC2

ELSE IF(FCTR.LE.0.993)THEN

 Second quarter covered is completely taxable.

 QWTFC(I,2)=QWCFC(I,2)

 QWTFC(I,3)=FCQD(3)*WTFC

 QWTFC(I,4)=WTFC-QWTFC(I,1)-QWTFC(I,2)-QWTFC(I,3)

ELSE

 Second and third quarter covered is completely taxable.

 QWTFC(I,2)=QWCFC(I,2)

 QWTFC(I,3)=QWCFC(I,3)

 QWTFC(I,4)=WTFC-QWTFC(I,1)-QWTFC(I,2)-QWTFC(I,3)

END IF

Where

CFCQD = Proportion of annual OASDI covered Federal Civilian wages paid in each quarter

FCQ = Sum of proportions of annual OASDI covered Federal Civilian wages paid in each quarter for quarters two to four

FCQD = Proportion of annual OASDI taxable Federal Civilian wages paid in each quarter

FCTR = Ratio annual OASDI taxable to covered Federal Civilian wages

I = Calendar year

TCFCD = Proportion of annual NIPA Federal Civilian wages paid in each quarter

QWCFC = Quarterly OASDI covered Federal Civilian wages

QWTFC = Quarterly OASDI taxable Federal Civilian wages

WTFC = Annual OASDI taxable Federal Civilian wages

WTFC2 = Total OASDI taxable Federal Civilian wages paid in quarters two to four

Quarterly OASDI taxable wage liabilities

WTLQFCEE(I, J) = QWTFC(I, J) * TRWEE(I)

WTLQFCER(I, J) = QWTFC(I, J) * TRWER(I)

WTLQFC(I, J) = WTLQFCEE(I, J) + WTLQFCER(I, J)
166

Where

I = Calendar year

J = Quarter

TRWEE = OASDI employee tax rate

TRWER = OASDI employer tax rate

WTLQFC = Quarterly OASDI taxable Federal Civilian combined employee-employer wage liabilities

WTLQFCEE = Quarterly OASDI taxable Federal Civilian employee wage liabilities

WTLQFCER = Quarterly OASDI taxable Federal Civilian employer wage liabilities

Military wages

Annual OASDI taxable wages

BACMW = 16439.95

ACMW = AWCML * BACMW / AWCML97

T = MAX / ACMW

IF(T.LT.0.060827432)THEN

 MTR=T-(.712875/2)*T**2

ELSE IF(T.LT.0.182482295)THEN

 MTR=(.71197/1.8)*T**1.8-(1.59752/2D0)*T**2+.97587*T+0.000542413

ELSE IF(T.LT.0.608274315)THEN

 MTR=-(1.75026/2D0)*T**2+(2.86837/3D0)*T**3-(1.90346/4D0)*T**4+1.10056*T-.006441373

ELSE IF(T.LT.1.094893767)THEN

 MTR=-(.700864/1.4)*T**1.4-(.40042/3.3)*T**3.3+(.197091/4.1)*T**4.1+1.33615*T-.056637087

ELSE IF(T.LT.1.703168082)THEN

 MTR=(21.3527/.3)*DEXP(-.3*T)-(21.1277/0.5)*DEXP(-.5*T)+(2.73027/1.1)*DEXP(-1.1*T)+4.34833*T-31.56802874

ELSE IF(T.LT.2.311442397)THEN

 MTR=-(33.3894/1.2)*T**1.2+(14.9436/1.6)*T**1.6-(2.58041/2.1)*T**2.1+21.3365*T-.872981629

ELSE IF(T.LT.3.163026438)THEN

 MTR=-(.076094/.3)*DEXP(-.3*T)-(1.59668/1.4)*DEXP(-1.4*T)-.0271355*T+1.182946986

ELSE IF(T.LT.4.257920205)THEN

 MTR=(.482918/1.5)*T**1.5-(9.21141/.9)*DEXP(-.9*T)+(25.93/1.5)*DEXP(-1.5*T)-1.14706*T+3.246003821

ELSE

 MTR=-(9.00723/1.8)*DEXP(-1.8*T)+1.000285789

END IF

WTML=MTR*WCML

Where

ACMW = Average OASDI covered military wages adjusted for level used to produce equations

AWCML = Average OASDI covered military wages

AWCML97 = Average OASDI covered military wages in 1997

BACMW = Average OASDI covered military wages in 1% sample data for 1997 used to produce equations

MAX = OASDI taxable maximum

MTR = Ratio of OASDI taxable to covered military wages

T = Ratio of the OASDI taxable maximum to average covered military wages

WCML = Annual OASDI covered military wages

WTML = Annual OASDI taxable military wages

Quarterly OASDI covered wages

CMLQD(1) = .97978*TCMLD(I,1)*MLPD(I,1)

CMLQD(2) = 1.002*TCMLD(I,2)*MLPD(I,2)

CMLQD(3) = 1.02145*TCMLD(I,3)*MLPD(I,3)

CMLQD(4) = .99689*TCMLD(I,4)*MLPD(I,4)

QWCML = CMLQD(J)*WCML

Where

CMLQD = Proportion of annual OASDI covered military wages paid in each quarter

I = Calendar year

J = Quarter

MLPD = Payday variable for military wages based on calendar

QWCML = Quarterly OASDI covered military wages

TCMLD = Proportion of annual NIPA military wages paid in each quarter

WCML = Annual OASDI covered military wages

Quarterly OASDI taxable wages

T=MAX/AWCML
167

IF(MLTR.LT.0.776)QML(1)=CMLQD(1)+.393565-.018307*T-3.44641/T+15.6381/T**2-40.0168/T**3+62.0449/T**4-

57.525/T**5+30.2498/T**6-7.8664/T**7+.674629/T**8

IF(MLTR.LT.0.952)QML(2)=CMLQD(2)+.844748-.0401062*T-7.24247/T+32.4957/T**2-83.3328/T**3+129.374/T**4-

122.526/T**5+68.2737/T**6-20.1479/T**7+2.34289/T**8

IF(MLTR.LT.0.985)QML(3)=CMLQD(3)-2.62266+.125592*T+22.5832/T-105.727/T**2+300.027/T**3-540.915/T**4+622.304/T**5-

441.658/T**6+175.722/T**7-29.8987/T**8

IF(MLTR.LT.1.)QML(4)=CMLQD(4)+2.37295-.111565*T-21.1954/T+106.049/T**2-330.637/T**3+658.869/T**4-

835.626/T**5+648.641/T**6-279.392/T**7+50.9246/T**8

IF(MLTR.LT.0.776)THEN

 QWTML(I,1:4)=QML(1:4)*WTML

ELSE IF(MLTR.LT.0.952)THEN

 QWTML(I,1)=QWCML(I,1)

 TOTWG1=WTML-QWTML(I,1)

 Q1=QML(2)+QML(3)+QML(4)

 QML(2:4)=QML(2:4)/Q1

 QWTML(I,2:4)=QML(2:4)*TOTWG1

ELSE IF(MLTR.LT.0.985)THEN

 QWTML(I,1)=QWCML(I,1)

 QWTML(I,2)=QWCML(I,2)

 TOTWG1=WTML-QWTML(I,1)-QWTML(I,2)

 Q1=QML(3)+QML(4)

 QML(2:4)=QML(2:4)/Q1

 QWTML(I,2:4)=QML(2:4)*TOTWG1

ELSE IF(MLTR.LT.1.)THEN

 QWTML(I,1)=QWCML(I,1)

 QWTML(I,2)=QWCML(I,2)

 QWTML(I,3)=QWCML(I,3)

 QWTML(I,4)=WTML-QWTML(I,1)-QWTML(I,2)-QWTML(I,3)

END IF

Where

AWCML = Average OASDI covered military wages

CMLQD = Proportion of annual OASDI covered military wages paid in each quarter

MLTR = Ratio of OASDI taxable to covered military wages

MAX = OASDI taxable maximum

I = Calendar year

Q1 = Sum of proportions of annual OASDI taxable military wages paid in each quarter for last three or two quarters in year

QML = Proportion of annual OASDI taxable military wages paid in each quarter

QWCML = Quarterly OASDI covered military wages

QWTML = Quarterly OASDI taxable military wages

T = Ratio of the OASDI taxable maximum to average covered military wages

TOTWG1 = Annual OASDI taxable military wages for all quarters except first or first and second

WTML = Annual OASDI taxable military wages

Quarterly OASDI taxable wage liabilities

WTLQMLEE(I, J) = QWTML(I, J) * TRWEE(I)

WTLQMLER(I, J) = QWTML(I, J) * TRWER(I)

WTLQML(I, J) = WTLQMLEE(I, J) + WTLQMLER(I, J)

Where

I = Calendar year

J = Quarter

TRWEE = OASDI employee tax rate

TRWER = OASDI employer tax rate

WTLQML = Quarterly OASDI taxable military combined employee-employer wage liabilities

WTLQMLEE = Quarterly OASDI taxable military employee wage liabilities

WTLQMLER = Quarterly OASDI taxable military employer wage liabilities

Federal

WCF = WCFC + WCML

QWCF = QWCFC + QWCML

WTF = WTFC + WTML

QWTF = QWTFC + QWTML

WTLQFEE(I,J) = QWTF(I,J) * TRWEE(I)

WTLQFER(I,J) = QWTF(I,J) * TRWER(I)

WTLQF(I,J) = WTLQFEE(I,J) + WTLQFER(I,J)

Where
168

I = Calendar year

J = Quarter

QWCF = Quarterly OASDI covered Federal wages

QWCF C = Quarterly OASDI covered Federal Civilian wages

QWCML = Quarterly OASDI covered military wages

QWTF = Quarterly OASDI taxable Federal wages

QWTFC = Quarterly OASDI taxable Federal Civilian wages

QWTML = Quarterly OASDI taxable military wages

WCF = Annual OASDI covered Federal wages

WCFC = Annual OASDI covered Federal Civilian wages

WCML = Annual OASDI covered military wages

WTF = Annual OASDI taxable Federal wages

WTFC = Annual OASDI taxable Federal Civilian wages

WTLQF = Quarterly OASDI taxable Federal combined employee-employer wage liabilities

WTLQFEE = Quarterly OASDI taxable Federal employee wage liabilities

WTLQFER = Quarterly OASDI taxable Federal employer wage liabilities

WTML = Annual OASDI taxable military wages

State and Local wages

Annual OASDI taxable wages

BACW = 21583.61

AWCSL = WCSL / ESLC * BACW / AWCSLOD97

S = MAX / ASLC

IF(S.LT.0.02316573)THEN

 SLTR=S-(1.1803/1.71)*S**1.71

ELSE IF(S.LT.0.463314609)THEN

 SLTR=-(1.54738/1.6)*S**1.6-(.421147/2.5)*S**2.5+(3.34881/.5)*DEXP(-.5*S)+4.39012*S-6.697774474

ELSE IF(S.LT.0.833966296)THEN

 SLTR=-(.756943/1.8)*S**1.8+(.485982/2.3)*S**2.3-(.175681/3.2)*S**3.2+.88749*S+.004652169

ELSE IF(S.LT.1.945921357)THEN

 SLTR=(3.4167/.3)*DEXP(-.3*S)-(7.26467/.9)*DEXP(-.9*S)+(4.57049/1.5)*DEXP(-1.5*S)+1.0378*S-6.245057503

ELSE IF(S.LT.3.243202261)THEN

 SLTR=-(2.40293/.2)*DEXP(-.2*S)+(6.44952/.4)*DEXP(-.4*S)-(5.64852/.6)*DEXP(-.6*S)-.278204*S+5.099074279

ELSE IF(S.LT.5.559775305)THEN

 SLTR=-(.0434955/.6)*DEXP(-.6*S)-(4.00403/1.7)*DEXP(-1.7*S)+.00006219*S+.997065459

ELSE IF(S.LT.18.53258435)THEN

 SLTR=-(.0272758/.5)*DEXP(-.5*S)+.0000671826*S+.997657785

 ELSE

 SLTR=-(.00861948/.7)*S**(-.7)+1.000492941

END IF

WTSL=SLTR*WCSL

Where

AWCSL = Average OASDI covered State and Local wages adjusted for average wage used to produce equations

AWCSLOD97 = Average OASDI covered State and Local wages for 1997

BACW = Average OASDI covered State and Local wages in 1% sample data for 1997 used to produce equations

ESLC = OASDI covered State and Local employment

MAX = OASDI taxable maximum

S = Ratio of the OASDI taxable maximum to average covered State and Local wages

SLTR = Ratio of OASDI taxable to covered State and Local wages

WCSL = OASDI covered State and Local wages

WTSL = OASDI taxable State and Local wages

Quarterly OASDI covered wages

CSLQD(1)=1.0131455*TCSLD(I,1)+SLPD(I,1)

CSLQD(2)=1.0431906*TCSLD(I,2)+SLPD(I,2)

CSLQD(3)=.9060524*TCSLD(I,3)+SLPD(I,3)

CSLQD(4)=1.0365866*TCSLD(I,4)+SLPD(I,4)

QWCSL=CSLQD(1:4)*WCSL

Where

CSLQD = Proportion of annual OASDI covered State and Local wages paid in each quarter

I = Calendar year

QWCSL = Quarterly OASDI covered State and Local wages

SLPD = Payday variable for State and Local wages based on calendar

TCSLD = Proportion of annual NIPA State and Local wages paid in each quarter

WCSL = Annual OASDI covered State and Local wages
169

Quarterly OASDI taxable wages

QSL(1)=(CSLQD(1)-.24087*(1.-1./SLTR))

QSL(2)=(CSLQD(2)-1.0492*(1.-1./SLTR)+.51259*(1.-1./SLTR**2)-.07643*(1.-1./SLTR**3))

QSL(3)=(CSLQD(3)-5.99032*(1.-SLTR**2)+13.238*(1.-SLTR**3)-11.3291*(1.-SLTR**4)+3.52237*(1.-SLTR**5))

QSL(4)=(CSLQD(4)+8.99897*(1.-SLTR**.25)-5.48866*(1.-SLTR**.5))

TQSL=QSL(2)+QSL(3)+QSL(4)

QSL(2:4)= QSL(2:4)/TQSL

QWTSL(I,1)=QSL(1)*WTSL

QWTSL(I,2:4)=QSL(2:4)*(WTSL- QWTSL(I,1))

Where

CSLQD = Proportion of annual OASDI covered State and Local wages paid in each quarter

I = Calendar year

QSL = Proportion of annual OASDI taxable State and Local wages paid in each quarter

QWTSL = Quarterly OASDI taxable State and Local wages

SLTR = Ratio of OASDI taxable to covered State and Local wages

WTSL = OASDI taxable State and Local wages

Quarterly OASDI taxable wage liabilities

WTLQSL(I,J) = QWTSL(I,J) * TRW(I)

Where

I = Calendar year

J = Quarter

TRW = OASDI combined employee-employer tax rate

WTLQSL = Quarterly OASDI taxable State and Local combined employee-employer wage liabilities

Private household quarterly OASDI taxable wages and liabilities

QWTPHH(I,J) = WCPHH(I) * QDPHH(J)

WTLQPHH(I,J) = QWTPHH(I,J) * TRW(I)

Where

I = Calendar year

J = Quarter

QDPHH = Proportion of annual OASDI taxable private household wages paid in each quarter

QWTPHH = Quarterly OASDI taxable private household wages

TRW = OASDI combined employee-employer tax rate

WCPHH = Annual OASDI covered private household wages

WTLQPHH = Quarterly OASDI taxable private household combined employee-employer wage liabilities

Farm taxable wages

Annual OASDI

BAFMW = 7467.91

AWCFM97 = ACFMW(1997)

F = MAX / (ACFMW * BAFMW / AWCFM97)

IF(F.LT.0.066953142)THEN

 FMTR=F- (1.30211/1.75)*F**1.75

ELSE IF(F.LT.0.401718855)THEN

 FMTR=-(1.18244/1.35)*F**1.35+(.25412/1.75)*F**1.75+1.24681*F-.001598087

ELSE IF(F.LT.0.669531425)THEN

 FMTR=-(.508764/.6)*DEXP(-.6*F)-(.300083/2.8)*DEXP(-2.8*F)+.0188542*F+.966550312

ELSE IF(F.LT.1.87468799)THEN

 FMTR=-(.638146/.6)*DEXP(-.6*F)-(.0322774/1.5)*DEXP(-1.5*F)-.033706*F+1.133974442

ELSE IF(F.LT.2.41031313)THEN

 FMTR=-(2.64644/1.1)*DEXP(-1.1*F)+(17.4638/2)*DEXP(-2*F)-(26.4191/2.5)*DEXP(-2.5*F)+.00686748*F+.909154345

ELSE IF(F.LT.4.82062626)THEN

 FMTR=-(1.06567/1.3)*F**1.3+(.073837/2.1)*F**2.1+1.31021*F-.007628879

ELSE IF(F.LT.6.427501679)THEN

 FMTR=-(.178355/.5)*DEXP(-.5*F)-(1.70356/1.3)*DEXP(-1.3*F)+.00115171*F+.959096096

ELSE IF(F.LT.10.7125028)THEN

 FMTR=-(.0474377/0.35)*DEXP(-.35*F)-(1.32456/1)*DEXP(-1*F)+.0016146*F+.957903052

ELSE IF(F.LT.11.38203422)THEN

 FMTR=-(.0581938/.35)*DEXP(-.35*F)+.00130453*F+.961918378
170

ELSE IF(F.LT.24.1031313)THEN

 FMTR=-(.0492564/.3)*DEXP(-.3*F)+.000761577*F+.97040299

ELSE

 FMTR=-(.00304904/.06)*DEXP(-.06*F)+1.000606299

END IF

TFMW=FMTR*WCFM

Where

ACFMW = Annual average OASDI covered farm wages

AWCFM97 = Annual average OASDI covered farm wages for 1997

BAFMW = Average farm wage in 1% sample data for 1997 used to produce equations

F = Ratio of taxable maximum to annual average OASDI covered farm wages adjusted for average wage used in 

equations

FMTR = Ratio of OASDI taxable to covered farm wages

MAX = OASDI taxable maximum

TFMW = Annual OASDI taxable farm wages

Quarterly OASDI wages and liabilities

QWTFM(I,J) = TTFMD(I,J) * TFMW

WTLQFM(I,J) = QWTFM(I,J) * TRW(I)

Where

I = Calendar year

J = Quarter

QWTFM = Quarterly OASDI taxable farm wages

TFMW = Annual OASDI taxable farm wages

TRW = OASDI com

TTFMD = Proportion of annual OASDI taxable farm wages paid in each quarter

WTLQFM = Quarterly OASDI taxable farm combined employee-employer wage liabilities

Quarterly OASDI taxable employee tips

QWTTIPSEE(I,J) = QDTIP(J) * WTTIPSEE(I)

QWTTIPSEE(I,2) = QWTTIPSEE(I,2) + WTTIPSSR(I)

WTLQTIPSEE(I,J) = QWTTIPSEE(I,J) * TRW(I)

Where

I = Calendar year

J = Quarter

QDTIP = Proportion of annual OASDI taxable tips received in each quarter

QWTTIPSEE = Quarterly OASDI taxable tips received by employees

WTLQTIPSEE = Quarterly OASDI combined employee-employer wage liabilities on taxable tips received by employees

TRW = OASDI combined employee-employer tax rate

WTTIPSEE = Annual OASDI taxable tips received by employees reported by employers

WTTIPSSR = Annual OASDI taxable tips received by employees self-reported on income tax returns

Private non-farm OASDI taxable wages and liabilities

Annual

WTPNF = WTER – WTFC – WTML – WTSL – TFMW – WTTIPSEE – WTTIPSSR

Where

TFMW = Annual OASDI taxable farm wages

WTSL = Annual OASDI taxable State and Local wages

WTFC = Annual OASDI taxable Federal Civilian wages

WTPNF = Annual OASDI taxable private non-farm wages excluding tips

WTTIPSEE = Annual OASDI taxable tips received by employees reported by employers

WTTIPSSR = Annual OASDI taxable tips received by employees self-reported on income tax returns

WTER = Annual OASDI taxable employer wages

Quarterly

BACW93 = 21912.00

NACW = BACW93 / ACW93 * AWC

X = MAX / NACW

IF(X.LT.0.91274)THEN

 TWTR=1D0+.990751*DEXP(X)**(-1)/(-1)-.013904602

ELSE IF(X.LT.2.05367)THEN
171

TWTR=1D0+(-.003129*X+(1.167562*DEXP(X)**(-1.17)/(-1.17)))-.065747345

ELSE IF(X.LT.4.791895)THEN

 TWTR=1D0+(.003962*X+(.770093*X**(-1.85053))/(-1.85053))-.06071106

ELSE

 TWTR=1D0+(.267708*X**(-.94))/(-.94)+.00066 

END IF

IF(TWTR.LT.0.70)THEN

 QP(1)=-(-0.000575+0.18692*DLOG(TWTR)-0.23133*DLOG(TWTR)**2-

0.10453*DLOG(TWTR)**3+0.04306*DLOG(TWTR)**4+0.01906*DLOG(TWTR)**5)-0.0325201+PD(1)+TCPD(I,1)

 QP(2)=-(0.00657+1.7015*TWTR-8.60615*TWTR**2+14.444*TWTR**3-9.97171*TWTR**4+2.42519*TWTR**5)-

0.0080956+PD(2)+TCPD(I,2)

 QP(3)=-(0.12167+1.31142*TWTR**3-6.31672*TWTR**4+8.03785*TWTR**5-3.15412*TWTR**6)+0.019325+PD(3)+TCPD(I,3)

 QP(4)=-(0.1548-0.41354*TWTR**5+0.25874*TWTR**7)+0.0197767+PD(4)+TCPD(I,4)

ELSE IF(TWTR.LT.0.88)THEN

 QP(1)=0.224763-0.237056*TWTR+PD(1)+TCPD(I,1)

 QP(2)=0.190385-0.209676*TWTR+0.00176*(TWTR-0.7)/(0.88-0.7)+PD(2)+TCPD(I,2)

 QP(3)=-0.052523+0.05309*TWTR+PD(3)+TCPD(I,3)

 QP(4)=-0.354571+0.38249*TWTR+PD(4)+TCPD(I,4)

ELSE

 QP(1)=0.968092-1.877574*TWTR+0.904348*TWTR**2+PD(1)+TCPD(I,1)

 QP(2)=-0.468266+1.148107*TWTR-0.690132*TWTR**2+PD(2)+TCPD(I,2)

 QP(3)=-0.850885+1.824094*TWTR-0.981557*TWTR**2+PD(3)+TCPD(I,3)

 QP(4)=0.350767-1.093966*TWTR+0.766972*TWTR**2+PD(4)+TCPD(I,4)

END IF

IF(PTR.LT.0.86)THEN

 QP(J)=QP(J)+ADJTP(J)

ELSE

 IF((ADJCP(J)-ADJTP(J)).NE.0D0)QP(J)=QP(J)+ADJTP(J)+((PTR-BPTR)/(1.-BPTR))**4*(ADJCP(J)-ADJTP(J))

END IF

QWTPNF(I, J) = QP(J) * WTPNF(I) + QWTTIPSEE(I, J) + QWTPHH(I, J)

QWTPNF(I, 2) = QWTPNF(I, 2) + WTTIPSSR(I)

Where

ACW93 = Annual average OASDI covered wage for 1993

AWC = Annual average OASDI covered wage for current year

BACW93 = Annual average OASDI covered wage for 1993 from actual data used to determine taxable to covered wage equations

I = Calendar year

J = Quarter

MAX = Annual OASDI taxable maximum

NACW = Annual average OASDI covered wage for current year adjusted for average from actual data used to determine 

equations

PD = Payday variable for private non-farm based on calendar

QP = Proportion of annual OASDI taxable private non-farm wages excluding taxable tips paid in each quarter

QWTPNF = Quarterly OASDI taxable private non-farm wages including tips

TCPD = Proportion of annual NIPA private wages paid in each quarter

TWTR = Ratio of OASDI taxable to covered wages computed using equations based on data for 1993

X = Ratio of annual OASDI taxable maximum to adjusted annual average OASDI covered wage (NACW)

Quarterly OASDI wage liabilities

WTLQPNF(I,J) = (QWTPNF(I,J) – QWTPHH(I,J)) * TRW(I)

Where

QWTPHH = Quarterly OASDI taxable private household wages

QWTPNF = Quarterly OASDI taxable private non-farm wages including tips

TRW = OASDI combined employee-employer tax rate

WTLQPNF = Quarterly OASDI tax liabilities from taxable private non-farm wages including tips, excluding private household 

taxable wages

Total quarterly OASDI taxable wages and wage liabilities

QWT(I,J) = QWTPNF(I,J) + QWTF(I,J) + QWTSL(I,J) + QWTFM(I,J)

WTLQ(I,J) = QWT(I,J) * TRW(I)

Where

I = Calendar year

J = Quarter

QWT = Quarterly OASDI taxable wages

QWTF = Quarterly OASDI taxable Federal wages
172

QWTFM = Quarterly OASDI taxable farm wages

QWTPNF = Quarterly OASDI taxable private non-farm wages including tips

QWTSL = Quarterly OASDI taxable State and Local wages

WTLQ = Quarterly OASDI taxable wage liabilities

2-4.12 OASDI quarterly taxable wage liability collections (WTLQC)

OASDI taxable private non-farm wages by sub-quarterly periods

PTR =WTP/WCP

MR =MAR(I)-.04346*(1.-PTR)+.08497*(1.-PTR)**2

JR =JUN(I)-.02627*(1.-PTR)-.26844*(1.-PTR)**2

SR =SEP(I)-.12321*(1.-PTR)-.02344*(1.-PTR)**2

DR =DEC(I)-.12468*(1.-PTR)-.20710*(1.-PTR)**2

MWTP(1)=QWTP(I,1)*MR

MWTP(2)=QWTP(I,1)-MWTP(1)

MWTP(3)=QWTP(I,2)*JR

MWTP(4)=QWTP(I,2)-MWTP(3)

MWTP(5)=QWTP(I,3)*SR

MWTP(6)=QWTP(I,3)-MWTP(5)

MWTP(7)=QWTP(I,4)*DR

MWTP(8)=QWTP(I,4)-MWTP(7)

Where

DEC = Proportion of fourth quarter OASDI covered private non-farm wages (excluding tips and household) paid in December

DR = Proportion of fourth quarter OASDI taxable private non-farm wages (excluding tips and household) paid in December

I = Calendar year

JR = Proportion of second quarter OASDI taxable private non-farm wages (excluding tips and household) paid in June

JUN = Proportion of second quarter OASDI covered private non-farm wages (excluding tips and household) paid in June

MAR = Proportion of first quarter OASDI covered private non-farm wages (excluding tips and household) paid in March

MR = Proportion of first quarter OASDI taxable private non-farm wages (excluding tips and household) paid in March

MWTP = OASDI taxable private non-farm wages (excluding tips and household) paid in last month and in first two months of 

quarter

PTR = Ratio of annual OASDI taxable private non-farm wages (excluding tips and household) to covered private non-farm wages

QWTP = Quarterly OASDI taxable private non-farm wages (excluding tips and household)

SEP = Proportion of third quarter OASDI covered private non-farm wages (excluding tips and household) paid in September

SR = Proportion of third quarter OASDI taxable private non-farm wages (excluding tips and household) paid in September

WCP = Annual OASDI covered private non-farm wages

WTP = Annual OASDI taxable private non-farm wages (excluding tips and household)

OASDI taxable private non-farm wages collected on in same quarter wages are paid

TRAT =RATEE(I,5)

CA =.95

MWCP(1)=QWSCPNF(I,1)*MAR(I)

MWCP(2)=QWSCPNF(I,1)-MWCP(1)

MWCP(3)=QWSCPNF(I,2)*JUN(I)

MWCP(4)=QWSCPNF(I,2)-MWCP(3)

MWCP(5)=QWSCPNF(I,3)*SEP(I)

MWCP(6)=QWSCPNF(I,3)-MWCP(5)

MWCP(7)=QWSCPNF(I,4)*DEC(I)

MWCP(8)=QWSCPNF(I,4)-MWCP(7)

RCSM =.80

QRMREQ=750.

QRWREQ=11250.

RMF=70786.*WSP(I)/1001400.

CALL ITERNU(QRMREQ,MWTP(2),MWCP(2),TRAT,RMF,PWCS(1))

CALL ITERNU(QRWREQ,MWTP(1),MWCP(1),TRAT,RMF,PWCE(1))

CALL ITERNU(QRMREQ,MWTP(4),MWCP(4),TRAT,RMF,PWCS(2))

CALL ITERNU(QRWREQ,MWTP(3),MWCP(3),TRAT,RMF,PWCE(2))

CALL ITERNU(QRMREQ,MWTP(6),MWCP(6),TRAT,RMF,PWCS(3))

CALL ITERNU(QRWREQ,MWTP(5),MWCP(5),TRAT,RMF,PWCE(3))

CALL ITERNU(QRMREQ,MWTP(8),MWCP(8),TRAT,RMF,PWCS(4))

CALL ITERNU(QRWREQ,MWTP(7),MWCP(7),TRAT,RMF,PWCE(4))

DO J=1,4

 QWTPC(I,J)=PWCS(J)+PWCE(J)*RCSM*CA

 QWTPF(I,J)=QWSTXPHH(I,J)-QWTPC(I,J)

END DO

Where
173

AWSCODXSRT = Annual average OASDI covered private non-farm wages (excluding household)

CA = Compliance allowance

DEC = Proportion of fourth quarter OASDI covered private non-farm wages (excluding tips and household) paid in 

December

I = Calendar year

J = Quarter

JUN = Proportion of second quarter OASDI covered private non-farm wages (excluding tips and household) paid in 

June

MAR = Proportion of first quarter OASDI covered private non-farm wages (excluding tips and household) paid in 

March

MWCP = OASDI covered private non-farm wages paid in third month and in first two months of each quarter

MWTP = OASDI taxable private non-farm wages paid in third month and in first two months of each quarter

PWCE = OASDI taxable private non-farm wages paid in the third month of each quarter

PWCS = OASDI taxable private non-farm wages paid in the first two months of each quarter on which taxes are 

collected in that quarter

QWSCPNF = Quarterly OASDI covered private non-farm wages

QRMREQ = Monthly deposit requirement

QRWREQ = Quarterly deposit requirement

QWSTXPHH = Quarterly OASDI taxable private non-farm wages (excluding household)

QWTPC = Quarterly OASDI taxable private non-farm wages on which employers deposit taxes in the quarter the wages 

were paid

QWTPF = Quarterly OASDI taxable private non-farm wages on which employers deposit taxes in the quarter after the 

wages were paid

RATEE(I,5) = OASDHI employee tax rate

RCSM = Proportion of OASDI taxable private non-farm wages wages paid in same quarter in which taxes are collected

RMF = Current year average wage size of firm

SEP = Proportion of third quarter OASDI covered private non-farm wages (excluding tips and household) paid in 

September

TRAT = OASDHI employee tax rate

WSP = Economy-wide (NIPA) private wages

SUBROUTINE ITERNU(A11,QPAR,QTOT,T,RMF,AMTOUT)

 R=QPAR/QTOT

 X=A11/(T*2.+.10)

 DO

 IWH=X*(.16011+.01998*LOG(X/RMF)-.01)

 FWH=T*2.*X*((-1.4402*LOG(1.+X/RMF)+1.)*(1.-R)+R)

 A1=IWH+FWH

 D=A11/A1

 N1=D*1000.

 IF(N1.EQ.999.OR.N1.EQ.1000)THEN

 RTAX=R+(1.-R)*(-1.07115*X/RMF+.38633*(X/RMF)**2+1)

 TOD=177.16+1142.7*DEXP(-(X/RMF))+1181.26*DEXP(-3.*(X/RMF))-907.88*DEXP(-4.*(X/RMF))+646.49*DEXP(-5.*(X/RMF))-

 165.09*DEXP(-6.*(X/RMF))-20.92*X/RMF-2906.07/(X/RMF+1.)**2+831.44/(X/RMF+1.)**3

 AMTOUT=QPAR-RTAX*TOD*QTOT

 RETURN

 END IF

 X=X*D

 END DO

END SUBROUTINE ITERNU

Where

A1 = Total (income plus FICA) taxes withheld

A11 = Deposit requirement

AMTOUT = OASDI taxable private non-farm wages paid in sub-quarterly period and collected on in same quarter

D = Ratio of deposit requirement to total taxes withheld

FWH = FICA taxes withheld

IWH = Income taxes withheld

N1 = Ratio of deposit requirement to total taxes withheld times 1000 (used to see how close we are to target)

QPAR = OASDI taxable private non-farm wages paid in sub-quarterly period

QTOT = OASDI covered private non-farm wages paid in sub-quarterly period

R = Initial ratio of OASDI taxable to covered private non-farm wages paid in sub-quarterly period

RMF = Current year average wage size of firm

RTAX = Ratio of OASDI taxable to covered private non-farm wages paid in sub-quarterly period

T = OASDHI employee tax rate

TOD = Proportion of liabilities to be deposited in quarter after that in which wages paid

X = Taxable wage amount needed to meet deposit requirement

OASDI taxable private wages collected on in same quarter wages paid and in following quarter
174

QWTPCQ(I,J)=QWTPC(I,J)+QWTPHHCQ(I,J)+QWTFM(I,J)

QWTPFQ(I,J)=QWTPF(I,J)+QWTPHHFQ(I,J)

OASDI taxable State and Local wages collected on in same quarter wages paid and in following quarter

SLTR=WTSL/WCSL

LMPW(1)=MARSL(I)-.00329*(1.-SLTR**2)

LMPW(2)=JUNSL(I)-.68187*(1.-SLTR**3)+.52206*(1-SLTR**4)

LMPW(3)=SEPSL(I)-1.33596*(1.-SLTR)+1.51187*(1.-SLTR**2)-.63523*(1.-SLTR**3)

LMPW(4)=DECSL(I)-2.03892*(1.-SLTR)+1.90430*(1.-SLTR**2)-.6633*(1.-SLTR**3)

DO J=1,4

 SLCR(J)=(1.-LMPW(J))+LMPW(J)*LMCRPR(I-16,J)

 QWTSLC(I,J)=SLCR(J)*QWTSL(I,J)

 QWTSLF(I,J)=QWTSL(I,J)-QWTSLC(I,J)

END DO

Where

DECSL = Proportion of OASDI taxable State and Local wages paid in fourth quarter which are paid in December

I = Calendar year

J = Quarter

JUNSL = Proportion of OASDI taxable State and Local wages paid in second quarter which are paid in June

LMCRPR = Proportion of OASDI taxable State and Local wages paid in final month of quarter on which employers are to deposit 

taxes in the same quarter

LMPW = Proportion of quarterly OASDI taxable State and Local wages paid in final month of quarter

MARSL = Proportion of OASDI taxable State and Local wages paid in first quarter which are paid in March

QWTSL = Quarterly OASDI taxable State and Local wages paid in quarter

QWTSLC = Quarterly OASDI taxable State and Local wages paid in quarter on which taxes are deposited by the employer in the 

same quarter

QWTSLF = Quarterly OASDI taxable State and Local wages paid in quarter on which taxes are deposited by the employer in the 

following quarter

SEPSL = Proportion of OASDI taxable State and Local wages paid in third quarter which are paid in September

SLCR = Proportion of OASDI taxable State and Local wages paid in quarter on which taxes are deposited by the employer in 

the same quarter

SLTR = Ratio of OASDI taxable to covered State and Local wages

WCSL = Annual OASDI covered State and Local wages

WTSL = Annual OASDI taxable State and Local wages

OASDI taxable wages collected on in same quarter wages paid and in following quarter

WTQCQ(I,J)= QWTPCQ(I,J)+ QWTSLC(I,J)+QWTF(I,J)

WTQFQ(I,J)= QWTPFQ(I,J)+ QWTSLF(I,J)

Where

I = Calendar year

J = Quarter

QWTF = Quarterly OASDI taxable Federal wages

QWTPCQ = Quarterly OASDI taxable private wages collected on in same quarter wages paid

QWTPFQ = Quarterly OASDI taxable private wages collected on quarter following that in which wages paid

QWTSLCQ = Quarterly OASDI taxable State and Local wages collected on in same quarter wages paid

QWTSLFQ = Quarterly OASDI taxable State and Local wages collected on in quarter following that in which wages paid

WTQCQ = Quarterly OASDI taxable wages collected on in same quarter wages paid

WTQFQ = Quarterly OASDI taxable wages collected on in quarter following that in which wages paid

Quarterly OASDI wage tax collections

WTLQC(I,1) = TRW(I-1) * WTQFQ(I-1,4) + TRW(I) * WTQCQ(I,J)

DO J = 2, 4

 WTLQC(I,J) = TRW(I) * (WTQFQ(I,J-1) + WTQCQ(I,J))

END DO

Where

I = Calendar year

J = Quarter

TRW = OASDI combined employee-employer tax rate

WTLQC = Quarterly OASDI wage tax collections

WTQCQ = Quarterly OASDI taxable wages collected on in same quarter wages paid

WTQFQ = Quarterly OASDI taxable wages collected on in quarter following that in which wages paid

2-4.13 Quarterly Self-Employed Net Income Tax Collections (SELQC)
175

DO J = 1, 4

 SELQC(I,J) = SECRCY(I,J) * SEL(I) + SECRPY(I,J) * SEL(I-1)

END DO

Where

I = Calendar year

J = Quarter

SECRCY = Proportion of OASDI taxable self-employment earnings collected on in same year earned

SECRPY = Proportion of OASDI taxable self-employment earnings collected on in year following that in which earned

SEL = OASDI taxable self-employment earnings liabilities

SELQC = Quarterly OASDI self-employed net income tax collections

2-4.14 SECA Appropriation Adjustments (MSECAAA)

MSECAAA = 0

DO L = I-2, I-9, -1

 MSECAAA = MSECAAA + SEAACO(I,J) * SELIAC(L)

END DO

Where

I = Calendar year

J = Quarter

L = Liability year

MSECAAA = OASDI SECA appropriation adjustment for quarter (assigned to last month in quarter)

SEAACO = Proportion of past year’s OASDI self-employment tax liability which will be reported in current quarter

SELIAC = Prior years’ OASDI self-employment tax liability
176

Appendix 2-2

Economic Acronyms

AA Appropriation adjustments 

ACE Average OASDI covered earnings

ACSE Average OASDI covered self-employed income

ACW

ACWC

ADJ_FSA_FC

AWEFC_N

AWI

AWSE

AWSGEFC

AWSGFC

AWSGFM

AWSGGEFC

AWSGGESL

AWSP

AWSPH

AWSPL

AWSSP

Average OASDI covered wage

Average economy-wide wage

Adjustment to lower federal civilian covered wages relative to NIPA 

wages due to a presumed increase in the relative amount placed into an 

FSA 

Average wage for Federal civilian employees not covered under OASDI

Average wage index calculated by SSA; based on the average wage of all 

workers with wages from Forms W-2

Economy-wide average wage

Average wage for the Federal government enterprises

Average wage for the Federal civilian government

Average wage for the military

Average wage for the Federal government & government enterprises

Average wage for State and local government and government enterprises

Average wages, private sector

Average wage in private household sector

Average wages, private sector, 2-year moving average

Average compensation, private sector

AWSSPBNFXGE

AWSSPES

Average compensation, private nonfarm business, excluding government 

enterprises

Average compensation, private sector, educational services

AWSSPF

AWSSPHS

AWSSPL

AWSSPSS

AWSUI

AWS_MEF

AYF

AYF_K

AYNF

AYNF_K

Average compensation, private farm, wage workers

Average compensation, private sector, health services 

Lagged average compensation for private sector workers

Average compensation, private sector, social services

Average wage of workers under UI 

Average wage for employees with any wages (covered and noncovered) 

posted to the MEF

Average proprietor income, private farm 

Ratio of average self-employment income to average wage-worker 

compensation for the agriculture sector

Average proprietor income, private nonfarm business

Ratio of average self-employment income to average wage-worker 

compensation for the nonagriculture sector

BEA The Bureau of Economic Analysis

BLS

CFCGEFC

CFCGESL

CFCGFC

The Bureau of Labor Statistics

Compensation of fixed capital, Federal government enterprises

Government consumption of fixed capital, Government enterprises, State 

& local

Compensation of fixed capital, Federal civilian
177

CFCGFM

CFCGSL

Federal Government Consumption Expenditures, Defense Consumption 

Expenditures

State & Local Government consumption expenditures, Gross output of 

general government, Value added, consumption of general government 

fixed capital

CMB_TOT

CML

Workers that have a combination of both OASDI covered wages and selfemployed income.

Ratio of Federal military OASDI covered wages to NIPA wages

COV

CP

Economic Sub-Process: Covered Employment and Earnings

Ratio of Private OASDI Covered to NIPA wages; OASDI private coverage 

ratio

CPI The Consumer Price Index for Urban Wage Earners and Clerical Workers 

(CPI-W) is an official measure of inflation in consumer prices, published 

by the BLS.

CPS

CR_UI 

CRAZ1

Current Population Survey, conducted monthly by the Bureau of Census 

for the Bureau of Labor Statistics. It is the source of historical monthly 

economic data (such as labor force, civilian noninstitutional population, 

and unemployment) used to project US employment.

Civilian pay raise

CSE_TOT

CSLA

Total OASDI covered self-employed income

Ratio of State and Local OASDI Covered to NIPA wages

CSRS Civil Service Retirement System

CSW

DNEDMIL

DRTP, DRTPP, 

DRTPN, DRTP1Q, 

DNRTP1Q, 

DPRTP1Q

Self-employed only workers (SEO) plus combination workers 

(CMB_TOT)

Decreases in the military population (as shown by the difference over four 

quarters)

Dummy variables for positive and negative 

economic cycle trends

E

E_FE

Total employment, CPS concept (i.e., average of monthly estimates of 

total wage and salary workers, plus self employed, plus unpaid family 

workers)

Civilian employment level at full employment (i.e., at potential GDP)

EA

EAS

EAW

Total agricultural employment

Civilian Employment Level, Self employed workers: agriculture, SA

Employment by class of worker, agricultural wage workers

EDMIL

EGFC

EGEFCPS

Total number serving in the US Armed Forces estimated by the 

Department of Defense and published by the Census Bureau

Federal civilian government employment

Employment, Establishment Data, All Employees: Government, Federal 

Government Enterprises, U.S. Postal Service
178

EGGEFC

EGGESL

EMPTRDI

EMPTRHI

EMPTROASI

ENA

ENAS

ENAU

ENAW

ENAWPBXGE

ENAWPH

ENAWSPBXGE

Employment, Establishment Data, All Employees: Government, Federal 

Government, SA

Employment, State & Local government enterprises 

DI employer tax rate

HI employer tax rate

OASI employer tax rate

Civilian Employment Level, Nonagricultural industries, 16 years and over, 

SA

Employment by class of worker, nonagricultural self-employed

Employment by class of worker, nonagricultural unpaid family workers

Employment by class of worker, nonagricultural wage workers 

Employment for private nonfarm business

Employment by class of worker, nonagricultural wage workers, private 

household workers

Employment for private nonfarm business and nonagricultural selfemployed

EO Total employment in the other immigrant population

EO_A Total employment in the other immigrant population who are temporarily 

authorized to reside or work in the US

EO_ESF Total employment in the other immigrant population whose reported 

earnings are posted to the Earnings Suspense File

EO_MEF Total employment in the other immigrant population whose earnings are 

reported and posted to the Master Earnings File

EO_MEFC Total employment in the other immigrant population whose earnings are 

reported and posted to the Master Earnings File and are OASDI-covered

EO_NA Total employment in the other immigrant population who have overstayed 

their authorization

EO_NO Total employment in the other immigrant population who were never 

authorized to reside or work in the US

EO_UND

EP

EPES_EST

EPHS_EST

EPSS_EST

Total employment in the other immigrant population that is strictly in the 

underground economy (i.e., with no earnings reported)

Employees in Private industries

Employees by industry, Private industries, Educational services

Employment for private health services

Employees by industry, Private industries, Social Assistance

ES Self-employed workers

ETP Effective annual taxable payroll, equal to total employer taxable OASDI 

wages plus total self-employed taxable income minus one half of the 

multi-employer refund wages 

EU Unpaid family workers

EW Wage and salaried workers

FERS Federal Employee Retirement System

GDP Gross domestic product
179

GDP05

GDPG

GDPGE

GDPGEFC

GDPGESL

GDPGF

GDPGFC

GDPGFM

GDPGGE

GDPGGEFC

GDPGGESL

GDPGSL

GDPPBNFXGE

GDPPF

GDPPH

GDPPNI

GDP, 2005$

GDP, General Government

GDP, Federal and State & local government enterprises

GDP, Federal civilian government enterprises

GDP, State & local government enterprises

GDP, General Government, Federal

GDP, Federal civilian

GDP, military

GDP, Federal and State & local government enterprises

GDP, Federal civilian government and government enterprises

GDP, State & local government and government enterprises

GDP, General Government, State & Local

GDP, private nonfarm business, excluding government enterprises

GDP, private business sector, farm

GDP, Private Households

GDP, Nonprofit institutions serving households

HI

HIFC_L

HIFM_L

HIP_L

HISL_L

KGDP05

Hospital insurance

HI Employer Liability, Federal Civilian

HI Employer Liability, Federal Military

HI Employer Liability, Private

HI Employer Liability, State & Local

Potential real GDP, 2005$

LC US labor force, equal to the sum of number of persons employed and 

number of persons seeking employment

LFPR Labor force participation rate, defined as the ratio of the number of persons 

in the US labor force to the number of persons in the US noninstitutional 

population.

M

MER

MRAZ

Military population 

Multi-employer refund wages

Military pay raise

N Civilian noninstitutional population

NCE Total noncovered employment

NIPA The National Income and Product Accounts, published by the BEA, 

providing historical estimates of quarterly earnings and output measures

NRA Normal retirement age

OASDI

OASDIFC_L

OASDIFM_L

OASDIP_L

OASDISL_L

Old-Age, Survivors, and Disability Insurance

OASDI Employer Liability, Federal Civilian

OASDI Employer Liability, Federal Military

OASDI Employer Liability, Private

OASDI Employer Liability, State & Local

OASDHI Old-Age, Survivors, Disability, and Health Insurance

OLI

OLI_CSRS1

OLI_FC

Employer contributions for employee pension and insurance funds

Contributions for CSRS employees’ pay

Other labor income, Federal civilian
180

OLI_FERS1

OLI_FERSFC

OLI_GGE

OLI_GHI

OLI_GHI_FC

OLI_GHI_P

OLI_GHI_SL

OLI_GLI

OLI_GLI_FC

OLI_GLI_P

OLI_GLI_SL

OLI_P

OLI_PPPS

OLI_PPS

OLI_RETFC

OLI_RETFM

OLI_RETSL

OLI_SL

OLI_SU

OLI_WC

OLI_WCP

OLI_WCSL

OLIF_RETFCO

OOH

OP

OP_A

Contributions for FERS employees’ pay

Employer contributions to Thrift Savings Plan for FERS employees

Other labor income, government and government enterprises

Other labor income by type, Employer contributions to pension and 

welfare funds, private welfare funds, Group health insurance

Employer contributions for employee pension & insurance funds, group 

health insurance, Federal civilian government sector

Employer contributions for employee pension & insurance funds, group 

health insurance, private sector

Employer contributions for employee pension & insurance funds, group 

health insurance, State & local government sector

Employer contributions for employee pension and insurance funds, 

Group life insurance

Employer contributions for employee pension & insurance funds, group 

life insurance, Federal civilian government sector

Employer contributions for employee pension & insurance funds, group 

life insurance, private sector

Employer contributions for employee pension & insurance funds, group 

life insurance, State & local government sector

Employer contributions for employee pension and insurance funds, 

private industries

Other Labor Income, Private Sector Pension and Profit Sharing 

Employer contributions for employee pension and insurance funds, 

Pension & profit-sharing

Employer contributions for employee pension and insurance funds, 

Publicly administered government employee retirement plans, Federal 

civilian

Employer contributions for employee pension and insurance funds, 

Publicly administered government employee retirement plans, Federal 

military

Employer contributions for employee pension and insurance funds, 

Publicly administered government employee retirement plans, State and 

local

Other labor income, State and local

Employer contributions for employee pension and insurance funds, 

Supplemental unemployment

Employer contributions for employee pension and insurance funds, 

Workers’ compensation

Private employer contribution to other labor income, total for workers' 

compensation 

Employer contributions to workers’ compensation, State and local

Other government contributions to Federal civilian retirement

Owner-occupied housing

Other immigrant population

Other immigrant population who are temporarily authorized to reside or 

work in the US
181

OP_NA

OP_NO

PGDP

PGDPAF

Other immigrant population who have overstayed their authorization

Other immigrant population who were never authorized to reside or 

work in the US

Gross Domestic Product Price Index, Units: 2005=100

Deflator for farm output

PIA Primary insurance amount

PIARR PIA replacement rate, defined as the ratio of a hypothetical medium 

scale worker’s PIA to his/her career average indexed earnings.

PGDP GDP price deflator

PBNFXGE Private nonfarm business excluding government enterprises

RCMB Proportion of wage workers who are also self-employed 

(CMB_TOT/WSW)

RCSE

RCWSF

RCWSM

RCWSP

RCWSSL

Covered self-employed ratio, defined as the ratio of total covered selfemployment income to total proprietor income (CSE_TOT/Y).

Ratio of compensation to wages in the Federal government

Ratio of compensation to wages in the military

Ratio of compensation to wages in the private sector

Ratio of compensation to wages in the State and local sector

RD Disability prevalence ratio, defined as the ratio of disabled worker 

beneficiaries to the disability-insured population.

RELMAX

RELMAX_UI

Ratio of the TAXMAX to averaged covered earnings

Ratio of the aggregate weighted average of the UI taxable maximum to 

the average UI wage

RET Earnings test ratio, defined the ratio of the maximum amount of earnings 

before an OASDI benefit is reduced to the average wage index. 

RFS

RGR_GHI

RHIP_L

Family size ratio, defined as the ratio of the number of children under 6 

to mothers of a certain age.

Product of HI tax rate, private coverage ratio, and the taxable ratio

RM Military ratio, the ratio of the US armed forces to the noninstitutionalized population.

RMER

ROASDIP_L

ROLI_PPPS

ROLI_SU

ROLI_WCP

Multi-employer refund wage ratio, defined as the ratio of multiemployer refund wages to total OASDI wages.

Product of OASDI tax rate, private coverage ratio, and the taxable ratio

Ratio of employer contributions to private pension and profit-sharing to 

private wages

Ratio of private employer contributions for employee pension and 

insurance funds, Supplemental unemployment to private wages

Ratio of private employer contribution to other labor income, total for 

workers’ compensation to private wages

RSET

RSOC_UIP

RSOC_WCP

Self-employed net income taxable ratio, defined as the ratio of total selfemployed taxable income to total OASDI wages.

Ratio of private employer contributions to social insurance, total for 

unemployment insurance, to private wages

Ratio of private employer contributions to social insurance, total for 
182

RSOCF_PBG

RSOCSL_WC

workers' compensation to private wages

Ratio of private employer insurance contribution to the Pension Benefit 

Guaranty Trust Corporation to private wages

Ratio of combined Private and State & local sector employer 

contributions to social insurance for workers' compensation to the 

combined Private and State and local sector employer contributions to 

workers' compensation

RTE Ratio of total employment to the sum of wage & salary, self-employed 

workers, and the military (TE/(EW + ES + military))

RTP A summary measure of the economic cycle equal to the ratio of real 

GDP to potential GDP

RU

RUIWS

RWCWS

Civilian unemployment rate defined as the ratio of the unemployed US 

labor force to the total US labor force

Effective tax rate for employer contributions to unemployment 

insurance

Effective tax rate for employer contributions to workers’ compensation

RWSC Covered wage ratio, defined as the ratio of OASDI covered wages to 

total wage and salary disbursements (WSC/WSD)

RWSD

RWSSPBNFXGE

Earnings ratio, defined as the ratio of total wage and salary 

disbursements to total wage and worker compensation(WSD/WSS)

Ratio of compensation to GDP in private business nonfarm excluding 

government enterprises

RWSSY Total compensation ratio, defined as the ratio of total compensation to 

gross domestic product (WSSY/GDP)

RWTEE Employee taxable ratio, defined as the ratio total employee OASDI 

taxable wages to total OASDI covered wages.

RY Income ratio, defined as the percentage of total compensation 

attributable to proprietor income (Y/WSSY).

S&L State and Local government

SEL Self-employment tax liabilities

SELQC Quarterly self-employed net income tax collections

SEO Workers that report only OASDI covered self-employed earnings

SEPR Self employed participation rate (the proportion of employed persons 

that are self employed) 

SET Total self-employed taxable income

SOC

SOC_FC

SOC_FM

SOC_GGE

SOC_P

SOC_SL

SOC_UIP

Employer contributions for government social insurance

Employer contributions for social insurance, Federal civilian sector

Employer contributions for government social insurance, Federal 

government, military sector

Employer contributions for social insurance, government and 

government enterprises

Employer contributions for social insurance, private industries

Employer contributions for social insurance, State and local sector

Private employer contributions to social insurance, total for 

unemployment insurance 
183

SOC_UISL

SOC_WCP

SOC_WCSL

SOCF_HI

SOCF_MIFM

SOCF_OASDI

SOCF_PBG

SOCF_RETRR

SOCF_UIF

SOCF_UIFC

SOCF_UIFED

SOCF_UIFM

SOCF_UIS

SOCF_WC

SOCSL_WC

State and local government employer contributions to social insurance, 

total for unemployment insurance

Private employer contributions to social insurance, total for workers' 

compensation

State and Local government employer contributions to social insurance, 

total for workers'compensation

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Hospital Insurance 

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Military Medical 

Insurance

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Old-age, Survivors, And 

Disability Insurance

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Pension Benefit 

Guaranty

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Railroad Retirement

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Federal Unemployment 

Tax

Total federal civilian government employer contributions to 

unemployment insurance

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Federal Employees’ 

Unemployment Insurance

Total federal government employer contributions to unemployment 

insurance, military

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, State Unemployment 

Insurance

Contributions for Government Social Insurance, Employer 

Contributions, Federal Social Insurance Funds, Worker’s Compensation 

Contributions for Government Social Insurance, Employer 

Contributions, State and Local Social Insurance Funds, Workers’ 

Compensation

SSA Social Security Administration

TAXMAX OASDI contribution and benefit base

TAXPAY Economic Sub-Process: Taxable Payroll

TCE Total OASDI covered employment

TE

TEFC_N

Total “at any time” employment

Total “at any time” employment, Federal civilian, without Federal 

civilian OASDI

TEO Total “at any time” employment in the other immigrant population
184

TEO_ESF Total “at any time” employment in the other immigrant population 

whose reported earnings are posted to the Earnings Suspense File

TEO_MEF Total “at any time” employment in the other immigrant population 

whose earnings are reported and posted to the Master Earnings File

TEO_MEFC Total “at any time” employment in the other immigrant population 

whose earnings are reported and posted to the Master Earnings File and 

are OASDI-covered

TEO_UND

TMAXUI_SL

TRATE_UI

TRATIO_UI

Total “at any time” employment in the other immigrant population that 

is strictly in the underground economy (i.e., with no earnings reported)

Taxable maximum for State & local unemployment insurance

TRSE OASDI self-employed tax rate

TRW

TXRP

Combined OASDI employee-employer tax rate

OASDI private taxable ratio

U The number of persons in the labor force who are unemployed

USEAR Economic Sub-Process: U.S. Earnings 

USEMP

WEFC_N

WS

Economic Sub-Process: U.S. Employment

Wages for Federal civilian employees not covered under OASDI

Compensation of Employees, Wage and Salary Accruals 

WSC Total OASDI covered wages

WSD

WSDP

WSGEFC

WSGFC

WSGFM

WSGGEFC

WSGGESL

WSP

WSPF

WSPH

WSPNI

WSPRRB

WS_MEF

Total wage and salary disbursements

Private wage and salary disbursements

Government Wages and Salaries, Federal civilian, Government 

Enterprises

Wage and salary accruals by industry, Government, Federal civilian

Wage and salary accruals by industry, Government, Federal, Military

Wages for the Federal government & government enterprises

Wages for State and local government and government enterprises

Compensation of Employees, Wage and Salary Accruals

Wage and salary accruals by industry, Private industries, Farms

Wage and salary accruals by industry, Private industries, Households

Wage and salary accruals by industry, Private industries, Nonprofit 

institutions serving households

Wages covered by Railroad Retirement Act

Total wages posted to the MEF

WSS

WSSG

WSSGE

WSSGEFC

WSSGESL

WSSGF

WSSGFC

Total wage worker compensation

Compensation for Federal and State & local government

Compensation for Federal and State & local government enterprises

Compensation of employees by industry, Government, Federal

Compensation of employees by industry, Government, State and local 

government enterprises

Federal Government Consumption Expenditures, Compensation of 

General Government Employees

Compensation of employees by industry, Government, Federal civilian
185

WSSGFM

WSSGGE

WSSGGESL

WSSGSL

WSSP

WSSPBNFXGE

WSSPES

WSSPF

WSSPH

WSSPHS

WSSPNI

WSSPSS

Compensation of employees by industry, Government, Military

National Income w/o Capital Consumption Adjustment, Government

Compensation for the State & local government and government 

enterprises

State & Local Government Consumption Expenditures, Compensation 

of General Government Employees

Compensation of employees by industry, Private industries

Compensation in private business nonfarm excluding government 

enterprises

Compensation of employees by industry, Private industries, Educational 

services

Compensation of employees by industry, Private industries, Farms

Compensation of employees by industry, Private industries, Households

Compensation of employees by industry, Private industries, Health 

services

Compensation of employees by industry, Private industries, Nonprofit 

institutions serving households

Compensation of employees by industry, Private industries, Social 

assistance

WSSY Total compensation for wage and salary workers and proprietors

WSW

WSW_MEF

Wage and salary workers that report some OASDI covered earnings

Total number of employees with any wages posted to the MEF

WTEE Total employee OASDI taxable wages

WTER Total employer OASDI taxable wages

WTL Annual OASDI wage tax liabilities

WTLQ Quarterly OASDI wage tax liabilities

WTLQC Quarterly OASDI wage tax collections

Y

YF

YNF

Total proprietor income

National Income, Proprietors’ income with Inventory Valuation (IVA) 

and Capital Consumption Adjustment (CCAdj): farm sector

National Income, Proprietors’ income with IVA and CCAdj: nonfarm
186

Process 3:

Beneficiaries
187

3. Beneficiaries

OCACT uses the Beneficiaries process to project the fully insured and disability insured 

population, the number of disabled workers and their dependent beneficiaries, the number of 

retired worker and their dependent beneficiaries, and the number of dependent beneficiaries 

of deceased workers. The Beneficiaries process receives input data from the Demography and 

Economics sections along with data received from the Social Security Administration and other 

government agencies. Output data is provided to the Economics and Trust Fund Operations and 

Actuarial Status processes.

The Beneficiaries Process is composed of three subprocesses: INSURED, DISABILITY, and OLDAGE AND SURVIVORS. As a rough overview, INSURED projects the number of people in the 

Social Security area population that have sufficient work histories for disability and retirement 

benefit eligibility. DISABILITY projects the number of disabled workers and their dependent 

beneficiaries. OLD-AGE AND SURVIVORS projects the number of retired workers, their 

dependent beneficiaries, and the dependent beneficiaries of deceased workers.

All programs output data on an annual basis.
188

3.1. INSURED

3.1.a. Overview

Insured status is a critical requirement for a worker, who has participated in the covered 

economy, to receive Social Security benefits upon retirement or disability. The requirement for 

insured status depends on the age of a worker and his (or her) accumulation of quarters of 

coverage (QC).

INSURED is a simulation model that estimates the percentage of the population that is fully 

insured (FPRO) and disability insured (DPRO) throughout the projection period. These 

estimates are used in conjunction with estimates of the Social Security area population 

(SSAPRO) to estimate the number of people that are fully insured (FINPOP) and disability 

insured (DINPOP). FINPOP is then used by the OLD-AGE AND SURVIVORS INSURANCE 

subprocess, and both FINPOP and DINPOP are used by the DISABILITY subprocess. FINPOP and 

DINPOP are projected by age, sex, and cohort.

For each sex and birth cohort, INSURED simulates 30,000 work histories that represent the nonother immigration population (LEGPOP). These histories are constructed from past and 

projected cover worker rates of the non-other immigration population, median earnings, and 

amounts required for crediting QC. 

The equations for this subprocess are given below: 

FPRO = FPRO(·) (3.1.1)

DPRO = DPRO(·) (3.1.2)

FINPOP = FPRO * SSAPOP (3.1.3)

DINPOP = DPRO * SSAPOP (3.1.4)

3.1.b. Input Data

All data are updated annually, except those that are noted.

Long-Range OCACT Data

Demography 

 Social Security area population as end of year (1940 – 2090) by age (0 -100, age 100 

including age 100 and older), marital status (single, married, widowed, divorced) and 

sex (M, F). (Workflow 3.a)

 “Other immigrant” population as end of year (1963 – 2090) by age (0 -100, age 100 
189

including age 100 and older) and sex (M, F). (Workflow 3.c)

 Number of new “net legal immigrants” (legal immigrants – estimated legal 

emigrants) entering the Social Security area each year (1940 – 2090) by age (14 – 69) 

and sex (M, F). (Workflow 3.d)

 The population granted deferred action for childhood arrivals (DACAs) as end of year 

(2012-2090) by age (0 -100, age 100 including age 100 and older) and sex (M, F). 

(Workflow 3.e)

Economics

 Annual estimates of covered workers posted to the Master Earnings File (MEF) by 

sex (M, F) and age (0 – 100) for years (1937 – 2090). (Workflow 3.b)

 Annual projection (2010 – 2090) of average wage index and median covered earnings. 

(Workflow 3.g)

 “Other immigrant” workers with earnings posted to the (MEF) by sex (M, F), age (16 –

100), and for years (1964 – 2090). (Workflow 3.h) 

Beneficiaries

 Disabled-worker beneficiaries at year end (2012 – 2090) by age (15 – 66), sex (M, F) 

and duration (0 – 10, duration 10 including duration 10 and above) from the 

previous year’s Trustees Report. These data are read in from files that are 

generated annually from the Beneficiaries/DISABILITY (#3.2) area. (Workflow 3.f)

Short-Range OCACT data

 FINPOP by age (14 – 95, age 95 including age 95 and older) and sex (M, F) from the 

end of year 1969 to the end of Short-Range projection period (2023). (Workflow 2.f)

(MKS-2014.4)

DINPOP by age (15 – 66) and sex (M, F) from the end of year 1969 to the end of 

Short-Range projection period (2023). (Workflow 2.g) (MKS-2014.4)

Other input data

 Historical series of annual median earnings of covered workers by age group (<20, 

20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 

80-84) and sex (M, F) for years 1937-2010. Data are updated using the data in the 

most recent Social Security Annual Statistical Supplement Table 4.B6. (Workflow 

2.b) (MKS-2014.7)

 Number of disabled workers by age (20-69) and sex (M, F) for years 1958-2012. 

Ages 66-69 are zeros. Data are updated using the data from the historical disability 

file “wkrben”. (Workflow 2.e) (MKS-2014.2)
190

 The amount required for crediting one quarter of coverage for years 1937-2010 from 

a OCACT web site. (Workflow 2.d) (MKS-2014.4)

 Historical series of annual median earnings of all covered workers for years 1937-

2010. Data are updated using the data in the most recent Social Security Annual 

Statistical Supplement Table 4.B6. (Workflow 2.c) (MKS-2014.4)

 The number of all covered workers (wage/salary workers, self-employed workers) by 

sex and amount of earnings for 2010 in the most recent Social Security Annual 

Statistical Supplement Table 4.B7 & 4.B9. These are used to produce the input data 

for the distribution of earnings (FRACMOD.f90). (Workflow 2.a) (MKS-2014.2)

(Updated for the 2014 TR)

 ANNUAL factor (comparability factor between quarterly and annual reporting of 

earnings) by age (13-84) and sex (M, F) for years prior to 1978. These data are not 

updated, but was expanded to age 84 for the 2014TR. (Workflow 4.d) (MKS-2014.4)

 SLCT factor (adjustment factor to bring simulated fully insured rate in line with 

historical fully insured rate) by age (13-84) and sex (M, F). These data are updated 

when needed. (Workflow 4.b) (MKS-2014.5) Updated for 2014 TR

 SRCH factor (adjustment factor to bring simulated fully insured rate in line with 

historical fully insured rate) by age (13-84) and sex (M, F). These data are updated 

when needed. (Workflow 4.c) (MKS-2014.5) Updated for 2014 TR

 DIADJ factor (adjustment factor to bring simulated disability insured rate in line with 

historical disability insured rate) by age (13-69) and sex (M, F). These data are 

updated when needed. (Workflow 4.e) (MKS-2014.1) (Did not change for the 2014 

TR)

3.1.c. Development of Output

Equation 3.1.1 & 3.1.2 -

Determining the QC distribution 

There are three variables playing important roles in the simulation process starting from age 13 

through 84 of a birth cohort by sex. They include historical and projected covered worker rates 

of the non-other population24 by age and sex, the amounts required for crediting QC, and a 

cumulative worker distribution by earnings level.

Covered worker rates of the non-other population (CPRO) are the ratio of the non-other 

covered workers to the non-other Social Security area population. A non-other population is its 

total population minus its other immigrant population. Historical and projected (total and other 

immigrant) numbers of covered workers and the Social Security area population, which are 

provided by the Economics and Demography sections respectively, are used to calculate the 

rates for ages 15 through 84. For ages, 13 and 14, INSURED estimates covered worker rates 

based on the covered worker rates at age 15. 

The law specifies the amount of earnings needed to earn one QC for each year of the historical 

period. Its projection assumes the same growth rate as the Social Security average wage index. 

The cumulative worker distribution by earnings level is ‘FRAC’. It is a function of covered 

earnings relative to median earnings. For a given ratio of covered earnings relative to median 

 

24 The insured model treats the DACA population (those granted deferred action for childhood arrivals) like legal 

permanent resident immigrants.
191

earnings, FRAC returns the percentage of covered workers whose earnings relative to median 

earnings are less than the given ratio. It is constructed based on the latest historical data. It is 

used for each age and sex and is assumed to remain constant throughout the projection period. 

The program uses FRAC to estimate the percentage of covered workers that earn 0, 1, 2, 3 or 4 

QC in a given year. Thus, for a particular age and sex, the percentage of covered workers 

earning at least n QC is defined as: 

QCDist = 1 – FRAC

n QC amount *

median earnings

 

   

, for n = 1, 2, 3, 4

 where median earnings is for that age and sex.

Simulation process – assigning QC to records

Once the QCDist is known, the simulation process begins with 30,000 records for each sex and 

birth cohort. Starting with the QC distribution at age 13, INSURED randomly assigns a number 

of QC (1, 2, 3 or 4) to these records based on QCDist. 

For ages 14 to 84, INSURED begins the simulation process by randomly selecting records to 

represent new net legal immigrants from the covered worker portion of 30,000 records. For 

each record, a number of QC (1, 2, 3 or 4) is assigned on a uniform basis. Once a record is 

assigned a number of QC, INSURED nullifies the previous earnings of the record. 

After the records for new immigrants are selected, the rest of the records for ages 14 to 84 are 

either non-covered workers or covered workers. The total number of records assigned as noncovered workers is set equal to (1-covered worker rate) * 30,000. These records receive no QC. 

To identify records as non-covered workers, INSURED uses two parameters (SRCH, SLCT), which 

vary by age and sex. 

SRCH sets a limit on the number of consecutive records to be searched for a non-covered 

worker. In general, the younger age groups have lower SRCH values. SLCT is the number of 

consecutive prior years in which no QC were earned that is required in order for a simulated 

record to be assigned as a non-covered worker. Lower SLCT values are set for the very young 

and older age groups. Sensitivity analyses show that insured percentages are negatively 

correlated with these two parameters. When the female covered worker rates approach the 

male rates, the female SRCH and SLCT values are graded toward the male values25.

For each sex and birth cohort, the simulation process of assigning records as non-covered 

workers uses the following approach. This approach is repeated until the targeted number of 

non-covered workers is achieved.

1. One of the records, which is designated as one for legal immigrants, is randomly 

selected as the starting record.

2. Beginning with the starting record, each record is examined until a record that 

matches the SLCT criterion is found.

3. However, if the number of records examined equals the value of SRCH and no 

record matches the SLCT criterion, then the record closest to the SLCT criterion is 

 

25 This occurs when the female covered worker rate is greater than 80 percent of the male rate. When the female 

rate is equal to or greater than the male rate, the female SLCT and SRCH parameters are set equal to the male 

parameters. The parameters are linearly interpolated when the female covered worker rate is between 80 and 100 

percent of the male rate.
192

assigned no QC as a non-covered worker.

Initially, values for SRCH and SLCT are the same as those used in the prior Trustees Report. 

Adjustments to these values are only made when the results are not consistent with historical 

data.

The final step of the simulation process is to use QCDist to randomly assign QC of 0, 1, 2, 3 or 4 

to the remaining covered worker records, which are not new net legal immigrants, for ages 14-

84.

Determining Insured Status

Once the simulation process is complete, the insured status for each record at any age can be 

determined based on the total QC assigned up to that age. The simulated non-other fully 

insured percentage (FSIM_LEG) is calculated as the percentage of the 30,000 simulated records 

meeting the QC requirements for insured status. The same calculation is applied to the 

disability-insured percentage (DSIM_LEG). 

For each sex and cohort, FSIM_LEG is determined at ages 13 to 84.

DSIM_LEG is determined at ages 13 to 69.

An adjustment, DINADD, is made to DSIM_LEG. This additive adjustment accounts for workers 

who fail to meet the requirement for disability-insured status solely because of having no 

earnings while receiving disability benefits. INSURED assumes that workers who have been on 

the disability rolls for more than 3 years would be in this situation26. Thus, DINADD is 

#of workers on the disability rolls morethan3 years

Social Security Area population

by age, sex, and cohort.

A small proportion of the other immigrant population, OTLPOP, is added to calculate the 

simulated fully insured rate of the Social Security Area population, FSIM. We assume that other 

immigrants who have their earnings posted to the MEF will have the same insurability as the 

non-other population. We project FSIM as 

SSAPRO

FSIM _ LEG *(LEGPOP CW _OTHER/CPRO)

by age, sex, and cohort.

Hence, the simulated fully insured rate of the other immigrant population FSIM _OTL is

OTLPOP

FSIM _ LEG *CW _OTHER/CPRO

by age, sex, and cohort.

FSIM_LEG is assumed to remain the same beyond age 84. FSIM_OTL is assumed to remain the 

same beyond age 69. For ages 70 and older, FSIM is projected by multiplying the non-other and 

other fully insured rates to their respective populations and dividing the sum by the Social 

Security Area population.

DSIM and DSIM_OTL are projected in a similar manner by using DSIM_LEG. If the simulated 

results for DSIM are not consistent with historical data, an additional age-sex-specific additive 

adjustment (DIADJ) is used to bring the simulated results in line with the historical estimates. 

Finally, incorporation of Short-Range projections produces FPRO and DPRO. For the first 10 

years, FPRO and DPRO are set equal to Short-Range estimates. The difference in terms of the 

 

26 Those who are on the rolls for less than 4 years are assumed to meet the requirement for disability-insured status 

based on their earnings histories.
193

percentage between the Long-Range and Short-Range projections at the end of 10th year is 

linearly phased out during the next ten years by cohort and sex. The Long-Range projections 

are assumed thereafter.

Number of Fully Insured and Disability Insured Workers

The numbers of Fully Insured and Disability Insured workers are obtained by applying FPRO and 

DPRO, respectively, to the Social Security area population. The result is an estimate of the 

number of people that are fully insured (FINPOP) and disability insured (DINPOP) by single year 

of age and sex, respectively. For a given age and sex, the proportion of the Social Security area 

population that is insured (FPRO) is assumed to be the same for each marital status. 
194

DISABILITY 

3.2.a. Overview

The Social Security Administration pays monthly disability benefits to disability-insured workers 

who meet the definition of “disability”. If they meet certain requirements, spouses and children 

of disabled-worker beneficiaries may also receive monthly benefits. 

DISABILITY projects the number of disabled-worker beneficiaries in current-payment status 

(DIB) at the end of each year by age at entitlement, sex, and duration from entitlement. We 

base the number of DIB at the end of each year on the number of disabled-worker beneficiaries 

who are currently entitled to benefits (CE). We calculate the number of CE at the end of year by 

adding the number of newly entitled CE (New Entitlements) during the year and subtracting the 

number of CE who leave the disability rolls (Exits) during the year to the number of CE at the 

end of the prior year. Disabled-worker beneficiaries who leave the disability rolls (Exits) do so 

by recovering from disabilities (Recoveries), by dying (Deaths), or by converting to retired 

worker status (Conversions). A disabled-worker beneficiary converts to retired worker status 

upon reaching Normal Retirement Age (NRA), the age at which a person first becomes entitled 

to unreduced retirement benefit.

DISABILITY also projects the number of future dependent beneficiaries of DIB by category, age, 

and sex. The six categories are minor child, student child, disabled adult child, young spouse, 

married aged spouse and divorced aged spouse. We generate the numbers of dependent 

beneficiaries of DIB by multiplying the relevant subset of the SSA area population (Exposures) 

by a series of probabilities that relate to the regulations and requirements for obtaining 

benefits (Linkages).

New Entitlements(year) = ExposureBOY



Incidence Rate(year) (3.2.1)

 where BOY is beginning of year.

Exits(year) = Recoveries(year) + Deaths(year) + Conversions(year) (3.2.2)

 where Recoveries(year) = CEBOY 



Recovery Rate(year) 

 where Deaths(year) = CEBOY 

 Death Rate(year).

CEEOY = CEEOY-1 + New Entitlements (year) – Exits(year), (3.2.3)

 where EOY is end of year, EOY-1 is end of prior year.

Dependent Beneficiaries of DIB EOY = Exposures EOY



Linkages EOY (3.2.4)

 

3.2.b. Input Data

Trustees Assumptions

Each year, the Trustees set the assumption for the ultimate age-sex-adjusted incidence rate and 

the ultimate age-sex-adjusted recovery rate. We achieve this by setting the age-adjusted 
195

incidence rate and the age-adjusted recovery rate for each sex. The ultimate level for the agesex-adjusted incidence rate and for the age-sex-adjusted recovery rate is set for the twentieth 

year of the projection period. Then for each age group and sex, we adjust the ultimate 

incidence rate to match a “target” incidence rate. The following chart shows these “target” 

incidence rates by age group and sex. 

Using a standard population of disability insured who are not in current pay as of December 

1999, the age-sex-adjusted incidence rate (on an entitlement rate basis) for the 2014 Trustees 

Report is 5.8 per 1,000. Using a standard population of DIBs as of December 1999, the age-sexadjusted recovery rate for the 2014 Trustees Report is 9.3 per 1,000. 

Long-Range OCACT Data

All data is updated annually except those noted otherwise. Population data are as of December 

31. We assume data as of December 31 of year z-1 is equal to data as of January 1 of year z. 

Workflow reference is bolded in parenthesis after each item.

Demography

 January Social Security area population by age, sex, and marital status27 (dimensioned 

(0:100,1:2,1:4)) for years 1970-2090. (3a)

 Probabilities of death by sex, age and year (1:2,15:148,2012:2090). (3b)

 Total children by sex of parent, age of parent and age of child (dimensioned 

(1:2,19:71,0:18)) for years 1970-2090. (3c)

 Total married lives by age of husband crossed with age of wife (dimensioned 

(14:100,14:100)) for years 1970-2090. (3d)

 Average number of children under 16 per couple with children by age group (<25, 25-

29,...,60-64) of head of household (dimensioned (1:9)) for years 1970-2090. (3e)

Economics

 Unemployment rates by age group (16-19, 20-24,...,60-64), sex and year (1990:2025). 

(3h)

Beneficiaries

INSURED subprocess #3.1

 Disability-insured population by age, sex and year (15:69,1:2,1969:2090) from the 

2014 Trustees Report. (3g)

 

27 Single, married, widowed, divorced.

Target Incidence Rates by Age Group

<19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 

Male 2.46

 

2.58

 

1.66

 

2.49

 

3.34

 

4.52

 

5.96

 

9.89

 

15.72

 

19.02

 

9.00

 Female 1.90

 

1.02

 

2 1.62

 

2.82

 

3.54

 

4.78

 

6.39

 

10.10

 

14.16

 

14.72

 

8.34

 

Age Group
196

 Disability-insured population by age, sex and year (15:69,1:2,1969:2090) from the 

2013 Trustees Report. (2j)

 Fully insured population by age, sex and marital status (14:95,1:2,1:4) for years 

1970-2090. (3f)

2013 Trustees Report DISABILITY subprocess #3.2

 Death rate projection factors from the 2013 Trustees Report by age group (15-19,20-

24,...,60-64), sex and year (1:2,1:10,2013:2090). (3i)

 Recovery rate projection factors and recovery rates from the 2013 Trustees Report 

by age group (15-19,20-24,...,60-64), sex and year (1:2,1:10,2013:2090). (3j) and (3k)

Other input data

We update only the most recent year data annually for this category except as noted otherwise 

below:

 The December 2013 data from the Master Beneficiary Record (MBR) containing the 

number of DIB by duration of entitlement, age of entitlement, sex and time of year 

(BOY or EOY) (0:55,15:66,1:2,1:2). (2b)

 December 2013 and December 2012 data from the MBR containing the number of 

disabled workers in current-payment status by age, sex and year 

[(15:66,1:2,1969:2013). (2a) and (2j)

 December 2013 data from the MBR containing the number of disabled workers in 

withheld or suspended status by age, sex and year [(15:66,1:2,1969:2013) and 

(15:66,1:2,1969:2012)]. (2a) 

 December data from the MBR containing the number of dependent beneficiaries by 

age, sex of the account holder, and year for the following beneficiary categories. (2a)

1) Minor child (0:17,1:2,1970:2013) 

2) Student child (18:21,1:2,1970:2013)

3) Disabled adult child (age group1:928,1:2,1970:2013)

4) Young spouse (19:64,1:2,1970:2013) 

5) Married aged spouse (62:100,1:2,1970:2013) 

6) Divorced aged spouse (62:100,1:2,1970:2013)

 We also read totals for each category.

 December data from the MBR containing the number of DIB awards by age, sex and 

year (15:67,1:2,1970:2013). (2c)

 December data from the MBR containing (1) the number of DIB total terminations 

(recoveries and deaths) and (2) the number of conversions29. These data are by sex 

and year (1:2,1970:2013). (2d)

 December data from the MBR containing the number of DIB deaths by age, sex and 

year (15:67,1:2,1975:2013). (2d)

 December data from the MBR containing the number of estimated DIB recoveries by 

age, sex and year (15:67,1:2,1975:2013). (2d)

 

28 Age groups 1 through 9 are 18-19, 20-24, 25-29,..., 55-59. 

29 Conversions are DIB beneficiaries who become eligible for old-age benefits due to reaching the normal retirement 

age.
197

 December data from the MBR containing the number of old-age beneficiaries who at 

some point in time were converted to retired worker status. This data is by age, sex 

and year: (65:95+,1:2,1970:2013) (2b)

 December data from the MBR containing the number of DIB entitled to the Hospital 

Insurance portion of Medicare by age group (<25,25-29,...,60-64,65+), sex and year 

(2:11,1:2,1973:2013). (2e)

 December data from the MBR containing the number of DIB awards by duration, 

age, sex and year (0:5,0:65,1:2,1993:2012). 2012 Awards were retrieved for this 

year’s TR, 1993-2011 Awards have not been updated. (2j)

 Retroactive factors30 by year (1969:2012). These values are estimated using OCACT 

beneficiary data. (2h)

All numbers in the following categories are updated annually unless otherwise noted.

 Average incidence rates by age and sex (15:65,1:2) for the base period 1998-2007 

based on awards data from 1998-2012 (also known as the base incidence rates). We 

update these values when time and data are available. Note that rates for ages 60 

through NRA are from the 2011 TR. (2j)

 Probability of death for DIB’s – in a multiple-decrement environment by duration, 

age and sex (0:10,15:65,1:2) for the base period 2001-2005. These numbers (also 

known as the base probabilities of death) are from Actuarial Study No. 122. We 

update these values when time and data are available. (2f)

 Probability of recovery for DIB’s – in a multiple-decrement environment by duration, 

age and sex (0:10,15:65,1:2) for the base period 2001-2005. These numbers (also 

known as the base probabilities of recovery) are from are from Actuarial Study No. 

122. We update these values when time and data are available. (2g)

 Initial Incurred but not reported (IBNR)31 factors by duration, age and sex (0:10, 15: 

69, 1:2) based on 2002 entitlements (awards data from 2002-2007). (2j) (Not 

Updated)

 Ultimate IBNR factors by duration, age and sex (0:10, 15: 69, 1:2) based on 1998-

2007 entitlements (awards data from 1998-2012). We update these values when 

time and data are available. The IBNR calculation linearly grades between the initial 

and ultimate IBNR factors during the ten years from 2008-2017. The ultimate IBNR 

factor table applies for all years after 2017. (2j)

 For each year 2000-2090, (1) the Normal Retirement Age (NRA), (2) the proportion 

of DIBs who stay on the DI roll for that age, and (3) the proportion of DIBs who 

convert to an old-age benefit during that year for that age. We update these values 

only when there is a change in the NRA or in the present law. (2k)

 For the years 2016-2026, weights for ages 61-66 for linear interpolating between 

one set of incidence rates at a particular NRA and another set of incidence rates at 

 

30 Retroactive factors for each calendar year are the ratio of the total monthly payments to DIBs to the monthly DIBs 

in current payment status times the average DIB monthly benefit. 

31 IBNR factors reflect the proportion of DIBs entitled to benefits who have been awarded since the year of their 

entitlement.
198

the next higher NRA. We update these values only when there is a change in the 

NRA or in the present law. (4b)

 The following linkages for the calculations of auxiliary beneficiaries; the probability 

that student is in an eligible school, the probability that adult child is disabled, the 

probability that beneficiary is not subject to the earnings test, and the probability 

that beneficiary was married 10 or more years are estimated and are updated when 

time and data are available. (4a)

 Short-Range/Long-Range adjustment (APROJ) factors by auxiliary beneficiary 

category (1:7) for years 2014-2033. These seven categories are; minor child, student 

child, disabled adult child, young wife, young husband, age wife and aged husband. 

We calculate these values by comparing Short-Range and Long- Range numbers for 

auxiliary beneficiaries. (2i)

 IPROJG, DPROJG and RPROJG adjustment factors used to adjust incidence, death and 

recovery rates to reconcile between the long-range model and the short-range 

model. IPROJG (1:11, 2, 2014-2090) and DPROJG (1:10, 2, 2014-2090) adjustment

factors are by age group, sex and year. RPROJG (2, 2014-2023) adjustment factors 

are by sex and year. (2l),(2m), and (4d) 

 Ultimate RPROJG values by sex and age group (1:2, 1:10) calculated to reach a target 

value. Updated when the probabilities of recovery for DIB’s are updated. (4d)

3.2.c. Development of Output

Equation 3.2.1 – New Entitlements

We calculate new entitlements by multiplying age-sex-specific incidence rates to the exposed 

population at the beginning of the year. The exposed population is the disability-insured 

population less the currently entitled population. We calculate future age-sex-specific incidence 

rates by multiplying the base incidence rates by the incidence rate projection factors (IPROJGs). 

For the first ten years of the projection (short-range period), IPROJGs by 5-year age group and 

sex are obtained by using regression equations with the change in unemployment in the two 

prior years as the independent variables. We describe the regression equations in detail in 

Appendix 3.2-1. Then, we run the IPROJGs through the main model and analyze the resulting 

incidence rates by age group and sex. We adjust the IPROJGs by age and sex to reach “target” 

incidence rates in the twentieth year of the projection period. These “target” incidence rates 

were determined based on a recent review of historical and projected award rates by age group 

and sex. For projection periods between the tenth and twentieth years, we linearly interpolate 

the IPROJGs between the ultimate IPROJGs values and the IPROJGs values at the end of shortrange period. Additional adjustments to the IPROJGs during the short-range period may be 

necessary for reconciliation between the long-range model and the short-range model. For the 

2014 Trustees Report, we made IPROJG adjustments in the short-range period to better project 

the drop in disability claims as we continue to recover from the recent economic recession. 
199

Equation 3.2.2 – Exits

The long-range model projects three types of exits from the disability rolls; death, recovery and 

conversion to an old-age beneficiary upon reaching normal retirement age (NRA). Deaths and 

recoveries are projected by multiplying the beginning currently entitled population by the 

probabilities of death only and recovery only, (

(d )

x q

) and (

(r)

qx

), respectively. Projected (

(d )

x q

) 

and (

(r)

qx

) by age, sex, and duration are calculated by multiplying the base probabilities by the 

respective projection factors by age group and sex for that year.

For the first ten years, we derive the recovery projection factors (RPROJGs) by age group and 

sex from linear interpolation between an estimated starting level for the RPROJGs and an 

estimated tenth-year projection target level for the RPROJGs. For each age group and sex, we 

calculate the starting RPROJGs the following way:

 RPROJGTR14(2013) = RPROJGTR13 (2013) 



actual recovery rate(2013) / estimated 

 recovery rate TR13(2013)

Because there is no apparent upward or downward trend, we use the average recovery rates 

for the last ten historical years as the target values for the 10th year (2023). Then, for each age 

group and sex, we calculate the tenth year’s RPROJGs as follows: 

 RPROJGTR14(2023) = RPROJGTR14 (2013) 



target value recovery rate (2023) / 

 actual recovery rate (2013)

For the second 10 years of the projection period, we linearly interpolate between the ultimate 

RPROJG value and the RPROJG value at the end of short-range period (2023). We assume 

attainment of ultimate recovery rates in the twentieth year of the projection period. Ultimate 

recovery rates by age group and sex are determined by analyzing historical recovery rates. We 

may make additional adjustments to the RPROJGs to reconcile with the short-range model. 

For the first year of the projection period, the death projection factors (DPROJGs) by age group 

and sex are determined so that they achieve a targeted death rate. The targeted death rate is 

determined by fitting an exponential curve to historical death rates for DIBs by age group and 

sex (see Appendix 3.2-1). For the rest of the projection period, we assume the DPROJGs 

improve at the same rate as the general population for that age group and sex. We calculate 

the DPROJGs for each year by 5-year age group and sex the following way: 

DPROJG(year) = DPROJG(year-1)

x 4 year 1999

age age age x

qx DIB





 

      

/ 

x 4 year 1 1999

age age age x

qx DIB

 



    

  

We may make additional adjustments to the DPROJGs to reconcile with the short-range model.
200

Equation 3.2.3 – Disabled-Worker Beneficiaries

The projection begins with the latest data available from the mainframe of disabled-worker 

beneficiaries in current-payment status. This data is from a 100 percent sample of the Master 

Beneficiary Record (MBR) at the end of the year. We split up disabled-worker beneficiaries by 

age at entitlement, sex and duration of entitlement. We convert this population to a currently 

entitled population by dividing each age, sex and duration cell by the appropriate duration-agesex-year-specific IBNR factor. An iterative process begins with new entitlements added to and 

exits subtracted from the previous year’s currently entitled population to get the following 

year’s currently entitled population with advancement of duration within the age of 

entitlement. We reduce this currently entitled population by multiplying by the appropriate 

duration-age-sex-year-specific IBNR factor. The result is the following year’s disabled-worker 

beneficiaries in current-payment status. The process repeats over each sex, age of entitlement 

and duration of entitlement throughout the projection period.

Equation 3.2.4 – Dependent Beneficiary of Disabled Workers

There are six dependent-beneficiary categories; minor child, student child, disabled adult child, 

young spouse, married aged spouse and divorced aged spouse. We disaggregate projections by 

age of the beneficiary and sex of the account holder. We detail below the linkages and 

exposures used in each category of dependent beneficiaries. 

Minor Child

Exposure: Single SSA population by single ages 0-17

Linkages: pMCAGA = Probability that parent is under NRA

pMCDIA = Probability that parent is disability insured given 

that the parent is under NRA

pMCDPA = Probability that disability insured parent under 

NRA is disabled

MCRES = Residual Factor

Student Child 

Exposure: Single SSA population by single ages 18-19

Linkages: pSCAGA = Probability that parent is under NRA

pSCDIA = Probability that parent is disability insured given 

that the parent is under NRA

pSCDPA = Probability that disability insured parent under 

NRA is disabled

pSCDPC = Probability that student is in an eligible school

SCRES = Residual Factor

Disabled Adult Child 

Exposure: Single SSA population by age groups 18-19, 20-24, 25-29,

 30-34, 35-39, 40-44, 45-49, 50-54, 55-59

Linkages: pDCAGA = Probability that parent is under NRA
201

pDCDIA = Probability that parent is disability insured given 

that the parent is under NRA

pDCDPA = Probability that disability insured parent under 

NRA is disabled

pDCDPC = Probability that adult child is disabled

DCRES = Residual Factor

Young Spouse 

Exposure: Married SSA population by sex and by single ages 20-64 

Linkages: pYSAGA = Probability that account holder is under NRA

pYSDIA = Probability that account holder is disability 

 insured given that the account holder is under 

 NRA

pYSDPA = Probability that disability insured account holder 

under NRA is disabled

pYSETB = Probability that young spouse is not subject to

 earnings test 

pYSMCB = Probability that young spouse has a minor child

 beneficiary in his/her care

pYSDCB = Probability that young spouse has a disabled child 

 beneficiary in his/her care 

YSRES = Residual Factor

Married Aged Spouse 

Exposure: Married SSA population by sex and by single ages 62-100

Linkages: pMSAGA = Probability that account holder is under NRA

pMSDIA = Probability that account holder is disability 

 insured given that the account holder is under 

 NRA

pMSDPA = Probability that disability insured account holder 

under NRA is disabled

pMSFIB = Probability that beneficiary is not insured

MSRES = Residual Factor

Divorced Aged Spouse 

Exposure: Divorced SSA population by sex and by single ages 62-100

Linkages: pDSDEA = Probability that account holder is living

pDSAGA = Probability that account holder is under NRA

pDSDIA = Probability that account holder is disability 

 insured given that the account holder is under 

 NRA

pDSDPA = Probability that disability insured account holder 

under NRA is disabled

pDSFIB = Probability that beneficiary is not insured

pDSDMB = Probability that beneficiary was married 10 or

 more years
202

We estimate the residual factors for each of the dependent categories using a 10-year Least 

Squares regression formula. We then hold these residual factor values constant for the 

duration of the long-range period. If the 10-year Least Squares method results in a negative 

residual factor, we hold the last historical residual factor instead.

We develop APROJ factors for a dependent beneficiary category to match short-range results 

during the first 10 years of the projection period. We phase these factors out linearly over the 

second ten years of the projection period.

Appendix: 3.2-1

The following information provides details about the regression equations used in determining 

incidence rates and IPROJG values by age group and sex for the first ten years of the projection 

period.

Male

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 16-19

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 16-19

Dependent Variable: 15-19 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.33275792

Standard Deviation: 0.59405720

Coefficient Intercept: 2.31341457

Coefficient Slope1: 0.19135398

Coefficient Slope2: 0.04134247

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 20-24

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 20-24

Dependent Variable: 20-24 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.55447936

Standard Deviation: 0.38142355

Coefficient Intercept: 2.26240088

Coefficient Slope1: 0.22468502

Coefficient Slope2: 0.07088968

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 25-29

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 25-29

Dependent Variable: 25-29 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.75823056

Standard Deviation: 0.16075460
203

Coefficient Intercept: 2.01277274

Coefficient Slope1: 0.14287995

Coefficient Slope2: 0.09638397

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 30-34

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 30-34

Dependent Variable: 30-34 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.25051490

Standard Deviation: 0.23415715

Coefficient Intercept: 2.52589296

Coefficient Slope1: 0.08336470

Coefficient Slope2: 0.05892174

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 35-39

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 35-39

Dependent Variable: 35-39 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.31129255

Standard Deviation: 0.26018889

Coefficient Intercept: 3.33022085

Coefficient Slope1: 0.10525609

Coefficient Slope2: 0.09431533

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 40-44

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 40-44

Dependent Variable: 40-44 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.51376614

Standard Deviation: 0.26976436

Coefficient Intercept: 4.41360446

Coefficient Slope1: 0.15146290

Coefficient Slope2: 0.16055039

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 45-49

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 45-49

Dependent Variable: 45-49 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.65948437

Standard Deviation: 0.30217939

Coefficient Intercept: 5.92193479

Coefficient Slope1: 0.21340895

Coefficient Slope2: 0.24971155
204

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 50-54 

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 50-54 

Dependent Variable: 50-54 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.70307246

Standard Deviation: 0.53887534

Coefficient Intercept: 9.62568981

Coefficient Slope1: 0.33610885

Coefficient Slope2: 0.54797127

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 55-59

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 55-59

Dependent Variable: 55-59 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.35154240

Standard Deviation: 0.92700601

Coefficient Intercept: 15.79653435

Coefficient Slope1: 0.31574104

Coefficient Slope2: 0.56501428

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 60-64

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 60-64

Dependent Variable: 60-64 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.08966668

Standard Deviation: 0.83840827

Coefficient Intercept: 16.50990580

Coefficient Slope1: 0.20964408

Coefficient Slope2: 0.23616431

Female
205

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 16-19

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 16-19

Dependent Variable: 15-19 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.23263088

Standard Deviation: 0.40471551

Coefficient Intercept: 1.58560823

Coefficient Slope1: 0.12742295

Coefficient Slope2: 0.05235043

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 20-24

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 20-24

Dependent Variable: 20-24 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.36763811

Standard Deviation: 0.33306933

Coefficient Intercept: 1.71774651

Coefficient Slope1: 0.21641350

Coefficient Slope2: 0.06079072

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 25-29

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 25-29

Dependent Variable: 25-29 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.50069915

Standard Deviation: 0.23737880

Coefficient Intercept: 1.88124701

Coefficient Slope1: 0.19426902

Coefficient Slope2: 0.09630699

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 30-34

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 30-34

Dependent Variable: 30-34 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.57026855

Standard Deviation: 0.22772055

Coefficient Intercept: 2.63257563

Coefficient Slope1: 0.18419440

Coefficient Slope2: 0.18591156

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 35-39

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 35-39

Dependent Variable: 35-39 incidence rates
206

Observation Period: 1994-2010

Adjusted R square: 0.61754167

Standard Deviation: 0.26018159

Coefficient Intercept: 3.72425696

Coefficient Slope1: 0.25568472

Coefficient Slope2: 0.22765106

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 40-44

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 40-44

Dependent Variable: 40-44 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.47103176

Standard Deviation: 0.38227017

Coefficient Intercept: 5.01756401

Coefficient Slope1: 0.31934478

Coefficient Slope2: 0.27438353

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 45-49

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 45-49

Dependent Variable: 45-49 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.73950860

Standard Deviation: 0.30029097

Coefficient Intercept: 6.51249772

Coefficient Slope1: 0.41826805

Coefficient Slope2: 0.46915740

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 50-54 

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 50-54 

Dependent Variable: 50-54 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.72399622

Standard Deviation: 0.41500275

Coefficient Intercept: 10.00615175

Coefficient Slope1: 0.36589766

Coefficient Slope2: 0.73749169

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 55-59

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 55-59

Dependent Variable: 55-59 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.42094172

Standard Deviation: 0.56350418
207

Coefficient Intercept: 14.39617925

Coefficient Slope1: 0.06040387

Coefficient Slope2: 0.76901938

Independent Variable: Unemp Rate(t)-Unemp Rate(t-1) for ages 60-64

Independent Variable: Unemp Rate(t-1)-Unemp Rate(t-2) for ages 60-64

Dependent Variable: 60-64 incidence rates

Observation Period: 1994-2010

Adjusted R square: 0.37201473

Standard Deviation: 0.44893248

Coefficient Intercept: 13.01705167

Coefficient Slope1: 0.18292360

Coefficient Slope2: 0.40922150

The following information provides details about the exponentially fitted equations used in 

determining death rates by age group and sex for the first year of the projection period.

Male

Independent Variable: Year

Independent Variable: 15-19 death rates

Observation Period: 2004-2013

Adjusted R square: 0.21337

Standard Deviation: 0.82042

Coefficient Intercept: 338.94188

Coefficient Slope1: -0.16756

Independent Variable: Year

Independent Variable: 20-24 death rates

Observation Period: 2004-2013

Adjusted R square: 0.56698

Standard Deviation: 0.07146

Coefficient Intercept: 59.06159

Coefficient Slope1: -0.02813

Independent Variable: Year

Independent Variable: 25-29 death rates

Observation Period: 2004-2013

Adjusted R square: 0.58773

Standard Deviation: 0.04610

Coefficient Intercept: 40.35661

Coefficient Slope1: -0.01888
208

Independent Variable: Year

Independent Variable: 30-34 death rates

Observation Period: 2004-2013

Adjusted R square: 0.79138

Standard Deviation: 0.04205

Coefficient Intercept: 57.70386

Coefficient Slope1: -0.02744

Independent Variable: Year

Independent Variable: 35-39 death rates

Observation Period: 2004-2013

Adjusted R square: 0.92341

Standard Deviation: 0.02300

Coefficient Intercept: 56.03582

Coefficient Slope1: -0.02650

Independent Variable: Year

Independent Variable: 40-44 death rates

Observation Period: 2004-2013

Adjusted R square: 0.89145

Standard Deviation: 0.03004

Coefficient Intercept: 60.52568

Coefficient Slope1: -0.02863

Independent Variable: Year

Independent Variable: 45-49 death rates

Observation Period: 2004-2013

Adjusted R square: 0.95509

Standard Deviation: 0.01461

Coefficient Intercept: 48.09344

Coefficient Slope1: -0.02232

Independent Variable: Year

Independent Variable: 50-54 death rates

Observation Period: 2004-2013

Adjusted R square: 0.92332

Standard Deviation: 0.01687

Coefficient Intercept: 42.50116

Coefficient Slope1: -0.01942

Independent Variable: Year

Independent Variable: 55-59 death rates

Observation Period: 2004-2013
209

Adjusted R square: 0.86972

Standard Deviation: 0.01072

Coefficient Intercept: 22.15179

Coefficient Slope1: -0.00922

Independent Variable: Year

Independent Variable: 60-64 death rates

Observation Period: 2004-2013

Adjusted R square: 0.84703

Standard Deviation: 0.01893

Coefficient Intercept: 33.58601

Coefficient Slope1: -0.01486

Female

Independent Variable: Year

Independent Variable: 15-19 death rates

Observation Period: 2004-2013

Adjusted R square: -0.01409

Standard Deviation: 0.64368

Coefficient Intercept: -130.52031

Coefficient Slope1: 0.06629

Independent Variable: Year

Independent Variable: 20-24 death rates

Observation Period: 2004-2013

Adjusted R square: 0.12509

Standard Deviation: 0.06882

Coefficient Intercept: 25.43422

Coefficient Slope1: -0.01146

Independent Variable: Year

Independent Variable: 25-29 death rates

Observation Period: 2004-2013

Adjusted R square: 0.09324

Standard Deviation: 0.06709

Coefficient Intercept: 22.86632

Coefficient Slope1: -0.01025

Independent Variable: Year

Independent Variable: 30-34 death rates

Observation Period: 2004-2013

Adjusted R square: 0.41573

Standard Deviation: 0.05150
210

Coefficient Intercept: 33.39405

Coefficient Slope1: -0.01543

Independent Variable: Year

Independent Variable: 35-39 death rates

Observation Period: 2004-2013

Adjusted R square: 0.83632

Standard Deviation: 0.01324

Coefficient Intercept: 22.63516

Coefficient Slope1: -0.00999

Independent Variable: Year

Independent Variable: 40-44 death rates

Observation Period: 2004-2013

Adjusted R square: 0.78299

Standard Deviation: 0.02762

Coefficient Intercept: 38.11328

Coefficient Slope1: -0.01759

Independent Variable: Year

Independent Variable: 45-49 death rates

Observation Period: 2004-2013

Adjusted R square: 0.87367

Standard Deviation: 0.01925

Coefficient Intercept: 36.83141

Coefficient Slope1: -0.01686

Independent Variable: Year

Independent Variable: 50-54 death rates

Observation Period: 2004-2013

Adjusted R square: 0.88980

Standard Deviation: 0.01531

Coefficient Intercept: 32.20361

Coefficient Slope1: -0.01447

Independent Variable: Year

Independent Variable: 55-59 death rates

Observation Period: 2004-2013

Adjusted R square: 0.91701

Standard Deviation: 0.01577

Coefficient Intercept: 38.20439

Coefficient Slope1: -0.01740
211

Independent Variable: Year

Independent Variable: 60-64 death rates

Observation Period: 2003-2012

Adjusted R square: 0.93827

Standard Deviation: 0.01419

Coefficient Intercept: 40.22909

Coefficient Slope1: -0.01833
212

3.3. Old-Age and Survivors Insurance

3.3.a. Overview

Every month, the Social Security program pays benefits to retired workers and their 

dependents. It also provides benefits to eligible dependents of deceased workers. The OLDAGE AND SURVIVORS subprocess projects the number of people expected to receive benefits 

over the next 75 years. The projection method is very similar to the method used for 

dependent beneficiaries of disabled workers in the DISABILITY subprocess. We compute the 

projection of beneficiaries by multiplying a subset of the Social Security area population by a 

series of probabilities of the conditions that a person must meet to receive benefits. The main 

program receives all necessary input data and performs all preliminary calculations. It then 

calls each individual beneficiary type subroutine where it makes all beneficiary calculations.

We categorize retired workers and their dependent beneficiaries as follows:

 retired workers (RWN) by age (62-95+), sex, and marital status (single, married, 

widowed, divorced)

 aged spouses of retired workers (ASRWN), by age (62-95+), sex of the account holder, 

and marital status of the beneficiary (married, divorced) 

 young spouses of retired workers (YSRWN) by age-group (under 25, 25-29,..., 65-69) 

and sex of the account holder

 minor, student, and disabled adult children of retired workers (MCRWN, SCRWN, and 

DCRWN, respectively) by age of the child (0-17 for minor, 18-19 for student, age 

groups 18-19, 20-24, ..., 55-59, 60+ for disabled adult) and sex of the account holder

Dependent beneficiaries of deceased workers include:

 aged spouses of deceased workers, ASDWN, by age (60-95+), sex of the account 

holder, marital status (widowed, divorced) and insured status (insured, uninsured)

 disabled spouses of deceased workers (DSDWN) by age (50-69), sex of the account 

holder and marital status (widowed, divorced)

 young spouses of deceased workers (YSDWN) by age-group (under 25, 25-29,..., 65-

69), sex of the account holder and marital status of the beneficiary (widowed, 

divorced)

 minor, student, and disabled adult children of deceased workers (MCDWN, SCDWN, 

and DCDWN, respectively) by age of the child (0-17 for minor, 18-19 for student, age 

groups 18-19, 20-24,..., 55-59, 60+ for disabled adult) and sex of the account holder

Lastly, we estimate the number of deaths of insured workers (LUMSUM) by 5-year age group 

(20-24, 25-29,..., 80-84, 85+) and sex.
213

Equations 3.3.1-13 indicates the flow of calculations of beneficiaries.

ASDWN  ASDWN() (3.3.1)

RWN  RWN() (3.3.2)

ASRWN  ASRWN() (3.3.3)

DSDWN  DSDWN() (3.3.4)

MCRWN  MCRWN() (3.3.5) 

MCDWN  MCDWN() (3.3.6)

SCRWN  SCRWN() (3.3.7)

SCDWN  SCDWN() (3.3.8)

DCRWN  DCRWN() (3.3.9)

DCDWN  DCDWN() (3.3.10)

 YSRWN YSRWN() (3.3.11)

 YSDWN YSDWN() (3.3.12)

LUMSUM  LUMSUM() (3.3.13)

The appendix 3.3-1 at the end of this section provides a listing with explanation of the 

acronyms used in this documentation. 

 

3.3.b. Input Data

We update all data annually unless otherwise noted. Timing of data received is denoted ‘BOY’ 

(beginning of year) or ‘EOY’ (end of year).

Long-Range OCACT Data

Demography

 Social Security area population by year (EOY 1970-2090), single year of age (0-100+), 

sex, and marital status (single, married, widowed, divorced)

 Deaths by year (during years 2013-2090), age group (20-24,...,80-84, 85+) and sex

 Average number of children per family by year (EOY 1970-2090), and age group of the 

householder (20-24,...,60-64)

 Children by year (EOY 1970-2090), single year of age (0-18), sex of primary account 

holder (parent), status of primary (62+ or deceased), and age of the other parent (15-

19,20-24,...,65-69, 70+, total ages)

 Married couples by year (EOY 1970-2090), age of husband (62-95+) and age of wife (62-

95+)

 Persons with an aged spouse by year (EOY 1970-2090), age group (15-24, 25-29,...,65-

69) and sex

 Probabilities of death by year (EOY 1941-2100), single year of age (-1,100), and sex
214

Economics

 Covered wages and employment in the Federal Civilian and State and Local Sectors 

(during years 1998-2090)

 Labor force participation rates for age 62 by year (during years 1970-2090) and sex

Beneficiaries

 Fully insured persons by year (EOY 1969-2090), age (14-95+), sex, and marital status 

(single, married, widowed, divorced)

 Disabled-worker beneficiaries in current pay by year (EOY 1970-2090), age (62-66) and 

sex

 Converted DI to OAI beneficiaries by year (EOY 1970-2090), age(65-95+) and sex

 Disability prevalence rates by year (EOY 1970-2090), age (50-66) and sex

Short-Range OCACT Data

 Insured aged spouses of deceased workers by year (EOY 1974-2013), age (60-95+) 

and sex

 Retired worker beneficiaries in-current-pay status by age (62-70, 70+) and sex for 

EOY 2012-2023

 We receive the following for EOY 2013:

a. Aged spouses of deceased workers by age (60-95+), sex and marital status 

(widowed, divorced)

b. Retired workers by age (62-95+) and sex

c. Aged spouses of retired workers by age (62-95+), sex and marital status (married, 

divorced)

d. Disabled widow(er)s by age (50-65), sex and marital status (widowed, divorced)

e. Minor children by age (0-17), sex of parent and status of parent (retired, deceased)

f. Student children by age (18-19), sex of parent and status of parent (retired, 

deceased)

g. Disabled adult children by age (18-60+), sex of parent and status of parent (retired, 

deceased)

h. Young spouses of retired workers by age group (under 25, 25-29,...,60-64, 65-69) 

and sex

i. Young spouses of deceased workers by age group (under 25, 25-29,...,65-69), sex 

and marital status (widowed, divorced)

j. Total parent beneficiaries

Note: Each year, we append data for the most recent historical year.

 We receive the following for EOY 2023:

a. Retired workers by age group (62-64, 65-69) and sex

b. Insured widows by age group (60-64,...,80-84,85+)

c. Uninsured widows by age group (60-64, 65+)

d. Total disabled widows
215

e. Female young spouses of deceased workers

f. Female aged spouses of retired workers by age group (62-64, 65+)

g. Female young spouses of retired workers

h. Minor children by status of parent

i. Student children by status of parent

j. Disabled adult children by status of parent

 Total amount of lump-sum death payments during 2012

Other Input Data

 For EOY 1970-2004, obtained from the MBR10PER dataset on the mainframe:

a. Aged spouses of deceased workers by age (60-95+), sex and marital status 

(widowed, divorced)

b. Retired workers by age (62-95+) and sex

c. Aged spouses of retired workers by age (62-95+), sex and marital status (married, 

divorced)

d. Disabled widow(er)s by age (50-64), sex and marital status (widowed, divorced)

e. Minor children by age (0-17), sex of parent and status of parent (retired, deceased)

f. Student children by age (18-19), sex of parent and status of parent (retired, 

deceased)

g. Disabled adult children by age (20-95+), sex of parent and status of parent (retired, 

deceased)

h. Young spouses of retired workers by age group (under 25, 25-29,...,60-64) and sex

i. Young spouses of deceased workers by age group (under 25, 25-29,...,65-69), sex 

and marital status (widowed, divorced)

j. Total parent beneficiaries

Note: We will not update this data.

 Number of beneficiaries with benefits withheld due to receipt of a significant 

government pension by sex and marital status (married, widowed) for EOY 2012 

from the 2013 Annual Statistical Supplement

 Age distribution of beneficiaries with benefits withheld due to receipt of a significant 

government pension by age (60-95+) and sex, computed as an average from the 

2009 through 2013 WEP 100-percent sample

 Proportions of disabled adult children of retired and deceased workers 

(proportioned by age and sex of the child) from the 2003 MBR ten-percent sample. 

(Note: The RSB program calculates disabled adult children by sex of the primary 

account holder, not by sex of the child. The RSB program outputs a file, which we 

use for Annual Update #9, which calculates beneficiaries by sex. Therefore, we 

apply the 2003 proportions to estimate the breakdown of disabled adult children by 

sex of the child. We will not update this input.). Not used for SOSI.
216

 Schedule of normal retirement age (NRA), delayed retirement credit, and actuarial 

reduction factors for ages more than 3 years below NRA and less than 3 years below 

NRA for years 1970-2090 from the Social Security website (Note: these values are 

only updated when there is a Social Security law change regarding the NRA)

 Prevalence rate regression coefficients (slopes and y-intercept value by sex)

 Regressed prevalence rate by sex for the most recent historical year

 Adjustment factors which account for the difference between estimated and actual 

historical retired worker prevalence rates by year (EOY 1970-2090), age (63-69) and 

sex

 Adjustment factors which account for the difference between projected beneficiary 

values for the tenth year of the projection period made by the Long-Range and 

Short-Range offices. Factors are computed for:

a. Retired workers by age group (62-64, 65-69) and sex

b. Insured widows by age group (60-64,...,80-84,85+)

c. Uninsured widows by age group (60-64, 65+)

d. Total disabled widows

e. Female young spouses of deceased workers

f. Female aged spouses of retired workers by age group (62-64, 65+)

g. Female young spouses of retired workers

h. Minor children by status of parent

i. Student children by status of parent

j. Disabled adult children by status of parent

3.3.c. Development of Output

We use several acronyms to describe the equations presented below. Acronyms not preceded 

by a subscript generally refer to the number of beneficiaries. For example, RWN refers to the 

number of retired workers. Acronyms preceded with a ‘p’ refer to probabilities. For example, 

pRWFIA

refers to the probability that a person is fully insured. 

Equation 3.3.1 – Aged Spouses of Deceased Workers (ASDWN)

Aged Spouses of Deceased Workers

Exposures: SSA population by age (60-95+), sex and marital status (widowed and 

divorced)

Linkages: pASDWDEA = probability that the primary account holder (PAH) is

deceased

pASDWFIA = probability that the PAH was fully insured at death

pASDWMBB = probability that the widow(er) is not receiving a youngspouse benefit for the care of a child
217

pASDWFIB = probability that the aged-widow(er) is or is not fully 

insured 

pASDWGPB = probability that the aged-widow(er)’s benefits are not 

withheld or offset totally because of receipt of a significant 

government pension based on earnings in noncovered 

employment

pASDWRES = probability that a widow(er) eligible to receive his/her own 

retired-worker benefits would instead apply for and eceive 

widow(er) benefits

We project the number of aged spouses of deceased workers (widow(er)s), along with all 

linkage factors, by age, sex of the account holder (sa=1 for male, sa=2 for female), marital 

status and insured status. Age ranges from 60 to 95+, marital status includes widowed 

(mb=1) and divorced (mb=2), and insured status includes insured (in=1) and uninsured 

(in=2). Note that all variables preceded by the letter p refer to calculated probabilities. We 

calculate the projected number of insured aged spouses of deceased workers age 60 to 70, 

and uninsured aged spouses of deceased workers age 60 to 95+ as follows:

(3.3.1)

For each sex we calculate the projected number of insured aged spouses of deceased 

workers over age 70 by applying termination rates to the population already receiving such 

benefits:

Where N is the age, and is the death rate for age N-1 in the given year

ASDWPOP represents the subset of the population from which we draw these beneficiaries. 

We set this equal to the Social Security area population (SSAPOPmb) for each possible 

marital status.

pASDWDEA represents the probability that the primary account holder (PAH) is deceased. 

For the widowed population, we set this factor equal to one. For the divorced population, 

we set this factor equal to the portion of the total widowed (SSAPOPwid) and married 

(SSAPOPmar) population who are widowed.
218

pASDWFIA represents the probability that the PAH was fully insured at death. For a given 

age of widow, AW, we assume that the age of her deceased husband, AH, ranges from AW6 to AW+12 with a lower and upper bound of 60 and 95+. Further, we assume that the 

more likely age of the husband is AW+3. For each age, we calculate pASDWFIA as a weighted 

average of the portion of the Social Security area population who are fully insured at each 

possible age of the husband (FINSAH). For example, for a widow age 70, we assume that the 

age of her husband is between 64 and 82, therefore we calculate the weighted average of 

the portion of the population who are fully insured males, applying the highest weight of 10 

to age 73, and a linearly reduced weight to zero for each age above and below 73. We use 

the same concept for widow(er)s with the assumption that the age of his deceased wife 

ranges from AH-12 to AH+6, with a greater likelihood of her age being AH-3. Let WEIGHT

represent the specific weight applied to each potential age of the spouse. 

pASDWMBB represents the probability that the widow(er) is not receiving a young-spouse 

benefit for the care of a child. A widow(er) can receive a young-spouse benefit up to age 69 

if he/she meets all other eligibility requirements. Since the minimum age requirement to 

receive a widow(er) benefit is 60, it is necessary to remove those receiving a young-spouse 

benefit (

ab YSDWN

), where ab represents the 5-year age bracket32. We assume a uniform 

breakdown to divide the age groups into single-age estimates.

For in = 1 (insured):

For in = 2 (uninsured):

 

32 There are no young spouses at NRA or above.
219

pASDWFIB represents the probability that the aged widow(er) is fully insured. For insured 

widow(er)s, pASDWFIB is the portion of the Social Security area population that is fully 

insured (FINS) at each age, sex, and marital status. For uninsured widow(er)s, pASDWFIB is 

simply one minus the probability for insured widow(er)s.

Where in represents the insured status of the account 

holder

pASDWGPB represents the probability that the aged-widow(er)'s benefits are not withheld or 

completely offset because of receipt of a significant government pension based on earnings 

in noncovered employment. According to the 1977 amendments, Social Security benefits 

are subject to reduction by up to two-thirds of non-covered government pension. GPWHLD

represents the total number of widow(er) beneficiaries (for all ages) expected to receive a 

significant government pension. rGPOAGE represents the ratio of the total for each given 

age. If a person is insured, this implies that he/she is eligible to receive Social Security 

benefits based on his/her own earnings regardless of a government pension. Therefore, we 

do not apply a factor. 

For in = 1 (insured):

 

For in = 2 (uninsured):

pASDWRES represents the probability that a widow(er), who is eligible to receive 

widow(er)’s benefits, will actually receive benefits. In particular, for in = 1, this factor is 

equivalent to the probability that a widow(er) eligible to receive his/her own retired-worker 

benefits would instead apply for and receive widow(er) benefits. For all historical years, we 

calculate 

year pASDWRES

as the ratio of ASDWN, the actual number of widow(er)s, to the 
220

number of persons meeting all previously mentioned requirements by age, sex, insured 

status, and marital status.

, year < TRYR,

where TRYR is the Trustees Report year.\

For each age, sex, insured status, and marital status, we use a least squares regression over 

the last ten years of historical data to determine a starting value in TRYR-1 for 

year pASDWRES

from which we project future values. In addition, for each sex, insured status, 

and marital status, we graduate the regressed values of 

TRYR1

pASDWRES

 over age using a 

weighted minimized third-difference formula to produce

ASDW ESTRES . ASDW ESTRES

are 

the preliminary estimates of 

TRYR9

pASDWRES

, the values in the tenth year of the projection 

period.

In addition, we apply adjustments to the widows (sa =1 ) by age group (60-64, 65-69 for 

insured; 60-64, 65+ for uninsured), 

ASDW SRADJ

, to the tenth year of the projection period

in order to match the projections made by the Short-Range office. We linearly interpolate 

the values of

year pASDWRES

for intermediate years between and 

(equal to

)

ASDW ASDW ESTRES SRADJ 

. After the 10th year, we linearly grade 

these adjustment factors to one over the 10 years beyond the end of the short-range 

period, thus gradually eliminating the effect of the short-range adjustment factors, so that 

we ultimately return to long-range projections. 

We also apply adjustments to the 70+ insured widows by age group (70-74, 75-79, 80-84, 

85+). The adjustments for the intermediate years between the TRYR and TRYR + 9 are 

applied on a cohort basis to the number of aged spouses of deceased workers (ASDWN) 

using the following formula:

Where h is the number of years the adjustment is applied for the cohort. For age 70, we 

linearly grade these adjustment factors to one over the 10 years beyond the end of the 

short-range period, thus gradually eliminating the effect of the short-range adjustment 

factors, so that we ultimately return to long-range projections.

Equation 3.3.2 – Retired Workers (RWN)

Retired Workers

Exposure: SSA population by age (62-95+), sex and marital status (single, married, 

widowed and divorced)

Linkages: pRWFIA = probability that the primary account holder (PAH) is insured

pRWDBB = probability that the PAH is not receiving a disabled-worker 
221

benefit

pRWWBB = probability that the PAH is not receiving a widow(er) benefit

pRWRES= retirement prevalence rate; probability that a fully insured

worker not receiving disability or widow(er)’s benefits would 

receive a retired-worker benefit

We project the numbers of retired-worker beneficiaries, along with all linkage factors, by 

age, sex, and marital status. Age ranges from 62 to 95+, and marital status includes single, 

married, widowed, and divorced (ms = 1 to 4). We calculate the projected number of 

retired-worker beneficiaries as follows:

 (3.3.2)

RWPOP represents the subset of the population from which we draw these beneficiaries. 

We set this equal to the Social Security area population (SSAPOPms) for ms = 1 to 4.

pRWFIA represents the probability that the primary account holder (PAH) is insured. We set 

this factor equal to the portion of the Social Security area population that is fully insured 

(FINS) for ms=1 to 4.

pRWDBB represents the probability that the PAH is not receiving a disabled-worker or 

disability-conversion benefit. We set this factor equal to the portion of fully insured 

workers who are neither disabled-worker beneficiaries nor converted from disabled-worker 

beneficiaries (DIBCON). ASDWN represents the number of aged spouses of deceased 

workers.

 

pRWWBB represents the probability that the PAH is not receiving a widow(er) benefit. We 

set this factor equal to the portion of fully insured workers that is not aged spouses of 

deceased workers. 
222

pRWRES

N,year represents the retirement prevalence rate, which is the probability that a fully 

insured worker not receiving disability or widow(er)’s benefits would receive retired-worker 

benefits as of the given age, N, for the given year. In order to estimate the future 

prevalence rate, the program first calculates the historical values of 

N year pRWRES,

.

For each historical year and sex, we calculate 

N year pRWRES,

as the ratio of RWN, the actual 

number of retired workers, to the number of persons meeting all previously mentioned 

requirements by age, sex, and marital status.

, N = 62-95+ and year < TRYR

Historical prevalence rates at age 62 follow an inverse relationship with (1) labor force 

participation rates (LFPRyear) at age 62, by sex, and (2) increases in the normal retirement 

age over the historical period. We assume this relationship holds in the projection period, 

and therefore we used it to calculate REGPRyear, the regressed prevalence rate based on the 

projected LFPRyear at age 62 for each year and sex, and the number of months from age 62 

to the normal retirement age (monthNRAyear). Note that we calculate prevalence rates on a 

cohort basis33. The regression equation used to estimate the prevalence rates is:

for male with 

an R2value of 0.906855,

and

for female 

with an R2value of 0.939876

We then set the future prevalence rate at age 62, 

year pRWRES62,

, equal to the sum of the 

regressed prevalence rate (REGPRyear) and ERROR, the difference between the actual 

prevalence rate and the regressed prevalence rate in the most recent historical year, which 

we phase out linearly. 

, N = 62 and year ≥ TRYR-1

To compute 

N year pRWRES,

for ages 63 to 69 in the projection period, we must calculate several 

preliminary variables. These include:

 MBAPIAN, for N = 62,70 (same for both sexes),



year ESTPRN

, for N = 63,69 and by sex,

 DIFFADJN, for N = 63,69 and by sex, 



year ESTPR2N

for N = 63,69 and by sex, and

 

33 For example, to calculate the projected number of 65 year olds in a given year, the prevalence rate at age 62 is 

needed. This is actually the prevalence rate that occurred three years ago at age 62.
223

MBAPIAN is the ratio of the monthly benefit amount (MBA) to the primary insurance 

amount (PIA) at age N and is calculated on a cohort basis for N = 62,70. We base the 

calculation of MBAPIAN on the normal retirement age (NRA), delayed retirement credits 

(DRC), and actuarial reduction factors, ARFLE3 when the difference between NRA and age 

at retirement is less than 3, and ARFGT3 when the difference is greater than 3 within each 

cohort. If a person retires after NRA, his/her benefits are increased by DRC for each year 

the age exceeds NRA. If a person retires before NRA, his/her benefits are decreased by 

ARFLE3 for each of the first three years that NRA exceeds the age, and further decreased by 

ARFGT3 for any remaining years.

year ESTPRN

, the estimated prevalence rate at age N, is then calculated as the prevalence 

rate at age 62 (

62, year(N62)

pRWRES

) plus an estimate on the expected portion of the remaining 

probability (

62, ( 62)

1

 



year N

pRWRES

), that a potential retired worker will actually retire by that 

given age. We base this estimate on MBAPIAN, assuming that the retirement decision by a 

worker is totally and completely influenced by the expected change in the portion of PIA 

that is payable at each age relative to the potential change after the initial eligibility.

, N = 63-69

In the first year of the projection period, an adjustment (DIFFADJN) is made which accounts 

for the difference between the actual and estimated prevalence rate at each age in the 

most recent historical years. For ages 63 to 69, the value used beginning in TRYR is the 

average of the last 5 years’ differences between the actual and estimated PR. With the 

exception of age 66, we hold this value constant throughout the projection period. 

For years in which the NRA increases, DIFFADJN is greater for a period of time both before 

and after the NRA transitions to a new age. This affects DIFFADJ66 due to the increase in 

NRA from age 65 to age 66 between the years 2000-2005, and the scheduled increase from 

age 66 to 67 over the years 2017-2022. Thus for the years preceding the change in NRA 

from age 66 to 67, we use the average from the last five historical years, which includes the 

increase in the difference between actual and estimated prevalence rates from when the 

NRA changed from age 65 to 66, in order to calculate DIFFADJ66 . After the NRA increases to 

age 67 in 2026, we use DIFFADJ266 which is the average difference between actual and 

estimated prevalence rates from the years 1995-1999 since at that time it is expected to 

decrease to the level it was prior to the increase in NRA to age 66 in the year 2000. In the 

years during which the NRA transitions to age 67 (2021-2026) we use a linear interpolation 

based on the number of months that the NRA has increased in order to phase out DIFFADJ66

and phase in DIFFADJ266. It will remain at DIFFADJ266 for the rest of the projection period. 
224

For age 70, we assume that the values of the latest actual 

N year pRWRES,

by sex change linearly 

to the ultimate level of 0.995 for male and 0.99 for female over the first 20 years of the 

projection period.

For year ≥ TRYR:

For ages 71 and older, we assume 

N year pRWRES,

stays constant at the level when the age was 

70 because there is no incentive to delay applying for benefits beyond age 70.

, for N = 71-95+ and year ≥ TRYR

In addition, we apply adjustments by age group (62-64, 65-69) and sex, 

RW SRADJ

, to the 

tenth year of the projection period in order to match the projections made by the ShortRange office. We also apply these adjustments to 

N year pRWRES,

for all years after TRYR+9. 

The values of

N year pRWRES,

for intermediate years are linearly interpolated between 

N,TRYR1

pRWRES

and 

N,TRYR9

pRWRES

.

For each age, retired workers are further broken down by age at entitlement, AE, by 

multiplying the number of retired workers at age N by the ratio of the incidence rate at AE

(

N AE  years prior) to the prevalence rate at age N.

, AE ≤ N

where we calculate the incidence rate for a given AE = N and year as the change in the 

prevalence rate at age N to the prevalence rate at age N-1 in the previous year.
225

Equation 3.3.3 – Aged Spouses of Retired Workers (ASRWN)

Aged Spouses of Retired Workers

Exposures: SSA population by age (62-95+, sex, and marital status (married and 

divorced)

Linkages: pASRWDEA = probability that the primary account holder (PAH) is not 

deceased

pASRWAGA = probability that the PAH is of the required age

pASRWFIA = probability that the PAH is fully insured

pASRWCPA = probability that the PAH is receiving benefits

pASRWMBB = probability that the beneficiary is not receiving a young- 

 spouse benefit

pASRWFIB = probability that the aged-spouse is not fully insured

pASRWGPB = probability that the aged-spouse’s benefits are not withheld 

because of receipt of a significant government pension based 

on earnings in noncovered employment

pASRWRES = probability that a person who is eligible to receive aged- 

 spouse benefits actually receives the benefits

We project the number of aged spouses of retired workers, along with all linkage factors, by 

age, sex of the account holder (sa = 1, 2), and marital status of the beneficiary. Age ranges 

from 62 to 95+, and marital status includes married (mb = 1) and divorced (mb = 2). We 

calculate the projected number of aged spouses of retired workers as follows:

 (3.3.3)

 ASRWPOP represents the subset of the population from which these beneficiaries are 

drawn, and we set it equal to the Social Security area population (SSAPOPmb) for mb = 1,2.

pASRWDEA represents the probability that the PAH is not deceased. For the married 

population, We do not apply a factor. For the divorced population, we set the factor equal 

to the portion of the total married and widowed population who are married.

pASRWAGA represents the probability that the PAH is of the required age, and we set it 

equal to the portion of the married population with a spouse (PAH) at least age 62.

Where mar(AA,AS) is the number of married couples where the age of the account holder is 

AA and the spouse of the account holder is age AS. 
226

pASRWFIA represents the probability that the PAH is fully insured, and we set it equal to the 

portion of married couples of the required age where the PAH is fully insured (FI_PAH). For 

example, when the program estimates the number of female aged spouse of retired 

workers, this factor will find the portion where their spouse, the male PAH, is fully insured.

pASRWCPA represents the probability that the PAH is receiving benefits. We set this factor 

equal to the portion of eligible married couples where the PAH is receiving benefits 

(RETIRED). If the beneficiary is divorced, we do not apply a factor, since it is not required 

for the retired worker to be receiving benefits for the divorced aged spouse to receive 

benefits.

pASRWMBB represents the probability that the beneficiary is not receiving a young-spouse 

benefit. If the beneficiary is age 70 or older or if the beneficiary is divorced, we do not 

apply a factor. Otherwise, we set this factor equal to the portion of potentially eligible 

widow(er)s where the spouse of the PAH is not receiving a young-spouse benefit 

(

ab YSRWN

), where ab represents the 5-year age group.34

For mb = 1 (married):

For mb = 2 (divorced):

 

34 There are no young spouses at NRA or above.
227

 

pASRWFIB represents the probability that the aged spouse is not fully insured, and is 

therefore not receiving a retired-worker benefit based on his/her own earnings. We set this 

factor equal to the portion of the married and divorced population that is not fully insured. 

For example, when the program estimates the number of female aged spouse of retired 

workers, this factor will find the portion of female beneficiaries that is fully insured. 

, mb = 1-2

pASRWGPB represents the probability that the aged-spouse's benefits are not withheld 

because of receipt of a significant government pension based on earnings in noncovered 

employment. GPWHLD represents the total number of aged spouse of retired-worker 

beneficiaries (for all ages) expected to receive a significant government pension. rGPOAGE

represents the ratio of the total for each given age. 

pASRWRES represents the probability that a person who is eligible to receive aged-spouse 

benefits actually receive the benefits. For all historical years, we calculate 

year pASRWRES

as 

the ratio of ASRWN, the actual number of aged spouses receiving benefits, to the number of 

persons meeting all previously mentioned requirements by age, sex, and marital status.

For year < TRYR:

For each age, sex , and marital status, we use a least squares regression over the last ten 

years of historical data to determine a starting value in TR-1 for 

year pASRWRES

from which we 

project future values. In addition, for each sex and marital status, we graduate the 

regressed values of 

TRYR1

pASRWRES

over age using a weighted minimized third-difference 

formula to compute 

ASRW ESTRES . ASRW ESTRES

are the preliminary estimates of 

TRYR9

pASRWRES

, the values in the tenth year of the projection period. For female spouses, 

we apply additional adjustments by age group (62-64, 65+), 

ASRW SRADJ

, to the tenth year 

of the projection period in order to match the projections made by the Short-Range office. 

We linearly interpolate the values of 

year pASRWRES

for intermediate years interpolated 

between 

TRYR1

pASRWRES

and 

TRYR9

pASRWRES

(equal to

ASRW ASRW ESTRES SRADJ 

). After the 
228

10th year of the projection period, we linearly grade these adjustment factors to one over 

the 10 years beyond the end of the short-range period, thus gradually eliminating the effect 

of the short-range adjustment factors, so that we ultimately return to the long-range 

projections.

Equation 3.3.4 – Disabled Spouses of Deceased Workers (DSDWN)

Disabled Spouses of Deceased Workers

Exposure: SSA population by age (50-69), sex and marital status (widowed and 

divorced)

Linkages: pDSDWDEA = probability that the primary account holder (PAH) is 

deceased

pDADWFIA = probability that the PAH was fully insured at death

pDSDWSSB = probability that the spouse is indeed disabled

pDSDWDEB = probability that the disabled spouse is not receiving 

 another type of benefit

pDSDWRES = probability that a person who is eligible to receive 

 disabled-spouse benefits actually receive the benefits

We project the number of disabled spouses of deceased workers, along with all linkage 

factors, by age, sex of the account holder (sa = 1 for male, sa = 2 for female) and marital 

status. Age ranges from 50 to 69, and marital status includes widowed (mb = 1) and 

divorced (mb = 2). We calculate the projected number of disabled spouses of deceased 

workers as follows:

 

, (3.3.4)

DSDWPOP represents the subset of the population from which we draw these beneficiaries, 

and we set it equal to the Social Security area population (SSAPOPmb) for mb = 1,2.

pDSDWDEA represents the probability that the primary account holder is deceased. For the 

widowed population, we do not apply a factor. For the divorced population, we set this 

factor equal to the portion of the total widowed and married population that is widowed.

pDSDWFIA represents the probability that the PAH was fully insured at death. Given the age 

of the widow, AW, we assume that the age of her deceased husband, AH, ranges from AW-6
229

to AW+12 with a lower and upper bound of 50 and 95+. Further, we assume that the more 

likely age of the husband is AW+3. For each age, we calculate pDSDWFIA as a weighted 

average of the portion of the Social Security area population that is fully insured (FINS), at 

each possible age of the husband. For example, if the widow is age 65, we assume that the 

age of the husband is between 59 and 77. Therefore, when we calculate the weighted 

average of the portion of the population who are fully insured males, we apply the highest 

weight of ten to age 68 and linearly reduce the weight to zero for each age above and 

below 68. We use the same concept for widow(er)s with the assumption that the age of his 

deceased wife ranges from AH-12 to AH+6, with a greater likelihood of her age being AH-3. 

Let WEIGHT represent the specific weight applied to each age. 

pDSDWSSB represents the probability that the spouse is indeed disabled. We set this factor 

equal to the disability prevalence rates (DISPREV) by age and sex received from the 

DISABILITY subprocess. 

pDSDWDEB represents the probability that the disabled spouse is not dually eligible for 

another type of benefit. We assume this factor remains at a constant level by sex.

pDSDWRES represents the probability that a person who is eligible to receive disabledspouse benefits actually receive the benefits. For all historical years, we calculate 

year pDSDWRES

as the ratio of DSDWN, the actual number of disabled spouses of deceased 

workers receiving benefits, to the number of persons meeting all previously mentioned 

requirements by age, sex, and marital status.

, year < TRYR
230

For ages 50 to 64, and each sex, and marital status, we use the average over 

the last five years of historical data to determine starting values for from 

which we project future values. We phase in these averages over five years using a linear 

interpolation between the five-year averages and . For female disabled 

spouses, we apply an adjustment, , to the tenth year of the projection period 

in order to match the projections made by the Short-Range office. We exponentially 

interpolate the values of for intermediate years between and 

(equal to ). After the 10th year of the projection 

period, we linearly grade the adjustment factors to one over the 10 years beyond the end of 

the short-range period, thus gradually eliminating the effect of the short-range adjustment 

factors, so that we ultimately return to the long-range projections.

For the projection period, for ages 65 to 69 where age is less than the NRA when the 

beneficiary was age 60, 

year pDSDWRES

is equal to 

year pDSDWRES

at age 64 times an adjustment 

that accounts for the additional ages as NRA changes.

, 65 ≤ age ≤ 69 

 and age < NRA at age 60 

Equation 3.3.5 – Minor Children of Retired Workers (MCRWN)

We project the number of minor children of retired workers, MCRWN, by age of the minor 

(am = 0 to 17) and sex of the account holder (sa = 1 for male, sa = 2 for female). 

For children of male retired workers:

 (3.3.5.1)

For the number of minor children of male retired workers, we multiply the number of 

children under the age of 18 with a father who is at least 62 years old and who’s other 

parent is not deceased ( ) by the total number of male retired workers ages 62 

to 71 ( ) divided by the total number of males in the population ages 62 to 71 

( ).

represents the probability that a child who is eligible to receive minor-child 

benefits actually receives them. In the historical period we calculate this as the ratio of the 

total number of minor children of retired workers actually receiving benefits to the total 

eligible to receive benefits.
231

For the projection period, we take the average of these ratios over the last ten historical 

years and phase it in using a linear interpolation between the average and . 

We apply an adjustment, 

MCRW SRADJ

, to the tenth year of the projection period in order 

to match the projections made by the Short-Range office. The adjustment factor is phased 

in over the first 10 years of the projection period with the full factor applied in the tenth 

year. After the 10th year of the projection period, we linearly grade these adjustment 

factors to one over the 10 years beyond the end of the short-range period, thus gradually 

eliminating the effect of the short-range adjustment factors, so that we ultimately return to 

the long-range projections.

For children of female retired workers:

(3.3.5.2)

mcrwPrcntFem represents the average over the last five historical years of the percentage 

of the total number of minor children of female retired workers, ages 0 to 17, to the total 

number of minor children for both male and female retired workers.

We maintain the total at this percentage over the entire projection period 

by multiplying our estimate of by mcrwPrcntFem divided by its 

complement. 

In order to distribute among the ages 0 to 17, we multiply by 

the proportion of beneficiaries at each age in the last historical year. 

Equation 3.3.6 – Minor Children of Deceased Workers (MCDWN )

Minor Children of Deceased Workers

Exposure: SSA population by age (0-17) and sex of the account holder

Linkages: pMCDWDEA = probability that the parent is either retired or deceased

pMCDWFIA = probability that the primary account holder (PAH) is fully 

insured
232

pMCDWRES = probability that a child who is eligible to receive minorchild benefits actually receive the benefits

We project the number of minor children of deceased workers, along with all linkage 

factors, by age of the minor (am = 0 to 17) and sex of the account holder (sa = 1 for male, sa

= 2 for female). We calculate it as follows:

(3.3.6)

MCDWPOP represents the subset of the population from which we draw these beneficiaries, 

and we set it equal to the Social Security area population (SSAPOP).

pMCDWDEA represents the status of the parent (PAH). This is set equal to the portion of the 

minor population where at least one parent is deceased. CHI_DEA represents the number 

of children having at least one deceased parent. 

pMCDWFIA represents the probability that the parent (PAH) is fully insured. We set this 

equal to the portion of the population aged 25 + am to 35 + am where the PAH is fully 

insured (FI_PAH).

pMCDWRES represents the probability that a child who is eligible to receive minor-child 

benefits actually receive the benefits. For all historical years, we calculate 

year pMCDWRES

as 

the ratio of MCDWN, the actual number of minor children of deceased workers receiving 

benefits, to the number of number of persons meeting all previously mentioned 

requirements by age and sex of the parent.

, year < TRYR

 For each age and sex of parent, we use a least squares regression over the last ten years of historical 

 data to determine a starting value in TR-1 for 

year pMCDWRES

from which we project future values. We 

apply an adjustment, 

MCDW SRADJ

, to the tenth year of the projection period in order to match the 

projections made by the Short-Range office. We linearly interpolate the values of 

year pMCDWRES

for 

intermediate years between the regressed values for

TRYR1

pMCDWRES

and 

TRYR MCDW 9

RES pMCDW SRADJ 

 . 
233

After the 10th year of the projection period, we linearly grade the adjustment factors to one over the 

10 years beyond the end of the short-range period, thus gradually eliminating the effect of the short-

 range adjustment factors, so that we ultimately return to the long-range projections.

 Equation 3.3.7-8 – Student Children of Retired and Deceased Workers (SCRWN and SCDWN)

Student Children of Retired Workers

Exposure: SSA population by age of the student (18-21) and sex of the account holder

Linkages: pSCRWDEA = probability that at least one parent is retired

pSCRWAGA = probability that the primary account holder (PAH) is age 62 

or older

pSCRWFIA = probability that the PAH is fully insured

pSCRWCPA = probability that the PAH is receiving benefits

pSCRWSSB = probability that the child is indeed attending school

pSCRWRES = probability that a child who is eligible to receive studentchild benefits actually receives the benefits

Student Children of Deceased Workers

Exposure: SSA population by age of the student (18-21) and sex of the account holder

Linkages: pSCDWDEA = probability that at least one parent is deceased

pSCDWAGA = probability that the PAH is age 62 or older (set to 1)

pSCDWFIA = probability that the PAH is fully insured

pSCDWCPA = probability that the PAH is receiving benefits (set to 1)

pSCDWSSB = probability that the child is indeed attending school

pSCDWRES = probability that a child who is eligible to receive student-

 child benefits actually receives the benefits.

We project the number of student children of retired and deceased workers, along with all 

linkage factors, by age of the student (as = 18 to 19) and sex of the account holder (sa = 1 

for male, sa = 2 for female). We calculate the projected number of student children of 

retired and deceased workers as follows:

POP DEA AGA FIA

CPA SSB RES

SCRWN SCRW pSCRW pSCRW pSCRW

pSCRW pSCRW pSCRW

    

 

 (3.3.7)

POP DEA AGA FIA

CPA SSB RES

SCDWN SCDW pSCDW pSCDW pSCDW

pSCDW pSCDW pSCDW

    

 

 (3.3.8)

SCRWPOP and SCDWPOP represent the subset of the population from which these 

beneficiaries are drawn, and we set them equal to the Social Security area population 

(SSAPOP).
234

pSCRWDEA and pSCDWDEA represent the status of the parent (PAH). For student children of 

retired workers, we set this equal to the proportion of the subset of the population where 

neither parents are deceased. For student children of deceased workers, we set this equal 

to the proportion of the subset of the population where at least one parent is deceased. 

CHI_DEA represents the number of student children having at least one deceased parent. 

pSCRWAGA and pSCDWAGA represent the probability that the PAH is age 62 or older. For 

student children of retired workers, we set this equal to the proportion of the student 

population that has one parent age 62 or older, CHI_62+. For student children of deceased 

workers, we set the factor equal to one.

pSCRWFIA and pSCDWFIA represent the probability that the PAH is fully insured. For student 

children of retired workers, we set this equal to the portion of the population aged 62 to 

64+as where the PAH is fully insured (FI_PAH). For student children of deceased workers, 

we calculate the factor similarly with the population being aged 25+as to 35+as.

pSCRWCPA and pSCDWCPA represent the probability that the PAH is receiving benefits. For 

student children of retired workers, we set this factor equal to the portion of the population 

aged 62 to 64+as where the PAH is receiving benefits (RETIRED). For student children of 

deceased workers, we set this factor equal to one.
235

pSCRWSSB and pSCDWSSB represent the probability that the child is indeed attending school 

(full-time elementary or secondary school). This factor is dependent upon the age of the 

child, and we calculate it as follows.

pSCRWRES and pSCDWRES represent the probability that a child who is eligible to receive 

student-child benefits actually receive the benefits. For all historical years, we calculate 

year pSCRWRES

and 

year pSCDWRES

as the ratio of SCRWN and SCDWN, the actual number of 

student children receiving benefits, to the number of number of persons meeting all 

previously mentioned requirements by age and sex of the parent.

, year < TRYR

, year < TRYR

For each age and sex of parent, we use a least squares regression over the last ten years of 

historical data to determine a starting value in TR-1 for

year pSCRWRES

from which we project 

future values. We apply an adjustment, 

SCRW SRADJ

, to the tenth year of the projection 

period in order to match the projections made by the Short-Range office. We linearly 

interpolate the values of 

year pSCRWRES

for intermediate years between the regressed values 

for 

TRYR1

pSCRWRES

and 

TRYR SCRW 9

RES pSCRW SRADJ 



. After the 10th year of the projection 

period, we linearly grade the adjustment factors to one over the 10 years beyond the end of 

the short-range period, thus gradually eliminating the effect of the short-range adjustment 

factors, so that we ultimately return to the long-range projections. We calculate the values 

of 

year pSCDWRES

similarly.

Equation 3.3.9-10 – Disabled Adult Children of Retired and Deceased Workers (DCRWN and 

DCDWN)

Disabled Adult Children of Retired Workers

Exposure: SSA population by age of the adult child (18-95)

Linkages: pDCRWAGA = probability that the primary account holder (PAH) is age 62 

or older

pDCRWDEA = probability that the parent is retired

pDCRWFIA = probability that the PAH is fully insured

pDCRWCPA = probability that the PAH is receiving benefits

pDCRWSSB = probability that the child is indeed disabled
236

pDCRWRES = probability that a child who is eligible to receive disabledchild benefits actually receive benefits

Disabled Adult Children of Deceased Workers

Exposure: SSA population by age of the adult child (18-95)

Linkages: pDCDWAGA = probability that the PAH is age 62 or older (set to 1)

pDCDWDEA = probability that the parent is deceased

pDCDWFIA = probability that the PAH is fully insured

pDCDWCPA = probability that the PAH is receiving benefits (set to 1)

pDCDWSSB = probability that the child is indeed disabled

pDCDWRES = probability that a child who is eligible to receive disabledchild benefits actually receive the benefits

We project the number of disabled adult children of retired and deceased workers, along 

with all linkage factors, by age group of the disabled adult child (ad = 1-10) and sex of the 

account holder (sa = 1 for male, sa = 2 for female). The age groups are 18-19, 20-24, ..., 55-

59, 60+. We calculate the projected number of disabled adult children of retired and 

deceased workers as follows:

 

POP AGA DEA FIA

CPA SSB RES

DCRWN DCRW pDCRW pDCRW pDCRW

pDCRW pDCRW pDCRW

   

  

 (3.3.9)

 

POP AGA DEA FIA

CPA SSB RES

DCDWN DCDW pDCDW pDCDW pDCDW

pDCDW pDCDW pDCDW

   

  

 (3.3.10)

We calculate all factors similarly to those for student children with the exception of the 

following. 

pDCRWDEA is set equal to the proportion of the married and widowed population who are 

married (for ages of the parent that are reasonable based on the given age range of the 

disabled child). We calculate pDCDWDEA similarly for disabled children of deceased workers.
237

pDCRWSSB and pDCDWSSB represent the probability that the adult child is indeed disabled. 

DCPREM is the preliminary calculation of this factor and we assume it to remain constant. 

For the projection period, for ad=6-10, we set pDCRWSSB and pDCDWSSB equal to the 

preliminary factor, plus an adjustment which accounts for the year.

0.012, ad = 1-2

0.009, ad = 3

0.007, ad = 4

0.006, ad = 5

0.005, ad = 6

0.004, ad = 7-10

pDCRWRES and pDCDWRES represent the probability that a child who is eligible to receive 

disabled-child benefits actually receive the benefits. For all historical years, we calculate 

year pDCRWRES

and 

year pDCDWRES

as the ratio of DCRWN and DCDWN, the actual number of 

disabled children receiving benefits, to the number of number of persons meeting all 

previously mentioned requirements by age and sex of the parent.

For year < TRYR:

We apply an adjustment,

DCRW SRADJ

, to the tenth year of the projection period in order to 

match the projections made by the Short-Range office. We linearly interpolate the values

of 

year pDCRWRES

for intermediate years between 

TRYR1

pDCRWRES

and 

TRYR DCRW 9

RES pDCRW SRADJ 



. After the 10th year of the projection period, we linearly grade 

the adjustment factors to one over the 10 years beyond the end of the short-range period, 

thus gradually eliminating the effect of the short-range adjustment factors, so that we 

ultimately return to the long-range projections. We calculate the values of 

year pDCDWRES

similarly.

Equation 3.3.11-12 – Young Spouses of Retired and Deceased Workers (YSRWN and YSDWN)

Young Spouses of Retired Workers

Exposure: SSA population by age (15-69), sex of account holder, and marital status 

DCPREM =
238

(married)

Linkages: pYSRWAGA = probability that the primary account holder (PAH) is of the 

required age.

pYSRWECB = probability that the young spouse has an entitled child in their 

care

pYSRWFSB = probability that the young spouse is not already receiving 

benefits based on another child in their care

pYSRWRES = probability that a person who is eligible to receive youngspouse benefits actually receive the benefits

Young Spouses of Deceased Workers

Exposure: SSA population by age (15-69), sex of the account holder and marital status 

(widowed and divorced)

Linkages: pYSDWDEA = probability that the PAH is deceased

pYSDWECB = probability that the young spouse has an entitled child in their

care

pYSDWFSB = probability that the young spouse is not already receiving

benefits based on another child in their care

pYSDWRMB = probability that the young spouse is not remarried

pYSDWRES = probability that a person who is eligible to receive youngspouse benefits actually receive the benefits

We project the number of young spouses of retired and deceased-workers, along with all 

linkage factors, by age group (ab = 1-10) of the young spouse and sex of the account holder 

(sa = 1 for male, sa = 2 for female). We also project young spouses of deceased workers by 

marital status of the young spouse (mb = 1 for widowed and mb = 2 for divorced). The age 

groups are under 25, 25-29 ..., 65-69. We calculate the projected number of young spouses 

of retired and deceased-workers as follows:

YSRWN YSRW pYSRW pYSRW pYSRW pYSRW      POP AGA ECB FSB RES

 (3.3.11)

YSDWN YSDW pYSDW pYSDW pYSDW pYSDW pYSDW       POP DEA ECB FSB RMB RES

(3.3.12)

YSRWPOP and YSDWPOP represent the subset of the population from which we draw these 

beneficiaries. We set YSRWPOP equal to the married Social Security area population 

(SSAPOPmar) and we set YSDWPOP equal to SSAPOPmb for mb = 1-2.
239

pYSDWDEA represent the probability that the PAH is deceased. For mb=1, we do not apply 

any factor. For mb=2, we set this factor equal to the portion of young spouses that is 

widowed.

pYSRWAGA and represent the probability that the PAH is of the required age. We set 

pYSRWAGA equal to the portion of the married population who has an aged spouse (AGSP). 

pYSRWECB and pYSDWECB represent the probability that the young spouse has an entitled 

child in their care. We set pYSRWECB equal to the portion of persons meeting the previously 

mentioned requirements who have a minor or disabled adult child in their care. We set 

pYSDWECB ,by marital status, equal to the portion of persons meeting the previously 

mentioned requirements who have a minor or disabled adult child in their care. 

ab MCRWN

and 

ab DCRWN

represent the total number of minor and disabled adult 

children of retired workers where the other parent (young spouse) is in the age bracket ab.

pYSRWFSB and pYSDWFSB represent the probability that the young spouse is not already 

receiving benefits based on another child in their care. We set this factor equal to one 

divided by the number of children in the average family (ASOFab) for the given age bracket 

of the spouse. For young spouses of retired workers, we do not apply a factor for sa=2.
240

pYSDWRMB represents the probability that the spouse is not remarried. We assume this 

factor remains constant at 0.600.

pYSRWRES and pYSDWRES represent the probability that a person who is eligible to receive 

young-spouse benefits actually receive the benefits. For all historical years, we calculate 

year pYSRWRES

as the ratio of YSRWN, the actual number of young spouses of retired workers 

receiving benefits, to the number of persons meeting all previously mentioned 

requirements by age, sex, and marital status. We calculate 

year pYSDWRES

similarly, using the 

number of young spouses of deceased workers.

For each age, sex , and marital status, we use a least squares regression over the last ten 

years of historical data to determine a starting value in TR-1 for 

year pYSRWRES

. In addition, for 

each sex and marital status we graduate the regressed values of 

TRYR1

pYSRWRES

over age 

using a weighted minimized third-difference formula to compute 

YSRW ESTRES . 

YSRW ESTRES

are the preliminary estimates of 

TRYR9

pYSRWRES

, the values in the tenth year of 

the projection period. For female young spouses, we apply an adjustment, 

YSRW SRADJ

, to 

the tenth year of the projection period in order to match the projections made by the ShortRange office. We exponentially interpolate the values of 

year pYSRWRES

for intermediate years 

between 

TRYR1

pYSRWRES

and 

TRYR9

pYSRWRES

(equal to 

YSRW YSRW ESTRES SRADJ 

). After the 

10th year of the projection period, we linearly grade the adjustment factors to one over the 

10 years beyond the end of the short-range period, thus gradually eliminating the effect of 

the short-range adjustment factors, so that we ultimately return to the long-range 

projections. We calculate the values of

year pYSDWRES

similarly.

Equation 3.3.13 – Number of Deaths of Insured Workers (LUMSUMab)

We project the number of deaths of insured workers by sex and 5-year age group (ab = 1-

14). Age groups include 20-24, 25-29,...,80-84, 85+. We calculate EXPOSUREab, the 

estimated number of lump-sum payments paid during the year for age group ab, as the 

number of total deaths during the year times the probability that the deceased was fully 

insured and has a surviving spouse or child. We calculate BASE as the ratio of the actual 

total amount of lump-sum death payments paid in TRYR-1 to the estimated total amount of 
241

lump-sum payments paid in TRYR-1. We then calculate 

LUMSUMab

for each year in the 

projection period.

 (3.3.13)

Appendix 3.3-1: Glossary

AB: age group of the beneficiary

AD: age of the disabled child

AGSP: married population where at least one spouse is age 62 or older

AM: age of the minor child

ARFGT3: actuarial reduction factor for ages more than 3 years below normal retirement age

ARFLE3: actuarial reduction factor for ages less than 3 years below normal retirement age

AS: age of the student child

ASDW: aged spouse of deceased worker by linkage factor, age (60-95+), sex of the account 

holder, marital status (widowed, divorced) and insured status (insured, uninsured). Linkage 

factors are:

ASDWPOP

: population of potential aged spouse of retired workers

pASDWDEA

: probability that the primary account holder (PAH) is deceased

pASDWFIA

: probability that the PAH was fully insured at death

pASDWMBB

: probability that the widow(er) is not receiving a young-spouse benefit for 

the care of a child

pASDWFIB

: probability that the aged widow(er) is fully insured

pASDWGPB

: probability that the aged-widow(er)'s benefits are not withheld or offset 

totally because of receipt of a significant government pension based on 

earnings in noncovered employment

pASDWRES

: probability that a widow(er) eligible to receive his/her own retiredworker benefits would instead apply for and receive widow(er) 

benefits

ASDWN: final number of aged spouse of deceased workers (product of all linkage factors)

ASOF: average number of children in a family, by age group (under 25, 25-29 ..., 60-64)

ASRW: aged spouse of retired worker by linkage factor, age (62-95+), sex of the account 

holder, and marital status of the beneficiary (married, divorced). Linkage factors are:

ASRWPOP

: population of potential aged spouse of retired worker beneficiaries

pASRWDEA

: probability that the primary account holder (PAH) is not deceased

pASRWAGA

: probability that the PAH is of the required age

pASRWFIA

: probability that the PAH is fully insured

pASRWCPA

: probability that the PAH is receiving benefits

pASRWMBB

: probability that the beneficiary is not receiving a young-spouse benefit

pASRWFIB

: probability that the aged spouse is not fully insured
242 pASRWGPB

: probability that the aged-spouse's benefits are not withheld because of 

receipt of a significant government pension based on earnings in 

noncovered employment

pASRWRES

: probability that a person who is eligible to receive aged-spouse benefits 

actually receive the benefits

ASRWN: final number of aged spouse of retired workers (product of all linkage factors)

AH: age of husband

AW: age of wife

BASE: ratio of actual to estimated total amount of lump-sum death payments paid in TRYR-1

CHI_62+: number of children having at least one parent aged 62 or older

CHI_DEA: number of children having at least one deceased parent

CON: number of persons converted from disabled-worker beneficiaries

DCDW: disabled child of deceased workers by linkage factor, age group of the child (18-19, 20-

24,..., 55-59, 60+) and sex of the account holder. Linkage factors are same as SCDW.

DCDWPOP

: population of potential disabled children

pDCDWAGA

: probability that the PAH is age 62 or older

pDCDWDEA

: probability that the parent is either retired or deceased

pDCDWFIA

: probability that the PAH is fully insured

pDCDWCPA

: probability that the PAH is receiving benefits

pDCDWSSB

: probability that the child is indeed disabled

pDCDWRES

: probability that a child who is eligible to receive disabled-child benefits 

actually receive the benefits

DCDWN: final number of disabled children of deceased workers (product of all linkage factors)

DCPREM: preliminary calculation of the probability that a child is disabled, by age

DCRW: disabled child of retired workers by linkage factor, age group of the child (18-19, 20-

24,..., 55-59, 60+) and sex of the account holder. Linkage factors are same as those for DCDW.

DCRWN: final number of disabled children of retired workers (product of all linkage factors)

DIB: number of disabled-worker beneficiaries

DIFFADJ: adjustment that accounts for the difference between the actual and estimated 

prevalence rate at each age in the most recent historical years

DISPREV: disability prevalence rate by age and sex

DRC: delayed retirement credit

DSDW: disabled spouse of deceased worker by linkage factor, age (50-69), sex of the account 

holder, and marital status (widowed, divorced). Linkage factors are:

DSDWPOP

:population of potential beneficiaries

pDSDWDEA

: probability that the primary account holder (PAH) is deceased

pDSDWFIA

: probability that the PAH was fully insured at death

pDSDWSSB

: probability that the spouse is indeed disabled

DEB pDSDW

: probability that the disabled spouse is not receiving another type of 

benefit
243 pDSDWRES

: probability that a person who is eligible to receive disabled-spouse 

Benefits actually receive the benefits

DSDWN: final number of disabled spouse of deceased workers (product of all linkage factors)

ERROR: actual prevalence rate minus the regressed prevalence rate in the most recent 

historical year

ESTPR: preliminary estimate of the prevalence rate for retired workers

ESTPR2: secondary estimate of the prevalence rate for retired workers

ESTRES: preliminary estimate of the RES factor for the tenth year of the projection period

EXPOSURE: estimated number of lump-sum payments by age group (20-24, 25-29,...,80-84, 

85+)

FACTOR: adjustment for calculation of MBB factor of aged spouse of deceased worker

FINS: portion of the SSA population that is fully insured

FI_PAH: portion of married population where one spouse is fully insured

FP: status of the parent (retired, deceased)

GPOAGE: portion, by age, of the total beneficiaries expected to receive a significant 

government pension

GPWHLD: total number of beneficiaries (for all ages) expected to receive a significant 

government pension

IN: insured status of the beneficiary

LFPR: labor force participation rates for age 62, by sex

LUMSUM: number of deaths of insured workers by sex and age group (20-24,...,80-84,85+)

MAR62PLUS: number of couples where both husband and wife are age 62 and over

MS: marital status of the primary account holder

MB: marital status of the beneficiary

MBAPIA: ratio of the monthly benefit amount (MBA) to the primary insurance amount (PIA) by 

age (62-70) and sex

MCDW: minor children of deceased workers by linkage factor, age of the child (0-17) and sex of 

the account holder. Linkage factors are:

MCDWPOP

: population of potential minor children

pMCDWDEA

: probability that the parent is either retired or deceased

FIA pMCDW

: probability that the PAH is fully insured

pMCDWRES

: probability that a child who is eligible to receive minor-child benefits 

actually receive the benefits

MCDWN: final number of minor children of deceased workers (product of all linkage factors)

MCRW: minor children of retired workers by linkage factor, age of the child (0-17) and sex of 

the account holder.

MCRWN: final number of minor children of retired workers (product of all linkage factors)

NRA: normal retirement age

PAH: primary account holder

REGPR: regressed prevalence rate for retired workers

RETIRED: number of retired workers receiving benefits

RW: retired workers by linkage factor, age (62-95+), sex, and marital status (single, married, 

widowed, divorced). Linkage factors are: 
244 RWPOP

: population of potential retired-worker beneficiaries

pRWFIA

: probability that the primary account holder (PAH) is insured

pRWDBB

: probability that the PAH is not receiving a disabled-worker benefit

pRWWBB

: probability that the PAH is not receiving a widow(er) benefit

RES pRW

: retirement prevalence rate; probability that a fully insured worker (not 

receiving disability or widow(er)’s benefits) would receive a retiredworker benefit

RWN: final number of retired workers (product of all linkage factors)

SA: sex of the account holder

SCDW: student children of deceased workers by linkage factor, age of the student (18-21) and 

sex of the account holder. Linkage factors are:

SCDWPOP

:population of potential student children

pSCDWDEA

: probability that the parent is either retired or deceased

pSCDWAGA

: probability that the PAH is age 62 or older

pSCDWFIA

: probability that the PAH is fully insured

pSCDWCPA

: probability that the PAH is receiving benefits

pSCDWSSB

: probability that the child is indeed attending school

pSCDWRES

: probability that a child who is eligible to receive student-child benefits 

actually receive the benefits

SCDWN: final number of student children of deceased workers (product of all linkage factors)

SCRW: student children of retired workers by linkage factor, age of the student (18-21) and sex 

of the account holder. Linkage factors are same as SCDW.

SCRWN: final number of student children of retired workers (product of all linkage factors)

SRADJ: adjustment to match short-range projections in 10th year of projection period

SSAPOP: Social Security area population by age (0:100), sex, and marital status (single, married, 

widowed, divorced)

SX: sex of the beneficiary

TRYR: first year of the projection period

WEIGHT: estimated probability applied to each possible age of the spouse, given the age of the 

primary account holder

YSDW: young spouse of deceased worker by linkage factor, age group (under 25, 25-29,...,65-

69), sex of the account holder and marital status (widowed, divorced). Linkage factors are:

YSDWPOP

: population of potential young spouse of deceased workers

pYSDWDEA

: probability that the primary account holder (PAH) is of the required age

pYSDWECB

: probability that the young spouse has an entitled child in their care

pYSDWFSB

: probability that the young spouse is not already receiving benefits based 

on another child in their care

pYSDWRMB: probability that the young spouse is not remarried

pYSDWRES

: probability that a person who is eligible to receive young-spouse benefits 

actually receive the benefits
245

YSDWN: final number of young spouse of deceased workers (product of all linkage factors)

YSRW: young spouse of retired worker by linkage factor, age group (under 25, 25-29,...,65-69) 

and sex of the account holder. Linkage factors are:

YSRWPOP

: population of potential young spouse of retired workers

pYSRWAGA

: probability that the primary account holder (PAH) is of the required age

pYSRWECB

: probability that the young spouse has an entitled child in their care

pYSRWFSB

: probability that the young spouse is not already receiving benefits based 

on another child in their care

pYSRWRES

: probability that a person who is eligible to receive young-spouse benefits

actually receive the benefits

YSRWN: final number of young spouse of retired workers (product of all linkage factors)
246

Process 4:

Trust Fund

Operations &

Actuarial Status
247

4. Trust Fund Operations and Actuarial Status

OCACT uses the Trust Fund Operations and Actuarial Status Process to project: (1) the annual flow 

of income from payroll taxes, taxation of benefits, and interest on assets in the trust fund, and (2) 

the annual flow of cost from benefit payments, administration of the program, and net railroad 

interchange. The annual flows are projected for each year of the 75-year projection period. In 

addition, this subprocess produces annual and summarized values to help assess the financial status 

of the Social Security program. 

The Trust Fund Operations and Actuarial Status Process is composed of three subprocesses: 

TAXATION OF BENEFITS, AWARDS, and COST. As a rough overview, TAXATION OF BENEFITS projects, 

for each year during the 75-year projection period, the amount of income from taxation of benefits 

as a percent of benefits paid. AWARDS projects information needed to determine the benefit levels 

of newly awarded retired workers and disabled workers by age and sex. COST uses information 

from the AWARDS and TAXATION OF BENEFITS subprocesses, as well as information from other 

processes, to project the annual flow of income and cost to the trust funds. In addition, COST 

produces annual and summarized measures of the financial status of the Social Security program, 

including selected summarized information used to prepare the Statement of Social Insurance.
248

4.1. TAXATION OF BENEFITS

4.1.a. Overview 

The 1983 Social Security Act specifies including up to 50 percent of the Social Security benefits 

to tax return filer’s adjusted gross income (AGI) for tax liability if a tax return filer’s adjusted 

gross income plus half of his (or her) Social Security benefits is above the specified income 

threshold amount of $25,000 as a single filer (or $32,000 as a joint filer). Subsequently, the 

1993 OBRA (Omnibus Budget Reconciliation Act) provided for taxation of up to 85 percent if a 

tax return filer’s adjusted gross income plus half of his (or her) Social Security benefits is above 

the specified income threshold amount of $34,000 as a single filer (or $44,000 as a joint filer). 

The proceeds from taxing up to 50 percent of the OASDI benefits, as a result of the 1983 Act,

are credited to the OASI and DI Trust Funds, while additional taxes on the OASDI benefits, as a 

result of the 1993 Act, are credited to the HI Trust Fund.

Income to the Trust Funds from such taxation is estimated by using ratios of taxes on benefits 

to benefits for the OASI and DI programs separately. These ratios, called “RTBs”, are applied to 

projected OASI and DI benefit amounts to estimate tax revenues to the OASI and DI Trust 

Funds. 

For the short range period (first 10 years of the projection), the Cost sub-process (4.3) uses OTA 

(Office of Tax Analysis)’s projected estimates for (1) the percent of benefits taxable and (2) the 

average marginal tax rates applicable to those taxable OASI and DI benefits. The multiplication 

of (1) and (2) produces projected RTBs under the 1983 Act (up to 50 percent of benefits 

taxable) and the 1993 OBRA (additional up to 35 percent of benefits taxable).

For the long range period (11th through 75th year of the projection period), the RTB ratios for 

OASI benefits and those for DI benefits under the 1983 Act and the combined 1983 + 1993 Act 

are computed with the following formula for each projection year.

RTB(yr) = RTB(tryr+9) * {AWI(tryr+9)/AWI(yr)}^P + 

 RTB(ultimate)*{1- AWI(tryr+9)/AWI(yr)}^P, (4.1.1) 

where

 tryr = first year of the projection period (year of the Trustees Report) 

 RTB(ultimate) = ratio of taxes on benefits to benefits assuming income 

 thresholds equal zero. 

 

 AWI = SSA average wage index series
249

 P = exponential parameter for a trend curve line.

Finally, the Cost sub-process (4.3) applies the projected RTB ratios to its own estimates of the 

projected OASI and DI benefit payments to produce taxation of benefit revenues to the OASI 

and DI Trust Funds.

4.1.b. Input Data

OCACT Data

Economics--process 2

- Projected SSA wage index series by year, updated yearly

- Projected COLAs and average wage index levels under the intermediate 

assumptions of the prior Trustees Report

Beneficiaries--process 3

- Projected OASI beneficiaries by age and sex under the intermediate assumptions 

of the prior Trustees Report

- Projected DI beneficiaries by age and sex under the intermediate assumptions of 

the prior Trustees Report

Trust Fund Operations--process 4

- Aggregate OASI and DI benefit ratios (as a ratio of total OASDI benefits) under 

the intermediate assumptions of the prior Trustees Report

Other

- Aggregate OASDI benefit payments for calendar year ni-3 (ni = initial projection 

year, e.g, 2011 for the 2014 Trustees Report)

Other input Data

- OTA’s projected percent of benefits taxable and average marginal tax rates by 

type of benefit (OASI and DI) for the short range period (updated yearly). 

- OTA’s ultimate ratios of taxes on benefits to benefits (i.e., with income 

thresholds, assumed equal to 0). Such ultimate ratios are provided on a 

combined OASDI benefit basis, and are expected to be updated annually based 

on OTA’s update. 

- Current Population Survey (CPS) data for year ni-3

- Personal income tax brackets for year ni-3 and tax year 2013

- Marginal income tax rates for year ni-3 and tax year 2013
250

- General filing requirement amounts (standard deduction amounts) for personal 

income tax purposes for year ni-3

- Ratios of taxable income to adjusted gross income by income level (IRS data) for 

tax year ni-3

- Treasury’s aggregate taxable benefit amount (IRS data) for tax year ni-3

- OTA’s estimated taxes on benefits for the OASDI and HI Trust Funds for tax year 

ni-3

4.1.c. Development of Output

For the short range period, the Cost sub-process (4.3) uses OTA’s projected RTBs for OASI and 

DI benefits under the 1983 Act, to project taxation of benefit revenues to the OASI and DI Trust 

Funds. 

For the long range period, formula 4.1.1 computes projected ratios of taxes on OASI benefits to 

OASI benefits and projected ratios of taxes on DI benefits to DI benefits under the 1983 Act (up 

to 50 percent of benefits taxable). This formula essentially provides more weight to the 

ultimate RTB ratios as time progresses, using the ratio of AWI (10th year) to AWI (projection 

year) as the “weight.” Additionally, an exponential parameter P value to the AWI “weights” is 

set judgmentally such that the estimate continues the short range trend into the transitional 

11th through 20th projection years before it approaches the ultimate RTB ratio. For the RTB 

ratios for up to 50 percent of benefits taxable, the P values were set at 0.97 and 0.92 to project 

smooth transitional RTB ratios for OASI and DI benefits, respectively.

The ultimate RTB ratios used in the projection are based on OTA’s ultimate ratios, reduced by 

about 4.5 percent. This reduction reflects estimates of the effect of the higher proportion of 

“old elderly” beneficiaries in the 2090 OASDI beneficiary population distribution relative to the 

2023 OASDI beneficiary population distribution, due to improved mortality. 

For the 2014 Trustees Report, the ultimate RTB ratios for up to 50 percent of OASI and DI 

benefits taxable were set at 0.054 and 0.027, as compared to 0.052 and 0.026 for the 2013 TR 

ultimate ratios. Slight increases in the ultimate RTB ratios reflect OTA’s updated zero threshold 

estimates. 

Lastly, the Cost sub-process (4.3) applies these projected RTB ratios to projected OASI and DI 

benefit payments to develop estimated taxation of benefit revenues to the OASI and DI Trust 

Funds.
251

4.2. AWARDS

Each year over 2 million workers begin receiving either retired-worker or disabled-worker 

benefits. The monthly benefits for these new awards are based on their primary insurance 

amount (PIA). The PIA is computed using the average indexed monthly earnings (AIME) and the 

PIA benefit formula as specified in the 1977 amendments. The AIME depends on the worker’s 

number of computation years, Y, and the earnings in each year. For retired-worker 

beneficiaries who have attained or will attain age 62 in 1991 or later, Y = 35.

The AWARDS subprocess (AWARDS) selects records from a 10 percent sample of newly entitled 

worker beneficiaries obtained from the Master Beneficiary Record (MBR).35 The selected 

sample, referred to as “sample”, contains 261,931 beneficiary records, and each record, r, 

includes a worker’s history of taxable earnings under the OASDI program as well as additional 

information such as sex, birth date, month of initial entitlement, and type of benefit awarded. 

To estimate the benefit levels of future newly entitled worker beneficiaries, AWARDS modifies 

the earnings records in the sample to reflect the expected work histories and earnings levels of 

future beneficiaries (equation 4.2.1). After the modifications, AWARDS computes an AIME for 

each record in the future sample of beneficiaries (equation 4.2.2). AWARDS subdivides the 

AIME value of each record into bend point subintervals36 (equation 4.2.3). As input to the Cost 

subprocess, the AIME values are used to calculate aggregate percentages of AIME in each bend 

point subinterval for each age at entitlement, sex and trust fund (equation 4.2.4). Equations 

4.2.1 through 4.2.4 outline the overall structure and solution sequence. The subscript n refers 

to the bend point subinterval and r refers to the sample record.

 Projected Earnings = Projected Earnings (∙) (4.2.1)

 AIME(r) = 

12

Highest Indexed Earnings( )





Y

Y r

 (4.2.2) 

 AIME (r) n = AIME n (∙) (4.2.3)

 PAPn = 





r

n

r

n

r

bp

AIME ( )

 (4.2.4)

where bpn is the length of the nth bend point subinterval,

Y is the number of computation years, and

AIMEn (r) is the AIME amount contained within the nth interval for record r.

 

35 A record is selected if the year of initial entitlement equals 2008 and the beneficiary is in current pay status as of 

Dec. 2008, 2009 or 2010. Retired beneficiaries over age 70 and disability beneficiaries under age 20 are excluded.

36 The current law PIA formula has two bend points. For the purposes of PAP, the Awards subprocess instead uses 

30 subintervals.
252

4.2.b. Input Data

Long-Range OCACT Projection Data

Demography–

 Total Social Security area population (as of July) by sex and age.

o From 1951 to 2090

o Updated annually

 Other-than-legal population (as of July) by sex and age

o From 1964 to 2090

o Updated annually

 Deferred Action for Childhood Arrivals (DACA) population (as of July) by sex and age.

o From 2012 to 2090

o Updated annually

Economics –

 Covered workers by sex and age—with earnings posted to the Master Earnings File 

(MEF) only. - used with CWHS data to project future earnings levels

o From 1951 to 2090 

o Updated annually

 Covered workers not in the other-than-legal population by sex and age —with 

earnings posted to the Master Earnings File (MEF) only

o From 1951 to 2090 

o Updated annually

 Average Wage Index (AWI), projected values.

o From 2013 to 2090

o Updated annually

 Total taxable earnings and number of workers with taxable earnings by age, sex, and 

year from the 2011 Continuous Work History Sample (CWHS).

o From 1951 to 2011

o Updated annually

 Historical Average Taxable Earnings (ATE) —with earnings posted to the Master 

Earnings File (MEF) only – used with CWHS data to project future earnings levels

o From 1951 to 2012 (only 2007-2012 data used in SOSI) 

o Updated annually

 Projected Average Taxable Earnings (ATE) —with earnings posted to the Master 

Earnings File (MEF) only – used with CWHS data to project future earnings levels

o From 2013 to 2090 

o Updated annually

 Projected Covered Worker Rate based on total covered workers/total population 

(not used in SOSI) 

o From 2013 to 2090

o Updated annually

 COLA (Cost Of Living Adjustment) – not used in SOSI

o From 2013 to 2090
253

o Updated annually

 Projected Wage Base (to reflect relative changes in relative taxable maximum levels 

over time)

o From 1951 to 2015

o Updated annually

Fully Insured –

 Historical and projected fully insured rates by sex and single year of age 14-95

o From 1969 to 2090

o Updated annually

Other input data

 10% Awards Sample from the MBR and Master Earnings File

o Newly entitled OASI / DI beneficiaries, whose initial entitlement year was 

2008, and are in current pay status as of Dec. 2008, 2009 or 2010.

 SSN

 Type of benefit

 Type of claim (retirement or disability)

 Sex

 Date of birth

 Date of initial entitlement

 Date of disability onset

 PIA amount

 Type of dual entitlement

 Dual entitlement status code

 PIFC

 LAF

 Eligibility year

 Trust fund

 Earnings histories for each worker from 1951 to 2007

o Generally updated annually, pending validation of the sample

 AWI, Average Wage Index, historical values

o From 1951 to 2012

o Data obtained from OCACT internet site.

o Updated annually

 Wage base

o From 1951 to 2014 

o Data obtained from OCACT internet site.

o Updated annually

 COLA, cost of living adjustment – not used in SOSI

o From 1975 to 2012

o Data obtained from OCACT internet site.

o Updated annually
254

 Amount of earnings needed to earn one quarter of coverage

o From 1951 to 2014

o 1978-2014 data obtained from OCACT internet site. 1951-1977 values 

estimated by applying projection methodology backwards from 1978.

o Updated annually

 Windfall Elimination Provision (WEP) factors, the % of sample cases affected by the 

WEP which will no longer be affected by the WEP, by sex and projection year 

o From 2013 to 2090

o Data obtained from OCACT internal calculations

o Updated annually 

 PIA bend points – not used in SOSI (expect for 1979 bend points)

o From 1979 to 2014

o Data obtained from OCACT internet site.

o Updated annually

4.2.c. Development of Output

All equations described below are projected separately for the OASI and DI program.

Equation 4.2.1 – Projected Earnings

In order to estimate future benefit levels, the work histories and earnings levels in the current 

sample must be modified to represent those for a sample of worker beneficiaries who are 

newly entitled in future years. Three distinct modifications are made to the earnings records. 

For each future year, changes are made to the earnings records in order to reflect:

 Changes in Wage Bases. 

For some years, the projected wage base (contribution and benefit base), on an AWI 

discounted basis, is higher than the historical wage base. Therefore, the taxable 

earnings of future beneficiaries may need to include covered earnings above the 

reported historical wage base. Thus, for each record with reported taxable earnings at 

the wage base in a given year, AWARDS imputes his/her covered earnings.

 Changes in Covered Worker Rates. 

Adjustments are made to work histories to be consistent with the projected changes in 

the economy-wide covered worker rates. Economy-wide covered worker rates are 

defined as the ratio of covered workers (from Economics subprocess) to the Social 

Security area population (from Demography subprocess).
255

 Earnings Experience in the CWHS37. 

Earning levels are modified to capture the changes to date that are reflected in the 

average taxable earnings reported in the CWHS by age and sex and the changes 

expected in the future.

Change in Wage Bases

The earnings posted in the sample are limited by the historical wage base (contribution and 

benefit base). Prior to 1975, the maximum annual amount of earnings on which OASDI taxes 

were paid was determined by ad hoc legislation. After 1974, however, the annual maximum 

level was legislated to be determined automatically, based on the increase in the Social Security 

Average Wage Index (AWI). Prior to these automatic wage base increases, a relatively large 

portion of workers earned amounts above the base. Additional legislation raising the annual 

maximum taxable amount occurred in 1979, 1980, and 1981 to improve the financial future of 

the OASDI Trust Funds. In addition, the AWI used in the automatic calculation of the annual 

taxable maximum was modified in the early 1990s to include deferred compensation amounts. 

Therefore, for each record in the sample with earnings at the wage base, AWARDS imputes 

covered earnings above the historical wage base in order to reflect higher maximum taxable 

amounts imposed on future newly entitled beneficiaries. Please refer to appendix 4.2-1 at the 

end of this subprocess for details of this imputation. Then, these projected covered earnings 

are compared to the wage base values that would be in effect for future samples of retired 

workers (using the “projected wage base” input file) to determine the taxable earnings to use in 

the benefit calculations. 

Change in Covered Worker Rates

The sample’s covered worker rate by age group and sex is defined as the ratio of (1) the number 

of those beneficiaries with covered earnings in the sample to (2) the total number of 

beneficiaries in the sample. For both males and females, the work histories are modified to 

reflect changes in the covered worker rates that would apply to a future sample of 

beneficiaries. These changes in the covered worker rates are based on changes in the 

economy-wide covered worker rates. The economy-wide covered worker rate is defined for an 

age-sex group in a particular period which represents a future sample cohort as the ratio of (1)

the number of “legal” workers in the economy in this group that have some earnings in this 

period, to (2) the total midyear “legal” population in this group in this period.38 Economy-wide 

covered worker rates are calculated separately for each age-sex group and each historical and 

projected calendar year based on input data from the Economics, Demography, and Fully 

Insured subprocesses.

 

37 This file is a 1% sample of individuals who had covered earnings at some point in their work histories.

38 For this purpose, we define the “legal” population as the total SSA area population minus the other-than-legal 

population (those in the U.S. illegally and those in the U.S. on a temporary basis, e.g., individuals with specific nonimmigrant worker or student visas) plus the DACA population (DACA individuals are included in the other-thanlegal data but are effectively made legal, for Social Security purposes, by recent legislation).
256

In projecting sample covered worker rates, examination is done of the change in adjusted

economy-wide covered worker rates, by age group, between the “base period” (representing 

individuals retiring in the sample year) and the “projection period” (representing individuals 

retiring in a year later than the sample year). The adjusted economy-wide covered worker 

rates in the base year take into account 60% of the change in projected age-62 fully insured 

rates relative to the base year, based on analyses of historical data. That is, for each projection 

year, the method estimates what the base year economy wide covered worker rates would 

have been if the fully insured rates in the base year matched those in the projection year. 

Details of how this change is used to estimate the change in a covered worker rate for retired 

workers from a current period in the sample to a future period are given below. The method 

used is the same for males and females in projecting sample covered worker rates. For 

additional explanation of this calculation, refer to example 1.1 in appendix 4.2-2 of this 

subprocess. 

Projected sample covered worker rate equals (for increasing economy-wide rates):

 The ratio of (1) the potential difference in the economy-wide male (or female) 

covered worker rate in the projection year to (2) the potential difference in the 

adjusted economy-wide male (or female) covered worker rate in the sample year 

(i.e., 1 – adjusted economy-wide male (or female) covered worker rate), 

multiplied by 

 The corresponding potential difference in the sample’s male (or female) covered 

worker rates (i.e,. 1 – sample male (or female) covered worker rate)). 

 The above result is subtracted from 1 to get the projected sample covered worker 

rate.

This presentation above presumes that economy-wide covered rates increase over time, which 

is very common for females but not always true for males. The calculation of the change in 

covered worker rate differs if there is a reduction in relevant economy-wide covered worker 

rates. Example 1.2 gives an example of the calculations done for males and females if 

economy-wide covered worker rates decline.

A similar procedure exists for projecting sample covered worker rates for disabled workers. See 

Example 1.3 for an example, for male disabled workers. 

Once the covered worker rates for the future sample of beneficiaries are determined, 

modifications to work histories of the sample to attain these rates are generally done by 

randomly removing or adding earnings.39 For males, the procedure is to select records 

randomly. However, for females, an additional selection criterion is included in order to 

achieve a specified distribution of the number of years of earnings for retired female 

beneficiaries. Female records with 10 or fewer years of earnings are not modified. A 

distribution limit is set for those female workers with 11 to 25 total years of career earnings 

 

39 Individuals in the sample affected by the Windfall Elimination Provision are less likely to have earnings removed 

or added by this process.
257

within the projection year. This distributional limit changes each projected year. In the first 

year after the sample year, the distribution limit for females is equal to the male distribution 

plus 97% of the difference between the initial male and female distributions within the sample. 

In each subsequent year, the percentage decreases by three percent until it reaches 0%. Thus, 

the females’ years of earnings distribution for those with 11 to 25 years of earnings is adjusted 

to approach that of the males. 

If a record is selected for adding earnings in a particular year, the amount of earnings added is 

based on the career earnings pattern of the selected record. When earnings are added to a 

record, AWARDS calculates the ratio of (1) the record’s Average Indexed Earnings, AIE40, to (2) 

the AIE of a hypothetical worker, w, whose year of birth and sex are the same as the record and 

whose annual earnings are set equal to average taxable earnings. For this purpose, average 

taxable earnings are determined by averaging the earnings over all records in the sample with 

the same sex and year of birth. Then, the preliminary amount of earnings41 in year t that is 

added to the record is

 Earnings(r, t) = PreATEf(sex, t) * 

AIE( )

AIE( )

w

,

where PreATEf(sex, t) is the average taxable earnings in year t, for those in the sample 

with the same sex as that of the record, for those retiring in year f. 

For additional explanation of this calculation, refer to example 2 in appendix 4.2-2 of this 

subprocess. Note that all earnings levels get further adjusted by earnings experience in the 

CWHS for recent workers, as discussed in the below section.

Earnings Experience in the CWHS

For historical years beginning with 1951, AWARDS uses average taxable earnings by age and sex 

(CWHSATEas) and numbers of covered workers by age and sex (CWHSCWas) as tabulated from the 

most recent CWHS file42. To estimate ATE levels for the first projection year, the AWARDS uses 

the average values of normalized numbers of workers and normalized taxable earnings from 

the last five historical years of the most recent CWHS file. These computed normalized values 

take into account changes in number of covered workers, and changes in ATEs, between each 

of the five historical years and the first projection year; this allows the comparison of 

corresponding values from different years in a way that reduces time series effects. For future 

years, AWARDS projects these values from this base year (the first projection year). Projections 

are made for each year after the base year through the end of the 75-year projection period 

 

40 AIE is the average indexed annual earnings, average over the highest Y years of earnings (similar to AIME, but an 

annual amount).

41 In this subprocess, earnings histories of projected beneficiaries are all reflected as wage-indexed earnings histories 

in the 2008 sample.

42 These historical values are tabulated by the Economic subprocess.
258

using projected economy-wide number of covered workers by age and sex and annual average 

taxable earnings (ATE) from the Economics process43. 

After the base year, the numbers of covered workers by age and sex (CWHSCWas) are projected 

by applying the annual growth rates by age and sex in the numbers of economy-wide covered 

workers produced by the Economics subprocess. In addition, CWHSATEas are projected. The first 

step is to determine preliminary CWHSATE'as by using the annual growth rate in the total 

economy-wide ATE. A further multiplicative adjustment is made to each CWHSATE'as such that 

the resulting aggregate average taxable earnings, determined by combining the projected 

values of CWHSCWas and CWHSATEas for the year, produces the same growth rate as the growth in 

average taxable earnings from the Economic process. 

For additional explanation of this calculation, refer to example 3 in appendix 4.2-2 of this 

subprocess.

The historical and projected CWHSATEas are then used to change the earnings histories of the 

sample of newly entitled beneficiaries so that the earnings better represent newly entitled 

beneficiaries in future years. For a given sex, trust fund, and earnings year, the expected annual 

average taxable earnings of a future sample is denoted as ATEf'. ATEf' equals the sample’s 

average taxable earnings for a specific earnings year, multiplied by the comparable changes in 

the CWHSATEas, that is, the wage-indexed changes in the CWHSATEas between the year of earnings 

in the sample of new beneficiaries and the year of earnings in the projected sample. ATEf' is 

then compared to the average taxable earnings of the sample (after adjustments to the 

records’ earnings levels for changes in wage bases and covered worker rates), denoted as ATEf 

and computed by sex, trust fund, and earnings year. The difference between these values is 

the amount by which the average annual earnings levels are adjusted. Let

f ATEf

δ(t)  ATE '  ,

for each year t. Denote the total workers in the sample in year t as TotalWorkers(t). Then, (δ(t) 

* TotalWorkers(t)) is the total amount of earnings which the model distributes for a given sex 

and age group in a way so that the average taxable earnings after distribution is ATEf'. 

For additional explanation of the calculation δ(t), refer to example 4 in the appendix 4.2-2.

When δ(t) is negative, earnings for the year are decreased. To achieve ATEf' for the given sex 

and age-group, AWARDS multiplies CoveredEarnings(r, t) by a ratio, 





  

ATE ( )

( )

ratio( ) 1

f

t

t

t .

 

43 These values are based on earnings posted to the Master Earnings File (MEF), excluding earnings posted to the 

suspense file.
259

The term, α, is an additional adjustment necessary because covered earnings near or above the 

wage base, may have either a partial effect or no effect on modifying ATEf to ATEf'. These α 

values vary by sex, trust fund, and whether earnings increase or decrease. In the 2014 Trustees 

Report, for OASI, α is set equal to -0.07 for both males and females when the earnings are 

decreasing; and to 0.03 for males and 0.04 for females when the earnings are increasing. 

Similarly, for DI, α is set equal to -0.08 for males and -0.09 for females when the earnings are 

decreasing; and to 0.04 for males and 0.01 for females when the earnings are increasing. These 

α values are set to best target ATEf' while making adjustments throughout as many of the 

sample records as possible. 

As AWARDS applies ratio(t) to Earnings(r, t) by each record, it makes sure that the total 

earnings adjustment in a year does not exceed δ(t)*TotalWorkers(t). For additional explanation 

of this calculation, refer to example 5 in appendix 4.2-1 of this subprocess.

Equation 4.2.2 – Average Indexed Monthly Earnings (AIME)

Step 1: Index Earnings

To compute an individual’s AIME, all taxable earnings after 1950 are considered. First, the 

earnings are indexed up to the index year, i, which is defined as the year of attaining age 60 for 

retired-worker beneficiaries (eligible for benefits at age 62). For disabled-worker beneficiaries, i

is set to be 2 years before the sample year. Thus, 













 



i

i

t

i

r t

Earnings(r,t), if t

, if t

AverageWage ( )

AverageWage ( )

Earnings( , )

IndexedEar nings(r, t )

Step 2: Determine Computation Years

For each record, the number of computation years, Y, is determined. For a retired-worker 

beneficiary in the sample, Y is 35. 

For a disabled-worker beneficiary, Y is calculated as follows:

Determine the number of elapsed years, which is equal to the year of disability onset (not 

later than the year the worker turned age 62) minus the greater of 1951 or the year the 

disabled worker turned age 22. 

 Elapsed Years  minYear of disability onset,Year attained age 62 max1951,Year attained age 22

Divide the elapsed years by five and truncate. Subtract this number (cannot exceed five) 

from the number of elapsed years. 

























  ,5

5

ElapsedYears Y Elapsed Years min
260

 Y must be at least 2.

Step 3: Determine AIME

Finally, an individual’s AIME is computed by summing the highest Y indexed earnings and 

dividing by the number of months in those years. Hence, for each record, 

Y 12

Highest Y Indexed Earnings (r)

AIME(r)





 .

Equation 4.2.3 – AIMEn(r)

The Possible AIME value is divided into 30 intervals (bend point subintervals). The length of 

each interval in 1979 dollars is given below:

 $45, if 0 < n ≤ 13 

 bpn = $100, if 14 ≤ n ≤ 18 

 $200, if 19 ≤ n ≤ 28 

$1000, if 29 ≤ n ≤ 30 

Thus, the interval points of AIME division given below in 1979 dollars, yk, are equal to 



k

n

n

1

 bp

and 

 $180, if k = 4 

 yk = $1085, if k = 18 

 $5085, if k = 30.

For each record (r), the values for bpn are indexed from 1977 to his/her indexing year i using the 

Social Security average wage index (AWI). So for n = 1 to 30,

AWI(1977)

AWI(i) bpn

(r)  bpn

Next the record’s AIME amount, AIME (r), is compared to the indexed intervals. If 







1

1

 bp ( )

k

n

n

r

< AIME (r) ≤ 



k

n 1

bp (r) n

,

then AIME (r) falls within the kth interval. And for n = 1 to 30,

 bp (r) n, if n < k

 AIMEn (r) = AIME (r) - 

k

n

n

r

1

bp ( )

, if n = k

 0, if n > k
261

Equation 4.2.4 – Potential AIME Percentages (PAPS)

Finally, for n = 1 to 30, AWARDS sums the values of AIMEn and bpn across all the records for 

years 2013 to 2090 by sex, age (20-65 for disabled workers, and 62-70 for retired workers), and 

trust fund. The ratio of these values gives the average potential AIME percentages (PAPS)

PAPn = 

bp ( )

AIME ( )

r

n

r

n

r

r





.

For an example of this calculation, refer to example 6 in appendix 4.2-2 of this subprocess. 
262

Appendix 4.2-1

This appendix provides additional details on how the AWARDS process imputes covered 

earnings above the historical wage base. 

To do this, AWARDS first computes the cumulative distribution, F, of the workers in the sample 

by their earnings level. Each historical wage base is divided into 20 equal intervals, n, and each 

interval length in year t is

20

WageBase(t)

IntervalLength(t)  .

The cumulative distribution F(n, t) is the proportion of workers whose earnings are less than 

IntervalLength(t)*n, for n = 1 to 20. Let NumberWorkers(n, t) be the number of workers whose 

earnings in year t fall within the nth interval, that is the earnings are greater or equal to 

IntervalLength (t)*(n-1) and less than IntervalLength (t)*n. Also, let TotalWorkers(t) be the total 

number of workers in the sample with earnings in year t. Then for any n, 1 ≤ n ≤ 20,

TotalWorkers ( )

NumberWorkers( , )

F ( , )

1

t

m t

n t

n

m



 .

Once F(n, t) is computed for n = 1 to 20, AWARDS extends the function for those who had 

earnings at the wage base. To extrapolate F past the historical base (define F(n, t) for n > 20), 

AWARDS groups the maximum earners in each year in the sample based on the number of 

years they had earnings at the wage base during the next four years (0, 1, 2, 3, 4). Under the 

assumption of uniform distribution within each group, AWARDS assigns an F(nr, t) value to each 

record with earnings at the tax maximum beginning with the group that has no other earnings 

at the tax maximum during the next four years and ending with the group that has maximum 

earnings in each of the next four years. Note that for these beneficiaries F(nr, t) > F(20,t). Once 

F(nr, t) is computed for these beneficiaries, values for F(n, t), where n>20 are estimated.

To find F(n, t), where n>20, the log odds transformation is utilized. The odds ratio, 

n,t)

n,t

n,t

1- F(

F( )

T( ) 

, where n < 20

is the ratio of (1) the proportion of beneficiaries with earnings levels below the nth interval to 

(2) the proportion of beneficiaries with earnings levels above the nth interval. Next, the natural 

logarithm of the odds ratio is computed, giving the log odds transformation,

 

















 

1- F( )

F( )

Y( ) ln T( ) ln

n,t

n,t

n,t n,t

, where n < 20

Utilizing the most linear portion of the function at the upper values of n, AWARDS regresses Y 

on those values. The regression line of Y has the form 
263

Y(n,t) *n

ˆ

  0  1

Finally, the amount of covered earnings of a record that has earnings at the taxable maximum is 

determined based on the F(nr, t) value assigned to the record. The F(nr, t) value for this record 

is used in the above equations to determine T(nr, t), Y(nr, t), and then nr, the non-integer value 

for n in the regression equation of 

Y

ˆ

above. Thus, if earnings(r, t) = wage base in year t then 

CoveredEarnings(r, t) IntervalLength(t) 

r n 

+ error44,

where nris the record’s non-integer value for n in the regression equation of 

Y

ˆ

above.

If earnings(r, t) < wage base in year t, then CoveredEarnings (r, t) = earnings(r, t)

At this point, AWARDS defines the expected taxable earnings of a future sample as,













future wage base (t), CoveredEarnings(r, t ) future wage base (t)

CoveredEarnings(r, t ), CoveredEarnings(r, t ) future wage base (t)

Earnings(r,t)

 

44 The difference between Y(20,t) and 

Y(20 )

ˆ

,t .
264

Appendix 4.2-2

This appendix provides examples to help understand the calculations described in the 

model documentation of the AWARDS subprocess. These examples do not reflect 

actual values.

Example 1.1: (OASI-Male with increasing economy-wide covered worker 

rates)—same method applies for females

Task: In projecting the 2008 sample of newly entitled male beneficiaries to represent 

newly entitled male beneficiaries in 2050, an adjustment to the earnings histories for 

those males age 40-44 is needed to reflect higher covered worker rates expected for 

males in this age group. 

This example illustrates the calculation of the projected covered worker rate for males 

who are age 40-44 in the projection period. We will be comparing the group of males 

age 40-44 in the base period with its counterpart group of males age 40-44 in the 

projection period.

Information given:

 Newly entitled retired male beneficiaries represented in the 2008 sample 

are age 40-44 in the base period, 1978-1990, and the counterpart group 

of males retiring in 2050 is age 40-44 in the projection period, 2020-2032.

 Based on the 2008 sample, the covered worker rate for males age 40-44 in 

the base period = 88.0%. 

 Fully insured rate for age 62 in 2007 = 92.0%

 Fully insured rate for age 62 in 2049 = 88.5%

 Unadjusted economy-wide covered worker rate for males age 40-44 = 

87.0% in the base period. 

 Economy-wide covered worker rate for males age 40-44 = 91.0% in the 

projection period.



Calculations: 

1. Adjusted economy wide covered worker rate in the base period = 

Unadjusted economy-wide covered worker rate in the base period * (1+ 

(Fully insured rate 2049/Fully insured rate 2007 - 1) * .6 ) = 85.0%

2. The potential difference in the economy-wide covered worker rate for 

males age 40-44 in the projection period is 100.0% - 91.0% or 9.0%. 

3. The potential difference in the adjusted economy-wide covered worker 

rate for males age 40-44 in the base period is 100.0% - 85.0% or 15.0%. 

4. The ratio from steps 2 and 3 is 9/15 or 60.0 %. 

5. The potential difference in the sample’s covered worker rate for the 

males age 40-44 in the base period is 100.0% - 88.0% or 12.0%.
265

6. The ratio from step 4 is multiplied by the potential difference in the 

sample’s covered worker rate for males age 40-44 in the base period to 

yield 7.2% (60.0 % * 12.0% = 7.2% ).

7. The amount in step 6 (7.2%) would be subtracted from 1 to yield the 

sample’s covered worker rate for males who are age 40-44 in the 

projection period (92.8%).

Example 1.2: (OASI-Male with decreasing economy-wide covered worker 

rates)—same method applies for females

Task: In projecting the 2008 sample of newly entitled male beneficiaries to represent 

newly entitled male beneficiaries in 2050, an adjustment to the earnings histories for 

those males age 40-44 is needed to reflect lower covered worker rates expected for 

males in this age group. 

This example illustrates the calculation of the projected covered worker rate for males 

who are age 40-44 in the projection period. We will be comparing the group of males 

age 40-44 in the base period with its counterpart group of males age 40-44 in the 

projection period.

Information given:

 Newly entitled retired male beneficiaries represented in the 2008 sample 

are age 40-44 in the base period, 1978-1990, and the counterpart group 

of males retiring in 2050 is age 40-44 in the projection period, 2020-2032.

 Based on the 2008 sample, the covered worker rate for males age 40-44 in 

the base period = 88.0%. 

 Fully insured rate for age 62 in 2007 = 82.0% 

 Fully insured rate for age 62 in 2049 = 92.0%

 Unadjusted economy-wide covered worker rate for males age 40-44 = 

79.2% in the base period. 

 Economy-wide covered worker rate for males age 40-44 = 75.0% in the 

projection period.

Calculations: 

1. Adjusted economy wide covered worker rate in the base period = 

Unadjusted economy-wide covered worker rate in the base period * 

(1+ (Fully insured rate 2049/Fully insured rate 2007 - 1) * .6 ) = 85.0%

2. The economy-wide covered worker rate for males age 40-44 in the 

projection period is 75.0%. 

3. The ratio from steps 2 and 1 is 75/85 or 88.2 %. 

4. The ratio from step 3 is multiplied by the sample’s covered worker rate 

for males age 40-44 in the base period to yield 77.6% (88.2 % * 88% = 

77.6% ).
266

The amount in step 4 (77.6%) would be the sample covered worker rate for males who 

are age 40-44 in the projection period.

Example 1.3: (DI-Male with decreasing economy-wide covered worker 

rates)

Task: In projecting the 2008 sample of newly entitled male DI beneficiaries to 

represent newly entitled male beneficiaries in 2040, an adjustment to the earnings 

histories for those males age 40-44 is needed to reflect lower covered worker rates 

expected for males in this age group. 

This example illustrates the calculation of the projected covered worker rate for males 

who are age 40-44 in the projection period. We will be comparing the group of males 

age 40-44 in the base period with its counterpart group of males age 40-44 in the 

projection period. If the projection period is less than 40 years from the sample year, 

the following calculations would apply; otherwise, the projected sample covered worker 

rate for DI males would be the same as the economy-wide covered worker rate for DI 

males age 40-44, adjusted in a manner similar to that described below.

Information given:

 Newly entitled retired male beneficiaries represented in the 2008 sample 

are age 40-44 in the base period, 1978-1990, and the counterpart group 

of males retiring in 2040 is age 40-44 in the projection period, 2010-2022.

 Based on the 2008 sample, the covered worker rate for males age 40-44 in 

the base period = 88.0%. 

 Adjusted economy-wide covered worker rate for males age 40-44 = 85.0% 

in the base period. This rate is the average of the most recent x years of 

“adjusted covered worker rates”, where x = the number of years with 

earnings in the sample for that age group. For each of the previous x 

years, the “adjusted covered worker rate” equals the economy-wide 

covered worker rate plus the difference between the sample covered 

worker rate for that age group and the economy-wide rate, limited by the 

maximum sample covered worker rate. 

 Adjusted economy-wide covered worker rate for males age 40-44 = 75.0% 

in the projection period. This rate is the average of the most recent x 

years of “adjusted covered worker rates”, where x = the number of years 

with earnings in the sample for that age group. For each of the previous x 

years, the “adjusted covered worker rate” equals the economy-wide 

covered worker rate plus the difference between the sample covered 

worker rate for that age group and the economy-wide rate, limited by the 

maximum sample covered worker rate. For projection years after (sample 

year -1), the difference is phased in from the difference in sample year – 1 

to the average difference for all historical earnings years. 
267

 Weighting factor 1 is 0.8 or (2040-2008)/40. 

 Weighting factor 2 is 0.20 or (1-0.8).

Calculations: 

1. The potential difference in the economy-wide covered worker rate for 

males age 40-44 in the projection period is 100.0% - 75.0% or 25.0%. 

2. The potential difference in the economy-wide covered worker rate for 

males age 40-44 in the base period is 100.0% - 85.0% or 15.0%. 

3. The ratio from steps 1 and 2 is 25/15 or 166 2/3 %. 

4. The potential difference in the sample’s covered worker rate for the 

males age 40-44 in the base period is 100.0% - 88.0% or 12.0%.

5. The ratio from step 3 is multiplied by the potential difference in the 

sample’s covered worker rate for males age 40-44 in the base period to 

yield 20.0% (166 2/3 % * 12.0% = 20.0% ).

6. The ratio from step 5 (20.0%) then is subtracted from 100% to yield 

80.0%.

7. Multiply weighting factor 1 (0.80) by the economy-wide covered worker 

rate for males age 40-44 in the projection period (0.75) to yield 60.0% 

(0.80*0.75)

8. Multiply weighting factor 2 (0.175) by the amount in step 6 (0.8) to yield 

16% (0.20*0.8)

9. Sum up the amounts in step 7 and 8 to yield the sample covered worker 

rate for DI males who are age 40-44 in the projection period (76.0%).

Example 2: 

Task: In projecting the 2008 sample of newly entitled male OASI beneficiaries to 

represent newly entitled male OASI beneficiaries in 2020, an adjustment to the earnings 

histories for those males age 40-44 is needed to reflect higher covered worker rates 

expected for males in this age group. The amount of increase in the covered worker 

rates is computed as 4 percent. To achieve this target, the desired number of records 

with zero reported earnings in this age group are randomly selected and assigned 

earnings.

This example illustrates the calculation of earnings to be assigned to a randomly chosen 

newly entitled retired male record with zero taxable earnings in the base year. 

Information given:

 Newly entitled retired male beneficiaries represented in the 2008 sample 

are age 40-44 in the base period, 1978-1990, and the counterpart group 

of males retiring in 2020 will be age 40-44 in the projection period, 1990-

2002.
268

 Based on the 2008 sample, a male record, r = 60,000, has been randomly 

selected to replace his zero taxable earnings reported in the base year 

1985 with an amount based on his career earnings pattern. This record is 

age 63 in 2008, and his year of birth is 1945.

 A beneficiary retiring at age 63 in 2020 will have a year of birth of 1957. 

And, the corresponding projection year to the base year of 1985 is the

projection year 1997.

 The Average Indexed Earnings for this record, AIE (60,000), is computed to 

be $43,465. Note: This value is calculated by (1) using the record’s annual 

taxable earnings reported each year through 2007, (2) converting them to 

2007 year dollars, and then (3) summing the highest 35 years of earnings 

and dividing by 35.

 The Average Indexed Earnings for a hypothetical worker, AIE(w) whose 

year of birth is 1945 is $41,995. This value is calculated as above given the 

hypothetical worker earned the average taxable earnings in each of the 

base years for males retiring at age 63 in the 2008 sample.

 The projected Average Taxable Earnings of males retiring in 2020 for the 

projection year 1997 (converted to 1985 dollars45) is PreATE2020(male,1997) 

= $18,718.

Calculations: 

1. The ratio of the Average Indexed Earnings for record number 60,000, 

AIE(60,000) to the Average Indexed Earnings of a hypothetical male 

worker born in 1944 and retiring at age 63, AIE(w) is $43,465/$41,995 or 

1.035. 

2. The amount in step 1 (1.035) would be multiplied by 

PreATE2020(male,1997), which is given as $18,718. This yields the amount 

of earnings assigned to record number 60,000 in the projection year 1997 

representing a sample retiring in 2020, Thus, Earnings(60,000,1997) = 

1.035 * $18,718 which equals $19,373.

Note that, at this stage of the process, the average taxable earnings, denoted 

PreATE2000(male,1997), have been computed using projected earnings adjusted for 

changes in the wage base. Adjustments to earnings for the earnings experience in the 

CWHS have not yet been applied. See the earlier section “Earnings Experience in the 

CWHS” and example 4 below for a description of this adjustment process.

Example 3: 

Task: The AWARDS subprocess estimates projected values of Average Taxable Earnings 

by age and sex using the values46 in the 2011 CWHS file supplied by the Economic 

subprocess as the base year on which to build our projections.

 

45 All projection year dollars are converted back to ‘sample year’ dollar amounts, using the Average Wage Index.
269

This example illustrates the calculation of the projected Average Taxable Earnings of the 

CWHS in 2013 for 42 year old females, CWHSATE42,female(2013). We will be using the 

number of female covered workers age 42 and the total taxable earnings for females 

age 42 as given the in 2011 CWHS data. 

Information given:

 The total number of female covered workers age 42 in 2012 (first 

projection year), calculated as the average of normalized values from last 

5 historical years in the CWHS is 16,475.

 The calculated total taxable earnings for females age 42 in 2012 (first 

projection year), calculated as the average of normalized values from last 

5 historical years in the CWHS is $ 594,579,316.

 The economy-wide number of covered workers for females age 42 in 2012 

is 1,649,300.

 The economy-wide number of covered workers for females age 42 in 2013 

is 1,613,787.

 The economy-wide Average Taxable Earnings (ATE) in 2012 is $35,061.24

 The economy-wide ATE in 2013 is $35,946.

Calculations: 

1. The growth in the economy-wide covered workers for females age 42 

from 2012 to 2013 is 1,613,787/1,649,300 or 0.978. 

2. The growth rate in the economy-wide covered workers for females age 

42 from step 1 is applied to the CWHS female covered workers age 42 in 

2012, CWHSCW42,female(2012) to yield the projected covered workers for 

females who are age 42 in 2013, CWHSCW42,female(2013), 0.978*16,475 or 

16,113 

3. The CWHS Average Taxable Earnings for females age 42 in 2012, 

CWHSATE42,female(2012) is computed using the given CWHS total taxable 

earnings and total covered workers for females age 42 in 2012 

($594,579,316 / 16,475 = $36,090).

4. The economy-wide growth rate in the average taxable earnings from 

2012 to 2013 is $35,946 / ($35,061)or 1.0252

5. The growth rate in step 4 (1.0252) is multiplied by the amount in 

step3($36,090) to estimate the preliminary CWHS Average Taxable 

Earnings for females age 42 in 2013, CWHSATE’42,female(2013) = $36,999 

(1.0252*$36,090)

 

46 Because not all earnings are posted for the most recent years for a given CWHS file, adjustment factors, based on 

historical trends, are applied by the Economic subprocess to complete these earnings. For the 2011 CWHS, 

adjustment factors were applied to data in years 2007 through 2011.
270

6. The value from step 5 ($36,999) is multiplied by the value in step 2 

(16,113) to yield the preliminary CWHS projected total taxable earnings 

for females age 42 in 2013 ($596,164,887).

7. Note a final multiplicative adjustment is made to CWHSATE’42,female(2013) to 

obtain CWHSATE42,female(2013). This adjustment is made once the 

preliminary taxable earnings have been computed for each age and sex in 

2013 in order to ensure the growth rate in the aggregate average taxable 

earnings between 2012 and 2013 matches the growth rate in the ATE 

from the Economic process.

Example 4: 

Task: In projecting the 2008 sample of newly entitled female beneficiaries to represent 

newly entitled female beneficiaries in 2050, the projected Average Taxable Earnings of 

females in the sample (ATEf)

47 for year t = 2020 must be adjusted by an amount, 

δ(2020), to meet a targeted Average Taxable Earnings(ATEf'') for 2020. 

This example illustrates the calculation of δ(2020) for the female cohort retiring at ages 

62 to 70 in the projection year 2050. The cohort is age 32 to 40 in the projection year 

2020. The value δ(2020) is the dollar amount in which the average annual earnings 

levels are adjusted for females in this age-group in the year 2020 and retiring in 2050. 

We will be comparing this group of females age 32-40 in the projection 2020 year with 

its counterpart group of females age 32-40 in the base year 1978. 

Information given:

 A cohort of newly entitled retired female beneficiaries retiring at ages 62 

to 70 represented in the 2008 sample are ages 32-40 in the base year, 

1978, and the counterpart group of females retiring in 2050 are ages 32-

40 in the projection year, 2020.

 Based on the 2008 sample, the average taxable earnings for females ages 

32-40 in the base year 1978 is $4,536. 

 For a sample projected to be retiring in 2050, the average taxable earnings 

(ATEf) for females in the projection year 2020 is $4,56448, after applying 

adjustments to the records’ earning levels for changes in the wage base 

and in covered worker rates. 

 Based on the 2011 CWHS data, the average taxable earnings for females 

ages 32-40 in the base year 1978 is $4,218.

 The projected average taxable earnings in the CWHS for females ages 32-

40 in the projection year 2020 (in 1978 dollar amounts) is $4,640.

 

47 The average taxable earnings have been computed using projected earnings adjusted for changes in the wage base 

and for changes in covered worker rates.

48 Amount is in 1978 dollars, ‘sample year dollars’.
271

Calculations: 

1. The increase in the average taxable earnings for females ages 32-40 in 

the CWHS from the base year 1978 ($4,218) to the projection year 2020 

($4,640)12 is 10 percent.

2. Based on the value from step 1 the expected annual average taxable 

earnings (ATEf'') of females ages 32-40 in 2020 and retiring in 2050 is 

$4,536*1.10 or $4,990 

3. The difference in ATEf'' ($4,990) and ATEf' ($4,564) yields the δ(2020) 

value $426 ($4,990 - $4,564 = $426).

Example 5: 

Task: In projecting the 2008 sample of newly entitled female OAB beneficiaries to 

represent newly entitled female OAB beneficiaries in 2033, for year t = 2023, δ(2023) is 

positive indicating an adjustment to earnings histories is needed to reflect higher 

average taxable earnings by this cohort for the projection year 2023. 

This example illustrates the calculation of the ratio(2023) in projection year 2023 for the 

female cohort retiring at ages 62 to 70 in the projection year 2033. The value, 

ratio(2023), is the adjustment ratio that will be applied to this cohort projected covered 

earnings in 2023 in order to achieve the targeted Average Taxable Earnings of this 

cohort for 2023.

Information given:

 Earnings in the projection year 2023 for a group of newly entitled female 

beneficiaries retiring in 2033 is the counterpart corresponding to earnings 

in the base year 1998 for the group of newly entitled female beneficiaries 

in the 2008 sample.

 The targeted average taxable earnings ATE2023' for the projection year 

2023 is $21,560.

 For newly entitled females retiring in 2033, the average taxable earnings 

(ATE2023) for the projection year 2023 is $19,97049. 

 δ(2023) , the difference in the targeted average taxable earnings ATE2023' 

and ATE2023, is calculated to be $1,590 ($21,560 - $19,970). 

 α is an additive constant to the ratio(2023). For female OABs the constant 

α when δ(t) is positive is 0.04.

Calculations: 

 The ratio(2023) multiplied to the covered earnings in 2023 for females 

retiring in 2033 is 1 + δ(2023) /ATE2023 + α = ( 1 + ($1,590/$19,970) + 0.04), 

or 1.12. 

 

49 Amount is in 1998 dollars, ‘sample year dollars’.
272

Example 6: 

Task: The AWARDS subprocess calculates the Average Indexed Monthly Earnings 

(AIME) of each beneficiary in the sample. The AIME values are then divided into 30 

intervals.

This example illustrates the division of a possible AIME value into intervals. 

Information given:

 An OAB beneficiary retired at age 64 in 2008

 This OAB beneficiary is record #150000 in the sample (r = 150000)

 The AIME for this individual is $3,000

 The initial eligibility year is 2006, the year the individual turned age 62.

 The length of each interval (bpn) in 1979 dollars is given in Equation 4.2.3. 

The length of each interval in 2006 dollars is given by the equation 

bpn (r) = bpn * AWI (2004) / AWI (1977) 

where bpn is the length of interval n in 1979 dollars

 The average wage index (AWI) for year 2004 is $35,648.55

 The AWI for year 1977 is $9,779.44

 When converting the intervals from 1979 dollars to 2006 dollars, there is a 

2-year lag in AWI values.

 AIMEn (150000) is the AIME value in interval n for Record #150000

Calculations: 

 The AIME for this individual ($3,000) is compared to the indexed intervals. 

It falls within the 16th interval.

 The AIME16 is the residual of $3,000 subtracting the cumulative indexed 

bend points up to 15th interval ($2861.58). The AIME for this individual in 

16th interval is $138.42

 AIMEn (150000) for interval 1 through15 equals bpn (150000) for the 

corresponding intervals, such that PAPn = AIMEn / bpn = 1 for these 

intervals

 AIME16(150000) = $138.42, such that PAP16 = $138.42 / $364.53 = 0.3797

 AIMEn (150000) for interval 17 through 30 equals 0, such that PAPn = 0 for 

these intervals.

 The following table details these results.

n 

bpn in 

1979 

dollars

bpn (r) in 

2006 

dollars

 bp ( )

1

n r

n

k

 i

n 2006 

dollars

AIMEn (r) 

in 2006 

dollars

PAPn 

in 2006 

dollars
273

1 $45 $164.04 $164.04 $164.04 1

2 45 164.04 328.08 164.04 1

3 45 164.04 492.12 164.04 1

4 45 164.04 656.16 164.04 1

5 45 164.04 820.20 164.04 1

6 45 164.04 984.24 164.04 1

7 45 164.04 1,148.28 164.04 1

8 45 164.04 1,312.32 164.04 1

9 45 164.04 1,476.36 164.04 1

10 45 164.04 1,640.40 164.04 1

11 45 164.04 1,804.44 164.04 1

12 45 164.04 1,968.48 164.04 1

13 45 164.04 2,132.52 164.04 1

14 100 364.53 2,497.05 364.53 1

15 100 364.53 2,861.58 364.53 1

16 100 364.53 3,226.11 138.42 0.3797

17 100 364.53 3,590.64 0 0

18 100 364.53 3,955.17 0 0

19 200 729.05 4,684.22 0 0

20 200 729.05 5,413.27 0 0

21 200 729.05 6,142.32 0 0

22 200 729.05 6,871.37 0 0

23 200 729.05 7,600.42 0 0

24 200 729.05 8,329.47 0 0

25 200 729.05 9,058.52 0 0

26 200 729.05 9,787.57 0 0

27 200 729.05 10,516.62 0 0

28 200 729.05 11,245.67 0 0

29 1000 3,645.25 14,890.92 0 0

30 1000 3,645.25 18,536.17 0 0
274

4.3. Cost

4.3.a. Overview 

The COST subprocess projects the trust fund operations for each year of the long-range 75-year 

period. The COST subprocess projects the income and cost for each trust fund (OASI and DI). 

The two components of non-interest income are payroll tax contributions and taxation of 

benefits50. The other component of income is interest earned on the trust fund assets. The 

three components of cost are scheduled benefits, administrative expenses, and the railroad 

interchange. Each of these components is projected for each trust fund (OASI and DI). The 

end-of-year assets is computed by taking the beginning-of-year assets (ASSETS), adding payroll 

contributions (CONTRIB), taxation of benefits (TAXBEN), and interest income (INT), and 

subtracting scheduled benefits (BEN), administrative expenses (ADM), and the railroad 

interchange (RR).

Equations 4.3.1 through 4.3.6 outline this overall structure and sequence.

CONTRIB = CONTRIB(·) (4.3.1)

BEN = BEN(·) (4.3.2)

TAXBEN = TAXBEN(·) (4.3.3)

ADM = ADM(·) (4.3.4)

RR = RR(·) (4.3.5)

INT = INT(·) (4.3.6)

 ASSETS ASSETS CONTRIB TAXBEN INT BEN ADM RR EOY BOY       

The COST subprocess produces annual values that help assess the financial status of the OASI, 

DI, and combined funds. These include the annual income rate (ANN_INC_RT), annual cost rate 

(ANN_COST_RT), and trust fund ratio (TFR) as outlined below. 

ANN_INC_RT = ANN_INC_RT (·) (4.3.7)

ANN_COST_RT = ANN_COST_RT (·) (4.3.8)

TFR = TFR(·) (4.3.9)

The COST subprocess also produces summarized values. These values are computed for the 

entire 75-year projection periods, as well as 25- and 50-year periods. These include the 

actuarial balance (ACT_BAL), unfunded obligation (UNF_OBL), summarized income rate 

 

50 As noted throughout the Trustees Report a third component of non-interest income is reimbursements from the 

General Fund of the Treasury. In the cost program such reimbursements are simply treated as payroll tax 

contributions.
275

(SUMM_INC_RT), summarized cost rate (SUMM_COST_RT), and closed group unfunded 

obligation (CLOSEDGRP_UNFOBL). 

ACT_BAL = ACT_BAL(·) (4.3.10)

UNF_OBL = UNF_OBL(·) (4.3.11)

SUMM_INC_RT = SUMM_INC_RT(·) (4.3.12)

SUMM_COST_RT = SUMM_COST_RT(·) (4.3.13)

CLOSEDGRP_UNFOBL = CLOSEDGRP_UNFOBL(·) (4.3.14)

The following notation is used throughout this documentation:

 ni represents the first year of the projection period-2014 for the 2014 TR

 ni+74 represents the final year of the projection period-2088 for the 2014 TR

 nf represents the last year the cost program will project-2090 for the 2014 TR

 nim1 is equal to ni-1

 nim2 is equal to ni-2

 ns is equal to ni+9

 nbase, the year of the sample, is equal to 2008

4.3.b. Input Data

Data received as input from the short-range office are presented first. Then data from long 

range and all other sources are identified separately for each equation.

Short-range OCACT Data

 Estimates for the first ten years of the projection period for the first six equations 

mentioned above. 

 Assets at the beginning of year ni.

All of this information is updated annually.

Long-range OCACT and other Data

i. Equation 4.3.1 – Tax Contributions (CONTRIB)

Economics-Process 2

 Projected effective taxable payroll for years nim1 through nf, updated yearly 

Other

 Projected employee/employer payroll tax rate, by trust fund and year, for years 

1981 through nf, updated as needed (e.g., as required due to legislative changes)
276

ii. Equation 4.3.2 – Scheduled Benefits (BEN)

Demography-Process 1

 Projected number of married and divorced people in the Social Security area 

population by age for end of years nim1 through 2100, updated yearly

Economics-Process 2 

 Historical COLA for year nim2, updated yearly

 Historical CPI for years 1990 through nim1, updated yearly

 Projected cost of living adjustment (COLA) for years nim1 through nf, updated yearly

 Historical SSA average wage index for year nim2, updated yearly

 Projected percent increases in the average wage index for years nim1 through nf, 

updated yearly

Beneficiaries-Process 3

 Initial and ultimate incurred but not reported (IBNR) DI beneficiary distribution by age, 

duration of disability (0 through 9 years and 10+ years) and sex, updated every few 

years (subprocess #3.2). Factors are read in such that there is a ten-year linear phase-in 

from initial factors to ultimate factors. The number of DI beneficiaries in currentpayment status is equal to the number of currently entitled DI workers times the IBNR 

factor.

 Projected number of disabled workers in current-pay status by sex, age in current-pay, 

and duration of disability (0 through 9 and 10+) for years nim1 through nf, updated 

yearly from subprocess 3.2

 Projected number of retired worker beneficiaries in current-pay status by sex, age in 

current-pay, and age at entitlement for years nim1 through nf, updated yearly from 

subprocess 3.3

 Projected number of auxiliary beneficiaries (by benefit category) of retired-worker, 

deceased-worker, and disabled-worker beneficiaries for years nim1 through nf, updated 

yearly from subprocess 3.3

 Projected number of disability insurance beneficiaries who convert to retirement 

insurance status upon the attainment of normal retirement age by age in current pay, 

for years nim1 through nf, updated yearly from subprocess 3.2 and 3.3

 Retired Workers 65+ by sex, and marital status (single, married, widowed, and divorced)

 Retired Workers 62+ by sex, and marital status (single, married, widowed, and divorced)

Other

 Total (aggregate) PIA and MBA, not actuarially reduced, of DI male and female workers 

in current payment status, updated yearly from the Table 1-A Supplement

 Total (aggregate) PIA and MBA, actuarially reduced, of DI male and female workers in 
277

current payment status, updated yearly from the Table 1-A Supplement

 Total (aggregate) PIA and MBA, not actuarially reduced, of newly awarded DI male and 

female workers, updated yearly from the Table 1-A Supplement

 Cumulative distribution of AIME dollars for newly entitled retired-worker beneficiaries 

by age (62 through 70) and sex, for years nim1 through nf, updated yearly from 

subprocess 4.2

 Cumulative distribution of AIME dollars for newly entitled disabled-worker beneficiaries 

by age (20 through 65) and sex, for years nim1 through nf, updated yearly from 

subprocess 4.2. Ages 15 through 19 are assumed to have the same distribution of 

dollars as does age 20. Age 66 is assumed to have the same distribution of dollars as 

does age 65

 Age distribution of newly entitled retired-worker beneficiaries in the AIME awards 

sample by sex, updated in years that the sample changes, from subprocess 4.2

 Starting average PIA matrix for retired-worker benefits for the year nim1, by age at 

entitlement, age in current-pay and sex, updated yearly

 Starting average PIA matrix for disabled-worker benefits, for the year nim1, by age in 

current-pay, duration and sex, updated yearly

 Starting average PIA matrix for beneficiaries who convert from disabled worker to 

retirement worker status for the year nim1, by age in current-pay and sex, updated 

yearly

 Starting average MBA matrix for retired-worker benefits for the year nim1, by age at 

entitlement, age in current-pay and sex, updated yearly

 Starting average MBA matrix for beneficiaries who convert from disabled worker to 

retirement worker status for the year nim1, by age in current-pay and sex, updated 

yearly

 Benefit relationships between worker and auxiliary benefits (linkages) for the year nim1, 

for all benefit categories and worker account holders of both sexes, updated yearly from 

qlink14.xls

 Retroactive payment loading factors for auxiliary beneficiary categories for all years, for 

each benefit category and both sexes, updated yearly

 Initial and ultimate post entitlement factors for retired workers by sex and duration 

updated yearly. Factors are read in such that there is a twenty-year linear phase-in from 

initial factors to ultimate factors. 

 Initial and ultimate post entitlement factors for disabled workers by sex and duration 

updated yearly. Factors are read in such that there is a twenty-year linear phase-in from 

initial factors to ultimate factors. 

 Initial and ultimate post entitlement factors for DI conversion workers by sex and 

duration updated yearly. Factors are read in such that there is a twenty-year linear 

phase-in from initial factors to ultimate factors. 

 Average excess MBA amounts for dually entitled wives and widows, updated yearly 

from Statistical Supplement

 Initial Windfall Elimination Provision (WEP) factors by sex and age for attributed year, 

updated every 2 years
278

 Ultimate WEP factors by sex read in as a percentage of the way from initial factors to 

one, updated every 2 years

 Year in which ultimate WEP factor is reached by age at initial entitlement (62-70), 

updated every 2 years

 Trendline by which WEP factors are phased-in from initial value to ultimate value, 

updated every 2 years

 Workers’ Compensation cumulative factors by duration that adjust benefits to account 

for decreasing offsets (i.e. – Workers Comp offsets decrease as duration increases), 

updated yearly

 Workers’ Compensation reduction factors (used in retroactive category) to reflect 

offsets starting and stopping in the year of DI entitlement

 Workers’ Compensation parameter to account for offsets that begin and end in the year 

of entitlement, updated yearly

 Dual entitlement regression coefficients for the number of wives, husbands, widows, 

and widowers, and the average excess amounts for wives and widows, updated yearly

 Dual entitlement widower excess amount as a percent of widow excess amount

 Dual entitlement husband excess amount as a percent of wife excess amount

 Dual entitlement average excess amounts and percentages of exposure population for 

December, year ni-1 for wives, husbands, widows, and widowers, updated yearly

 Target values for ratios relating to the above dual entitlement categories, updated 

yearly

 Number of years in which the difference between the results from the regression 

coefficients and targeted values are phased in for the five above dual entitlement 

categories, updated yearly

 Adjustment factors for average retired and disabled worker benefit amounts (PIA and 

MBA) in current-pay at durations 0 and 1, by sex and age, updated yearly 

 Number of months retroactive benefits are received by a worker who is paid 

retroactively in their year of entitlement, by trust fund

 Adjustment factors for average retired worker retroactive benefit amounts by sex, 

updated yearly 

 Adjustment factors for disabled worker retroactive benefit amounts in current-pay at 

durations 0, 1 and 2+, by sex and age, updated yearly 

 Adjustment factors to apply to long-range average benefit levels in the first 20 years of 

the projection period by sex and trust fund, updated yearly 

 Average retired worker PIA in the 75th year of the projection period by sex, updated 

yearly

iii. Equation 4.3.3 – Taxation of Benefits

Trust Fund Operations and Actuarial Status

 Taxation of benefits as a percentage of scheduled benefits by trust fund for years 

nim1 through nf, updated yearly from subprocess #4.1
279

iv. Equation 4.3.4 – Administrative Expenses

Economics-Process 2

 Average wage indexes for years nim1 through nf, updated yearly

 Ultimate value of productivity factor for the period ni through nf updated yearly

Beneficiaries-Process 3

 Total number of beneficiaries in current-pay status by trust fund for years nim1 through 

nf, updated yearly

v. Equation 4.3.5 – Railroad Interchange

Economics-Process 2

 Increase in the average wage index for years nim1 through nf, updated yearly

 Ultimate value of productivity factor for the period ni through nf updated yearly

Trust Fund Operations and Actuarial Status

 Taxation of benefits as a percent of the amount of benefits scheduled to be paid, by 

trust fund for years nim1 through nf, updated yearly (use same factors as in equation 

4.3.3)

Other input data

 Nominal annual yield rate on the combined OASDI trust fund for years ni through nf, 

updated as the ultimate real interest rate and ultimate CPI are changed by the Trustees 

 Ratio of railroad retirement OASI and DI average benefits to overall OASI and DI average 

benefits, updated yearly

 Number of railroad beneficiaries (retirement and disability) for December of year nim3, 

updated yearly

 Average taxable earnings in railroad employment for year nim2, updated yearly

 Historical data on average railroad employment, 1960 through nim2

 Average worker benefit by sex and trust fund, updated yearly from the December nim1 

Table I-A Supplement

 Expected railroad new awards as a percent of the average of historical employment data

 Auxiliary loading factor by trust fund, updated yearly using 10 years of historical 

financial interchange benefit data

 Railroad initial mortality rate by trust fund for year nim2, updated yearly using 10 years 

of historical financial interchange benefit data

 Railroad mortality improvement rate by trust fund, updated yearly using 10 years of 

historical financial interchange benefit data

vi. Equation 4.3.6 – Interest Income

Economics-Process 2

 Annual increase in the CPI for years ni through nf, updated yearly
280

Trustees assumptions

 Ultimate real interest rate, updated annually

Other input data

 Factors for exposure to interest rate for benefits, payroll, and taxation of benefits, 

updated yearly

 Factors for exposure to railroad interchange and administrative expenses, updated 

periodically

vii. Equations 4.3.7 through 4.3.13 – Annual Values and Summarized Values

All inputs for equations 4.3.7 through 4.3.13 are estimated internally in the Cost program.

viii.Equation 4.3.14 – Closed Group Unfunded Obligation

Demographics-Process 1

 Single year population and mortality rate data for years 1941 through 2101, updated 

yearly

Economics-Process 2

 Historical and projected single-year COLA data and average wage indexing series (AWI) 

data for years 1975 through 2090 (for COLA) and 1951 through 2090 (AWI), updated 

yearly

 Projected number of covered workers by single year of age 0-74 from year ni through 

2090, updated yearly

 Historical and projected number of covered workers by single year of age 75-99 from 

year ni-21 through 2090, updated yearly

 Projected number of covered workers by single year of age 100+ from year ni through 

2090, derived from total age 100+ projections from year ni-1 through 2090

 Ultimate assumed annual average wage increase, wg_ult, updated yearly

Beneficiaries-Process 3

 Total projected disabled workers by age for years 2013 to 2090, updated yearly

 Total projected aged spouses, divorced aged spouses, surviving aged spouses and 

divorced surviving aged spouses by sex, single year of age (up to 95+) and for years 2013 

to 2090.

Awards-Process 4

 Projected number of workers and total taxable earnings by single year of age (15-80) 

and sex from 2013 to 2090, updated yearly

 Other

 Total count of beneficiaries in 20 of the 28 beneficiary categories (excluding retired 

workers, disabled workers, aged spouses (married, divorced, and dually entitled 

excess), and aged widow(er)s (married, divorced, and dually entitled excess) from 
281

the December 2013 Master Beneficiary Record (MBR)51—updated yearly

 Total benefits paid in 20 of the 28 beneficiary categories (excluding retired workers, 

disabled workers, aged spouses (married, divorced, and dually entitled excess), and 

aged widow(er)s (married, divorced, and dually entitled excess) from the December 

2013 MBR—updated yearly

 Consumer Price Index data from 1951-1974 from Bureau of Labor Statistics

 Number of covered workers and average taxable earnings by single year of age 1-14 

for years 1991-2010 from 1 percent Continuous Work History Sample (CWHS), 

updated yearly to include year ni-4

 Number of covered workers and average taxable earnings by single year of age 75-

99 for years 1991-2010 from 1 percent CWHS, updated yearly to include year ni-4

 Distribution of assumed age differentials between aged spouses and workers 

ranging from -12 years to 15 years seniority for the worker by sex of the beneficiary, 

age of the beneficiary 62-74, and marital status obtained from the December 2012 

MBR—updated every 3 years

 Distribution of assumed age differentials between widow(er)s and workers ranging 

from -6 years to 12 years seniority for the male (generally not updated)

 Factors to apply to the 95+ “in current pay” counts of retired workers, aged spouses, 

surviving aged spouses, divorced spouses, and dually entitled spouses expanding the 

single age counts through 119, updated yearly

4.3.c. Development of Output

i. Equation 4.3.1 – Payroll Tax Contributions (CONTRIB)

It would be natural to estimate the payroll tax contributions by trust fund by multiplying the 

applicable employer/employee tax rate by effective taxable payroll. However, tax contributions 

are reported on a cash basis. That is, tax contribution amounts are attributed to the year in 

which they are actually received by the trust funds, while taxable payroll is attributed to the 

year in which earnings are paid. In other words, the lag between the time the tax liability is 

incurred and when the taxes are actually collected must be reflected. If lag represents the 

proportion of incurred payroll taxes estimated to be received by the trust fund (tf) in year yr, 

then tax contributions (CONTRIB) are given by the formula

( , ) ( , ) ( )

(1 ) ( , 1) ( 1)

CONTRIB tf yr lag tax rate tf yr payroll yr

lag tax rate tf yr payroll yr

  

     

for 

yr ns  .

 

51 For disabled adult children of deceased workers and lump-sum beneficiaries, data were extracted from a 1-

percent sample of the December 2013 MBR, mainframe dataset ACT.TAPEL.CAN1213. For the other 18 auxiliary 

beneficiary categories, data was extracted from the 100 percent December 2013 MBR, mainframe dataset 

ACT.TAPEH.MBR100.D1312.CANSORT.
282

The value of lag is estimated from the combined OASI and DI tax contributions estimated to be 

collected in the final year of the short-range period, ns, and is given by

 

 

2 2

1 1

2

1

( , ) ( , 1) ( 1)

( , ) ( ) ( , 1) ( 1)

tf tf

tf

CONTRIB tf ns taxrate tf ns payroll ns

lag

taxrate tf ns payroll ns taxrate tf ns payroll ns

 



   



    

 



.

For the first ten years of the long-range period, tax contributions are set equal to those 

provided by the short-range office. The same value of lag is used for all years, and both trust 

funds, thereafter.

ii. Equation 4.3.2 – Scheduled Benefits (BEN)

(1) Disabled-Worker Benefits

Disabled Worker Beneficiary Matrix

The number of disabled-worker beneficiaries for a given year and sex is provided from the 

subprocess 3.2. For each projection year, two matrices are provided – one for males and 

one for females. The structure of each matrix is as follows:

 11 columns. The columns are indexed by duration of disability (0-9 and 10+).

 52 rows. These rows correspond to the age in current pay, ages 15 through 66.

 

The COST subprocess, however, only uses 10 durations (0-8 and 9+), and 47 ages (ages 20 

through 66). This requires a manipulation of the matrix of DI beneficiaries in current-pay 

status from subprocess 3.2. For ages in current pay greater than or equal to 30, the 

duration 9 and 10+ columns of this matrix are added to give the total number of duration 9+ 

beneficiaries. For ages (ag) between 20 and 30 inclusive, the number of beneficiaries in 

current-pay aged ag and duration ag-20 is the value provided by the DISABILITY subprocess 

added to the number of people in current pay aged ag-j and duration ag-20 for j=1,..,5. 

(For example, the number of people aged 20 of duration 0 is combined with the number of 

people aged 15, 16, 17, 18 and 19 of duration 0; the number of people aged 21 of duration 

1 is combined with the number of beneficiaries in current-payment status aged 16, 17, 18, 

19, and 20 of duration 1, and so on. In other words, the five nonzero diagonal of the matrix 

provided by the DISABILITY subprocess is “combined with” the diagonal directly below it 

and then zeroed out.) 

Building the Average Benefit Matrix for Disabled Workers

In each projection year, the COST subprocess produces an average benefit matrix for each 

sex. Each matrix is a 47 by 10 matrix whose entries are the average benefit amounts of 

disabled worker beneficiaries whose age in current pay is indexed by the rows (ages 20 

through 66) and whose duration of disability is indexed by the columns (durations 0 through 

8 and 9+). 
283

The 100 percent Master Beneficiary Record (100% MBR) extract is processed by a side 

model. The final product of the side model is two matrices of average benefit levels, one 

for males and one for females, for December nim1 (2013 for the 2014 TR). 

For a given year of the projection period, a new average benefit matrix is obtained by 

moving the average benefit matrix from the previous year one year forward. The next few 

paragraphs describe this procedure.

In general, for each age in current-pay, the age and duration are incremented by 1 and the 

previous PIA amount is given a cost of living adjustment. In addition, the beneficiaries are 

given a workers’ compensation adjustment and a post-entitlement adjustment. For each 

duration 

j  0,1,...,7,

and 8+ and sex, let the workers’ compensation offset factor be 

denoted

wkcomp yr sx dur ( , , ).

We have, for durations 0 through 8, that 

( , , , ) ( 1, , 1, 1) (1 ( ))

(1 ( , , )) ( , , ).

avgmba yr sx ag dur avgmba yr sx ag dur COLA yr

wkcomp yr sx dur PEadj yr sex dur

     

  

A more careful explanation of the factors, 

wkcomp yr sx dur ( , , )

and 

PEadj yr sex dur ( , , ),

is 

given later in this document. See the section titled Average PIAs and MBAs for DisabledWorker Beneficiaries, below. To move duration 8 average benefits to duration 9+ average 

benefits, both average benefits are given a cost of living adjustment and a post-entitlement 

adjustment (see section “Post-Entitlement Adjustments”). The resulting duration 9+ 

average benefit is the weighted average of the adjusted prior year duration 8 and 9+ 

average benefits, weighted by the prior year’s numbers of beneficiaries in current-pay 

status for durations 8 and 9+ respectively.

The only column that does not follow this procedure is the duration 0 column. The duration 

0 column corresponds to newly entitled disabled-worker beneficiaries. The following 

sections describe how average benefits are determined for this group of beneficiaries. 

Average Benefits for Newly Entitled Disabled-Worker Beneficiaries

The potential AIME percentage values for newly entitled disabled-worker benefits (DPAPs) 

are obtained from the AWARDS subprocess. The two bendpoints of the PIA formula, BP1 

and BP2, are indexed by the increase in the average wage index. In 1979 dollars, the values 

of BP1 and BP2 are $180 and $1,085 respectively. The AIME dollars between 0 and BP1 are 

divided into four intervals (each of length $45 in 1979 dollars). The AIME dollars between 

BP1 and BP2 are divided into fourteen intervals (nine of length $45 and five of length $100, 

in 1979 dollars). Twelve additional intervals are added beyond BP2 (ten of length $200 and 

two of length $1,000, in 1979 dollars). 

To determine the average PIA for newly entitled beneficiaries, the DPAP values for each of 

the thirty intervals of AIME dollars are multiplied by the dollar amounts attributable to each 
284

interval (the length of the interval) and by the associated PIA factors. The distribution of 

prior year disability onset and current year disability onset is taken into consideration. 

It is assumed that this distribution is 6 months for prior year disability onset and 6 months 

for current year disability onset. In the formulas below, j = 1 signifies current year disability 

onset and j = 2 signifies prior year disability onset.

Let:



( max( 60,1 )) _ ( , , )

(1977) j

avgwg yr ag j Wage Idx sx ag yr

avgwg

  



for j=1,2.

 1

62

(1 ( 1)) (1 ( )) 64

_ ( , )

(1 ( ( 62)) 64 66.

ag

k

COLA yr COLA yr ag

Cum COLA ag yr

COLA yr k ag



     



 

     





2

63

1 ( ) 63

_ ( , )

(1 ( ( 62)) 63 66.

ag

k

COLA yr ag

Cum COLA ag yr

COLA yr k ag



  



 

     







6 1

,

12 2

wj  

j=1,2.

 PIA_factori represent the PIA factor for interval i (equal to 0.90 for intervals i=1..,,4, 

0.32 for intervals i=5,...,18, and 0.15 for intervals i=19,...,30). 

 AIME_dollarsi represent the length of interval i, expressed in 1979 dollars.

 dpapi (yr,sx,ag) represent the DPAP value for newly entitled disabled workers in 

year yr whose sex is sx and age is ag. 

To take into account the workers’ compensation offset to disability benefits, administrative 

data is reviewed, from which a factor is developed and applied to the average award 

benefit. We now describe how this factor, 

facm p yr sx 2 ( , )

is computed. The table 1-A 

supplement, for each month in 2013, contains total award PIA and MBA data for disabled 

workers, by sex, for beneficiaries both non-actuarially reduced and actuarially reduced. Let 

totmba DIB nar yr sx _ _ ( , )

and 

totpia DIB nar yr sx _ _ ( , )

be the total annual MBA and PIA 

respectively for DIBs that are not actuarially reduced as found in the table 1-A data. In the 

historical period 2000-nim1 we define 

facm p yr sx 2 ( , )

to be the ratio of the total MBA to the 

total PIA for those not actuarially reduced. In other words, 

_ _ ( , ) 2 ( , ) .

_ _ ( , )

totmba DIB nar yr sx facm p yr sx

totpia DIB nar yr sx



In the period ni through ns+9, facm p yr sx 2 ( , )is defined as follows. Let 

y facm p nim sex 1 2 ( 1, ) 

y y 2 (1.0 1) / 3.0  

1/20

2 ( 1, ) 2 ( , ) max( 1 2,min( 1 2, 2 ( 1, ) )).

2 ( 11, )

facm p yr sx facm p yr sx y y y y facm p yr sx

facm p yr sx

  

        
285

Projected values of facm2p are therefore held within a delta of y2 from the last historical 

year of facm2p. 

This value is further adjusted by the variable facm2p_param to reflect the offset amounts 

that end within the first entitlement year. For the 2014 TR the data suggests this factor 

should be .32. As a result, for yr = ni, ... , ns+9,

2 ( , ) (1 ) 0.32

0.32 0.68 .

facm p yr sx x x

x

   

  

The factor reaches its ultimate value in years ns+10 and later.

The preliminary average PIA for newly entitled disabled worker beneficiaries may now be 

defined. It is equal to 

30

1

2

1

_ ( , , ) _ _ ( , , )

_ ( , , ) _ ( , )

2 ( , ).

i i i

i

j j j

j

LR awdpia sx ag yr PIA factor AIME dollars dpap yr sx ag

w Wage Idx sx ag yr Cum COLA ag yr

facm p yr sx





  

 

      







Once these average PIAs of newly entitled disabled-worker beneficiaries are computed, 

their values are filled into the average PIA matrices for duration 0 for the appropriate 

entitlement age. 

Average PIAs and MBAs for Disabled-Worker Beneficiaries

An overall average PIA of newly entitled disabled worker beneficiaries for each sex and 

projection year is computed by taking the weighted average of awdpia(sx,ag,yr), the 

weights being the number of disabled workers in current payment status of duration zero. 

This value is denoted awdpia(sx,yr). 

In addition, an overall average PIA and MBA for all disabled worker beneficiaries in currentpayment status is computed by finding the weighted average of the average PIAs for each 

age in current-pay and duration with the number of people in current pay for each of these 

ages and durations. The average PIAs were already reduced by a workers’ compensation 

offset factor, as briefly described above; a more careful description is given in this section. 

To get the average MBAs, we apply a factor that reflects the differences in average MBAs 

and PIAs for disabled workers, isolating only the trend in cases with an actuarial reduction. 

We also provide a relatively small reduction to reflect offsets starting and stopping in the 

year of DI entitlement that are not captured by the current method. There is an additional 

adjustment to the weighted average disabled worker PIA and MBA amounts in current pay 

at durations 0 and 1 to account for benefit level differences that are phased out by duration 

2. 
286

Workers’ Compensation Offset Factors

For each duration 

j 1,...,7,

and 8+ and sex we define a workers’ compensation factor. 

This factor is applied to the average worker PIA matrix as mentioned above. It is 

denoted

wkcomp yr sx dur ( , , ). Let facm p pct dur 2 _ ( )

be defined as in the following table. 

Duration Cumulative product above set at x% of 

way between original facm2p and 1

1 0.465294

2 0.652346

3 0.734595

4 0.785673

5 0.831964

6 0.856982

7 0.876671

8+ 0.897059

Then 

wkcomp yr sx dur ( , , )

is defined so that 

1

2 _ ( ) 2 ( , ) (1 ( , , )).

dur

j

facm p pct dur facm p yr dur sx wkcomp yr sx j



    

This is an iterative process that first computes 

wkcomp yr sx ( , ,1)

by solving the above 

equation with dur set equal to 1. The remaining factors for higher durations are then 

computed recursively using the above formula.

Trend in Average MBA to Average PIA

This trend is captured in a factor denoted

Fam p yr sx 2 ( , ). The table 1-A supplement as of 

the end of December 2013 contains total in-current pay PIA and MBA data for disabled 

workers, by sex, for beneficiaries both non-actuarially reduced and actuarially reduced. Let 

totmba nar yr sx _ ( , )

and 

totpia nar yr sx _ ( , )

be the total MBA and PIA respectively for DIBs that 

are not actuarially reduced as found in the table 1-A data. Similarly, let 

totmba ar yr sx _ ( , )

and 

totpia ar yr sx _ ( , )

be the total MBA and PIA respectively for cases that 

are actuarially reduced. In the historical period 2000-nim1 we define 

fam p yr sx 2 ( , )

to be 

the ratio of the total MBA to the total annual PIA for those not actuarially reduced. In other 

words, 

_ ( , ) _ ( , ) 2 ( , ) .

_ ( , ) _ ( , )

totmba ar yr sex totpia nar yr sex Fam p yr sex

totpia ar yr sex totpia nar yr sex







In the period ni through ns+10, facm p yr sx 2 ( , )is defined as follows:

 y fam p nim sex 1 2 ( 1, ) 

 y y 2 (1.0 1) / 3.0  
287 1/20

2 ( 1, ) 2 ( , ) max( 1 2,min( 1 2, 2 ( 1, ) )).

2 ( 11, )

fam p yr sx fam p yr sx y y y y fam p yr sx

fam p yr sx

  

        

The factor reaches its ultimate value in years ns+10 and later.

More Workers’ Compensation Offsets 

As mentioned above, we also we provide a relatively small reduction to retroactive benefits 

to reflect offsets starting and stopping in the year of DI entitlement that are not captured by 

the current method. Based on historical administrative data, we set these factors by 

duration as follows: 

Duration Percentage Reduction

0 0.2223%

1 0.1596%

2 0.0608%

3 0.0260%

4 0.0055%

+ 0%

We define wkcomp_red(dur) to be 1 minus these percentage reductions. 

By law, disabled workers attaining age 65 are no longer subject to the workers’ 

compensation offset. Therefore, all DI worker benefit levels are adjusted at age 65 to 

eliminate the effect of the offset.

Adjustment to Average Benefit Levels at Durations 0 and 1

Average disabled worker PIA and MBAs are adjusted further at durations 0 and 1 by a 

factor, DI_RI_facs, designed to account for average benefit level changes that occur when 

beneficiaries come on the rolls retroactively. This is different than projected retroactive 

payments discussed elsewhere in the documentation. These adjustments are phased out 

by duration 2, when the vast majority of disabled workers have started receiving benefits.

Adjustment to Short-Range Average Benefit Levels

Average PIAs are further adjusted by a factor, icp_adj, designed to adjust the long-range 

aggregate average disabled worker in current pay monthly benefit amount (MBA) to the 

value projected by short range in the short-range period. This is done for each year of the 

short-range period and for each sex with the adjustment in year ns linearly phased down to 

0 by year ns+10.

Computation of Average MBA for DI Workers

The disabled worker PIA as presented in the average benefit matrix was already 

incremented by age and duration using a COLA and a workers’ compensation adjustment. 

The average PIA by year, age and duration, is denoted 

avgpia yr ag sx dur ( , , , ). The overall 

average MBA by year and sex is the weighted average of 
288 avgpia yr ag sx dur Fam p yr sx dur ( , , , ) 2 ( , , ), 

the weights being the number of DI workers in 

current payment status by age, sex, and duration.

DI Conversions

Disabled-worker beneficiaries convert to retired-worker beneficiary status (called DI 

conversions) at normal retirement age (NRA). The average new DI conversion benefit for a 

given sex at age NRA is the weighted average of the average DI worker benefits from the 

prior year for that sex and age NRA-1, weighted by the number of people in current pay in 

each duration and then increased by the current year COLA and adjusted by the appropriate 

Post-Entitlement factor. The average DI conversion benefit for a given sex and single age 

NRA+1 through 95+ is the average DI conversion benefit from the previous year for the 

same sex and age cohort increased by the current year COLA and adjusted by the 

appropriate Post-Entitlement factor. Both the average conversion benefit for each sex and 

single age NRA through 95+ and the number of people in current pay for these ages are 

used in the computation of average retired worker benefits (see subsection (2) below).

Post-Entitlement Adjustments

As cohorts of beneficiaries age, their average benefit level will likely change for reasons 

other than just the COLA increase. The two primary reasons for this are post-entitlement 

work, which could lead to a re-calculation of one’s benefit, and a known correlation 

between greater lifetime earnings and lower mortality rates. The Cost process uses postentitlement factors by sex and duration to account for the expected dynamic benefit levels. 

One percent December MBR data from the most recent 10 historical years are used to 

calculate post-entitlement factors.

For disabled workers we calculate separate factors for those in current pay (ICP) who are 

younger than 50 and those ICP who are age 50 or older. We use separate factors for each 

sex and each duration (0-9

+

). Initial and Ultimate factors are calculated before being read 

into the cost program with initial factors set to the most recent 3-year historical average 

and ultimate factors at the most recent 10-year average.

For retired workers we calculate separate factors for those ICP who converted from DI 

status and those ICP who came on the rolls as a retired worker. We use separate factors for 

each sex and each duration (0-12+). Initial and ultimate factors are calculated before being 

read into the cost program with initial factors set to the most recent 3-year historical 

average and ultimate factors at a 10-year average. For females the ultimate postentitlement factors are adjusted further to reflect the trend that female retired workers are 

starting to have earnings and benefit levels more similar to men. Therefore female ultimate 

post-entitlement factors are calculated in the program as 90% of the male 10-year average 

plus 10% of the female 10-year average. 
289

All initial factors are phased-in linearly to the ultimate factors over the first 20 years of the 

projection period (reaching the ultimate values in ni+19).

(2) Retired-Worker Benefits

Retired-Worker Beneficiary Matrix

The number of retired-worker beneficiaries for a given year and sex is provided from 

subprocess 3.3. Two matrices are provided – one for males and one for females. The 

structure of each matrix is as follows:

 10 columns. The first 9 columns are the age at entitlement, ages 62 through 70. 

The last column is the number of disabled workers who are projected to convert 

to retired-worker beneficiary status (DI conversions) at normal retirement age.

 34 rows. These rows correspond to the age in current pay, ages 62 through 94 

and ages 95+. 

Note that the entries on the diagonal at ages 62 through 70 (where age in current-pay 

equals age at entitlement) are the number of new entitlements projected for that year. 

Building the Average Benefit Matrices for Retired Workers

In each projection year, the COST subprocess produces four average benefit matrices. For 

each sex there are two matrices, an average monthly benefit amount (MBA) matrix and the 

average primary insurance amount (PIA) matrix. Each matrix has the same structure as the 

beneficiary matrices. In other words, each matrix is a 34 by 10 matrix whose entries are the 

average benefit amounts of retired worker beneficiaries whose age in current pay is 

indexed by the rows and whose original age at entitlement is indexed by the columns. The 

final column simply gives the average benefits for DI conversions at the various ages in 

current pay. 

The 100% MBR extract is processed by a side model. This side model computes a starting 

matrix for year ni-1. This starting matrix contains the four initial benefit matrices, 

constructed using the most recent data. For a given year of the projection period, the 

average benefit matrix is updated from its previous year’s value incrementing each benefit 

amount (PIA or MBA) by one year of age and increasing it by a cost of living adjustment 

(COLA) and by the appropriate post entitlement factor (see section “Post-entitlement 

adjustments”) for males and females. Adjusted age 94 benefits and age 95+ benefits are 

averaged, based on the respective number of beneficiaries in current pay in the prior year, 

to get the new average benefit for age 95+. DI conversion benefits are handled similarly, 

except the average conversion benefit for each age 65 through 66 is combined (as a 

weighted average) between the DI conversion average benefits computed in subprocess 3.2 

and the DI conversions of the corresponding age already receiving benefits (provided by 

subprocess 3.3).
290

The entries along the diagonal, the average benefits of newly entitled beneficiaries by age, 

must still be computed. The remainder of this section will explain how these average 

benefits are computed. Once these are computed, all entries are computed and the 

average benefit matrix for the year is complete.

Average Benefits for Newly Entitled Retired Worker Beneficiaries

The potential AIME percentage (OPAPs) values for newly entitled retired-worker benefits 

are obtained from subprocess 4.2. The OPAPs for the projection of average benefits for 

newly entitled retired-worker beneficiaries are modified by the COST subprocess. The 

reason is that the age distribution of newly entitled retired worker beneficiaries differs 

between the projection year and the year of the awards sample. To incorporate the 

change in the age distribution of projected newly entitled retired-worker beneficiaries for 

determining their average benefits, we use a “shuttling method” that allocates appropriate 

portions of the OPAPs by different ages to represent a specific age in the projection year. 

The resulting new potential AIME percentages, by age and sex, are denoted OPAP1. 

Additional details about the shuttling method are given in Appendix 4.3-1.

Average benefits for newly entitled retired-worker beneficiaries are calculated by sex and 

single years of age 62 through 69, and ages 70+. The two bendpoints of the PIA formula, 

BP1 and BP2, are indexed by the increase in the average wage index. In 1979 dollars, the 

values of BP1 and BP2 are $180 and $1,085 respectively. The AIME dollars between 0 and 

BP1 are divided into four intervals (each of length $45 in 1979 dollars). The AIME dollars 

between BP1 and BP2 are divided into fourteen intervals (nine of length $45 and five of 

length $100, in 1979 dollars). Twelve additional intervals are added beyond BP2 (ten of 

length $200 and two of length $1,000, in 1979 dollars). 

To determine the average PIA for newly entitled beneficiaries, the OPAP1 values for each 

interval of AIME dollars are multiplied by the dollar amounts attributable to each interval 

(the length of the interval). More precisely, let

 PIA_factori be the PIA factor for subinterval i (equal to 0.90 for intervals i=1,4, 0.32 

for intervals i=4,...,18, and 0.15 for intervals i=19,...,30), AIME_dollarsi be the length 

of subinterval i,

 opap1i (yr,sx,ag) be the modified PAP value for retired workers newly entitled in 

year yr whose sex is sx and whose age is ag. 

 wff(yr,sx,age) be a reduction factor to account for the Windfall Elimination 

Provision.52

 

52 The Windfall Elimination Provision (WEP) reduces the first PIA formula factor from 90% to as low as 40% for 

individuals who receive a pension based on specified categories of non-covered employment, primarily non-covered 

state and local government employees and federal workers receiving a pension under the Civil Service Retirement 

System. The cost process uses initial factors by sex and age, ultimate factors, years in which ultimate factors are 

reached and phase-in trend lines to the ultimate factor, all supplied by a side model.
291



( ( 62)) _ ( , )

( 2)

avgwg yr ag Wage Idx ag yr

avgwg nbase

 







62

_ ( , ) (1 ( ( 62)))

ag

k

COLA Idx ag yr COLA yr k



    

Then the average PIA for these newly entitled retired worker beneficiaries is equal to

30

1

_ ( , , ) _ ( , ) _ ( , ) ( , , )

_ _ 1 ( , , ). i i i

i

LR awdpia sx ag yr Wage Idx ag yr COLA IDX ag yr wff yr sx age

PIA factor AIME dollars opap yr sx ag



  

   

This formula incorporates the fact that the PAP values are the estimated cumulative 

distribution of AIME dollars. The average award MBA for a worker beneficiary is then the 

average newly entitled PIA multiplied by the appropriate actuarial reduction factors and 

delayed retirement credits, arfdrc(ag,yr), based on age at initial entitlement. 

Once these average benefits of newly entitled retired-worker beneficiaries are computed, 

their values are filled into the appropriate average benefit matrices.

For summary purposes, the COST subprocess computes an average PIA and MBA for all 

male and female newly entitled retired-worker beneficiaries. These are just the respective 

weighted averages of the average PIAs and MBAs by age and sex, the weights being the 

number of newly entitled retired-worker beneficiaries. Similarly, average PIA and MBA for 

all retired worker beneficiaries in current pay are computed, by sex. 

Adjustment to Average Benefit Levels at Durations 0 and 1

Average retired worker PIA and MBAs are adjusted at durations 0 and 1 by a factor, 

OA_RI_facs, designed to account for average benefit level changes that occur when 

beneficiaries come on the rolls retroactively. This is different than projected retroactive 

payments discussed elsewhere in the documentation. These adjustments are phased out 

by duration 2, when the vast majority of retired workers have started receiving benefits.

Adjustment to Short-Range Average Benefit Levels

Average PIAs are further adjusted by a factor, icp_adj, designed to be the factor that adjusts 

the long-range aggregate average retired worker in current pay monthly benefit amount 

(MBA) to the value projected by short range in the short-range period. This is done for each 

year of the short-range period and for each sex with the adjustment in year ns linearly 

phased down to 0 by year ns+10.

(3) Annualizing Benefits 

Scheduled benefits are calculated by trust fund and projection year. For each year, scheduled 

benefits for each trust fund are found by adding up the appropriate benefit categories. 

This section applies to all benefit amounts except the “dual entitlement excess amount.” If a 

retired worker beneficiary is also entitled to auxiliary spouse or widow(er) benefits and these 

auxiliary benefits are greater, then the amount by which the auxiliary benefit exceeds the 
292

worker’s MBA is the dual entitlement excess amount. The four categories of excess amounts 

(dually entitled wives, widows, husbands, and widowers) are projected separately. More 

information is found in subsection (4).

The first step is to determine average benefits by category. A list of the beneficiary categories 

follows. An odd category number refers to the male account holder, while an even category 

number refers to the female account holder. As an example, for category 4, the aged married 

spouse is the aged married husband of the retired female worker.

Category Beneficiary Type

# (cat)

Old-Age Insurance Beneficiaries

1 & 2 Retired worker (includes DI conversions)

3 & 4 Aged married spouse

5 & 6 Aged divorced spouse

9 & 10 Young spouse with child

11 & 12 Child < 18

13 & 14 Student child

15 & 16 Disabled adult child

Disability Insurance Beneficiaries

17 & 18 Disabled worker

19 & 20 Aged married spouse 

21 & 22 Aged divorced spouse

25 & 26 Young spouse with child

27 & 28 Young child

29 & 30 Student child

31 & 32 Disabled adult child

Survivors Insurance Beneficiaries

33 & 34 Aged married widow

35 & 36 Aged divorced widow

39 & 40 Young married disabled widow

41 & 42 Young divorced disabled widow

43 & 44 Aged parent

45 & 46 Young married widow with child

47 & 48 Young divorced widow with child

49 & 50 Young child

51 & 52 Student child

53 & 54 Disabled adult child

55 & 56 Lump sum death benefit ($255)

For the worker categories, the prior sections describe the computation of average benefit levels 

at the end of each year. For a specific auxiliary beneficiary category, the average monthly 

benefit at the end of each year (avgben) is determined by multiplying:

 The linkage factor (the assumed relationship between an auxiliary beneficiary’s benefit 

level and the corresponding worker benefit level) by 
293

 The relevant average PIA or the average monthly benefit of the primary account holder 

(the worker beneficiary account on which the auxiliary beneficiary is entitled to receive 

the benefit).

In order to annualize benefits for each beneficiary category, two values are used. The 

beginning-of-year average benefit equals the average monthly benefit in December of the prior 

year. The end-of-year benefit equals the monthly average benefit of the worker beneficiary for 

December of the current year without the cost of living adjustment (COLA). The average 

benefit by category for each month is found by taking a weighted average of the benefits at the 

beginning and end of the year, the weights being the fractions of the year the prior and current 

year’s beneficiaries have been exposed. Since the new COLA takes effect in December of the 

year, the new COLA must be reflected in the December benefits. If cp(cat,yr) is the number of 

beneficiaries in category cat for year yr, and avgben(cat,yr) is the average monthly benefit for 

category cat for year yr, then the amount of aggregate benefits paid in year yr is given by the 

formula:

11

i=0

AggBen( , ) 

(12 ) = ( , 1) ( , 1) ( , ) ( , ) .

12 12

yr cat

i i cp cat yr avgben cat yr cp cat yr avgben cat yr   

           

For all beneficiary categories expect for the lump-sum benefit, the aggregate benefit amount is 

increased by the retroactive payments that were projected to be paid during the year. See 

section (5), below. 

(4) Dually Entitled Beneficiaries and Benefits

Number of Dually Entitled Beneficiaries

There are four categories of dually entitled beneficiaries. They are the dually entitled wives (1), 

widows (2), widowers (3), and husbands (4). To project the number of dually entitled 

beneficiaries for each category we combine a regression equation with two coefficients each, a 

slope of 

( )

1

k

a

and a y-intercept of 

( ) k

b

, with a third factor, 

( ) ( ) k

c yr

derived from a process we 

describe as “add factoring”:

( ) ( ) ( ) ( )

1

PIA(yr,M) PIA(yr,F) PctExp ( ) ( )

PIA(yr,M)

k k k k yr a b c yr 

  

(k=1,2,3,4) project the percentage of the exposed population entitled to wife (1), widow (2), 

widower (3), and husband (4) benefits. 

PIA(yr,sex) is the average PIA of all retired worker beneficiaries in current pay by sex, wageindexed to the year of the sample and PctExp(yr) is the percentage of the entitled population in 

the category that is dually entitled. Only the male (widower and husband) dually entitled 
294

regression equations warrant the usage of a non-zero y-intercept. We use the “add factoring” 

method with variable 

( ) ( ) k

c yr to account for the expected future comparative work history 

changes that will affect dual entitlement populations.

To derive 

( ) ( ) k

c yr

, suppose that 

( ) k

ult is the value obtained from the regression equation 

without add-factoring in the final year of the projection period. Therefore 

( ) ( ) ( )

1

PIA( 74,M) PIA( 74,F)

.

PIA( 74,M)

k k k ni ni ult a b

ni

  

 



Let 

( ) k

targ

be the target value we estimate for the final year of the projection period. Let 

phaseyrs

be the number of years it takes to fully phase in the target value. Then we have 

( ) ( )

( ) ( ) min( 2012, ) .

k k

k

targ ult c yr yr phaseyrs

phaseyrs



  

The following table displays the coefficients, target values, and phase-in years for each type of 

beneficiary.

k Type

( )

1

k

a

( ) k

b

Target Value

( ) k

targ

Add-factoring Phase-in 

Years

phaseyrs

1Wife 0.86539 0.0 0.20 40

2Widow 1.85623 0.0 0.57 35

3Widower -0.31581 0.16772 0.10 41

4Husband -0.04744 0.02162 0.0085 38

In the above equations, the average PIA of newly entitled beneficiaries by sex has already been 

computed (see subsection (2) above). 

Average Excess Amount for Dually Entitled Beneficiaries

The projection of the average excess amounts for two categories of dually entitled beneficiaries 

(wives and widows) is similar to that of the number of dually entitled beneficiaries. The 

structure of the equations used to project these amounts is similar to the equations used to 

project the percentage exposures.

The equations used to project the average excess amount each have two terms. Each equation 

k has one coefficient, 

( )

1

,

k

b

calculated in a side model for the cost process based on historical 

series of the other terms in the equations and an add factor adjustment (denoted “c

(k)”) similar 

to the process for the number of dually entitled beneficiaries. A target value in the 75th year of 

the projection period is used in deriving the “add-factor” adjustment. The two equations
295

( ) ( ) ( )

1

PIA(yr,M) PIA(yr,F) AvgExcPct ( ) ( )

PIA(yr,M)

k k k yr b c yr 

 

(

k 1,2

) project the average excess benefit amounts of wife (1) and widow (2) beneficiaries as 

a percentage of the male retired worker benefit. 

The derivation of 

( ) ( ) k

c yr

is similar to the case of the exposure percentages as described 

above. Suppose that 

( ) k

ult is the value obtained from the regression equation without addfactoring in the final year of the projection period. Therefore 

( ) ( )

1

PIA( 74,M) PIA( 74,F)

.

PIA( 74,M)

k k ni ni ult b

ni

  





Let 

( ) k

targ

be the target value we estimate for the final year of the projection period. Let 

phaseyrs

be the number of years it takes to fully phase in the target value. Then we have 

( ) ( )

( ) ( ) min( 2012, ) .

k k

k

targ ult c yr yr phaseyrs

phaseyrs



  

The table below shows the regression coefficients and other relevant adjustments in the 2014 

Trustees Report.

k Type

( )

1

k

b

Targeted Value (2088) in 

Nominal Dollars

( ) k

targ

Add-factoring 

Phase-in Years

phaseyrs

1 Wife 0.43400 3,616 38

2 Widow 1.21183 9,401 33

The average excess amount of widower beneficiaries is estimated to be a fixed percentage 

(50%) of the average excess amount of widow beneficiaries. That is, 

(3) (2) AvgExcPct ( ) 0.50 AvgExcPct ( ). yr yr  

The average excess amount of husband beneficiaries is estimated as a percentage of average 

excess amount of dually entitled wives. This percentage is measured to be 75.15% in 2012 and 

is assumed to linearly phase to 80% in the 20th year of the projection period (2033 for 2014TR).

Annualizing Excess Amounts

The process to annualize excess amounts is very similar to the process for annualizing auxiliary 

benefits. 
296

For each dual entitlement category, the number of beneficiaries is simply

( ) ( ) ( , ) PctExp ( ) ExposedPop ( ). cat cat cp yr cat yr yr   With this method, however, no linkage 

factor is used. Instead, the projected average excess amount, as explained above, is used. 

Therefore,

11

( ) ( )

i=0

AggExcess( , ) 

(12 ) = ( , 1) ( 1) ( , ) ( ) .

12 12

cat cat

yr cat

i i cp cat yr AvgExcAmt yr cp cat yr AvgExcAmt yr   

           

(5) Retroactive Payments

Frequently, beneficiaries start receiving payments later than their actual entitlement date, such 

that they receive a “catch-up” lump-sum amount for the time delay, in addition to regular 

monthly benefits going forward. These lump-sum amounts, which we call retroactive 

payments, apply for all beneficiary categories (except for the one-time $255 death benefit) but 

are more significant for disabled workers because of the frequent and sometimes lengthy time 

lag in getting an allowance on their application, as well as the 12 months of retroactivity 

allowed at the time of benefit filing. This section discusses how retroactive benefit payments 

are projected for all beneficiary categories. 

Disabled Workers

For each age and sex the DI area (subprocess 3.2) provides two cumulative distributions (one 

initial and one ultimate), by duration, of incurred but not reported cases. Denote this by

ibnr yr sex age dur ( , , , )

where for years ni-10 to 2008 ibnr factors are set to the initial 

distribution, for years 2018 and after ibnr factors are set to the ultimate distribution, and for 

years 2009 to 2017 we linearly interpolate. The number of disability beneficiaries who will 

eventually receive benefits (by age, sex, and duration) is 

dibce yr sex age dur dibcp yr sex age dur ibnr yr du ( , , , ) ( , , , ) / ( , ,max(15, ), ).    r sex age dur dur

The associated frequency distribution of incurred but not reported cases is 

ibnr freq yr sex age dur _ ( , , , ).

In other words, we have 

ibnr freq yr sex age ibnr yr sex age _ ( , , ,0) ( , , ,0) 

for duration 0 and 

ibnr freq yr sex age dur ibnr yr dur sex age dur ibn _ ( , , , ) ( , , , ) ( , , , 1)      r yr dur sex age dur for 

higher durations, 

1 9   dur

and 10+.

Let dur be a duration, 0 through 9 or 10+. Define



0

_ ( ) (1 ( )).

dur

j

Cum COLA dur COLA yr j



   
297

 For 

i  0,...,10,

2 0

5 0 and 0 

_ ( )

12 0 and 0

6 0 and 

dur

dur i

Num Months i

dur i dur

dur i dur

 



  

 

   



  

Then the aggregate retroactive payments for disabled workers, in millions, are defined to be 

Retro_DIB(sex,yr)

66 10

20 0 0

6

( , , , 0) _ ( ) _ ( , , )

( , , ) _ ( )

_ ( )

.

10

dur

age dur i

dibce yr dur sex age Num Months i ibnr freq sex age dur

dibpia age sex dur wkcomp red dur

   Cum COLA dur

 

      dibpia sex age dur ( , , )

is further altered in this formula by the adjustment factors in array: 

DI RI facs sex age dur _ _ ( , , ,2)

These adjustments to dibpia are designed to account for the small differences between average 

benefit levels for all beneficiaries and levels for those receiving retroactive credit.

Retro_DIB(sex,yr) is simply added to the disabled worker benefit category by year and sex.

Retired Worker Beneficiaries

Retired worker beneficiaries are assumed to have, on average, 0.7 months of retroactive 

payments. Hence 

70

62

( , , , ) 0.7 ( , , , )

1000 _ ( , ) .

1 ( ) age

oabicp yr sex age age oabmba yr sex age age

retro OAB sex yr

 COLA yr

 







In the above formula, 

oabicp yr sex age age ( , , , )

is the number of newly entitled beneficiaries at 

age equal to age (age in current pay equals age at entitlement equals age) and 

oabmba yr sex age age ( , , , )

is the corresponding average benefit. 

oabmba year sex age age ( , , , )

in the above formula is further altered by the adjustment factors:

retrom adj sex _ ( ) {0.9025,1.0118} 

These adjustments to oabmba are designed to account for the small differences between 

average benefit levels for all beneficiaries and levels for those receiving retroactive credit.

The aggregate retroactive benefits for retired worker beneficiaries are simply added to the 

retired worker benefit category by year and sex.
298

Auxiliary Beneficiary Categories

Retroactive payments for auxiliary beneficiaries are treated as a loading of the aggregate 

annual benefits by auxiliary category. That is, each auxiliary benefit category has a loading 

factor to represent retroactive payments on top of regular monthly benefits, and the aggregate 

annual benefits by category are increased by this loading factor.

(5) Aggregate Scheduled Benefits (BEN)

(6)

Aggregate benefits by trust fund, BEN(tf,yr), are computed as follows. For each year of the 75-

year long-range projection period, the aggregate benefits by category (including retroactive 

payments, as described above) are summed up to give total annual scheduled benefits by trust 

fund. In the short-range period, the long-range values are overridden by the values estimated 

by the short-range office. The difference between long-range scheduled benefits and shortrange benefits in the 10th year of the short-range period is called the scheduled benefits 

adjustment. In the 10 years after the end of the short-range period, the long-range scheduled 

benefits are adjusted by linearly grading the scheduled benefits adjustment to zero. From the 

20th year forward, the projection is the pure long-range value. 

iii. Equation 4.3.3 – Taxation of Benefits (TAXBEN)

The short-range office provides taxation of benefits levels by trust fund in the short-range 

period. These implicitly give, for each year, an estimated taxation of benefits factor, by trust 

fund, equal to the estimated taxation of benefits as a percentage of benefits scheduled to be 

paid. The long-range office projects these factors independently for every year of the 

projection period, also by trust fund. (See subprocess 4.1.) The difference in the factors 

between the two offices at the end of the short-range period is phased out linearly over the 

next ten years. The long-range projection of taxation of benefits is estimated by multiplying the 

projected taxation of benefits factors by the benefits scheduled to be paid, by trust fund. If 

taxben_factor(tf,yr) is the percentage of scheduled benefits for the year, by trust funds, 

estimated to be collected as taxation on benefits, then

TAXBEN tf yr taxben factor tf yr BEN tf yr ( , ) _ ( , ) ( , )  

for

yr ns  10.

iv. Equation 4.3.4 – Administrative Expenses (ADM)

v.

Administrative expenses are estimated separately by trust fund. In the short-range period, the 

short-range office provides the estimates of administrative expenses by trust fund. Thereafter, 

administrative expenses are computed by multiplying the previous year’s administrative 

expenses by three factors: annual changes in total beneficiaries, annual changes in AWI, and 

one minus annual productivity growth. As a formula, if ticp(tf,yr) is the total estimated number 

of beneficiaries in current-pay status by trust fund and year, AWI(yr) is the average wage index 

in year yr, and prod is the ultimate assumed annual growth in productivity, then
299 ( , ) ( , 1) [ ( , )/ ( , 1)] [ ( )/ ( 1)]

(1 )

ADM tf yr ADM tf yr ticp tf yr ticp tf yr AWI yr AWI yr

prod

     

 

for 

yr ns  .

vi. Equation 4.3.5 – Railroad Interchange (RR)

vii.

Railroad interchange is disaggregated by trust fund and projection year. The long-range office 

does a projection for each year in the 75-year period. In the short-range period (first 10 years 

of the 75-year projection period), the short-range office provides the estimates of railroad 

interchange by trust fund and the long-range projection is overridden in these years. Over the 

next five years of the projection period, the estimate of the railroad interchange is a linear 

interpolation between the short-range projection at the end of the short-range period and the 

long-range projection five years hence. During the final 60 years of the projection period, the 

projection is as estimated by the long-range office.

By trust fund, the total cost in calendar year yr, rr_cost(tf,yr) is the sum of railroad benefits and 

railroad administrative expenses, less the sum of railroad contributions and railroad taxation of 

benefits. By trust fund, the railroad interchange component of total cost is then equal to 

RR tf yr rr tf yr rr tf yr yield yr ( , ) [0.25 _cost( , 2) 0.75 _cost( , 1)] [1 1.25         ( )].

Note that the railroad cost is increased by the yield rate on the combined OASDI trust funds for 

year yr. This formula takes into account the fact that that there is a delay from the end of the 

fiscal year to the time of valuation.

The total cost in year yr is broken down in to four components: railroad benefits in year yr, 

railroad administrative expenses in year yr, railroad contributions in year yr, and railroad 

taxation of benefits in year yr. 

Projections of numbers of newly entitled retired workers are determined by analyzing the ratio 

of new entitlements to previous levels of railroad employment using 1997-2011 new 

entitlement data in the analysis. After initial entitlement, a mortality rate of 5.7% for 2012 is 

assumed based on analysis of recent Railroad Retirement Board financial interchange data, with 

mortality assumed to improve thereafter. For projections of newly entitled disabled workers a 

similar trend analysis based on prior employment is used. A “mortality” rate (deaths plus 

recoveries plus conversions to retired worker benefits) is determined using Railroad Retirement 

Board (RRB) data, with the rate assumed constant thereafter.

Assuming a 90/10 male/female split, the average benefit level for an OASI railroad worker is 

calculated as a ratio to the average OASI retired worker benefit. This ratio is constant 

throughout the projection period and is derived by comparing MBR and Railroad Board data. 

Additionally a constant loading factor based on the same data is applied to aggregate worker 

benefits to determine the aggregate benefit amounts for auxiliary OASI beneficiaries. This same 
300

approach is used to determine similar constants for DI railroad benefits. The aggregate 

disabled worker railroad benefits (and beneficiaries) are estimated in the same way. 

It is assumed that the ratio of OASI taxation of benefits to OASI benefits and DI taxation of 

benefits to DI benefits are both the same for railroad taxation of benefits. The railroad taxation 

of benefits is estimated by multiplying the railroad benefits by these ratios.

Administrative expenses for railroad are computed separately by trust fund. They are set at 

levels determined by short range in the short-range period. For years ni+10 to nf, they are 

computed similarly to OASDI administrative expenses. Administrative expenses in yr-1 are 

multiplied by (a) the change in the total number of worker beneficiaries, (b) the annual change

in average wage, and (c) one minus the ultimate productivity growth.

Railroad contributions are estimated, by trust fund, to be total railroad employment, multiplied 

by average railroad earnings, multiplied by the combined OASDI employer/employee tax rate. 

Railroad earnings are assumed to grow with the increase in the average wage index, and 

railroad employment is assumed to decrease over time, both of which are in line with the 

Railroad Retirement Board’s own “most likely” projections.

vi. Equation 4.3.6 – Interest Income (INT)

In the short-range period, the projection of interest income by trust fund is provided by the 

short-range office. In each year of the short-range period, the annual yield rate is defined as 

the ratio of interest earned by a fund to the average level of assets held by the fund during the 

year. 

The ultimate annual yield rate on each trust fund is equal to the nominal yield, which is the real 

interest rate increased for inflation. As a formula,

ultimate yield rate real interest rate inflati      (1 ) (1 ) 1. on rate

To get the yield rate for each year between the end of the short-range period (ns) and 5 years 

later, ns+5, when the ultimate yield rate is assumed to be reached, the program linearly 

interpolates between the values for years ns and ns+5. 

The projection of interest income in a given year is the yield rate for that year multiplied by the 

average level of assets. As a formula,

INT tf yr yield tf yr avg assets tf yr ( , ) ( , ) _ ( , ).  

The amount of assets in a trust fund at the end of a given year is estimated from the level of 

assets at the beginning of the year by:

 Increasing the level for the tax contributions and taxation of benefits income received 

during the year (each exposed to the point in the year in which they are estimated to be 

received, on average), and 
301

 Decreasing the level for scheduled benefits, railroad interchange, and administrative 

expenses paid during the year (each exposed to the point in the year in which they are 

estimated to be disbursed, on average). 

For all years of the projection period, tax contributions are given an exposure of 0.518, taxation 

of benefits are given an exposure of 0.625, railroad interchange is given an exposure of 

0.583,

and administrative expenses are given an exposure of 0.5. For scheduled benefits, 

separate OASI and DI exposures are determined through a side model. The exposure, 

ben_exp(yr), is larger than 0.5 in the early years of the projection period for both trust funds, 

whereas in later years OASI benefit exposures fall below 0.5 with DI remaining above 0.5. The 

reason is that in the past, benefits were always paid on the 3rd of each month. Now benefits 

are paid out throughout the month, based on the birth date of the beneficiary. The reason for 

the differences between trust funds is that benefits are paid on the third of the month 

(exempting check cycling53) for a higher proportion of DI beneficiaries due primarily to (1) 

concurrent receipt of SSI benefits, or (2) state payment of Medicare premiums. The average 

assets held by the trust funds for a given year is estimated by the formula

_ ( , ) ( , ) 0.518 ( , )

0.625 ( , ) ( , ) ( , )

0.583 ( , ) 0.5 ( , ).

BOY avg assets tf yr ASSETS tf yr CONTRB tf yr

TAXBEN tf yr ben_exp tf yr BEN tf yr

RR tf yr ADM tf yr

  

   

   

viii.Equations 4.3.7, 4.3.8 and 4.3.9 – Annual Values

The annual income rate for a trust fund is computed as the sum of payroll tax contributions plus 

taxation of benefits as a percentage of taxable payroll. 

( , ) ( , ) _ _ ( , ) .

( )

CONTRB tf yr TAXBEN tf yr ANN INC RT tf yr

payroll yr





The annual cost rate for a trust fund is computed as the total cost of providing scheduled 

benefits from that fund as a percentage of taxable payroll. If

COST tf yr BEN tf yr RR tf yr ADM tf yr ( , ) ( , ) ( , ) ( , ),   

then

( , ) _ _ ( , ) .

( )

COST tf yr ANN COST RT tf yr

payroll yr



The trust fund ratio measures the amount of beginning of year assets that can be used to pay 

total cost. It is expressed as a percentage:

 

53 Under check cycling many Social Security beneficiaries filing for benefits after April 1997 are paid on either the 

2

nd, 3rd, or 4th Wednesday of each month. In the past, benefits were always paid on the 3rd day of each month.
302 ( , ) ( , ) .

( , )

ASSETS tf yr BOY TFR tf yr

COST tf yr



ix. Equations 4.3.10, 4.3.11, 4.3.12, and 4.3.13 – Summarized Values

Present values of cash flows during the year are computed using the yield rate on the combined 

OASDI trust fund for that year. Each component of trust fund operations is exposed, with 

interest, to the point in the year in which, on average, it is received or disbursed. These 

exposure levels, ben_exp(tf,yr), are the same as described above. These exposed levels are 

then discounted to January 1 of the year of the Trustees Report, ni. If yield(j) is the annual yield 

rate on the combined OASDI trust funds for year j and v(yr) is the discounting factor for the 

year, then

1

( ) .

[1 ( )]

yr

j ni

v yr

yield j 

 

For a given year, and trust fund, 

_ ( , ) (1 0.518 ( )) ( , ) ( ),

_ ( , ) (1 0.625 ( )) ( , ) ( ),

_ ( , ) (1 _ ( , ) ( )) ( , ) ( ),

_ ( , ) (1 0.583 ( ))

PV TAX tf yr yield yr TAX tf yr v yr

PV TAXBEN tf yr yield yr TAXBEN tf yr v yr

PV BEN tf yr ben exp tf yr yield yr BEN tf yr v yr

PV RR tf yr yield yr

    

    

    

   ( , ) ( ),

and _ ( , ) (1 0.5 ( )) ( , ) ( ).

RR tf yr v yr

PV ADM tf yr yield yr ADM tf yr v yr

 

    

The target fund for a year is next year’s cost. Its present value is computed as 

PV TARG tf yr BEN tf yr RR tf yr ADM tf yr v yr _ ( , ) [ ( , 1) ( , 1) ( , 1)] ( ),       

Taxable payroll is exposed to the middle of the year when computing present values:

PV PAYROLL yr yield yr payroll yr v yr _ ( ) (1 0.5 ( )) ( ) ( ).     

We also define

PV INC tf yr PV TAX tf yr PV TAXBEN tf yr _ ( , ) _ ( , ) _ ( , )  

and

PV COST tf yr PV BEN tf yr PV RR tf yr PV ADM tf yr _ ( , ) _ ( , ) _ ( , ) _ ( , ).   

Summarized rates are calculated using beginning of period assets and a target fund. Let yr1 = 

the first year of the valuation period and yr2 = the ending year of the valuation. Then the 

summarized income rate is: 
303 2

1

2

1

1

1 2

( , ) _ ( , )

_ _ ( , , ) .

_ ( )

yr

BOY

j yr

yr

j yr

ASSETS tf yr PV INC tf j

SUMM INC RT tf yr yr

PV PAYROLL j





 

    







The summarized cost rate is similarly computed:

2

1

2

1

2

1 2

_ ( , ) _ ( , )

_ _ ( , , ) .

_ ( )

yr

j yr

yr

j yr

PV COST tf j PV TARG tf yr

SUMM COST RT tf yr yr

PV PAYROLL j





    

  





The 75-year actuarial balance is computed for a period beginning January 1 of the Trustees 

Report year, ni. It includes both beginning of period assets and a target fund. Therefore, 

75 ( ) _ _ ( , , 74) _ _ ( , , 74). ACTBAL tf SUMM INC RT tf ni ni SUMM COST RT tf ni niyr

   

In general, an actuarial balance may be computed for any given subperiod of the projection 

period. In general, actuarial balances for a subperiod beginning on January 1 of year 

ni

and 

continuing through the end of year yr are computed using 

,

( ) _ _ ( , , ) _ _ ( , , ). ACTBAL tf SUMM INC RT tf ni yr SUMM COST RT tf ni yr ni yr 

The unfunded obligation of a trust fund for a given period is the excess of the present value of 

the net cash deficits for each year of that period over the trust fund balance at the beginning of 

the period. The unfunded obligation for the period beginning on January 1 of year ni and 

continuing through the end of year yr is computed using

_ ( , ) [ _ ( , ) _ ( , )] ( , ).

yr

BOY

j ni

UNF OBL tf yr PV COST tf j PV INC tf j ASSETS tf ni



   

Note that the unfunded obligation excludes the target fund.

x. Equation 4.3.14—Closed Group Unfunded Obligation

The closed group is defined as individuals who attain specified ages in the first year of the 

projection period (ni). The Statement of Social Insurance displays unfunded obligations for 

closed groups (1) attaining 15 or later in 2014, (2) attaining 62 or older in 2014, and (3) 

attaining 15 to 61 in 2014. For each year of the projection period, closed group calculations 

attribute a portion of the items in equations 4.3.1 through 4.3.6 to individuals falling in the 

defined closed group. The calculation of the closed-group unfunded obligation, then, uses the 

equation above but only considering the present values of cost and income attributable to the 

closed group. 
304

The following information, developed elsewhere in the “Cost” process, is used for developing 

closed group unfunded obligation amounts:

 Total number of workers and total taxable earnings by single year of age 15-80 and sex, 

years 1951 through nf, updated yearly 

 Taxable payroll, years ni through nf, updated yearly

 Payroll tax income, years ni through nf, updated yearly

 Income from taxation of benefits, years ni through nf, updated yearly

 Scheduled benefits by beneficiary category, years ni through nf, updated yearly

 Railroad interchange, years ni through nf, updated yearly

 Administrative expenses, years ni through nf, updated yearly

 Yield rate on the combined OASDI trust funds, years ni through nf, updated yearly

 Population counts for all retired workers, spouses, divorced spouses, and widow(er)s 

by year, sex, and age 95-119 (read in as a percentage of 95+ counts)

 Distribution of assumed age differentials between aged spouses and workers 

ranging from -12 years to 15 years seniority for the worker by sex of the beneficiary, 

age of the beneficiary 62-74, and marital status

 Distribution of assumed age differentials between aged widow(er)s and deceased 

workers from -6 years to +12 years

It is important to note that, for dependent beneficiaries, the age of the worker, on whose 

account the benefits are based, determines whether that beneficiary would fall in the closed 

group. For instance, if the closed group were defined as individuals attaining age 15 or later in 

2014, a 3-year-old minor child receiving benefits in 2014 on the account of a retired worker 

aged 63 would be considered part of this closed group because the account holder was at least 

age 15 in 2014. The following describes how the various components of income and cost are 

allocated to the defined closed group in question:

Payroll Tax Contributions

Closed group taxable payroll is defined as the percentage of OASDI taxable payroll attributable 

to the closed group in question. An input file of closed group payroll factors, containing these 

percentages by year from 2014 through 2113, is used by the cost program to compute payroll 

tax contributions attributable to the closed group. For each year, the closed group payroll 

factors are determined as follows:

 The number of projected workers by single year of age (ages 0-119) and sex are 

multiplied by the associated average earnings by age/sex.

 Then, the portion of total taxable earnings attributable to the closed group is 

calculated. 

For each year of the projection period, the number of workers and average taxable earnings by 

single year of age and sex are determined as follows:
305

 For ages 0-99, the number of projected workers comes directly from Economics group 

projections.

 For ages 100-119, the total age 100+ amounts from Economics are distributed among 

ages 100 through 119 for each projection year using mortality rates derived by looking 

back 10 years and multiplying these populations by 10 years of mortality increases by 

sex.

 For ages 0-14, the average taxable earnings are obtained by analyzing historical 1991-

2010 data of the average earnings at each age relative to age 15 average earnings, and 

judgmentally assigning a ratio (to age 15 average earnings) for each age.

 For ages 15-80, the average taxable earnings by age and sex come directly from the 

AWARDS subprocess. 

 For ages 81-99, the average taxable earnings are calculated as a weighted average of the 

average earnings for ages 76-80, weighted by the ratios of earnings between these ages 

and each age 81 through 99 for earnings years 1991 through 2010. This is similar to the 

method to calculate average earnings for ages 0 through 14, though here we are using 

five anchor years instead of one. For ages 100-119, average taxable earnings are set 

equal to the average earnings calculated for age 99.

Benefits

Methodologies for computing benefits attributable to the closed groups differs among benefit 

categories, as described below:

Retired Workers

For each age in current pay, the number of beneficiaries is multiplied by the corresponding 

average benefit amount across all ages of entitlement. The same applies for DI conversion 

cases. Retroactive benefits for the current year, by age, are then calculated. The closedgroup factors for old-age benefits for each year are found by summing the benefit amounts 

attributable to the specified closed group, as a proportion of total retired worker benefits 

for all ages. This process is done separately by sex. 

Disabled Workers

For each age from 20 to the year before normal retirement age, the program adds the 

products of the number of beneficiaries for each duration and the PIA for that duration. 

Retroactive benefits for the current year, by age, are then calculated. The closed-group 

factors for disability benefits are found by summing the total benefit amounts attributable 

to the closed group, as a proportion of total disabled worker benefits for all ages. This 

process is done separately by sex. 
306

Aged Spouses and Divorced Aged Spouses

Closed Group calculations are done separately (although in the same manner) for aged 

wives, aged divorced wives, aged husbands, and aged divorced husbands. The number of 

aged spouse beneficiaries in each beneficiary category in current pay status (no dual 

entitlement) is provided by single year of age (up through 119). Then, for each single year 

of age, the program allocates total numbers of workers by age, from 12 years younger to 15 

years older than the aged spouse using an assumed category-specific distribution. Next, for 

each age of worker in current pay, the number of workers is multiplied by the weighted 

average retired worker benefit for that age; this is done for all ages. The closed group 

factors, then, are obtained by determining the proportion of total benefit amounts 

attributable to the given closed group (based on the worker’s age).

For aged spouses in dual entitlement status (i.e. aged spouses with a smaller worker 

benefit) we do similar calculations with the assumption that the age distributions are equal 

to the combined distribution of non-dually entitled aged spouses and aged divorced 

spouses. The calculations are done separately for male and female beneficiaries.

Aged Widows and Divorced Aged Widows

Closed Group calculations are done separately (although in the same manner) for aged 

widows, aged divorced widows, aged widowers, and aged divorced widowers. The number 

of aged widow(er) beneficiaries in current pay status (no dual entitlement) is provided by 

age from 60 to 119. For each single year of age, the program allocates total number of aged 

widows by age of the deceased husband (age the husband would have been if he had not 

died), from 6 years younger to 12 years older than the aged widow using an assumed 

distribution. The same distribution is used in reverse order for aged widowers leading to an 

age range of 12 years younger to 6 years older for the deceased wife. For each age of 

deceased spouses aged 119 or younger, a real wage growth factor is applied to reflect 

ultimate real wage growth taken to the power of the number of years younger than age 

119. 

(119 ker ) (1 _ 1.027) deceased wor age Benefitadj wg ult 

  

This exponent is intended to reflect differences in average levels of benefits, with younger 

deceased spouses having higher benefits based on real wage growth. The closed group 

factors, then, are obtained by determining the proportion of total benefit amounts 

attributable to the closed group. 

For aged widow(er)s in dual entitlement status (i.e. aged widow(er)s with a smaller worker 

benefit) we do similar calculations with the assumption that the age distributions are equal 

to the combined distribution of non-dually entitled aged widow(er)s and aged divorced 

widow(er)s.
307

Other Beneficiary Categories

For the 20 other dependent beneficiaries of retired workers, disabled workers, and 

deceased workers, an input file of closed group benefit factors is created, which represents 

the proportion of total (open-group) projected benefits in that category attributable to the 

given closed group age and year. This file is used by the cost program to compute amounts 

from each beneficiary category attributable to the closed group. The file, separately 

created for each closed group run, contains closed group benefit factors for ages 0 through 

150 for each of the 20 beneficiary categories by sex of the account holder (worker). These 

input files are created by examining a recent sample of Master Beneficiary Record (MBR) 

data1for each of the beneficiary categories by age of the worker, and projecting future 

distributions by age of the worker based on population and, for survivor benefits, projected 

deaths by age. Then, adjustments are made for real wage growth to reflect different 

benefit levels by birth cohort. 

Taxation of Benefits

Since taxation of benefits is related to benefits, the closed-group taxation of benefit amounts 

are computed by multiplying the total (open-group) taxation of benefit amounts by Trust Fund, 

by the corresponding total closed-group benefit factors by Trust Fund. 

Administrative Expenses

Since administrative expenses are also assumed to be related to benefits, the closed-group 

administrative expense amounts are computed by multiplying the total (open-group) 

administrative expenses by Trust Fund), by the corresponding total closed-group benefit factors 

by Trust Fund.

Railroad Interchange

Since the railroad interchange has both a payroll tax and benefit component, each component 

is multiplied by its corresponding closed-group factor. That is, total payroll tax contributions 

arising from railroad interchange are multiplied by the closed group payroll factor discussed 

above in the “Tax on Contributions” section. Total railroad benefits, by Trust Fund, are 

multiplied by the aggregate closed-group benefit factors by Trust Fund. Closed-group railroad 

administrative expenses and closed-group railroad taxation of benefits are also estimated by 

applying aggregate closed group benefit factors by Trust Fund. The final amount is then the 

difference in the components (closed group railroad income less closed group railroad cost).
308

Appendix 4.3-1

Shuttling Method

In this appendix, we discuss the “shuttling method”. 

The shuttling method as presented in the COST model attempts to reorganize the PAPs 

obtained from subprocess 4.2, which maintains a static age-sex distribution of newly 

entitled beneficiaries, in such a way that captures the changing age-sex distribution of 

newly entitled beneficiaries provided by subprocess 3.3. The age-sex distribution of the 

sample (subprocess 4.2) and those newly entitled from subprocess 3.3 are aligned in the 

sample year. This alignment persists throughout all years in the long-range period. When 

we refer to the age-sex distribution from subprocess 3.3 in what follows below, we refer to 

the aligned age-sex distribution. 

Let oadsrs be the age/sex distribution of the sample from subprocess 4.2. Let oabicp be the 

number of newly entitled beneficiaries by age and sex from subprocess 3.3. Let total be the 

total number of newly entitled beneficiaries by sex. The ratio 

( , )

,

( )

oabicp sex age

total sex

for age = 62, ... , 70 gives the age-sex distribution from subprocess 3.3. 

The array rsb_oadscp_sampleyr is the age-sex distribution from subprocess 3.3 from the 

sample year (2008 for the 2014 Trustees Report).

The value of oadscp is defined, by age and sex, to be:

( , ) ( , ) ( , ) _ _ ( , ).

( )

oabicp sex age oadscp sex age oadsrs sex age rsb oadscp sampleyr sex age

total sex

  

Starting with the 2010 Trustees Report, we set all values of rsb_oadscp_sampleyr to the 

corresponding value of oadsrs. Therefore, the value of oadscp is now defined to be:

( , ) ( , ) .

( )

oabicp sex age oadscp sex age

total sex



Despite the fact that we eliminated the actual 

alignment we will still refer to this as the aligned age-sex distribution as obtained from 

subprocess 3.3 (with the assumption that the alignment left the age-sex distribution 

unchanged).

For each sex, we construct a matrix oads. Construction of this matrix uses two different 

age-sex distributions: the age-sex distribution of the awards sample and the aligned age-sex 

distribution of newly entitled beneficiaries from subprocess 3.3. The matrix oads is a 

9 9 

matrix whose rows and columns are indexed consecutively by the ages 62,..., 70. We index 

the rows of this matrix by the age of entitlement of a worker in the projection (ageentRSB) 

and the columns of this matrix by the age of entitlement of a worker in the Awards sample 

(ageentAWD). 
309

More precisely, for a given sex, let oadsrs be the age distribution of the sample; oadscp be 

the aligned age distribution of newly entitled beneficiaries from subprocess 3.3. Both 

arrays oadsrs and oadscp are indexed by age, ages = 62, ..., 70. For a given sex, the matrix 

oads is constructed with the following properties:

70

62

( ) ( , )

ageentAWD

oadscp ageentRSB oads ageentRSB ageentAWD



 

and

70

62

( ) ( , ).

ageentRSB

oadsrs ageentAWD oads ageentRSB ageentAWD



 

In other words, the matrix oads has the following properties:

 The sum of the entries in any row of the matrix is the value of the distribution of the 

projection for the age corresponding to the row.

 The sum of the entries in any column of the matrix is the value of the distribution of 

the Awards sample for the age corresponding to the column.

Let opap be the original potential AIME percentages (PAPs) passed to subprocess 4.3 from 

subprocess 4.2. This is a 

9 30 

matrix whose rows are indexed by ages at entitlement 62, 

..., 70 and whose columns are indexed by benefit interval 1, ..., 30 (represented by the 

variable i in the formulas below). The values of opap are modified and the results are the 

PAPs used by process 4.3, called opap1. As a formula, 

70

62

70

62

1( , )

( , ) ( , )

.

( , )

ageentAWD

ageentAWD

opap ageentRSB i

opap ageentAWD i oads ageentRSB ageentAWD

oads ageentRSB ageentAWD













This formula may be rewritten as follows.

70

70

62

62

1( , )

( , ) ( , ) .

( , ) 



  



ageentAWD

ageentAWD

opap ageentRSB i

oads ageentRSB ageentAWD opap ageentAWD i

oads ageentRSB ageentAWD

It follows that opap1 may be interpreted as a reweighting of opap. We have 

70

,

62

1( , ) ( , ) ageentRSB ageentAWD

ageentAWD

opap ageentRSB i w opap ageentAWD i



  

with weights 

,

( , )

.

( ) ageentRSB ageentAWD

oads ageentRSB ageentAWD

w

oadscp ageentRSB

 

As matrices, 

opap w opap 1 

(and is another 

9 10 

matrix). 

Consider the following example. In this example, the projection year is 2040, and the sex is 

males. 
310

The oadsrs vector (from subprocess 4.2) is as follows.

62 63 64 65 66 67 68 69 70

Male 0.3989 0.1267 0.0703 0.1118 0.2668 0.0119 0.0058 0.0040 0.0040

The unaligned age-sex distribution (from subprocess 3.3) is as follows.

62 63 64 65 66 67 68 69 70

Male 0.2430 0.1382 0.0583 0.1124 0.1601 0.1672 0.0361 0.0356 0.0490

The age-sex distribution (from subprocess 3.3) in 2008, the year of the sample is assumed 

the same as the oadsrs vector and therefore is as follows.

62 63 64 65 66 67 68 69 70

Male 0.3989 0.1267 0.0703 0.1118 0.2668 0.0119 0.0058 0.0040 0.0040

Hence the aligned age-sex distribution (from subprocess 3.3), that is, the oadscp vector, is 

as follows.

62 63 64 65 66 67 68 69 70

Male 0.2430 0.1382 0.0583 0.1124 0.1601 0.1672 0.0361 0.0356 0.0490

The matrix oads, computed in this subprocess (4.3) is as follows. An explanation of how this 

matrix is generated appears below. 

\ 62 63 64 65 66 67 68 69 70 Total

62 0.2430 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2430

63 0.1382 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1382

64 0.0177 0.0406 0.0000 0.0000 0.0000 0.

ageentRSB ageentAWD

0000 0.0000 0.0000 0.0000 0.0583

65 0.0000 0.0861 0.0263 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1124

66 0.0000 0.0000 0.0440 0.1118 0.0043 0.0000 0.0000 0.0000 0.0000 0.1601

67 0.0000 0.0000 0.0000 0.0000 0.1672 0.0000 0.0000 0.0000 0.0000 0.1672

68 0.0000 0.0000 0.0000 0.0000 0.0361 0.0000 0.0000 0.0000 0.0000 0.0361

69 0.0000 0.0000 0.0000 0.0000 0.0356 0.0000 0.0000 0.0000 0.0000 0.0356

70 0.0000 0.0000 0.0000 0.0000 0.0236 0.0119 0.0058 0.0040 0.0040 0.0490

Total 0.3989 0.1267 0.0703 0.1118 0.2668 0.0119 0.0058 0.0040 0.0040 1.0000

Note that the column total is oadsrs and the row total is oadscp. The oads matrix is 

determined using these row and column sum constraints. The nonzero entries of the oads

matrix zigzag down and to the right. Starting at the upper left hand corner, the lesser of 

oadsrs and oadscp is put there. So 

oads(62,62) 0.2430. 

In this case (as is usually the case), oadscp is less. Since, the difference 

oadsrs oadscp oadscp (62) (62) 0.3989 0.2430 0.1559 0.1382 (63)      

, by the row sum 

constraint we are forced to have 

oads(63,62) 0.1382. 

Now we have that the row sum is 

oadscp(63). We want the column sum to be oadsrs(62). 

We move down to 

oads(64,62). Since 

oadsrs oads oads oadscp (62) (62,62) (63,62) 0.3989 0.2430 0.1382 0.01         77 0.0583 (64),

the entry 

oads(64,62) 0.0177.  
311

With the column sum constraint met, we move one spot to the right. Since 

0.0583 0.0177 0.0406 (63) 0.1267,     oadsrs

the entry 

oads(64,63) 0.0406.  

The row sum is now 

oadscp(64),

and we move one down. Since 

0.1267 0.0406 0.0861 (65) 0.1124,     oadscp

the entry 

oads(65,63) 0.0861.  

Since 

oadscp oads oadsrs (65) (65,63) 0.1124 0.0861 0.0263 0.0703 (64),      

by the 

row sum constraint we are forced to have 

oads(65,64) 0.0263. 

The row constraint is now met, and we move down to 

oads(66,64). For the column sum to 

be 

oadsrs(64)

we have 

oads(66,64) 0.0703 0.0263 0.0440.  

Since the column constraint is now met, we move right to 

oads(66,65). 

oadsrs oadscp oads (65) 0.1118 (66) (66,64) .1601 .0440 .1161,      

so by the column 

constraint 

oads(66,65) 0.1118. 

Now we can get the row total to match 

oadscp(66)

by setting 

oads(66,66) 0.1601 0.1118 0.0440 0.0043.    

Now that this row constraint is met, we move one row down to 

oads(67,66). Since 

oadsrs oads oadscp (66) (66,66) 0.2668 0.0043 0.2625 0.1672 (67),      

by the row sum 

constraint we are forced to have 

oads(67,66) 0.1672  

Now that this row constraint is met, we move one row down to 

oads(68,66). Since 

oadsrs oads oads oadscp (66) (67,66) (66,66) 0.2668 0.1672 0.0043 0.09         53 0.0361 (68),

by the row sum constraint we are forced to have 

oads(68,66) 0.0361.  

Continuing with this logic, 

oads(69,66)

is set to 

oadscp(69) 0.0356, 

and we move one row 

down to 

oads(70,66). By the column sum constraint we are forced to have

oads(70,66) 0.2668 0.0356 0.0361 0.1672 0.0043 0.0       236.

Now the column constraint is satisfied and we move right to 

oads(70,67). By the column 

constraint, this entry is forced to be 0.0119.

Moving to column 68, again, by the column constraint, the entry 

oads(70,68)

is forced to be 

0.0058. 

Moving to column 69, again, by the column constraint, the entry 

oads(70,69)

is forced to be 

0.0040. 

Finally, by the row and column constraints, the last entry, 

oads(70,70),

is 0.0040.
312

To obtain the w matrix, normalize the rows by dividing by the row sum. 

\ 62 63 64 65 66 67 68 69 70

62 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

63 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

64 0.3036 0.6964 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0

ageentRSB ageentAWD

.0000

65 0.0000 0.7660 0.2340 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

66 0.0000 0.0000 0.2748 0.6983 0.0269 0.0000 0.0000 0.0000 0.0000

67 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000

68 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000

69 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000

70 0.0000 0.0000 0.0000 0.0000 0.4787 0.2414 0.1176 0.0811 0.0811

Then, as matrices, 

opap w opap 1 ,  

as one may verify. For display purposes the 

transposes of opap and opap1 are shown.

opap 62 63 64 65 66 67 68 69 70

1 0.9996 0.9992 0.9991 0.9986 0.9994 0.9972 0.9953 0.9912 0.9953

2 0.9891 0.9865 0.9833 0.9826 0.9933 0.9615 0.9527 0.9171 0.9533

3 0.9606 0.9578 0.9554 0.9493 0.9807 0.8879 0.8409 0.7970 0.8760

4 0.9272 0.9235 0.9258 0.9168 0.9654 0.8248 0.7671 0.7051 0.8071

5 0.8943 0.8913 0.8982 0.8901 0.9488 0.7887 0.7233 0.6403 0.7601

6 0.8635 0.8623 0.8759 0.8631 0.9333 0.7557 0.6881 0.6029 0.7176

7 0.8340 0.8355 0.8511 0.8374 0.9186 0.7224 0.6478 0.5677 0.6813

8 0.8051 0.8077 0.8252 0.8122 0.9034 0.6976 0.6182 0.5451 0.6483

9 0.7768 0.7785 0.7997 0.7870 0.8870 0.6759 0.5870 0.5185 0.6127

10 0.7475 0.7494 0.7741 0.7608 0.8706 0.6548 0.5655 0.4989 0.5817

11 0.7181 0.7198 0.7468 0.7332 0.8526 0.6305 0.5505 0.4844 0.5570

12 0.6880 0.6908 0.7197 0.7043 0.8344 0.6107 0.5319 0.4665 0.5426

13 0.6567 0.6584 0.6852 0.6741 0.8145 0.5915 0.5131 0.4509 0.5244

14 0.6055 0.6043 0.6358 0.6260 0.7772 0.5511 0.4883 0.4185 0.4896

15 0.5330 0.5255 0.5604 0.5562 0.7153 0.5091 0.4520 0.3819 0.4445

16 0.4578 0.4494 0.4818 0.4818 0.6477 0.4682 0.4147 0.3558 0.4007

17 0.3832 0.3754 0.4042 0.4125 0.5809 0.4330 0.3708 0.3353 0.3645

18 0.3118 0.3058 0.3368 0.3506 0.5153 0.3966 0.3265 0.2920 0.3397

19 0.2192 0.2170 0.2553 0.2678 0.4245 0.3439 0.2870 0.2576 0.3097

20 0.1232 0.1289 0.1661 0.1785 0.3151 0.2740 0.2280 0.2177 0.2732

21 0.0577 0.0651 0.0936 0.1045 0.2137 0.2031 0.1749 0.1731 0.2202

22 0.0151 0.0200 0.0326 0.0419 0.1013 0.1014 0.1046 0.1137 0.1546

23 0.0006 0.0010 0.0023 0.0039 0.0113 0.0134 0.0176 0.0264 0.0519

24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
313

27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

opap1 62 63 64 65 66 67 68 69 70

1 0.9996 0.9996 0.9993 0.9992 0.9988 0.9994 0.9994 0.9994 0.9974

2 0.9891 0.9891 0.9873 0.9857 0.9831 0.9933 0.9933 0.9933 0.9715

3 0.9606 0.9606 0.9586 0.9572 0.9518 0.9807 0.9807 0.9807 0.9185

4 0.9272 0.9272 0.9246 0.9240 0.9206 0.9654 0.9654 0.9654 0.8743

5 0.8943 0.8943 0.8922 0.8929 0.8939 0.9488 0.9488 0.9488 0.8434

6 0.8635 0.8635 0.8627 0.8655 0.8685 0.9333 0.9333 0.9333 0.8175

7 0.8340 0.8340 0.8350 0.8392 0.8434 0.9186 0.9186 0.9186 0.7919

8 0.8051 0.8051 0.8069 0.8118 0.8183 0.9034 0.9034 0.9034 0.7707

9 0.7768 0.7768 0.7780 0.7835 0.7932 0.8870 0.8870 0.8870 0.7489

10 0.7475 0.7475 0.7488 0.7552 0.7675 0.8706 0.8706 0.8706 0.7294

11 0.7181 0.7181 0.7193 0.7261 0.7402 0.8526 0.8526 0.8526 0.7100

12 0.6880 0.6880 0.6900 0.6976 0.7121 0.8344 0.8344 0.8344 0.6916

13 0.6567 0.6567 0.6579 0.6647 0.6810 0.8145 0.8145 0.8145 0.6725

14 0.6055 0.6055 0.6047 0.6117 0.6329 0.7772 0.7772 0.7772 0.6365

15 0.5330 0.5330 0.5278 0.5337 0.5617 0.7153 0.7153 0.7153 0.5859

16 0.4578 0.4578 0.4519 0.4570 0.4864 0.6477 0.6477 0.6477 0.5336

17 0.3832 0.3832 0.3778 0.3822 0.4149 0.5809 0.5809 0.5809 0.4833

18 0.3118 0.3118 0.3076 0.3131 0.3514 0.5153 0.5153 0.5153 0.4323

19 0.2192 0.2192 0.2177 0.2260 0.2687 0.4245 0.4245 0.4245 0.3661

20 0.1232 0.1232 0.1272 0.1376 0.1789 0.3151 0.3151 0.3151 0.2836

21 0.0577 0.0577 0.0629 0.0718 0.1045 0.2137 0.2137 0.2137 0.2037

22 0.0151 0.0151 0.0185 0.0230 0.0410 0.1013 0.1013 0.1013 0.1069

23 0.0006 0.0006 0.0009 0.0013 0.0037 0.0113 0.0113 0.0113 0.0169

24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
314