Technical Paper Series

Congressional Budget Office

Washington, DC

OVERVIEW OF THE 

CONGRESSIONAL BUDGET OFFICE 

LONG-TERM (CBOLT)

POLICY SIMULATION MODEL

Josh O’Harra (e-mail: josh.oharra@cbo.gov)

John Sabelhaus (e-mail: john.sabelhaus@cbo.gov)

Michael Simpson (e-mail: michael.simpson@cbo.gov)

Congressional Budget Office

Washington, DC

January 2004

2004-1

Technical papers in this series are preliminary and are circulated to stimulate discussion and

critical comment. These papers are not subject to CBO’s formal review and editing

processes. The analysis and conclusions expressed in them are those of the authors and

should not be interpreted as those of the Congressional Budget Office. Papers in this series

can be obtained at www.cbo.gov (Select Publications and then Technical Papers).
Abstract

The Congressional Budget Office Long-Term (CBOLT) policy simulation model was developed to

answer budgetary and distributional questions about Social Security, Medicare, and other long-term

policy issues. CBOLT has three distinct solution modes for making projections: static simulations with

a fixed macro environment and actuarial projection modules like those used by the Social Security

Administration (SSA) and Center for Medicare and Medicaid Studies (CMS); a macro growth model

environment with SSA/CMS-style actuarial projection modules; and an integrated micro/macro model

with economic and policy outcomes based on a representative population sample. The first mode gives

answers that approximate those of SSA and CMS actuaries, the second indicates the first-order effects

of considering macroeconomic feedbacks, and the third provides the opportunity to consider behavioral

responses and conduct detailed distributional analysis. CBOLT can be solved using either fixed or

stochastic values for the key exogenous input variables, so the model is capable of generating

confidence intervals for budgetary and distributional outputs. 
Contents Page 

1. Introduction ............................................................................................................................... 1

2. CBOLT’s Three Solution Modes ................................................................................................ 3

2.1 Actuarial Projections in the Static Macro Environment .................................................... 3

2.2 Actuarial Projections in the Growth Model Framework .................................................. 6

2.3 Dynamic Micro Simulation in the Growth Model Framework ......................................... 9

3. CBOLT Static Solutions ........................................................................................................... 10

3.1 Exogenous Parameter and Policy Settings in CBOLT Static Solutions ........................... 11

3.2 Actuarial Population Projections .................................................................................. 13

3.3 Labor Force and Employment ..................................................................................... 14

3.4 Social Security Payroll Taxes, Beneficiaries, Benefits, and Trust Funds ......................... 15

4. CBOLT Macro Growth Model Solutions ................................................................................. 18

4.1 Exogenous Parameter and Policy Settings in CBOLT Macro Growth Solutions ............ 19

4.2 Production, Incomes, and Factor Prices ....................................................................... 22

4.3 Medicare ................................................................................................................... 25

4.4 Other Federal Budget Outcomes ................................................................................ 26

4.5 State and Local Budgets ............................................................................................. 27

4.6 Foreign Transactions ................................................................................................... 27

4.7 Private Savings ............................................................................................................ 28

4.8 Next Period Capital ................................................................................................... 30

5. CBOLT Integrated Micro/Macro Solutions .............................................................................. 31

5.1 Integrating a Dynamic Micro Model into the Growth Framework ................................. 32

5.2 Preparing the CWHS Micro Base File ......................................................................... 34

5.3 Demographic Transitions in the Micro Sample .............................................................. 39

5.4 Individual Labor Supply and Earnings .......................................................................... 45

5.5 Social Security Benefit Claiming and Awards ............................................................... 49

5.6 Distributional Analysis ................................................................................................. 51

6. Stochastic Simulation Using Monte Carlo Draws for Inputs ........................................................ 53

6.1 Beyond Low-, Medium-, and High-Cost Projections ................................................... 54

6.2 Time-Series Processes for Exogenous Inputs ............................................................... 55

6.3 Properties of Stochastic Simulations ............................................................................. 56

References ..................................................................................................................................... 59

Figures ........................................................................................................................................... 61
1The authors would like to thank Amy Harris, Kevin Perese, Steve Lieberman, Noah

Meyerson, Joel Smith, and Julie Topoleski of CBO’s Health and Human Resources Division for their

support and helpful comments. 

1

1. Introduction1

This paper describes the Congressional Budget Office Long-Term (CBOLT) policy simulation

model. CBOLT is used to analyze how sensitive Social Security, Medicare, and other long-term

projections are to demographic, economic, and behavioral assumptions. The model is designed to

examine the effects of various federal budget policy options, including detailed alternative Social

Security tax and benefit rules and Medicare eligibility and cost growth assumptions. In addition, it can

be used to simulate the effects of trust fund investment, private accounts, and other fundamental policy

changes that have been suggested during reform discussions. Standard simulation outputs include

macroeconomic and budgetary projections along with various types of distributional tables.

CBOLT integrates several modeling techniques into one tool for analyzing long-term policy

issues. The methods employed in a particular CBOLT simulation include various combinations of

actuarial algorithms for projecting population and entitlement program finances, a macro growth model

with consumption and labor supply feedbacks, a detailed federal budget accounting framework, and

micro simulation models that operate on samples of the population. Different combinations of these

techniques produce three distinct solution “modes”: a static macro environment with actuarial

projections like those used by the Social Security Administration (SSA) and Center for Medicare and

Medicaid Studies (CMS); a macro growth framework with actuarial projection modules; and a fully

integrated dynamic micro/macro growth model that aggregates economic and policy outcomes for a
2

representative sample of the population. Any of these three types of projections can be implemented in

either deterministic (fixed-input) or stochastic (Monte Carlo-generated input) simulation environments.

Each of the three CBOLT solution modes plays a role in CBO’s analysis of long-term budget

policy; the first mode provides answers that approximate the SSA and CMS actuaries’ methods. The

second can be used to examine the first-order effects of considering macroeconomic feedback from

policy changes. The third provides the best data for considering more detailed behavioral responses

and implementing detailed distributional analysis. Interrelationships between CBOLT solution modes

— modes one and two share the same core actuarial projections modules, and modes two and three

share the same macro growth model framework — are useful for disentangling the differences in policy

conclusions produced by different models. In particular, by solving the model in the various modes and

with various input assumptions, CBO can determine the sensitivity of projections to exogenous

assumptions and to modeling techniques.

This paper provides an overview of the capabilities of CBOLT. It is organized around the three

solution modes, beginning with a more careful description of how each of the these modes differs and

then explaining each solution mode further. The description first includes a discussion of which

exogenous inputs and policy parameters the user can set in a given type of simulation and then provides

details about the simulation modules themselves. In many instances, although the discussion of how

(and why) certain modules operate is surface level only, readers are referred to the detailed analysis of

those simulation modules that is available in other CBO papers. The concluding section of this paper

provides a brief introduction to CBOLT’s stochastic capabilities, which apply to all three solution

modes in roughly the same manner.
3

2. CBOLT’s Three Solution Modes

CBOLT has evolved through three distinct phases, each time retaining the simulation

capabilities established in previous versions. Its three evolutionary steps have created a static model

with a fixed macro environment and actuarial projection modules that mimic the approach used by SSA

and CMS in their long term projections, a macro growth model environment with actuarial projection

modules and detailed federal budget rules, and a macro growth environment with detailed federal

budget rules and an integrated dynamic micro simulation model. This section describes the relationships

between the three solution modes. In the most general sense, the first two share many of the same

actuarial projection techniques but differ in terms of the macroeconomic environment. The second and

third solution modes basically share the same macroeconomic environment but differ in terms of how

demographic, economic, and program outcomes are projected.

2.1 Actuarial Projections in the Static Macro Environment

Completed in late 2000, the first version of CBOLT was focused on the Social Security system

and designed to generally mimic SSA’s Office of the Chief Actuary’s (OCACT) methodology. The

model replicates some OCACT techniques and calibrates around remaining differences so that it

generates a baseline that matches intermediate SSA projections (given the same input assumptions). 

More importantly, it has sufficient detail to ensure that responses to policy changes or input assumptions

are very close to those reported in OCACT’s annual Trustees Report. 

The actuarial modules of this version of CBOLT are “cell-based,” meaning that they operate

on the U.S. population through detailed age, sex, and marital status groups. In addition, statistics such
4

as population counts, average benefits, the number of eligible beneficiaries, the number of disabled, and

the number of workers are associated with each cell. Aggregating cell statistics for each year results in

annual totals of revenues and expenditures. (Further manipulation produces statistics such as the

combined trust fund balance). The flow of year-by-year cell-based projections is straightforward —

first it solves for population by age and sex, then labor force by age and sex, then Social Security

covered worker counts, payroll taxes, beneficiary counts, benefit amounts, and finally trust fund

outcomes (see Figure 1). 

Just as in the SSA actuaries’ approach, some exceptions to the cell-based structure in CBOLT

actuarial modules are present. For instance, some variables are tracked only in aggregate over time

rather than by age and sex in each year. Within that class of aggregate variables, some (such as Social

Security cost and income rates, trust fund balances, and so on) are appropriately tracked only as totals

since there is no further detail to exploit. To keep the model simple, however, other variables are

modeled as aggregate concepts. For example, although separate earnings distributions for each age

and sex group could have been included in the model, CBOLT relies instead on a univariate earnings

distribution for the entire workforce in each year. The disadvantage of adopting this simplifying

assumption is that the actuarial modules do not automatically generate a distribution of payroll tax

contributions by age and sex, which is data that would be useful for cohort-level distributional analysis.

Another exception to the cell-based rule in CBOLT’s actuarial solution mode is one that adds

detail instead of simplification. That exception relates to the computation of OAI and DI worker benefit

awards, which are computed using a micro sample of recent beneficiaries. The limited micro simulation

is then used within the actuarial framework to capture historical heterogeneity in workers’ earnings
2This version of the model was used in producing, for example, Uncertainty in Social

Security’s Long-Term Finances: A Stochastic Analysis (Congressional Budget Office 2001).

3See, for example, President’s Commission to Strengthen Social Security (2001) and Social

Security Administration Office of the Chief Actuary (2001).

5

histories. What results is a model that allows the effect of policy changes to generate a more

representative stream of simulated future benefits. The cohort of recent beneficiaries is “re-sampled” in

each projection year, with work and earnings histories adjusted to compute future benefit awards that

are consistent with other model assumptions. 

The static model incorporates a stochastic simulator for nine major demographic and economic

input assumptions. This feature allows the model to generate probability distributions as well as point

estimates for the actuarial projections. The nine assumptions — the same assumptions the Social

Security Trustees vary for their annual low- and high-cost scenarios — are the fertility rate, rate of

mortality improvement, number of immigrants, real wage growth, inflation, unemployment rate, real

interest rate, disability incidence rate, and disability termination rate. For each input, mean values are

set equal to the OCACT intermediate input assumptions specified in the annual Trustees Report.

Deviations from those means are modeled on the basis of historical variability.2

Embedded in all versions of CBOLT is a simple dynamic micro model that simulates outcomes

for the same “example” workers analyzed in the report issued by the President’s Commission to

Strengthen Social Security (PCSSS) and in other SSA distributional analyses.3 Future earnings of the

example workers analyzed are derived by applying fixed age-earnings factors to the projected average

wage index, which is a variable in the actuarial model. To solve for benefits, benefit formula

parameters are applied directly to predicted example worker earnings histories. An advantage of
4See, for example, Harris, Sabelhaus and Simpson (2003) or Sabelhaus (2003).

6

CBOLT’s integrated framework is that the stochastic simulator values for variables, such as real wage

growth and inflation, that determine the average wage index also act directly on the example workers. 

Thus, variability in example-worker outcomes occurs automatically. That feature is useful when

analyzing both expected outcomes and variability in outcomes for example workers under alternative

benefit rules.4

2.2 Actuarial Projections in the Growth Model Framework

Completed in mid-2001, the second version of CBOLT integrated the core actuarial modules

into a macro growth framework with detailed federal modules, including a detailed Medicare actuarial

model developed to mimic the CMS actuaries’ approach. It also replaced the baseline macroeconomic

assumptions provided by OCACT for the first ten years of the projection with those forecasted by

CBO. In this version, the macro model generates endogenous values for wage growth and interest

rates that replace the exogenous values specified in static simulations. Through this mechanism, the

model can be used to estimate the macroeconomic effects of potential policy changes. The macro

predictions are in turn influenced by the actuarial and policy assumptions. Detailed federal budget

modules are included, and various tax and spending assumptions are controlled by user specifications

across dozens of options about policymaker behavior. Simulation options range from making tax and

spending levels constant as a share of GDP to specifying movements in various budgetary parameters
5See, for example, CBO’s January 2003 Budget and Economic Outlook and December

2003 Long-Term Budget Outlook. 

7

to close deficits or stay within GDP-based debt ceilings. This version of the model is used to produce

the long-term budget projections in several CBO analyses.5

 It is instructive to view the growth model solution (mode two) as simply adding more structure

around the SSA static model solution (mode one). (See Figure 1). The same steps needed to solve

the static model each year are also needed to solve the growth model — in fact, the models share much

of the same computer code. Still, the growth model adds several steps that are not included in the

static simulations. For instance, although population and labor force are solved for identically in both

models, the growth model uses a Cobb-Douglas production function with exogenous total factor

productivity growth to solve for total output. Given exogenous price indexes, output is allocated

between income to labor and income to capital using standard first-order conditions. Outcomes of this

process are real wage growth and interest rates, which replace the exogenous values used in mode-one

static solutions.

Solving the growth model requires estimating values for the capital stock because capital (along

with labor and exogenous productivity) determines total production. Solving for capital requires first

solving for public and private saving, which together determine total investment. Thus, in addition to

solving for Social Security budgetary outcomes (as in the static model), the growth framework also

computes outcomes for Medicare and other federal revenues and spending, state and local budgets,

and foreign transactions, especially capital flows. Those additional modules solve for government and

foreign saving, which combine with the private saving decision to determine aggregate investment, and
6

 Like the OASDI actuarial modules the Medicare modules do not attempt to mimic the short

run projections produced by the CMS actuaries, focusing only on the long run techniques. 

8

thus next-period capital stock. The nature of the private saving decision is a key element of model

design that affects dramatically the long-run growth model solution and, therefore, the model allows for

analysis based on various user-determined saving responses. 

A significant part of the effort involved with adding the macroeconomic environment was

introducing a comprehensive federal budget sector, including actuarial Medicare modules that mimic the

long run projection techniques used by the CMS actuaries. CMS takes the SSA population

projections as given, so those CBOLT modules overlap. Given population, the CMS approach is to

project Medicare outlays by category (and across Parts A and B) using age-sex indexes and assumed

“excess” cost growth–the amount by which per capita Medicare spending growth exceeds per capita

GDP growth.6 In subsequent work, the Medicare actuarial modules were refined to include a “time

until death” factor for spending and more careful tracking of eligibility and participation. 

Although mode-two growth model solutions add significant economic structure to mode-one

static macro solutions, the ability to run stochastic simulations and operate on example workers is not

affected. The only noticeable change for stochastic simulations reflects the change in inputs; rather than

set expected values and stochastic processes for real wage growth and real interest rates, the expected

level of total factor productivity growth and the expected gap between the interest rate and the return

on capital are set exogenously, and then actual values for those inputs vary stochastically. Finally,

results for example workers are produced in a mode-two simulation just as they are in a mode-one

static macro solution.
9

2.3 Dynamic Micro Simulation in the Growth Model Framework

The third version of CBOLT was initially run in Fall 2002, and work refining the micro modules

continues as of this writing. This version differs from the second model in that actuarial estimates for

crucial variables are replaced with values from a dynamic micro simulation on a 1:1000 representative

population sample. For each of the 300,000+ observations in the longitudinal micro sample, CBOLT

simulates birth, death, immigration, marital transitions, marital pairings, labor force participation, hours,

earnings, Social Security benefit claiming, and Social Security benefit levels. 

The dynamic micro model replaces the outputs from many of the actuarial modules, substituting

aggregated individual outcomes for group-level (cell-based) projections (see Figure 2). For example,

labor supply and earnings are determined person-by-person, rather than group-by-group. Still, much

of the growth model structure is the same as in mode two, because, for example, it does not make

sense to track aggregate budgetary outcomes at the individual level. As a result, just as modes one and

two share much of the same actuarial computer code, modes two and three share many growth model

components. 

Although details of the micro modules are discussed below, it is worthwhile to note the reasons

for replacing the actuarial modules with a dynamic micro simulation while retaining modes one and two. 

One criticism of the actuarial modules used in solution modes one and two is the lack of detail in certain

parts of the model; for example, in solution modes one and two, taxable payroll is not tracked by age
7

It is important to note here that the dynamic micro simulation in CBOLT is still evolving; some

micro behaviors are in the model, but others, such as pension coverage, health status and health

spending, and life-cycle consumption behavior, are still under development.

10

and sex. Ultimately, a representative micro sample allows the most flexibility in terms of distributional

analysis and the ability to capture micro level behavior.7

Although the fully integrated micro/macro environment is in many ways the culmination of the

initial CBOLT development effort, some computational costs must be considered. The first two

solution modes are computationally intensive, requiring nearly 250 megabytes of RAM and about 90

seconds for a 100 year simulation. The third solution mode, however, is a world apart. A CBOLT run

with the dynamic micro model requires about 1.4 gigabytes of RAM (more than was available in most

off-the-shelf PCs offered for sale in 2003) and about 20 minutes to run (depending on the processor). 

This difference becomes particularly important in a stochastic environment; modes one and two can

produce 400 runs with random draws stochastically overnight. The fully integrated micro/macro model

(mode three) requires about 150 hours of processing time for the same simulation. Even with this

amount of processing time, these runs are feasible. By breaking the simulation across multiple machines

a run of 400 draws can be (and are) completed over a weekend or during a week of evenings. Still,

the computational intensity of solving the full micro model underscores the utility of preserving several

solution modes.

3. CBOLT Static Solutions

In its original conception, CBOLT was designed to mimic, but not necessarily to replicate, 

SSA projections. To meet that goal, varying levels of detail were included in the model. The idea was
11

to build a tool that would provide answers similar to SSA’s about the budgetary effects of changes in

Social Security tax and benefit rules, as well as changes in key demographic and economic input

assumptions. This version of CBOLT is referred to as the static version because it does not allow for 

feedbacks resulting from the federal budget, interest rates, or wage growth. 

Many of the projection modules in CBOLT’s actuarial (mode one and mode two) simulations

are either greatly simplified versions of the SSA actuaries’ approach or ad hoc ratio calculations

derived directly from their projections. For example, the CBOLT actuarial modules do not project

how the number of auxiliary (spouse, children, widow) beneficiaries will change over time or across

different policy regimes (though most auxiliary outcomes are projected directly in the dynamic micro

modules). Rather, such beneficiary estimates are generated by applying fixed ratios of auxiliary

beneficiaries to relevant population subgroups. The ratios themselves are derived from detailed

estimates provided by the SSA actuaries. One convenient property of CBOLT is, however, its ability

to change auxiliary beneficiary counts according to changes in population mortality, fertility, or

immigration — only its ratios to population are fixed. CBOLT’s reliance on OCACT population

projections underscores the important point that in modes 1 and 2 CBOLT is not an alternative to the

SSA actuaries’ models; rather, it is dependent on SSA actuaries for annual input files.

3.1 Exogenous Parameter and Policy Settings in CBOLT Static Solutions

A good starting point for describing how CBOLT operates in its static version is to focus on

what input variables are required in those simulations (see Figure 3). The list of Social Security policy

parameters includes most of the options discussed when policy changes are under consideration: OASI
8CBOLT actually has a number of ways for using historical Ibbotson (2000) data to project

returns on financial assets. For a discussion, see Sabelhaus and Smith (2003).

12

and DI payroll tax rates, benefit formula parameters, normal retirement age, and so on. Some policy

parameters, such as tax rates, operate on aggregate values in the static model, others, such as benefit

formula parameters, operate on the aged micro sample, thereby capturing the important interaction

between heterogeneity in the population and the benefit formula. 

The basic demographic and economic input required for CBOLT static solutions is the same as

that listed at the front of the annual Social Security Trustees Report. The three demographic

assumptions (rates of mortality improvement, fertility, and immigration) determine population by age and

sex for each year of the projection. The four economic assumptions (inflation, unemployment, real

wage growth, and the real interest rate) and the two behavioral assumptions (disability incidence and

termination) affect various parts of the model. Some inputs, such as real wage growth, unemployment,

and inflation, affect benefit outcomes because they play a role in the aging process for the micro sample

used in the “cohort re-sampling” benefit projections described further below. Other inputs only impact

aggregate outcomes. For example, the interest rate interacts with aggregate trust fund outcomes to

determine interest paid or received by the system. 

CBOLT can also simulate many different types of trust fund investment or individual account

policies. The model can simulate the effects of investing trust fund assets in equities or corporate bonds

controlling for the assumed rate of return on the investments. When the model is solved stochastically,

it can project the expected value and uncertainty of those returns.8 Six sets of parameters go into an

individual account simulation; users set contribution rates, participation rules and behavior,
13

administrative costs and general fund transfer rules, portfolio allocation and annuitization assumptions,

and a selection of benefit offset rules. Finally, users can specify behavioral responses to trust fund

shortfalls. The options include accumulating debt (often called the “current law” scenario), raising

payroll taxes, reducing new benefit awards, or reducing all benefits.

3.2 Actuarial Population Projections

The first step in a CBOLT simulation is the population projection. Population evolves each

year through births, deaths, and immigration. In the CBOLT actuarial modules, population is tracked 

as of January 1st of each year by single year of age (0 to 100), by sex, and by four marital status groups

(never married, married, widowed, and divorced). As a result, more than 800 cells are included in the

population matrix; 101 for age, multiplied by two for sex, multiplied by four for marital status groups.

The mortality process in CBOLT uses a standard stationary population calculation, with

baseline rates of mortality improvement fixed across 20 age and 2 sex groups, consistent with the

Social Security actuaries’ approach. Users can set both the initial and ultimate rates of mortality

improvement by age and sex for the simulation, or set cohort life expectancy by sex in each year. 

Fertility is also a standard actuarial calculation, with exogenous age-specific annual fertility rates

determining the number of births. Immigration is set exogenously in annual levels, and the distribution

by age and sex is taken from SSA projections. Marital behavior is the only demographic process in the

static solution mode that does not closely approximate the SSA approach. CBOLT actuarial modules

make no attempt to model marital distributions; instead, they are set equal to SSA projections for each
14

year. Thus, the distribution of the population across marital states within each age and sex group is

invariant to overall population size.

3.3 Labor Force and Employment

As with the population modules, the goal of CBOLT actuarial labor force modules is to mimic

the approach of the SSA actuaries. To that end, CBOLT predicts labor force participation for 103

separate groups by age, sex, marital status, and presence of children. The participation rate for each

group is based on a time-series equation estimated using March Current Population Survey data for the

period from 1968 through 1999. Explanatory variables differ across groups, but they include factors

such as percentage in school, percentage in the military, real GDP, disability prevalence, spouse labor

force participation rates, number of children, and age- and sex-specific time trends. For the group

aged 62 to 70, Social Security policy affects labor force participation insofar as benefits (relative to

past earnings) and the Social Security earnings test affect the percentage of people in the labor force.

Applying these estimated labor force participation rates to the population matrix (actually, the

“noninstitutional population” matrix, which is fixed by age and sex relative to the main model “SSA

Area Population” concept) yields an estimate of labor force by age and sex. Resulting labor force

estimates are then multiplied by exogenous unemployment rates (the overall rate is set by the user, and

age- and sex-specific rates vary automatically) to solve for the employment matrix by age and sex. 

Finally, employment numbers are used to estimate the number of workers covered by Social Security in

a given year. 
15

3.4 Social Security Payroll Taxes, Beneficiaries, Benefits, and Trust Funds

Social Security modules pick up where labor force and employment leave off. The first step is

to apply fixed covered worker rates by age, sex, and year. These rates vary by age and sex for two

reasons: first, some age/sex groups are more likely to have uncovered employment; and second, the

concepts of covered workers and employment differ systematically because employment is weighted

across months of the year, and covered workers is an “any job during the year” concept. Estimates of

covered workers by age and sex feed through to other Social Security modules. 

The second step is to compute aggregate taxable payroll by estimating the portion of earnings

above the taxable maximum using a fixed univariate earnings distribution and taxable maximum policy

parameter, aged forward using real wage and covered employment growth. The univariate earnings

“look up table” comes from SSA actuaries. The SSA actuaries build in a slight shift in the distribution

for the first 10 years, then fix the (real) values for the remainder of the simulation. In this table, as the

taxable maximum varies, the underlying total taxable payroll changes. Given taxable payroll, the model

multiplies by the (exogenous user-specified) OASI and DI payroll tax rates to solve for aggregate

payroll tax contributions during the year.

OAI worker beneficiary counts are computed using insured worker rates, by age and sex,

which in principle are based on past covered worker counts, but in practice are nearly 100 percent 

and constant for eligible age groups. DI worker beneficiary counts by age and sex are based on

(exogenous user-specified) DI incidence and termination rates applied to underlying age- and sexspecific probabilities. Counts for dual beneficiaries (those beneficiaries entitled to benefits based both

on their own earnings and someone else’s earnings) are computed using an SSA formula that predicts
9For a detailed discussion of the differences between cohort re-sampling and dynamic microsimulation, see Harris, Sabelhaus, and Simpson (2004).

16

such “duals” on the basis of the relative earnings of men and women. Finally, other auxiliary beneficiary

counts (CBOLT includes a total of 25 outlay categories) are determined by applying exogenous ratios

to population subgroups derived from the main population matrix. For example, widow beneficiaries

are a fixed fraction of the number of widows ages 60 and older, where the fraction in each year is

computed using SSA actuarial projections.

New OAI and DI worker benefit awards in the actuarial model are computed using a static

micro simulation that operates on a sample of beneficiaries taken from the 2001 Continuous Work

History Sample (CWHS), the same data set that plays an important role in the CBOLT dynamic micro

model. For the actuarial model, CBOLT loads earnings and labor force participation histories of about

15,000 beneficiaries who began claiming OASDI benefits in 2001. For each of the 15,000

beneficiaries in the sample, earnings and labor force participation rates are adjusted to be consistent

with the actuarial/macro economic labor force and earnings outcomes that CBOLT generated over

those beneficiaries’ working lives. For example, new benefit awards for 62-year-olds in 2050 are in

principle consistent with the actual labor force participation of 61-year-olds in 2049, 60-year-olds in

2048, and so forth. The technique of reusing the same sample each year to compute new benefit

awards is referred to here as “cohort re-sampling” to distinguish it from the techniques used in the

integrated micro/macro model (mode 3).9

The CBOLT actuarial benefits module uses this micro sample together with the relevant policy

parameters to compute new awards. Yet, even with its ability to capture the interaction between
17

individual heterogeneity and detailed policy rules, CBOLT’s static micro simulation approach has

shortcomings; first, for any given cohort of beneficiaries, retrospective micro earnings may not be

consistent with the aggregate, or economywide, earnings for the year in question, and, because of the

approach, it is impossible to reconcile aggregate and micro earnings (across all working cohorts) in the

static model. Second, although distinct shifts in the profiles of earnings of the 2001 beneficiary group

by age or by sex have already been observed, the model does not alter its profiles for future cohorts. 

Both of those problems are resolved in the CBOLT dynamic micro simulations described below. 

While the static micro simulation generates new benefit awards, existing OAI and DI worker

average benefits by age, sex, and age at entitlement are simply aged forward using the cost of living

adjustment and fixed ratios derived from SSA data. Those ratios control for the effects of differential

mortality and re-computations on average benefits within a cohort. Auxiliary benefit levels are based on

fixed SSA ratios to OAI and DI worker benefits. Thus, those levels move up and down as policy

parameters for the new OAI and DI worker benefits change.

Using payroll taxes derived from the univariate earnings distribution and benefits derived by

multiplying beneficiary counts by average benefits, CBOLT “trust fund” modules generate the few

additional inflows and outflows through the use of simple ratios. These include administrative costs

relative to system costs; benefit tax relative to benefit levels; and exogenous general fund transfers and

transfers to Railroad Retirement. The exogenous interest rate on new trust fund issues is then used to

“grow” the trust funds forward over time, which completes the computations for a given simulation year.
10See Congressional Budget Office (2001).

18

4. CBOLT Macro Growth Model Solutions

One of the simplifying assumptions of the SSA-style (mode one) actuarial projection is that the

macroeconomy does not respond to changes in system finances. All of the major economic variables

are specified exogenously by the user in a mode-one CBOLT run. In mode two and mode three key

economic determinants of OASDI finances are generated using a macro growth framework. The

difference in modeling strategy can be thought of as embedding the SSA actuarial model within a

comprehensive model of the macroeconomy, one that by necessity considers the behavior of all

government and private actors to determine how much the economy grows from one year to the next. 

In that sense, Social Security affects the macroeconomy directly through its impact on overall federal

government deficits, and potentially by inducing behavioral reactions to changes in payroll tax and

benefit rules. One crucial component of the comprehensive federal budget sector is Medicare; just as

the actuarial modules in the first version of CBOLT were designed to mimic the approach used by the

SSA actuaries, the Medicare modules were designed to be consistent with the approach used by the

CMS actuaries. 

The 10-year model described most recently in the paper CBO’s Method for Estimating

Potential Output: An Update and used by CBO for many years serves as the starting point for the

CBOLT macro growth model.10 Although the CBOLT model uses the same five-sector production

decomposition and the same production function and capital input specifications as the 10-year model,

it builds on the 10-year framework by adding more detail in the federal budget and by introducing
19

endogenous labor supply and private saving behavior. Even with those differences, however, the

description of the 10-year model is an excellent starting point for readers who wish to learn more about

the CBOLT macro growth model environment.

4.1 Exogenous Parameter and Policy Settings in CBOLT Macro Growth Solutions

As with static (mode-one) macro simulations, it is instructive to begin with the list of parameters

and exogenous values that are specified in a CBOLT macro growth simulation (see Figure 4). The list

of parameters includes most of the options in a static (mode-one) macro simulation, except the real

wage differential and the real interest rate. In a mode-two or -three solution, those variables are

generated by the production function and associated first-order conditions that determine wage rates

and the return on capital. In addition to user settings that overlap with static runs, several options are

offered in the economic inputs and other policy rules categories. The “other policy rules” and

“behavioral assumptions/parameters” are entirely new.

The sole exogenous determinant of output (real GDP) at any time is total factor productivity. 

The other two inputs to the production function — total hours worked and the capital stock — are

determined by the model itself. Also on the list of economic inputs are several assumptions that

determine real wage growth, given values for GDP and labor input. Those inputs include the gap

between the consumer price index, CPI-W, and GDP price indexes; the average hours growth rate;

and the growth oftaxable earnings as a share of total compensation. These real-wage determinants are

dealt with explicitly by SSA actuaries in the section of the annual Trustees Report that explains their

estimate of long-run real wage growth. The CBOLT macro framework also considers three other
20

economic inputs. First, because investment is differentiated by type, the trajectory for computer prices

(which have fallen dramatically in recent years) is specified. Second, the capital share of output (the

exponent in the Cobb-Douglas production function in the nonfarm business sector) is set exogenously. 

It should be noted that this feature also affects the historical levels of total factor productivity. Finally,

the gap between the 10-year interest rate (which is solved for in the growth model) and the 5-year

interest rate (which best approximates the rate on new issues of OASDI trust fund assets) is specified

as an exogenous parameter.

Additional “other” policy rules in the macro growth framework generally address non-Social

Security government budgeting behavior (see Figure 5). There are a few non-Social Security

categories of federal spending that get detailed attention in CBOLT. Medicare is modeled at a very

detailed level (more below) while Medicaid and Supplemental Security Income (SSI) have spending

indexes that vary by age and sex. Also, the medical spending categories are interacted with exogenous

“excess” cost growth parameters that specify how much per person costs outpace per person GDP

growth. Thus, as the population ages, spending on those programs will rise relative to GDP

automatically, but the rate at which the gap widens (which depends on the “excess” cost growth) is

effectively exogenous. 

Detail on other federal revenues and spending is fairly minimal. For example, no attempt is

made to model federal personal or corporate income tax. CBOLT simply applies an effective tax rate

to aggregate taxable income. Many options are available for setting those effective rates, some of

which involve endogenous policy responses. Similarly, spending rates are generally fixed percentages

of GDP, and various rules set the exact trajectory of spending percentages. Exceptions to fixed
21

spending rules involve a few age- and health-related categories (Medicare, Medicaid, and

Supplemental Security Income) for which the age composition of the population and assumed health

cost growth affect outcomes.

CBOLT’s detailed federal budget modules also allow users to specify numerical debt ceilings

and tax or spending responses as those limits are approached. This element is necessary to ensure

plausible paths of future economic growth and model sustainability. Users can set debt limits ranging

from 0 to 500 percent of GDP, and they can specify whether the government will respond to those

limits with tax increases or spending cuts. Debt limits influence behavioral responses (private saving and

labor supply) because the labor supply is sensitive to changes in tax rates and because saving is affected

by both tax and interest rates (and interest rates are affected by the level of debt in the growth model).

Finally, several behavioral options must be specified in a growth model simulation. Currently,

CBOLT includes several types of private savings equations, each differing in terms of how government

debt affects private savings (see Figure 6). Some of the savings equations have user-specified

parameters that determine the magnitude (elasticity) of responses, generally with respect to changes in

the interest rate. The default rule targets the capital-output ratio, which effectively fixes the real interest

rate, and thus creates a stable baseline economy such as the one projected by SSA. Also included is

an option that allows for a response of labor input (aggregate hours worked) to changes in tax rates. 

This result implies an important interrelationship between policy and economic activity. If mounting

debt leads to a tax increase, labor supply falls, because the substitution effect is assumed to dominate

the income effect.

The final behavioral assumption is for the excess cost growth in the component programs of
22

Medicare and Medicaid; these inputs set gaps between GDP growth and health care costs that

determine the extent to which health program costs will outpace growth in GDP. Medicare costs grow

with age and sex (offset to some extent because, as life expectancy rises, people have longer “time until

death” and thus lower costs in a given age/sex group) which means the aging population will raise

program costs relative to the size of the economy. However, based on historical experience, a larger

share of the growth in health costs is “excess,” meaning that it cannot be explained by simple

demographics and GDP growth. 

4.2 Production, Incomes, and Factor Prices

The first two steps in the growth model macro solution are the same as those employed in the

CBOLT static (mode-one) model macro solution (see Figure 1). That is, in a given year, the model

first solves for population, then for labor force and employment, using the same computer code

described in modes one and two throughout. CBOLT also includes an hours worked response to

changes in personal tax rates. This innovation is one of the two routes (together with private savings,

discussed below) by which policy affects economic growth.

Significant differences between mode one and mode two solutions begin after employment is

calculated. A static simulation moves directly to solving for Social Security budgetary outcomes. The

growth model, however, takes the derived labor input, combines it with capital input (solved for at the

end of the previous year) and exogenous total factor productivity, then solves for real output. Once

real output and exogenous prices have been determined, the model distributes nominal output through

various types of income.
23

As noted, in a macro growth solution, total labor input (hours worked) is solved for by using the

derived employment numbers from the actuarial modules and the assumed average hours growth rate,

set as an exogenous parameter. CBOLT allocates total hours across the four productive sectors of the

economy. Government — federal, state, and local — hours worked are based on the amount of real

purchases in those sectors, where real purchases are determined by budget policy. Given productivity

in the government sector, the model solves for the labor input needed to generate the specified level of

spending. Hours worked in the farm and household service sectors are relatively small and are set

constant relative to overall labor input. The residual, after allocations to other sectors are subtracted,

goes to the nonfarm business sector, which accounts for the lion’s share of output. The Cobb-Douglas

production function in the nonfarm business sector is then used to determine real output in that sector.

Output in all other sectors is determined by the hours worked allocation. The sum of real output across

the four sectors is total real GDP.

Given aggregate production (real GDP), the basically exogenous GDP price index determines

total nominal GDP, which is in turn broken down into income components using the first-order

conditions from the production function and other National Income and Product Account (NIPA)

accounting identities. The first step is to subtract capital consumption (depreciation) from nominal

GDP, a straightforward process with fixed depreciation rates applied to the existing capital stock. The

second step is to subtract indirect business taxes, such as excise and property taxes, that are accounted

for in total sales (nominal GDP) but not included in calculations of national income. Similar to the hours

worked allocation, the level of indirect taxes is a function of government budget assumptions. In the

final step before allocating incomes to labor and capital, a statistical discrepancy between production
24

and income measures that involves a significant net addition to output before solving for incomes must

be resolved. 

Once those subtractions and additions have been accomplished, the growth model has

generated the level of aggregate income that will be allocated between labor and capital income. The

procedure is simple and consistent with the Cobb-Douglas production function used to identify the

initial determinants of output. Total earnings grow with output; capital income is the residual after labor

earnings are subtracted. Then, the rate of real wage growth (used in the Social Security actuarial

modules) is total earnings divided by hours worked, adjusted for earnings as a share of compensation,

average hours growth, and the gap between GDP and CPI-W. The rate of return on capital is the

residual capital income divided by the capital stock. As a result, the 10-year government interest rate

varies with the derived return on capital, and the real interest rate on new OASDI issues (used in the

Social Security actuarial modules) moves one-for-one with the 10-year rate because (as described

above) the gap between the two interest rates is an exogenous parameter. Thus, the two exogenous

inputs in a static CBOLT simulation (real wage growth and the interest rate on OASDI assets) are now

generated endogenously by the growth model.

Referring back to the schematic that details differences between static and growth model

solutions (see Figure 1) shows the two solutions coming back together at this point. Indeed, the next

step in solving for federal budget outcomes is to run all Social Security modules, exactly as they exist in

the static solutions but with a different set of real wage growth and interest rate values because those

are now generated by the growth model. In the growth model, however, the entire federal budget, not

just the Social Security budget, is important because overall federal taxes, spending, and deficits affect
11For details, see Sabelhaus, Simpson, and Topoleski (2003).

25

economic growth.

4.3 Medicare

After Social Security, Medicare is modeled with the most detail in CBOLT. As with the

original OASDI actuarial modules, the initial goal was to produce projections which look similar to

those produced by the responsible actuaries, in this case, CMS. Medicare is modeled at the subprogram level, with individual calculations made for inpatient hospital, skilled nursing facilities, home

health care, hospice, outpatient hospital and other Part B services, physician, group plans and

prescription drugs. In CBOLT, Medicare outlays can be projected using spending indexes that are

fixed by age and sex as CMS does, or, there ia an alternative in which the parameters are further

adjusted for “time-until-death” in each age-sex group. The additional detail of time-until-death

modeling is needed because as people live longer they can expect to have additional years of healthy,

low medical cost living. Modeling without time-until-death will overstate the costs of the Medicare

system.11 

The other similarity between Medicare and Social Security is a detailed tracking of trust fund

balances and reconciliation with CBO’s ten-year baseline. Thus, the model predicts not only spending

on Medicare beneficiaries on a NIPA basis but also tracks trust fund inflows, outflows, and balances

for Parts A and B. In late 2003, the model was upgraded to include the new prescription drug benefits

and financing.
12One of the key model refinements underway is adding more unified budget detail, which will

make it possible to show CBOLT results using standard CBO budget table formats.

26

4.4 Other Federal Budget Outcomes

Few non-Social Security categories of federal spending receive detailed attention in CBOLT. 

Medicaid and Supplemental Security Income all have fixed age/sex spending indexes tied to excess

cost growth parameters that specify the amount by which per-person costs outpace wage growth. 

Thus, as the population ages, spending on those programs automatically increases relative to GDP, but

the rate at which the gap widens (which depends on excess cost growth) is fixed exogenously. 

In CBOLT, most federal budget inflows and outflows are solved for by using simple spending

and revenue rules (see Figure 5). These rules have descriptive identifiers such as “balance non-trust

fund budget,” which means adjust taxes as a share of GDP to meet that target. This type of targeting

behavior causes the only true “looping” in a CBOLT run; federal budget modules first compute what

would happen with no change in taxes or spending, then re-solve using tax and spending rates that are

consistent with whatever target the user sets. Again, those rates are usually simple ratios to GDP, not

detailed manipulation of (for example) personal income tax brackets and rates. 

The ultimate goal of federal budget calculations (including the initial step when Social Security

finances are calculated) is to compute the size of the federal deficit (both on and off budget). CBOLT

uses both NIPA and unified accounting to solve for total investment; thus, government deficits (along

with net foreign investment and private savings) determine the level of investment in capital for next

period production.12 CBOLT does have rules that maintain national debt (accumulated deficits) below
27

a specified critical value relative to GDP above which federal debt-ceiling rules mandate tax increases

or spending cuts.

4.5 State and Local Budgets 

State and local budgets are treated even more simply than the federal budget. Most inflow and

outflow calculations are based on fixed ratios of taxes and outlays to GDP. The exception is Medicaid,

because states must pick up part of the tab as costs rise. Thus, the state and local spending module

works differently than the federal sector module; however, because the goal is to keep state and local

deficits a constant fraction of output, taxes are adjusted when Medicaid expenditures change. This

convention keeps the focus of the model on federal government behavior and is consistent with state

budgeting practices.

4.6 Foreign Transactions

Just as the focus of government modules is on determining deficits, the foreign transactions

focus is on international capital flows, especially net foreign investment, which subtracts directly from

the pool of other savings used to finance capital investment. Although in principle net foreign investment

should be affected by the (endogenous) interest rate in the model, in its current version, CBOLT ratios

of capital flows to GDP are effectively exogenous. CBOLT tracks gross and net assets held in the

United States and other countries and solves for net exports using NIPA identities on capital flows. 

That net exports value is a key piece of the NIPA product side- identity in CBOLT, which is the

textbook GDP condition — it equals consumption, plus investment, plus government purchases, plus
28

net exports. Therefore, in the current model, the income to foreign capital ratio does not result in higher

future foreign investment. 

At this point in the model, total output (GDP) is known, government purchases and net exports

have been solved for, thus the only computation required to complete the model solution is to divide

what remains of GDP between consumption and investment. 

4.7 Private Savings

The CBOLT private savings module determines how consumption and investment respond to

different policy regimes. It is, therefore, key in determining how the growth rate of the economy will

vary under different policy regimes (see Figure 6). After government and foreign transactions modules

have run, CBOLT solves for aggregate potential consumption, which is GDP minus government

purchases and net exports. Potential consumption is then allocated between actual consumption and

investment using a private savings rule. Because it is assumed that consumers will allocate some income

between consumption and saving, NIPA income-side identities are used in conjunction with productside identities in CBOLT. The amount of income that they save is added back to the pool of savings

for investment, and it becomes capital in the next period. 

Before describing CBOLT’s alternative equations, it helps to begin with a quick review of how

different savings equations operate. All of CBOLT’s savings equations operate on aggregate variables

in a myopic framework. Currently, the only dimension along which alternative savings equations differ

is in the extent to which private savings offset government deficits. 
29

At one extreme, CBOLT allows a “targeted capital to output ratio” option which effectively

neutralizes the impact of government deficits on economic growth — there is no “crowding out.” In

that case, private savings has a one-for-one response to government deficits. Under this equation, as

Social Security and other aging and health-related programs follow trajectories into deficit in future

decades, private savings will increase such that the allocation of potential consumption between

investment and actual consumption will be unaffected by those deficits. This extreme behavior is often

assumed for comparability to SSA projections, because it generates a stable baseline economy with

fixed real interest rates.

CBOLT also allows for other alternatives in how government deficits affect private saving

where the impact is less than one-for-one and acts through changes in the interest rate. Initially, when

government deficits rise, the amount of investment falls because the pool of funds for investment shrinks. 

However, reduced investment slows growth in the capital stock, which, given the first-order conditions

of the production function, raises interest rates. That increase in interest rates triggers savings responses

in three of CBOLT’s five savings equation options. 

 The simplest option for linking interest rates and private savings is the “constant savings

elasticity,” in which users set the responsiveness of the private savings rate to changes in the interest

rate. One variant is the “variable savings elasticity,” in which the responsiveness of the savings rate

changes as the level of the interest rate itself rises — think of this as the economy becoming more

“Ricardian” as debt grows and interest rates rise. The third variant operates on the same principle but

ties back to the complete offset model above; in the “variable deficit offset” savings model, the

percentage of any given deficit offset by private savings varies with the level of the interest rate. At very
30

high rates, the offset is one-for-one (the Ricardian case), while at lower rates the offset is much more

modest. In all three of those variants there is some user-defined parameter that determines the degree

of savings responsiveness to interest rates and thus the degree to which private savings offsets

government deficits.

To some extent, the choice of which savings equation to use (and how to set parameter values

on those with parameters) cannot be made independently of assumptions about long-run fiscal policy. 

If assumptions about effective tax and spending rates caused government debt to skyrocket in the

future, the savings equation that was selected would matter tremendously. If, however, budgets were to

be balanced every year, the various CBOLT savings equations would predict very similar paths for

private savings.

Under the constant savings elasticity model, rapid growth in debt pushes interest rates well out

of their historical ranges. This solution highlights whether constant elasticity is a reasonable response

under very high-debt conditions. Indeed, under some elasticity values and some fiscal policy

assumptions, CBOLT will crash because the underlying economic environment (and/or the specified

policy, depending on one’s viewpoint) is unrealistic. 

4.8 Next Period Capital 

At the end of each simulation year, CBOLT solves for total investment, which is the sum of

private savings, federal government savings, state and local government savings, and net foreign

investment. Total investment is then allocated across several types of capital, and each capital stock

grows by the amount of investment minus depreciation. By definition, some types of capital —
31

residential, for example — do not contribute to output in the nonfarm business sector. Types of capital

that affect output are weighted by service flows to determine aggregate capital input for the CobbDouglas production function in the next period.

5. CBOLT Integrated Micro/Macro Solutions

With CBOLT, the move from static to macro growth model solutions involved adding

economic structure around SSA-style actuarial modules. The innovation introduced in CBOLT’s third

solution mode is to replace most of the actuarial methods with a dynamic micro-simulation model. That

model starts by using longitudinal demographic and economic data for a representative sample of the

population as of some base year. In each year of the projection, the model simulates demographic and

economic life events for each member of the sample. Ultimately, those micro level events include

calculations of payroll tax liabilities and Social Security benefits. Thus, the dynamic model provides an

alternative to actuarial modules for computing OASDI and other program finances — directly

aggregating outcomes across the representative micro sample. 

Dynamic micro-simulation is both conceptually and computationally more difficult than actuarial

modeling. A dynamic micro model must go beyond projecting average outcomes across age/sex

groups — the technique used in a cell-based model — and capture heterogeneity within each group. 

Capturing this type of heterogeneity at the individual level involves adding random components to the

estimated equations that explain demographic and economic transitions. Thus, the typical dynamic

micro module involves looping over the entire sample, computing the probability of transition for each
32

person in the sample, then using a computer-generated random number to determine the actual

outcome. 

It is easy to see why, when the micro sample is large, the number of computations in a given

simulation is much higher than in an actuarial model. If, for instance, the representative sample includes

about 300,000 observations, and approximately 10 demographic and economic outcomes are being

produced per person, then about 3 million micro calculations must be made per year for each of the 75

years of a given simulation. This increased number of calculations shows up in model solution times; a

CBOLT static mode-one or growth model/actuarial (mode-two) simulation takes about 90 seconds; an

integrated micro/macro solution (mode three) takes about 20 minutes, but the exact time depends on

the clock speed of the computer.

5.1 Integrating a Dynamic Micro Model into the Growth Framework

The best place to begin describing CBOLT’s dynamic micro model is with the demographic

and economic variables tracked for each person in the representative micro sample (see Figure 7). For

each person in the 1:1000 sample, the micro model tracks date of birth (and thus implicitly age),

immigration year, sex, education, marital status, marital partner, earnings, labor force participation,

Social Security benefits and beneficiary status, and a host of intermediate variables needed to compute

transitions. That list of variables is consistent with the goals of CBOLT. Still, it suggests the limitation

of replacing the actuarial model with the micro model. For example, because there is currently no link

between parents and children in the micro model, children’s auxiliary benefits cannot be computed on

the basis of their parents’ earnings. The list of variables tracked for the micro sample also suggests what
33

sort of transition processes are needed to dynamically age the sample forward each year. On the

demographic front, each person in the existing micro sample faces some probability of death, and

individuals ages 16 and older face the probability of marital transitions, depending on their current

marital status. In this approach, individuals who transition into marriage are matched to potential

spouses in a simulated “marriage market.” Person-level economic outcomes include labor force

participation, hours worked, unemployment spells, and ultimately, earnings. Given demographic and

economic variables, a final set of equations computes beneficiary status (claiming behavior) for OAI

and DI worker benefits. Longitudinal earnings histories combined with detailed program rules are used

to compute benefit levels. 

In some cases the dynamic micro and actuarial modules work together to determine outcomes. 

The most noticeable example of this is the basic demographic processes: births, deaths, and

immigration. For these transitions the actuarial modules determine how population evolves — year-byyear and by age and sex — and the micro modules simply allocate outcomes across the representative

sample of the population. This feature has the immediate advantage that basic demographic processes

are still controlled through the same parameter settings as in mode-one and mode-two simulations. 

Nonetheless, future development plans for CBOLT include improving how it models basic

demographics. 

Other than basic demographics, the dynamic micro modules provide a distinct alternative to the

actuarial modules. Indeed, when the dynamic micro model runs, most of the actuarial modules used in

mode-one and mode-two simulations are turned off. A good example of how this works is illustrated in

the labor supply and employment modules. As described above, the actuarial modules — in both static
13One of the on-going parts of the CBOLT project is updating the micro base file as the

CWHS data set is updated. As of this writing, the model is being updated to the 2001 input file. 

34

and growth model modes — use 103 time-series equations to project group-level labor force

participation. Fixed unemployment rates determine employment by age and sex and in the growth

model, multiplication by exogenous average hours determines total hours worked. In the micro model,

each person’s labor supply is determined through a sequence of equations for labor force participation,

hours, and unemployment, and total hours worked is summed directly in the micro sample. From the

perspective of aggregate production, the choice between the underlying labor supply modules is

irrelevant because the only important variable in the aggregate production function is total hours

worked.

5.2 Preparing the CWHS Micro Base File

Because no single data set includes all of the demographic and economic data needed for a

CBOLT dynamic micro simulation, a much more complex effort, using several data sets, is required to

create a base data file with all of the required information. Moreover, because micro data are generally

available only with a lag, CBOLT uses the dynamic micro model to project micro outcomes for the

years between when the data set ends and the simulation begins. 

One of the first choices in dynamic micro simulation is where to start when developing the base

file. CBOLT uses administrative data from SSA’s Continuous Wage History Survey — a 1:100

sample of all Social Security numbers ever issued. As of 1998, CWHS included several million

observations.13 Because it is based on administrative data records rather than surveys, it provides the
35

best earnings and benefit data available to researchers. In addition, its sample size far exceeds any

survey data set. (CBOLT actually reduces the CWHS sample by a factor of ten — to a 1:1000

sample — and even with that reduction, the sample is still orders of magnitude larger than alternatives

such as Panel Study of Income Dynamics (PSID) survey data.) Still, the administrative nature of the

data set has one serious drawback: it contains only the most basic demographic, labor supply, and

earnings data. The CWHS also suffers from missing values for some crucial data, particularly dates of

death for people who never received Social Security benefits. Moreover, earnings before 1978 are

only reported up to the Social Security taxable maximum, and the reported earnings above the taxable

maximum through the first few years of the 1980s are thought to be suspect. 

Before its use in CBOLT, CWHS data must undergo a number of procedures to prepare it for

use as a base file. The goal is to develop a representative sample of the U.S. population as of the end

of 2002, with all the micro variables listed above (see Figure 7). The starting point is a raw CWHS file

with limited demographics, earnings, and beneficiary data for a 1:100 sample of all Social Security

numbers issued through 1998. The end product is a 1:1000 sample that represents the U.S.

population during the 1984 to 1998 period (historical information on earnings back as far as 1951 is

retained for individuals alive in 1984). Seven steps are required to complete the transformation:

Step One. The first step in working with the CWHS sorts the individual records randomly. 

This guarantees that in later steps, the process of sampling from the data or matching mates will be truly

random and not reflect the systematic order of the CWHS. The original extracts of micro data appear

to be in order of year of SSN assignment, thus the older observations are listed first. In addition, the
36

active and inactive CWHS files are concatenated together in the original extract program, thus

observations with observed deaths are listed last.

Step Two. The second step in base-file preparation focuses on creating a representative one

percent sample as of the CWHS sample year (currently 1998). As noted, the CWHS does not have

accurate dates of death for many people in the sample because the nature of the data collection makes

that a priority only for people already receiving benefits (SSA wants to ensure they stop making

payments at death). Although CWHS is a 1:100 sample of Social Security numbers, the failure to

accurately record deaths leaves a file with observations that total more than one percent of the

population in 1998. For the base file to be representative, dates of death must be imputed for the

“extra” people, and those people are then removed from the sample. Because the CWHS also does

not record immigration status, an individual who immigrates to the United States at age 40 appears as

part of the population for the previous 40 years, which also inflates historical population totals. To

solve this problem and better target the CWHS to match historical population counts, dates of

immigration are imputed. Earnings and beneficiary status are used as indicators of either death or

immigration. For instance, a worker who stops earning but does not go on OAI or DI is more likely to

have died. And a worker who starts earning abruptly is more likely to be newly immigrated. In years

prior to the mid-1980s, when only covered earnings were recorded, these indications could be

confused with those of people moving in to or out of uncovered work, which is one reason why the

micro base file is developed to be representative only starting in 1984. A demographics program

removes excess observations from the CWHS by targeting historical population, worker beneficiary

and covered worker counts from SSA. Using micro earnings and beneficiary status, the program
37

targets people ages 15 to 100 to fill age and sex cells, including observations with positive earnings —

counted as covered workers — and then observations with OAI or DI worker beneficiary status. 

Finally, population age and sex cells are filled with individuals who are most likely to still be alive.

Step Three. The third step in preparing the CWHS completes the earnings histories for the

population. As noted above, data on earnings above the taxable maximum are not reliable until the

mid-1980s. As a result, earnings above that maximum must be imputed for all sample members who

were in the work force before 1984. In addition, deferred compensation must be imputed for 1984 to

1998 along with late postings in 1998; although the CWHS was released in 1999, it often takes two

years to accurately record all FICA earnings because of “late postings.”

Step Four. Once the 15-year representative longitudinal sample with complete earnings

histories is complete, the 1:1000 micro sample is selected at random by choosing one out of 10

observations from the full micro sample.

Step Five. The fifth step imputes additional demographic and economic characteristics onto

the CWHS sample, including marital status as of 1983 and education. The approach often involves

working backward with equations estimated from supplemental data sets. The same statistical

relationships used in the dynamic micro model projections (labor force participation depends on marital

status, especially for younger and middle-aged women; earnings depends on age, sex, and education

status) are reversed when imputing characteristics (younger and middle-aged working women are less

likely to be married; higher earners are more likely to have high education). Marital histories back to

age 16 are also imputed, including the number of previous marriages.

Step Six. The sixth step is to assortatively match married individuals in the population. 
14See Perese (2002).

38

Because the CWHS does not include information on marital status, it also lacks information on spouses. 

Linking spouses is critical for projecting auxiliary Social Security benefits. Thus, extensive work was

undertaken to produce an algorithm that matches mates. The mate-matching project considers all

individuals married in 1983 and matches them together on the basis of earnings, age, and imputed

education. (This same algorithm is used in all future projections.14) Once individuals are matched, it is

necessary to align marital histories such that current spouses entered their most recent marriage in the

same year. This project takes about an hour to run because it creates more than 56,000 unions. A

married individual’s record contains a link to the record of the spouse that can, in turn, be used to

determine future marital transitions and auxiliary benefit awards and amounts.

Step Seven. The final step is to fill in missing micro data for 1984 through 1998 and then to

simulate the entire micro sample forward to the end of 2002. This historical simulation uses much of the

same computer code employed in the CBOLT dynamic micro model during the projection years, but

differs at points where aggregate outcomes for certain processes are known from other data sources. 

For example, other data sources provide aggregate information on marital distributions, labor force

participation, and hours worked that are then used as targets to calibrate micro processes. Those

calibration ratios also have information about how well the micro processes are tracking history. This

information about possible systematic biases endemic in the data or techniques helps to resolve “jumpoff” problems associated with simulating forward in time. The result is a micro file that has the intended

heterogeneity but is also consistent with known aggregates.
15One of the oft-noted determinants of mortality (and other demographic processes to follow)

that is missing in CBOLT is race. Although race is a strong determinant of several demographic and

economic outcomes, there are several problems with including race, most notably the lack of good

racial identifiers on the underlying CWHS data set used in the model.

39

5.3 Demographic Transitions in the Micro Sample

Person-level demographic variables in the CBOLT dynamic micro model (see Figure 7) include

years of birth and immigration, sex, marital status, and (if married) a linked spouse. The basic

demographic processes (birth, immigration, death) are simple extensions of the actuarial techniques

used in other CBOLT simulation modes. Marital transitions and mate matching are much more

complicated, however, and rely on several estimated transition equations as well as a simulated

“marriage market” that unites brides and grooms each year.

The basic demographics in a CBOLT dynamic micro simulation are controlled by the

corresponding actuarial processes — mortality, fertility, and immigration (see Figure 8). In the case of

mortality, the probability that someone in the micro sample dies in the current year is a function of their

age and sex and is solved for as the ratio of expected deaths to beginning population. For example, if

1,000 individuals were in a particular age-cohort group in the micro sample at the end of year one, and

the actuarial model predicts 990 people in the same age-cohort group at the end of year two, then the

probability that someone in the group will die is (1000 - 990)/1000, or 1 percent. Each person in that

group gets a random number — distributed uniformly between zero and one — in the mortality module. 

If that random number is less than .01, that person dies. One improvement is the addition of earnings as

a determinant in the mortality probability equation. This controls for observed “differential mortality” in

death rates across income while still preserving the number of deaths.15 
16In the case of marital transitions, see O’Harra and Sabelhaus (2002).

40

The result of this process is that the expected number of deaths in a given age-cohort group will

always match the actuarial model, although random variation will cause actual outcomes to differ. Still,

CBOLT has a built-in solution that prevents random variation from affecting long-run results. If too

many or too few people die in a particular year, the model will self-correct in the subsequent year,

because the underlying probability of death is generated by comparing the actual age-cohort count in

the micro sample with the value in the main population matrix, which is generated by the actuarial

model. 

Immigration and births are even simpler to calculate. (Note that as of yet, CBOLT does not

attempt to link parents and children in the micro model. Consequently, the number of births remains

exogenous to that model and is still determined by the actuarial modules.) The only transition code

needed for new births is a random number that determines sex. The number of immigrants is also fixed

in aggregate, as is the expected distribution by age and sex.

The first set of transition processes that rely on estimated micro transition equations are marital

events. As with most of the transition equations, what follows is a brief discussion of those processes,

but details can be found in a series of Technical Papers.16 CBOLT includes four marital status

categories: never married, married, divorced, and widowed. Thus, four transitions are modeled: first

marriage, divorce, widowing, and remarriage. In the micro model, widowing occurs automatically,

through a combination of the mortality probabilities and links between spouses. The other three

processes are affected in various ways by age, sex, education, earnings, cohort effects, and marital

history up to that point.
41

The 1996 Survey of Income and Program Participation (SIPP) panel data, linked with SSA

earnings records, are used to estimate all of the marital transition equations for CBOLT. SIPP’s

retrospective marriage history module allows for construction of marital histories for each person in the

sample ages 16 or older through 1996. Patterns that emerged from those marital histories motivated

the strategy for estimating actual transition equations used in CBOLT. The approach CBOLT employs

for marital transitions is to estimate separate equations for each age/sex group, where the effects of

cohort can be separated from the effects of age and where the impact of other independent variables

— earnings, education, marital history — are allowed to vary by age.

The first pattern observed from SIPP marital history data is that distinct differences are evident

across cohorts in event probabilities at a given age. For example, the probability of a first marriage

before the age of 25 has decreased dramatically over time. The probability of a first marriage after the

age of 25 has, however, remained stable or even increased during the same time, such that the

cumulative probability of a first marriage by age 40 is only marginally lower for later cohorts. The fact

that an important determining variable — simulation year — has different effects on different age groups

within the sample suggests that a flexible approach is required when estimating transition equations.

In CBOLT, the key to such flexibility is estimating separate transition equations for each singleage group. The alternative is to use many interactions between variables and potential nonlinear effects

for a given variable. That, however, imposes unnecessary structure on the relationships and may

actually introduce significant multi-collinearity. One problem with the single-year-of-age approach is

that SIPP sample sizes get small, therefore the actual estimations use an “age-centered” strategy that

includes data for the age group being analyzed plus groups within two years of age on either side, but
42

weighted slightly less than the group being analyzed. As a result, CBOLT has, for example, separate

first-marriage equations for every age/sex group between the ages of 16 and 70. When considering

these issues, allowing for the effects of different explanatory variables across groups is important. 

Consider, for example, the effects of education: educational attainment affects first-marriage

probabilities much more at younger ages (when people are still in school) than at older ages.

The other two marital transitions — divorce and remarriage — use a similar age-centered 

estimation strategy. Again, the value of flexibility is evident. In addition to basic demographics and

earnings, equations for these transitions use marriage history to capture important longitudinal

differences in marriage patterns; for example, the probability of divorce for people who are already

once-divorced is higher than for those who have never been divorced. In addition, findings about

divorce and remarriage have implications for OASDI outlays; men with low earnings and women with

high earnings are both more likely to divorce, which suggests a self-sufficiency aspect to divorce

decisions. 

A key decision in micro marriage equations, and in most micro transitions, is how to extend

unexplained trends into the future. As noted, the flexible age-centered approach is good at separating

age and cohort effects, but it does not provide particular guidance about what to do with unexplained

cohort trends. An example is the unexplained decrease in first-marriage probabilities among the young: 

should CBOLT assume the downward trend will continue further, stop where it is, or reverse? Here

the usual CBOLT solution is to assume no further trends beyond identified cohort effects for young age

groups and allow those to work their way through future age groups. Interestingly, this assumption
17See Perese (2002).

43

generates answers about future marital status distributions that are very similar to SSA actuarial

projections.

The final demographic process that is modeled entails solving a complicated algorithm that links

brides and grooms.17 A brief description of the process follows. Each year, a few thousand brides

and grooms are simulated to experience CBOLT wedlock either through a first marriage or a

remarriage transition. The goal of this CBOLT simulated marriage market is to unite couples in such a

way that the joint distribution of husband/wife characteristics in the CBOLT micro world resembles the

joint distribution observed in actual data.

The data set used to analyze the joint distribution of husband/wife characteristics is the same

matched SIPP, with SSA earnings records used for marital transition equations. In the case of mate

matching, the estimation period is restricted to marriages that occurred in 1994, 1995, or 1996. In

those years, about 1,200 marriages were observed in SIPP, about two-thirds of which were first

marriages (sorted by husband’s marriage order); the remainder were second or higher-order marriages.

Characteristics considered in the mate-matching estimation are straightforward; basic

demographics such as age difference between husband and wife are key, but determinants such as

education and earnings are also important for simulating distributional and program outcomes. In

particular, the process of “assortative” mating suggests that couples will tend to have appropriately

correlated ages, educational attainments, and earnings, which has strong implications for distributional

analysis when programs (such as Social Security) have rules based on both individual and spousal

characteristics. The goal for the CBOLT algorithm is to develop a means to simulate the joint
44

distributions of age, education, and earnings observed in actual data without imposing too much

uniformity on the resulting couples. Algorithms that directly impose maximized matches between

husbands and wives (by finding each person’s most suitable mate) cause too many perfect marriages

(for example, husbands who are exactly one year older than their wives). 

The CBOLT solution is to estimate logistic equations where the probability of the union

between two individuals depends on the indicated variables: age gap, education gap, and earnings gap. 

The estimation phase involves considering every woman (bride) or man (groom) in SIPP a potential

match for every other man or woman, where actual couples get successful outcomes and every other

potential couple gets a value of zero. Thus, the estimation is made within a huge data set with few

successes and many failures.

CBOLT’s marriage market uses estimated probabilities of husband/wife unions in conjunction

with random numbers to generate matches whose joint distribution of characteristics is consistent with

SIPP data. Each year, the model proceeds groom-by-groom, first computing the probability of union

for all potential brides, then proceeding bride-by-bride (where the brides are randomly ordered) using a

random number compared with the probability of union to determine whether a match occurs. One of

the tricks that makes this approach efficient is that the probability of union for any given potential bride

is normalized by the probability of union for the most likely bride; thus, perfect matches happen without

randomness (if the groom gets that far in his list) but there is still, for example, a 50 percent chance of

union between a groom and a bride who is (statistically) half as likely to marry that groom as his ideal

mate. This statistical randomness eliminates the tendency of mate-matching algorithms to produce too
18For more details on these equations, see Harris and Sabelhaus (2002). 

45

many perfect marriages. This mate-matching algorithm successfully reproduces the joint distributions of

husband/wife characteristics exhibited in the underlying SIPP data.

5.4 Individual Labor Supply and Earnings

One of the most striking features of individual labor supply and earnings patterns is the

tremendous amount of idiosyncratic (unexplained) but highly autocorrelated variation in the data. The

extensive unexplained heterogeneity means that standard control variables such as education, marital

status, cohort, and age may all have statistically significant effects on labor supply and earnings, but the

overall fraction of the variation in labor supply or earnings explained by those determinants is quite low. 

The autocorrelated nature of the series means that if a person’s outcomes diverge from the equationbased expected value in one direction or another in a given year, they are likely to continue to do so in

subsequent years.

The sequence of equations CBOLT uses to predict labor force participation, the part-time/fulltime decision, and the level of earnings are all structured to accommodate these observations.18 In

participation and hours equations, the probability of working full-time this year is strongly correlated

with whether one worked full-time last year. In earnings equations, each person is assumed to have a

permanent earnings differential that measures the extent to which their expected earnings differ from

expected earnings for someone with their same characteristics. Because of “permanent shocks,” that

earnings differential evolves over time so that it can capture variations in lifetime earnings patterns

observed in the data. Even so, current earnings are highly correlated with lagged earnings.
46

CBOLT micro labor force participation equations are estimated separately for men and women

using pooled cross-sections from the annual (March) supplement to the Current Population Survey for

1976-2001. This estimation strategy uses the same age-centered technique employed for marital

transitions. Use of this approach provides the most flexibility in terms of how independent variables

(age, marital status, beneficiary status, cohort, and lagged participation) affect the probability of

working. The equations reflect expected correlations (married women are less likely to work, for

example) but also capture important cohort effects (younger female cohorts have higher participation in

the labor force and are likely to continue to do so as they age).

CBOLT’s full-time participation equation is similar to the overall participation equation.

Because the Census Bureau’s Current Population Survey (CPS) does not provide longitudinal details

about hours worked, the full-time equation is estimated using data from the 1968-1992 Panel Survey of

Income Dynamics (PSID). Again, the model is estimated separately for men and women, and

explanatory variables include age, marital status, cohort, lagged participation, and lagged full-time

participation. As with labor force participation, the lagged values introduce the observed

autocorrelation (persistence over time) in workforce attachment.

In the CBOLT micro model, unemployment is modeled very simply; because the aggregate

unemployment rate is an exogenous variable, the only task is to distribute unemployment events across

groups. Unlike the actuarial model, the micro model has a problem allocating unemployment. In the

aggregate, a 4 percent unemployment rate for a given group implies that the labor supply should be

lowered by 4 percent for that group to generate a measure of labor input. At the micro level, however,

this does not imply that 4 percent of the people should be unemployed, because individuals are not
47

usually unemployed for a full year. To accommodate this phenomenon in the micro model, a simple

regression that relates the fraction of people experiencing an unemployment spell to the aggregate

unemployment rate is run on CPS summary statistics. Thus, for example, a 4 percent unemployment

rate is consistent with 7.5 percent of the workforce experiencing unemployment spells of varying lengths

in a given year. CBOLT allocates those spells differently across part- and full-time employees on the

basis of CPS historical data.

Because of the approach CBOLT takes in estimating earnings equations it is necessary to solve

for hours of labor input per person. Under this approach, earnings equations are estimated separately

for men and women using CPS data for 1976 through 2001. The dependent variable in those

equations is the log of real, full-time equivalent, earnings — that is, what the person would have earned

in 1993 dollars if he or she had worked full-time. Before the equations are estimated, each part-time

worker’s hourly earnings are adjusted upwards to be consistent with the full-time equivalent concept. 

The historical values are then adjusted for overall nominal wage growth over time, so that the units are

consistent over the 25 years used in the estimation.

Creating full-time equivalent earnings before estimating earnings equations is a means for

isolating idiosyncratic differences across people. Consider, for example, two 30-year-old females with

the same educational attainment who both work full-time for salaries of $30,000 per year. If one of

those women was assigned part-time status in the following year, her earnings would be adjusted

downward to account for the reduction in hours worked. Moreover, a full-time wage premium would

be deducted. The woman who continued to work full-time would keep earning $30,000. If the parttime worker was to return to work full-time, her earnings would be reinstated to the level of her
48

continuously full-time counterpart. Without the full-time equivalent adjustment, such a sudden increase

would register as an unexplained earnings “shock” when, in reality, it is completely explained by

changes in the labor force decision.

Explanatory variables in male and female full-time equivalent earnings equations include age

(single year of age dummies), education, education-age interaction, and cohort trends. These equations

produce the typical rising concave patterns of earnings — by age — that are observed in cohort-level

data. Overall, however, the explanatory power of the equations is fairly low; only a fraction of variance

in earnings is explained by control variables. In a sense, this implies that estimating earnings equations is

actually only part of the overall process of predicting earnings. Indeed, modeling the unexplained

residuals is at least as important as capturing the explained variation.

The pattern of errors estimated from the earnings equation suggests a useful strategy for

predicting individual earnings. First, for a given person, the errors are highly autocorrelated. That is, if

a person’s earnings are above the equation-predicted value in one year, they are likely to be above in

subsequent years. This correlation can be considered as distinguishing between two people who have

the same measured characteristics — age, sex, cohort, education — but unmeasured differentiating

characteristics such as skill or occupation. The equation predicts the average outcome for the two

people at any point in their lives, but the higher earner generally remains above the predicted line, and

the lower earner generally remains below it. 

The second observation is that, although the tendency to be a high or low earner is evident in

the data, some statistically random movement occurs in people’s relative standing. Put simply, this

means that a person’s unexplained earnings — referred to as his or her “permanent earnings
19In the current version of CBOLT, the only benefits not computed directly using the micro

sample are for children and parents, because those linkages do not yet exist at the micro level. That part

of the model is currently under development.

49

differential” in the CBOLT micro model — will evolve randomly over time. The random evolution of

the unexplained earnings component can be captured using a number of statistical methods; the method

used in CBOLT is to specify “permanent” shocks to the earnings differential terms. This works in

conjunction with the (also random) “transitory” earnings shocks to determine actual earnings in a given

year. 

To summarize, in the CBOLT micro model, every individual’s full-time equivalent earnings are

composed of three components: the first is predicted by the earnings equation; the second is the

permanent earnings differential, which evolves randomly over time; and the third is the current year

transitory shock, which captures other unexplained variation. This decomposition of the earnings

process is fairly complicated, but it is essential to generating longitudinal earnings profiles that show

variation within and across people that are consistent with actual data.

5.5 Social Security Benefit Claiming and Awards

The primary goal of building the CBOLT micro model was to create a method for projecting

Social Security benefits under baseline and alternative policies. The emphasis placed on the various

processes speaks to this goal; we need realistic longitudinal labor force and earnings patterns and

marital histories in order to simulate benefit awards under all the various OASDI programs.19 In

addition to having the underlying micro data to which rules are applied, the model also needs a set of

behavioral rules that control the initiating of benefit claiming. 
20A preliminary version of this module was used by Harris and O’Harra (2001) to investigate

the outlook for women’s Social Security benefits. 

50

In the case of OAI and DI worker and spousal auxiliary benefits the CBOLT micro model has

all the information needed to compute benefit amounts given claiming status. In the dynamic micro

model, actual benefit calculations are made using the same code that was developed for CBOLT’s

static-aged micro model mode-one and mode-two actuarial simulations. That code already computes

worker benefit awards using detailed policy rules. The only innovation of the dynamic micro model is

that the sample of beneficiaries is now representative. Otherwise, inputs to the benefits calculator —

age, current year, and earnings histories — are identical.20

CBOLT’s micro model captures both aggregate and individual-level factors in the claiming

behavior of DI and OAI workers. With respect to DI, the overall rates of incidence and termination by

age and sex are controlled by the same input parameters utilized in the actuarial model (which

incorporates expected trends in DI). The micro model is, however, careful to differentiate who —

within an age-sex group — is likely to go on DI in a given year. The important variable by which to

differentiate within an age/sex group is earnings. Because low earners are more likely to claim disability

insurance, the expected benefits for DI recipients are lower than if one took a random sample of

earners from a given age/sex group and assigned them to the DI program. 

In the case of OAI worker claiming behavior, the initial approach tested for CBOLT was a

structural retirement model that would predict retirement behavior by assuming people rationally

compute potential benefits under alternative retirement dates, then choose a retirement date by making
21For details on this research, see Harris (2002).

51

a trade-off between benefit levels and the lure of leisure.21 Unfortunately, structural models are not very

good predictors of people’s behavior in this area. In particular, important reference ages seem to be

statistically more significant for explaining retirement than benefit trade-offs. With Social Security, the

classic example is that most people retire at age 62 when early retirement is available, even though

actuarial reductions associated with early retirement make retirement at any time between age 62 and

65 a proposition that is basically neutral. Absent self-selection, such “irrationality” makes structural

modeling based on a rational agent’s hypothesis difficult.

The OAI worker claiming module in CBOLT takes this age-referencing behavior as a starting

point to which a policy response is added. The approach is to use the retirement behavior of the most

recent observed cohorts — those who retired in the late 1990s — as a reference to determine a

baseline probability of claiming at all ages greater than or equal to the early retirement age. The second

step is to hypothesize that decreases in benefits relative to average economywide earnings — the socalled replacement rate — will cause people to work marginally longer, which is implemented by

decreasing their claiming probabilities. Because the degree of responsiveness to benefit changes is an

exogenous parameter that is under the user’s control, sensitivity analysis is possible.

5.6 Distributional Analysis

In addition to the goal of producing better budgetary projections, a distinct benefit of using

dynamic micro simulation is the ability to undertake distributional analysis, defined here as the tracking

of tax and benefit outcomes across groups and time. In the standard SSA actuarial approach
52

distributional analysis is not an intrinsic feature of the model. Policy makers are provided with results

for hypothetical example workers who bear no necessary relationship to real people; they are

effectively historical averages adjusted for average wage growth. The dynamic micro model, on the

other hand, automatically produces a representative sample in every forecast year, so that distributional

results across a number of dimensions are possible. 

The limits on distributional analysis in any given model are determined by the lowest level of

detail in that model. Thus, in the group-level actuarial modules, it is possible to track benefit outcomes

by age, sex, and birth year. However, because taxes are computed using the aggregate univariate

distribution, comprehensive distributional analysis by age, sex, and birth year is not possible. This is

why hypothetical example workers, with earnings fixed relative to the average wage index, are used by

the SSA actuaries in their distributional tables. 

The dynamic micro model allows a much more comprehensive look at distributional effects. 

The basic demographic and economic variables tracked at the person-level allow the sample to be

divided up in many imaginable ways. Standard CBOLT distributional tables consider taxes and

benefits by sex, birth year, lifetime earnings, marital status, and other basic variables. Also, the

“representativeness” of the CBOLT micro sample is not suspect like it is for example workers. 

CBOLT tables for the “average” worker truly reflect an average outcome, not an outcome fixed relative

to the average wage. 
22The SSA actuaries also introduced stochastic analysis in their latest (2003) Trustees Report,

using a Monte Carlo simulation approach that was largely patterned on CBOLT. When it comes to

generating ranges, however, their main focus is still on low and high cost projections. 

53

6. Stochastic Simulation Using Monte Carlo Draws for Inputs

All of the CBOLT simulation techniques described thus far assume a deterministic solution

where the user defines a model solution mode and associated options or behavioral parameters, policy

rules, and values for the exogenous input variables. For stochastic solutions the first two steps are the

same, but the actual values for the exogenous inputs are drawn from a probability distribution using a

Monte Carlo simulation technique. In CBOLT, users of stochastic runs can alter the expected values

for exogenous inputs, after which actual inputs vary symmetrically around those expected values.22 

Any given stochastic simulation yields a viable scenario, which means some chance exists that

the particular set of exogenous inputs chosen in that set of random draws will be observed in the real

world, even though it may not be the most likely scenario. The utility of stochastic simulations becomes

apparent after the model has been solved many times using different sets of draws for the inputs each

time. Because the probability distribution for each input has a bell-shaped distribution where values are

likely to bunch around the expected value, repeated stochastic simulations will generate a distribution of

system financial outcomes that also exhibits bunching. After a sufficient number of simulations have

been run, it is possible to make inferences about the probability distribution of the same budgetary and

distributional outcomes generated by a deterministic simulation.
54

6.1 Beyond Low-, Medium-, and High-Cost Projections

Each year, Social Security actuaries produce three sets of projections for their annual Trustees

Report. Of those, the projection that generates the most attention is the intermediate one, because it is

considered the most likely scenario. To produce the intermediate forecast the actuaries use values for

the nine major input assumptions provided by the Trustees — fertility rate, rate of mortality

improvement, number of immigrants, real wage growth, inflation, rate of unemployment, interest rate,

disability incidence rate, and disability termination rate. 

To characterize uncertainty about the projections, the actuaries produce two other simulations

— the low-cost and high-cost scenarios. For those simulations, all inputs are set to some alternative

value, each consistent with either improved or deteriorated system finances. For example, in the lowcost scenarios, actuaries use a lower rate of mortality improvement, higher fertility rate, higher number

of immigrants, higher real wage growth, lower inflation, lower unemployment rate, and lower rate of DI

incidence, because those values for the inputs all push the OASDI system toward increased solvency. 

In the high-cost scenario, the reverse is true (for example, mortality is assumed to improve more

rapidly).

Several problems are inherent in using the high- and low-cost scenario approach to measuring

uncertainty about projections. First, the gap between high-/intermediate- or low-/intermediate-values

for a given input has no probabilistic interpretation based on evaluation of historical data. Second, in

the alternative scenarios, all inputs move together, even though no statistical basis exists for expecting

that type of correlation. Finally, rather than adding both high frequency (annual) and long-run variation,

the actuaries only alter long-run values for the input assumptions. CBOLT stochastic simulations
23 The equations are described in detail in the December 2001, Congressional Budget Office

paper, Uncertainty in Social Security’s Long-Term Finances: A Stochastic Analysis.

55

address all of those problems; the estimation phase involves identifying the time-series properties of

each input and the correlations between inputs. Actual implementation involves drawing annual values

for each input using estimated equations.

6.2 Time-Series Processes for Exogenous Inputs

The estimation phase of the stochastic simulations involves empirical investigation of each input

assumption using time-series analysis. The focal point of the time-series analysis is measuring the

dynamics of a given input assumption relative to a benchmark of white noise variation; this variation is

characterized as the completely unexplained, uncorrelated movement around some central tendency. 

Time-series analysis examines each input assumption variable with the belief that a white noise process

exists and can be isolated after all empirically identifiable correlations (over time and with other

variables) have been removed. 

Time-series processes for CBOLT’s input assumptions are described here briefly (see Figure

9).23 In looking at these processes, the first noteworthy point is that the list of stochastic inputs differs in

static and growth model solutions. In a static run, the model needs input values for real wage growth

and interest rates; however, those input values are effectively replaced by total factor productivity

growth and the gap between 5- and 10-year rates in a growth model simulation. In addition, because

of correlations between variables, equations for some of the other inputs also change when moving from

mode-one to mode-two solutions. 
56

The descriptions of time-series processes (see Figure 9) for the inputs use standard

econometric shorthand notation. For example, AR-1 implies an autoregressive equation with one

lagged value. Consequently, mortality equations, the rate of improvement in each age group (actually

the rate of improvement differenced from its own mean), depend on one lagged value and a white noise

process. The ARMA(4,1) equation for fertility implies both autoregressive and moving average

components to the dynamic process. Finally, the VAR notation indicates a vector autoregression. In

those equations, each variable depends on its own lagged values and the lagged values of other

variables in the VAR. 

6.3 Properties of Stochastic Simulations

CBOLT’s stochastic simulator is, in effect, a separate model that runs at the beginning of each

simulation year. Using the time-series equations and other necessary information (usually lagged values

of the stochastic variables), the simulator generates input values for the current year. The main model

then solves for the current year with that stochastic set of inputs, just as if the inputs had been set that

way by the user in a deterministic run. A standard CBOLT stochastic simulation involves 500 to 1,000

complete CBOLT simulations, each with a different trajectory for the inputs.

Although computationally intensive, stochastic simulation provides valuable lessons about the

long-term projections. For example, one finding that was surprising at first is that the expected financial

outcomes in a stochastic simulation differ from a deterministic solution even when the inputs are set to

the same expected values. This occurs because of asymmetries in OASDI system responses with

respect to symmetric variation in the inputs. That is, if inflation is expected to be 3 percent, the time-
24See Harris, Meyerson, and Smith (2001) or Sabelhaus and Meyerson (2000) for an

application to uncertainty about trust fund investment policies.

57

series equations imply that 2.5 percent or 3.5 percent are equally likely outcomes in a stochastic

simulation. The positive financial effect of raising inflation from 3 percent to 3.5 percent does not,

however, necessarily match the negative effect of lowering inflation from 3 percent to 2.5 percent.

Stochastic simulations also provide interesting findings with respect to overall levels of

uncertainty about projections. First, although the ranges for inputs used by the Social Security actuaries

in their high- and low-cost scenarios seem reasonable, conclusions about overall system finances do

not. For instance, the low-cost scenario suggests that through the 75-year evaluation period system

solvency is within the range of plausible outcomes because the summary balance is actually slightly

positive. However, the base-case CBOLT stochastic simulation in the static (mode-one) solution with

the expected values of the inputs set to the actuaries’ intermediate values suggests only a small chance

of solvency through 2077.24

In addition, CBOLT stochastic simulations suggest that uncertainty grows dramatically as

simulations are done farther into the future and the reason for that uncertainty can be tracked back to a

handful of input assumptions. The range of plausible outcomes for measures such as the gap between

cost and income rates rises dramatically as one projects farther in time, with standard confidence

intervals swamping the size of the gap itself by the end of the evaluation period. In the near term,

however, system finances seem quite predictable because many of the important determinants of

variability in near-term finances are already known — population characteristics, earnings histories for

the soon to be retired, and so on. In the future, those determinants are, of course, much more
25See Harris, Sabelhaus, and Simpson (2003). 

58

uncertain. For instance, the effect of variability in fertility on the size of the population is much lower at

the beginning of the simulation than it is 30 years later because the size of the population at the beginning

is known, whereas the size of the population that can give birth 30 years from now can only be

projected. As a result, future fertility will be the product of cumulative random fertility, mortality, and

immigration. One of the important lessons from stochastic analysis is that the current OASDI system is

self-correcting with respect to some input assumptions, such as inflation, because both costs and

incomes are affected. Other variables only cause movements in one direction — mortality

improvement is a good example. Thus, reforms aimed at improving the expected financial status of the

system should also be evaluated in terms of their impact on the variability of outcomes. 

Stochastic analysis is also important for distributional analysis. For example, although it is

widely acknowledged that policies involving investment in private assets will yield uncertain returns, it is

not clear that the uncertainty about individual benefit levels would change dramatically for individual

accounts created using a modest carve-out with a simple benefit offset mechanism.25 Some analysts

contend that individual accounts should be analyzed using just the “risk free” interest rate, while others

contend the risk premium should be included. Stochastic analysis offers an alternative: the risk premium

is included on an expected basis, but the historical variability in equity returns provides an estimate of

the range of possible outcomes. 
59

References

Board of Trustees, Federal Old-Age and Survivors Insurance and Disability Insurance Trust Funds,

The 2003 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors

Insurance and Disability Insurance Trust Funds (Washington, D.C.: U.S. Government

Printing Office, March 2003).

Congressional Budget Office, The Long-Term Budget Outlook (Washington, D.C.: Congressional

Budget Office, December 2003)

Congressional Budget Office, The Budget and Economic Outlook: Fiscal Years 2004-2013

(Washington, D.C.: Congressional Budget Office, January 2003).

Congressional Budget Office, Uncertainty in Social Security’s Long-Term Finances: A Stochastic

Analysis (Washington, D.C.: Congressional Budget Office, December 2001).

Congressional Budget Office, CBO’s Method for Estimating Potential Output: An Update

(Washington, D.C.: Congressional Budget Office, August 2001).

Harris, Amy Rehder, “Modeling Retirement Behavior: A Test of the Option Value Model Using the

Health and Retirement Study.” (Draft) Washington, D.C.: Congressional Budget Office, (April

2002). 

Harris, Amy Rehder, and Josh O’Harra, “The Impact of Marriage and Labor Force Participation

Trends on the Social Security Benefits of Women.” Proceedings of the National Tax

Association (November 2001) 

Harris, Amy Rehder, Noah Meyerson, and Joel V. Smith, “Social Insecurity? The Effects of Equity

Investments on Social Security Finances,” National Tax Journal, LIV(3) (2001), pp. 645-

668.

Harris, Amy Rehder, and John Sabelhaus, “Projecting Longitudinal Earnings for Long-Term Policy

Analysis,” Technical Paper 2003-2. Congressional Budget Office (April 2003).

Harris, Amy Rehder, John Sabelhaus, and Michael Simpson, “Uncertainty About OAI Worker

Benefits Under Individual Accounts.” (Draft) Congressional Budget Office (May 2003). 

Harris, Amy Rehder, John Sabelhaus, and Michael Simpson, “Projecting Long-Run OASDI Benefits:

Implications of Adopting a Dynamic Micro-Simulation Approach.” (Draft) Congressional

Budget Office (January 2004).
60

Ibbotson Associates, Stocks, Bonds, Bill and Inflation 2000 Yearbook: Market Results for 1926-

1999 (Chicago: Ibbotson Associates, 2000).

O’Harra, Josh, and John Sabelhaus, “Projecting Longitudinal Marriage Patterns for Long-Term Policy

Analysis,” Technical Paper 2002-2. Congressional Budget Office, (October 2002).

Perese, Kevin, “Mate Matching for Micro-Simulation Models,” Technical Paper 2002-3,

Congressional Budget Office (November 2002).

President’s Commission to Strengthen Social Security, Strengthening Social Security and Creating

Personal Wealth for All Americans (Washington, D.C.: Report of the President’s

Commission to Strengthen Social Security, December 2001), available at

www.csss.gov/reports/Final_report.pdf.

Sabelhaus, John and Noah Meyerson, “Uncertainty in Social Security Trust Fund Projections.”

National Tax Journal. 53:3, 515-530. (September 2000). 

Sabelhaus, John, Julie Topoleski, and Michael Simpson, “Incorporating Longevity Effects in LongTerm Medicare Projections.” (Draft) Congressional Budget Office (November 2003).

Sabelhaus, John, “For Whom the Bill Tolls: Distributing the Cost of Maintaining Social Security

Solvency.” (Draft) Congressional Budget Office, (May 2003). 

Smith, Joel V. and John Sabelhaus, “Alternative Methods for Projecting Equity Returns: Implications

for Evaluating Social Security Reform Proposals” Technical Paper 2003-8. Congressional

Budget Office (August 2003).

Social Security Administration, Office of the Chief Actuary, “Internal Real Rates of Return Under the

OASDI Program for Hypothetical Workers” (Washington, D.C.: SSA Actuarial Note No.

144, June 2001).
61

Figure 1.

Year-by-Year Solution Sequence for Static and Growth Model Simulations

Solution Mode 1

(Static Macro)

Solution Modes 2 and 3

(Growth Model)

1. Population 1. Population

2. Labor Force 2. Labor Force

3. Production and Output

4. Incomes and Factor Prices

3. Social Security Covered Workers 5. Social Security Covered Workers

4. Social Security Payroll Taxes 6. Social Security Payroll Taxes

5. Social Security Beneficiary Counts 7. Social Security Beneficiary Counts

6. Social Security Benefit Amounts 8. Social Security Benefit Amounts

7. Social Security Trust Funds 9. Social Security Trust Funds

10. Medicare

11. Other Federal Revenues and Spending

12. State and Local Budgets

13. Foreign Transactions

14. Private Saving

15. Next Period Beginning Capital Stock
62

Figure 2.

Actuarial Versus Dynamic Micro Simulation Modules in 

Growth Model Solutions (Modes 2 and 3)

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic Micro Simulations

1. Population C Apply mortality rates by age and

sex to population matrix, solve for

number of deaths.

C Apply fertility rates by age to

female population matrix, solve for

number of births.

C Add immigrants; overall count is

exogenous, distribution by age and

sex uses fixed percentages.

C Apply fixed marital distribution to

population by age and sex; solve

for counts of never married,

married, divorced, and widowed.

C Use actuarial modules to update

overall population matrix by age and

sex.

C Apply mortality, fertility, and

immigration rates to micro sample;

add and drop observations in micro

sample through births, immigrations,

and deaths.

C Apply marital transition equations

across four possible marital states.

C Put observations entering marriage

(from never married, divorced, or

widowed) through mate-matching

algorithm.

2. Labor Force C Solve for labor force participation

rates across 103 age, sex, and

marital status groups; independent

variables include trends and Social

Security benefit replacement rates.

C Apply labor force participation and

exogenous unemployment rates to

population matrix to yield

employment matrix by age and sex;

multiply by exogenous average

hours to solve for total hours

worked.

C Predict labor force participation for

each person in the micro sample;

determinants include age, sex, cohort

trends, marital status, benefit levels,

and taxes.

C Predict part versus full time and hours

worked for each observation; same

determinants.

C Apply exogenous unemployment rates

to micro sample.

C Sum hours worked in micro sample to

solve for total hours worked.
63

Figure 2, Continued

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic Micro Simulations

3. Production

and Output

C Solve for aggregate production

based on total hours worked (from

actuarial modules), total capital

stock, and exogenous total factor

productivity.

C Solve for aggregate production based

on total hours worked (from micro

sample), total capital stock, and

exogenous total factor productivity.

4. Incomes and

Factor Prices

C Solve for total earnings and capital

income (given aggregate

production and exogenous prices)

using first-order conditions from

production function.

C Compute rate of real wage growth,

which is earnings divided by hours

worked, adjusted for earnings as a

share of compensation and other

factors.

C Solve for interest rate by dividing

net capital income by capital stock.

C Perform same first three steps as in

the actuarial model.

C Compute individual earnings for

observations in the micro sample with

positive labor supply; apply

permanent and transitory shocks and

overall real wage growth.

5. Social

Security

Covered

Workers

C Apply fixed covered worker rates

by age and sex to aggregate

employment matrix solved for in

labor force module.

C Assign covered worker status to

micro sample; randomly set small

fraction of earnings for workers not

covered by Social Security.

6. Social

Security Payroll

Taxes

C Compute aggregate taxable payroll

on the basis of fixed univariate

earnings distribution and taxable

maximum policy parameter, aged

forward using real wage and

covered worker growth rates.

C Compute aggregate taxable payroll by

summing taxable earnings (covered

earnings below the taxable maximum)

across all observations in the micro

sample.
64

Figure 2, Continued

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic Micro Simulations

7. Social

Security

Beneficiary

Counts

C Compute OAI worker beneficiary

count using insured worker rates

by age and sex for people ages 62

through 70, which in principle is

based on past covered worker

counts but in practice is nearly 100

percent and constant.

C Compute DI worker beneficiary

counts by age and sex on the basis

of exogenous DI incidence and

termination rates applied to

underlying age- and sex-specific

probabilities.

C Compute dual beneficiaries using

an SSA formula that predicts duals

on the basis of earnings of men

versus women.

C Compute other auxiliary

beneficiaries largely on the basis of

exogenous ratios of population

subgroups; for example, widow

beneficiaries are a fixed fraction of

the number of widows ages 60 and

older.

C Compute OAI worker beneficiary

count by applying OAI worker

claiming equation to all observations in

the dynamic micro model with age

greater than or equal to early

retirement age. The main explanatory

variable in the equation is the level of

benefits relative to earnings

(replacement rate) with one parameter

controlling response to benefit

changes.

C Compute DI worker beneficiary

counts by applying micro DI incidence

and termination equations; exogenous

DI incidence and termination rates by

age and sex determine overall flows,

with person-level relative

probabilities of DI affected by

individual relative earnings.

C Compute dual and other auxiliary

beneficiary counts on the basis of

actuarial modules, though

implementation of micro-level

nonworker beneficiary status is under

way.
65

Figure 2, Continued

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic Micro Simulations

8. Social

Security Benefit

Amounts

C Compute new OAI and DI worker

beneficiary awards using “static”

micro simulation on CWHS

samples of 1996 beneficiaries with

earnings and labor force

participation adjusted to be

basically consistent with

actuarial/macro labor force and

earnings outcomes.

C “Age” forward existing OAI and

DI average worker benefits by age,

sex, and age at entitlement using

fixed ratios derived from SSA

data; those ratios control for the

effects of differential mortality and

recomputations on average benefits

within a cohort.

C Compute auxiliary benefits are

based on fixed (SSA) ratios to

OAI and DI workers.

C Compute new OAI and DI worker

benefits using dynamic micro

simulation with earnings and labor

force participation automatically

consistent with actuarial/macro labor

force and earnings outcomes (since

those are based on the same micro

sample). Existing OAI and DI worker

average benefits are the result of

tracking benefits for those who remain

alive; that implies the effect of

differential mortality will not be

observed in benefits unless mortality

rates are adjusted for earnings.

C Compute auxiliary benefits in the same

way as actuarial benefits, though

implementation of nonworker benefits

is under way.

9. Social

Security Trust

Funds

C Apply OASI and DI tax rates to

taxable payroll, add benefits tax,

and solve for total system income.

C Multiply average benefits by

number of beneficiaries across

age/sex groups and for each

worker/auxiliary outlay category,

add administrative costs, and solve

for total system costs.

C Set increments for OASI and DI

trust funds using costs, income, and

interest rates.

C Apply OASI and DI tax rates to

individual earnings up to taxable

maximum, add benefits tax, and solve

for total system income.

C Sum benefits paid to OAI and DI

worker beneficiaries, sum actuarialbased auxiliary outlays, add

administrative costs, and solve for

total system costs.

C Set increments for OASI and DI trust

funds using costs, income, and interest

rates.
66

Figure 2, Continued

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic Micro Simulations

10. Other

Federal

Revenues and

Spending

C Solve for aggregate outlays in

Medicare and other “age-related”

programs using population matrix

and fixed age/sex spending

indexes.

C Solve for other (non-age-related)

federal spending using fixed ratios

to GDP.

C Solve for aggregate personal and

corporate income taxes, indirect

business taxes, and non-Social

Security social insurance taxes

using fixed ratios to aggregate

taxable income.

C Adjust tax and/or spending rates if

a deficit rule is activated and

recompute outlays and revenues to

meet deficit rule.

C Perform the same steps as in the

actuarial modules; no micro detail is

available for taxes and spending

(except Social Security).

11. State and

Local Budgets

C Use fixed ratios of taxes and

outlays (except age-related) to

GDP to set most values.

C Adjust taxes when expenditures on

age-related programs change such

that state and local deficit is

constant as a share of GDP. 

C Perform the same steps as in the

actuarial modules; no micro detail is

available for state and local taxes or

spending.

12. Foreign

Transactions

C Use fixed ratios of capital flows to

GDP to determine net exports and

net foreign investment.

C Calculate net exports to close out

product side of GDP accounts and

net foreign investment to help

determine next period capital.

C Perform the same steps as in the

actuarial modules.
67

Figure 2, Continued

Solution Mode 2

Actuarial Module Simulations

Solution Mode 3

Dynamic-Micro Simulations

13. Private 

Savings

C Determine private savings using

one of several options: complete

savings offset (Ricardian), constant

saving elasticity, variable savings

elasticity, and variable deficit offset.

With complete savings offset, as

federal debt increases, private

savings matches one for one.

Constant savings elasticity means

that as debt rises, the capital stock

initially falls, then interest rates rise,

causing savings to rise and offset

part of initial loss. For the other

options, the degree of deficit offset

or response to interest rates varies

with the level of debt; if debt gets

very high, economy is effectively

Ricardian.

C Perform the same steps as in the

actuarial modules. Implementation of

micro-level savings responses (based

on forward-looking behavior) is under

way.

14. Next

Period

Beginning

Capital Stock

C Solve for total investment as the

sum of private savings, federal

government saving, state and local

government saving, and net foreign

investment.

C Allocate investment across several

types of capital; each capital stock

grows by investment minus

depreciation, overall stocks are

then weighted to determine

aggregate capital input for

production function.

C Perform the same steps as in the

actuarial modules.
68

Figure 3.

Policy Parameters and Exogenous Input Assumptions

in CBOLT Static Macro (Mode 1) Simulations

Social Security Basic Parameters Economic Input Assumptions

Payroll Tax Rate CPI-W Inflation 

COLA Differential Unemployment Rate

Benefit Formula Replacement Factors Real Wage Differential

Number Years AIME Computation Real Interest Rate 

Normal Retirement Age Disability Incidence Rate

Actuarial Reduction Rate Disability Termination Rate

Delayed Retirement Credit Equity and Corporate Bond Return Rates

Bend Points Individual Account Parameter Settings

Taxable Maximum IA Administrative Cost/Transfer Rules

Demographic Input Assumptions IA Contribution Rates

Mortality IA Participation Rules and Behavior

Fertility IA Portfolio Allocation

Immigration IA Annuitization Assumptions

Exogenous Trust Fund Assumptions IA Benefit Offset Rules

Administrative Expense Rate Other Policy Rules

Benefits Tax Rate Trust Fund Investment Policy

General Fund Transfers Low-Wage Supplementation

Transfers to Railroad Retirement Trust Fund Shortfall Responses
69

Figure 4.

Policy Parameters and Exogenous Input Assumptions

in CBOLT Macro Growth (Modes 2 and 3) Simulations

Social Security Basic Parameters Economic Input Assumptions

Payroll Tax Rate CPI-W Inflation 

COLA Differential Unemployment Rate

Benefit Formula Replacement Factors Disability Incidence Rate

Number Years AIME Computation Disability Termination Rate

Normal Retirement Age Equity and Corporate Bond Return Rates

Actuarial Reduction Rate Total Factor Productivity

Delayed Retirement Credit Gap Between CPI-W and GDP Prices

Bend Points Gap Between Computer/Other Prices

Taxable Maximum Gap Between 10- and 5-Year Interest Rates

Demographic Input Assumptions Average Hours Growth Rate

Mortality Improvement Growth of Taxable Earnings/Compensation

Fertility Capital Share of Output

Immigration Individual Account Parameter Settings

Exogenous Trust Fund Assumptions IA Administrative Cost/Transfer Rules

Administrative Expense Rate IA Contribution Rates

Benefits Tax Rate IA Participation Rules and Behavior

General Fund Transfers IA Portfolio Allocation

Transfers to Railroad Retirement IA Annuitization Assumptions

Other Policy Rules IA Benefit Offset Rules

Trust Fund Investment Policy Behavioral Assumptions/Parameters

Low-Wage Supplementation Private Savings Functional Form 

Trust Fund Shortfall Responses Private Savings Response Elasticity

Federal Tax/Spending Policy Hours Worked Elasticity and Marginal Rate

Debt-Ceiling Response Policy Medicare Excess Cost Growth
70

Figure 5.

CBOLT Federal Tax, Spending, Debt-Limit, and Trust Fund Shortfall Rules

Federal Tax Rules

C Fix revenues at year 10 percentage of GDP for all years after year 10.

C Fix revenues at year 1 percentage of GDP for all years after year 10.

C Temporarily spend non trust fund surpluses after year 10.

C Permanently cut taxes/raise spending to balance 2011 non trust fund budget.

C Temporarily spend non trust fund surplus in all years.

C Spend non trust fund surplus and fix percentage of GDP after.

C Balance non trust fund budget after year 10.

C Balance non trust fund budget in all years.

C Temporarily spend non trust fund surplus years 1 to 10, use year 10 percentage of GDP after.

Federal Spending Rules

C Fix spending at year 10 percentage of GDP for all years after year 10.

C Fix spending at year 1 percentage of GDP for all years after year 10.

C Temporarily spend non trust fund surpluses after year 10.

C Permanently cut taxes/raise spending to balance 2011 non trust fund budget.

C Temporarily spend non trust fund surplus in all years.

C Temporarily spend non trust fund surplus years 1 to 10, use year 10 percentage of GDP after.

Federal Debt-Ceiling Rules

C Choose no debt ceiling or ceilings of 0, 50, 100, 200, or 500 percent of GDP.

C Choose whether to use tax increases or spending cuts to stay under the ceiling.

Trust Fund Shortfall Rules

C Accumulate debt (current law benefits).

C Raise payroll taxes.

C Reduce new benefit awards.

C Reduce all benefits.
71

Figure 6.

CBOLT Aggregate Private Savings Equations for Growth Model Simulations

Savings

Equation Description Properties

Targeted

Capital to

Output Ratio

Private savings adjusts basically dollar

for dollar to government deficits in

current year, in order to keep capital

to output ratio fixed over time.

Complete offset eliminates any impact

of government deficits on investment

and thus economic growth. This is the

growth model version of the “static”

solution.

Constant

Savings

Elasticity

Private savings is a fixed percentage of

GDP, adjusted (on a fixed elasticity

basis) for changes in interest rates. 

Default parameter = 0.2.

As government debt rises, interest

rates rise, and private savings starts to

rise with a lag. The smaller the

elasticity is, the less the increase in

private savings and the higher the

crowding out.

Variable 

Savings

Elasticity

Modified version of the constant

savings elasticity in which the

responsiveness of private savings

depends on the level of the interest rate

through a logistic function. Default

parameter = -0.33.

Similar to the constant elasticity, but

the degree of responsiveness changes

as debt rises, so the variable elasticity

actually becomes more “Ricardian” as

interest rates rise.

Variable 

Deficit 

Offset

Variable version of the complete

savings offset; rather than have private

savings offset every dollar of

government debt, the fraction offset

varies with the interest rate. 

Default parameter = -2.0.

Like variable savings elasticity, the

degree of responsiveness changes as

debt rises, so the variable elasticity

actually becomes more “Ricardian” as

interest rates rise.

Partial 

Savings 

Offset

Similar to complete offset model in that

the fraction of debt offset does not

vary, but here the fraction is below

one. 

Default parameter = 0.4.

Interest rate has no effect on offset

parameter, so equation can actually

lead (under high debt levels) to

unstable economy, because private

savings becomes too low to fund any

new investment.
72

Figure 7.

Person-Level Variables Tracked in the CBOLT Dynamic Micro Model

Variable Description/Possible Values

Birth Year Year of birth; given simulation year, derive age

Immigration Year Year of immigration

Sex 1=male, 2=female

Education 0=<14 year of education, 1=14+

Marital Status For each age 0 to 100, 1=never married, 2=married,

3=divorced, 4=widowed

Need Spouse Logical variable = true if in marriage market

Divorce Eligible Indicator if divorced with marriage of 10+ years

Earnings For each age 16 to 90, level of actual earnings

Percentage Earnings Uncovered For each age 16 to 90, the fraction of person’s earnings not

covered by Social Security

Full Time Equivalent Earnings For current age, level of earnings if the person worked full

time with no unemployment

Lifetime Earnings Average earnings through current year

Lifetime Earnings Quintile Classifier for lifetime earnings

Labor Force Participation For each age 16 to 90, 0 = not in labor force, 1 = part time

with unemployment, 2 = part time, 3 = full time with

unemployment, 4 = full time

Permanent Earnings Differential Evolving heterogeneous earnings component; subject to

“permanent” shock each year

Social Security Benefit Level of current-year benefit

Social Security Benefit Type For each age 16 to 100, 1=OAI, 2=DI, 3=OAI dual, 4=SI

dual, 5(6)=OAI (div) aged spouse, 7(8)=SI (div) aged

widow, 9(10)=SI (div) disabled widow, 11(12)=DI (div)

aged spouse

IA Participation Indicator for whether participating in IA

DI Earnings Decile Earnings classifier for DI incidence equation

Spouse Identifier Pointer variable to spouse’s information
73

Figure 8.

Person-Level Transition Processes in the CBOLT Dynamic Micro Model

Transition

Process

Micro

Determinants Estimation/Implementation in Dynamic Model

Mortality Age, sex,

cohort, disability

insurance status

C No estimation; mortality rates in actuarial model determine

population by age and sex in each year; ratio of micro

sample to target population by age and sex determines

mortality probability.

C Actual mortality based on comparing derived probability

to a random number; within age/sex groups people on DI

more likely to die.

Fertility None C No estimation; fertility rates in actuarial module determine

the number of new births.

C New observations are added to the micro sample at a rate

of 1:1000 births.

Immigration None C No estimation; immigration rates in actuarial module

determine the number of new immigrants added to the

micro sample at a rate of 1:1000.

C Age/sex distribution of immigrants is based on the fixed

actuarial distribution but with a random component

because rounding to nearest 1,000 loses too much detail.

First Marriage Age, sex,

earnings,

education,

cohort

C Separate logit equations for probability of first marriage

estimated by single year of age (17 through 60) using

matched SIPP/SSA earnings data.

C Actual marital events are based on comparing derived

probability to a random number.

Divorce Age, sex,

education,

earnings, cohort,

marital history

and current

duration

C Separate logit equations for probability of divorce

estimated by single year of age (17 through 70) using

matched SIPP/SSA earnings data. 

C Actual divorce events are based on comparing average of

derived probabilities for couple to a random number.

Widowing None C Happens automatically in model when linked spouse is deallocated in mortality module.
74

Figure 8, Continued

Transition

Process

Micro

Determinants Estimation/Implementation in Dynamic Model

Remarriage Age, sex,

education, 

earnings, cohort, 

duration since

last marriage

C Separate logit equations for probability of remarriage

estimated by single year of age (17 through 70) using

matched SIPP/SSA earnings data.

C Actual remarriage events are based on comparing derived

probability to a random number.

Mate Matching Age difference

between bride

and groom;

bride’s marriage

number;

education and

earnings gaps

between bride

and groom 

C Logistic equations for probability of husband/wife union

estimated using matched SIPP/SSA earnings data set with

marriages that occurred in 1994, 1995, or 1996.

C Separate equations estimated for men’s first and higher

order-marriages. 

C Actual unions in CBOLT are based on comparing the

probability of a particular couple getting married to a

random number in a stochastic “marriage market.”

Labor Force

Participation

Age, sex, marital

status,

beneficiary

status, lagged

participation,

cohort

C Separate logit equations for probability of working for

ages 16 through 90 estimated using pooled March CPS

data for 1966 through 2001. 

C Labor force participation events are based on comparing

derived probability to a random number.

Part Time/

Full Time

Age, sex, marital

status, lagged

participation,

lagged part

time/full time, 

cohort

C Separate logit equations for probability of working full

time (conditional on working) for ages 16 through 90

estimated using PSID data for 1968 through 1992.

C Working full time event is based on comparing derived

probability to a random number.

Unemployment Age, sex, part

versus full time

C Unemployment rates are set exogenously, translated from

weighted average rates to probability of spells and

allocated across part versus full time workers on the basis

of CPS data.

C Actual implementation is based on comparing the

probability of spell to random number, assigning average

spell to all unemployed people.
75

Figure 8, Continued

Transition

Process

Micro

Determinants Estimation/Implementation in Dynamic Model

Earnings Age, sex,

education,

cohort

C Separate equations for men and women estimated using

CPS data for 1976 through 2001.

C Independent variable is the log of real full-time equivalent

earnings.

C Actual implementation is based on decomposing personspecific error term into (1) a highly auto-correlated

“permanent differential”that evolves through iid

“permanent” shocks and (2) an uncorrelated “transitory”

shock; thus, each person gets two random numbers each

year that together (with demographics) determine

earnings.

Taxable Payroll None C No estimation; taxable payroll is all earnings below taxable

maximum parameter.

DI Claiming and

Benefits

Age, sex, lagged

relative earnings,

eligibility status 

C OACT incidence probabilities are adjusted to reflect

information derived from historical earnings of DI

claimants using CWHS.

C Actual DI claiming is based on comparing adjusted

probabilities to a random number.

C Benefit amounts are based on an individual’s earnings and

DI benefit parameters.

OAI Claiming

and Benefits

Age, sex, OAI

benefits relative

to earnings,

eligibility status

C Conditional claiming probabilities by age and sex are

computed using CWHS in 1997 and 1998 for eligible

claimants.

C Additional benefit replacement rate parameter in logistic

equation is used to adjust conditional probability; that

introduces an effect of changes in benefit replacement

rates on future cohorts. 

C Actual OAI claiming are based on comparing adjusted

conditional probabilities to a random number.

C Benefit amounts are based on an individual’s earnings and

OAI benefit parameters; includes retirement earnings test

and recomputations.
76

Figure 9.

Stochastic Inputs in CBOLT Monte Carlo Simulations

Variable Description of Stochastic Process

Mortality Improvement Separate AR-1 equations for each of 21 age and 2 sex groups

estimated using data for 1900 through 1995. Model draws

correlated errors across 42 groups using multivariate normal

distribution.

Fertility ARMA(4,1) equation for overall fertility rate estimated using data

for 1917 through 1997.

Immigration ARMA(4,1) equation for total immigration estimated using data for

1901 through1995.

Unemployment VAR model with two lags each on unemployment, inflation, and

real interest rate (static) or real interest gap (growth model)

estimated using data for 1954 through 1999.

Inflation VAR model with two lags each on unemployment, inflation, and

real interest rate (static) or real interest gap (growth model)

estimated using data for 1954 through 1999.

Real Wage Growth

(Static Model)

Level of nominal wage is a function of three economic variables;

equation estimated using data for 1954 through 1999; real wage is

nominal wage minus inflation.

Total Factor Productivity

(Growth Model)

A white noise process; standard deviation of innovations is 

computed using data for 1950 through 2000.

Real Interest Rate

(Static Model)

VAR model with two lags each on unemployment, inflation, and

real interest rate (static) or real interest gap (growth model)

estimated using data for 1954 through 1999.
77

Figure 9, Continued

Variable Description of Stochastic Process

Interest Rate Gap

(Growth Model)

VAR model with two lags each on unemployment, inflation, and

real interest rate (static) or real interest gap (growth model)

estimated using data for 1954 through1999.

DI Incidence AR-1 model for overall DI incidence rate estimated using data for

1975 through 1998.

DI Termination AR-1 model for overall DI termination rate estimated using data

for 1975 through 1998.

Equity Returns Several options for equity returns, all based on data for Ibbotson

large cap returns during the period from 1954 through 1999. First

option is a white noise process; second includes inflation,

unemployment, and interest rate (level or gap, depending on

solution) estimated over the period from 1954 through 1999; third

includes same economic variables and a log dividend price ratio,

estimated over the 1954-1999 period.

Corporate Bond Returns Level of bond returns is a function of inflation, unemployment, and

interest rate (level or gap, depending on solution mode), estimated

over the 1954-1999 period.