TREASURY’S PANEL MODEL FOR TAX ANALYSIS 

by 

James R. Nunns, Deena Ackerman, James Cilke 

Julie-Anne Cronin*, Janet Holtzblatt, Gillian Hunter 

Emily Lin, and Janet McCubbin 

OTA Technical Working Paper 3 

The authors are grateful to the staff of SOI and participants in the Tax Economist Forum for comments 

and suggestions. 

Director of Economic Issues at the AARP Public Policy Institute. 

represent those of the U.S. Department of the Treasury, the Joint Committee on Taxation, the 

Economist at OTA. The views expressed in this paper are those of the authors and do not necessarily 

Janet Holtzblatt is a Senior Analyst with the Congressional Budget Office. 

Financial Economists at OTA. James Cilke is a Senior Economist at the Joint Committee on Taxation. 

All the authors participated in building the Panel Model while working at the Office of 

Tax Analysis (OTA). Currently, James R. Nunns is the Director of Tax Policy for the New Mexico 

Taxation and Revenue Department. Deena Ackerman, Julie-Anne Cronin and Gillian Hunter are 

 Emily Y. Lin was formerly a Financial 

Janet McCubbin is the 

Congressional Budget Office, the New Mexico Taxation and Revenue Department, or the AARP. 

Office of Tax Analysis 

U.S. Department of the Treasury
 

1500 Pennsylvania Avenue, NW 

Washington, D.C. 20220 

* U.S. Department of the Treasury. julie.anne.cronin@do.treas.gov 

OTA Technical Working Papers is an occasional series of reports on the research, models and 

datasets developed to inform and improve Treasury’s tax policy analysis. Views and opinions 

expressed are those of the authors and do not necessarily represent official Treasury positions or 

policy. OTA Technical Working Papers are distributed in order to document OTA analytic 

methods and data and invite discussion and suggestions for revision and improvement. 

Comments are welcome and should be directed to the authors. 

July 2008
 
 

TREASURY’S PANEL MODEL FOR TAX ANALYSIS 

by 

James R. Nunns, Deena Ackerman, James Cilke 

Julie-Anne Cronin*, Janet Holtzblatt, Gillian Hunter 

Emily Lin, and Janet McCubbin 

OTA Technical Working Paper 3 

July 2008
 

OTA Technical Working Papers is an occasional series of reports on the research, models and 

datasets developed to inform and improve Treasury’s tax policy analysis. Views and opinions 

expressed are those of the authors and do not necessarily represent official Treasury positions or 

policy. OTA Technical Working Papers are distributed in order to document OTA analytic 

methods and data and invite discussion and suggestions for revision and improvement. 

Comments are welcome and should be directed to the authors. 

Office of Tax Analysis 

U.S. Department of the Treasury
 

1500 Pennsylvania Avenue, NW 

Washington, D.C. 20220 

* U.S. Department of the Treasury. julie.anne.cronin@do.treas.gov 

The authors are grateful to the staff of SOI and participants in the Tax Economist Forum for comments 

and suggestions. All the authors participated in building the Panel Model while working at the Office of 

Tax Analysis (OTA). Currently, James R. Nunns is the Director of Tax Policy for the New Mexico 

Taxation and Revenue Department. Deena Ackerman, Julie-Anne Cronin and Gillian Hunter are 

Financial Economists at OTA. James Cilke is a Senior Economist at the Joint Committee on Taxation. 

Janet Holtzblatt is a Senior Analyst with the Congressional Budget Office. Janet McCubbin is the 

Director of Economic Issues at the AARP Public Policy Institute. Emily Y. Lin was formerly a Financial 

Economist at OTA. The views expressed in this paper are those of the authors and do not necessarily 

represent those of the U.S. Department of the Treasury, the Joint Committee on Taxation, the 

Congressional Budget Office, the New Mexico Taxation and Revenue Department, or the AARP. 
 

Table of Contents 

Treasury’s Panel Model for Tax Analysis ....................................................... 1 

I. Introduction .................................................................................. 1 

II. Data Used in the Model .................................................................... 3 

A. Data Requirements for a Panel Model ............................................... 3 

B. Cohort Panel ............................................................................. 4 

C. Refreshment Panel ....................................................................... 4 

D. Permanent Non-Filers (PSID) ................................................................. 4 

E. Information Return Data ............................................................... 5 

F. Estate Tax Data .......................................................................... 5 

G. Age and Gender ......................................................................... 5 

H. Imputations .............................................................................. 6 

III. Extrapolation ................................................................................. 7 

A. General Approach ...................................................................... 7 

B. Defining Cells ........................................................................... 8 

C. Matching at the Micro Level .......................................................... 8 

D. Hitting Aggregate Targets ............................................................. 9 

E. Second Extrapolation ................................................................... 9 

F. Updating the Extrapolation ............................................................. 9 

IV. Distributional Analysis with the Model ................................................... 10 

A. Methodology Summary ................................................................. 11 

B. Measuring Welfare ...................................................................... 12 

C. Measuring Consumption .......................................................................... 15 

D. Measuring Taxes ................................................................................... 15 

E. Illustration: Tax Burdens under Current Law .............................................. 16 

F. Illustration: Comparison to Single-Year Table ............................................. 18 

G. Illustration: Distributional Analysis of Recent Legislation ............................ 20 

V. Dynamic Tabulations with the Model ..................................................... 22 

A. Illustration: EGTRRA and JGTRRA .......................................................... 22 

B. Simulation 1: Effect of Demographic and Economic Dynamics .................. 23 

C. Simulation 2: Effect of EGTRRA and JGTRRA Phase-ins ....................... 24 

D. Simulation 3: Effect of EGTRRA and JGTRRA Sunsets ......................... 24 

E. Simulation 4: Effect of the AMT on EGTRRA and JGTRRA Benefits ......... 25 

F. Combined Effect of Phase-in, Sunset, and AMT on EGTRRA Benefits ......... 25 

i 
VI. Conclusions ................................................................................................... 28 

A. How the Model Improves Tax Analysis ...................................................... 28 

B. Limitations of the Model ......................................................................... 29 

Appendices 

A. Basic Data 

1. Constructing the Tax Return Panels ................................................. A.1 

2. Data from the PSID ................................................................... A.2 

3. Estate Tax Data ........................................................................ A.3 

B. Weighting the Panel ...................................................................... B 

C. Imputations 

1. Consumption .......................................................................... C.1 

2. Tuition Expenses for Postsecondary Education .................................. C.2 

3. Health Insurance Status ............................................................... C.3 

4. Transfer Income ....................................................................... C.4 

D. Extrapolation 

1. Notes .................................................................................... D.1 

2. Algebraic Description ................................................................ D.2 

3. Shortcut Update ....................................................................... D.3 

4. Estate Tax Data ......................................................................... D.4 

E. Equivalence Scales ........................................................................ E 

ii 
TREASURY’S PANEL MODEL FOR TAX ANALYSIS 

Synopsis 

Over time an individual’s demographic or economic status may change in ways that significantly 

change how they are affected by current or proposed federal tax laws. In addition, some 

provisions of the tax law have effects over multiple years, and the effects of some tax provisions 

change over time due to phase-ins, phase-outs, and other factors. Current economic analyses of 

the effect of federal tax laws are generally based on data from “cross-section” samples, so 

analyses capture the demographic and economic circumstances of individuals and the provisions 

of federal tax law only at a single point in time. Treasury’s Office of Tax Analysis has developed 

a new model based on data from a “panel” sample that included the same individuals for 10 

years. This model captures the changing demographic and economic circumstances of 

individuals and the effects of changes in tax law over the entire 10-year budget window. 

Analyses based on the model will provide policy makers and the general public a deeper 

understanding of the effects of current and proposed tax policies. 

I. Introduction 

Treasury’s Office of Tax Analysis (OTA) and its counterparts in the Congress, the Joint 

Committee on Taxation (JCT) and the Congressional Budget Office (CBO), are responsible for 

providing policy makers with economic analyses of the effects of current and proposed federal 

tax law. Typical analyses address such issues as the effect of the law on government receipts, 

the effect on the distribution of the tax burden, and the effect on incentives to work, save, and 

engage in other economic activities. Until recently, these analyses have been based almost 

exclusively on data from “cross-section” samples, or models based on such data. A cross-section 

sample is one drawn from a population at a point in time, such as a sample of all tax returns filed 

in a particular year. Such samples are designed to accurately represent the characteristics of the 

population for the year or other point in time covered by the sample. 

OTA, JCT, and CBO have routinely extrapolated cross-section samples so that they represent the 

population in each of a number of future years. These extrapolated cross-sections have allowed 

us to analyze the effects of changes in the tax law in each of these future years. Such 

extrapolations, however, do not allow us to analyze the effects of changes in the tax law on 

individuals over a period of time longer than a year, since in these extrapolated cross-sections an 

individual in the sample represents a different set of individuals in each year of the extrapolation. 

For example, if the sample is extrapolated over ten years, a 35-year old married man with two 

children and no sources of income other than wages in the first year still represents a 35-year old 

married man with two children and income solely from wages in the tenth year. The 

extrapolated cross-section will have a 45-year old male in the tenth year that represents the 

original 35-year old, but he will be an entirely different person, and no ten-year “history” will 

connect the two. 

- 1 ­
 

 

 

In addition, some provisions of the tax law have effects over multiple years and the effects of 

some tax provisions change over time due to phase-ins, sunsets, and other factors. Until 

recently, economic analyses of the effect of federal tax laws have been almost exclusively based 

on data from “cross-section” samples. Such analyses capture the demographic and economic 

circumstances of individuals and the provisions of federal tax law only at a single point in time, 

even though the actual provisions would not be well described by this snapshot. 

To be able to analyze the effect of changes in tax law on individuals over time, each individual in 

the sample must represent the same set of individuals over time in the extrapolation. Also, for 

each individual we must know the changes in their demographic and economic characteristics in 

each year of the extrapolation period. Over time an individual’s demographic or economic status 

may change in ways that significantly change how they are affected by current or proposed 

federal tax laws. We must know, for example, whether individuals change their dependency or 

marital status; have children and possibly other dependents of their own; enter and leave the 

workforce, perhaps permanently at retirement; change their patterns of consumption and savings; 

reallocate their assets; and relocate among local and state fiscal jurisdictions. Data on such 

demographic and economic changes must come from a “panel” sample, one in which the same 

individuals are sampled over multiple years. 

Treasury’s Office of Tax Analysis has developed a model based on data from a “panel” sample 

that included the same individuals for 10 years. Analyses based on OTA’s panel model provide 

policy makers and the general public a more complete understanding of the effects of current and 

proposed tax policies. This model captures the changing demographic and economic 

circumstances of individuals and the effects of changes in tax law over the entire 10-year budget 

window. Ten year distributional analyses using Treasury’s panel model were included in the 

final report of the 2005 Tax Reform Panel.1 

Distributional analyses with models using multi-year data have been developed by Davies et al. 

(1984) and Fullerton and Rogers (1993). Their models used small samples of representative 

taxpayers to simulate lifetime paths of income and tax burdens. The model developed at OTA is 

based on a much larger and detailed panel data set and is capable of analyzing tax proposals in 

greater detail than these models, but covers a shorter time period than entire lifetimes. Unlike 

Fullerton and Rogers’ model, the OTA panel model is not a general equilibrium model. 

Three previous papers2

 have described the design of Treasury’s new panel model and presented 

some preliminary distributional analyses and dynamic tabulations from the model. This paper 

builds on the three papers by providing greater detail on the development of the model as well as 

updated distributional and dynamic tabulation analyses. The paper is organized into six sections 

1

 Report of the President’s Advisory Panel on Federal Tax Reform, “Simple, Fair, and Pro-Growth: Proposals to 

Fix America’s Tax System,” November 2005, ( http://www.taxreformpanel.gov/final-report/ ) 

2

 See Cilke, et al. (2000), Cilke, et al. (2001), and Cronin et. al. (2003). All work on this project by James Cilke was 

performed while he was employed by OTA. 

- 2 ­
and includes five appendices. Section II describes the data used in the panel. Section III details 

the extrapolation methodology, including how it is updated for new assumptions and additional 

years. Sections IV and V describe the distributional and dynamic tabulation methodologies and 

analyses. Section VI concludes with an overview of the benefits as well as the limitations of the 

model. 

The appendices include working papers that describe each component of the model building 

process. Appendix A describes the basic data including the 1987 to 1996 cohort panel (A1), the 

PSID (A2), and the estate tax data (A3). Appendix B describes the weighting of the refreshed 

panel. Appendix C describes the model’s consumption (C1), higher education (C2), health 

insurance (C3), and transfer payment (C4) imputations. Appendix D describes the extrapolation 

(D1) and includes an algebraic description of the extrapolation (D2), a shortcut for updating the 

extrapolation (D3), and the methodology for extrapolating the estate tax (D3). Appendix E 

describes the equivalence scaling used in the model. 

II. Data Used in the Model 

A. Data Requirements for a Panel Model 

The primary data source for representing the population in each year of the budget period in 

Treasury’s new model is a ten-year panel sample of individual income tax returns. This panel 

consists of a “cohort” segment and a “refreshment” segment. The “cohort” segment represents 

all individuals in the population who were tax return filers or dependents of these filers in 1987, 

the initial year of the panel. The refreshment segment represents all individuals in the population 

who became non-dependent tax return filers after 1987, and their dependents. The panel thus 

represents the entire U.S. filing population in each of the ten years from 1987 through 1996 at 

the level of tax return units. It also contains annual links between dependent filers and their 

parents, so it is possible to form (tax) families in each year. The detailed information on income, 

deductions, exemptions, etc. required for distributing individual income tax burdens is included 

for each return in all years of the panel. The income information includes the components of 

capital income required for distributing the burden of the corporate income tax. The income 

information also includes most of the components required for constructing our economic 

welfare measure (discussed below). 

The filing population, however, is not sufficient for all the analyses that OTA performs. In 

particular, distributional analyses present the distribution of all federal taxes, both current law 

and any law that might be proposed, for the entire U.S. population. For these analyses, the 

model needs a panel of permanent non-filers. This panel was imputed from the Panel Study of 

Income Dynamics (PSID). 

All panel returns have also been linked to Form W-2 information, which provides earnings at the 

individual worker level, which is required for distributing payroll tax burdens within tax families 

for the analysis of marriage penalties (or bonuses) and similar equity considerations. Age and 

gender have been linked to the panel returns from Social Security administration data. 

Consumption, which is required for the distribution of excise taxes, has been imputed from the 

- 3 ­
 

 

 

 

 

Consumer Expenditure Survey. In addition, the tax calculators required to compute individual 

income tax liabilities under alternative tax laws have been developed for each year of the panel. 

B. Cohort Panel 

The panel model is based on data from several sources for the 10-year period 1987 through 1996. 

Most of the data are from tax and information returns filed with the IRS. The initial (“cohort”) 

panel sample was designated from the individual income tax returns for tax year 1987 that were 

filed with IRS in 1988 and included in the annual cross section sample drawn by the Statistics of 

Income (SOI) Division in 1988. This sample consists of approximately 88,000 non-dependent 

tax returns. All non-dependent taxpayers, including secondary taxpayers on joint returns, who 

were represented in this 1987 sample were designated as members of the initial panel, as were all 

dependents of these taxpayers.3 For the next nine years (i.e., through tax returns filed for tax 

year 1996), the SOI Division included in the initial panel sample any tax return filed that 

reported any panel member as a primary or secondary taxpayer. Estate tax data were collected 

for all members of the cohort panel for whom an estate tax return was filed. 

C. Refreshment Panel 

The initial panel sample represents the 1987 filing population, but it does not represent the filing 

population in subsequent years. Over time, the filing population grows, on net, due to 

immigration, dependents becoming non-dependents, and the movement of 1987 non-dependent 

non-filers into the filing population. To represent this growth in the filing population, the initial 

panel sample was supplemented with a “refreshment” panel of non-dependent filers who filed 

after 1987, and their dependents. The refreshment panel was constructed from the annual SOI 

cross section samples for tax years 1988 through 1996. The SOI samples for all these years 

included a simple random sample based on the same ending digits of Social Security numbers 

used by primary taxpayers. Primary taxpayers in this simple random sample in any year from 

1988 through 1996 who were not in the initial sample for 1987 were designated as members of 

the refreshment sample and their returns filed in any subsequent tax year through 1996 were 

included in the refreshment panel.4

 SOI also obtained any estate tax return filed for any of these 

panel members who died between 1987 and 1996. 

3

 Taxpayers and their dependents represented on returns for 1987 that were filed in subsequent years (through 1990) 

were also designated as members of the initial panel. With the addition of these late-filed returns, the initial panel 

sample represents the entire 1987 filing population. 

4

 To calculate sample weights, it was necessary to determine which of the three groups a member of the refreshment 

panel belonged to because the groups represent different parts of the U.S. population. Non-dependent non-filers in 

1987 (and their dependents) are part of the 1987 U.S. population that is not represented on 1987 tax returns, 

dependents of 1987 filers who subsequently become filers are part of the 1987 U.S. population and are represented 

on 1987 tax returns, and immigrants are not part of the U.S. population until the year of immigration. Age in 1987 

and presence of pre-1987 earnings (both from Social Security Administration records) were used to determine which 

group refreshment panel members belonged to. Note that the refreshment panel is not a true panel because 

secondary taxpayers, including primary taxpayers who became secondary taxpayers (e.g., because they married), 

were not followed. Missing records were imputed to make this a true panel. 

- 4 ­
 

 

D. Permanent Non-Filers (PSID) 

The initial and refreshment panels represent the population of individuals who were represented 

as a taxpayer or dependent on a return filed in any year between 1987 and 1996. The remainder 

of the U.S. population consists of those individuals who were never represented on a return 

between 1987 and 1996. The Panel Study of Income Dynamics (PSID) was used to represent 

this “permanent non-filer” segment of the population. The basic approach was to form tax units 

from PSID units, determine whether each tax unit was above or below the tax filing threshold in 

each year, and then select the units that were below the threshold in every year from 1987 

through 1996.5 These units form the basis of the permanent non-filer population. Together, the 

main sample, the refreshment sample, and the permanent non-filer sample form a panel roughly 

representative of the American population for the entire period (once weighted). 

E. Information Return Data 

For individuals represented on tax returns, wages reported on W-2 forms were available in all 

years and other income reported on 1099 forms was available in some years. The W-2 

information was used to split earnings on joint returns, which is necessary for computing payroll 

taxes for each spouse and for use in analyzing marriage penalties and related issues. The W-2 

information was also used in combination with 1099 information and PSID data to impute 

income for years in which the return unit did not file a return. 

F. Estate Tax Data 

Estate tax data for panel members who died and for whom an estate tax return was filed are 

included. Limited estate tax data for all panel members and more detailed data for most panel 

members were available. Beginning with filing year 1994, SOI included all panel members in 

the samples for the annual estate tax studies. Data collected for these studies include detailed 

information about assets owned by the decedent, liabilities and the estate tax calculation. Prior 

to 1994, the estate tax returns of panel members were included in the annual SOI studies only if 

they met the usual sampling criteria.6

 Most panel decedents met the sampling criteria and were 

included in the study. By the end of processing year 2000, over 6,000 estate tax returns had been 

filed for panel members; SOI data were available for 88 percent of these estates. Returns 

Transaction File (RTF) data were obtained for the remaining estates, which were primarily 

returns for smaller estates filed prior to 1994 that did not meet the regular SOI sampling criteria. 

The RTF data include taxable estate and subsequent tax calculations, but do not include total 

gross estate or deductions. Gross estate and deductions were imputed for panel members for 

whom only RTF records were available. 

5

 The latest PSID year available was 1993 when this work was done, so the PSID file was extrapolated through 1996 

using essentially the same methodology subsequently used to extrapolate the entire model. This methodology is 

described later in the paper. 

6

 The SOI estate tax study is based on a stratified random sample of estate tax returns. Sample criteria include year 

of death, age, and gross estate. 

- 5 ­
 

 

 

G. Age and Gender 

Individuals represented on tax returns were linked to Social Security administration records (the 

“DM-1 file) to obtain the date of birth, date of death, and gender of each person with a valid 

social security number (SSN) on every return. Ages for persons with missing SSNs were 

imputed to the extent possible.7 Likewise, if the year-of-birth information obtained from the 

DM-1 was clearly wrong, the age was adjusted. For example, individuals on the panel are 

constrained to be under 107 years of age and non-dependent taxpayers and spouses are 

constrained to be at least 15 years old. 

H. Imputations 

The primary data described above either exclude or only partially include key variables that are 

required to measure economic welfare and tax burdens. There are four groups of these variables: 

consumption, education, health insurance and employer characteristics, and transfer payments. 

Imputations for each category were made for each individual in the tax return and PSID panels 

for all years. 

Consumption. A family’s share of consumption devoted to items subject to excise taxes needed 

to be imputed in order to measure the relative tax burden imposed on the consumption of these 

items. Consumption shares for these items were computed from the Consumer Expenditure 

Survey (CEX) conducted by the Bureau of Labor Statistics. We imputed 15 broad categories of 

expenditures as well as 3 subcategories of expenditures subject to excise. We also imputed 

smoking status and consumption using the National Institute of Health Survey. More detail on 

the data and methods used in these imputations is in Appendix C1. 

Higher education. The status of late teens and adults as higher education students and the 

characteristics of the higher education institution a student attends were required to analyze the 

effect of the tax credits and above-the-line deduction for higher education. The 1995-1996 

National Postsecondary Student Aid Study (NPSAS), produced by the Department of Education, 

provided information on a student’s year in school, the degree program, whether the student was 

full-time or part-time, and the level and control of the institution the student was attending (e.g. 

public vs. private, 2-year vs. 4-year). Year-by-year profiles of a student’s entire higher education 

status were constructed from the NPSAS. A separate tax data file was constructed for 1999 that 

provided information on the number of tax filers and dependents who were students, whether 

income-eligible students (or their parents, for dependent students) claimed the tax credit, an 

estimate of Pell grant or other aid, and whether a student attended an in-state or out-of-state 

institution. The NPSAS and 1999 tax files were statistically matched based on the age, marital 

status, family size and income of the student (or of the parents, for dependent students). 

7 In general, the imputed age of a married person was the age of his or her spouse. If the person claimed an extra 

elderly standard deduction, the imputed age was at least 65. If the return filed form 8615, the imputed age was 

under 14 years. The imputation of ages to dependents with missing SSNs depended on whether the person was 

missing in 1987 or not. Dependents first appearing after 1987 were usually given very low ages (0 or 1). 

- 6 ­
 

The combined matched NPSAS/1999 tax file was then statistically matched to the first year of 

the (extrapolated) panel (2004) based on age, earnings, total income, family size, dependency 

status, and status (for married students) as a primary or secondary filer. In this match, the 

weights from the 1999 tax records were exhausted, so that the panel had the target number of 

students. In each succeeding year of the panel a new match was performed to the combined 

matched NPSAS/1999 file to impute new beginning students. To be eligible for the match an 

individual on the panel could not be a student currently or have been a student in the preceding 

year. These matches hit separately generated targets for students by type of institution. A tuition 

level was then assigned to each student in each year, based on the type of institution, whether the 

student was in-state or out-of-state, and the year. 

Health insurance. Health insurance coverage and premium amounts were imputed to the model 

to enable the model to analyze the effect of a wide variety of tax and health reform proposals. 

Employer-provided health coverage and tax preferred premium amounts were needed to be 

imputed to the model to analyze the effect of current law tax preferences. In addition, nongroup 

insurance coverage and premiums, out-of-pocket expenses, public coverage and uninsured status 

were needed to analyze the effects of reform proposals including exclusions and tax credits. 

Because no single health survey includes the necessary items, several surveys were linked 

together through matching techniques. First data from the Robert Wood Johnson Employer 

Health Insurance Survey8

 (RWJ) were statistically matched using employer and employee 

characteristics to the Current Population Survey (CPS) to determine premiums, employer and 

employee contributions, and other plan characteristics for employer-provided health insurance. 

Then the CPS (with the matched RWJ plan variables) was matched to the panel in order to 

determine who is covered by employer, nongroup, Medicare and Medicaid health insurance and 

to identify the residual category of uninsured individuals. Nongroup health insurance premiums 

from the National Health Insurance Survey (NHIS) were assigned to individuals in the nongroup 

market. Finally, data from the Medical Expenditure Panel Survey (MEPS) were used to impute 

medical expenditures. Tax data already on the panel provided information on itemized medical 

expenses and the above-the-line deduction for health insurance for self-employed individuals. 

Transfer payments. Cash income, the income concept used on the panel model, is the sum of 

income reported on income tax returns plus nontaxable cash transfer payments (including food 

stamps) and taxes excluded from income sources (the employer share of payroll taxes and the 

corporate income tax). Unemployment benefits and some Social Security benefits were reported 

on tax returns, and the remainder of Social Security benefits for filers was available in some 

years from forms 1099-SSA. Missing years of Social Security benefits were imputed, based on 

benefit levels in surrounding years. Social security and unemployment benefits were imputed to 

non-filers from the PSID, and other cash transfer payments (including food stamps) were 

imputed to filers from the PSID. Transfer payments by type were benchmarked to totals from 

administrative data for each program. 

8

 Premiums from the employer survey in the MEPS were not used, because there was no way to link premiums to 

family income. The RWJ survey included both premiums and wage data, enabling a link since wages are the major 

component of income. 

- 7 ­
 

Detailed descriptions of the consumption, education, health insurance, and transfer payment 

imputations can be found in Appendix C. 

III. Extrapolation 

A. General Approach 

The original panel data cover the 10-year period 1987-1996, and the budget period is also a 10­

year period (currently, 2008-2017). The basic approach was to first divide the 1987 and 1996 

non-dependent panel records into a number of matching cells and to align the income and 

weights within each 1987 cell with the income and weights in the corresponding 1996 cell. The 

records on the two files were then matched and the income adjustments from the 1987 records 

(now adjusted to 1996) carried forward to all years of the panel (which now represents 1996­

2005). Finally, aggregate income by source was adjusted to actual or budget forecast targets. 

These steps were then repeated, with 1987 now matched to 2004 so that the extrapolated file 

covers the 2004-2013 budget period (the first budget period for which the model was in full 

production). The extrapolated file is then updated for population and income growth and 

adjusted to budget forecast targets each year so that it covers the current budget period (currently 

2008-2017). A more detailed description follows. 

B. Defining Cells 

Seven variables define “super” matching cells, and each super matching cell was then divided 

into income and age ranking quantiles. The super matching cells were defined by marital status 

(single, married, head of household), three broad age groups, gender (for unmarried record 

units), second earner status (for married record units), number of dependents (none, one, two or 

more), filing status, and income type (six types that depend on the mix of income, e.g., one type 

requires that income is at least 90 percent wages and that losses from all sources are less than 

$3,000). Within each super cell in the 1996 file, all (weighted) records were then ranked into 

income quantiles. Each quantile was required to have at least 10 unweighted records, or the 

quantile was collapsed into surrounding quantiles. Income from each source on the records in 

the corresponding super cell on the 1987 file was then grown by the ratio of the super-cell 

average for the source on the 1996 file to the corresponding super-cell average for the source on 

the 1987 file. Total (grown) income was then computed for each 1987 record, and the 1987 file 

divided into income cells using the dollar quantile breaks from the 1996 file. Within each 

income cell, age quantiles were then computed for each file, and the smaller number of breaks 

(based on the requirement of at least 10 unweighted records in each cell on each file) was used to 

define the age cells within the income cells.9

 The final step before performing the match was to 

proportionately adjust the weights within each cell on the 1987 file so that the total of the record 

weights matched the total of the record weights in the corresponding cell in the 1996 file. 

9

 The preceding steps resulted in some cells with fewer than 10 unweighted observations. These cells were 

collapsed into surrounding cells. The process of defining age quantiles was repeated using adjusted income on the 

1987 file to improve file alignment. 

- 8 ­
 

 

 

 

C. Matching at the Micro Level 

For the match, records within each cell on both files were sorted by income and age, so the first 

(top income) record from the 1987 file was linked to the first (top income) record on the 1996 

file. If the (adjusted) weight of the matched 1987 record exceeded the weight of the matched 

1996 record, the weight of the 1987 record was reduced by the weight of the 1996 record, and 

the 1987 record was reused in the next match. Otherwise, the 1987 record kept its (remaining) 

weight and the next 1987 record was matched to the same 1996 record, taking its remaining 

weight (or some of it). This process continued until all 1987 records were matched (which 

meant all the 1996 records had been matched also, since the two files had identical total weights 

within each cell). 

D. Hitting Aggregate Targets 

The next step was to make final adjustments to income items and to adjust the level of 

deductions on the 1987 records. The level of total (grown) income on each 1987 record was first 

adjusted to the total income on the matched 1996 record.10 Then, each amount of income from 

each source was adjusted so that the total amount from that source on the 1987 file matched the 

total amount from the 1996 file. The growth in total income (or, total income excluding capital 

gains) from the original 1987 amount on each extrapolated 1987 record was used to adjust 

itemized deductions. The amount of each extrapolated itemized deduction on each record was 

then adjusted so the total amount on all records hit known 1996 targets. 

Once the extrapolation of non-dependent 1987 records was completed, adjustment factors (the 

ratio of the extrapolated 1996 amount to the original 1987 amount) were computed for each 

income source and deduction for each record. These adjustment factors were then applied to 

each subsequent year of the extrapolated file, including returns filed by dependents, to give an 

initial extrapolation to the 1996-2005 period. The final step was to proportionately adjust each 

income and deduction item on each record for each year so that the aggregate amount hit known 

(or forecast) targets. Since the targets are for filers, the filing status of records was determined 

and the initial adjustment applied only to filers.11 The same adjustment factors were then applied 

to items on non-filer records. This resulted in a final extrapolated file for the 1996-2005 period. 

E. Second Extrapolation 

The second extrapolation followed the same procedure described above, with the 1987 file 

matched to the 2004 extrapolated file, resulting ultimately in the 2004-2013 extrapolated file 

10 Adjustment factors were capped above at 10 and below at 0.1. 

11 Filing status was first determined by comparing the record’s income to the filing threshold for the filing status, 

eligibility for refundable credits, presence of wages, and other indicators of filing. Records just above or below 

filing based on these indicators were then brought into or moved out of filing status to hit a target for the total 

number of fliers for the year. Another round of adjustments might be required after incomes, etc. were adjusted to 

targets. 

- 9 ­
 

 

 

used to produce the simulations reported in this paper.12 Because records were split in both 

stages of the extrapolation, the number of records in the model increased from 1.4 million in the 

initial 1987-1996 file to 3.9 million in the 2004-2013 extrapolated file. 

F. Updating the Extrapolation 

The current base extrapolation (for 2004-2013) is updated twice each year. The two updates 

follow the same methodology except that the first update uses winter Budget assumptions and 

the second is based on Mid-Session Review assumptions. 13 The first update also moves the 

Budget window one year forward to the current Budget window (e.g. from 2007-2016 to 2008­

2017) and increases the return weights. A step-by-step algebraic description of the “short-cut” 

method for updating the extrapolation is given in Appendix D3. 

In brief, the short-cut extrapolation proceeds as follows. Each income item on each return is 

grown by the aggregate rate of growth for that income item, where the initial amount of 

aggregate income for an item is the sum for that item across all returns as measured in the current 

base extrapolation and the target amount as given by the winter Budget (or Mid-Session Review) 

assumptions. For example, for the 2008-2017 winter Budget (WB08) update, if the target 

amount of wages for 2008 as given by the WB08 assumptions is x percent higher than the sum of 

all wages in 2004 as measured in the current base extrapolation, then wages for every 2004 

return would be increased by x percent to update it to 2008 levels. 

Exogenous non-income items (e.g. itemized deductions) for each return are first grown by the 

return’s forecasted rate of growth of total income (either with or without capital gains14). For 

example, if a return’s total income with capital gains in 2008 as calculated using the income 

growth factors (above) is y percent greater than it was in 2004 as measured under the current 

base extrapolation, then charitable contributions are first grown by y percent. Most itemized 

deductions are then adjusted for aggregate forecasted differential rates of growth for the 

deduction relative to total income (or total income without gains). For example, if on average 

charitable contributions are expected to grow slightly slower than total income between 2004 (as 

measured by the current base extrapolation) and 2008 (as forecast) then charitable contributions 

on all returns for 2008 would be lowered by the ratio of the average growth rate for charitable 

contributions over the average growth rate for total income between 2004 and 2008. 

IV. Distributional Analysis with the Model 

The panel model has allowed OTA to extend its distributional analysis from a single year to a 

multi-year period. We can use a multi-year measure of income to rank families, and calculate a 

multi-year measure of tax burdens. In contrast to the single-year measure, our multi-year 

12 Estate tax returns are extrapolated separately, using the general methodology Treasury uses to extrapolate the 

estate tax file used for revenue estimating purposes. See Appendix D4. 

13 Updated transfer targets are generally not available in time for the winter Budget update. 

14 See Appendix D3 for a complete list of relevant growth factors. 

- 10 ­
 

measure of income allows us to capture some lifecycle effects, better measure temporary relative 

to permanent changes in demographic and economic circumstances, and more accurately rank 

savers and non-savers. It also allows us to better measure the burden of temporary provisions 

and provisions with unindexed parameters. 

For example, workers typically have lower wages when they are young than they will earn later 

in their working lives, and have no earnings when they retire. Likewise, current earnings might 

rank an individual temporarily out of work too low in terms of longer term welfare and current 

income might lead to too high a ranking if an individual has a one-time large capital gains 

realization. 

In contrast to our single-year measure of tax burdens, our multi-year measure of tax burdens 

captures tax laws that follow lifecycle patterns, have multiple year effects, or that are temporary, 

phase in over time or are not indexed for inflation. For example, a proposed child credit would 

not be shown to benefit a childless couple if only their current demographic circumstances were 

taken into account, but the couple would benefit if they had children in a subsequent year. 

Payroll taxes only apply to current workers, but they would affect a young student who was 

about to enter the labor force, and they did affect the retiree during the years he or she was in the 

labor force. 

Other provisions have multiple-year effects. These include the deduction or exclusion of 

contributions to IRAs and other retirement plans, the inside buildup on retirement plan 

contributions which is taxed upon withdrawal, and the deferral of tax on unrealized capital gains. 

A sunset provision will effectively repeal the tax cuts enacted under the Economic Growth and 

Tax Relief Reconciliation Act of 2001 (EGTRRA) and the Jobs and Growth Tax Relief 

Reconciliation Act of 2003 (JGTRRA) in 2010. Because the AMT exemption amount is not 

indexed for inflation, many more taxpayers will be affected each year. A multi-year measure can 

evaluate such provisions on a year-by-year basis, and therefore fully reflect their effects 

throughout the period. 

A. Methodology Summary 

The panel model allows Treasury to extend the time period of analysis to a multi-year period, but 

the objective of our distributional methodology is otherwise unchanged from the single-year: to 

provide a comprehensive measure of the effect of current and proposed tax laws on the 

distribution of tax burdens. Implementing that objective in a multi-year context leads us to the 

following choices in each dimension of distributional analysis. 

Taxes included. As under our single-year methodology, 15 all federal taxes are included. 

Incidence assumptions. These are unchanged from the single-year methodology. 

Time period of analysis. The time period of analysis will be the ten-year budget period that 

begins in the succeeding calendar year. For example, the current budget period is 2008 through 

15 See “U.S. Treasury Distributional Analysis Methodology” (Cronin, 1999; http://www.treas.gov/offices/taxpolicy/library/ota85.pdf ) for a complete description of Treasury’s single year methodology. 

- 11 ­
 

 

 

2017. The same period will be used for the measurement of income and for the measurement of 

tax burdens. 

Unit of analysis. Family membership in many cases changes over the ten-year budget period. 

The basic unit of analysis over the budget period must therefore be individuals. In each year of 

the budget period, as explained below, cash income and tax burdens are first computed at the 

family level, and then allocated to individuals. These individual-level results are then annuitized 

to display the results. 

Ranking individuals by multi-year cash income. In each year of the ten-year budget period, the 

annual cash income of each family is measured, adjusted for family size (see Equivalence Scales 

below), and then allocated to each individual member of the family. Thus each individual present 

in the first year of the budget period will have a size adjusted value of cash income for every 

year of the budget period.16 The level real annuity equivalent of these individual streams of cash 

income over the budget period is then calculated and individuals are ranked by this measure into 

quintiles or fixed dollar classes. Each of these steps is discussed in some detail below. 

Tax burden measures. The steps involved are similar to those used for ranking families by 

income. In each year of the ten-year budget period, the tax burden of each family in that year 

under current law and under proposed law is computed, using the relevant tax law in effect in 

that year. These tax burdens are then adjusted for family size, and allocated to each individual 

member of the family. The level annuity equivalent of each individual’s stream of tax burdens 

over the budget period is then calculated. 

B. Measuring Welfare 

OTA assumes that all family members in a given year have equal welfare. Families are assumed 

to act as an economic unit, sharing both resources and costs. This is implicit in OTA’s single 

year income measure and also applies to the multi-year measure. The sources of income do not 

matter in assigning income to family members. For example, if all family income is derived 

from the earnings of one member, that member is still assigned the same level of welfare as the 

non-working members. Likewise, tax burdens are assumed to be shared equally among family 

members. 

Cash income is used as a proxy for welfare. An important difference between income and 

welfare measures is that an income measure omits the value of leisure time. If two otherwise 

identical families (or individuals) have the same wage income but have different levels of work 

effort, they will have the same rank under an income measure even though their welfare is 

obviously not equivalent. 

Another important difference is that welfare measures control for differences in prices faced by 

consumers. If two otherwise identical families have the same income but one lives in a high 

cost-of-living area, the two families would have the same rank under an income measure even 

16 Individuals who die during the Budget period will have fewer than ten years of income. The present value and 

annuity formulas are adjusted accordingly. 

- 12 ­
 

 

though the purchasing power of the family living in the high cost of living area is considerably 

lower. Finally, forward-looking welfare measures consider the degree of uncertainty 

surrounding earnings and consumption choices; income measures do not. 

OTA’s choice of income as a proxy for welfare is based on several considerations. Although 

income may not be a good proxy for welfare in some instances, income is measured with a 

relatively high degree of precision whereas the value of leisure, the prices faced by different 

families, and uncertainty of income and consumption streams could only be measured with 

imprecision. Further, income is a more familiar concept than welfare and therefore may better 

convey relative rankings to policy makers. 

Cash income. Cash income includes wages and salaries17, net income from a business or farm, 

taxable and tax-exempt interest, dividends, rental income, realized capital gains, cash transfers 

from the government, and retirement benefits. Employer contributions for payroll taxes and the 

federal corporate income tax are added to place cash income on a pre-tax basis. Annual cash 

income is measured on a family rather than on a tax return basis. The cash incomes of all 

members of a family in a single year are added to arrive at a family's annual cash income. This 

annual measure is then adjusted for family size, and each family member is allocated the size 

adjusted level of cash income for their family in that year. For example, if in 2008 the Smiths 

are a family of three with two adults and one minor child where one adult earns $50,000 and 

there is no other cash income, then the Smith's size-adjusted family cash income is $31,867 

(family size adjustments are described below). This amount is assigned to each of the three 

individuals in the family for 2008. 

Equivalence scales. The multi-year period of OTA’s new distributional analyses necessitates an 

explicit adjustment for family size. In these multi-year analyses, the income and tax burdens of 

base-year families are measured over the ten-year budget period. Because family composition 

changes over the budget period, a base-year family’s income and tax burden cannot be measured 

in each year of the budget period. Instead, the income and tax burdens for each individual who 

existed in the base year are measured in each year. The equivalence scale used for this measure 

determines how a family’s income is translated into the income of each individual in the family 

in each year. If the equivalence scale assumed perfect returns to scale, each individual in a given 

family in a given year would be assigned the family’s income for that year. At the other 

extreme, if the equivalence scale assumed no returns to scale then the family’s income in each 

year would be divided by the number of individuals in the family. OTA’s equivalence scale falls 

between these two extremes. 

The specific equivalence scale we have chosen adjusts for family size (i.e., for economies of 

scale as family size increases), but not for other economic or demographic differences among 

families. The scale was derived from the official U.S. poverty levels by computing the 

coefficient on the size of the family that yields the same scale as the U.S. poverty levels for 

17 Fringe benefits are not included in cash income. 

- 13 ­
 

 

 

 

 

 

 

 

i 

families of one and three persons.18 We chose this scale because it closely matches utility-based 

scales found in the literature, and it is straightforward to compute for any family size. It also 

approximates the U.S. poverty thresholds for most people.19 See Appendix E for more 

information on equivalence scales. 

Annuitization. As discussed above, OTA measures cash income over multiple years, adjusts 

each family’s cash income for family size in each year of the budget period, and allocates the 

adjusted amount to each individual in the family. This gives us a stream of adjusted cash income 

over the budget period for each individual present in the first year of the budget period. We 

perform similar calculations to arrive at each individual’s tax burden streams. We then annuitize 

the ten year streams of income and taxes in order to arrive at a summary measure of an 

individual’s “ten year” income and tax burden. 

The annuitization process works as follows. For purposes of illustration, we limit the discussion 

to the calculation of the level annuity of income. The calculation of the level annuity of tax 

burden is analogous. For each individual present in the first year of the budget period, we 

calculate the present discounted value of each individual’s equivalenced income stream using the 

actual inflation rate as measured by the CPI-U and an assumed real rate of interest of 4 percent. 

Then, we calculate the level real annuity over the budget period that has the same present 

discounted value as the equivalenced income stream for each individual present in the first year 

of the budget period. OTA uses the actual expected inflation rates20 in each year of the budget 

period but if the inflation rate were constant, the annuity formula would appear as follows: 

i ) 

where: 

indexes individual members of the sample; 

Ai = the level annuity value of income for individual i; 

18 The coefficient is called the “family size elasticity” (fse). The fse in the equivalence scale we use (.41) was 

derived from the U.S. poverty levels for one and three person families in 1998 ($8,316 and $13,003, respectively) by 

solving the following formula: $8,316/1fse = $13,003/3fse . 

19 By design, OTA’s equivalence scale exactly matches the poverty thresholds for families of size 1 and 3, and it 

closely matches the poverty threshold for families of size 2. Ninety percent of all families in 2000 were made up of 

1, 2, or 3 members. A constant FSE of .41 does not approximate the U.S. poverty thresholds for large families. A 

much higher FSE would be necessary to capture the large additional amounts of income allowed large families 

under the poverty thresholds. 

20 Expected inflation rates are the same as those used for revenue estimating purposes and are updated twice 

annually for winter budget and the Mid-Session Review. 

PDV (C I _ 

N (i) 

R
 

∗
 

1
 

1
 

1
 

1

Ai ⎤
 

⎥

⎥⎦

⎞⎤

⎟

⎠

π
 

⎥ R ⎦ 
 

⎞

⎟

⎠

⎛ +π ⎜

⎝ +

+
 

+

−
 

1 

1 

⎡

⎢

⎢⎣

⎡

⎢

⎣ =

⎛

⎜

⎝

−
 

- 14 ­
 

 

 

 

 

π = the assumed constant rate of inflation; 

R = an assumed nominal interest rate such that the real interest rate equals 4%; 

N(i) = a count of the years individual i appeared in the sample, and 

PDV(C_Ii) is the present discounted value of cash income for individual i over the 

budget period. 

C. Measuring Consumption 

Shares of consumption devoted to items subject to excise taxes are required to estimate the 

distributional effect of these taxes.21 Consumption shares for these items were computed from 

the Consumer Expenditure Survey (CEX) conducted by the Bureau of Labor Statistics. To 

obtain a sufficiently large sample from the CEX, all consumer units reporting in the first quarter 

of the 1995 and 1996 surveys were used. Units that did not report in all four quarters were 

matched to similar units and the missing quarters of expenditures imputed, so that all units had a 

full year of expenditures. Expenditures for each unit were then divided between durables and 

non-durables, and the share of non-durable expenditures was computed for each item subject to 

an excise. Seven matching variables for the resulting CEX file and each year of the panel file 

were then computed: family type (four categories), age of unit head, gender of unmarried unit 

heads, number of children, number of adults other than the head and spouse, consumption 

centile, and presence of a smoker. On the panel, consumption was defined as after-tax (cash) 

income.22 Smoking status was imputed to all individuals over age 13 in all years of the panel, 

based on the probability of smoking by age, income quantile, and marital status from the 

National Institute of Health survey. 

The initial match of the CEX was to the 1987 panel file. A penalty function was used to insure 

that only similar units were matched. A panel unit (a “tax family”) retained its consumption 

shares from the initial match in subsequent years unless its family type changed, its current 

income rank was more than 10 centiles away from its 1987 rank, or it changed smoking status. If 

any of these three changes occurred in a year, the unit was re-matched to the CEX file to obtain 

new consumption shares. See appendix C1 for more information on the consumption 

imputation. 

21 See Cronin (1999; http://www.treas.gov/offices/tax-policy/library/ota85.pdf ) for a description of the basic 

methodology Treasury uses to distribute excise taxes and customs duties. 

22 Note that cash income many types of savings including: contributions to 401(k) type accounts, inside build up on 

tax preferred savings vehicles, and accrued capital gains (including housing capital gains). Net capital losses were 

capped at $50,000 in the computation of consumption. In addition, a lower bound was imposed on consumption of 

half the poverty level for a single individual, with this amount adjusted for larger families using the family 

equivalence scale. An upper bound on consumption of $5 million was also imposed. 

- 15 ­
 

 

D. Measuring Taxes 

Distributional analysis on the panel model begins with the distribution of each tax to tax families 

in each year. A tax family is defined as the non-dependent primary taxpayer, the taxpayer’s 

spouse, and their dependents for income tax purposes. Individual income tax liabilities are 

distributed to payers (which includes dependent filers), payroll taxes to individual workers, 

corporate income to capital income generally, excises taxes to families according to their 

consumption patterns and their total labor and capital income, and the estate and gift taxes to 

decedents. Tax liabilities, by tax, are aggregated at the family level. Family-level income and 

tax liabilities in each year are then divided by an equivalence scale and assigned to each 

individual in the family. 

E. Illustration: Tax Burdens under Current Law 

As measured above, each individual present in the first year of the budget period has a stream of 

income and tax liabilities for each year he or she is present in the panel. Annuity equivalent 

streams of income and tax burdens are then calculated and used in the ten year distribution tables 

produced by Treasury. Table 1 below shows the percent distribution of individual annuitized 

cash income and federal taxes by type over the budget period (2007-2016) under policy baseline 

law.23 

As discussed above, although we assume that families share income and tax burdens in a 

particular year, only individuals can be followed over time. Table 1 therefore distributes 

individual income and tax burdens. Every individual in the U.S. population in the first year of 

the budget period (including children) is included in the table. If a family maintains the same 

membership across all ten years (i.e. no births, deaths, marriages or divorces, and no children 

that become non-dependents), then all members of the family will be ranked exactly the same in 

the table. In all other cases, each member will have a ranking that reflects his or her income or 

tax burdens over time. For example, if a family composed of a husband and wife in year 1 is 

divorced in year 5 and both remarry in year 7, then their ranking will be a weighted average of 

their incomes under both marriages and the time during which they were single. Likewise, if a 

23 Policy baseline law assumes the tax cuts enacted by EGTRRA and JGTRRA are extended. Note that the estate 

tax is repealed under fully phased in policy baseline law. 

- 16 ­
 

 

 

 

 

 

Table 1
 

Distribution of Individuals, Annuitized Individual Cash Income, and Federal Taxes Over the Budget Period
 

under Policy Baseline Law1
 

(Annuitized Individual Ten Year Income Levels and Taxes) 

Annuitized 

Individual Ten 

Year Cash 

Income Quintile 

Individuals 

Annuitized 

Individual 

Ten Year Cash 

Income 

Total Federal 

Taxes 

Individual 

Income Taxes 

Corporate 

Income Taxes Payroll Taxes Excises & 

Customs Duties Estate 

--- Percent Distribution ---

Lowest2 

Second 

Third 

Fourth 

Highest 

19.8 

20.0 

20.0 

20.0 

20.0 

3.1 

7.6 

13.0 

20.5 

56.2 

0.4 

3.8 

10.3 

19.8 

65.6 

-1.3 

0.1 

6.0 

16.2 

78.9 

0.6 

2.2 

6.1 

13.7 

77.0 

2.7 

9.4 

17.6 

26.9 

43.4 

2.0 

6.9 

13.2 

21.0 

56.9 

0.1 

1.0 

0.7 

0.4 

97.0 

Total2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Top 10 

Top 5 

Top 1 

10.0 

5.0 

1.0 

41.2 

30.7 

16.6 

49.3 

37.5 

20.8 

63.2 

50.2 

29.1 

65.0 

54.2 

35.5 

25.0 

14.4 

4.5 

41.3 

30.6 

16.4 

88.9 

83.3 

58.3 

--- As a Percent of Income ---

` 

Lowest2 

Second 

Third 

Fourth 

Highest 

3.2 

12.5 

19.6 

24.1 

29.0 

-5.5 

0.1 

5.8 

10.0 

17.7 

0.5 

0.8 

1.2 

1.8 

3.6 

7.5 

10.7 

11.6 

11.3 

6.6 

0.6 

0.9 

1.0 

1.0 

1.0 

0.00 

0.01 

0.00 

0.00 

0.1 

Total2 24.8 12.6 2.7 8.6 1.0 0.04 

Top 10 

Top 5 

Top 1 

29.8 

30.3 

31.3 

19.3 

20.6 

22.1 

4.2 

4.7 

5.7 

5.2 

4.0 

2.3 

1.0 

1.0 

1.0 

0.1 

0.1 

0.1

 Department of the Treasury

 Office of Tax Analysis
 

1

 The Policy Baseline assumes the current law tax cuts are extended. 

2

 Families with negative incomes are excluded from the lowest income quintile but included in the total line. 

NOTE: Quintiles begin at individual annuity equivalent income of: Second $15,216; Third $29,612; Fourth $47,663; Highest $75,223; 

Top 10% $105,510; Top 5% $146,840; Top 1% $349,780. 

couple with one child remains married for ten years but their child leaves the family after five 

years, the two adult members will have the same rank but their child will have a different rank 

that will reflect his or her years as a non-dependent. Further, if the couple has the same income 

in the years with and without the child, their individual adjusted income in the years without the 

child will be higher to reflect the fact that their real income is higher.24 

Although individuals who are not part of the U.S. population in the first year of the budget 

period are not explicitly included in the table, births and immigration (like deaths and 

emigration) will affect the measurement of income and tax burdens either directly by generating 

24 Note that the increase in income due to having one less family member will be relatively small due to the assumed 

returns to scale for family size. For example, if the family's income were $100,000 (with and without the child), 

each individual in the family would be assigned an income of $63,735 in the case with the child and $75,262 

without the child. 

- 17 ­
 

 

income or tax liabilities in the succeeding years, or indirectly through their impact on 

equivalence scales or the application of tax law (e.g. child tax credits). 

The ten-year distributions of income and tax burdens in Table 1 show that policy baseline law is 

progressive. The lowest quintile of individuals has an total federal effective tax rate of 3.2%; the 

second lowest quintile has an effective tax rate of 12.5% and the middle quintile has an effective 

rate of 19.6%. The second highest quintile has an effective rate of 24.1% and the top quintile has 

an effective rate of 29.0%. 

The top quintile's share of ten-year income is 56.2% and their share of the ten year measure of 

total federal tax burden is 65.6. In contrast, the lowest quintile's share of income is 3.1% and 

their share of total federal taxes is 0.4%. 

The individual income tax is the largest federal tax, just over half of all federal tax burdens. It is 

also very progressive. Because of refundable individual income tax credits, the lowest quintile's 

effective individual income tax rate is -5.5% under the ten year tables. The second quintile of 

individuals bears almost no individual income tax burden and the middle quintile's effective 

individual income tax rate is only 5.8%. In contrast, the effective individual income tax rate is 

10.0% for the second highest quintile, 17.7% for the highest quintile, and 22.1% for the top 1%. 

After the individual income tax, the payroll tax is the next largest federal tax, almost 35% of the 

total federal tax burden. Payroll tax burdens are progressive through the third quintile, flatten 

out and then become regressive. This pattern is explained by two factors. First, only earned 

income is subject to payroll taxation and the composition of income changes as we move up the 

income distribution. Lower income individuals tend to have relatively more transfer and other 

non-wage income; middle income taxpayers tend to have mostly earned income; and higher 

income taxpayers have increasingly more capital income. Second, the FICA/SECA portion of the 

payroll tax is capped at relatively high earnings. 

The corporate income tax is the third largest federal tax, accounting for almost 11% of the total 

tax burden. Like the individual income tax, most of the burden of the corporate income tax is 

borne by higher income taxpayers. In contrast, excises and customs (4% of total federal tax 

burdens) are fairly flat. The estate tax is the most progressive tax in the table but it accounts for 

very little of the tax burden (0.2%) because it is phased out by 2010. 

F. Illustration: Comparison to Single-Year Table 

Table 2 shows distributional results comparable to Table 1, but estimated using the single-year 

methodology and assuming fully phased in policy baseline law at 2007 levels of income. 25 As 

expected the ten-year table (Table 1) has relatively flatter distributions of both income and total 

tax burdens. Because the ten-year tables "average" good years with bad years, the extremes that 

may be present in a single-year table are smoothed out. For example, if a person realizes a large 

amount of capital gains in a particular year they might appear to be higher income in a single 

year table than they would in a multi-year table. Likewise a person might have a short spell of 

25 Fully phased in policy baseline law excludes temporary provisions (ignoring the EGTRRA and JGTRRA sunsets) 

and adjusts for the effects of unindexed parameters. 

- 18 ­
 

 

 

 

low income during college years or unemployment spells but might have higher wages in other 

years. 

Table 2
 

Distribution of Families, Cash Income, and Federal Taxes under Fully Phased In Policy Baseline Law1
 

(Fully phased in Law at 2007 Income Levels) 

Family Cash 

Income Quintile Families Family Cash 

Income 

Total Federal 

Taxes 

Individual 

Income Taxes 

Corporate 

Income Taxes Payroll Taxes Excises & 

Customs Duties Estate 

--- Percent Distribution ---

Lowest2 19.5 2.0 0.3 -0.7 0.3 2.0 1.0 0.0 

Second 20.0 6.1 2.2 -1.2 1.6 5.9 4.5 0.0 

Third 20.0 11.6 8.1 3.7 5.1 14.1 10.8 0.0 

Fourth 20.0 20.1 18.1 14.0 13.7 24.9 19.8 0.0 

Highest 20.0 60.9 71.3 84.2 79.1 53.0 63.7 0.0 

Total2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 0.0 

Top 10 10.0 45.1 54.4 68.6 68.7 32.5 47.4 0.0 

Top 5 5.0 33.9 41.3 54.9 59.9 18.8 35.6 0.0 

Top 1 1.0 18.8 22.7 31.8 42.5 5.4 19.7 0.0 

--- As a Percent of Income ---

Lowest2 3.7 -3.8 0.4 9.1 0.5 

Second 8.8 -2.4 0.7 9.0 0.7 

Third 17.3 3.8 1.2 11.2 0.9 

Fourth 22.4 8.3 1.8 11.4 0.9 

Highest 29.0 16.5 3.5 8.0 1.0 

Total2 24.8 12.0 2.7 9.2 1.0 

Top 10 30.0 18.2 4.1 6.7 1.0 

Top 5 30.3 19.4 4.8 5.1 1.0 

Top 1 29.9 20.2 6.1 2.6 1.0

 Department of the Treasury

 Office of Tax Analysis 

1

 The Policy Baseline assumes the current law tax cuts are extended. 

2

 Families with negative incomes are excluded from the lowest income quintile but included in the total line. 

NOTE: Quintiles begin at cash income of: Second $13,310; Third $28,507; Fourth $50,448; Highest $87,758; 

Top 10% $128,676; Top 5% $177,816; Top 1% $432,275. 

The highest income quintile's share of the single-year cash income is 60.9% compared to 56.2% 

in the ten-year table. The bottom quintile's share of income is 2.0% in the single-year table 

compared to 3.1% in the ten year tables. The highest income quintile's share of the single-year 

total federal tax burdens is 71.3% compared to 65.6% in the ten-year table. The bottom quintile's 

share of total federal tax burdens is 0.3% in the single-year table compared to 0.4% in the tenyear table. 

Because the distribution of both income and tax burdens are flatter in the ten-year tables, the 

difference in effective rates between the two tables depends on what happens to the relative 

shares of income and tax burdens between the two tables. For the top quintile the relative shares 

move together and the effective rate for total federal taxes is the same in the ten-year and singleyear tables. For the lowest quintile the effective rate is lower in the ten-year tables. This is not 

- 19 ­
 

surprising since these are individuals who are consistently ranked low and are eligible for 

refundable individual income tax credits in multiple years. All other quintiles have higher 

effective rates in the ten-year table than in the single-year table. The top 1% also has a higher 

effective rate in the ten-year table. 

The individual income tax appears more progressive in the ten-year table, ranging from an 

effective tax rate of -5.5% for the lowest quintile to 22.1% for the top 1%, compared to an 

effective tax rate of -3.8% for the lowest quintile and 20.2% for the top 1% in the single-year 

table. 

G. Illustration: Distributional Analysis of Recent Legislation 

Table 3 provides two measures of the results of the distributional analysis of recent legislation. 

The top panel of Table 3 shows the percent of total tax liabilities by income quintile. The bottom 

panel of Table 3 shows by income quintile effective tax rates, indexed to the average effective 

rate for the tax law. Indexed rates isolate how the relative tax burdens of different income 

groups have been affected by the changes in the various tax laws. 

Several legislative trends are apparent from both measures presented in Table 3. First, every 

major tax law, if in effect over the entire 2004-2013 budget period, would have reduced the 

relative tax burdens on individuals in the first three income quintiles, with a large cumulative 

effect between TRA86 law and EGTRRA plus JGTRRA law. These relative reductions are due 

primarily to the expansions of the EITC, the adoption and expansion of the child tax credit and 

its refundability feature, and the new 10 percent bracket. Second, the fourth income quintile 

would have had a cumulative reduction in relative tax burdens since TRA86 by either measure, 

but the pattern is mixed, with relative reductions through OBRA93 and relative increases 

subsequently. Third, relative tax burdens on individuals in the highest income quintile would 

have been increased by every major law since TRA86. These relative net tax increases are due 

primarily to increases in individual income tax rates (but, not in EGTRRA and JGTRRA). 

The panel model provides a 10-year, forward-looking dimension to the distributional effects of 

federal tax laws. A different dimension is provided by analyses based on cross-section models. 

For example, the Congressional Budget Office (CBO) has periodically published its analysis of 

the distribution of federal taxes in effect in selected prior years.26 CBO’s analyses capture the 

combined effects of historical changes in the tax laws and in the demographic and economic 

circumstances of households between years. Such retrospective analyses inform policy makers 

and the general public about actual historical developments in federal tax policy, which shape 

perspectives on future tax policies. 

26 See CBO (2006), "Historical Effective Tax Rate: 1979 to 2004," 

(http://www.cbo.gov/ftpdocs/77xx/doc7718/EffectiveTaxRates.pdf ). 

- 20 ­
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3
 

Distribution of Total Federal Taxes Under Alternative Tax Laws Over the 2004-2013 Budget Period1,2,3
 

Income 

Quintile4 TRA 86 OBRA 90 OBRA 93 

TRA97 

Fully Phased 

In 

EGTRRA and JGTRRA 

As Enacted 

Without 

Phase-ins 

Without 

Phase-ins or 

Sunsets 

Without 

Phase-ins, 

Sunsets or 

AMT 

--- Percent Distribution ---

Lowest5 1.0 0.9 0.6 0.5 0.4 0.4 0.4 0.4 

Second 4.8 4.7 4.2 4.1 3.9 3.8 3.8 3.9 

Third 10.7 10.6 10.1 10.0 10.0 10.0 10.0 10.2 

Fourth 19.5 19.4 18.6 18.8 18.9 19.1 19.3 19.4 

Highest 63.7 64.2 66.2 66.4 66.6 66.5 66.4 65.9 

Total5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Top 10 47.7 48.3 50.9 50.9 50.9 50.7 50.3 49.8 

Top 5 36.2 36.9 39.7 39.6 39.4 39.1 38.6 38.2 

Top 1 20.1 20.6 23.1 22.9 22.6 22.3 21.7 21.9 

--- Indexed Effective Rate ---

Lowest5 32.0 29.0 17.9 16.6 13.7 12.6 11.5 11.9 

Second 62.4 60.7 54.8 52.6 50.1 49.4 48.7 50.2 

Third 81.4 80.5 76.6 75.9 75.6 75.6 75.9 77.6 

Fourth 95.3 94.5 90.9 91.5 92.3 93.0 94.0 94.9 

Highest 114.2 115.1 118.8 119.1 119.4 119.3 119.1 118.1 

Total5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

Top 10 117.3 118.8 125.2 125.1 125.1 124.5 123.7 122.3 

Top 5 119.9 121.9 131.4 130.8 130.4 129.2 127.5 126.2 

Top 1 124.0 127.5 142.9 141.6 139.7 137.5 134.0 135.2 

1

 The calculations are based on a representative sample of individual income tax returns filed over the 10-year period 1987 through 1996 and a sample of nonfilers taken from the 

Panel Survey on Income Dynamics (PSID). The combined samples have been extrapolated to the Budget period, 2004-2013. 

2

 The taxes included are individual and corporate income, payroll (Social Security and unemployment), excises, customs duties, and estate and gift taxes. The individual income tax 

is assumed to be borne by payers, the corporate income tax by capital generally, payroll taxes (employer and employee shares) by labor (wages and self-employment income), excises 

on purchases by individuals in proportion to relative consumption of the taxed good and proportionately by labor and capital income and excises on purchases by businesses and 

customs duties proportionately by labor and capital income, and the estate and gift taxes by decedents. 

3 Income and tax liabilities over the Budget period (2004-2014) are on an annuitized, family-equivalenced basis for each individual present in the first year of the Budget period 

(2004). All individual members of a family in a year are assumed to share equally in the family's income and tax liabilities for the year. Each family's income and tax liabilities for a 

year is divided by an equivalence scale and these equivalenced amounts are assigned to each member of the family. The equivalence scale increases with the size of the family and is 

related to the official poverty thresholds. Dividing by the equivalence scale is necessary to put the income and tax liabilities of individuals in families of different sizes on a 

comparable basis. The year-by-year amounts of each individual’s income and tax liabilities are then converted to their present value in the first year of the Budget period. Each 

present value amount is then converted to the level real annuity over the Budget period with the same present value. 

4

 The income measure is individual annuitized cash equivalent income. It is a summary measure of an individual's cash income over the Budget period (see footnote 3). Cash income 

consists of wages and salaries, net income from a business or farm, taxable and tax-exempt interest, dividends, rental income, realized capital gains, cash transfers from the 

government, and retirement benefits. Employer contributions for payroll taxes and the federal corporate income tax are added to place cash income on a pre-tax basis. 

5 Individuals with negative incomes are excluded from the lowest income quintile but included in the total line. 

- 21 ­
 

 

 

 

IV. Dynamic Tabulations with the Model 

Conventional models can be used to analyze who will benefit in any particular year of the budget 

period, but cannot be used to analyze who will benefit in more than one year. Treasury’s new 

panel model is capable of capturing the dynamic changes in taxpayers’ circumstances and the tax 

law over the entire budget period, adding the missing time dimension to this type of tax analysis. 

For example, in any one year of the budget period only taxpayers with children and certain 

income and income tax characteristics will benefit from the expansion of the child tax credit in 

EGTRRA. The benefit will depend on the precise year because it drops to its pre-EGTRRA 

level after 2010. But in each year of the budget period, some currently childless taxpayers will 

have children and some taxpayers with children will have changes in their income and income 

tax characteristics, and these taxpayers will benefit from expansion of the credit in years prior to 

the sunset. 

Treasury’s new panel model can be used to perform a wide range of analyses that capture the 

dynamics of both the tax law and individuals’ demographic and economic circumstances. The 

illustration below addresses the question: How many taxpayers will benefit from the provisions 

in EGTRRA and JGTRRA? 

A. Illustration: EGTRRA and JGTRRA 

Two sets of simulations illustrate dynamic analysis with the model.27 The first set addresses the 

question: How many taxpayers will benefit from the fully phased in level of the provisions of 

EGTRRA and JGTRRA in the first year and in any year of the budget period? The second set 

addresses the question: What effect do the phase-ins and sunsets in EGTTRA and JGTRRA, as 

well as the alternative minimum tax (AMT) have on the number of taxpayers who will benefit 

from the fully phased in level of the provisions of the legislation over the budget period? The 

effect of the AMT is included in the second set, even though EGTRRA and JGTRRA contained 

only temporary provisions affecting the AMT, because the AMT can limit or eliminate the 

benefit of several provisions of EGTRRA and JGTRRA.28 All of these simulations cover only 

non-dependent taxpayers who filed a return in the first year (2004) of the budget period and were 

alive at the end of the budget period (whether or not they filed a return in any year after 2004). 

Certain provisions of EGTRRA and JGTRRA are at fully phased in levels in 2004 and remain 

fully phased in until they sunset. These provisions include the new 10 percent rate bracket for 

head of household filers, reductions in tax rates above 15 percent, reduced rates on dividends and 

capital gains for taxpayers in the regular tax 25 percent and higher brackets, expansion of the 

child and dependent care tax credit, and EITC simplification. Other provisions are at fully 

27 These simulations were performed under 2004 law before enactment of the Working Family Tax Refief Act 

(WFTRA) of 2004 which extended the capital gains and dividends tax relief from to 2010. 

28 The AMT does not allow personal exemptions, the standard deduction or certain credits, and effectively treats 

rates below 26 percent as “preferences.” Further, the AMT exemption is not indexed for inflation, while regular 

income tax parameters are indexed. The effect of the broad base and lack of indexing of the AMT will be to reduce 

or eliminate the benefit of some EGTRRA and JGTRRA provisions for an increasing number of taxpayers over 

time. 

- 22 ­
 

 

phased in levels in 2004, but then revert to the levels set by the EGTRRA phase-in schedule. 

These include the new 10 percent bracket (with full bracket widths) for single and joint filers, 

widening of the 15 percent bracket and expansion of the standard deduction for joint filers, and 

expansion of the child credit to $1,000. The remaining provisions are not fully phased in (with 

some not even beginning to phase in) until after 2004. These include the zero rate on dividends 

and capital gains for taxpayers in the regular tax 10 and 15 percent brackets (effective only in 

2008), the repeal of the personal exemption phase-out (PEP) and the limitation on itemized 

deductions for higher-income taxpayers (“Pease”), and the increase in the EITC phase-out range 

for joint filers. All provisions sunset at the end of 2010 except the reduced rates on dividends 

and capital gains, which sunset at the end of 2008 (currently 2010, see footnote 27). The benefit 

of reduced rates on dividends and capital gains, expansion of the child credit, and the EITC 

provisions are not limited by the AMT. 

B. Simulation 1: Effect of Demographic and Economic Dynamics 

The first simulation illustrates the effect of changes in individuals’ demographic and economic 

circumstances on the benefit of tax provisions in EGTRRA and JGTRRA. For this simulation, 

EGTRRA and JGTRRA law is held constant with all provisions set at their fully-phased-in levels 

over the entire budget period (i.e., the phase-ins and sunsets do not apply), and the benefit of the 

provisions is not reduced by the AMT (the AMT is effectively repealed). Tax liability on each 

return in the panel in each year was first calculated under the law in effect prior to EGTRRA 

(“pre-EGTRRA” law), but with all provisions fully-phased-in and with the AMT effectively 

repealed.29 Tax liability was then recalculated on each return under fully phased in, no sunset, 

no AMT EGTRRA and JGTRRA law. A taxpayer would benefit from one or more of the 

provisions of EGTRRA and JGTRRA in a year if their calculated liability under this EGTTRA 

and JGTRRA law is less than their calculated liability under pre-EGTRRA law. Additional 

calculations of tax liability for each year were then done to determine whether each specific 

provision would benefit the taxpayer. In these calculations, tax liability under no sunset, no 

AMT EGTRRA and JGTRRA law with only one fully-phased-in provision was used. If liability 

calculated using this law was less than the liability calculated under pre-EGTRRA law, the 

provision would provide the taxpayer a benefit in that year. 

The number of taxpayers who would benefit from the provisions of EGTRRA and JGTRRA was 

then counted in two ways: the number who would benefit in the first year of the budget period 

(2004), and the number who would benefit in any year of the budget period (2004-2013). These 

numbers were then divided by the total number of taxpayers, to give the percentage of taxpayers 

who would benefit.30 These percentages are shown in Table 4. Changes in individuals’ 

demographic and economic circumstances over the budget period have a significant effect on the 

percentage that would benefit from the provisions of EGTRRA and JGTRRA over time. For 

29 Effectively repealing the AMT in pre-EGTRRA law is necessary to isolate the effects of EGTRRA and JGTRRA. 

30 In all of these dynamic simulations, the number of taxpayers rather than the number of tax returns is used in the 

counts because taxpayers can be followed over time but tax return units can change (e.g., when two single taxpayers 

marry and start filing a joint return). Because the panel follows the same taxpayers over ten years and taxpayers 

who died during the period have been excluded from these simulations, the total number of taxpayers is the same in 

all ten years (158.7 million). 

- 23 ­
 

 

example, in the first year 34.7 percent of taxpayers would benefit from the reduction of tax rates 

above 15 percent, whereas over ten years 60.7 percent would benefit in at least one year. 

Similarly, in the first year 28.0 percent of taxpayers would benefit from the expansion of the 

child tax credit, whereas over ten years 41.5 percent would benefit. In the first year, some tax 

return filers do not benefit from any of the major provisions of EGTRRA because they have no 

income tax liability under pre-EGTRRA law and do not qualify for the expanded refundability of 

the child credit. But over time, nearly all taxpayers, 94.4 percent, would benefit. 

Table 4
 

Simulation 1: EGTRRA and JGTRRA Fully Effective Over the Budget Period, 2004-20131
 

Major Provision of EGTRRA or JGTRRA 

Percent of Taxpayers Benefiting: 

In First Year of the 

Budget Period 

In At Least One 

Year During the 

Budget Period 

(Simulation 1) 

Increase 

(Percent of taxpayers 

who gain benefits over 

time) 

New 10% Bracket 78.0% 91.9% 13.8% 

Reduction in Rates Above 15% 34.7% 60.7% 26.0% 

Reduced Rates on Dividends and Capital Gains 20.9% 42.7% 21.8% 

Repeal of PEP and Pease 7.2% 18.4% 11.2% 

Marriage Penalty Relief (Except Wider 15% Bracket) 25.5% 49.6% 24.2% 

Expansion of Child Tax Credit 28.0% 41.5% 13.4% 

Expansion of Child and Dependent Care Tax Credit 4.3% 10.1% 5.8% 

EITC Simplification Provisions 0.3% 3.1% 2.7% 

Any of the Major Provisions 84.5% 94.4% 9.9% 

1 In Simulation 1 provisions do not phase in over time or sunset, and the alternative minimum tax (AMT) is treated as 

repealed. 

C. Simulation 2: Effect of EGTRRA and JGTRRA Phase-Ins 

In this simulation, tax liability for each return for each year of the budget window was first 

calculated using fully-phased-in, no AMT pre-EGTRRA law, as in Simulation 1. Then, tax 

liability was calculated for each year using no sunset, no AMT EGTRRA and JGTRRA law, 

with provisions that were not at fully-phased-in levels (according to the phase-in schedule) set at 

their pre-EGTRRA law level. In this simulation as in the Simulation 1, there are no sunsets so 

that the fully-phased-in level of all provisions of EGTRRA and JGTRRA are in effect for 2011 

through 2013 (2008 through 2013 for the reduced rates on dividends and capital gains provision). 

If the first calculation results in a higher tax liability than the second, the taxpayer would benefit 

from at least one of the fully-phased-in provisions of EGTRRA and JGTRRA. Additional 

calculations of tax liability for each year were then required to determine the specific major 

provision or provisions that would benefit the taxpayer. 

In this simulation, taxpayers would only benefit from the widening of the 15 percent bracket for 

joint filers (which also reduces rates above 15 percent) in 2004 and again starting in 2008; full 

EITC marriage penalty provisions, the widening of the 10 percent bracket for single and joint 

- 24 ­
 

 

filers,31 and the zero rate on dividends and capital gains for taxpayers in the regular tax 10 and 15 

percent brackets starting in 2008; the full increase in the standard deduction for joint filers in 

2004 and again starting in 2009; the full increase in the child credit to $1,000 in 2004 and again 

starting in 2010; and the completed phase-out of PEP and Pease starting in 2010. 

In Simulation 1 taxpayers could potentially benefit from the fully-phased-in level of all 

provisions of EGTRRA and JGTRRA in all ten years of the budget period. In this simulation, 

taxpayers could potentially benefit from some provisions of EGTRRA and JGTRRA for as few 

as four years. So, the phasing in of some provisions of EGTRRA and JGTRRA reduces the 

percentage of taxpayers who could benefit in at least one year of the budget period, as shown in 

Table 5. The reduction in the percentage of taxpayers who would benefit is significant for 

provisions that phase in slowly, such as marriage penalty relief and expansion of the child credit. 

In both absolute and relative terms, the greatest percentage reduction is for the repeal of PEP and 

Pease, the provisions with the slowest phase in. 

Table 5 

Simulation 2: Effect of EGTRRA and JGTRRA Phase Ins Over the Budget Period, 2004-20131 

Percent of Taxpayers Benefiting from Fully Phased In 

Major Provision of EGTRRA or JGTRRA 

Provision At Least One Year During the Budget Period: 

Laws Phased In 

(Simulation 2) 

Laws Fully 

Effective Over the 

Budget Period 

(Simulation 1) 

Reduction 

(Percent of taxpayers 

losing full benefits due 

to phase ins) 

New 10% Bracket 91.1% 91.9% -0.8% 

Reduction in Rates Above 15% 58.7% 60.7% -2.0% 

Reduced Rates on Dividends and Capital Gains 39.5% 42.7% -3.2% 

Repeal of PEP and Pease 14.4% 18.4% -4.0% 

Marriage Penalty Relief (Except Wider 15% Bracket) 46.1% 49.6% -3.5% 

Expansion of Child Tax Credit 38.7% 41.5% -2.8% 

Expansion of Child and Dependent Care Tax Credit 10.1% 10.1% 0.0% 

EITC Simplification Provisions 3.1% 3.1% 0.0% 

Any of the Major Provisions 94.4% 94.4% 0.0% 

1 In Simulation 2 provisions phase in over time as scheduled under current law but do not sunset, and the alternative 

minimum tax (AMT) is treated as repealed. 

D. Simulation 3: Effect of EGTRRA and JGTRRA Sunsets. For this simulation, the 

calculations of tax liability are the same as for Simulation 2, except that the EGTRRA and 

JGTRRA sunsets in 2008 and 2010 are in effect for all provisions. Adding the sunset means, 

for example, that taxpayers could benefit from the full increase in the child credit to $1,000 

in only two years (2004 and 2010) and the completed phase-out of PEP and Pease in only 

one year (2010). The added effect of the sunset on the percentage of taxpayers who would 

31 For head of household filers, the 10 percent bracket is always fully phased in. It is also effectively fully phased in 

any year for single and joint taxpayers with taxable incomes in the 10 percent bracket specified in 2005 law. 

- 25 ­
 

 

benefit is shown in Table 6. The largest effect, as might be expected, is on the reduced rates 

on dividends and capital gains provision, the only provision that sunsets in 2008. Reductions 

in rates above 15 percent, marriage penalty relief, and expansion of the child credit are also 

significantly affected. Repeal of PEP and Pease is affected the most in relative terms. 

Table 6 

Simulation 3: Effect of EGTRRA and JGTRRA Sunsets Over the Budget Period, 2004-20131 

Percent of Taxpayers Benefiting from Fully Phased In 

Provision At Least One Year During the Budget Period 

Major Provision of EGTRRA or JGTRRA 

(with Laws Phased In): 

Laws Sunset 

(Simulation 3) 

Laws Phased In 

(Simulation 2) 

Reduction 

(Percent of taxpayers 

losing full benefits due 

to sunsets) 

New 10% Bracket 89.7% 91.1% -1.4% 

Reduction in Rates Above 15% 53.1% 58.7% -5.6% 

Reduced Rates on Dividends and Capital Gains 28.1% 39.5% -11.4% 

Repeal of PEP and Pease 9.7% 14.4% -4.6% 

Marriage Penalty Relief (Except Wider 15% Bracket) 40.5% 46.1% -5.6% 

Expansion of Child Tax Credit 35.4% 38.7% -3.3% 

Expansion of Child and Dependent Care Tax Credit 9.8% 10.1% -0.3% 

EITC Simplification Provisions 2.1% 3.1% -1.0% 

Any of the Major Provisions 93.5% 94.4% -0.9% 

1 In Simulation 3 provisions phase in over time and sunset as scheduled under current law, but the alternative minimum tax 

(AMT) is treated as repealed. 

E. Simulation 4: Effect of the AMT on EGTRRA and JGTRRA Benefits 

This simulation recalculates tax liability in the same manner used for Simulation 3, except that 

the AMT is in effect for the second and subsequent calculations. The added effect of the AMT is 

shown in Table 7. The largest reductions in the percentage of taxpayers that would benefit are 

for the rate cut provisions, because some lower rates are effectively “preferences” under the 

AMT. The percentage of taxpayers who would benefit from repealing PEP and Pease would be 

reduced by more than half by the AMT. 

- 26 ­
 

Table 7
 

Simulation 4: Effect of the AMT on EGTRRA and JGTRRA Benefits Over the Budget Period, 2004-20131
 

Percent of Taxpayers Benefiting from Fully Phased In 

Provision At Least One Year During the Budget Period 

Major Provision of EGTRRA or JGTRRA 

(with Laws Phased In and Sunset): 

AMT in Effect 

(Simulation 4) 

AMT Not in Effect 

(Simulation 3) 

Reduction 

(Percent of taxpayers 

losing full benefits due 

to the AMT) 

New 10% Bracket 87.1% 89.7% -2.6% 

Reduction in Rates Above 15% 50.0% 53.1% -3.1% 

Reduced Rates on Dividends and Capital Gains 28.1% 28.1% 0.0% 

Repeal of PEP and Pease 4.6% 9.7% -5.1% 

Marriage Penalty Relief (Except Wider 15% Bracket) 38.6% 40.5% -1.8% 

Expansion of Child Tax Credit 35.4% 35.4% 0.0% 

Expansion of Child and Dependent Care Tax Credit 8.7% 9.8% -1.1% 

EITC Simplification Provisions 2.1% 2.1% 0.0% 

Any of the Major Provisions 93.5% 93.5% 0.0% 

1 In Simulation 4 provisions phase in over time and sunset as scheduled under current law, and the alternative minimum tax 

(AMT) is in effect. 

F. Combined Effect of Phase-In, Sunset, and AMT on EGTRRA Benefits 

The combined effects of the phase-in, the sunset, and the AMT on the percentage of taxpayers 

who would benefit from the major provisions of EGTRRA and JGTRRA are shown in Table 8. 

The combined effect of these three dynamic features of current law is fairly significant for some 

provisions. Nearly 11 percent of all taxpayers who potentially could benefit from the fullyphased-in reduction in tax rates above 15 percent over the budget period would not receive this 

benefit. Nearly 15 percent of all taxpayers would not benefit from the fully-phased-in reduction 

in rates on dividends and capital gains, and 11 percent would not receive the full benefit of the 

marriage penalty provisions. The percentage of taxpayers who would benefit from repeal of PEP 

and Pease would be reduced by three-fourths. 

- 27 ­
 

Table 8
 

Simulations 1-4 Combined: Effect of Phase Ins, Sunsets, and the AMT on EGTRRA and JGTRRA Benefits Over the
 

Budget Period, 2004-2013
 

Percent of Taxpayers Benefiting in Any Year During the 

Major Provision of EGTRRA or JGTRRA 

Budget Period: 

Laws Phased In, 

Sunset, and with 

the AMT in Effect 

(Simulation 4) 

Laws Fully 

Effective Over the 

Budget Period 

(Simulation 1) 

Reduction 

(Percent of taxpayers 

losing full benefits due 

to the phase ins, 

sunsets, and the AMT) 

New 10% Bracket 87.1% 91.9% -4.8% 

Reduction in Rates Above 15% 50.0% 60.7% -10.7% 

Reduced Rates on Dividends and Capital Gains 28.1% 42.7% -14.6% 

Repeal of PEP and Pease 4.6% 18.4% -13.8% 

Marriage Penalty Relief (Except Wider 15% Bracket) 38.6% 49.6% -11.0% 

Expansion of Child Tax Credit 35.4% 41.5% -6.0% 

Expansion of Child and Dependent Care Tax Credit 8.7% 10.1% -1.4% 

EITC Simplification Provisions 2.1% 3.1% -1.0% 

Any of the Major Provisions 93.5% 94.4% -0.9% 

Notes to Tables 4-8: 

(1) The figures in all simulations are based on a representative sample of individual income tax returns filed over the 10­

year period 1987 through 1996, and a sample of nonfilers taken from the Panel Survey on Income Dynamics (PSID). The 

combined samples have been extrapolated to the Budget period, 2004-2013. 

(2) The percentages shown for all simulations are based on the projected 2004 population of individuals who: i) are not 

dependents of another taxpayer in 2004 (or any subsequent year through 2013); ii) file a return in 2004 (but may or may not 

file in any subsequent year through 2013); and iii) are alive in 2013. Each nondependent individual is counted separately 

(i.e., each spouse in a joint return unit is counted). 

(3) For single and joint filers with income taxed above the 10-percent rate, the 10-percent bracket provision is considered 

fully phased in only in years when the bracket width is fully phased in. 

(4) The reduction in rates on dividends and capital gains is considered fully phased in for taxpayers in the regular tax 10­

percent and 15-percent brackets only when their rate on dividends and capital gains is zero. 

- 28 ­
VI. Conclusions 

A. How the Model Improves Tax Analysis 

Treasury’s new panel model is able to analyze the effect of changes in tax law on individuals 

over time. It captures the changing demographic and economic circumstances of individuals and 

the effects of changes in tax law over the entire 10-year budget window, not just a single year. 

In addition, because it is based on a very large data set the Treasury model is capable of 

analyzing tax proposals in great detail, more so than multi-year models based on representative 

taxpayers. 

B. Limitations of the Model 

Length of time period. The panel model uses ten years of data as opposed to a single year which 

gives policy makers a better understanding of how policy changes affect taxpayers over time. 

But ten years is still only a snapshot compared to a taxpayer’s lifetime. A twenty year old 

taxpayer may be lower income than he or she would be at age 30, and he or she might marry, 

become a joint filer and have children or attend college over the next ten years. Our analyses 

using the panel model will capture the interaction of these income and demographic changes 

with proposed tax changes, but it will not capture interactions beyond the first ten years. If the 

twenty year would not take advantage of proposed changes in retirement savings incentives until 

he or she were 30, then he or she would not be measured as benefiting from such a proposal in a 

ten-year analysis. If the analysis were extended to cover the remaining lifetime of such a 

taxpayer it would give a more complete picture of the lifetime benefits of a proposal. 

Ten years is also too short a time period to fully capture lifetime income rankings. Very short 

term income fluctuations such as one-time capital gains realizations or short periods of 

unemployment are better measured with a ten year income measure. But separate identification 

of lifetime poor from “ten-year” poor which may arise from natural lifecycles is only partially 

achieved. An individual who is 18 at the beginning of the ten year period and in the lowest 

income quintile may be very different in terms of lifetime income from the individual who is 40 

and also ranked in the lowest income quintile on the ten year tables. 

Historical basis. The extrapolation approach that the model uses retains the richness of the 

original data while retargeting certain data fields to reflect the latest data from other sources. 

Not all fields are targeted, however, and not all trends can be captured. For example, if 

individuals after 1996 began delaying retirement, divorced in greater numbers, had fewer or 

more children than they did before 1996, the more recent trends would not be reflected in the 

model. 

Other analyses required for a complete picture. Distributional analyses and dynamic analyses are 

only two of the tools that should be used to judge tax policies. Distributional analysis by itself 

only measures one aspect of fairness, vertical equity: the degree to which individuals with 

greater abilities to pay taxes pay greater amounts. It does not measure the other aspect of 

fairness, horizontal equity: whether individuals with equal abilities to pay taxes pay equal 

amounts. 

- 29 ­
Some other types of analyses used to judge tax policies include discussions of efficiency gains or 

losses; discussions of simplicity gains or losses; carefully constructed examples of taxpayers 

affected by particular proposals (e.g. elderly taxpayers, taxpayers with children, small business 

owners); and counts of winners and loser. 

- 30 ­
 

 

APPENDIX A.1 

Basic Data: Constructing the Tax Return Panels 

by 

James Cilke 

and 

James R. Nunns 
Constructing the Tax Return Panels 

The panel model was designed to represent the entire U.S. population in each year of the ten-year 

period 1987 through 1996. The primary source of data for the panel model came from a panel of 

tax returns (the “cohort” panel) based on the SOI cross-section sample for 1987. This sample 

represented most of the U.S. population in 1987, all those individuals who were represented on a 

tax return as taxpayers (including spouses on joint returns) and taxpayers’ dependents. Returns 

in the 1987 cross-section sample that were filed by taxpayers who were not dependents of 

another taxpayer were included in the cohort panel sample. The non-dependent taxpayer(s) and 

any dependents reported on each of these returns were considered a “tax family” headed by the 

primary taxpayer on the return, and all members of all of these tax families were panel members. 

Any return filed by any panel member over the succeeding nine years, 1988 through 1996, was 

identified and included in the cohort panel. Any return filed by a “new” dependent (one reported 

for the first time after 1987) was also included in the cohort panel. In years after 1987 some nondependent panel members who remained in the non-dependent U.S. population (i.e., had not 

become a dependent, died or emigrated) did not file a return, or filed but the return was not 

identified. All such “missing” returns were imputed by OTA. With these imputed returns, the 

cohort panel represents over the ten-year panel period all individuals reported on 1987 returns 

(the 1987 “filing population”) and all new dependents (primarily newborns) of this population. 

The U.S population that was not reported on 1987 returns (the 1987 “non-filing population”) was 

represented by two additional panels. In both panels, individuals were arranged into tax families 

following the income tax filing status and dependency rules, and the basic data unit was a (nondependent) tax return. One of these panels represented the portion of the 1987 non-filing 

population that remained in the non-filing population in all subsequent years through 1996. This 

“permanent non-filer” panel was constructed from the PSID. The basic approach was to form 

tax units from PSID individuals, compute taxable income for the tax unit, and select units with 

incomes below the filing threshold in every year from 1987 through 1996 for addition to the tax 

panel. The PSID panel is described in Appendix A.2. 

The other (“refreshment”) panel was a tax return panel based on the SOI cross-section samples 

for 1988 through 1996. These samples were designed in such a way that in each year they 

included a simple random sample of returns with a primary filer who had not filed a tax return in 

any prior panel year (“new filers”). These new filers included non-dependent taxpayers, and 

their dependents, who were in the 1987 non-filing population. These new filers also included 

new non-dependent filers who had been dependents in prior panel years. Since dependents of 

1987 filers were panel members and any returns they filed (as dependents or non-dependents) 

were included in the cohort panel, to avoid double representation of any returns they filed as 

non-dependents these returns were zero weighted. Non-dependent immigrants to the United 

States after 1987, and their dependents, were also included in the new filers. Any return filed by 

any new filer reporting as the primary taxpayer in a subsequent year through 1996 was 

automatically included in the SOI cross-section samples, so these returns could be identified and 

included in the refreshment panel. 

A.1 - 1
 
 

Whether a return was “missing” in any year of the refreshment panel, and therefore imputed by 

OTA, depended on the type of new filer. For the first group, 1987 non-dependent non-filers, a 

missing non-filed return was imputed for 1987 and all subsequent years prior to the first year of 

filing, and a missing (filed or non-filed) return was imputed for any year thereafter (through 

1996) that a return was missing and the taxpayer had not become a dependent, died or emigrated, 

or had not become a secondary filer.1 For the second group, 1987 dependents who became nondependents, it was assumed that any return they had filed as a dependent was already represented 

in the cohort panel, so no missing return was imputed. In any year after the first year of filing 

(through 1996), a return (filed or non-filed) was imputed for any year a return was missing and 

the taxpayer had not become a dependent again, died or emigrated, or had not become a 

secondary filer. For the third group, post-1987 immigrants, it was assumed that the nondependent taxpayer filed in their first year in the United States, so no missing returns were 

imputed for any prior year. If any immigrant taxpayer stopped filing, it was assumed they had 

emigrated or had become a secondary filer, and no missing return was imputed for years after the 

last year of filing. A missing (filed or non-filed) return was imputed if there was a gap in filed 

returns. With these imputed returns, the refreshment panel represents over the ten-year panel 

period all individuals who were not reported on 1987 returns (the 1987 “non-filing” population) 

but were reported on a subsequent return by 1996, new non-dependent filers, new immigrants to 

the United States, and all new dependents (primarily, newborns) of any of these groups. 

This Appendix describes how the members of the cohort and refreshment tax return panels were 

designated, how panel members were followed over time and the tax returns they filed were 

identified for each panel year, and how “missing” returns of panel members were imputed. 

Some issues related to panel weights are mentioned, but the full description of weighting is 

contained in Appendix B. 

I. The Cohort Panel 

The Statistics of Income (SOI) Division of IRS draws a sample of the individual income tax 

returns filed each year. The returns filed each year are primarily for the preceding calendar year 

(e.g., 1987 returns were generally filed in 1988), and SOI samples are normally identified by the 

calendar (or “tax”) year preceding the year the sample is drawn (which is referred to as the 

“processing” year). Some returns filed each year are for earlier years and some returns cover 

fiscal (non-calendar) years or periods shorter than a year (part-year returns).2

 There are also 

some returns filed each year that SOI does not sample: amended returns (because the original 

returns were sampled) and tentative returns (because the revised return will be sampled). 

SOI samples have two basic components, a simple random sample and a stratified probability 

sample. The simple random sample is based on the last four digits of primary filers’ SSNs, 

1

 Since the “refreshment” panel was selected only on primary filers, when a single taxpayer married and became a 

secondary filer on a joint return they were no longer included in the panel. However, they were still represented in 

the panel, as the spouse of a newly-married panel member. 

2

 Tax returns must generally be filed on the 15th day of the fourth month following the end of the taxpayer’s 

accounting period (which generally must have a month ending), so SOI samples may include fiscal or part year 

returns that end as late as August (and possibly even later) of the processing year. 

A.1 - 2
 
 

 

 

 

 

 

where these digits are from a set of these SSN endings designated by the Social Security 

Administration and known as the Continuous Work History Sample (CWHS). Since SSN 

endings are random (or very nearly so) and cannot end in 0000, each CWHS ending represents 1 

in 9,999 individuals, making the probability of selection of a return filed by a primary taxpayer 

with that ending 1/9,999. The stratified portion of the SOI sample selects returns based on the 

level of income or other return characteristics used to place the returns in strata. Sampling rates 

vary from the CWHS rate up to 100%. CWHS returns are selected first within each of the strata, 

with the remaining returns selected randomly, based on a transformation of primary filers’ 

SSNs.3 

In the latter part of the 1980s, SOI samples were smaller in even-number (“lean”) years than in 

odd-numbered (“fat”) years to meet OTA modeling needs.4

 The lean year sample consisted of 

one CWHS ending (about 10,000 returns) and a stratified probability sample of about 80,000 

returns.5

 The fat year sample consisted of the lean year sample plus a second CWHS ending and 

higher sampling rates (producing approximately another 20,000 returns) in the stratified 

probability sample.6

 In addition, sample rates for returns with foreign-source income were 

periodically increased to provide more reliable estimates of such income, increasing the sample 

by about 6,000 returns.7 

A. Selection from the 1987 SOI Sample 

Most cohort panel members were designated from the returns in the annual cross-section sample 

drawn by the SOI Division from the 107 million returns filed during 1988, i.e., the “1987” SOI 

cross-section sample drawn in processing year (PY) 1988. Since (tax year) 1987 was a “fat” 

year and also a year that covered foreign-source income, the SOI sample drawn in PY88 was 

about 126,000 returns of the 107 million returns filed. Some of the returns in the PY88 sample 

were not considered in designating panel members: 

3

 The transformation creates an 11-digit number that is truncated to a 5-digit pseudo random number (0 to 99999) 

that corrects for the non-randomness of SSNs. The sample rate for a stratum is achieved by selecting returns with 

the low-order digits of the transform (taking into account the CWHS selection rate) that represent the sample rate. 

For example, if the sample rate for a stratum was 2 percent and one CWHS ending was in the CWHS portion of the 

sample, the non-CWHS portion of the sample in the stratum would be sampled at 1.99 percent (.02 less the CWHS 

sample rate of .0001 (rounded), less the probability a CWHS ending is transformed to the non-CWHS portion or .02 

x .0001, which is negligible), so transforms of 0 through 1989 would be used. 

4

 At the time, OTA planned to develop a new individual tax model (ITM) every other year, using the latest fat-year 

SOI sample. 

5

 The lean year sample was referred to as the “Level 1” sample. 

6

 The additional sample in fat years was referred to as the “Level 2” sample. The Level 2 sample was identified 

every year by SOI, but only processed and included in the SOI cross-section file in fat years. Another (“Level 3”) 

sample was identified every year by SOI, but never processed. 

7

 Foreign-source income is identified on tax returns by the presence of Form 1116 (Foreign Tax Credit) or Form 

2555 (Foreign Earned Income). The over sample of returns with foreign-source income were considered to be part 

of the Level 1 sample. 

A.1 - 3
 
 

 

 

 

 

 

 

 

 

 

 

•	 In order to keep the panel at what was thought to be a manageable size, the additional returns 

selected in the fat year portion of the stratified probability sample were generally excluded.8 

•	 The over sample of returns with foreign-source income was likewise excluded. 

•	 Because dependents can only be panel members if they appear on a return filed by a nondependent taxpayer (including spouses) designated as a panel member, returns filed by 

dependents were excluded.9,10 

•	 Because the panel was designed to represent economic and demographic information on a 

calendar year (rather than a processing year) basis, prior year returns were not used directly 

to designate panel members. However, the non-dependent taxpayers (including spouses) and 

their dependents represented on prior year returns were designated as panel members if they 

were represented on a 1987 return11 that was: i) included in the PY88 SOI cross-section 

sample; ii) included in the 1985-based sales of capital assets (SOCA) panel for PY88; or iii) 

posted to the Individual Master File (IMF) in any year from PY89 through PY94.12 

With these exclusions, the remaining returns in the PY88 sample that were used to designate 

panel members were the 1987 returns filed by non-dependents that had a primary filer with one 

of the two CWHS endings (approximately 20,000 returns), or that were included in the lean year 

portion of the stratified probability sample (approximately 70,000 returns). A few of these 

90,000 returns, however, were excluded for technical reasons: 

•	 As explained below, returns filed by panel members for years after 1987 had to be identified 

by panel members’ SSNs, which could not be matched if the SSN was misreported on the 

8

 A few of these non-CWHS Level 2 returns were used to designate panel members because SOI adjusted the 

achieved non-CWHS portion of the Level 1 sample to correspond to the originally designated targets for each 

stratum. So, conversely, some Level 1 non-CWHS, non-dependent, 1987 returns were not used to designate panel 

members. 

9

 Returns were identified as having been filed by a dependent if the dependent checked the dependent checkbox on 

the return or if a Form 8615 (“kiddie tax”) was attached to the return. As discussed below, some individuals who 

were claimed as a dependent on another return filed returns that had neither of the dependent indicators. 

10 Although dependent returns were not used to designate panel members, a small number of the returns filed by 

dependents that were in the PY88 SOI sample were filed by dependents who were panel members, so these returns 

were included in the panel. 

11 The 1987 return had to be filed by the primary taxpayer on the prior year return, and only spouses and dependents 

on the 1987 return were designated as panel members (i.e., spouses and dependents that were reported on the prior 

year return but not on the 1987 return were not designated as panel members). 

12 So long as the SSN of the primary on the pre-1987 return was correctly reported on that return and the 1987 

return, the 1987 return should have been identified by SOI and the primary, spouse, and dependents made panel 

members. Note, however, that some of the primaries on these pre-1987 returns filed a 1987 return in PY88, but that 

return was not included in the SOI cross-section sample and, as explained below, no EOYTICK process was run for 

PY88, so the 1987 return was never identified by SOI and the primary, spouse, and dependents reported on the 

return never became panel members. 

A.1 - 4
 
 

 

 

 

 

1987 return. The small number of 1987 returns with erroneous primary SSNs were excluded, 

which meant that no one (primary, spouse, or dependents) reported on these returns were 

designated as panel members.13 

•	 A few amended and tentative returns are unintentionally included in the SOI sample each 

year because they cannot be identified as such during sampling. A few returns are also 

sampled that report no income information. These returns remain in the SOI sample, but are 

effectively excluded because they are given a weight of zero. The amended, tentative, and no 

income returns included in the PY88 SOI sample were entirely excluded from the panel 

sample. 

•	 A return with a filing period that ended in 1987 or 1988 was considered to be a “1987” return 

for purposes of designating panel members. Because of fiscal-year and part-year returns, in a 

few instances two “1987” returns for same (non-dependent) taxpayer(s) appeared in the 

sample. Any part year returns were eliminated, and returns ending in 1988 were considered 

1988 returns.14 

In addition to the returns identified in the PY88 sample, late filed returns for 1987 with a CWHS 

primary that were and identified in the PY89 and later samples were added to the cohort panel by 

OTA. The identification and other aspects of these returns are discussed in the major section of 

the paper on the “refreshment” portion of the panel. The imputation of “missing” returns for 

panel members is discussed in the major section of the paper on imputation of missing returns. 

B. Following Panel Members Designated from the 1987 SOI Sample 

The panel was intended to include every tax return filed by every panel member for the 1987 

through 1996 period. This “following” of panel members required checking the SSNs of all 

panel members against the SSNs of all tax return filers (including spouses) in every year through 

1996. This section explains how this checking was accomplished. 

The (non-dependent) primary filers and spouses,15 and their dependents, reported on the 1987 

returns in the 1987 (PY88) SOI sample described above were all designated panel members.16 

13 Returns with correct primary but erroneous secondary SSNs were retained in the panel. 

14 OTA eliminated the part year returns, and imputed a 1987 return if the taxpayer(s) only had a 1988 return in the 

PY88 sample. This treatment did not affect panel membership. 

15 Initially, in addition to spouses reported on joint returns, all spouses reported on married filing separate (MFS) 

returns were designated as panel members. In many cases the SSNs of spouses were not reported or misreported on 

MFS returns, making it difficult or impossible to follow these spouses. Eventually, it was decided to drop (by zero 

weighting) all returns of spouses designated on 1987 returns classified as MARS 3 returns (MFS returns which 

indicated the spouse filed a separate return), but to keep spouses designated on 1987 returns classified as MARS 6 

returns (MFS returns on which an exemption was taken for the spouse because the spouse had no income and did 

not file a separate return). 

16 Non-dependent primaries, spouses, and dependents reported on a pre-1987 (but not a 1987) return included in the 

TY88 sample were “tentative” panel members until they filed a 1987 return and became panel members, as 

described above, so were followed as if they were panel members through PY94. 

A.1 - 5
 
 

 

 

The SSNs of all non-dependent primaries, spouses (“secondaries”), and dependents from these 

returns were placed on a tickler file. However, the requirement for reporting the SSNs of 

dependents was new in 1987 and only applied to dependents over the age of four, so many 

dependents’ SSNs were not reported on 1987 returns. Most of these missing dependent SSNs 

were identified on the 1988 and 1989 returns filed by the non-dependent panel members who 

(presumably) claimed them in 1987, and also placed on the tickler file. 

The SSNs on this tickler file of panel members designated from the PY88 SOI was checked 

against the SSNs of every primary and secondary filer on every return filed beginning in PY89. 

This tickler file was created before the beginning of SOI’s processing for the year, and was 

therefore called the “beginning of year tickler” or BOYTICK. Returns identified from 

BOYTICK were processed by SOI along with, and in the same manner as, returns SOI selected 

for their cross-section sample for each year. The BOYTICK was updated in 1990 and 1991 to 

add missing panel member dependents’ SSNs, and also updated when panel members’ SSNs 

were corrected (see blow). 

After 1987, some panel members filed tax returns with new spouses or reported new dependents. 

These “visitors” were not designated panel members, but so long as they were reported on the 

return of a panel member any return they filed was to be included in the panel.17 To find these 

returns, each year SOI created an “end of year tickler”, or EOYTICK file that contained the 

SSNs of all spouses and dependents who were not themselves panel members but who were 

reported on a panel member’s return for the year. The EOYTICK file was then checked against 

the SSNs of all primary and secondary filers on all the returns that had been filed for the year. 

These EOYTICK returns were then run through SOI’s processing system. 

The combined BOYTICK and EOYTICK process was designed to capture all returns filed by all 

panel members for 1988 through 1996 (i.e., through PY97). It would have been necessary to run 

the EOYTICK process for 1987 (PY88) in order to capture the returns filed be panel dependents 

that year, but this wasn’t done because the design of the panel and the EOYTICK process were 

not in place by the end of PY88.18,19 So, nearly all dependents’ returns for 1987 were missing 

from the panel files created by SOI, and had to be imputed by OTA (see below).20 In addition, 

because of restrictions on the size of the BOYTICK file, the SSNs of panel member dependents 

identified in PY89 and PY90 were moved from the BOYTICK to the EOYTICK files for (tax 

years) 1991, 1992, and 1993. These SSNs were then inadvertently deleted from the EOYTICK 

17 The panel was designed to represent “tax families” in each year. A tax family is the (non-dependent) taxpayer, 

spouse, and dependents, and includes the income (and taxes) of all family members. Since separately filing MFS 

spouses were eventually dropped from the panel (see footnote 14), only tax returns filed by new dependents were 

ultimately retained in the panel. 

18 In addition, as noted above, many panel member dependents’ SSNs were not reported on 1987 returns. 

19 The EOYTICK process also would have identified any 1987 returns filed by “tentative” panel members who only 

had a prior-year return included in the PY88 SOI Level 1 sample. 

20 As noted above, a handful of panel dependents’ returns for 1987 were selected in the PY88 SOI sample. 

A.1 - 6
 
 

 

file, so most of the returns filed by these dependents for these years21 were not identified and 

processed by SOI.22 For 1993, however, SOI was able to obtain these dependents’ return records 

from the Individual Returns Transactions File (IRTF), which is essentially the input file for SOI 

processing. 

The BOYTICK/EOYTICK process operated only through PY97, so it did not capture any 1988­

1996 returns of panel members (or visitors) that were late-filed in PY98 or later years. Some of 

these late-filed returns were identified by OTA from the PY98 and preliminary PY99 SOI crosssection samples, and added to the tax return panel. 

C. SSN Cleanup 

Returns filed by panel members and visitors were identified solely through the SSNs contained 

on the BOYTICK and EOYTICK files. When the SSN of a panel member or visitor was missing 

or incorrect, any return they filed would be missed in the BOYTICK/EOYTICK process. 

Further, an incorrect SSN on the BOYTICK or EOYTICK files could match the SSN of an 

“intruder”, someone who was neither a panel member nor a visitor, and the intruder’s return 

could report SSNs of other intruders -- a spouse or dependents -- that would then get added to the 

BOYTICK or EOYTICK file and could generate additional intruder returns. Any missing or 

incorrect SSN would make it impossible to link, or would provide an incorrect link, to other 

records, such as W-2s and the DM-1 file. An incorrect SSN for a spouse or dependent reported 

in one or more years could also make a tax family appear to have more members than it actually 

had, and in some instances could affect cross-section weights. Thus, missing and incorrect SSNs 

could result in both too few and too many returns on the panel file, too many members of a tax 

family, no or incorrect links to other tax information, and incorrect weighting. Because of the 

critical importance of SSN cleanup to creation of the tax return panel, SOI and OTA devoted 

considerable resources to the task. 

Missing SSNs. A tax return ordinarily must have a valid primary SSN to post to the Individual 

Master File (IMF).23 However, in a very few instances a return with a missing primary SSN was 

identified for the panel by the secondary’s SSN. OTA attempted to fill in these missing primary 

SSNs in the manner, described below, used to fill in missing secondary SSNs. A primary SSN 

could also be incorrect (see section below on correcting SSNs), and an SSN reported in the 

primary position in one year could be omitted from the secondary position on a return for a 

subsequent year. 

At the time the panel returns were being selected, a tax return could have posted to the IMF with 

a missing secondary SSN. When the secondary SSN was missing on a panel return, SOI would 

compare the secondary’s name on any returns filed for subsequent years with the same primary 

SSN to determine whether the same secondary was filing with the primary. If the secondary 

21 Note that some of these individuals in these years were now filing as nondependents. 

22 Other difficulties in the BOYTICK/EOYTICK process resulted in some of these dependents’ returns to be missed 

in (tax years) 1989 and 1990 as well. 

23 The IMF is IRS’s administrative control file for individuals. 

A.1 - 7
 
 

 

appeared not to have changed and the secondary’s SSN was reported on one of these returns, it 

was carried back to the returns with a missing secondary SSN. If the secondary’s SSN was not 

reported on any subsequent return, SOI would assign a unique number to the secondary (or 

secondaries, if they appeared to have changed) and add the number to the return for each year.24 

In certain cases, OTA filled in missing secondary SSNs that had not been filled in by SOI. One 

case is when a joint return was filed in year t-1 and year t+1 with the same primary and 

secondary SSNs, but only the primary SSN was reported on the joint return for year t. Another 

case was when a joint return for year t-1 reported both the primary and secondary SSNs, the joint 

return for year t reported only the primary SSN, and the primary filed a separate return in year 

t+1. Note that in cases like this if the secondary filed a separate return in year t SOI added the 

missing secondary SSN to the year t joint return filed by the primary, but the filing status on this 

return was changed to married filing separate (MARS 3).25 OTA changed the filing status on 

such returns back to joint (MARS 2). 

Any return on which the number of claimed dependents exceeded the number of reported 

dependent SSNs was identified as having (one or more) missing dependent SSNs. Missing SSNs 

for dependents were far more numerous than missing secondary SSNs. As indicated above, 1987 

was the first year that dependents’ SSNs were required to be reported on tax returns, and the 

requirement only applied to dependents over the age of four, so the SSNs for many dependents 

were missing from 1987 returns. Since dependents claimed on (non-dependent) panel 

members’1987 returns were also panel members, it was critical for creating the panel to find any 

missing SSNs for these dependents. To find these missing SSNs, SOI examined the SSNs of 

dependents reported on the 1988 and 1989 returns filed by the 1987 return primary and 

secondary, and added back to the 1987 return and to the BOYTICK file the SSNs that appeared 

to be missing.26 After these missing SSNs were found and added to BOYTICK, any return filed 

by these dependents would be identified in SOI processing and added to the panel. SOI did not 

examine returns filed after 1989 for missing 1987 dependents’ SSNs. Further, the EOYTICK 

file for each year only included the SSNs of visitor (non-panel member) dependents and spouses 

reported on panel members’ returns in the current processing year.27 So if the SSN of a visitor 

dependent (e.g., a child born after 1987) was missing for a year and the dependent filed a return, 

SOI would not be able to identify it and add it to the panel. This was true even if the SSN was 

subsequently reported and could have been carried back to the earlier years, since SOI did not 

have the capability of going back to prior year’s IRTF records. So, SOI did not attempt to add 

back missing SSNs of visitor dependents, or of panel dependents who did not file a return. 

24 SOI-assigned secondary identifiers were added to about 1,000 returns in panel. 

25 However, SOI did not change the tax computations to correspond to the new filing status, and did not require that 

the filing status of the year t return filed by the secondary be appropriate (i.e., either head of household (MARS 4) 

or MFS (MARS 3)). 

26 Subsequently, OTA was able to use date of birth information from the DM-1 file to determine that in a few 

instances SOI had carried back the wrong dependent’s SSN from the 1988 or 1989 return, and substituted the correct 

one. (SOI apparently did not have access to the DM-1 file when it carried back dependents’ SSNs.) 

27 As noted above, certain dependent panel members’ SSNs were meant to be moved from the BOYTICK to the 

EOYTICK file starting in tax year 1991 (PY92), but these SSNs were then inadvertently deleted from the 

EOYTICK file. 

A.1 - 8
 
 

 

 

Finally, SOI did not check reported dependent SSNs to see if they were actually the SSN of the 

primary or secondary taxpayer. As a result, there were more than 50,000 dependent SSNs 

missing from the panel returns delivered to OTA. 

Dependent SSNs are required on returns in order to match each dependent on each return to files 

such as the DM-1, which provide information about the dependent that is used in OTA’s 

modeling directly, and also to impute missing returns (see blow). OTA therefore attempted to 

fill in the remaining missing dependent SSNs. OTA first checked all returns with dependents 

and deleted any reported dependent SSN that was actually the SSN of the primary or secondary 

taxpayer. The dependent SSNs reported on the last return filed by the primary and secondary 

were then checked against the DM-1 to determine whether each of the dependents was born 

before that year; i.e., could have been a dependent in the prior year.28 These potential prior-year 

SSNs were then used to fill in any missing SSNs on the prior year return. This process was 

repeated, moving backward in time through the panel file to the earliest year with a missing 

dependent SSN.29 About 35,000 of the missing dependent SSNs were filled in by this process. 

For the remaining instances of missing dependent SSNs, OTA assigned a unique number, 

keeping the same number for what appeared to be the same dependent over time. 

Incorrect SSNs. There are two sets of circumstances that indicate that an SSN might be 

incorrect. One is when the SSN is reported on multiple returns for the same tax year and should 

either have appeared on only one return, or appears inconsistently on two returns. The SSN of 

any non-dependent primary taxpayer should appear on only one return in a tax year, except for 

the primary SSN on a MARS 3 or head of household return that also appears as the SSN of the 

spouse on a MARS 3 return. The SSN of a spouse (secondary) on a joint or a MARS 6 return 

should also appear only on that one return for a tax year. The SSN of a claimed dependent 

should not appear as the SSN of a non-dependent primary or secondary taxpayer on a return for 

the same tax year. Similarly, the SSN of a MARS 3 spouse should not appear on a joint or single 

return (but can appear on a MARS 3 or head of household return) for the same tax year. 

In some cases, the multiple or inconsistent appearance of an SSN was simply the result of an 

incorrect SSN being reported on one of the returns. In other cases, though, mistakenly or 

intentionally the same individual filed multiple returns or was reported inconsistently across 

returns. The main tool for determining whether an incorrect SSN was reported on a return was 

the age, gender, and name control (the first four letters of an individual’s last name (or names, if 

their last name has changed over time)), from the DM-1 file. If the name control matched the 

first four letters of the name on only one return and the age and gender were consistent (e.g., 

spouses had to be at least 16 years old and of opposite gender), the SSN on that return was 

assumed to be correct and the SSN on the other return incorrect. If the name control matched on 

28 There were many instances of dependents apparently being initially claimed in the year prior to their DM-1 year 

of birth. (This likely occurred when the child was born after the end of the tax year, but before the return was filed.) 

Absent any other conflicting information, in these instances the SSN was assumed to be correct and could be carried 

back to the year prior to the DM-1 birth year. 

29 If the “parents” (primary and secondary) filed a joint return in year t, when a dependent SSN was missing, but 

separate returns in year t+1 (or year t+2 if the year t+1 return was missing) with one of the returns reporting the 

potentially missing dependent SSN, the SSN was carried back only if the dependent’s name control matched one of 

the parent’s in both years. 

A.1 - 9
 
 

both returns and the age and gender were consistent, or the comparisons were inconclusive, the 

individual was assumed to have filed both returns or to have been inconsistently reported on the 

two returns. OTA retained this multiple or inconsistent reporting in the panel. 

When one of a pair of multiple or inconsistently reported SSNs was determined to be incorrect 

and the (correct) SSN was that of a panel member or visitor, the return with the incorrect SSN 

could be deleted from the panel (unless a panel member was a spouse on the return). If the 

incorrect SSN was reported for a panel member or visitor, the SSN was deleted and therefore 

“missing”. The procedures described above were then used to fill in the missing SSN. In 

addition, since the correct SSN in the pair was that of an “intruder”, someone who was neither a 

panel member nor a visitor, all returns in the panel filed by them, their spouse, or their 

dependents were deleted from the panel (unless their spouse on a return was a panel member or 

visitor). About 3,800 intruder returns were deleted from the panel. 

A second set of circumstances in which an SSN might be incorrect is when the reported SSN of a 

spouse or a dependent changes over time. In some cases, the SSN change is simply the result of 

incorrect reporting of the SSN on the earlier (or later) return(s).30 In other cases, of course, the 

SSN changed because the spouse or dependent had changed. To help determine whether the 

SSN had been incorrectly reported or had actually changed, OTA developed a routine that 

compared the two SSNs. Each of the nine digits in the two SSNs was compared, and a score of 2 

assigned if they were identical; a score of 1 if the digits were not identical, but the digit in one of 

the SSNs was identical to the preceding or succeeding digit in the other SSN31 (i.e., a possible 

transposition error); and a score of 0.5 if the digit in one SSN was a 1 and in the other a 7 or was 

a 3 in one SSN and an 8 in the other (i.e., a possible transcription error). The possible scores 

ranged from a high of 18, when the two SSNs were identical, to a low of zero. 

The SSN scoring routine was applied to a sample of returns for which the SSN of the spouse had 

changed. The age, gender, and name control information from the DM-1 file was also used to try 

to determine whether the SSN was incorrectly reported or the spouse had changed. The results 

from this sample indicated that the two SSNs were almost certainly for the same individual if the 

scoring routine gave a score of 15 or higher (i.e., one of the SSNs was incorrectly reported and 

the spouse had not changed), and that the two SSNs were highly likely to be for two different 

individuals if the score was below 15 (i.e., the SSNs were different and there had been a change 

in spouses). 

The SSN scoring routine was then applied in all instances in which the SSN of the spouse 

changed. Based on the results from the sample, if the score was less than 15 the two SSNs were 

generally assumed to be correct. If the score was 15 or greater, one of the SSNs was assumed to 

30 If a return was filed with the same SSN as the incorrectly reported SSN, the returns should have been picked up 

by the BOYTICK/EOYTICK process and then would appear as a multiple or inconsistent reporting of an SSN (the 

first set of circumstances). SOI only checked the SSNs of spouses and dependents if another return with that SSN 

was filed. 

31 If the leading or trailing digit was missing from one of the SSNs there would be 8 transcription errors (which 

alone would have given a score of 8), but in this case a score of 16 was given. 

A.1 - 10
 
 

be incorrect, and the age, gender, and name control information from the DM-1 file was used to 

determine which of the two SSNs was the correct one.32 

The SSN scoring routine was also applied in all instances in which the SSN of a dependent 

appeared to have changed. If the score was under 15 no action was taken. If it was 15 or over 

the name controls from the DM-1 file of the two dependents and the parent(s) (primary and any 

secondary) were compared. If only one dependent SSN’s name control matched the parent’s(s’), 

it was assumed to be the correct SSN and the other to be incorrect. Any return filed with the 

incorrect SSN was deleted from the panel. If both or neither dependent SSN’s name control 

matched the parent’s(s’), both SSNs were assumed to be the correct. 

Other SSN Cleanup Issues. There were several other issues concerning SSNs that OTA dealt 

with, or examined but did not (or could not) deal with. One issue was the appearance on some 

returns of more SSNs for dependents than the number of claimed dependents. Typically, this 

occurred in a year that a dependent was transitioning out of a household (e.g., a child graduating 

from high school or college and leaving home), or following a divorce. In such cases, OTA tried 

to determine which dependent SSN should not have been reported and deleted it. 

Another issue was the sudden appearance, in some cases for only one year, of a dependent SSN 

on a return. After filling in missing and correcting incorrect dependent SSNs, it was decided that 

in most cases there was quite plausible reason for the SSN to suddenly appear: the dependent 

was a newborn (which could be confirmed from the DM-1), or adopted, or a child whose parents 

alternated the dependency allowance, or a child who had left home and been independent and 

now returned as a dependent, or an elderly parent or other relative who became a dependent. 

OTA had no other information that might have indicated a suddenly appearing dependent SSN 

was incorrect, so no changes were made in any of these instances. 

A third issue was ITINs, which are pseudo SSNs assigned by IRS in instances where an 

individual does not have a valid SSN. Typically, individuals assigned ITINs are immigrants who 

are in the process of obtaining an SSN. When these individuals do obtain an SSN and use it for 

filing, it is not possible to link their returns over time because IRS does not associate their SSN 

with their ITIN. Therefore, non-dependent panel members with ITINs in 1987 could not be 

followed once they began to file with an SSN. All OTA could do in these cases was to impute a 

return for the missing years (see below). In a few cases, OTA was able to replace the ITIN for a 

dependent with the SSN reported on a subsequent return by comparing the dependent’s name 

across the years. 

D. Other Following Issues 

Several circumstances made following certain panel members difficult or impossible. Some 

returns filed by a dependent panel member did not have an associated return filed by the head or 

spouse of the tax family. This circumstance occurred for several reasons. The head (and any 

spouse) of the tax family could be panel members who did not file a return for the year because 

they were not required to file, or filed a return but the return was not identified by SOI because 

32 Generally, the spouse had to be over age 15 and within 30 years of the age of the other spouse, to be of opposite 

gender, and to have a name control that matched the other spouse’s. 

A.1 - 11
 
 

of missing or incorrect SSNs, or filed a return for the year after the panel was selected.33 In 

addition, some dependent panel members changed tax families and typically the head (and any 

spouse) of the new tax family was not a panel member so their return was not included in the 

panel. If such dependent panel members did not file a return they were entirely lost from the 

panel. 

A few members of the panel married other panel members or became dependents of other panel 

members, making them members of two panel tax families. In this circumstance, OTA 

duplicated the returns filed by these panel members after they switched tax families and 

associated one of the returns with each of the tax families. The panel member was treated as a 

“visitor” in the second tax family. 

In principle, once a panel member died they were no longer followed in the panel. In a number 

of cases, however, a panel member who had died (according to the date of death from the DM-1 

file) continued to be reported as a spouse or dependent on panel returns. OTA retained this 

apparent misreporting. 

II. The Refreshment Panel 

The cohort tax return panel represents over the 1987 through 1996 period the members of the 

population reported on a tax return filed for 1987, and the new dependents of this population. 

The refreshment tax return panel represents, over the same period, the members of the population 

who were first reported on a tax return filed for a year between 1988 and 1996, and new 

dependents of this population. The refreshment panel represents individuals (nondependent 

family heads, spouses, and dependents) who were U.S. citizens or residents in 1987 and not 

reported on a tax return for 1987 but who were reported on a tax return in at least one year 

between 1988 and 1996; individuals who were not U.S. citizens or residents in 1987 but who 

subsequently became U.S. citizens or residents and were reported on a tax return between 1988 

and 1996; and new dependents of either group. In addition, because of the way it was 

constructed, the refreshment portion represents dependents in the 1987 filing population once 

they became nondependents. Since these individuals are also represented in the cohort panel, the 

dependent members of the cohort panel were given individual weights of zero once they became 

nondependents. Combined, the cohort and refreshment tax return panels represent all individuals 

who were citizens or residents of the United States and reported on a tax return at least once 

between 1987 and 1996, and the filers in each year represent the cross-section of tax returns filed 

in that year. The remainder of the U.S. population, individuals never represented on a tax return 

between 1987 and 1996 (the “permanent nonfiling population”) was represented by records from 

the PSID (see Appendix A.2). 

The same two primary filer CWHS SSN endings that were included in the PY88 SOI crosssection sample used to designate the cohort portion of the panel were included in the SOI crosssection samples for PY89 and all subsequent years. OTA used this consistency in the SOI 

sample design to identify the CWHS returns filed after PY88 that were not represented by the 

cohort portion of the panel designated from the PY88 sample. Returns filed with “new” CWHS 

33 SOI’s processing of the panel ended in PY97 (although IRTF records for panel members continue to be obtained). 

OTA checked the PY98 and preliminary PY99 SOI cross-section files for returns of panel members. 

A.1 - 12
 
 

 

 

SSNs could be identified by comparing the CWHS SSN of the primary filer on the returns 

against the primary and secondary SSNs reported on all 1987 returns filed in PY88. CWHS 

primaries on 1987 non-dependent returns filed in PY88 were all designated panel members, so 

none of their SSNs could be “new”. However, since CWHS returns are only designated by the 

primary SSN, it was possible for there to be a secondary CWHS SSN on a 1987 return filed in 

PY88 that was not included in the panel but would be represented by the panel. OTA was able to 

identify these secondary CWHS SSNs from a tickler file that included the primary and secondary 

SSNs reported on all nondependent returns filed in PY88; these also could not be “new”.34 The 

remaining, “new” CHWS returns were identified from the non-dependent returns for 1988 

through 1996 included in the SOI cross-section files for PY89 through the preliminary file for 

PY99. 

The primaries, secondaries, and dependents reported on late-filed 1987 new CWHS returns were 

added as members of the cohort panel. Conceptually, individuals reported on these 1987 new 

CWHS returns represent the non-dependent taxpayers (and their dependents) who filed no return 

in PY88, but filed a 1987 return in a later year. The portion of the cohort panel drawn from the 

PY88 SOI sample did not represent such individuals at all, so these late-filed CWHS returns all 

had a probability of selection of 2 in 9,999 and their weight is the inverse of this selection 

probability. 

The 1988 through 1996 new CWHS returns constitute the refreshment panel. The primaries 

reported on the earliest new CWHS return filed are members of the refreshment panel; all 

secondaries and dependents reported on these and later returns are “visitors”. Conceptually, 

refreshment returns should represent the population between 1988 and 1996 who were not 

represented on 1987 tax returns either because they were in the non-filing population in 1987 or 

not in the population at all in 1987 (i.e., immigrated), and their new dependents. However, 

refreshment returns also include the (non-dependent) returns filed by individuals who were 

dependents in 1987 but who subsequently became non-dependents, a group also represented in 

the cohort panel. There is no direct way to identify which refreshment returns represent the 1987 

nonfiler and post-1987 immigrant populations from the 1987 dependents who become nondependents. To avoid double counting the last group, OTA zero weighted any dependent in the 

cohort panel once they filed as a non-dependent. As a consequence of this zero weighting, a 

secondary on a joint refreshment return could only have a probability of selection in the 1987 

population if they filed as a non-dependent for 1987. Rather than check all refreshment 

secondary SSNs against the tickler of PY88 SSNs, OTA assumed that if the secondary was age 

21 or over in 1987 they filed as a non-dependent in 1987, and if they were under age 21 in 1987 

they were a dependent (or not in the filing population). 

Note that the new CWHS returns were added to the refreshment panel after SOI had completed 

processing of the cohort panel, so the SSNs of panel members from these returns were never part 

of the BOYTICK/EOYTICK process, and the CWHS portion of the SOI cross-section sample 

only follows primaries. So, returns filed by secondaries following a divorce from the primary or 

34 This tickler file included a few non-dependent primary CWHS SSNs that were not included in the cohort portion 

of the panel. Apparently, these individuals filed “no income” returns, which are excluded from the SOI sample. 

Since filing a “no income” return is essentially equivalent to not filing, OTA left these in the pool for “new” CWHS 

SSNs. 

A.1 - 13
 
 

 

 

 

the death of the primary are generally not in the refreshment panel, nor are any returns filed by 

dependents. Further, since only primary SSNs are used to designate the CWHS portion of the 

SOI cross-section sample, if a primary married and reported as the secondary on the subsequent 

joint return, or a primary switched their SSN reporting position (i.e., became the secondary), the 

return would generally disappear from the refreshment panel.35 

III. Imputing Missing Returns 

Only under certain circumstances would a return be considered missing, and therefore imputed. 

These circumstances differed, depending on how the characteristics of the individual included in 

the panels. This section first describes the circumstances under which a return was considered 

missing, and then the imputation process. 

A. Determining Whether a Return Was Missing 

In general, members of the cohort panel should be reported on a return in every year of the panel, 

and members of the refreshment panel in every year of the panel following their first appearance 

(in every year of the panel for 1987 nonfilers). If members of each panel were not reported on a 

return captured in the panel in all these years, in most circumstances a missing return would be 

imputed. However, there are a number of circumstances in which a member of either panel 

would not be reported on a return, so no missing return would be imputed. These circumstances 

differed, depending on the panel and the characteristics of the panel member. 

Nondependent Cohort Panel Members. These panel members should be reported on a return in 

every year of the panel, 1987 through 1996, unless: (i) they died (as determined from the DM-1 

file or as reported on their final tax return); (ii) they were believed to have emigrated from the 

United States (which OTA determined was the case if they stopped filing and their prior return(s) 

was filed from a foreign address; or (iii) they became a dependent (determined if they began 

filing as a dependent or were under age 21 and ceased filing; see next paragraph for when a 

dependent return was determined to be missing). Note that a missing return could have been 

filed but not captured for the panel,36 or the panel member could have been a nonfiler in the 

missing year. The section below on imputation describes how OTA determined whether the 

panel member was a filer or nonfiler in a missing year. 

Dependent Cohort Panel Members. In any year a return for a dependent panel member was not 

captured in the cohort panel, the return would be considered missing only if OTA determined the 

dependent actually filed a return; nonfiler returns were never considered missing for dependents 

and therefore never imputed. OTA calculated the likelihood each dependent without a filed 

return was a filer using a probit equation estimated using characteristics of dependent filers and 

35 Note that such individuals were still represented in the refreshment panel, as new spouses reporting as 

secondaries, or as new CWHS primaries. 

36 There are several reasons why a filed return by a nondependent (or a dependent) might not have been captured for 

the cohort panel, including the lack of EOYTICK processing in 1987 and problems with EOYTICK processing in 

1991 through 1993; missing or incorrect SSNs, returns that were late filed and not processed through the preliminary 

PY89 SOI cross-section sample, and errors in return processing. It is also possible that the taxpayer was required to 

file, but failed to do so. 

A.1 - 14
 
 

their parents captured in the TY1995 “family” cross-section sample. The target for the 

(weighted) number of missing dependent returns in each year was the difference between the 

number filed that were captured for the panel and the number from the SOI cross-section sample 

for the year. Targets were computed for ten age groups of dependents. These targets were then 

hit by setting the appropriate cutoff from the probit equation for each age group. Note that once 

a cohort panel member who was a dependent in 1987 started to file as a nondependent their 

returns were zero weighted (to avoid double counting with the new nondependents in the 

refreshment panel), so none of these nondependent returns was considered missing. 

Refreshment Panel Members. Only nondependent primaries were members of the refreshment 

panel, so in no circumstances was a separate return for a secondary or dependent considered 

missing. Whether a return was considered missing for a refreshment panel member varied by the 

portion of the population the member represented: 1987 dependents who became nondependents 

by 1996; post-1987 immigrants; or nondependent nonfilers in the U.S. population in 1987. As 

noted above, there was no direct way to determine the portion of the population a refreshment 

panel member belonged to, but age from the DM-1 file and past earnings from the Social 

Security earnings file provided indirect information that OTA used to make the determination. 

If a refreshment panel member was under age 21 in 1987, they were considered to have been a 

dependent in 1987 and to have remained a dependent (or non-citizen) until the year they entered 

the refreshment panel by filing as the primary filer on a nondependent CWHS return. Since 

returns filed by dependents were represented by the cohort panel, no return for returns for these 

refreshment panel members was considered missing for any year prior to the year they entered 

the refreshment panel. After the first year of filing a (filer or nonfiler) return would be 

considered missing if no return was captured in the refreshment panel for the year and in a 

subsequent year the panel member filed a (nondependent) return. 

Post-1987 immigrants were randomly selected from the returns filed by new refreshment panel 

members each year who were 21 or over in 1987 and had no recorded earnings in any prior year. 

The number of returns selected was determined by targets for immigration in the year (which 

included spouses and dependents).37 Immigrants were assumed to file a return the year they 

entered the United States, so no return was considered missing for any year before they entered 

the refreshment panel. Further, if an immigrant ceased filing, it was assumed that either they had 

emigrated from the United States, or had remained in the United States but married and began 

filing as the secondary (effectively changing their status to “visitor”). In neither circumstance 

would a return for them be considered missing. Only if there was a gap in returns filed, with 

earnings reflected on the Social Security earnings record, would a (filer or nonfiler) return for an 

immigrant be considered missing. 

New refreshment panel members over age 21 in 1987 who had recorded earnings in a prior year, 

or who had no recorded earnings and were not randomly selected as immigrants, were 

considered to have been nondependent nonfilers in 1987. Returns for 1987 through the year 

prior to the panel member’s entry into the refreshment panel were considered missing. If the 

member was male, he was assumed to have been a single nonfiler in 1987 and in all subsequent 

37 These targets were taken from Table 12, “Immigrants Admitted by Age and Sex, Fiscal Year 1986-96” in the 

1996 Statistical Yearbook of the Immigration and Naturalization Service. 

A.1 - 15
 
 

 

years prior to the year he entered the refreshment panel. If the member was female, she was 

assumed to have been a single nonfiler in 1987 but thereafter could have been single or married, 

and a filer or nonfiler. In addition, after the first year of filing a (filer or nonfiler) return would 

be considered missing if no return was captured in the refreshment panel for the year and in a 

subsequent year the panel member filed a return. 

B. Imputing Missing Returns 

The first step in the imputation process for nondependents who had filed in a previous year was 

to determine whether the missing return was a filer or a nonfiler return. To make this 

determination, the filing status in the missing year was assumed to be the same as in the last filed 

return38 unless a spouse or dependent(s) on that return filed a return in the missing year that 

would indicate a change in status. For example, if the individual with a missing return had filed 

as joint on their last filed return but their spouse filed a separate return (as a single, head of 

household, or joint with another taxpayer) in the missing year the individual’s filing status in the 

missing year would be set to single (or head of household if they had dependents). The filing 

status on the missing return and the age of the nondependent taxpayer(s) on the return were then 

used to compute the filing threshold (the combination of the standard deduction, including the 

extra deduction for the elderly and personal exemption(s) for the taxpayer(s)) for the missing 

return. If the filing threshold exceeded the earnings of the nondependent taxpayer(s) shown on 

the Social Security earnings record(s) by at least $50, the missing return was determined to have 

been filed; otherwise, it was a nonfiler return. 

In the case of 1987 nonfilers, in which the missing returns were nonfiler returns with no 

preceding filed return, for females the filing status on the first filed return was assumed to apply 

in all preceding missing years, except that for 1987 the filing status was always assumed to have 

been single. For males, the filing status in all preceding nonfiling years was assumed to be 

single, irrespective of the filing status on the first filed return. 

For missing nondependent returns that were determined to have been filed, earnings were taken 

from the Social Security earnings file, with an adjustment if earnings were at the OASDI 

earnings cap.39 Earnings were then split between wages and self-employment income based on 

the split in the last filed return. The implied growth rate in any self-employment income was 

used to adjust the amount of all active business income40 on the last filed return to the imputed 

level on the missing return. All other sources of income and any itemized deductions, as well as 

the number and type of dependents (absent a change in filing status or a dependent becoming a 

38 In the relatively rare cases in which returns for more than two years in a row were missing, the next filed return 

was used to determine filing status and as the source for other items if it was filed closer in time than the last filed 

return. 

39 The earnings on the Social Security earnings file available to OTA were capped at the OASDI earnings cap for the 

year. The adjustment for those over the cap was to assume earnings were the same as on the most recent filed return 

if earnings were above the cap in that year also, and otherwise to randomly add wages of between 0 percent and 15 

percent to the cap. 

40 Active business income includes income from sole proprietors, farm proprietors, active partnership income, and 

active SBC income. 

A.1 - 16
 
nondependent), were simply carried over from the last file return. Note that if filing status 

changed from joint to single or head of household between the last filed and missing return, 

active business income on the last filed return was split between the spouses in proportion to 

their self-employment income and that split was used to impute the active business amounts on 

the missing return, while all unearned income and itemized deductions on the last filed return 

were split evenly between spouses. 

For missing nonfiler returns, which were only imputed for nondependents, earnings were taken 

from the Social Security earnings file and assumed to be only from wages. Amounts of other 

income from pensions, unemployment compensation, alimony, interest, and IRA distributions 

were taken from the last filed return (or first filed return, if there was no preceding filed return), 

subject to a randomized constraint that kept these sources of income, plus earnings, below the 

filing threshold. Nontaxable Social Security benefits and tax-exempt interest were assumed not 

to have changed from the last filed return (except that only half of tax-exempt interest was 

carried over if the filing status changed from joint to single or head of household). 

Missing returns of dependents were imputed through matching of returns with similar 

characteristics of the dependent and the dependent’s parents. 

A.1 - 17
 
APPENDIX A.2 

Basic Data: Data from the PSID 

by 

James Cilke 

and 

Janet McCubbin 
 

 

Data from the PSID 

Some members of the U.S. population were never tax-return filers or dependents of filers during 

the 1987 through 1996 period. Observations from the Panel Survey of Income Dynamics (PSID) 

are used to represent this permanent non-filer segment of the population. The basic approach 

was to form tax units from PSID individuals, compute taxable income for the tax unit, and select 

units with incomes below the filing threshold in every year from 1987 through 1996 for addition 

to the tax panel. The PSID was also used in combination with Social Security earnings histories 

and Form 1099 information returns to impute income for years in which tax panel members (who 

were represented on tax returns for some years during the 1987 – 1996 period) did not file a 

return, and to impute transfer payments to tax panel members in all years. 

The PSID was initiated in 1968. It represents individuals and families residing in the contiguous 

United States in 1968, and their direct descendants. The initial sample was comprised of 1,872 

low-income families in SMSA’s and in non-SMSA’s in the south with heads under age 60 from 

the Survey of Economic Opportunity sample, plus 2,930 households from an equal probability 

sample of households in the contiguous United States. About 18,000 individuals were included 

in the initially sampled families. 

Data for PSID individuals and families associated with interviews conducted beginning in 1988 

were used to supplement the panel model.1

 PSID interviews gather income information for the 

preceding year, and it was assumed that the structure of the PSID family unit at the time of each 

interview had not changed from the end of the preceding (income) year. Thus, the 1988 PSID 

interview corresponded to tax year 1987 for the tax return panel, etc. When the panel model was 

created, final PSID data were available through interview year 1993 (tax year 1992).2 Over 

27,000 individuals are associated with the PSID interviews conducted between 1988 and 1993. 

I. PSID Units and Tracking Rules 

The PSID definition of a family is a group of individuals living together who are related by 

blood, marriage, or adoption. In addition, the PSID treats unmarried couples as part of the same 

family if the cohabitation lasts for more than one interview period.3 Individuals are classified as 

PSID family members if they are residing in an interviewed family unit (and are not temporary 

visitors or roommates) or are members of the family unit but temporarily away and in an 

institution (such as a school, jail, hospital or the military). 

The male member of a couple (married or unmarried) is always designated as the family head 

unless he is severely disabled. The female member of the couple is designated as the wife if 

1

 Data for interview year 1987 was also processed, but ultimately used only to assign tax family IDs. 

2

 As discussed below, the PSID panel for tax years 1987-1992 was extrapolated to cover the 1993 – 1996 period. 

3

 See Survey Research Center (1991), page 13. 

A.2 - 1
 
 

 

 

 

 

married to the head or as the “wife” (in quotes) if not married to the head. In most years, only 

one member of the family unit (usually the head but sometimes the spouse or cohabitor) was 

interviewed, and provided information about all of the family members. In general, detailed 

information is provided about the family head and spouse or cohabitor, and less detailed 

information is provided about other family members.4 

All non-institutionalized members of 1968 PSID families are sample members. In addition, 

persons aged 25 and younger in 1968 who were institutionalized but associated with PSID 

families (for example, children away at school and members of the military) are designated as 

sample members. The descendants of the original 1968 sample members are also designated as 

sample members. Nearly all sample members are followed in subsequent years.5 

In general, PSID interviews do not cover institutionalized persons age 18 or over. If a sample 

member age 18 or over moves to an institution (such as a prison, college dormitory, or the 

military), then the PSID records this fact, tracks the location of the sample member, and attempts 

to interview the sample member when they leave the institution. 

In general, the PSID does not attempt to follow individuals once they become non-respondents. 

However, beginning with the 1990 wave, the PSID attempts to recontact individuals who had 

become non-respondents between 1985 and 1989, and who were expected to have achieved age 

65 by 1990. In addition, the PSID began to follow non-sample members aged 65 and older if 

they survived all of the sample members in their PSID family, or if they moved out of the family 

unit. In the 1993 and 1994 waves, the PSID attempted to recontact a sample of all nonrespondents who dropped out of the sample in 1991 or earlier, but who shared a family affiliation 

with an individual who was still responding to the survey in 1992. 

New non-sample members of PSID family units (such as persons who marry or cohabitate with 

sample members) are interviewed, but (with the exception of some elderly persons, discussed 

above) non-sample members are not followed if they leave the PSID family. 

An important implication of this sample design is that immigrants entering the United States 

after 1968 (and individuals who resided in Hawaii and Alaska in 1968) are not represented in the 

core sample. To address this and other issues, the PSID sample design was modified in 1989 and 

again in 1997. These modifications do not affect our use of the PSID, because we are most 

concerned with immigration that occurred between 1968 and 1987 (that is, with immigration that 

4

 Note that if an originally sampled female family head marries (or cohabitates with) a non-sample member, then the 

sample member female is redesignated a wife (or “wife”) and the non-sample member male is designated the head. 

See Hill (1992), page 40. 

5

 See Hill (1992) page 11. Prior to the 1993 wave, sample members under the age of 18 who split off from their 

family units were not followed unless they set up their own households. Beginning in 1993, the PSID attempts to 

follow minors who leave their PSID family unit even if they move in with other adults rather than marrying or 

establishing their own household. When these minors are followed, the PSID attempts to interview an adult in the 

new household. As a result, it is possible that both the head and the wife/“wife” in the new household will be nonsample members. Prior to 1993, every PSID family has a sample member as the head, wife/“wife” or both. 

A.2 - 2
 
 

 

 

occurred between the beginning of the PSID and the beginning of the tax panel). Census data 

suggest that 4 percent of the total U.S. population in 1987 had immigrated since 1968.6 

II. PSID File Structure and Income Data 

PSID data through the 1993 interview consists of: (1) 26 annual family files for 1968 through 

1993, (2) one cross-year individual file covering 1968 through 1993, and (3) additional income 

information for other family unit members (OFUM) collected in 1993. 

The family level files include one record for each PSID family interviewed that year. The family 

file includes all family level information, plus detailed income (for the prior year) and other 

information about the family head and wife/“wife.” The individual file includes one record for 

each person ever in a PSID family (including non-sample members), and contains individual 

level demographic and income variables. (There is information for heads and wives/“wives” on 

both the individual file and the family files; however, the information on the family files is 

usually more detailed.) The 1993 OFUM file contains more detailed income information about 

family members who are not the head or wife/“wife.” 

The annual family files for interview years 1988 through 1993 and the 1993 OFUM file had to 

be matched to the cross-year individual file to obtain complete information on all individuals for 

the period. Then a fixed-length individual level file with individual income amounts was 

created.7

 These individuals were then recombined into tax units, which often differ from PSID 

family units. 

In general, labor income, asset income, AFDC, SSI, other welfare, social security, veteran’s 

pensions, other retirement income, unemployment compensation, worker’s compensation, and 

other transfers are available from the PSID.8 The creation of individual level income amounts 

requires imputations for some persons other than the head or wife/“wife”. First, until the 1991 

interview, labor and asset income was not shown separately for other family unit members 

(OFUM). In these years, for each person on the individual file the total of labor and asset 

income is shown, along with an indicator of whether the individual had asset income, labor 

income, or both. On the family file, total asset income for all OFUM and total labor income for 

all OFUM are provided. These amounts include only the income earned by the other family 

members while they were part of the family unit. Asset and labor income of OFUM with only 

asset or only labor income was assigned, and the total of these amounts subtracted from the 

family file totals. If there was only one OFUM with both types of income, the amounts of each 

were the residual OFUM totals. If there was more than one OFUM with both types of income, 

6

 Beginning with the 1990 interview wave, the PSID includes a supplemental sample of 2,043 Latino households, to 

increase the representation of Hispanics in the PSID and partially account for the absence of post-1968 immigrants 

in the PSID. The PSID weights are constructed to allow researchers to use only the core PSID sample, only the 

Latino sample, or both samples together. Since we are interested in the period beginning with the 1988 wave, we 

use only the core sample to add non-filers to the model. 

7

 Note that some variables are non-missing only for heads and wives/“wives,” and some are non-missing only for 

other persons. 

8

 Child support received and help from relatives are also available, but are not used in the panel model. 

A.2 - 3
 
 

residual OFUM asset income was divided among them according to the amount their total 

income while in the PSID unit during the year,9

 or by the amount of time each of them was in the 

family. (E.g., if each such person was in the unit all year, then the residual OFUM asset income 

is allocated evenly among them. If one person was in the unit longer, then more of the total asset 

income was allocated to that person.) Then residual OFUM labor income was allocated to each 

such person so that the sum of their asset and labor income equaled their total asset plus labor 

income shown on the individual file. 

Prior to the 1993 interview, transfer payments to OFUM (other than social security, which is 

always identified) must also be assigned to the various types of transfers. This exercise is trivial 

except when more than one OFUM has more than one type of transfer income. A procedure 

similar to that described for labor and asset income was used to allocate each individual’s nonsocial security transfer amount across the types of transfers reported for the other family 

members. 

Finally, as noted above, the income amounts reported for OFUM are, in general, “prorated” so 

that they reflect only the amount earned by other family members while they are residing with 

the head and wife/”wife”. Proration factors which can be used to annualize the income of up to 

five OFUM are provided on the family file. In addition, the number of months that each person 

lived in the unit can be determined from data on the individual file. This information is used to 

obtain annual income amounts for all persons.10 

III. Creating Tax Units and Taxable Income 

The first step in creating tax units was to assign each member of a PSID family unit in each year 

one of the following statuses: primary taxpayer, secondary taxpayer, dependent child, deceased, 

away from the family in an institution (e.g. school or a nursing home), and missing. 

The first three statuses were assigned using the PSID family head indicator, relationship to 

family head and marital status. Initially, primary taxpayers consisted of PSID family heads. 

Secondary taxpayers consisted of persons whose relationship to the family head was wife or 

husband.11 Dependent child status was assigned to all other family unit members who were a: 

child, stepchild, foster-child, grandchild, great grand child, nephew, or niece of the family head 

or the family head’s wife. All other unmarried family members under age 21 were also assumed 

to be dependent children. Later, the return status of any dependent child who was over 24 years 

and had income above the filing threshold was changed to return head. 

9

 For most OFUM, the family file contains a proration factor equal to the fraction of the OFUM’s income that was 

earned while the OFUM was in the PSID unit. If the PSID proration factor was not available, the fraction of time in 

the family was used. 

10 In most cases, the proration factor calculated from the months data in the individual file is the same as that 

provided on the family file. It differs when the individual’s income was not earned evenly during the year so that 

the fraction of his or her income earned while he or she was in the unit is not equal to the fraction of the year spent 

in the unit. PSID proration factors from the family file were used whenever they were available. 

11 As noted above, wives are considered PSID family heads only when the husband is severely disabled, so female 

family heads are rare. 

A.2 - 4
 
 

 

 

Female cohabitors (i.e., “wives” of PSID heads) who were PSID sample members were assigned 

a status of primary taxpayer. Female cohabitors who were non-sample members were assigned 

the missing status. Similarly, the partners of female cohabitors were assigned the primary 

taxpayer status if they were PSID sample members and were assigned a status of missing if they 

were not PSID members.12 

A status of deceased, away from the family, or missing was easily determined from PSID 

indicators. All other persons not yet assigned were made into primary taxpayers (or secondary 

taxpayers if married and female). No tax return units were assigned a status of married filing 

separately or surviving spouse. An unmarried person with a dependent child was assigned a 

head of household filing status. 

Marital status for the creation of married filing jointly tax returns from primary and secondary 

taxpayers was determined by the presence of a married-pairs indicator variable. Typically, both 

spouses in a couple have a married pairs indicator equal to ‘1’. If there was a second married 

couple in the house, their pairs value is ‘2’. The pairs variable together with the interview 

number is used to link the individual records of married couples. 

In general, adjusted gross income (AGI) is the sum of labor income, asset income, pension 

income, and unemployment compensation. (For simplicity, Social Security income is ignored at 

this stage.) For married persons, AGI is the sum of these income components for the primary 

taxpayer and spouse. “Taxable income,” defined in the PSID as the sum of labor and asset 

income, is also available, and in some cases, “taxable income” was inexplicably greater than the 

sum of labor income, asset income, pension income, and unemployment compensation. 

Therefore for married couples and PSID heads (except those linked to a female cohabitor), AGI 

was set to the maximum of “taxable income” for the head and wife or the sum of their labor 

income, asset income, pension income, and unemployment compensation. When “taxable 

income” was greater than the sum of labor income, asset income, pension income, and 

unemployment compensation, the excess was defined as “other” income. 

All positive labor income from the PSID is assumed to be wages, all positive asset income is 

assumed to be interest income, and all positive retirement income (except social security) is 

assumed to be pension income. If any income source was negative, it was added to “other” 

income (and removed from the original source). No other dollar values were carried from the 

PSID to the tax panel file. 

A tax family ID variable was created from the PSID interview number. For family heads and 

spouses, the tax family ID number was equal to the 1987 PSID interview number. If a 1987 

PSID child was assigned the status of primary taxpayer in any year, 8000 was added to the 

interview number. If a second child in the same PSID family was assigned the status of primary 

taxpayer, 8001 was added to the interview number. Similarly, 16000 (or 16001) was added to 

12 Non-PSID sample members have a zero weight, and would have a zero weight when added to the tax panel. 

Setting such person’s status to missing was a simple way of not creating a return for that person, as the return would 

have a zero weight in any case. 

A.2 - 5
 
 

 

 

the 1987 interview number of non-child family members assigned the status of primary taxpayer 

in any year.13 

Several additional assumptions were made in creating tax units. The number of dependent 

exemptions on a parent’s return was first set equal to the number of “dependent children” in the 

family unit under age 18 plus the number of “dependent children” age 18 or over with the same 

tax ID as the parents. In cases where the number of dependent exemptions reported on the PSID 

was greater than the number of “dependent children” determined by the preceding rules, the 

following assumptions were made about the “missing” dependent(s). If a “missing” dependent 

had been imputed in the previous year, it was assumed that the same “missing” dependent 

appeared in the current year. If one or more dependents was “missing” in 1987, age was 

imputed using a uniform distribution between 0 and 19. This assumption was repeated in 

following years (1988 to 1992) if the youngest parent was over age 44, or when there were three 

or more “missing” dependents. If the youngest parent was under age 45, the “missing” 

dependent was assumed to be a newborn. In all cases, each imputed “missing” dependent was 

given a 50 percent chance of being male. All dependents were assumed to be “children at home” 

dependents. 

No tax returns were created for dependents, regardless of their income. However, as in the tax 

panel, a person who was initially a dependent could become a non-dependent primary or 

secondary taxpayer. 

Individuals who were deceased, missing, or in an institution in all (tax) years 1987 through 1992 

were dropped from the file. 

IV. Weighting the PSID Units 

For 1968, the PSID family weight and the PSID individual weight is simply the inverse 

probability that the family was selected for the study. In subsequent years, individual weights 

depend on both the initial probability of selection and non-response adjustments, and the family 

weight is the average of the individual weights of the sample and non-sample members of the 

family unit.14 Non-sample members of PSID families (for example, new spouses or long-term 

cohabitors of panel members) generally receive a zero PSID weight.15 New sample members 

(that is, the children of sample members) receive a PSID weight equal to the average of their 

13 If a person’s was not interviewed in 1987, the 1987 interview number and all of the other information for 1987 is 

missing. PSID non-panel members with missing 1987 data were deleted. For the remaining cases, if the 1988 

interview number was present, the record was matched to another record with the same 1988 interview number, and 

the missing tax family identification variable was set equal to the 1987 record number from the matched record. If 

the 1988 interview number was also missing, the record was assigned a unique number, which serves as a unique 

identifier. 

14 Non-response adjustments were re-estimated every five years from 1969 through 1989, and in 1993. See Survey 

Research Center (1998), pages 28-30 for details. 

15 However, as noted above, a supplemental file of cross-section weights in which non-sample members can have 

positive weights was developed for the 1993 wave. 

A.2 - 6
 
 

 

 

 

 

 

parent’s weights.16 Thus, for example, the 1993 weights are designed to yield unbiased estimates 

of individuals and families who are eligible for the 1993 PSID population (i.e., all living 

individuals who were residents in the contiguous United States in 1968, and their direct 

descendants). 

Note that the individual weight on the file for year t represents both a cross-section and 

longitudinal weight for persons interviewed in year t. In other words, the weight used to analyze 

the data for year t is the same weight used to analyze the period (t-k,...t). This is problematic for 

the analysis of the period (t-k..., t) if an individual responded to the survey in year t and in year 

t-k, but did not respond in some intervening years. The year t weight will be biased upward 

because it accounts for the individual’s earlier non-response. This is a small problem in 1992 

and earlier interviews, because few individuals attrited and then reappeared. The problem 

becomes larger in 1993 and later interview years, because of efforts to recontact a sample of 

prior year non-respondents yielded a substantial number of observations with incomplete data. 

To address this problem, zero longitudinal weights were assigned to reappearing respondents 

who were non-respondents in the 1988 or 1989 interviews.17 In other words, sample members 

who have a non-zero longitudinal weight for 1993 should have interview data for at least 1988 

and 1989 (unless they were born to or adopted by a sample member between 1990 and 1993). 

The maximal (i.e., last non-zero) individual core sample weight assigned for the 1988 through 

1993 interview period is the starting point for purposes of weighting permanent non-filers in the 

tax panel. This weight is multiplied by 688 to account for the PSID scaling factor. 18 The result 

is equivalent to the cohort weight for tax panel members. For unmarried persons, the return 

weight is this new cohort weight for the taxpayer. For joint returns, the return weight is the 

average of the weights for the primary and secondary taxpayers. No tax return was created if the 

primary taxpayer (the primary or secondary in the case of a married couple) did not have a nonzero PSID weight. 

V. Extrapolating the PSID 

When the panel model was built, final PSID data were available only through the 1993 interview 

year (corresponding to income year 1992). Therefore the PSID panel for tax years 1987 through 

1992 was extrapolated to cover the 1993 through 1996 period using a methodology similar to 

that used to extrapolate the full 1987-1996 tax panel to the budget period. The extrapolation 

methodology for the full tax panel is discussed in Section III of the paper and in Appendix D.19 

16 See Survey Research Center (1998), pages 27-28, and Hill (1992) pages 22-23. 

17 See Survey Research Center (1998), pages 25 and 32, for details. 

18 Prior to 1989, the PSID weights were scaled so that they ranged from 1 to 99. (This was done to save space.) The 

file structure was modified after 1989, and beginning in 1989, weights may take up three digits. However, they are 

still scaled by the same factor(s) used prior to 1989 (i.e., attrition adjustments have increased the weights so that 

they are now three digits, but they still do not sum up to the population.) 

19 There are two important differences between the extrapolation of the PSID and the extrapolation of the tax panel. 

One is that many fewer matching cells could be used in the PSID extrapolation because the PSID sample is 

significantly smaller than the tax panel sample. The other is that the extrapolated PSID records for 1993 through 

1996 were appended to the matching records for 1987 through 1992 to obtain a 10-year set of records, whereas the 

A.2 - 7
 
 

 

To complete the PSID tax population file, returns were created for individuals who were missing 

in some years, but not deceased. For the years they were missing, individuals were assumed to 

be single with no income from earnings or investments. It was further assumed that their 

pension and their Social Security income were both equal to their last observed values. 

As missing individuals usually never returned, it is likely that some of them died and their death 

was not noted in the PSID survey. Therefore, some individuals who were missing for more than 

two years and who never returned were assumed to have died after their first year of missing. 

After imputing a return for the missing year, a random number was drawn from the uniform 

distribution. That number was compared to a value equal to twice their overall probability of 

dying, given their age and gender. If the random number was less than this value, the person was 

assumed to die in the following year. Otherwise, the process was repeated for the next year. 

After the full tax year 1987 – 1996 file was constructed, PSID panel members who never had 

income above the filing requirement and were never the dependent of a person with income 

above the filing requirement were identified. These permanent non-filers were added to the tax 

panel file. 

References 

Heeringa, Steven G. and Judith H. Connor (1999), “1997 Panel Study of Income Dynamics – 

Analysis Weights for Sample Families and Individuals,” June 1. 

Hill, Martha S. (1992). The Panel Study of Income Dynamics – A Users Guide, Sage 

Publications, Newbury Park, CA. 

Lupton, Joseph, Frank P. Stafford, and Tecla Loup (1999). “Guide to the Family ‘Income Plus’ 

Files, Readme, 1994-1997 Family Income and Components Files,” March 11. 

Survey Research Center, Institute for Social Research, The University of Michigan (1991). “A 

Panel Study of Income Dynamics: Procedures and Tape Codes – 1998 Interviewing Year, 

Volume I,” Ann Arbor, MI. 

Survey Research Center, Institute for Social Research, The University of Michigan (1998). “A 

Panel Study of Income Dynamics: Procedures and Codebooks – Guide to the 1993 

Interviewing Year,” Ann Arbor, MI. 

Survey Research Center, Institute for Social Research, The University of Michigan (1999). 

“Panel Study of Income Dynamics 1994, 1995 and 1997 Family Income and Components 

Codebook,” Ann Arbor, MI. 

tax panel (with appended 10 year PSID sets of records representing permanent nonfilers) was a 10-year sample and 

was extrapolated as a 10-year block beyond 1996. 

A.2 - 8 
 

APPENDIX A3 

Basic Data: Estate Tax Data 

by 

Deena Ackerman 

and 

Janet McCubbin 
Estate Tax Data 

I. Creating the File 

Beginning with filing year 1994, SOI has included all 1987-forward individual panel members 

SSNs in its annual estate tax studies. Prior to 1994, a panel member who died and for whom an 

estate tax return was filed would be included in the SOI study only if it met the usual estate tax 

study sampling criteria. Returns transaction file (RTF) data are available for all estate tax returns 

filed. Thus, limited estate tax data are available for all panel members and more detailed data for 

many panel members. 

SOI and RTF data for filing years 1987 through 2000 were matched to the 1987-forward 

individual panel. The match yields 6,038 estate tax returns matched to panel members, including 

members of the refreshment portion of the panel. SOI data are available for 5,338 panel 

members. 

Number of Unweighted Estate Tax Observations Matched to Panel, by Filing Year 

Estate Tax 

Filing Year 

With Positive Panel Weights With Zero Panel Weights 

SOI RTF Total SOI RTF Total Total 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

Total 

11 8 19 0 0 

122 77 199 0 2 

208 122 330 2 2 

328 73 401 1 3 

291 148 439 2 2 

280 127 407 1 3 

332 102 434 5 0 

498 9 507 3 5 

481 0 481 4 1 

490 0 490 5 0 

516 0 516 4 6 

578 0 578 4 5 

528 0 528 6 5 

632 0 632 6 0 

5295 666 5961 43 34 

0 

2 

4 

4 

4 

4 

5 

8 

5 

5 

10 

9 

11 

6 

77 

19 

201 

334 

405 

443 

411 

439 

515 

486 

495 

526 

587 

539 

638 

6038 

A.3 - 1
 
 

 

 The RTF data include most items from the first page of Form 706. This includes taxable estate 

and subsequent tax calculations. The RTF data do not include the key components of taxable 

estate: total gross estate and deductions.1

 They also do not include asset detail. The available 

RTF data appear to be reliable, even though they are not as thoroughly tested and edited as SOI 

data. Comparisons of RTF and SOI data for a sample of observations indicate that in nearly all 

cases, the RTF data match the SOI data. Taxable estate on the RTF was inconsistent with that on 

the SOI file in about 2 percent of tested cases. Estate tax liability after credits on the RTF was 

inconsistent with the tax on the SOI in less than 3 percent of cases. 

Common variable names and definitions have been created for all years of data. 

II. Testing the Panel Estate Tax Estimates 

The estate tax data matched to the panel and weighted using the individual panel weights 

indicate that estate tax returns were filed for 633,519 persons represented by panel members who 

died between 1987 and 1996. In comparison, the SOI data indicate that 617,052 returns were 

filed for individuals who died from 1987 to 1996. The panel estimate for number of returns is 20 

percent lower than the SOI estimate for the 1991 year of death and 38 percent higher for the 

1992 year of death. 

Overall, the panel estimate for number of returns is about 3 percent higher than the SOI estimate 

for the ten-year period, the panel estimate of taxable estate is 7 percent higher, and the panel 

estimate of estate tax after credits is 13 percent higher. If the 1992 year of death is excluded, the 

panel estimate for number of returns is 1 percent lower than the SOI estimate for the period, the 

panel estimate for taxable estate is 3 percent higher, and the panel estimate of estate tax after 

credits is 8 percent higher. 

Comparison of Panel and SOI Estate Tax Estimates, 1987 through 1996 years of death 

Dollar amounts in thousands 

Year of Death 

Panel Estimates SOI Estimates 

Number of 

Returns Taxable Estate Estate Tax 

Number of 

Returns Taxable Estate Estate Tax 

1987 

1988 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

Total 

44,429 

43,094 

53,106 

56,964 

45,998 

83,093 

77,063 

73,546 

65,043 

91,183 

633,519 

34,913,784 5,462,614 

47,342,260 10,226,032 

50,261,948 9,907,185 

56,842,371 10,097,862 

52,308,510 10,913,823 

82,064,297 17,366,500 

72,550,989 12,145,140 

66,259,819 11,544,479 

76,013,483 16,542,043 

90,389,492 17,795,066 

628,946,953 122,000,746 

43,996 

45,039 

50,458 

54,328 

57,490 

60,392 

68,991 

71,342 

78,272 

87,330 

617,639 

38,062,852 

41,692,033 

48,640,780 

51,156,471 

54,102,790 

57,512,355 

67,388,010 

67,707,542 

76,420,686 

86,667,116 

589,350,635 

6,393,717 

7,432,810 

8,953,032 

9,212,028 

9,626,357 

10,481,478 

12,573,827 

12,321,738 

14,269,444 

16,352,185 

107,616,617 

1

 The RTF does include an indicator of the size of gross estate. However, it is fairly crude, and does not appear to 

have been updated as the filing requirement was increased from $60,000 to $600,000 and the distribution of estates 

changed. The relevant size classes (i.e., the size classes that virtually all observations fall into) are $500,000 under 

$1 million, $1 million under $5 million, and $5 million or more. For 1998 tax year, 44 percent of panel members 

with estate tax returns fell into the lowest class, 51 percent in the middle class, and 5 percent in the highest class. 

A.3 - 2
The discrepancies between the SOI and the panel are to some extent unsurprising. The SOI 

sample is much larger than the panel sample, and the differences between the SOI and panel 

estimates may be due to sampling variation, particularly in the panel. The SOI sampling rates for 

1987-1996 years of death range from 10 percent to 35 percent of returns per year. The effective 

tax rates for decedents in the panel averages about 0.7 percent. 

The estimates for tax year 1992 are of some concern (the panel estimates are 38 percent higher 

than the SOI for number of returns, 43 percent higher for taxable estate and 66 percent higher for 

tax). In addition, the fact that the panel estimates (particularly for number of returns) seem to 

become more volatile beginning with 1991 might be of some concern. 

The estate tax returns of panel members for 1992 do not appear to have been misclassified into 

the wrong year of death. With few exceptions, the year of death on the individual panel (derived 

from Social Security records) matches the year of death in the estate tax data (as reported on the 

return). However, the year of death is missing from the panel data in about 10 percent of cases. 

The 1992 estimates for average age at death and other factors do not appear very different from 

that for other years. It does appear that both the unweighted number of returns and the average 

weight increase sharply for 1992. 

A.3 - 3
 
 

APPENDIX B 

Weighting the Panel 

by 

Janet McCubbin 
 

Weighting the Panel 

I. Weighting the 1987-Forward Panel 

Initial weights for the cohort portion of the 1987-forward panel were derived by Mathematica 

Policy Research, under contract to the Statistics of Income Division. These weights were 

modified by OTA and augmented with weights for refreshment portion of the sample. Weights 

designed to represent the tax filing population for 1987 through 1996 are described in this 

section. Adjustments to the weights during the extrapolation of the panel to the budget period 

are described below. 

II. Weighting the Cohort Portion of the Panel 

The populations represented by the panel include individuals who filed a tax return as a 

nondependent for tax year1987, the dependents that they claimed in 1987, the tax returns filed by 

these two groups of individuals, and the tax families headed up by these two groups of 

individuals, in 1987 and in subsequent years.1

 There are six different weights on the file 

delivered to OTA: PWGT, SWGT, RETWGT, FWGT01, FWGT02 and FWGT03. 

PWGT is nonzero on returns whose primary filer is a panel member. SWGT is nonzero on 

returns whose secondary filer is a panel member. Together, PWGT and SWGT for year t sum to 

the number of members of the panel universe who are filers in year t, including dependents who 

file their own returns. For a given individual, the nonzero values of PWGT and SWGT do not 

change. However PWGT is only “turned on” (i.e., nonzero) when that individual files as a 

primary and SWGT is only turned on when that individual files as a secondary. 

RETWGT is nonzero on returns filed by panel members. RETWGT for year t sums to the 

number of returns filed by members of the panel universe in year t, including those filed by 

dependents. 

FWGT01 is nonzero on one return in each tax family that is headed by a panel member. 

FWGT01 sums to the number of tax families that have a panel member as a filer (either primary 

or secondary) on the principal (that is, nondependent) return. 

FWGT03 is nonzero on every return filed by dependents who are claimed by panel members 

(including returns filed by dependents who are not themselves panel members). FWGT03 sums 

to the number of returns filed by dependents in the FWGT01 families. 

FWGT02 is equal to FWGT01+FWGT03, and is nonzero for every return in a tax panel family. 

FWGT02 sums to the number of returns filed by tax families that have a panel member as a filer 

on the principal (that is, non-dependent) return. Summing FWGT02 is equal to the number of 

1

 This is true with one exception: we do not have the 1987 returns of 1987 dependent filers, unless these returns 

happened to be selected in the 1987 SOI. 
 

 

 

 

 

 

returns filed by the sum of FWGT01 tax families. Note that RETWGT, FWGT02 and FWGT03 

are all return weights. However, FWGT02 need not be equal to RETWGT.2 

The derivation of RETWGT, PWGT and SWGT is as follows. In 1987, the probability that a 

return is selected into the panel is known, and RETWGT for 1987 is the inverse of that 

probability of selection. The probability that any individual (primary, secondary or dependent) is 

a panel member is equal to the probability that the nondependent return on which they appeared 

was selected in 1987. Hence PWGT is equal to the 1987 value of RETWGT in any year that the 

panel member appears as a primary, and SWGT is equal to the 1987 value of RETWGT in any 

year that the panel member appears as a secondary. 

For years after 1987, PWGT and SWGT do not change (except to “turn on” and “turn off.”) 

However, returns have multiple probabilities of selection, as the original panel member filing 

units join with other filing units. Consider the following four possible cases for new joint 

returns: a panel member files jointly with another panel member; a panel member files jointly 

with a non-panel member who was a 1987 nondependent filer; a panel member files jointly with 

a non-panel member who was a 1987 dependent (filer or nonfiler); a panel member files jointly 

with a non-panel member who was not a 1987 filer or dependent. It does not matter whether the 

panel member is the primary or secondary on the new return. Assume that the panel member is 

the primary, with probability of selection in 1987 equal to Prob(P). (i.e., Prob(P) = 1/PWGT = 

1/RETWGT for 1987.) 

In the first case, where the secondary is also a panel member, the secondary’s probability of 

being in the panel, Prob(S), is also known, and is equal to 1/SWGT. Assuming that the 

probabilities of selection for P and S are independent, the probability that their new joint return is 

in the panel is therefore 1/[Prob(P)+Prob(S)-Prob(P)Prob(S)]. 

Next, assume that the secondary was a non-dependent filer in 1987, but is not a panel member. 

In this case, SWGT is zero, but Prob(S) is known and is not zero. Again the probability that the 

new joint return is 1/[Prob(P)+Prob(S)-Prob(P)Prob(S)]. Note that even though SWGT is zero 

and S is not a panel member, her probability of selection contributes to (and reduces) the weight 

of the new joint return.3 

Now suppose that the secondary was a dependent filer in 1987. In this case, the fact that S was 

eligible for selection in 1987 is known, but her selection probability is unknown.4 Mathematica 

2

 For example, if a panel member files a dependent return, but is not claimed as a dependent by a panel member, 

then RETWGT will be nonzero but FWGT02 will be zero. (In other words, if a dependent is not claimed by a panel 

member filer, then the dependent is not counted as part of a panel tax family, even though the dependent is a panel 

member.) Conversely, if a non-panel member who is the dependent of a panel member files a return, then 

RETWGT will be zero but FWGT02 (and FWGT03) will not be zero. It is also possible for RETWGT and 

FWGT02 to both be nonzero, but different. 

3

 Note that the return weight is the same whether the panel member marries another panel member, or whether the 

panel member marries a non-panel member. In both cases, RETWGT=1/[Prob(P)+Prob(S)-Prob(P)Prob(S)]. 

4

 Taxpayers should identify themselves as dependent filers if they can be claimed as dependents, even if they are not 

in fact claimed as dependents (because, e.g., their parents do not file a return). Therefore it is possible that some 

dependent filers were not eligible for selection. However, the number of such filers is likely to be negligible. 

B - 2
 
 

Policy Research (MPR) then “applied the method of multiplicity weighting to calculate 

RETWGT, setting the weight equal to one-half the sum of PWGT and SWGT (one of which was 

zero).” 

If the secondary was not a dependent filer or nondependent filer in 1987, but was under age 21 at 

the end of 1987, then MPR assumes that the secondary was eligible to be selected as a dependent 

in 1987. Then as in the preceding case, the return weight is half of PWGT. 

Finally, if the secondary was not a dependent or nondependent filer in 1987 and was age 21 or 

over at the end of 1987, then MPR assumes that the secondary was not in the 1987 filing 

population. In this case, Prob(S) is 0, and the return weight is 1/[Prob(P)]. 

Assuming that all non-filers under age 21 were dependents in 1987 overstates the number of 

dependent children; and assuming that all non-filers over age 21 were not dependents in 1987 

understates the number of dependent adults. MPR asserts that by choosing 21 as the cutoff, the 

errors are, on average, approximately offsetting. 

In order to determine weights for joint returns in the panel with new non-panel member 

spouses after 1993, the probability of selection for the new person (i.e., Prob(S) in the above 

cases) is assumed to be equal to the individual’s CWHS probability of selection. To the extent 

that some of the new persons were not eligible for selection in 1987, this will overstate the 

probabilities of selection and understate the return. 

III. Weighting the Post-1987 Entrants to the Filing Population 

The panel does not represent individuals who were not filers or claimed dependents in 1987. 

New individuals include nondependent primaries in 1988 or later who were not filers or 

dependents in 1987; nondependent secondaries in 1988 or later who were not filers or 

dependents in 1987; dependents in 1988 or later who were not filers or dependents in 1987. (It 

may also include persons represented on tax year 1987 returns that were filed very late.) To 

represent these new individuals, CWHS returns of primaries who were not primaries or 

secondaries in 1987 were added to the panel. Some of these individuals were dependents in 

1987. Therefore to avoid overrepresenting persons who were dependents in 1987, weights of 

zero are assigned to returns of panel members who were dependents in 1987 as they become 

non-dependent filers. The CWHS returns represent all new nondependent filers. 

The probability of selecting a new return in any year after 1987 is equal to the CWHS probability 

of selecting the primary, Prob(P). The individual weights for the primary and secondary and the 

return weight are all 1/Prob(P). 

Note that the CWHS returns do not represent a true panel. Therefore the probability that a return 

is selected to refresh the panel in 1989 is not contingent on whether any of the individuals on the 

return were selected in 1988. The probability of selection in 1989 is contingent only on whether 

the primary or secondary were filers in 1987, and on the primary’s SSN. This makes the 

weighting very easy, but it creates problems for analysis. If a secondary divorces the primary 

B - 3
 
and is not selected based on her own SSN (or based on the primary SSN of a new joint return), 

then she is lost from the panel. Similarly, if a CWHS primary becomes a secondary and his 

spouse is not selected, then the primary is lost from the panel. (This would occur if the primary 

and secondary continue to file jointly, but switch positions on the return.) 

References 

Czajka, John. (1999). “Responses to David’s Responses, September 23, 1999.” 

Westat (1999). “Meeting Minutes, September 24, 1999.” 

B - 4
 
 

APPENDIX C.1 

Imputations: Consumption 

by 

James Cilke 

and 

Julie-Anne Cronin
 

B - 5
 
 

Consumption 

I. Introduction 

A measure of each family’s relative consumption of taxed goods is needed to distribute excise 

taxes. The tax data used by Treasury’s Office of Tax Analysis (OTA) to develop its panel model 

does not include information on consumption. This paper describes the methodology used to 

impute consumption to panel members. 

We had three goals in mind when trying to impute consumption. First, to accurately reflect total 

consumption by category of good for all families and across income classes. Second, to provide 

reasonable variation in consumption within and across socio-economic classes. Third, to 

maintain realistic cross-correlations among broad categories of consumption. 

There were a number of strategies we could have used to impute consumption. We could have 

attempted to estimate a system of demand equations, the sum of which would have been equal to 

total consumption. Such a system, if well-constructed, would preserve cross correlations among 

types of goods within a family. Unfortunately the price and quantity information needed to 

estimate such a system was not available. 

Alternatively, we could have used regression analysis to measure expenditures on particular 

goods as a function of the income and demographic information available in both the 

consumption and tax data. Such a methodology would not, however, have maintained cross 

correlations across goods. Variation in consumption patterns would also have been limited to 

known regressors. 

To maintain cross correlations as well as variation in the consumption data, we chose to perform 

an unconstrained match between the Consumer Expenditure Survey (CEX) and the tax data. 

Because the match was unconstrained (some families were matched repeatedly and others in the 

consumption data were not used) the imputed consumption on the panel does not have the same 

level of variation as the consumption data but more so than if we had used a regression 

technique. 

Below we discuss i) the consumption data; ii) the matching methodology; iii) the separate 

imputation for cigarette consumption; and iv) some results from the match. 

II. The Consumer Expenditure Survey 

A. Design 

We imputed shares of consumption from the CEX. It is designed to represent the civilian noninstitutionalized population. In the CEX, the unit of analysis is the “consumer unit” (CU), 

defined as one of the following: i) all related members of a household; ii) a person living alone 

or in a group but who is financially independent; or iii) two or more persons living together who 

use their joint income to make joint expenditures. 

C.1 - 1
 
 

 

 

 

The CEX data is collected in quarters. Each quarter is nationally representative and includes 

approximately 5,000 CUs. The sample for each quarter is divided in three (each nationally 

representative) so that only one-third of the quarter sample is interviewed in the first month of 

the quarter, one third in the second month of the quarter, and one-third in the last month of the 

quarter. The CUs are questioned about the previous three months consumption (e.g. a CU in the 

first quarter of 1995 that is interviewed in January will provide consumption information from 

October through December of 1994). 

The CEX has a rotating panel nature. Each selected CU is interviewed for 5 quarters. The first 

quarter is not recorded. In any given quarter, approximately 20 percent of the CUs interviewed 

will be in their first interview, 20 percent in their second interview, 20 percent in their third 

interview, etc. 

B. Limitations 

Although the CEX provided the most detailed consumption information available, it did have 

some severe limitations. As mentioned above, it lacked price and quantity information, making 

it difficult to use the data to create a system of demand equations. It also suffered from poor 

income data, attrition bias, and underreporting of key consumption categories. 

Transfer and capital income, in particular, were underreported in the CEX; making it especially 

difficult to measure the consumption to income ratios for the bottom and top ends of the income 

distribution. As a result, we did not use income as a matching variable. Instead we derived an 

annual consumption measure in the tax data and matched families in the two data sets by annual 

consumption. Our proxy for consumption in the tax data was positive after-tax cash income.1 

Each quarter of the CEX was nationally representative. We chose two quarters (the first quarter 

of 1995 and the first quarter of 1996) and followed the families represented in these quarters 

forward and backward to get a full year of consumption.2 We used two quarters, as opposed to 

one in order to increase the size of the CEX sample. Because consumption for any given family 

was only collected for four quarters, the two samples did not overlap. 

Many families, however, were missing one of the four interviews that made up a year’s 

consumption. The missing interviews were imputed by substituting the consumption of a 

similar family in the same interview quarter. Special care was taken with families in their final 

interview since the final interview included consumption items not recorded in previous 

interviews. 

1

 Cash income excludes many forms of savings such as employee and employer contributions to defined benefit and 

defined contribution pension plans, inside build-up on defined contribution pension plans, accrued (but not realized) 

capital gains (including personal residences), and inside build-up on other tax-preferrred savings accounts (IRAs, 

529s etc.).

2

 For example, if a family was in its final interview in the first quarter of 1995, we picked up that family’s 

consumption from the second, third and fourth quarters of 1994 to create an annual consumption measure. All
 

consumption amounts were indexed to the first quarter of 1996 by the CPI-U. 

C.1 - 2
 
 

 

The CEX, like many surveys, underreported alcohol, gasoline, and cigarette consumption. 

Because the reporting of cigarette consumption was particularly poor, we separately imputed 

cigarette usage using information from the National Institute of Health Survey (NIHS). Section 

IV below describes this imputation. 

C. File Preparation 

The Bureau of Labor Statistics organized the CEX data for each interview quarter into 4 files: 

the family file, MTAB, ITAB, and the diary file. The family file contained aggregated 

consumption data as well as general demographic information. The MTAB file contained 

detailed expenditure information for each universal classification code (UCC) for each month of 

each interview quarter. The ITAB file contained detailed income information for each month of 

the interview quarter. The diary file contained monthly expenditures on certain minor 

expenditures. The CEX data for our consumption imputation was drawn primarily from the 

family file and was supplemented by the MTAB and ITAB files. The diary file was not used. 

We imputed 15 broad categories of expenditures as well as three subcategories of expenditures 

subject to excise. The aggregate expenditure data were drawn from the family file and included 

expenditures on food, alcoholic beverages, housing, apparel, transportation, health, 

entertainment, personal care, reading, education, tobacco, miscellaneous items, charitable 

contributions, and pensions and life insurance. We also collected demographic information that 

was used in the match: the number of adults and number of children in the family, and the 

gender, age, and marital status of the family head. 

Because the broad categories of expenditures included durable goods, they had to be adjusted 

before they could be used to create annual consumption measures.3

 Durable goods are consumed 

over time so expenditures on durable goods in a given year do not accurately reflect consumption 

of the durable in that year. Ideally expenditures on a car, for example, would be spread over the 

car’s usable lifetime. Unfortunately, with only four quarters of consumption data, it was not 

possible to know which families were currently “consuming” previously purchased durable 

goods. 

Our next best alternative was to exclude the purchases of durable goods from our measure of 

consumption.4

 Using the definition of durable goods found in the National Income and Product 

Accounts (NIPA Table 2.4) and the MTAB file, we created a nondurable good measure for each 

consumption category. Table 1 lists the durable goods in each expenditure category and shows 

their relative magnitude. 

3

 They did not, however, include housing purchases which, along with principal repayments, are considered a form
 

of savings not consumption. 

4 Although we matched on nondurable expenditures, we imputed both durable and nondurable expenditures. 

C.1 - 3
 
 

Table 1: Description and Amount of Durable Goods 

Aggregate Consumption Category Durables NonDurables 

Description $B Description $B $B 

Food 548 No durables 

Alcoholic beverages 36 No durables 

Housing 1,056 Furntiture, appliances, and computers 

Apparel 160 Jewelry and watches 

Transportation 663 New and used motor vehicles, tires, tubes and other parts 

Health 120 Orthopedic and opthalmic products and appliances 

Entertainment 156 Recreation and sports equipment 

Personal care 28 No durables 

Reading 15 Books and maps 

Education 37 Books and maps 

Tobacco 23 No durables 

Miscellaneous 67 No durables 

Charitable contributions 98 No durables 

Pensions and life insurance 264 No durables 

Total 3,270 

0 

0 

101 

15 

353 

8 

56 

0 

5 

4 

0 

0 

0 

0 

541 

548 

36 

952 

146 

312 

114 

100 

28 

11 

32 

23 

67 

98 

264 

2,730 

We also used the MTAB file to collect amounts for the consumption of gasoline, air travel and 

telephone services. These expenditures were relatively narrow in scope and were not separately 

identified in the family file but are subject to excise taxes. 

The ITAB was used to adjust the miscellaneous expenditures only collected in the final 

interview.5 Because these amounts were only collected for one-fourth of the sample (those 

families in the final interview), they were multiplied by 4 during BLS processing, so that the sum 

of expenditures in this category represented the population total. Because we were annualizing 

the data, we had a final interview for each family and, therefore, needed to “undo” the BLS 

adjustment to these expenditures by dividing them by 4. 

III. Matching the CEX to the Tax Data 

To impute consumption to the panel model, we performed an unconstrained statistical match 

between tax families on the panel and families from the CEX. The CEX file consisted of 

approximately 7,600 families with levels of total annual consumption expenditures (at 1996 

levels), including expenditures on 20 additional specific items. Many of these items are subject 

to, or are strongly related to Federal excise taxes. 

Our a priori notion was that a family’s consumption pattern would remain fairly constant over 

time and that significant changes in consumption patterns would occur only when a family 

experienced a major change in membership or income. The basic strategy was to first match 

base year 1987 tax families to the consumption file. If the family was stable in subsequent years, 

then consumption shares of each item would remain the same for those years. Alternatively, 

families that experienced a major change would be rematched to a new CEX record. 

 The ITAB file primarily contains income, not expenditure data. The “income” item included as part of the 

miscellaneous expenditures are “employee business expenses.” 

C.1 - 4
 

5
 

 

 

In total, we used seven matching variables. The first, Family Status Class, contained four 

possible values: non-married without children, non-married with children, married without 

children, and married with children. The marital status refers to the head of the family. A child 

was defined as a person under the age of 18. The child status referred to the entire family. The 

existence of any child living with the family, independent of its relationship to the head of the 

family, resulted in the designation “with children.” The Family Status variable was required to 

match exactly. 

The second matching variable was the age of the primary taxpayer, which matches to the age of 

the family head on the CEX file. For non-married families, the gender of the family head was 

the third matching variable. The fourth matching variable was the number of children in the 

family. In the tax records, this variable was defined as the number of dependents aged 17 and 

under. 

The fifth matching variable was the number of “extra” adults in the family, persons age 18 and 

over excluding the family head and spouse (if married). In the tax records, this variable was 

defined as dependents age 18 and over. Because CEX families were not tax families, some 

family members in the CEX were not claimed as a dependent by the family head. As a result, 

the number of children and “extra” adults in the CEX file was sometimes larger than in the tax 

file. For this reason, the penalties for mis-matches on the number of children and the number of 

extra adults was relatively small. 

The sixth matching variable was a consumption ranking variable. On the tax panel, consumption 

was proxied by total positive income after-tax. Total positive income was the sum of the 

positive income items included in AGI, including all Social Security benefits, tax exempt 

interest, and the foreign earned income exclusion. From this, Federal income taxes and own 

FICA and SECA taxes were subtracted.6 

Tax families were then ranked by centile. For example, the median family had a rank of 50 and 

the top 1 percent of families had a rank of 100. Since the lowest 7 or 8 percent of families had 

zero positive after-tax income, we decided to collapse the first 10 ranks into a single value of 1 

and subtracted 10 points from each persons rank over 11. The resulting income ranks range from 

1 to 90. 

We created a similar ranking variable on the CEX file, except that families were ranked by their 

total consumption. Again, the values of this variable ranged from 1 to 90. Table 2 below 

summarizes the matching variables and the penalty assigned to a mis-match. The actual 

matching CEX record to a particular tax record was the one with the lowest calculated penalty. 

A single CEX record was matched multiple times. 

Ideally, we would have liked to subtract all personal taxes, including health insurance, estate taxes, and state and 

local income taxes. Unfortunately, these additional taxes were not available in the tax data prior to the consumption 

imputation. 

C.1 - 5
 

6 
 

Table 2: Mis-Matching Penalties 

Matching Variable Penalty 

Family Status Class 

Age of Primary 

Gender (for non-joints) 

Number of children in the family1 

Number of extra adults2 

Consumption Rank3 

Smoking status 

Infinite 

200 points 

500 points 

75 points per person 

75 points per person 

100 points per centile 

1000 points 

1

 Children are defined as persons age 17 and under
 

2

 Extra adults are defined as persons age 18 and older, not including the head of household 

(primary) or spouse.
 

3

 In the tax data, consumption is defned as total positive income, after-tax.
 

The only CEX information brought over to the tax file were the expenditure shares (expenditures 

on item x divided by total expenditures). On the panel model, total consumption by the tax 

family was proxied by positive after-tax cash income. The values for the 20 individual 

consumption items are created by multiplying the imputed shares from the CEX by total 

consumption on the panel to arrive at the separate consumption categories. 

If, in the next year, the tax family remained stable, then total consumption for the family was the 

previous year’s total consumption adjusted by the percentage change in the family’s total 

positive after-tax income.7

 So, for example, if a family’s positive after-tax income rose by five 

percent then total consumption also rose by five percent. 

If a tax family was not stable, then a new match to the CEX was performed. Families were 

considered unstable if: 1)The family status variable changed (e.g. an unmarried person married 

or a couple had a child); 2) The family’s income rank changed by more than 10 points relative to 

the original matching year; or 3) The family’s smoking status changed. 

IV. Tobacco Imputation 

We imputed the consumption of tobacco to individuals not families. Unlike most other goods, 

tobacco was not universally consumed. It is also habit forming but only at the individual level. 

Whether or not a family was designated as “smoking” depended on its membership. As family 

membership changed over time so did the smoking status of the family. 

 The percentage change in income is capped at plus or minus 25 percent. 

C.1 - 6
 

7
A. Imputing Smoking Status. 

The first step to imputing a smoking status to each individual on the tax file was to randomly 

assign each individual in the tax file a “Smoking Selection Cutoff” number. It was simply a 

random, uniformly distributed number between zero and one. Once assigned, the number never 

changed. 

Next, using data from the National Institute of Health Survey (NIHS), we constructed a table of 

smoking probabilities by family income class, age class, and marital status for all adults (see 

Table 3). The table included a total of 60 cells. As shown in the table, unmarried persons in the 

lowest income class and in the 34-45 age class had the highest probability of smoking (46 

percent). The lowest probability of smoking (2 percent) was associated with married persons in 

the highest income class, over 65 years of age. 

C.1 - 7
 
 

Table 3: Percent of Adults Who Smoke by Income, Age, and Marital Status 

Family Age 

Income 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 & over All ages 

--- All adults ---

$0 to $20,000 30 34 43 40 31 12 29 

$20,000 to $30,000 35 30 39 35 29 9 29 

$35,000 to $55,000 25 25 30 27 23 9 25 

$55,000 to $75,000 20 22 27 21 19 9 22 

over $75,000 23 16 15 15 12 4 15 

All Incomes 27 25 29 25 22 10 24 

--- Married ---

$0 to $20,000 21 29 39 33 26 11 25 

$20,000 to $30,000 34 27 33 35 27 8 25 

$35,000 to $55,000 19 23 28 25 23 8 23 

$55,000 to $75,000 20 19 25 21 16 8 20 

over $75,000 * 12 14 14 13 2 13 

All Incomes 25 21 24 21 19 8 20 

--- Single ---

$0 to $20,000 31 36 46 46 34 13 31 

$20,000 to $30,000 36 33 44 35 32 13 33 

$35,000 to $55,000 27 30 32 30 22 10 28 

$55,000 to $75,000 20 29 36 21 36 13 26 

over $75,000 22 26 23 27 11 9 22 

All Incomes 28 32 39 34 30 13 29 

Source: National Institute of Health Survey, 2000. 

* fewer than 40 observations 

Table 4 shows the overall percent distribution of the adult population within each of the NIHS’ 

income classes. For example, 25.3 percent of the adult population fell in the lowest income 

class, 23.5 percent in the next income class, and so on. In the tax data, we calculated the dollar 

breaks in each year (1987 to 1996) using the panel model’s family cash equivalent income 

measure so that the percent of the adult population in each class matched the distribution in 

Table 3. 

C.1 - 8
 
 

The next step was to impute smoking status to each adult person in each year.8 This was 

accomplished by simply comparing each adult person’s Smoking Selection Cutoff number with 

the appropriate probability of smoking given the person’s income, age and marital status. If the 

selection number was less than the probability number, the person was a smoker in that year.9 

Table 4: Distribution of NHIS Families by Income Class 

Family Income Class Percent Distribution 

$0 to $20,000 

$20,000 to $35,000 

$35,000 to $55,000 

$55,000 to $75,000 

over $75,000 

25.3 

23.5 

21.9 

14.0 

15.3 

All Incomes 100.0 

Table 3 only applied to the adult population. Additional information from the NIHS suggested 

that more than half of all smokers began smoking before age 18. As a result, we decided to 

extend the imputation to dependents between the inclusive ages of 13 and 17. The selection 

probabilities for the youngest age classes were adjusted by the following amounts: 0.18 for 13 

year olds, 0.28 for 14 year olds, 0.44 for 15 year olds, 0.64 for 16 year olds, and 0.79 for 17 year 

olds. 

The final output from the imputation routine were two arrays. The first showed for each person, 

the years in which he or she smoked. The second showed for each non-dependent return, the 

number of smokers on the return for that year. The imputation process captured the notion that 

people tend to smoke year after year. The process almost assures that a person in year t who was 

in the same income, age, and marital status class in year t+1 would have the same smoking 

status. However, the imputation process did not capture any smoking relationships among 

family members. Each person was treated independently. So for example, in our imputation 

smokers were not more likely to be married to another smoker, and children of smokers were not 

more likely to smoke. 

V. Some Results from the Imputation 

8

 For our purposes, adults are all independent filers and nonfilers regardless of age and all dependents age 18 or 

over. 

9

 The age and marital status distributions on the panel model are slightly different from those in Table 2. The end 

result is that slightly too many people are assigned a smoking status. To correct this, we increased each person’s 

Smoking Selection Cutoff value by 0.012. 

C.1 - 9
 
Tables 5a-5d show the distribution of total consumption by category across quintiles of 

nondurable consumption. Tables 6a-6d show the distribution of total consumption by quintile of 

nondurable consumption across consumption categories. Tables 7a-7d show the same 

distributions for excise goods. All tables labeled “a” refer to distributions for the four quarters of 

expenses for all families interviewed for the CEX in the first quarter of 1994; “b” tables refer to 

expenses for families interviewed in the first quarter of 1995; “c” tables refer to the first year of 

the imputation (1987); and “d” tables refer to the last year of the imputation (1996). 

In general, and as shown in tables 6a-6d, the distributions for the imputed panel years (“c” and 

“d” tables) are very similar to the distributions found in the annualized CEX data (“a” and “b” 

tables). Only the distributions of health and education expenditures show relatively minor 

differences. The CEX shows slightly more health and less education expenditures for families 

with less overall consumption than the imputations for 1987 and 1996. 

Total education expenditures, although they may be a large component of a family’s budget in a 

particular year, are relatively small in size as a percent of total expenditures for all families, only 

1 percent (see Tables 7a-7d). Tuition expenses which are crucial to the measurement of tuition 

tax credits are being separately imputed (see Appendix C2 for more detail). 

The health expenditure in this imputation is of limited value. Health expenditures in the CEX 

only include out-of-pocket expenses. Expenses reimbursed by health insurers are not included. 

In fact, out-of-pocket expenses may be negative when a family is reimbursed in the current year 

for expenses incurred in a prior year. 

Because the consumption of health services is a major component of overall consumption and 

the center of many tax policy decisions, the value of employee provided health insurance, was 

separately imputed (see Appendix C3 for more detail). 

Although the distributions of consumption in the CEX and the imputation are very similar, the 

levels of consumption imputed to the panel model are about 50 percent higher than in the CEX 

for all goods except tobacco. Consumption on the panel model is generally much greater than in 

the CEX. Because we only impute consumption shares, not total consumption, from the CEX, 

we maintain the panel model’s greater level of total consumption in the imputation. 

C.1 - 10
 
Table 5a: Four Quarters of Expenses for CU's Present in 1994(1)-- Percent Distribution Across Expenditure Q

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 7 9 7 8 5 5 10 5 8 7 5 10 4 

2nd 12 14 12 12 10 12 13 9 13 11 7 19 10 

3rd 17 17 17 16 15 19 19 15 17 18 11 22 15 

4th 24 24 24 24 25 25 23 25 26 26 18 26 24 

Highest 41 35 41 40 46 38 35 46 37 38 60 23 48 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Nondurable Goods ---

Lowest 7 9 7 8 5 5 10 5 8 8 3 10 4 

2nd 12 14 12 12 10 11 14 9 13 13 6 19 10 

3rd 16 17 17 16 15 17 19 14 17 18 10 22 15 

4th 24 24 24 24 25 25 23 25 26 26 18 26 24 

Highest 41 35 41 40 44 42 35 48 37 35 63 23 48 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Durable Goods ---

Lowest 6 7 3 6 8 5 5 17 

2nd 12 12 8 12 12 8 8 12 

3rd 19 15 13 21 20 17 17 13 

4th 25 24 20 25 26 25 26 19 

Highest 38 42 57 36 35 44 44 39 

Total 100 100 100 100 100 100 100 100 
Table 5b: Four Quarters of Expenses for CU's Present in 1995(1)-- Percent Distribution Across Expenditure Q

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 7 10 7 8 5 5 10 5 8 7 5 13 4 

2nd 12 15 14 13 11 12 15 11 13 13 7 19 10 

3rd 18 19 17 17 17 22 20 16 19 18 11 22 14 

4th 23 22 22 22 24 25 22 23 23 24 19 25 21 

Highest 39 34 40 40 44 37 33 46 37 38 58 22 52 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Nondurable Goods ---

Lowest 7 10 7 8 5 6 10 5 8 8 4 13 4 

2nd 13 15 14 13 11 12 15 10 13 14 6 19 10 

3rd 18 19 17 17 17 19 20 16 19 19 11 22 14 

4th 23 22 22 22 24 24 22 23 23 24 19 25 21 

Highest 40 34 40 40 43 40 33 47 37 35 61 22 52 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Durable Goods ---

Lowest 5 7 2 5 8 4 4 13 

2nd 11 12 8 11 14 12 10 13 

3rd 22 19 13 24 18 16 15 14 

4th 25 23 19 25 23 24 25 21 

Highest 37 39 57 35 37 44 46 39 

Total 100 100 100 100 100 100 100 100 
Table 5c: 1987 Panel Imputation -- Percent Distribution Across Expenditure Quintile 

(1987 Expenditure Levels) 

Nondurabl 

e 

Consumpt Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 7 10 10 8 6 6 6 5 7 7 9 15 2 

2nd 12 13 17 13 10 13 12 10 13 12 11 19 8 

3rd 17 18 16 17 15 19 18 16 18 17 16 20 16 

4th 24 23 21 23 23 25 25 24 25 25 14 25 24 

Highest 40 36 36 39 46 38 39 45 38 39 50 20 49 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Nondurable Goods ---

Lowest 7 10 10 8 6 6 6 5 7 7 7 15 2 

2nd 12 13 17 13 10 12 12 10 13 13 10 19 8 

3rd 17 18 16 17 16 18 18 15 18 18 16 20 16 

4th 23 23 21 23 23 24 25 24 25 26 14 25 24 

Highest 41 36 36 39 45 40 39 46 38 37 53 20 49 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Durable Goods ---

Lowest 7 8 3 6 7 5 7 22 

2nd 12 11 5 14 10 11 10 18 

3rd 18 15 13 19 17 17 15 16 

4th 25 23 21 25 26 24 25 14 

Highest 38 43 58 35 40 44 43 29 

Total 100 100 100 100 100 100 100 100 

C.1 - 13
 
Table 5d: 1996 Panel Imputation -- Percent Distribution Across Expenditure Quintile 

(1987 Expenditure Levels) 

Nondurabl 

e 

Consumpt Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 7 9 8 8 5 5 8 5 7 7 6 15 3 

2nd 12 14 15 13 10 14 11 10 13 12 12 19 8 

3rd 17 18 17 18 16 18 17 16 18 17 15 21 16 

4th 24 24 22 23 24 25 26 24 25 26 18 23 25 

Highest 40 35 38 39 46 38 38 45 37 38 49 22 48 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Nondurable Goods ---

Lowest 7 9 8 8 5 5 8 5 7 7 5 15 3 

2nd 12 14 15 13 10 12 11 10 13 12 10 19 8 

3rd 17 18 17 18 16 18 17 16 18 18 15 21 16 

4th 24 24 22 23 24 25 26 24 25 26 18 23 25 

Highest 40 35 38 38 44 40 38 45 37 36 51 22 48 

Total 100 100 100 100 100 100 100 100 100 100 100 100 100 

--- Durable Goods ---

Lowest 5 8 2 5 8 4 6 15 

2nd 13 10 6 15 12 10 10 20 

3rd 18 16 11 19 17 15 15 16 

4th 25 24 21 25 26 25 25 18 

Highest 39 42 60 36 38 45 43 31 

Total 100 100 100 100 100 100 100 100 

C.1 - 14
 
Table 6a: Four Quarters of Expenses for CU's Present in 1994(1)-- Percent Distribution Across Expenditure C

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

24 20 17 17 

15 

1 1 1 1 

1 

35 32 30 30 

31 

4 4 4 5 

5 

17 22 24 22 

20 

6 5 5 4 

4 

4 4 4 5 

5 

1 1 1 1 

1 

1 0 1 1 

0 

1 1 1 1 

2 

1 2 1 1 

1 

1 1 1 1 

1 

Total 100 17 1 31 5 21 4 5 1 0 1 1 1 

--- Nondurable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

28 25 22 21 

18 

1 1 1 1 

1 

38 35 34 33 

33 

4 5 5 5 

6 

9 

11 12 12 

12 

7 6 5 5 

4 

3 3 3 4 

4 

1 1 1 1 

1 

1 0 0 0 

0 

1 1 1 1 

2 

2 2 2 1 

1 

1 1 1 1 

2 

Total 100 21 1 34 5 12 5 4 1 0 1 1 1 

--- Durable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

21 18 14 18 

20 

1 2 2 2 

4 

64 70 72 67 

62 

2 1 1 1 

1 

9 7 9 

10 

12 

1 1 1 1 

1 

2 1 0 1 

1 

Total 100 18 3 66 1 10 1 1 

C.1 - 15
 
Table 6b: Four Quarters of Expenses for CU's Present in 1995(1)-- Percent Distribution Across Expenditure C

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

24 20 18 17 

15 

1 1 1 1 

1 

36 33 30 30 

32 

4 4 4 5 

5 

16 19 24 22 

19 

6 5 4 4 

3 

3 4 4 5 

5 

1 1 1 1 

1 

1 0 0 0 

0 

1 1 1 1 

1 

2 2 1 1 

1 

1 1 1 1 

2 

Total 100 17 1 31 5 21 4 5 1 0 1 1 1 

--- Nondurable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

28 24 22 20 

18 

1 1 1 1 

1 

37 36 33 33 

34 

4 4 5 5 

6 

9 

11 12 12 

11 

6 6 5 4 

4 

2 3 3 3 

4 

1 1 1 1 

1 

0 0 0 0 

0 

1 1 1 1 

2 

2 2 1 1 

1 

1 1 1 1 

2 

Total 100 21 1 34 5 11 5 3 1 0 1 1 1 

--- Durable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

26 20 16 17 

19 

1 2 1 2 

4 

61 65 73 69 

63 

2 2 1 1 

1 

8 

10 

7 

10 

11 

0 0 0 0 

0 

1 1 1 1 

1 

2 1 0 1 

1 

Total 100 18 2 67 1 10 0 1 1 

C.1 - 16
 
Table 6c: 1987 Panel Imputation -- Percent Distribution Across Expenditure Category 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

23 19 17 17 

15 

2 2 1 1 

1 

35 34 32 31 

31 

4 4 4 5 

6 

18 22 23 21 

19 

4 4 4 4 

4 

4 4 4 5 

5 

1 1 1 1 

1 

0 0 0 1 

0 

2 1 1 1 

2 

1 1 1 1 

0 

0 1 1 1 

1 

Total 100 17 1 32 5 20 4 5 1 0 1 1 1 

--- Nondurable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

27 23 21 20 

17 

2 2 1 1 

1 

38 37 35 34 

33 

4 5 5 5 

6 

9 

12 12 12 

11 

4 4 5 5 

4 

3 3 3 4 

4 

1 1 1 1 

1 

0 0 0 0 

0 

1 1 1 1 

2 

2 1 1 1 

0 

0 1 1 1 

2 

Total 100 20 1 34 5 11 4 4 1 0 1 1 1 

--- Durable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

22 16 16 17 

21 

1 1 2 2 

4 

64 71 70 67 

60 

2 1 1 2 

1 

8 9 

10 10 

12 

1 1 1 1 

1 

3 1 1 1 

1 

Total 100 18 3 65 1 10 1 1 

C.1 - 17
 
Table 6d: 1996 Panel Imputation -- Percent Distribution Across Expenditure Category 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption Total Food Alchohol Housing Apparel Transport Health 

Entertain 

ment 

Personal 

Care Reading Education Tobacco 

Misc - all 

qtr 

M

--- All Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

100 100 100 100 

100 

24 19 18 16 

15 

1 1 1 1 

1 

37 34 33 31 

31 

4 4 4 5 

6 

16 22 22 21 

19 

4 3 4 4 

4 

3 4 4 5 

5 

1 1 1 1 

1 

1 0 0 1 

0 

1 1 1 1 

1 

2 1 1 1 

0 

1 1 1 1 

1 

Total 100 17 1 32 5 20 4 5 1 0 1 1 1 

--- Nondurable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

Total 

100 100 100 100 

100 

100 

28 23 21 20 

18 

20 

1 2 1 1 

1 

1 

39 38 36 34 

33 

35 

4 5 5 5 

6 

5 

9 

12 12 12 

11 

11 

5 4 4 5 

4 

4 

3 3 3 4 

4 

4 

1 1 1 1 

1 

1 

0 0 0 0 

0 

0 

1 1 1 1 

1 

1 

2 1 1 1 

0 

1 

1 1 1 1 

2 

1 

--- Durable Goods ---

Lowest 

2nd 

3rd 

4th 

Highest 

Total 

100 100 100 100 

100 

100 

26 15 17 18 

20 

19 

1 1 2 2 

4 

3 

59 73 69 67 

61 

65 

2 1 1 1 

1 

1 

8 8 9 

10 

12 

10 

1 1 1 1 

1 

1 

2 1 1 1 

1 

1 

C.1 - 18
 
Table 7a: 4 Quarters of Expenses for 1994(1)- -- Distribution of
 

Goods Subject to Excise Across Expenditure Quintiles
 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption 

Goods 

Subject to 

Excise Alcohol Airline Gasoline Telephone Tobacco 

--- Percent Across Quintile ---

Lowest 8 7 2 7 11 10 

2nd 14 12 8 14 16 19 

3rd 19 17 11 19 20 22 

4th 25 24 22 26 23 26 

Highest 35 41 57 34 30 23 

Total 100 100 100 100 100 100 

--- Percent of Total Nondurable Expenditures ---

Lowest 11 1 0 4 3 2 

2nd 12 1 1 5 3 2 

3rd 11 11532 

4th 10 11421 

Highest 812321 

Total 10 11421 

C.1 - 19
 
Table 7b: 4 Quarters of Expenses for 1995(1)- -- Distribution of
 

Goods Subject to Excise Across Expenditure Quintiles
 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption 

Goods 

Subject to 

Excise Alcohol Airline Gasoline Telephone Tobacco 

--- Percent Across Quintile ---

Lowest 8 7 2 8 10 13 

2nd 15 14 6 15 17 19 

3rd 20 17 14 21 20 22 

4th 23 22 21 24 23 25 

Highest 34 40 57 32 29 22 

Total 100 100 100 100 100 100 

--- Percent of Total Nondurable Expenditures ---

Lowest 11 1 0 4 3 2 

2nd 12 1 1 5 3 2 

3rd 11 11521 

4th 10 11421 

Highest 812321 

Total 10 11421 

C.1 - 20
 
Table 7c: 1987 Panel Imputation -- Distribution of Goods Subject 

to Excise Across Expenditure Quintiles 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption 

Goods 

Subject to 

Excise Alcohol Airline Gasoline Telephone Tobacco 

--- Percent Across Quintile ---

Lowest 9 10 2 8 10 15 

2nd 14 17 7 14 16 19 

3rd 19 16 14 20 20 20 

4th 23 21 22 25 23 25 

Highest 35 36 55 33 30 20 

Total 100 100 100 100 100 100 

--- Percent of Total Nondurable Expenditures ---

Lowest 11 2 0 4 3 2 

2nd 11 2 1 4 3 1 

3rd 10 11431 

4th 9 1 1 4 2 1 

Highest 812320 

Total 9 1 1 4 2 1 

C.1 - 21
 
Table 7d: 1996 Panel Imputation -- Distribution of Goods Subject 

to Excise Across Expenditure Quintiles 

(1987 Expenditure Levels) 

Quintile of 

Nondurable 

Consumption 

Goods 

Subject to 

Excise Alcohol Airline Gasoline Telephone Tobacco 

--- Level ($ millions) ---

Lowest 21,003 2,808 714 7,889 6,132 3,460 

2nd 37,461 5,450 2,420 15,173 10,238 4,180 

3rd 47,955 6,019 4,584 20,177 12,366 4,809 

4th 61,061 7,672 7,840 26,157 14,123 5,269 

Highest 89,428 13,543 18,427 34,618 17,977 4,863 

Total 256,908 35,491 33,986 104,014 60,835 22,582 

--- Percent Across Quintile ---

Lowest 8 8 2 8 10 15 

2nd 15 15 7 15 17 19 

3rd 19 17 13 19 20 21 

4th 24 22 23 25 23 23 

Highest 35 38 54 33 30 22 

Total 100 100 100 100 100 100 

C.1 - 22
 
APPENDIX C.2 

Imputations: Tuition Expenses for Postsecondary Education 

by 

James Cilke 

and 

Julie-Anne Cronin 
 

Tuition Expenses for Postsecondary Education 

I. The Data 

We used two micro-data sources to impute tuition expenses for postsecondary education. 

These imputations were needed to properly simulate the two tax credits for higher 

education expenses as well as the temporary above-the-line deduction for education 

expenses. The first file, produced by the Department of Education, was the 1995-1996 

National Postsecondary Student Aid Study (NPSAS). This file contained the results of a 

survey of postsecondary students which asked a multitude of questions about the 

financial and demographic characteristics of students and their parents, the types of 

degrees or certificates they were seeking, and the attributes of the schools they were 

attending. The second file was an extract from the 1999 INSOLE file and contained tax 

information for people who either received a 1098T Information Return or who filled out 

Form 8863 to claim a credit for higher education expenses. 

A. The NPSAS 

The first step in the imputation process was to extract certain specific pieces of 

information from the NPSAS. The NPSAS is very rich in information about higher 

education but did not include everything we needed to impute student status to panel 

members. Further, like most survey studies, some important data items were missing for 

some records. In general, missing values were assumed to be random events and 

therefore imputed in the same proportions as non-missing values. 

A number of variables were estimated using the NPSAS data. The AGI of independent 

students and the AGI of parents of dependent students were estimated as total (cash) 

income less Social Security transfers,1

 AFDC payments, other non-taxable transfers and 

child support payments. Whether or not a married student was the primary taxpayer was 

estimated based on whether or not the student had more earnings than his/her spouse. 

The higher earner was assigned primary status. In cases where the earnings were the 

same or zero, the male was assigned primary status. 

Four variables were created to indicate a person’s status as a student. The first variable 

(ISTART) indicated whether a student was in their first year of higher education. A 

second variable (ILASTYR) indicated whether a student was in their last year of higher 

education. A third variable (BGRAD) indicated whether a graduating high school senior 

would become a beginning graduate student in the following year. Finally, a fourth 

variable (ITOGO) indicated the number of additional years the student would attend a 

higher education institution 

1

 The level of Social Security benefits in AGI could also have been estimated but its omission is unlikely to 

have affected our results. Ultimately, we wanted to create a variable for AGI class. Taxable Social 

Security benefits were unlikely to move a high income return to another income class and benefits are not 

taxable at the low end. 

C.2 - 1
 
 

 

ISTART was set to 1 if the student was a first year freshman, a first year graduate 

student, enrolled in less than a two year program, or in the process of changing from an 

undergraduate to a graduate student. (Starting graduate students were treated as starting 

students, even if they were in school in the previous year.) ILASTYR was set to 1 if the 

student was a graduating senior or was receiving his degree, or if the student’s own plans 

for the following year did not include going to school. BGRAD was set to 1 for 

graduating seniors and other degree earners who also indicated that they were planning 

on attending school in the following year. 

Everybody who was not in their last year was given at least one additional year of higher 

education to go. Undergraduates in a traditional 2-year program or less were initially 

assumed to have one remaining year to go. Undergraduates in traditional 4-year 

programs were initially assumed to have either one, two, or three additional years of 

education to go, depending on whether the student classified himself as a junior, 

sophomore, or freshman. Starting graduate students in a Masters program were assigned 

two additional years, while continuing Masters students were assigned one additional 

year. Beginning graduate students in either a Ph.D. program or other professional 

program were initially assigned four additional years to go (ITOGO=4). Otherwise 

ITOGO for Ph.D. and other students in professional programs was set to 5 minus the 

number of years completed, but not less than one. For all other graduate students, 

ITOGO was set to 2 for beginning students and 1 for continuing students. 

The last group of students who needed an imputation for the amount of time remaining 

were graduating high school seniors who were immediately continuing on to a 

postsecondary school. We calculated the distribution of ITOGO for all starting 

postsecondary students and applied this distribution to the group of continuing seniors. 

After the initial assignment of ITOGO, a few adjustments were made. First, if the person 

was less than a half-time student, then ITOGO doubled. Next, 30% of all students with 3 

or more years to go, 20% of all students with 2 year to go, and 10% of student with 1 year 

to go were randomly assigned an additional year to go. 

B. The 1999 INSOLE File 

From the population of tax returns filed for tax year 1999, SOI drew a sample of 

approximately 177,000 returns. This sample was extensively edited, and weighted to 

represent the population of tax returns filed for 1999.2

 The SOI sample included both 

dependent (approximately 7,000) and non-dependent (approximately 170,000) returns. 

Dependent returns were sampled independently from their parent returns. To prevent 

double counting, we only worked with non-dependent returns.3 

2

 More technically, the sample represented individual tax returns filed in processing year 2000. For more 

information on SOI's sampling methodology, see "Individual Income Tax Returns 1999," published by the 

Internal Revenue Service. 

3

 All dependent students were represented on the non-dependent return on which they were claimed. 

C.2 - 2
 
 

 

 

 

 

 

 

For each non-dependent tax return, we extracted a set of items found on Form 1040, the 

primary individual income tax return form, and Form 8863, the form used to compute the 

tax return's education credit.4

 We supplemented our SOI data with data reported on 

Form 1098t. 

Because 1098ts are issued by all postsecondary institutions (eligible for Title IV funds) to 

their enrolled students, these forms allowed us to identify the postsecondary student 

population in the tax data. In addition, the 1098t’s contained a taxpayer identification 

number (TIN) for the issuing school, and information on the individual's level of 

enrollment (graduate or undergraduate) and time commitment (less than half-time or at 

least half-time). By exactly matching the 1098ts to the tax returns, we were able to 

compare the pool of potentially credit-eligible students (those who received a 1098t) to 

those for whom an education credit was actually taken (those who appeared on a Form 

8863). Using the school identifier, we determined the type (two-year or four-year) and 

control (public or private) of the school attended. Of the 170,000 non-dependent returns 

in our sample, approximately 24,000 (14 percent) received at least one 1098t.5 

II. Statistical Match Between the Tax File and the NPSAS. 

The next step in the imputation process was to statistically match the file of highereducation students derived from the 1999 INSOLE file with the NPSAS data. Two 

unconstrained statistical matches were performed; one for students who were dependents 

of others and another for independent students.6 In the case of dependent students, the 

initial matching variables were: 

− Age of student, 

- 7 age classes of the student’s (primary) parent7

, 

- 14 AGI classes of the student’s parent, 

- Marital status of the student’s parent(s), 

- Number of dependents exemptions claimed by parents8, 

- Graduate student status, 

4

 From Form 1040 we obtained most of the income fields, certain deductions used to offset total income, 

and most of the tax liability and tax credit fields. We also obtained filing status (joint, non-joint), type of 

form (1040, 1040A, 1040EZ), number of dependents, and the return's population weight. For each return 

that included a Form 8863, we obtained most of the items on that form. These items included total 

qualified Hope expenses, total qualified lifetime learning expenses, their combined credit amounts, and the 

credit remaining after applying phase-out and tax liability limitations. Approximately 5,000 tax returns in 

the sample included a Form 8863. 

5

 A single return may receive more than one 1098t, and an individual on a return can receive multiple 

1098ts (for example if he or she transferred during the course of the year or moved from undergraduate to 

post-graduate education). Among those returns that received at least one 1098t, 83 percent received only 

one, 15 percent received two, and 2 percent received three or more. Approximately 24,000 individuals in 

our sample received at least one 1098t. Among those individuals, 86 percent received only one, 12 percent 

received two, and 2 percent received three or more. 6

 Dependency here was defined according to tax rules, not the financial aid rules set the Department of 

Education. 

7

 Age classes for the student’s parent were Under 45, 45-49, 50-54, 55-59, 60-64, 65-69, and 70 and older. 

8

 Dependent classes were 1, 2-4, and 5 or more. 

C.2 - 3
 
 

 

 

- Type of graduate student program9, 

- Full time versus part time status, and 

- Private vs. public schools. 

All tax records were matched to a NPSAS record. Each tax record and each NPSAS 

record was placed into a unique matching cell. For every two identically-defined 

matching cells, the first tax record was linked with the first NPSAS record; the second tax 

record was linked to the second NPSAS record, and so on until all of the tax records were 

matched. If the tax cell had more records than the NPSAS cell, the process was repeated 

after the last NPSAS record was used. 

In some cases, there were no NPSAS records that had the same matching characteristics 

as a tax record cell. In these cases, the matching cells needed to be expanded. We first 

expanded cells by eliminating the cells defined by the number of dependent exemptions 

claimed by the student’s parents. Next we eliminated the cells defined by the AGI class 

of the student’s parents. In the next pass, we allowed for students on the tax file to match 

with NPSAS students with a 1 year age difference. We also eliminated distinctions based 

on the age of the parent and the marital status of the parent, and the type of graduate 

school program. With these expansions, nearly all tax records were matched. In the final 

pass, we used matching cells defined by type of school (public vs. private), attendance 

intensity (full vs. part time) and level of program (graduate vs. undergraduate). 

The matching process for independent students was the same. The variables used to 

define the matching cells for independent students were: 

- 9 age of student classes10, 

- 14 AGI of the student classes,11
 

- Marital status of the student,12
 

- Number of dependents exemptions claimed by the student (zero or 1 or more), 

- Graduate student status, 

- Type of graduate student program, 

- Full time vs. part time status, and 

- Private vs. Public schools. 

Like the match with dependent students, the cell expansions first occurred by eliminating 

the distinction for number of dependent exemptions. The AGI class of the student 

distinction was then eliminated. With this expansion, all but 6 non-dependent tax records 

were matched. 

Once these matches were complete, there were two linked data files, one for students who 

were dependents of others and one for non-dependent students. The combined number of 

9

 Graduate student programs were classified into two types; regular and professional. Professional
 

programs largely consisted of medical programs and legal programs. 

10 Classes were, in years, under 20, 20-21, 22-23, 24-29, 30-34, 35-39, 40-49, 50-64, and 65 and over. 11 Same classes as AGI of Parents. 

12 The categories were joint primary, joint secondary, and non-joint. 

C.2 - 4
 
 

 

records was exactly equal to the number of records on the tax extract file of students 

taken from the 1999 INSOLE file. These NPSAS-Tax files were used to impute student 

status and tuition expenses to panel members. 

III. Statistical Match between 2004 panel records and the NPSAS-Tax File 

The next step in this process was to statistically match people on the panel to students on 

the matched NPSAS-Tax file. The first step was to build extracts of potential students 

from 2004 panel records. Three types of potential students were extracted: dependents of 

other taxpayers, independent spouses on joint returns, and independent primaries on 

panel returns13. All non-dependent people on the panel in 2004 and all dependents age 16 

or over were eligible to receive an education imputation. 

The following variables were used in the statistical match between dependent students. 

- Age of Student, 

- 7 Age classes of the student’s (primary) parent, 

- 14 AGI classes of the student’s parent14, 

- Wages as a percent of AGI on parent’s return.15
 

The following variables were used in the statistical match between spouses on joint 

returns. 

- Age of Student16, 

- 14 AGI classes, 

- Wages as a percent of AGI classes, 

- Wages earned by student,17
 

- With dependent children flag. 

The following variables were used in the statistical match between non-dependent 

primaries18. 

- Age of Student, 

- 14 AGI classes, 

- Wages as a percent of AGI classes, 

- Wages earned by student, 

- With dependent children flag, 

13 It was not necessary to split independent primaries between joint returns and non-joint returns because 

marital status was used as a hard matching variable. 

14 Age classes and AGI classes for the student’s parents in this match were the same as used in the earlier 

match. See footnotes 2 and 3.
 15 Wages as a percent of AGI classes were: >100%, 95%-100%, 90%-95%, 75%-90%, 50%-75%, 25%­

50%, 0%-25%, and exactly 0%. 

16 For students aged 50 or more, the student’s age was rounded to an even number. 17 Wages of the student classes were (in thousands): zero, 0-5, 5-10, 10-20, and over 20. 18 Same definitions as used in the match to non-dependent spouses. 

C.2 - 5
 
- Filing status (joint vs. non-joint). 

Based on tabulations from the 1999 tax file of students, the number of dependent students 

attending a higher-education institution in 1999 was approximately 6.7 million. The 

number of spouses on joint returns was approximately 3.3 million and the number of nondependent primaries was 9.4 million. These three values became the targets for the 

statistical match. 

Similar to the previous matches, each panel record and each NPSAS-Tax record were 

placed into a unique matching cell. For every two identically-defined matching cells, one 

holding panel records and the other holding NPSAS-Tax records, the first panel record 

was linked with the first NPSAS-Tax record. However, this time, the weight of the panel 

record was subtracted from the weight of the NPSAS-Tax record. If the weight of the 

NPSAS-Tax record was still positive, it was available for a second match. The same 

NPSAS-Tax record was then matched to the next panel record in the cell. Conversely, if 

the weight on the NPSAS-Tax record was reduced to zero, then the next NPSAS-Tax 

record was used in the cell. This process insured that nearly all of the NPSAS-Tax 

records within a cell were used in the match. 

When matching to spouses and to non-dependent primaries, only a single pass through 

each file was needed. The matching cells did not need to be expanded. Further, the 

number of students on the matched file was very close to the target number of students. 

When matching to dependents, the matching cells needed to be expanded. First, the age 

of the student’s parent for students aged 25 and higher was dropped. Next the number of 

classes for the age of the student’s parents was reduced to 4 and the number of AGI 

classes were cut in half and the number of wages as a percent of AGI classes were cut in 

half. 

Student Information Carried to the Panel. With the match of 2004 panel persons 

completed, the next step was to carry the matched student information to the panel. The 

variables carried and linked from the NPSAS-Tax file to the matched panel records 

included (but were not limited to): 

- A pointer to the NPSAS file, 

- A pointer to the 1999 INSOLE student tax file, 

- A graduate/undergraduate student indicator, 

- An indicator for the length of the undergraduate program (2 or 4-year, other) 

- An indicator for the current level of the undergraduate student (freshman, sophomore, 

junior, senior, or other), 

- An indicator for the kind of undergraduate “degree” being sought (certificate, 

associates degree, bachelors degree, or non-degree). 

- An indicator for the kind of graduate program for the student (regular, professional, 

or other) 

- An indicator for the kind of graduate “degree” being sought (masters, Ph.D., 

“professional”, and other) 

C.2 - 6
 
 

 

- A public/private school indicator, 

- A full-time/part-time attendance indicator, 

- An in-state/out-of-state student indicator, 

- A flag indicating whether the actual student in 1999 was apparently eligible for an 

education credit but failed to take the credit. 

- A flag indicating the student receives a Pell Grant. 

- The number of years of study remaining, 

- An indicator for just-starting students, 

- An indicator for students in their final year, 

IV. Imputing Student Status to 2005 to 2013 records 

At this point, we had a student indicator imputation for people on the panel in 2004. 

Using the years-of-study remaining variable, we had also imputed their student-status in 

the following year and beyond.19 For many students, 2004 was their final year of study. 

So, we needed a student indicator for people beginning school after 2004. 

To accomplish this, we largely repeated the statistical matching process used to impute 

student status for 2004. First, we determined how many beginning students were needed 

to hit our targets in a particular year. We then extracted potential students from the panel. 

The only additional criteria was that the person could not have been a student in the 

current year or the prior year.20 An almost identical matching methodology was used to 

link new beginning students to the NPSAS-TAX file. One difference was that only 

starting students from the NPSAS-Tax file were matched. Another difference is that a 

match usually produced too many students. So, among the pool of successful matches, 

just enough students in each of the three categories were randomly selected to hit the 

target number of students for the match. The student population was expected to grow 

during the 2004-2013 period by, on average, by 1.4% per year. 

V. Imputing Tuition Values 

Tuition values were imputed to the panel model according to the level and control of the 

student’s institution and whether or not the student lived in or out of state. In-state tuition 

amounts at public and private institutions also varied by state, as given in the Digest of 

Education Statistics (see attached Table). For 4-year out-of-state private tuition, we used 

3 values; 30% of such students paid $11,500 (in 1999 dollars), 40% paid $14,500, and 

30% paid $19,500. We assumed that all out-of-state students at 4-year public institutions 

paid the median 4-year public tuition of $9000 or the average 2 year public tuition of 

$7182 per year, respectively. Tuition values for 25% of students attending school halftime or more were reduced by 50% to roughly approximate students attending more than 

half time but less than full-time. Tuition values for less than half-time students were 

19 The imputation process assumed the student would remain in school for as long as the years-remaining 

variable suggested, regardless of the possible changing circumstances of the student (except death). 

20 Its entirely possible a person who was a student two years prior to the current year to be imputed a 

student status a second time. Further, we did not control the kind or type of student the person could be on 

his second imputation. 

C.2 - 7
 
reduced by 75 percent. Values for students in their first or last year of study were 

reduced by 50%. The imputed values of tuition were forecasted to grow over time by 

tuition specific inflation indices that varied by the institution’s level and control (e.g. 

tuition at 2 year public institutions was generally forecasted to grow at a slower rate than 

tuition at 4-year public institutions). The forecasted values were based on historical data 

from the College Board and ITM projections. 

C.2 - 8
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Average Tuition 1999-2000 Academic Year for Full Time Equivalent Students by Level, Control and State* 

4 year 2 year 

Grad

 1st 

Professional 

Public Private Public 

In State Out of State In State Out of State In State Out of State Private

National average 3,290 8,640 14,301 1,330 4,521 7,182 13,500 22,000 

Low (bottom 30 %) 11,500 

Medium (middle 40%) 14,500 

High (top 30%) 19,500 

Median 9,000 

By State:

 Alabama 2,727 8,403 8,697 1,504 4,571 8,383

 Alaska 2,808 9,627 8,320 1,998 5,777 7,182

 Arizona 2,205 9,024 7,539 879 4,658 7,182

 Arkansas 2,684 7,564 8,157 1,020 2,698 5,448

 California 2,588 9,407 14,784 357 4,136 7,182

 Colorado 2,731 9,550 13,124 1,561 7,182

 Connecticuit 4,394 10,392 19,352 1,853 6,077 14,332

 Delaware 4,562 9,009 8,007 1,528 4,479 9,139

 DistCol 2,040 6,487 17,606 1,330 4,397 8,383

 Florida 2,134 7,810 12,282 1,319 4,386 8,383

 Georgia 2,489 8,165 12,275 1,295 4,362 8,383

 Hawaii 2,919 9,738 7,320 1,028 4,807 7,182

 Idaho 2,420 9,239 12,490 1,220 4,999 7,182

 Illinois 3,941 8,998 14,364 1,463 4,863 7,583

 Indiana 3,570 8,627 15,077 2,126 5,526 7,583

 Iowa 2,934 7,991 13,708 2,021 5,421

 Kansas 2,416 7,473 10,405 1,327 4,727 7,583

 Kentucky 2,620 8,296 9,394 1,279 4,346 8,383

 Louisiana 2,410 8,086 14,293 901 3,968 8,383

 Maine 4,094 10,092 20,218 2,712 6,936 14,332

 Maryland 4,455 8,902 17,432 2,256 5,207 9,139

 Massachusetts 4,064 10,062 19,611 2,024 6,248 14,332

 Michigan 4,403 9,460 10,399 1,714 5,114 7,583

 Minnesota 3,818 8,875 15,252 2,367 5,767 7,583

 Mississippi 2,866 8,542 8,585 968 4,035 8,383

 Missouri 3,628 8,685 11,481 1,427 4,827 7,583

 Montana 2,904 9,723 8,765 1,926 5,705 7,182

 Nebraska 2,779 7,836 10,990 1,361 4,761 7,583

 Nevada 1,997 8,816 7,372 1,189 4,968 7,182

 New Hampshire 5,917 11,915 16,142 3,725 7,949 14,332

 New Jersey 5,105 9,552 15,982 2,102 5,053 9,139

 New Mexico 2,262 7,142 9,787 736 2,414 5,448

 New York 3,948 8,395 19,927 2,549 5,500 9,139

 North Carolina 2,007 7,683 15,756 682 3,749 8,383

 North Dakota 2,840 7,897 9,156 1,867 5,267 7,583

 Ohio 4,375 9,432 14,471 2,417 5,817 7,583

 Oklahoma 2,128 7,008 9,642 1,216 2,894 5,448

 Oregon 3,571 10,390 16,124 1,607 5,386 7,182

 Pennsylvania 5,473 9,920 16,682 2,140 5,091 9,139

 Rhode Island 4,242 10,240 17,275 1,746 5,970 14,332

 South Carolina 3,577 9,253 11,636 1,279 4,346 8,383

 South Dakota 3,124 8,181 10,508 2,687 6,087 7,583

 Tennessee 2,598 8,274 11,841 1,276 4,343 8,383

 Texas 2,538 7,418 13,288 893 2,571 5,448

 Utah 2,150 8,969 3,463 1,480 5,259 7,182

 Vermont 6,845 12,843 17,989 2,781 7,005 14,332

 Virginia 3,948 9,624 12,464 1,273 4,340 8,383

 Washington 3,253 10,072 14,670 1,620 5,399 7,182

 West Virginia 2,444 7,501 11,954 1,526 4,926 7,583

 Wisconsin 3,213 8,270 13,900 2,117 5,517 7,583

 Wyoming 2,373 9,192 14,301 1,278 5,057 7,182 

Source: Digest of Education Statistics 

* Data in bold was used in the model. 

C.2 - 9
 
APPENDIX C.3 

Imputations: Health Insurance Status 

by 

Gillian Hunter 

and 

Emily Lin 

C.3 - 1
 
 

 

 

Health Insurance Status 

I. Summary 

Health insurance coverage and premium amounts were imputed to the model to enable 

the model to analyze the effect of a wide variety of tax and health reform proposals. 

Employer-provided health coverage and tax-preferred premium amounts were needed to 

analyze the effect of current law tax preferences. In addition, nongroup insurance 

coverage and premiums, out-of-pocket expenses, public coverage and uninsured status 

were needed to analyze the effects of reform proposals including newly proposed tax 

deductions, exclusions and credits. 

Because no single health survey includes the necessary items, a variety of surveys were 

linked together through matching techniques. First, data from the 1997 Robert Wood 

Johnson Foundation Employer Health Insurance Survey1

 (RWJF) were statistically 

matched using employer and employee characteristics to the Current Population Survey 

(CPS) to determine premiums, employer and employee contributions, and other plan 

characteristics for employer-provided health insurance. Then data from the CPS (with 

the matched RWJF variables) were matched to the panel in order to determine who is 

covered by employer, nongroup, Medicare and Medicaid health insurance and to identify 

the residual category of uninsured individuals. Nongroup health insurance premiums 

from the National Health Insurance Survey (NHIS) were assigned to individuals in the 

nongroup market. Finally, data from the Medical Expenditure Panel Survey (MEPS) 

were used to impute out-of-pocket medical expenditures. Tax data already on the panel 

provided information on itemized medical expenses and the above-the-line deduction for 

health insurance for self-employed individuals. 

II. Matching the RWJF Employer-Provided Health Insurance Variables to the CPS 

Data from the RWJF, including employer-provided health insurance premiums, 

employees’ share of the premium and other plan characteristics (e.g., out-of-pocket 

limit), were statistically matched to the individuals identified as policyholders of 

employer plans in the CPS. The RWJF variables were then brought over to the Panel 

Model through the statistical match from the CPS to the Panel Model. 

A. The RWJF Survey 

The RWJF is a national probability sample of public employers and private 

establishments. For public employers, the data indicate whether the government is a 

federal, state, or local agency. For private establishments, the RWJF contains 

information about the industry of the employer and the size of the firm. The survey also 

collects information on the distribution of the hourly wage paid to the employees and 

1

 Premiums from the employer survey in the MEPS were not used, because there was no way to link 

premiums to family income. The RWJF survey included both premiums and wage data, enabling a link 

since wages are the major component of income. 
whether the employer offers health insurance. If health insurance is offered by an 

employer, the data have information about the number of plans offered and, for each 

plan, the monthly premium for the single and family policy, the employees’ share of the 

premium, the number of workers enrolled in the plan, and the share of the enrollees in the 

single and family policy. 

B. Matching Cells - RWJF 

The following four variables from the RWJF and CPS were used to define the matching 

cells: 

1. Policy type: single or family; 

2. Industry and employment sector: 9 private industries and 3 levels of governments; 

3.	 Firm size (applicable only for private-sector employers): fewer than 10 workers, 

10-24 workers, 25-99 workers , 100-499 workers, 500-999 workers, or more than 

1,000 workers; and 

4. Hourly wage: below $7, $7-$10, $10-$15, and $15 or over (in 1997 dollars). 

The CPS sample selected for the match consisted of private- and public-sector employees 

who participated in their employer’s health insurance plan. 

The data were adjusted in several ways to facilitate the match. First, because the nine 

major industries in the RWJF were grouped by the establishments’ two-digit SIC codes 

and the CPS used the 2002 NAICS codes, certain re-mappings between the two industry 

coding systems were created. 

Second, firm size was top coded at 25,000 in the RWJF, and, for these establishments, the 

industry variable was missing. To deal with the missing-industry problem, the fraction of 

RWJF policyholders in the largest-firm category who had an unknown industry was 

calculated (separately for single and family policies), then the same percentage of CPS 

policyholders in the largest-firm category was randomly selected and a separate statistical 

match was conducted for these policyholders. Because all of these workers were 

employed in large private establishments, the matching cells in this case were defined by 

the type of policy and hourly wage. 

Third, the RWJF included a wage category of below $5, but a small percentage of 

policyholders were classified in this category. To reduce the number of thin cells, the 

two wage categories, below $5 and $5-$7, were combined. 

Fourth, wages in the RWJF file were in 1997 dollars whereas the CPS wages were in 

2004 dollars. Instead of indexing the RWJF wage cutoffs to the 2004 level, we 

calculated, within each RWJF policy-type-industry-firm-size cell (i.e., non-wage 

partitions), the percentage of policyholders in each of the four wage categories, below $7, 

C.3 - 2
 
 

 

 

$7-$10, $10-$15, and $15 and over. Based on this wage distribution, the CPS 

policyholders in the corresponding cell were ranked by their wages and assigned into the 

four wage categories. 

Cells with small unweighted CPS counts (fewer than 20 in general) were combined with 

similar cells for better matching results. Small cells were combined with the next cell 

along the wage and/or firm size dimension to increase the cell size, and cells were never 

combined across industries, employment sectors, or types of policy. 

C. Matching the Records - RWJF 

The RWJF data were re-arranged so that each record represented a policy (single or 

family) of a health plan in a wage category. The RWJF and CPS records were grouped 

into the matching cells as defined above and randomly ordered within a cell. Each RWJF 

record carried the plan data and a policyholder count calculated as the number of 

employees enrolled in the policy who were in the specific wage category. Based on this 

policyholder count, each RWJF record was given a ranked percentile within the cell. To 

match the RWJF records to the CPS file, a CPS record received a random draw from a 

uniform distribution and was matched to the RWJ record with the nearest ranked 

percentile in the matching cell. An unconstrained match was conducted: The matched 

file had all the CPS records, but the RWJ records were not required to be used up and 

could be repeatedly used. 

III. Matching CPS Health Coverage Variables to the Family Panel 

Then data from the CPS (with the matched RWJ plan variables) were statistically 

matched to the panel in order to impute health insurance coverage. 

A. The CPS Survey 

The CPS is a national probability sample of about 60,000 households. The March 2004 

CPS ASEC (Annual Social and Economic Supplement) survey asks respondents to 

identify each type of health insurance coverage (employer, nongroup, Medicare, 

Medicaid, SCHIP, and several other types of public coverage) for each household 

member during the preceding calendar year.2

 Individuals who are not reported as having 

coverage are assigned to the residual category of uninsured. 

B. Matching Cells - CPS 

Both Panel Model records and CPS records were divided into cells by family type, 

gender, age, and employment status. 

Family Types. Family types included singles, head of household with children, joint 

filers without children and joint filers with children. Single and head of household filers 

 Some have questioned whether respondents report coverage for the previous year or whether they may 

erroneously report current coverage. 

C.3 - 3
 

2
 

 

 

were sub-divided by gender. Nonfilers and CPS records were put into similar categories. 

This required changing from the CPS family unit to a tax unit. Subfamilies and adult 

family members who were not a head of household or a spouse were put into their own 

separate units. For the Panel Model, independent filers who can be claimed as child 

dependents were included only as child dependents. Adult dependents were excluded 

from the analysis. 

Age Types. The age categories were: <25, 25-34, 35-44, 45-54, 55-64, 65+.3 Single and 

head of household filers were put into the appropriate age category. Joint filers were 

divided into four categories: both filers under age 65; both filers 65 or older; male head 

under 65 but female head 65 or older; and male head 65 or older but female head under 

65. If both filers were under age 65, the unit was grouped in the appropriate age category 

by the age of the older filer. 

Employment Status. Because the majority of individuals under 65 who have health 

insurance are covered by employer plans, employment is an important factor in health 

insurance coverage. In addition, the tax treatment of employer-provided health insurance 

differs from the tax treatment of health insurance purchased by self-employed 

individuals.4 As a result, self-employed individuals needed to be matched separately 

from other employed individuals. Hence, tax-filing-units were divided sequentially into 

one of four employment categories: self-employed claiming a health insurance deduction, 

self-employed but not claiming a health insurance deduction, employed or not employed. 

In the tax panel, wages were based on the combined annual wages (and pension 

contributions reported on a W-2) of both filers for joint returns. Self-employment income 

reported on schedule SE (i.e., net farm income plus net nonfarm income) was determined 

on a return basis. In the CPS wages were similarly based on the combined annual wages 

(but did not include pension contributions) of the head of household and his, or her, 

spouse. Self-employment income as reported in the CPS was aggregated across the head 

of household and the spouse but only one of these individuals was required to have 

positive self-employment income for the unit to be categorized as self-employed. 

The tax panel self-employed health insurance deduction amount was used to determine 

nongroup5

 health insurance coverage and premiums for the self-employed. The CPS was 

used to assign all other health insurance statuses. 

Tax-filing units with positive self-employment income claiming a self-employed health 

insurance deduction were matched to CPS units with positive self-employed income and 

nongroup insurance. These units were classified as self-employed with nongroup 

3 If both filers were 65 or older or if a singe filer was 65 or older, an additional age break at 85 years was 

added for tax-filers classified as not employed. See below for discussion of employment status. 

4

 Employee contributions to health insurance made through a Section 125 cafeteria plan and employer 

contributions are excluded from an employee’s taxable income for employment and income tax purposes. 

In contrast, self-employed individuals may under certain circumstances deduct premiums for health 

insurance regardless of whether they itemize but only up to the amount of their self-employed income. 

5

 For ease of exposition, these policies are referred to as nongroup health insurance; however health 

insurance that is not subsidized by an employer is a more accurate description. 

C.3 - 4
 
 

 

 

 

 

insurance. Otherwise if self-employment income was positive, if wages plus selfemployed income exceeded a dollar threshold ($1,000 in $2003), and if self-employed 

income exceeded a percentage threshold (40 percent of the sum of self-employment 

income and wages), a unit was classified as self-employed but without nongroup health 

insurance. If a unit did not fit into these first two categories, then it was classified as a 

wage earning tax-filing unit if the sum of self-employment income and wages exceeded 

the dollar threshold. Otherwise a tax-filing unit was classified as “not employed”.6 

Cell Aggregation. In general, cells with fewer than 500 observations were collapsed into 

larger cells across age, gender, and employment status. A family can have multiple 

kinds of health insurance (employer-provided, nongroup, Medicare, Medicaid etc.) or 

multiple policies (both employers may provide health coverage). In addition, a family 

may insure all, some or none of its family members. As a result, the cells were collapsed 

in a way that preserved the most common combinations. Because virtually all individuals 

65 or older are eligible for Medicare, tax filing units with an aged head (>=65) were not 

aggregated with those without an aged head. Because the health insurance decision is 

very different for singles than for married couples and different for couples with children 

than for couples without children, tax filing units with one head were not aggregated with 

units with two heads and tax filing units with children were not aggregated with units 

without children. As expected, nonaged tax filers classified as wage earners had more 

observations and hence finer age breaks, as did childless tax filers over 65 years of age 

who were classified as not employed. 

C. Matching the Records - CPS 

A tax unit’s wages as well as total income is likely to affect the decision to purchase 

health insurance. Higher wage jobs are more likely to have access to employer-provided 

health insurance than lower wage jobs. As income rises, individuals are more likely to 

purchase health insurance because it is more affordable, because they have more assets to 

protect and because they are less likely to have access to free care in the event of an 

expensive medical outcome. Wages is the major component of income for some groups 

of tax filers while nonearned income (including self-employment income) is the major 

component for others. In the matching process, records within a cell were disaggregated 

into finer cells by the less important component of income and then were ranked by the 

more important component of income. Generally, tax-filing units categorized as 

employed and less than 65 years of age were ranked by wage, and the remaining units 

were ranked by nonwage income (including self-employed income). 

For employed taxpayers under 65 years old, income is comprised mostly of wages. As a 

result, this group was first sub-divided by whether there was substantial nonwage income 

(including self-employed income) and then ranked by wage.7 Specifically, the group was 

6 Tax-filing units with negative self-employment income were classified as wage earners or not employed 

depending on whether wages exceed the dollar threshold. 

7

 For the CPS, the annual salary variable (or the hourly wage variable multiplied by the usual hours per 

week and weeks worked per year variables) was used to determine the annual wage. 

C.3 - 5
 
 

 

 

sub-divided by whether nonwage income exceeded 10% of wages. Within each subdivision, the CPS record with the highest wage in a cell was matched to the record with 

the highest wage in the Panel model, and so forth. 

For joint taxpayers the decision whether to purchase health insurance and in what 

combination depends on whether each spouse is working, whether one or both employers 

offer health insurance (correlated with each spouse’s wage), and on total family income. 

In order to capture the essence of this complicated relationship, the group of joint 

taxpayers categorized as employed and under age 65 was subdivided by whether a female 

taxpayer worked, earned below the median of female taxpayers in the group, or earned 

above the median. Each of these three groups was further sub-divided by whether the 

combined nonwage income exceeded 10% of total wages. Then records within each of 

the six resulting sub-groups were ranked by the male’s wage.8 

Units categorized as not employed were ranked by total cash income.9 This method of 

matching was also done in several situation where small cell sizes resulted in aggregation 

across employment categories (e.g., female taxpayers over 65 with children) and where 

wage breaks discussed below resulted in very small cells (e.g., self-employed joint 

taxpayers without children but with the male over 65 and the spouse under 65). 

For the remaining units, units categorized as self-employed and units with taxpayers over 

age 65 (except as noted above), the initial methodology (breaking by nonwage income 

and ranking by wage) was reversed to reflect the fact that nonwage income is a more 

substantial component of total income for many units in these groups. These groups were 

generally sub-divided by wage(s) and then ranked by nonwage income (including selfemployment income). 

In the matching process, tax panel weights were used to determine the total population. 

CPS weights were used to construct the CPS file for the match but the match was not 

constrained to use all the CPS weighted observations. The weights on all CPS records in 

each cell were adjusted (pro rata) so that they summed to the total of the weights in the 

corresponding Panel Model cell. 

CPS records could be split into two records as part of the matching process, but panel 

records were not split. When a CPS record was split into two or more records, the weight 

of the original record was split between the new records. If the combined weights of two 

or more (original or split) CPS records were required to equal the weight of a panel 

record, the CPS record in the group with the highest weight was matched to the panel 

record. 

8

 Employed units with the male taxpayer over age 65 but with the spouse under age 65 (and no dependent 

children) were ranked in a similar manner. 9 In the Panel, cash income includes wages, 401(K) contributions, taxable and nontaxable pension and 

annuity distributions, taxable IRA distributions, taxable and nontaxable Social Security income, taxable and 

tax exempt interest, dividends, self-employed income, other net income or loss, alimony, Veteran’s 

benefits, welfare and other transfer payments, Supplemental Security Income, unemployment compensation 

in AGI, and worker’s compensation. For purposes of matching only the income of the primary and 

secondary taxpayer were included in the total. 

C.3 - 6
 
 

The match within a cell began by taking the top ranked record from each file and 

comparing their weights. Any left over weight from the adjusted CPS was compared 

with the second panel record. If the CPS weight was less than the panel weight, 

additional CPS records in the ranked order were processed until a match was completed. 

This process was continued until all the panel records in the cell were matched to a CPS 

record. 

D. Initial and Subsequent Years 

In the initial year, health insurance status was assigned as discussed below to each 

member of the tax unit. In subsequent years, tax-filing-units that did not have a life 

changing event kept the initial match and all the resulting health insurance statuses. 

Units that had a life changing event were re-matched to the CPS and may have had a 

change in health insurance statuses as a result. Life changing events include a change in 

family type or family composition (reflected in a change in person number), a change 

from dependent status to independent status, a change in employment type, a 15 

percentile change in income, a 50 percent change in a filer’s wage, or attainment of age 

65 for at least one taxpayer. New entrants to the panel were matched in the year of entry. 

E. Matching within the Unit 

The CPS health insurance status and other variables for individuals within the unit were 

attached to Panel individuals following a set of rules. For single individuals, the CPS 

health variables were assigned to the panel observation. For married individuals, the CPS 

health variables of the male household head were assigned to the male taxpayer in the 

panel and the female household head were assigned to the female taxpayer. Depending 

on the kind of coverage, dependent coverage was assigned based on rules described 

below. 

F. CPS Health Insurance Variables Carried to the Panel Model 

The following describes how the CPS health insurance variables carried to the panel 

model were derived. One key measure of the impact of tax reform is the change in the 

number of uninsured individuals. The number of uninsured individuals is the residual of 

the total population less those individuals with identified coverage. Hence Medicare and 

Medicaid coverage status was included in the imputation along with employer-provided 

health insurance and nongroup coverage. Note the CPS health insurance status was for 

calendar year 2003. 

Employer-Provided Health Insurance. Employer-provided health insurance included 

coverage from a current or former employer as well as Federal Tri-care coverage, 

including CHAMPUS, military, and Veterans benefits.10 Dummy variables were created 

to indicate whether the coverage was single or family and whether the employer paid “all, 

10 CHAMPUS, military and Veterans benefits could not be separately distinguished. 

C.3 - 7
 
 

 

 

part or none” of the premium11 for each individual in the household. For individuals 65 

years of age or older, employer-provided health insurance was categorized as primary12 

or secondary payer depending on whether the policyholder was working at the time of the 

survey. If any child in the CPS unit had employer coverage, then all children in the panel 

unit were assumed to have employer coverage. 

Medicare. For individuals 65 years of age or older, dummy variables were created for 

whether Medicare was the primary or secondary payer. Dummy variables were also 

created for each household head under age 65 covered by Medicare due to disability. 

Medicaid/SCHIP . The Medicaid/SCHIP population was divided into three groups: aged 

Medicaid participants; adult Medicaid participants or eligibles; and child 

Medicaid/SCHIP participants or eligibles. For filers, a dummy variable was created to 

indicate Medicaid coverage. In addition, an adult eligibility flag for Medicaid was 

created in the CPS file by randomly assigning eligibility based on AGI. If any child in 

the CPS unit was covered by Medicaid or SCHIP, all children eighteen years of age or 

younger were assigned Medicaid/SCHIP coverage in the panel unit. A child eligibility 

flag for Medicaid and SCHIP was also created in the CPS file by randomly assigning 

CPS units whose children were not covered by Medicaid, SCHIP or nongroup health 

insurance to the eligible category based on AGI. Flags were also created for SSI 

recipients to potentially identify units where at least one member may be disabled. 

Nongroup Health Insurance. A dummy variable was created indicating nongroup 

coverage. As discussed above, the nongroup coverage variable was used to determine the 

self-employed with nongroup coverage cell. In addition, the nongroup dummy was 

carried to the panel for non-self-employed adults and their children under nineteen years 

old (or in school and less than 24 years of age.) Because families with several children 

may purchase nongroup coverage for only one child, if only one child in the CPS unit had 

nongroup health insurance, then only one child in the panel was randomly assigned 

nongroup health insurance. If more than one child in the CPS unit had nongroup health 

insurance, then all children in the panel unit were assigned nongroup health insurance. 

Uninsured. Individuals without any identifiable health insurance were designated to be 

uninsured. 

IV. Matching NHIS Nongroup Premiums to the Family Panel 

NHIS single premiums were matched by: 3 age groups (older tax filer < 45; 45-54; 55­

64); by 3 income groups (< $20,000; $20,000 - $75,000; $75,000+); and by gender of the 

policyholder. Within these cells, a hot-decking procedure was used to assign single 

11 The employer paid “all” category was assigned to individuals covered by Tri-care. Individuals with 

missing data, including individuals for whom the policyholder could not be identified, were assumed to 

receive a part contribution from their employer. 

12 Employer-provided health insurance was assumed to be primary and Medicare was assumed to be 

secondary if the policyholder was working even though in some cases the employer-provided health 

insurance may be secondary if the insurance was obtained from a former employer. If the policyholder was 

not working, Medicare was primary and employer-provided health insurance was secondary. 

C.3 - 8
 
 

 

 

 

nongroup premiums to each tax-filing unit identified as having nongroup coverage on the 

family panel. In addition, childless couple premiums and family with children premiums 

were assigned to each tax-filing unit. The childless couple and couples with children 

premiums were calculated by multiplying the (male) single premium by the appropriate 

ratio below. These ratios were calculated from the NHIS data. 

Ratio of family premiums to self-only by age and child status 

Age group 

19-29 30-44 45-54 55-64 

Couples without 

Children 1.34743 1.77032 1.84675 1.63109 

Couples with 

Children 1.96309 2.25903 1.59680 1.70766 

V. Matching MEPS Out-of-Pocket Medical Expenditures to the Family Panel 

Out-of-pocket medical expenditures for individuals who had employer coverage for an 

entire year were used to impute out-of-pocket medical expenditures (including 

individuals without employer coverage) to individuals less than 65 years of age in the 

Panel Model.13 Because medical expenses are heavily skewed, relatively few individuals 

with very high expenses account for a large portion of total spending, two years of data 

were combined to increase the sample size. Nonconsecutive years were used to avoid 

overweighting high cost medical events that begin in one year and end in the next. 

Expenses from 1996 and 1998 were indexed to 1997 to match the year of the data for 

employer premiums from the RWJF data. 

Out-of-pocket expenses were matched by gender type and by five age groups (<19, 20­

29, 30 - 39, 40 - 49, and 50 - 64). Within a gender/age cell, expenses were calculated at 

the following percentiles: (5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99, 100th). Expenses 

were averaged between percentile breaks. Individual were randomly assigned a 

percentile and thus an expense amount. 

VI. Health Insurance Variables Determined by Panel Data 

The original tax panel provides data for three items. As previously mentioned, the self 

employed health insurance deduction provided nongroup insurance premiums for selfemployed units claiming a self-employed health insurance deduction. Medical expenses 

that exceed 7 1⁄2 % of AGI were in the panel data for itemizers. Finally, since virtually 

everyone over age 65 participates in Medicare, age from the family panel was used to 

assign Medicare for the aged. 

13 Including out-of-pocket medical expenses for individuals with employer-provided health insurance 

enables modeling of proposals that would provide a tax preference for or a cap on the combined value of 

premiums and out-of-pocket expenses. 

C.3 - 9
 
 

APPENDIX C.4 

Imputations: Transfer Income 

by 

James Cilke 

and 

Janet Holtzblatt 
 

 

 

 

Transfer Income 

I. Transfer Imputations 

Transfers, such as social security and welfare benefits, represent income to recipients and 

thus should be included in a comprehensive measure of income.1 To the extent that 

certain transfers are taxable, information on the incidence and the amount of transfers are 

available from tax returns and information returns. However, to a large extent, transfers 

are not taxable. We thus turn to other sources, including the Panel Survey on Income 

Dynamics, to impute nontaxable transfer income to the panel model. Using data from 

the National Income and Product Accounts (NIPA), targets were set for ten types of 

transfer income from 1987 through 1996. These are: 

Fully taxable transfers 

• Unemployment insurance 

Partially taxable transfers 

•	 Social security 

• Tier 1 railroad retirement benefits (“social security equivalent benefits”) 

Nontaxable transfers 

•	 Aid for Families with Dependent Children (AFDC) 

•	 Food stamp benefits2 

•	 Veterans cash benefits 

•	 Supplemental Security Income (SSI) 

•	 Low-income Home Energy Assistance Program (LIHEAP) 

•	 Other cash public assistance (including general assistance and other state 

programs) 

•	 Workers compensation 

Beyond 1996, the transfers are extrapolated using information from both the NIPA and, 

to the extent that the transfers are federally funded, OMB’s most recent budget forecast. 

A. Fully Taxable Transfers: Unemployment insurance 

Taxpayers must include total unemployment benefits in gross income. For filers, we 

used the reported amounts of unemployment benefits on the tax returns in the panel 

model. We compared these amounts with comparable information from the annual SOI 

Individual cross-section files. We found that unemployment benefits reported on the 

panel model were fairly consistent with the amounts reported on the SOI cross-section. 

1 Non-cash transfers are not included in our comprehensive definition of income due to difficulties in 

measuring their value. Examples of non-cash transfers include Medicare, Medicaid, school lunches, 

housing assistance, workers compensation health benefits, and veterans’ education and health benefits. 

2

 Strictly speaking, food stamps are not a cash transfer. Food stamp benefits are more fungible than other 

noncash transfers and are sometimes referred to as “near-cash” transfers. Thus, we include this benefit in 

our expanded income measure. 
 

For non-filers, we used the amounts of unemployment compensation as reported on the 

PSID. We then compared the sum of unemployment benefits reported by filers and nonfilers to the administrative targets. In each year, there was a shortfall. Since the panel 

model had tracked reasonably well the amounts reported by filers, the shortfall was made 

up by imputing unemployment benefits to non-filers. 

To qualify for the imputed unemployment benefits, non-filers had to meet three criteria. 

First, they had to report earnings in the current or prior year. Second, they could not 

report unemployment benefits in the prior year. Third, the sum of AGI plus the imputed 

unemployment benefits had to be less than the filing threshold (otherwise, the 

unemployment benefits would have been taxable and reported on their tax returns). In 

addition, a fourth criteria applied to years other than 1987 (the first year of the panel) or 

1992 and 1993 (years during which an economic downturn occurred). The fourth 

criterion was that earnings in the current year had to fall by more than ten percent from 

the prior year. 

The amount of unemployment compensation imputed to non-filers was set equal to the 

average benefit received by non-filers with reported benefits and a decline in earnings. 

B. Partially Taxable Transfers: Social Security and Tier 1 Railroad Retirement Benefits 

Social security and Tier 1 railroad retirement benefits (which are equivalent to social 

security benefits) are partially taxable. Thus, some information about social security 

benefits is available from tax returns. Filers with taxable social security benefits report 

both the full amount and the taxable portion of social security benefits. In addition, the 

Social Security Administration and Railroad Retirement Board report social security and 

tier 1 railroad retirement benefits to individuals on SSA-1099s and RRB-1099s. For tax 

years 1989 and 1993 through 1996, we had a file that contained an exact match between 

the returns in the panel file and the SSA-1099s and RRB-1099s. For the non-filers, we 

used the amounts of social security benefits that they reported on the PSID. 

Not surprisingly, the amount of social security reported on the panel model fell short of 

the targets based on administrative data. To make up for this shortfall, we imputed social 

security benefits to records without benefits in several stages. 

First, we identified individuals who appeared to be missing social security benefits for 

some years (possibly because of the incomplete nature of the 1099 file). Beginning in 

1988, benefits were imputed if an individual had reported benefits in year t-1 but did not 

report benefits in year t. The imputation was subject to two constraints: 

•	 The social security earnings test (which phases-out benefits above an earnings 

threshold); and 

•	 The income levels (“base amounts”) at which a portion of social security 

benefits become taxable. Benefits were assumed to be zero if, when added to 

AGI, they would have been subject to the income tax and thus reported on tax 

returns. 
The imputed benefit (prior to the imposition of the earnings test) was set equal to the 

benefit in year t, adjusted by the social security cost-of-living adjustment. We repeated 

this imputation methodology in reverse, starting in 1993. 

Second, benefits for 1987 and 1988 were imputed to individuals who were deceased or 

did not file a return in 1989, did not have actual or imputed benefits in 1987 and 1988, 

and were at least 62 years of age. As in the first step, the imputation of benefits was 

subject to two constraints (the social security earnings test and the income thresholds for 

the taxation of benefits). In this stage, the imputed benefit (prior to the imposition of the 

earnings test) was set equal to $4,984 in 1987 and $5,180 in 1988. 

Third, a very small amount of social security benefits was imputed to the panel model as 

part of the process of imputing filing returns. 

In the fourth step, benefits were imputed for dependents of taxpayers within certain age 

groups who had not reported benefits. In 1996 only, the SOI file was matched to 1099s 

for dependents as well as the primary and secondary taxpayer. We used information 

from this matched file to calculate the selection probabilities for imputing social security 

benefits to dependents. These included: 

•	 Dependent children on 1987 returns. 

•	 Dependent children whose parent died sometime between 1987 and 1995. 

Benefits were imputed to the surviving children beginning in the year following 

the parent’s death. 

•	 Dependent children, between the ages of 2 and 18, who were claimed by a filer in 

any year between 1988 and 1996 but who were not previously claimed by the 

filer. 

•	 Non-child dependents, aged 30 or older, on 1987 returns. 

•	 Non-child dependent, aged 30 or older, who were claimed by a filer in any year 

between 1988 and 1996 but who were not previously claimed by the filer. 

The imputed benefit was set equal to the average benefit for the year for other individuals 

in that age class (or half if the person died during the year). 

To make up the remaining shortfalls in social security benefits on the panel model, we 

imputed benefits randomly to non-dependent filers who met the following criteria: 

•	 For 1987 imputations: Earnings in 1987 and 1988 were below the point where 

the average benefit would be phased out by the social security earnings test, and 

their modified adjusted gross income was below the base amounts for taxation of 

social security benefits. 

•	 For 1988 and later imputations: Same criteria as above but also restricted to 

individuals 55 and older 

Aged-based probabilities were used in imputing benefits to non-dependent filers. 
 

 

Finally, we imputed benefits to individuals who received social security benefits in year 

t-1 and year t+1 but not year t (subject to income tax and earnings test constraints 

described above). The imputed benefit was equal to the prior year social security benefit 

adjusted for inflation. If, however, the individual had earnings in excess of the earnings 

test threshold, then the imputed benefit was set equal to the person’s maximum observed 

benefit in 1995 (adjusted by a deflator). 

C. Non-taxable Transfers: Means-tested Transfers and Workers Compensation 

Means-tested transfers are not taxable. These include 

•	 Aid for Families with Dependent Children (AFDC) (since 1996, Temporary 

Assistance for Needy Families) 

•	 Food stamp benefits 

•	 Veterans cash benefits 

•	 Supplemental Security Income (SSI) 

•	 Low-income Home Energy Assistance Program (LIHEAP) 

•	 Other cash public assistance (including general assistance and other state 

programs) 

Thus, tax returns do not contain any information on the receipt of such public and private 

transfers. Workers compensation also is not taxable and thus not reported on tax returns. 

To obtain information on non-taxable transfers, we performed an unconstrained statistical 

match between the panel and the constructed PSID panel file. The PSID asks individuals 

about the receipt of AFDC, veterans cash benefits, SSI, other cash public assistance, and 

workers compensation.3

 In addition, the PSID asks families about their receipt of food 

stamp benefits and LIHEAP. Prior to the match, we aggregated transfer income at the 

family level for all items. 

The statistical match employed a penalty function where, in each year, every tax family 

on the panel was compared with every tax filing unit on the constructed PSID file. The 

PSID record with the lowest penalty was the matching record. 

The filing units were matched based on demographic characteristics, including: 

•	 family status; 

•	 age; 

•	 number of dependents; and 

•	 whether the primary taxpayer or his or her spouse had died in the current or 

following year. 

In addition, the filing units were matched based by the filing unit’s relative income 

ranking (using modified adjusted gross income) and the presence and amount of certain 

types of income (investment income, social security, taxable pensions, and 

3

 More detailed information is available for the head of the household and his or her spouse than for other 

family members in the household. 
 

unemployment compensation). Finally, for years after 1987, filing units were matched 

based on the presence of certain types of transfer income in the prior year. 

During the match, penalties were assigned when discrepancies in the income and 

demographic variables occurred between the PSID constructed panel file and the tax 

panel. Transfer income is generally underreported in household surveys like the PSID. 

To help compensate for such shortfalls, we gave a bonus to potential matches reporting 

transfer income. Further, for certain types of transfers, we assumed that families 

generally continue to receive that type of transfer year after year. Under this assumption, 

we penalized potential matches to PSID records with certain types of transfer income if 

that type of transfer income was not reported in the prior year. 

After the match was completed, we adjusted the means-tested transfer amounts on the 

panel model to hit targets based on the administrative data. We also eliminated meanstested benefits for filing units with adjusted gross income in excess of $50,000 (1987 

dollars). 

We then compared the totals for workers compensation on the panel model to the 

administrative data targets. The administrative data targets included both public (federal 

employee, state programs, and black lung) and private forms of workers compensation. 

However, we observed that the amounts of workers compensation on the panel model fall 

greatly short of the administrative data targets. Speculating that some workers 

compensation was reported in the PSID as “other transfers,” we designated a portion of 

this miscellaneous account as workers compensation for returns without such benefits. 

Even after this imputation step, the amounts of workers compensation on the panel model 

fell short of the targets. We next determined the probability of receiving workers 

compensation based on family and income characteristics. Using these probabilities, we 

then imputed workers compensation to other returns without such benefits. 

After matching the aggregate targets for the non-taxable transfers, we then compared 

average benefits on the Panel Model with estimated average benefits based on 

administrative data. If the averages on the panel model exceeded the administrative 

averages by more than ten percent, we reallocated transfer income. First, we imputed 

transfer income to certain low-income returns without any transfer income. Then, to 

ensure that we still met the aggregate targets, we reduced proportionately all the imputed 

transfer amounts. 

II. Transfer Targets and Extrapolation 

As noted above, we compared the amounts of transfer income on the panel model to 

administrative data targets. Through 2002, the targets for federal and state transfers were 

derived from the National Income Products and Accounts (NIPA) Table 3.12 

(“Government Social Benefits”). Data on workers compensation paid by private funds 

came from NIPA Table 6.11C (“Employer Contributions for Employee Pension and 

Insurance Funds by Industry and by Type”). An adjustment was made to the NIPA target 
for social security benefits to take out the portion of benefits received by the 

institutionalized population (who are not included in the panel model). 

Beginning in 2003, the targets for federally-funded transfers are based on OMB 

projections for the most recent President’s budget. OMB provides these data on a fiscal 

year basis, which we adjust to a calendar year basis. For non-federally-funded transfers, 

the most recent NIPA data are adjusted for projected changes in the consumer price index 

(as estimated in the most recent President’s budget). 

In some instances, we also compared the average transfer amount received by filing units 

on the panel model to administrative data. Our sources for the number of recipients 

included the Social Security Bulletin Annual Statistical Supplement or the Statistical 

Abstract of the United States. In some instances, information on the number of recipients 

was available only for specific points-in-time (typical month), and it was necessary to 

estimate the total number of recipients throughout the year. 
APPENDIX D.1 

Extrapolation: Notes 

by 

James Cilke 
Notes 

I. Overview 

Aging or extrapolating the panel model is a multi-staged process. Fundamentally, we 

will extrapolate the first year of the panel (1987 records) to the first year of our budget 

cycle (currently 2004). We will accomplish this by first extrapolating 1987 records to 

1996 levels using a complicated semi-constrained statistical match. The relative growth 

from 1987 to 1996 will be applied to all records in the file. Thus, as a result of the match, 

1995 records will be aged to 2004 levels. The, next step will be to repeat the process 

with a statistical match between 1987 records and 1995-extrapolated-to-2004 records. 

The basic strategy under the first semi-constrained match is to link a non-dependent 1987 

return (filer and non-filer) with a non-dependent 1996 “Cross-section” return. The dollar 

values on the 87 return will be adjusted by a series of growth factors so that total income 

on the 87 return equals the matched 96 return. Weights on 87 returns will be adjusted so 

that the weighted number of 1987 returns within a matching cell equals the weighted 

number of 1996 returns in the same cell. 

Initially, the same dollar-adjustment factors applied to a panel member’s 1987 return will 

be applied to all future years in the panel. Thus, the relationship between dollar items 

across years for a panel member will remain unchanged. For example, if wages on the 

panel member’s 1988 return is twice the amount on his 1987 return, then the extrapolated 

1997 return will, initially, have twice the wages as the extrapolated 1996 return. 

However, the extrapolated file may not hit known or predicted targets for certain sources 

of income or types of itemized deductions. So, as a final step, income and deduction 

items will be uniformly adjusted to hit target amounts. 

II. Income Sources for Defining the Income Type Cell 

The first step was to collapse the various sources of income into separate categories. 

These categories are used to classify returns by their type of income. 

1.	 Wage income: (wages plus unemployment compensation less IRA contributions 

and the foreign earned income exclusion). 

2.	 Investment income: (interest, dividends, capital gains in AGI, and supplemental 

gains in AGI). 

3.	 Retirement income: (taxable pensions, taxable IRA distributions, and Social 

Security benefits). 

4.	 Business income: (positive amounts of income included in AGI from sole 

proprietors, partners, SBCs, and rents). 

5.	 Business losses: (amounts of losses which reduce AGI from sole proprietors, 

partners, SBCs, rents, and NOLS) 

6.	 Other income: (all other positive amounts of income included in AGI). 

D.1 - 1
 
7.	 Other losses: (all other negative income amounts that reduce AGI and all nonbusiness related adjustments to AGI (e.g., alimony paid, penalty on early 

withdrawal of savings, and the foreign housing exclusion.) 

8.	 Non-taxable transfers income: (The sum of non-taxable transfers imputed to the 

panel model.) 

9.	 Other non-taxable incomes: (Tax exempt interest and non-taxable pensions to the 

extent that the non-taxable portion is less than the taxable portion.) 

Passive losses allowed from the phase-in rule occurred in 1987 but not 1996. Thus, to 

make the income measures comparable, I disallow any passive losses allowed under the 

phase-in rule. This step either raises business income in 4 or lowers business losses in 5. 

For filers, the total target income (or matching income) is the sum of the first 7 income 

types. For a return, this sum can be described as AGI plus non-taxable Social Security 

benefits plus those business adjustments that otherwise reduce AGI. For non-filers, the 

total target income is the sum of all 9 income categories 

III. Defining the Super Matching Cells 

The next step is to place the 87 and 96 returns into “super” matching cells. The super 

matching cells are defined as follows. 

A) Marital status (ISTAT): 

1 = Singles and MFS returns 

2 = Joints 

3 = Head-of-house and Widower returns 

B) Age of primary class (IAGECL): 

1 = Under 40 years 

2 = 40 to 59 years 

3 = 60 years and up 

C) Gender (for ISTAT=1 or ISTAT=3) (ISEX): 

1 = Male 

2 = Female 

D) Two-earner indicator (for ISTAT=2) (ITWO): 

1 = 0 or 1-earner couple 

2 = 2-earner couple 

E) Number of dependents) (IDEP_NO): 

1 = zero 

2 = One 

3 = Two or more. 

F) Filer status (IFILST): 

D.1 - 2
 
1 = Non-filer or filer with zero AGI and zero Social Security benefits. 

2 = Filer 

G) Income type (INC_TYPE): 

1 = Wage income is over 90% of total targeted income and total losses from all 

sources is less than $3001. 

2 = Capital gains is over $3000 and capital gains is over 25% of total targeted 

income, or, investment income (dividends, interest, and capital gains) is over 

50% of total targeted income. 

3 = Positive business income is over 50% of total targeted income. 

4 = Retirement income is over 50% of total targeted income. 

5 = Total targeted income is zero or less (regardless of the above assignments). 

6 = Everybody else. 

IV. Collapsing Thin Cells 

Categorizing returns with these cell definitions created a number of “thin” cells. In other 

cases, the number of sample observations within a cell was sufficient. However, there 

was a wide disparity between the weighted number of returns in the 87-cell compared 

with the similarly-defined 96-cell. So, the next step was to eliminate certain cell 

boundaries. I applied the following rules to collapse the cells. For non-filers 

(IFILST=0), I eliminated the two-earner distinction, the number of dependents 

distinction, and the age class distinction. In addition, for joint non-filers, I eliminated the 

income type distinction. Further, for head-of-household non-filers, I eliminated the 

gender distinction. 

For filing singles, I eliminated the two-earner distinction and the number of dependents 

distinction. For filing joints, I eliminated the gender distinction. I also eliminated the 

two-earner distinction for joints where the dominate source of income was investment 

income or retirement income (INC_TYPE=2 or INC_TYPE=4). For elderly joints, I 

eliminated the number of dependents distinction. 

For head-of-household and widower returns, I eliminated the number of children 

distinction. Further, if the income type was the catch-all category (INC_TYPE=6), then I 

eliminated the age distinction and the gender distinction. Finally, if the head-of-house or 

widower was elderly, I eliminated the number of dependents distinction. 

In the end, the matching procedure formed 121 super matching cells. 

V. Determining Income Growth Rates within Super-Cells 

At this point, each 1987 record is placed into one (and only one) 87-level super-cell, and 

each 1996 record is placed into one 96-level super-cell. Within each super-cell, and for 

each file, I calculated the average level of each income source. The average only 

includes those records with that particular income source. That is, I calculated average 

incomes for: 

D.1 - 3
 
 

 

01) Wages, 

02) Unemployment compensation, 

03) Interest, 

04) Dividends, 

05) Capital gains, 

06) Income from Sole Proprietors and farms, 

07) Income from partnerships, SBCs, and rents plus royalties, 

08) Losses from partnerships, SBCs, and rents plus royalties, 

09) Income from taxable pensions and taxable IRA distributions, 

10) Social Security benefits,
 

11) Other incomes, 

12) Other losses, 

13) Total targeted income, 

14) Total targeted income less capital gains, 

15) Total non-taxable transfers income
 

16) AFDC Transfers income
 

17) Supplemental Security income
 

18) Veteran’s cash benefits 

19) Workers compensation benefits, 

20) Food Stamps 

21) Other Welfare payments 

22) Other transfer income
 

Now then, for each income source and within each super-cell, dividing the 1996 average 

by the 1987 average yields a growth rate factor. These growth factors will be used later 

on to grow a 1987 record to 1996 levels.1
 

VI. Defining Income Sub-Matching Cells – on 1996 Records 

At this point, I have the 1996 records within a super-cell sorted by total income. I also 

know the weighted number of records within each super-cell. The next step is divide the 

super matching cells into roughly equally-sized sub-matching cells. By equally-sized, I 

mean that the weighted number of returns within each sub-cell of a given super-cell will 

be approximately the same. The sub-cell boundaries are to be determined by age and 

total income quantiles. Our goal is to put at least 20 unweighted observations in each 

sub-cell. However, we would accept cells with 10 or more observations. 

The first step was to determine the sub-cell breaks based on income. Observations within 

each super-cell were sorted, highest to lowest, by total income. Next, for each super-cell, 

I compared the number of observations within similarly-defined super-cells in the 87 and 

96 files; the minimum of these two values would be used to determine the initial number 

of income breaks. The following table shows the initial number of income classes the 

super-cell would be divided into: 

1

 The adjustment factors are capped at 4.0 and 0.25. 

D.1 - 4
 
------------- ------------- 

 

 

 

 

 

 

 

 

 

 

Min number of Initial 

observations number of 

87 vs. 96 Income classes 

2000 and up 10 

1620 - 1999 09 

1280 - 1619 08 

980 - 1279 07 

720 - 979 06 

500 - 719 05 

320 - 499 04 

180 - 319 03 

80 - 179 02 

Under 80 01 

Say that, based on the above step, I will divide a given super-cell into 5 sub-cells. I 

would take the top 1/5th (20%) of the weighted number of records within each 1996 

super-cell and assign them a code indicating they are in the top sub-cell within the supercell. Finally, I would note and save the income value of the last record placed in the cell. 

As a general rule, the higher a return’s income, the lower the weight on the return is 

likely to be. So, given the above procedure and example, it is likely that more the 1/5th of 

the sample number of records were placed in the highest income sub-cell. So, at this 

point, I re-determine the number of additional income-derived sub-cells. 

Here, 

87adj = Ni

87 * (1. - (ai96 / Ni96 )), and 

96adj = Ni

96 – a

i

96 

where Ni

87 and Ni96 are the original sample number of records in the with 1987 and 1996 

super-cells, and ai

96 is the sample number of these records going into the top income subcell. I then use the following table to determine the number of additional income subcells (up to a maximum of 9) I will create. The row is determined by the minimum of 

87adj and 96adj. 

D.1 - 5
 
------------- ------------- 

 

 

 

 

 

 

 

 

 

 

 

Min number of Additional 

observations number of 

87adj vs. 96adj Income classes 

1740 and up 09 

1360 - 1739 08 

1020 – 1359 07 

760 - 1019 06 

540 - 759 05 

360 - 539 04 

220 - 359 03 

100 - 219 02 

Under 100 01 

Continuing with my example, say I have enough remaining observations in the super-cell 

to create 4 additional sub-cells. I have already sorted observations by income and put 

those observations with the highest income in the first income sub-cell. So, the second 

income sub-cell is filled with the next 25% of the remaining weighted observations. The 

third income sub-cell is filled with the next 25%, and so on until all observations within 

any given super-cell are placed in an income-based sub-cell. Again, I note the level of 

total income received by the last return in the sub-cell. 

VII. Defining Income Sub-Matching Cells – on 1987 Records 

At this point, every 1996 record is a member of one and only one income-defined subcell. In the next step, I put 1987 records into equally-defined sub-cells. First, I 

“extrapolate” each 1987 record by the series of growth factors described above. That is, 

the wages on each 1987 return within a super-cell are adjusted by the average per-capita 

change in wages in that super-cell between 1987 and 1996. Similarly, unemployment 

benefits are adjusted by the unemployment growth factor, interest income is adjusted by 

the interest income growth factor, and so on.2

 The individual income components are 

then summed to create an extrapolated total income. Finally, each 1987 observation is 

placed into one income-defined sub-cell according to the same income breaks used to 

define the sub-cells on 1996 records. 

VIII. Defining Age Sub-Matching Cells 

In the next step, I divide each of the income-defined sub-cells into finer sub-cells that are 

defined by age breaks. Starting with the 96 file, I sort the observations by age within 

each income-define sub-cell. (I added a random univariate number between 0 and 1 to 

everybody’s age to assist in rank-ordering observations with equal ages.) Next, I 

compared the number of observations within similarly-defined sub-cells in the 87 and 96 

2

 In this step, incomes from transfer payments except Social Security and Unemployment Compensation 

are adjusted by the growth factor for total transfers income. I will use the growth factors for specific 

transfer sources later on. Similarly, at this stage, I do not use the “Total Income except Gains” growth 

factor. 

D.1 - 6
 
 

 

 

 

 

 

 

 

 

 

 

 

files; the minimum of these two values would be used to determine the number of age 

breaks. The following table shows the number of age classes the sub-cell would be 

divided into: 

Min number of 

observations 

in a sub-cell Number of 

87 vs. 96 Age classes 

------------- -------------

200 and up 10 

180 - 199 09 

160 - 179 08 

140 - 159 07 

120 - 139 06 

100 - 119 05 

80 - 99 04 

60 - 79 03 

20 - 59 02 

Under 20 01 

For example, say a given income-defined sub-cell can be divided into 4 age-defined subcells. I would take the youngest 1/4th of the records within that sub-cell and assign them 

a code indicating they are in the first age-defined sub-cell. Additionally, I would note 

and save the age value of the last record placed in the cell. I would then use these same 

age breaks to assign 87 records into age-defined sub-cells. 

IX. Collapsing Thin Cells 

The just described puts each 1987 and 1996 tax record into a unique matching sub-cell. 

Unfortunately, the procedure allowed for the possibility that the number of observations 

within the sub-cell is too thin (e.g., under 10 observations). Recall that the income breaks 

and age breaks used to define the sub-cells on the 87 file came from breaks which were 

designed to evenly allocate records on the 96 file. 

So, I began by finding those matching sub-cells on the 87 file that had less than 10 

observations. If so, I merged the cell with an adjoining cell with higher income. For 

example, if I divided a super-cell into 5 income-defined sub-cells and the third sub-cell, 

on the 87 file, had less than 10 observations, I simply joined the third cell with the second 

cell. To keep the cell definitions consistent, I merged the same two cells on the 96 file. 

Next I repeated this same procedure to catch the rare cases where a combined sub-cell 

still had less than 10 observations. Next, I repeated this same procedure except that I 

joined those top income sub-cells containing less than 10 observations with an adjoining 

sub-cell with less income. Finally, in the case where a super-cell was divided into two 

age-defined sub-cells and one of the sub-cells had less than 10 observations, I merged the 

two sub-cells into a single matching cell. As a final step, I sort-ordered the records in 

each cell by total income (highest to lowest). 

D.1 - 7
 
X. Reweighting Each 87 Record 

To summarize, each 1987 record and each 1996 record are placed into a uniquely-defined 

matching cell. Each matching cell is defined as a super-cell that is further divided into 

sub-cells or matching cells based on the income and age of each record. For each 

matching cell for 1996 records, there is a corresponding (identically identified) matching 

cell for 1987 records. The next step is to uniformly adjust the return weights of each 

1987 record such that the sum of the weights in the matching cell equals the sum of the 

weights in the corresponding 1996 matching cell. Thus, after all 87 weights are adjusted, 

the weighted sum of all 1987 records will equal the weighted sum of all 1996 records. 

XI. Reallocate 1987 Returns into Sub-Cells All Over Again 

Recall from above that each super-cell is divided into a number of sub-cells based on the 

sample number of returns within the super-cell. So for example, if I decided to divide a 

super-cell into 5 income sub-cells, I would place approximately 1/5th of the weighted 

number of returns within the super-cell into each sub-cell. In the case of 1987 returns, I 

used the original 1987 weights. 

We then decided that it would be more appropriate if we filled sub-cells of 1987 records 

using each 1987 record’s adjusted weight. So, I repeated the process of filling 1987 

records into sub-cells. That is, I repeated the statistical matching process beginning after 

the point where I determined each sub-cell’s income break points. The end result is that 

some 1987 records were placed in a different matching sub-cell. (We could have 

repeated the process a third time. However, we assumed that greatest benefit from this 

iterative process would come in the first step.) 

XII. The Match 

At this point, I was ready to do the actual statistical match. Working, within each 

matching cell, I linked the top record with the 1987 cell to the top record in the 1996 cell. 

The weight on this matched record is the minimum of the 1996 record’s weight or the 

1987 record’s adjusted weight. If the 1987 record had the greater weight, I would then 

match the same 1987 record a second time to the next 1996 record. This time, however 

the record weight of the 1987 record would be reduced by that minimum weight from the 

previous match. Again, the weight of this matched record would be the minimum of the 

two record weights. I would continue matching the 1987 record until his record weight 

was completely exhausted. Similarly, a 1996 record would be matched multiple times if 

it had the larger weight in its first match. Recall that, by design, the sum of the weights 

of the 1987 records in the matching cell exactly equaled the sum of the weights of the 

1996 records. At the end of this step, every 1987 record is linked to one or more 1996 

records. Interestingly, the only information from the 1996 record that it kept is the 1996 

record’s total income amount. 

XIII. Blowing Up Dollar Values From 1987 to 1996 Levels 

D.1 - 8
 
 

 

 

 

At this point in the process I am ready to begin adjusting the exogenous dollar values on 

the now-split 1987 returns. I start by hitting each dollar value by one of the super-cell 

growth factors (described above). In effect, for income items, I repeat the process used in 

the matching process (described above) of growing 1987 total income to 1996 levels.3 

However, I apply a growth factor to all exogenous dollar items including credits and 

itemized deductions. Non-income items are adjusted by either the Total Income growth 

factor, or the Total-Income-Except-Gains growth factor. 

After adjusting all dollar amounts, I then recalculate total income. I then compare this 

amount to the 1996 total income amount derived from the statistical match. For each 

micro-record, I calculate the ratio of the statistically matched 1996 value and the 87-now96 total income value. This ratio becomes a second dollar adjustment factor and is 

applied to all exogenous dollar values.4 

Next, I hit each income item with an across-the-board factor such that the weighted sum 

of that income hits the weighted sum found on original 96 returns. For example, after 

adjusting wages on each return by the super-cell growth factor and further adjusting 

wages by the micro-level statistical match factor, I discovered that total wages on 87­

now-96 returns was slightly (1.9%) short of the original amount of total wages on 

original 96 returns. So, I bumped up wages on every return by 1.9% such that wages on 

the 87-now-96 returns equals wages on the original 96 returns. I performed a similar 

procedure for the remaining income items with targets. The following table enumerates 

the specific income targets 

Wages (in AGI) 

Tax exempt interest 

Taxable interest 

Dividends 

Taxable Pensions and IRA distributions 

Capital Gains in AGI 

Schedule C Income 

Schedule C losses 

Schedule E Partnership income 

Schedule E Partnership losses 

Schedule E SBC income 

Schedule E SBC losses 

Schedule E Rental income 

3

 This time, however, each transfer income item is grown by its own super-cell growth factor. Further, for 

returns with total positive income, loss items are not allowed to grow beyond their initial levels. Similarly, 

for returns with net losses, the growth factors for positive income items are capped at 1.0. 

4

 If the 87-now-96 record contains and IRA adjustment, both the numerator (target-96 value) and 

denominator (extrapolated 96 value) are reduced by the IRA adjustment. Further, if capital gains on the 87­

now-96 record is exactly at the loss limit (i.e., -3000 or –1500 for MFS) then gains are removed from the 

calculated ratio. The ratio of the target 1996 value over the 87-now-96 value is capped at 10.0 and 0.10. 

Finally, I set the ratio to 1.00 in the rare cases where the target 96 value has a different sign than the 

extrapolated 96 value. 

D.1 - 9
 
 

Schedule E Rental losses 

Schedule F Farm income 

Schedule F Farm losses 

Social Security (and Railroad Retirement) benefits 

Unemployment compensation 

All other income included in AGI 

All other losses included in AGI 

AFDC/TANF benefits 

Other welfare benefits 

Veterans cash benefits 

Supplemental Security income 

Workman’s compensation (cash) benefits 

Food stamps 

As part of this last step, I calculate, for each record, the change in total income and the 

change in total income without gains. For example, say the cumulative effect on a return 

of adjusting each income item is to raise total income (and total income without gains) by 

2%. I would then raise certain non-income items (e.g., itemized deductions) by 2%. 

Most non-income items are hit by the income-without-gains factor. Non-income items 

hit by the total income factor include charitable contributions, investment interest 

expenses, and state and local income taxes. 

Not too surprisingly, total values for itemized deductions on 87-now-96 returns drifted 

from those values on the original 1996 returns. To correct, I applied a set of across-theboard adjustment factors to selected itemized deduction amounts. In particular, I hit 

targets for charitable contributions, home mortgage interest expenses, investment interest 

expenses, state and local income taxes, state and local property taxes, medical expenses, 

and miscellaneous expenses. (About the only exogenous non-income values that were 

not specifically targeted relate to personal and business credits.) 

To summarize this step, each exogenous dollar value on each non-dependent 1987 return 

is blown up by a series of adjustment factors. The first factor is the average growth of 

some item within a super matching cell. The second factor is a micro-level factor applied 

to all exogenous values and is designed to adjust total income on the 1987 return to hit 

the total income amount that was on the matching 1996 return. The third factor is applied 

to income items such that the weighted sum of each specific income item hits the known 

1996 totals for that item. For non-income items, the third factor is equal to the combined 

affect on total income or total income without gains. The fourth factor, applied to 

itemized deductions only, adjusts deductions such that the weighted sum of the deduction 

for itemizers hits the known 1996 amounts. 

XIV. Extrapolating Beyond 1996 

Once the set of adjustment factors for aging a 1987 non-dependent return to 1996 levels 

are established, aging any subsequent return falls easily into place. Each person’s 

subsequent returns, from 1988 to 1996, are first adjusted by the same set of adjustment 

D.1 - 10
 
 

factors used on the 1987 return. So, 1998 returns are aged to 1997 levels, 1989 returns to 

1998 levels, and so on. Then, to control for any drift, each specific income item is hit 

with and single adjustment factor such that the weighted sum of that income item hits the 

known or target amount for that item. Note that the targets for 1998, 1999, and 2000 

levels are obtained from tabulations from the Individual SOI files for those years. For 

years 2001 and beyond, the targets, in general, are the same as used to extrapolate OTA’s 

Individual Tax Model. In both cases, the targets are for tax filers only. So, before 

applying the income-specific factors, I first must assign a filer/non-filer status variable to 

each return. 

Initially, any return that was actually filed and processed by SOI is treated as a “filing“ 

return in an extrapolated year. In addition, any return with income included in AGI that 

is above the filing threshold or any return with a non-zero tax liability is treated as a filer. 

The filing threshold for any return is simply the sum of the personal exemptions for 

primary taxpayer (and spouse if joint) plus the standard deduction, including any 

additional amounts for being over 64 years of age and/or being blind. I then compare this 

count of filers with the target number of returns. If I need additional returns, I first begin 

by converting non-filers with the highest AGIs into filers. If possible, I convert just 

enough non-filers so that the total number of filers hits the target number. If, after 

exhausting the pool of non-filers with positive AGI, I am still short of filers, I will then 

randomly convert non-filers with zero AGI into filers. Conversely, if I initially have too 

many filers, I will convert those filers without liability and with income below the filing 

threshold into non-filers. I start by randomly selecting returns without earned income to 

become a non-filer. 

With the filer/non-filer flag in hand, I can then compare income totals on extrapolated 

panel returns with targeted amounts. Note that once I determine an adjustment factor for 

a given income source, I will apply that factor to all returns, both filers and non-filers. 

This may cause some non-filers to switch to filers. So, the process of determining an 

income adjustment factor is an iterative one. 

D.1 - 11
 
APPENDIX D.2 

Extrapolation: Algebraic Description 

by 

James R. Nunns 
 

 

 

Algebraic Description 

This paper provides a step-by-step algebraic description of the first extrapolation of the 

panel from the historic 1987-1996 period to the 1996-2005 period. The second 

extrapolation of the panel, to the 2004-2013 period (the “04 budget period”), is nearly 

identical to the first extrapolation so is not described separately. However, the 

descriptions of steps in the first extrapolation include notes explaining any differences in 

the second extrapolation. The paper includes references to the spreadsheets used to 

develop the target levels or growth rates used in the extrapolations. 

This paper does not describe the formation of super-cells, the formation of income and 

age quantiles within super-cells, reweighting records, or the micro-level record match 

(which results in record splitting). These are all described in detail in Jim Cilke’s “Panel 

Extrapolation Notes.” 

The paper is divided into three sections. Section I describes the extrapolation of 1997 

returns1

 to 1996 levels. Section II describes the remainder of the first extrapolation, of 

1988-1996 returns to 1997-2005 levels. Section III provides documentation for the 

targets used in the extrapolations. 

I. Extrapolation of 1987 Returns to 1996 Levels 

It is assumed that all non-dependent returns in the 1987 and 1996 files have been placed 

in corresponding super-cells (with certain super-cells collapsed2) and placed in 

corresponding income and age quantiles within super-cells (with certain quantiles 

collapsed3), the weights of all 1987 returns in each super-cell have been adjusted 

proportionately so that the weighted number of returns matched the weighted number of 

returns in the corresponding 1996 super-cell, and the micro-level record match (with 

record splitting) to have already taken place. Note that the record match involves 

adjustments to the income on 1987 returns that is nearly identical to Steps 1-3 described 

below. 

Steps 1 and 2 adjust the average income from each income source on 1987 returns to the 

average for 1996 returns in the same super-cell. 

1

 Throughout, the term “return” is used to describe the basic units used in the extrapolations, which for 

filers are income tax returns and for non-filers are the unit that would have been used for filing an income 

tax return. Unless otherwise noted, only non-dependent return units are used in computing the factors used 

in the extrapolation. 

2

 Super-cells were collapsed if they contained too few observations (unweighted returns) in the cell, or if 

the weighted number of observations in the 1987 and corresponding 1996 cell were too disparate. See 

“Panel Extrapolation Notes” for a description of the specific super-cells that were collapsed. 

3

 Income and age quantiles were collapsed into surrounding quantiles if there were too few (generally, 

defined as less than 10) observations in the 1987 super-cell. See “Panel Extrapolation Notes.” 

D.2 - 1
 
 

 

 

 

 

In these and subsequent steps, upper-case variables (such as Yj for income from source 

j ) denote weighted amounts for a group (or all) returns, whereas lower-case variables 

(such as yij for income from source j on return i ) denote unweighted return-level 

amounts or scalars (such as a S

j which is defined in Step 1). 

Step 1
 

Compute for each income item j in each super-cell S the ratio a S

j where: 

S S 96 S 96 S87 S87	 St a j = (Yj / N j )/(Yj / N j ) and Yj is aggregate of income from

 source j in super-cell S in year 

t 

N St is the number of returns with j

income from source j in supercell S in year t 

Note that the a S

j ratios are capped at 4.0 and 0.25. 

Note also that somewhat different groupings of income sources are used in defining the 

a S

j ratios than are used in defining the c j ratios in Step 7, below. (See Jim Cilke’s 

“Panel Extrapolation Notes.”) 

Step 2 

Apply a S

j to income item j on 1987 return i (in super-cell S ). 

87 E1 S 87 yij = a j yij where yij is income from source j for 

return i 

and 87E1 denotes the first stage of the 

extrapolation of 1987 to 1996 

Note that a S

j applies to any return filed by a dependent on return i . 

Steps 3-5 adjust the total income on each 1987 return to the total income on the matched 

1996 return. 

D.2 - 2
 
 

 

 

 

Step 3 

87E1 87E1 Compute a new total income for each 1987 return i : yi = ∑yij 

j 

Note that for filers total income omits all non-taxable sources of income except nontaxable social security benefits. 

Note also that if the sign on total income on a return was switched by the application of 

the a S

j factors, the a Sj factors were set to 1.0 for all loss items on that return (if the return 

initially had positive total income) or for all positive items (if the return initially had 

negative total income) and total income was recomputed.1 

Step 4 

Compute for each 1987 return i the ratio bi where: 

96 87E1 96 bi = yi / yi where yi is the total income on the 

1996 return matched to 1987 

return i 

Note that IRA contributions at the contribution limit and capital losses at the loss limit 

96 87E1 were not included in yi or yi for this computation. Further, bi was capped at 10.0 

96 87 E1 and 0.1 and set to 1.0 if the signs of yi and yi were different. 

Step 5 

Apply bi to all income items of unit i as adjusted in Step 2 to give the second stage of 

the 1987 extrapolation to 1996: 

87 E 2 87 E1

yij = bi yij
 

Note that bi applies to any return filed by a dependent on return i . 

Steps 6-8 adjust aggregate income from each source on all returns from Step 5 to actuals 

for 1996. (For the years 2001 and later of the first extrapolation, described in Section II, 

and for the second extrapolation, forecasts would be used in Step 7.) 

Step 6 

1

 It is unclear from “Panel Extrapolation Notes” what, if anything, was done if the sign on total income 

remained switched. 

D.2 - - 3 ­
 

 

 

 

Compute for all returns (including dependents’ returns) the sum of each income item j 

as adjusted in Step 5: 

87 E 2 87E 2 87 E 2 Yj = ∑Yij where Yij is the weighted amount of 

i 

87 E 2

yij 

Note that for taxable income items, the sum is computed only for filers, since the actuals 

(or forecast targets) are only for filers (see Step 7). 

Step 7 

Compute the ratio c j for each income item j where: 

Yj 

87T 87E 2 Yj 87T

c j = /Yj where is the actual (or forecast target) 

for Yj in 1996 (2001 and later) 

Note that for taxable income items the ratio is computed only for filers, since the actuals 

(or forecast targets) are only for filers. 

Step 8 

Apply c j to income item j, as adjusted in Step 5, on all returns (filer, including 

dependent, and non-filer) to give the third stage of the extrapolation of 1987 returns to 

1996 levels. This is the final stage of the first extrapolation for income items. 

87 E3 87 E 2 S 87 yij = c j yij = c j bi a j yij 

Steps 9-11 apply the income adjustment factors from the preceding steps to non-income 

items that are “exogenous” (i.e., are not determined from other items on a return). Nonincome items include some adjustments to income, itemized deductions, certain credit 

amounts (e.g., child care expenses for the CDCTC, the amount of the general business 

credit), AMT timing preferences, and non-taxable IRA and pension distributions. These 

items are quite similar to the non-income items that are grown by Stage 1 per capita 

growth factors in the extrapolation of the ITM, but Jim Cilke’s code needs to be 

consulted to determine exactly which items are included. 

Step 9 

Compute the total income, and total income without capital gains, as adjusted through 

Step 8 on each 1987 return i : 

D.2 - - 4 ­
 

 

 

 

 

87 E3 87 E3

yi = ∑ yij 

j 

and 

87E3 87E3 87 E3 ( yi - yig ) where yig is capital gains on return i 

Step 10 

Compute the ratio of total income, with and without capital gains, from Step 9 to the 

original amounts for 1987 on each 1987 return i : 

W 87E3 87 di = yi / yi 

and 

WO 87E3 87E3 87 87 di = ( yi - yig )/( yi - yig ) 

Step 11 

Apply di

W to charitable contributions, investment interest expense, and state and local 

income taxes (items denoted with the subscript k in the following steps) on all returns i 

in 1987 (that have itemized deductions); 

Apply di

WO to all other exogenous non-income items (those denoted with the subscript l 

in the following steps) on all returns i in 1987. 

Application of these factors brings exogenous non-income items up to the third stage of 

the extrapolation of 1987 returns to 1996 levels. 

87 E3 W 87 t xik = di xik where xik is the amount of non-income 

item k on return i in year 

(actual or extrapolated) t 

and 

87 E3 WO 87 t xil = di xil where xil is the amount of non-income 

item l on return i in year 

D.2 - - 5 ­
 

 

(actual or extrapolated) t 

Steps 12-14 adjust the aggregate amounts of certain (targeted) itemized deductions1 on all 

returns from Step 11 for differential rates of growth of these deductions relative to the 

forecast growth rates for total income or total income without capital gains. For 1996­

2000, actual levels of the factors used for itemized deductions and of total income with 

and without capital gains were used in computing relative growth rates. For 2001 and 

later, forecasts were used. The method of computing rates of growth is described in 

Section III of the paper. 

Step 12 

Compute the per capita growth rates of total income and total income without capital 

gains and the per capita growth rates for the targeted itemized deductions. Note that 

since only filers have itemized deductions, total income is as defined for filers. 

rY = (Y t / Nt )/(Y t −1 / Nt −1 ) 

Y t t Y t −1 t −1 Nt−1rY −G = (( -Yg )/ Nt )/(( -Yg )/ ) 

rk = the per capita growth rate computed from Budget forecast variables for targeted 

 itemized deduction k (see Section III of the paper) 

rl = the per capita growth rate computed from Budget forecast variables for targeted 

 itemized deduction l (see Section III of the paper). 

Step 13 

Compute the ratio ek or el for each targeted itemized deduction k or l : 

ek = rk / rY 

el = rl / rY −G 

Step 14 

Apply ek ( el ) to itemized deductions k ( l ), as adjusted in Step 13 of all returns i in 

1987. This is the fourth, and final, stage of the extrapolation for these itemized 

deductions. 

1

 The specific itemized deductions adjusted in Steps 12-14 are: charitable contributions, investment interest 

expense, and state and local income taxes (items denoted with the subscript k ) and home mortgage interest 

expense, state and local property taxes, and medical expenses (items denoted with the subscript l ). 

D.2 - 6
 
 

 

87 E3

= ek xik xik 

87E3

= el xil xil 

With the re-computation of endogenous variables, those computed from other (now 

extrapolated) variables on the return, the extrapolation of 1987 returns to 1996 levels is 

complete. 

II. Extrapolation of 1988-1996 Returns to 1997-2005 Levels 

Step 1 

Apply the combined factor c j bi a S

j to each income item j on all returns filed by any 

person represented on return i in 1987 in any year t (= 1988,...,1996). (These persons 

are members of the “cohort” portion of the panel.) 

For returns filed by persons represented on “refreshment” returns (i.e., persons who first 

appear in the panel on a return filed2 after 1987 by a non-dependent who was either a 

dependent in 1987 or immigrated to the United States after 1987), the super-cell S was 

identified in the first year the person filed a return. There is no factor bi for refreshment 

returns, so the combined factor reduces to c j a S

j . 

tE1 S t yij = c j bi a j yij for returns filed by cohort members of the panel 

tE1 S t yij = c j a j yij for returns filed by refreshment members of the panel 

Steps 2-10 

These Steps are analogous to Steps 6-14 of Section I, applied year-by-year. All of the 

formulas are the same, with the addition of a superscript t for the year. Step 10 

completes the first extrapolation of the panel, to 1996-2005. 

The second extrapolation, to the 2004-2013 period (the “04 budget period”), begins with 

a match of the 1987 records to the 2004 records from the first extrapolation. This 

requires the formation of super-cells and income and age quantiles within super-cells in 

the 2004 records,3

 reweighting 1987 records, and a micro-level record match of 1987 to 

2

 No person represented in the refreshment segment of the panel entered the panel as a non-filer, but could 

become a non-filer in a subsequent year. 

3

 The super-cell and income and age quantiles for 1987 records were formed in the first extrapolation. 

However, some different collapsing of super-cells or income or age quantiles might be required for 

consistency with the collapsing in the 2004 records. 

D.2 - 7
 
2004 records. The second extrapolation then proceeds in steps that are nearly identical to 

the steps for the first extrapolation, so they are not described separately in this paper. 

III. Source of Targets Uses in the Extrapolation 

Targets for all variables were taken directly from, or derived from, the Administration’s 

FY 04 Budget economic assumptions. Targets for income items reported on tax returns 

are determined by the Revenue Estimating Staff (RED) using historical relationships 

between tax return items and related income items included in the economic assumptions 

for the Budget. These targets are used in the second stage of the extrapolation of the 

ITM, so are referred to as “Stage 2” targets. These targets were obtained from RED and 

used directly in the panel model extrapolation, except for the transfer payments targets 

(social security and unemployment). Targets for all transfer payments, including social 

security and unemployment, covering all (filer and non-filer) recipients, were obtained 

from OMB. The following two tables show each targeted income item. 

Items Reported on Tax Returns (Excluding Transfer Payments) 

Income Item 

Line on 

1040 Variable Name 

Wages 7 Wages and Salaries 

Taxable interest 8a Interest 

Tax-exempt interest 8b 

Dividends 9 Div in AGI 

Business (Schedule C) 

income 

12 

Sole Proprietor Income 

Business (Schedule C) loss 12 

Capital gain in AGI 13 Pos CG, total 

Taxable IRA and pension 

distributions 

15b + 16b Pension in AGI 

Partnership (Schedule E) 

income 

17 (part) Partnership Net + (post-TRA) 

Partnership (Schedule E) 

loss 

17 (part) Partnership Net – (post-TRA) 

S Corp (Schedule E) income 17 (part) Small Business + (post-TRA) 

S Corp (Schedule E) loss 17 (part) Small Business – (post-TRA) 

Rental (Schedule E) income 17 (part) Rent Net + (post-TRA) 

Rental (Schedule E) loss 17 (part) Rent Net – (post-TRA) 

Farm (Schedule F) income 18 (part) Farm Net + 

Farm (Schedule F) loss 18 (part) Farm Net - 

All other items of gross 

income (except social 

security and unemployment) 

10+11+ 

13 (loss) 

+14+ 

17 (part) 

+21 

<No explicit Stage 2 target. 

MAGI is targeted, but it 

excludes some items of gross 

income and includes other 

items.> 

D.2 - 8
 
 

 

Transfer Payments 

Transfer Payment 

Social Security & Railroad Retirement 

Unemp Comp [Unemployment Compensation] 

Workers Comp [Workers’ Compensation Cash Benefits] 

Veterans Pen/Dis [Veterans’ Cash Pension and Disability Benefits] 

SSI [Supplemental Security Income] 

AFDC/TANF 

Other Cash Assistance 

Food Stamps 

Several steps, and two spreadsheets, were used to compute the adjustment factors for 

certain itemized deductions, ek and el (see Steps 11 and 12 of Section I). Most of the r 

factors (Step 11 of Section I) are “Stage I” per capita growth factors from the ITM 

extrapolation, or computed directly from these factors. For the 1996 and 1997, the Stage 

I per capita growth factors were taken from the 01MS (mid-session) extrapolation of the 

v15 ITM, and re-indexed to 1996.4 The 04WB (winter budget) Stage I factors from RED 

were used for the 1998-2013 period, and re-indexed to1996 for the first extrapolation and 

to 2004 for the second. 

The remaining r factors require series for personal income, state and local income taxes, 

state and local property taxes, and consumer interest expense. For 1996 and 1997, these 

factors were computed from actuals from the NIPA and indexed to 1996. For the 1998­

2013 period, these factors were computed from the Administration’s “Black Box” 

forecasts for the 04WB and indexed to1996 for the first extrapolation and to 2004 for the 

second. 

The following table shows for each targeted itemized deduction’s e factor the per capita 

growth factors used in the numerators (the rk and rl factors) and the specific source for 

the factor. 

Factors for Targeted Itemized Deductions 

Targeted Numerator ( r factor) 

Itemized 

Deduction Stage I Factor Black Box Factor 

Investment 

Interest -- consumer interest expense 

Charitable FCHAR --

4

 The v15 ITM is based on the 1995 SOI, so Stage I growth factors for it are indexed to 1995 (i.e., 1995 = 

1.0 for all factors). 

D.2 - 9 
Contributions 

S&L Income 

Taxes -- S&L personal tax receipts 

Mortgage 

Interest --

50% personal 

income, 50% 

consumer 

interest expense 

Medical FMED --

S&L Property 

Taxes -- S&L property tax receipts 

The denominators for the e factors, rY and rY −G , were calculated from the Stage 2 

targets for MAGI, social security benefits, and capital gains. 

D.2 - 10
 
 

APPENDIX D.3 

Extrapolation: Shortcut Update 

by 

James R. Nunns 
 

 

 

Short-Cut Update of the Panel Extrapolation to the WB 07 Budget Forecast 

This paper provides a step-by-step algebraic description of the short-cut method for 

updating the current extrapolation of the panel, which is to the 04 budget period (2004­

2013), to the WB forecast for the 07 budget period (2007-2016). This paper is a sequel 

to, Appendix D.2, “Algebraic Description of Panel Extrapolation,” which should be read 

before this paper.1

 The update method described here is a short-cut because it simply 

applies adjustment factors to income and other items in the current extrapolation without 

creation of super-cells or any reweighting of returns. 

The next section of the paper describes the short-cut method of updating the results from 

the current extrapolation for 2004-2013 to 2007-2016. Section II describes the separate 

income items (and groupings of items) that are to be used in the extrapolation and the 

extrapolation factors for detailed components of income and for non-income items. 

I. Update the Results from the Current Extrapolation for 2004-2013 to 2007-2016 

Levels 

This update generally follows the last nine of the ten steps in Part II of the current 

extrapolation (see Appendix D.2, “Algebraic Description of Panel Extrapolation”). 

Return-level (unweighted) variables are denoted with lower-case letters, y and x , and 

weighted and aggregate variables by upper case letters, Y and X . Superscripts generally 

give the Budget forecast (04 or 07) first, followed by the year, t , where t = 

2004,...,2013 in Steps 1-3, and 2007,...,2016 in Steps 4-9. Note that for the 04 Budget 

extrapolation, each year is lagged three years from the 07 Budget extrapolation, although 

they are the same year for each return; for example, a (original 1987) return that 

represents 2004 in the current (04 Budget) extrapolation (i.e., superscript 0404) will 

represent a 2007 return in the updated (WB 07 Budget) extrapolation (i.e., superscript 

0707). 

Step 1 

Compute for all returns (including dependents’ and “refreshment” returns) the sum of 

each income item j : 

Yj 

04t t t = ∑Yij 

04 where Yij 

04 is the weighted amount of 

i 

04t yij 

1

 In Appendix D.2, “Algebraic Description of Panel Extrapolation,” the extrapolation to the 2004-2013 

period is referred to as the “second” extrapolation, to distinguish it from the beginning extrapolation (to the 

1996-2005 period), but is referred to in this paper simply as the “current” extrapolation. 

D.3 - 1
 
 

 

 

Note that for income items included in gross income (except social security and UI 

benefits), the sum is computed only for filers (including dependent filers) since the 

targets for these items are only for filers. 

Step 2 

Compute the ratio ct

j for each income item j where: 

t 07T (t +3) Yj 04t 07T (t +3) c j = Yj / where Yj is the WB 07 Budget target for 

 income source j in year 

t + 3 

Note that for income items included in gross income (except social security and UI 

benefits), the ratio is computed only for filers (including dependent filers) since the 

targets for these items are only for filers. 

Step 3 

Apply ct

j to income item j on all returns (filer, including dependent, and non-filer) to 

give the updated (WB 07 Budget) extrapolation for income items. 

07(t+3) t 04t yij = c j yij 

Steps 4 and 5 use the income as adjusted in the preceding steps to develop adjustment 

factors, and Step 6 applies these factors or the ct

j ratios to components of 1040 income 

items (e.g., the detailed components from Schedules C, D, E, F) and to exogenous nonincome items. 

Step 4 

Compute the total income, and total income without capital gains, from Step 3 on each 

return i in each year t : 

07t 07t yi = ∑ yij
 

j
 

and 

07t t 07t ( yi - yig 

07 ) where yig is capital gains on return i 

Step 5 

D.3 - 2
 
 

 

 

 

 

 

 

 

 

 

 

Compute for each year t the ratio of total income, with and without capital gains, from 

Step 4 to the corresponding amounts from the current (04 Budget) extrapolation for each 

return i : 

tW 07t / 04(t−3) di = yi yi 

and 

tWO 07t t 04(t−3) 04(t−3) di = ( yi - yig 

07 )/( yi - yig ) 

Step 6 

Apply di

tW to state and local income taxes, investment interest expense, and charitable 

contributions (items denoted with the subscript k in the following steps) on all returns i 

in each year t (that have itemized deductions); 

Apply di

tWO to medical expenses, state and local property and other (non-income) taxes, 

home mortgage interest expense, casualty and theft losses, and miscellaneous itemized 

deductions (those denoted with the subscript l in the following steps) on all returns i in 

each year t (that have itemized deductions). 

tW tWO	 t Apply di , di , or the appropriate c j ratio to the components of income and the 

remaining exogenous non-income items (those denoted with the subscript m ) on all 

returns i in each year t . See Section II for a description of which factor applies to each 

of these items. 

Application of these factors updates the extrapolation of components of income and 

exogenous non-income items (except itemized deductions; see Steps 7-9) to the WB 07 

Budget forecast. 

07t tW 04(t −3)	 *t xik = di xik where xik	 is the amount of itemized 

deduction k on return i in year 

t 

07t tWO 04(t −3)	 *t xil = di xil where xil	 is the amount of itemized 

deduction l on return i in year 

t 

and 

07t t 04(t−3)	 *t x = where is the amount of income im c j xim	 xim 

or component or non-income 

tW 04(t−3) di xim item m on return i in year t

 or 

D.3 - 3
 
 

tWO 04(t−3) di xim
 

Steps 7-9 adjust the amounts of itemized deductions on all returns from Step 6 for 

differential growth rates forecasted for these deductions relative to total income or total 

income without capital gains. Note that Steps 7 and 8 are computed in the spreadsheet: 

Panel Model_Extrapolation to WB FY07 budget period.xls. Also, there is no differential 

growth rate for casualty and theft and miscellaneous itemized deductions, so the ratios for 

them (Step 8) are 1.0 in all years (i.e., the ratios need not be applied). 

Step 7 

Compute from the WB 07 Budget forecast the per capita growth rates of total income and 

total income without capital gains and the per capita growth rates for itemized 

deductions. 

t Y t / Y t −1 Nt−1

rY = ( Nt )/( / ) 

t t Nt Y t −1 t −1

rY −G = ((Y t -Yg )/ )/(( -Yg )/ Nt−1 ) 

rk

t = the per capita growth rate computed from Budget forecast variables for 

 itemized deduction k (see Section II of the paper) 

rl

t = the per capita growth rate computed from Budget forecast variables for

 itemized deduction l (see Section II of the paper) 

Step 8 

Compute the ratio ek

t or elt for each targeted itemized deduction k or l : 

t t t ek = rk / rY 

t t t el = rl / rY −G 

Step 9 

Apply ek

t ( elt ) to itemized deductions k ( l ), as adjusted in Step 6 of all returns i in each 

year t . This completes the update of the extrapolation for these itemized deductions. 

07t t 07t = ek xik xik 

07t t 07t xil = el xil
 

D.3 - 4
 
II. Income Forecasts and Extrapolation Factors 

07T (t +3) t Table 1 shows sources for the Yj variables in Step 2, above, to compute the c j 

factors applied to the corresponding income items in Step 3, and the ek

t and elt factors for 

Step 9 (Steps 7 and 8 are performed in a spreadsheet). 

t tW tWO Table 2 indicates which c j factor or whether di or di applies to each component of 

income or each exogenous non-income item in Step 6. 

D.3 - 5
 
Table 1. Sources of Targets for Income Items 

Line on 

(2001) 1040 

(Schedule 

E) 

SOI 

Variable 

(TY2001) 

ITM (v17) 

Variable 

ITM WB 07 Budget 

Extrapolation Variable 

V

Stage 2 Target Stage 1 Factor 

Income Items with Targets for Filers Only 

Wages 7 E00200 was 

Wages and 

Salaries FWAGE 

Taxable interest 8a E00300 intst Interest FINTER Tax-exempt interest 8b E00400 texint 

Dividends 9 E00600 dbe Div in AGI FDIV S&L income tax refunds 10 E00700 sitr 

FSLTAX 

Alimony received 11 E00800 FWAGE 

Business (Schedule C) income or loss 12 E00900 bil 

Sole 

Proprietor 

Income FNFBUSI 

Capital gain in AGI 13 (pos.) E01000 + E01100 <calculator variables> Pos CG, total FLCAPGL Capital loss in AGI 13 (neg.) 

Other gains or losses 14 E01200 suppgl FLCAPGL 

Taxable IRA distributions 15b E01400 tirad 

Taxable IRA 

Distributions FTIRAD 

Taxable pension distributions 16b E01700 ptpen 

Pension in 

AGI FPENS 

Rental income 

17 (part of 

26) (pos.) E27310 – E25800 – 

E27200 

rent Rent Net + FRENTG 

Rental loss 

17 (part of 

26) (neg.) Rent Net - FRENTL 

Royalty income or loss 

17 (part of 

26) E25800 royal FRENTG 

D.3 - 6
 
Line on 

(2001) 1040 

(Schedule 

E) 

SOI 

Variable 

(TY2001) 

ITM (v17) 

Variable 

ITM WB 07 Budget 

Extrapolation Variable 

V

Stage 2 Target Stage 1 Factor 

Partnership income 

17 (part of 

32) (pos.) 

-E25920 

+E25940 +E25980 

-E25960 -E26110 

part 

Partnership 

Net + FPRTNRG 

Partnership loss 

17 (part of 

32) (neg.) 

Partnership 

Net - FPRTNRL 

S Corp. income 

17 (part of 

32) (pos.) 

-E26100 -E26160 

+E26170 

-E26180 

+E26190 

sbc 

Small 

Business + FSCORPG 

S Corp. loss 

17 (part of 

32) (neg.) 

Small 

Business - FSCORPL 

Estate and trust income or loss 17 (37) E26500 estu FGNP Farm rental income or loss 17 (40) E27200 frnt FFBUSI 

Farm income 18 (pos.) E02100 fil Farm Net + FFBUSI Farm loss 18 (neg.) Farm Net - 

Other 

income 

NOLs 

21 

E02540 nols 

Gambling E02800 gamble FGNP 

Other E02600 othinc Sec. 911 income exclusion E02700 forexc 

FWAGE < 

calculator 

caps> 

Income Items with Targets for All Units (Filers and Non-Filers) 

Social security & RRB benefits 

ssinc 

Unemployment compensation ucagix Workers compensation 

D.3 - 7
 
Line on 

(2001) 1040 

(Schedule 

E) 

SOI 

Variable 

(TY2001) 

ITM (v17) 

Variable 

ITM WB 07 Budget 

Extrapolation Variable 

V

Stage 2 Target Stage 1 Factor 

Veterans cash pension & disability benefits SSI AFDC/TANF State general assistance State energy assistance Other State cash assist. Food stamps 

1 The panel model variable is the same as the ITM variable unless otherwise noted. N/A indicates the variable is not on the panel mode

D.3 - 8
 
Table 2. Sources of Targets for Income Detail Items and Non-Income Items 

Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

Additional 1040 Income Items 

Total IRA distributions 15a E01300 girad FTIRAD Total pension distributions 16a E01800 ptpenex FPENS ptpe

<computed> fellow FGNP 

<computed> resid <computed 

post-extrap> 

<computed> agimod 

W-2 Items 

o401k_p 

FWAGE 

o401k_s 

hiexcl_p N/A 

hiexcl_s N/A depcare_p FCPI N/A depcare_s N/A 

Schedule B (Interest and Ordinary Dividends) Detail 

Savings bond interest 3 E21100 savbond FINTER 

Schedule C (Sole Proprietor) Detail 

<2001 levels 

computed 

pre-extrap.> 

aschcg 

FNBUSI 

aschcl pschcg pschcl aschc 

pschc 

 <computed> alt_pschc 

31 (all Cs) E90440 sch_c N/A 

D.3 - 9
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

31 (1st C) E91440 schc_1 N/A 

31 (2nd C) E92440 schc_2 N/A 

31 (3rd C) E93440 schc_3 N/A 

Schedule D (Capital Gains) Detail 

<All short-term items> 

E21610 

smiscg 

FSCAPGL 

E21615 

smiscl 

E21700 

spartg 

E21750 

spartl 

6 E21800 p69stc 

E21850 

stg 

E21900 

stl 

E22260 

sndedl 

5 E21775 nspartgl 

 <legacy> cystg  <legacy> cystl 

N/A sresc

<All long-term items> 

E22310 

lmiscg 

FLCAPGL 

E22315 

lmiscl 

E22350 

lpartg 

E22360 

lpartl 

13(f) E22370 lcgd 14(f) E22390 p69ltc 

E22400 

ltg 

E22500 

ltl 

E23300 

lndedl 

17 E23660 undyco 

D.3 - 10
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

 <legacy> cyltg  <legacy> cyltl  <computed> pcapl 

2 E21600 nssocagl N/A 

4 E21620 nsmiscgl 

11(g) E22325 lmisc28 N/A 12(g) E22367 lpart28 N/A 13(g) E22375 lcgd28 N/A 14(g) E22395 p69ltc28 N/A 

9(f) E22300 nlsocagl 

11(f) E22320 nlmiscgl 12(f) E22365 nlpartgl 

E22305 

lsoca28 N/A 

32 E24590 gain10 N/A 36 E24600 gain20 N/A 

E24610 

gain25 N/A 

E24550 

gain28 N/A 

N/A lresc

N/A linstN/A llikekN/A llikeN/A l479

Collectibles at 28% 15(g) E22550 cg28pct <no growth> N/A 

Schedule E Detail – Rents 

<2001 levels 

computed 

rentg FRENTG 

(if rent +) 

rentl 

D.3 - 11
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

pre-extrap.> rent FRENTL 

 3 (total) E25350 trent (if rent -)

23 

E25830 edisrent 

Schedule E Detail – Royalties 

FRENTG 

22 

E25800 

royal 

(= troyal + rentdepr) 

(if royal +) 

FRENTL 

(if royal -) 

Schedule E Detail – Partnership 

<All items> 

<2001 levels 

computed 

pre-extrap.> 

apart 

FPRTNRG 

(if part +) 

FPRTNRL 

(if part -) 

ppart 

part 

 <computed> alt_ppart 

27(k) E25980 apartg 

27(i) -E25960 apartl 27(j) -E26110 

27(h) E25940 ppartg 27(g) -E25920 ppartl 

Schedule E Detail – S Corporations

<All items> 

<2001 levels 

computed 

pre-extrap.> 

asbc 

FSCORPG 

(if sbc +) 

FSCORPL 

(if sbc -) 

psbc 

sbc 

27(k) E26190 asbcg 

27(i) -E26180 asbcl 27(j) -E26100 

27(h) E26170 psbcg 27(g) -E26160 psbcl 

Schedule E Detail – Estate and Trust 

D.3 - 12
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

<All items> 

<2001 levels 

computed 

pre-extrap.> 

aestu 

FGNP 

psetu 

estu 

33a(f) E26380 aestug 

33b(e) -E26360 aestul 33a(d) E26340 psetug 33b(c) -E26320 pestul 

Schedule E Detail – Farm Rental Income 

<All items> 

<2001 levels 

computed 

pre-extrap.>

 pfrntg 

FFBUSI 

pfrntgl 

39 E27200 frnt 

Schedule E Detail – Total 

<computed> sch_e 

<computed 

post-extrap> sch

Form 8582 (Passive Losses) Detail 

<Rental items +> 1a E65300 z1a FRENTG 

1d E65600 z1d 

<Rental items -> 1b E65400 z1b FRENTL 

<Partnership items +> 2a E66000 z2a FPRTNRG 

2d E66300 z2d 

<Partnership items -> 2b E66100 z2b FPRTNRL 

 <computed> agiuks FGNP E68000 

allow01 

E90640 

plossnd 

FCPI 

E68500 

ndirap 

E68505 

ndiras 

D.3 - 13
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

Schedule F (Farm Proprietor) Detail 

<All items> 

<2001 levels 

computed 

pre-extrap.> 

 afarmg

FFBUSI 

 afarml pfarmg pfarml afarm 

pfarm

 <computed> alt_pfarm 

36 (all Fs)) E95640 farm N/A 

36 (1st F) E96640 farm_1 N/A 36 (2nd F) E97640 farm_2 N/A 

E95660 

pfarmnd N/A 

Form 2555 (Foreign Earned Income Exclusion) Detail 

Form 1040 -- Adjustments to Income (“Above the Line Deductions”) 

IRA contributions 

23 E03150 adjira 

<no growth> adjira

adjira

zadjira N/A 

Student loan interest 24 E03210 ed_ded FCPI N/A 

Moving expenses 26 E03280 exmove FWAGE 

Archer MSA 25 E03600 

FMED 

N/A 

Self-employed health 28 E03270 healse 

Keogh contributions 29 E03300 keogh FG401 Alimony paid 31a E03500 FCPI alimp

ebe_

D.3 - 14
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

Interest penalty 30 E03400 pen_

secad

SECA deduction (50%) 27 E03260 (compuOther adjustments E03900 

FGNP 

Foreign housing exclusion 

32 (margin 

entry) E04000 forhe 

<computed 

<computed> adjust post-extrap> 

Schedule A (Itemized Deductions) 

Medical and dental 1 E17500 ttmed FMED 

9 E18300 ttsltx 5 E18400 slitx 

State and local taxes FSLTAX 6 E18500 retx 

7 E18800 pptx 8 E18900 otsltx 

Home mortgage interest 10 + 11 E19300 hmie FHOMEI 

12 E19530 points 

Investment interest 

13 (part) E19575 oth_iie FINTEXP 

cie 

cie_d

Charitable contributions 

18 E19700 ttcon FCHARG 

15 E19800 tcash 

Casualty and theft 19 <computed> ttcasu FCPI 

D.3 - 15
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

Miscellaneous 

deductions, 2% floor 23 E20400 

t2misc 

[pre-limitation] 

FCPI 

dedm

[compu

allowa

ebe 

Gambling losses 27 (part) E20900 fdgamb FCPI 

Other miscellaneous 27 (part) E21000 fdmisc FCPI 

fdoth

Form 4952 (Investment Interest Expense Deduction) 

3 E58950 gr_tiie FINTEXP 

 <computed> tiie_oth 

5 E59100 yinv_exp FGNP 

 <computed> yinv_uks E58980 

tcga_inv FLCAPGL 

<legacy> 

supp_inv 

 <legacy> nstgl_inv SLCAPGL E58990 

cg_elct <no growth> 

 <computed> pct_elct 

Special Tax Amounts 

Tax from Form 8615 40 E74160 txkidx 

<computed 

post-extrap> 

Tax from Form 8814 or 4972 40 E05700 spcta_ FTAXGR spcta

Form 8615 (Kiddie tax) 

2 E72900 dedkid FGNP 

 <computed> tih_nocg N/A 

D.3 - 16
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

 <computed> oui_nocg N/A 

E73650 

<SOI no 

longer 

collects> inlcgh FLCAPGL 

 <computed> inlcgo N/A 

 <computed> unuse_cg N/A 

<computed> dmfsh <no growth> 

 <computed> unk_kidi FDIV 

 <computed> stxkid FSLTAX 

 E74160 txwoui 

<computed 

post-extrap> 

 E82100 amtcr_cf FTAXGR 

6 E73200 tikidh <computed 

post-extrap> 

7 E73300 ouikid 

Form 8814 (Parents’ Elective Kiddie Tax) 

1a (1st 8814) E83060 

FINTER 

1a (2 

nd 8814) E83460 

1a (3rd 8814) E83860 

E83140 

FDIV 

E83540 E83940 

6 (4+ 8814) E84210 pe_agi4 

E83185 

tcgkid1 

FLCAPGL 

N/A 

E83585 

tcgkid2 N/A 

E83985 

tcgkid3 N/A 

3 (total) E84185 pe_lcgd N/A 

D.3 - 17
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

 E83160 dikid1 

<computed 

post-extrap> 

E83560 

dikid2 

E83960 

dikid3 

 <computed> dikidt E84180 

pe_inagi_ 

9 (4+ 8814) E84220 pe_tax4 

Form 6251 (AMT) 

17 E60100 amtnol 

FGNP 

E62000 

amtnol_alt 

25 E62900 amtftc 

 <computed> ucamti 

<legacy> entpreft <set to zero> 

 <computed> prefaj 

FPREF 

 <computed> timepref N/A 

 <computed> adjwui 

 <computed> pref_impt  <computed> amtuexm 

<computed> 

d_line27_diff 

FLCAPGL 

N/A 

cg25pct_diff 

N/A 

capgains_diff N/A 

Form 1116 (Foreign Tax Credit) 

<computed> tftccr FGNP 

Form 2441 (CDCTC) 

1d (total) E32700 ccecyr FCPI 

18 E32840 exclcc 

6 E33300 cccpyr 

10 E33420 emplycc 

D.3 - 18
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

11 E33450 cclost 13 E33460 empcce 19 E33480 taxcce 

 <computed> cce_nq 

15 E32880 ccei_p FWAGE 

16 E32890 cdei_s 

F2441 ccqi <no growth> 

Schedule R (Elderly Credit) 

E28100 

tdiinc 

FCPI 

11 E28200 eldtxi 

13c E28400 eldnti 

Form 8863 (Hope and Lifetime Learning Credits) 

<imputed> 

tuition_hope1 

FCPI 

N/A 

[Tuiti

expen

imput

post-ex

from 

differ

variabl

tuition_hope2 

tuition_life 

 <computed> tuition_hope 

<computed 

post-extrap> 

Form 8839 (Adoption Credit) 

T86100 

adopt_exp_1 FCPI N/A 

T86110 

adopt_exp_2 

From 3800 (General Business Credit) 

1a E53220 cyitc FBUSCR 

1b E53240 jobs 1d E53260 fuel 

1e E53280 raecr 

D.3 - 19
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

1f E53300 lihcr 

1g E53305 enoil 1h E53310 diacr 

1i E53315 elecpr 1j E53316 indemp N/A 

1k E53317 sstips N/A 

1m E53312 ? [New markets] N/A 

1n E53319 ccdccr 

1p E53313 cycelpcr 

3 E53340 pttcr 5 E53380 pdiscr 6 E53400 gbcyo 

1c E53250 to_workcr N/A 

1o E53314 tr_alaska N/A 1l E53318 orphan_dr N/A 

18c E53487 susp_re N/A 

esop

Form 8396 (Mortgage Interest Credit) 

3 E64000 cytmicr 

FGNP 

6 E64060 tmc_cyo1 5 E64040 tmc_cyo2 4 E64020 tmc_cyo3 

Form 8844 (Empowerment Zone Credit) 

10 

E75000 tezecr FGNP N/A 

Other Credits 

E07500 ezecr_ 

FBUSCR 

E07900 

fuels_nc 

E08000 othcr_ 

D.3 - 20
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

E07950 dc_homecr_ FGNP N/A 

<computed> unlimcr 

Schedule SE (Self Employment (SECA) Tax) 

1 (total) E29000 sefarm 

FFBUSI 

1 (p) E29020 sefarmp 

1 (s) E29050 sefarms 

15 (total) E31150 optfarm 

15 (p) E31170 optfarmp 

15 (s) E31200 optfarms 

2 (total) E29070 Senonf FNFBUSI 

or 1.0 

<factor 

computed> 

2 (p) E29100 senonfp 

2 (s) E29120 senonfs 

17 (total) E31220 optnonf 

17 (p) E31250 optnonfp 

17 (s) E31300 optnonfs 

5a (total) E30100 chwas 

FWAGE 

5a (p) E30200 chwasp 

5a (s) E30300 chwass 

8c (p) E29400 wasp_oas 

8c (s) E29450 wass_oas 

 <computed> seernp <computed 

post-extrap> 

<computed> seerns <computed> seern 

Other Taxes 

56 E10000 adveic FWAGE 

57 E10050 schh_tx N/A 

Payments 

D.3 - 21
 
Item 

Line on 

2001 

Schedule or 

Form 

SOI 

Variable 

(TY 2001) ITM (v17) Variable 

ITM WB 07 

Budget 

Stage 1 

Factor 

Pan

Mod

Variab

59 E10700 with FWAGE 

60 E10900 esttax FTAXGR 

EITC 

61b 

E59520 utearn FWAGE 

<computed> ueicei FCPI 

E59680 eicoff <no growth> N/A E59720 eicref N/A 

Social Security Taxes 

E30700 seta_p 

<computed 

post-extrap> 

[Al

calcula

but diff

E30800 seta_s E30600 seta_ 

<computed> secat_ 

E82910­

E11200 ss_employee name

1 The panel model variable is the same as the ITM variable unless otherwise noted. 

N/A indicates the variable is not on the model. 

D.3 - 22
 
 

APPENDIX D.4 

Extrapolation: Estate Tax Data 

by 

Deena Ackerman 
Estate Tax Data 

This appendix describes the annual extrapolation of the estate tax model to the current budget 

period. Every year we target the official Winter Budget forecast of estate and gift tax liabilities 

(net of refunds) when computing estate tax liability under current law. Targets for Pre-EGTRRA 

and TRA86 liabilities are computed relative to the Winter Budget forecast. 

I. Estate and Gift Tax Liability Estimates 

Every year, the Revenue Estimating Division of the Office of Tax Analysis forecasts estate and 

gift tax receipts for that year’s budget window. To construct these forecasts, the revenue 

estimator uses the most recent SOI estate tax file (currently 2005) augmented with SOI estate tax 

data from 2001. The recent data is augmented with older data because the relatively high 

exemption level under current law means that the most recent data includes no information on 

smaller estates. (In 2005 the exclusion level was $1.5 million. In contrast, in 2001 the 

exemption was only $675 thousand.) Information on the smaller estates is necessary for 

forecasting liabilities in the later years of the budget period, (and in the panel, for estimating 

liabilities under pre-EGTRRA and TRA86 law). Although these estates contribute only a small 

share of total estate tax revenue, there are a relatively large number of such estates. 

II. Creating a Set of Targets 

The panel staff is also supplied with a set of unadjusted estate tax liabilities under current law 

(EGTRRA), and under TRA86 and TRA87 law (Pre-EGTRRA). The unadjusted liability 

estimates used in this model differ slightly from the official Revenue Estimating liability 

estimates because the official estimates involve additional off-model adjustments to account for 

discrepancies in when the estate taxes are paid. Therefore, we create a set of factors (one for 

each year in the budget window) that adjust the first round estimates for the off-model 

adjustments. 

The unadjusted estate tax liability estimates for TRA86 and for TRA87 are multiplied by these 

factors to create the relevant adjusted estate tax liability estimates. Gift taxes estimates are 

calculated as a fixed percentage of the adjusted estate tax liabilities under pre-EGTRRA and 

TRA86 law. These shares are 14 and 15 percent respectively. 

D.4 - 1
 
III. Estimating Extrapolated Estate Tax Liabilities On the Panel Model. 

Once all six targets (estate and gift taxes, each under the three laws) for each year have been 

specified, the next step is to estimate extrapolated estate tax liabilities on the panel model. For 

each panel member for whom an estate tax return was filed, estate tax liability is estimated under 

current law, pre-EGTRRA law and TRA86 law in the following way. 

For each panel member who died during the period, the gross value of their estate is inflated 

from the year of death to their extrapolated year in the budget window. Thus, with a budget 

period of 2008-2017, the extrapolated year of death would be 2011 for a panel member who died 

in 1990 (the 4th year of the panel). The gross value of the estate is inflated by a factor that 

captures macroeconomic and demographic growth changes over the relevant period. 

Then, by applying a simplified estate tax model calculator to the gross estate, we compute an 

estimate of the estate tax owed under each of the three tax regimes. 

In each case, estimates are provided as if the panel members “died” in their extrapolated year of 

death, and again when the date of death is extrapolated to the first year in the budget period. By 

extrapolating all deaths to the first year in the budget period, we are able to mitigate some of the 

effects of the relatively small number of deaths in any given year. 

IV. Rescaling the Panel Estimates to Hit the Targets 

As above, estimated liability will differ from the official estimates due to off-model calculations. 

However, estimated liability will also differ from the targets due to differences between the 

characteristics of the SOI sample and the panel sample. Using the six targets specified above 

(the current law liability estimates for estate and gift, along with the scaled estimates under preEGTRRA law and TRA86 law), we create an additional set of factors which force the panel 

estimates to be identical in aggregate to the adjusted SOI estimates for each law in each year. 

Current law gift tax liabilities are computed so that the ratio between targeted estate tax liability 

and targeted gift tax liability is maintained. 

D.4 - 2
 
APPENDIX E 

Equivalence Scales 

by 

Julie-Anne Cronin 
Equivalence Scales 

I. Overview 

This paper describes the equivalence scale that Treasury’s Office of Tax Analysis (OTA) uses in 

its multi-year distributional analyses, and shows how that scale may affect the distribution of 

income and tax burdens. 

In its single-year tables, OTA does not use an equivalence scale to adjust income for family size. 

Families are ranked based only on the family’s income; for example, a family of four with 

$40,000 of income is ranked as having the same ability to pay as a family of one with $40,000. 

Implicitly, this treats families as if they enjoy perfect returns to scale; additional family members 

do not lower the well-being of the family. 

In contrast, the multi-year period of OTA’s new distributional analyses necessitates an explicit 

adjustment for family size. In these multi-year analyses, the income and tax burdens of baseyear families are measured over the ten-year budget period. Because family composition 

changes over the budget period, a base-year family’s income and tax burden cannot be measured 

in each year of the budget period. Instead, the income and tax burdens for each individual who 

existed in the base year are measured in each year. The equivalence scale used for this measure 

determines how a family’s income is translated into the income of each individual in the family 

in each year. If the equivalence scale assumes perfect returns to scale, each individual in a given 

family in a given year would be assigned the family’s income for that year. At the other 

extreme, if the equivalence scale assumes no returns to scale then the family’s income in each 

year would be divided by the number of individuals in the family. OTA’s equivalence scale falls 

between these two extremes. 

II. Assumptions 

The following assumptions hold under both single-year and multi-year analyses: 

•	 Equal welfare of family members: Families are assumed to act as an economic unit, sharing 

both resources and costs. This is implicit in OTA’s single-year income measure, and also 

applies to the multi-year, equivalence scale-adjusted measure. The sources of income do not 

matter in assigning income to family members. For example, if all family income is derived 

from the earning of one member, that member is not assigned a different level of welfare 

than the non-working members. Likewise, tax burdens are assumed to be shared equally 

among family members. 

•	 Income as a proxy for welfare: Income is used as a proxy for welfare. An important 

difference between income and welfare measures is that an income measure omits the value 

of leisure time. If two otherwise identical families (or individuals) have the same wage 

income but have different levels of work effort, they will have the same rank under an 

income measure even though their welfare is obviously not equivalent. 

E - 1
 
 

Another important difference is that welfare measures control for differences in prices faced by 

consumers. If two otherwise identical families have the same income but one lives in a high 

cost-of-living area, the two families would have the same rank under an income measure even 

though the purchasing power of the family living in the high cost of living area is considerably 

lower. Finally, forward-looking welfare measures consider the degree of uncertainty 

surrounding earnings and consumption choices; income measures do not. 

OTA’s choice of income as a proxy for welfare is based on several considerations. Although 

income may not be a good proxy for welfare in some instances, income is measured with a 

relatively high degree of precision whereas the value of leisure, the prices faced by different 

families, and uncertainty of income and consumption streams could only be measured with 

imprecision. Further, income is a more familiar concept than welfare and therefore may better 

convey relative rankings to policy makers. 

OTA’s new equivalence scale-adjusted measure relies on an additional assumption: 

•	 Children are equivalent to adults: The equivalence scale adjusts for family size but makes no 

other adjustment for family composition. Children are assigned the same welfare level as the 

adults in the family. For example, a family composed of one adult and three children will 

have the same adjustment factor as a family of four adults. Some equivalent scales allow for 

differential adjustments for children. Other scales go further and make more refined 

adjustments by age. 

III. Types of Equivalence Scales 

There are two basic types of equivalent scales: scales based on equivalent needs and scales based 

on equivalent welfare. The equivalent needs approach considers the level of income necessary to 

achieve a minimal standard of living for a given family type. The U.S. poverty thresholds are an 

example of an equivalent needs scale. Equivalent welfare scales are based on traditional 

consumer theory and measure the level of income necessary to achieve the same level of welfare 

(“utility”) across family types. In this approach, children increase the welfare of the family but 

also increase costs. 

The equivalence scale OTA uses is based on the U.S. poverty thresholds, a well-known set of 

government statistics. Rather than use the U.S. poverty thresholds which show increasing 

returns to scale for small families and relatively constant returns to scale for large families, OTA 

follows Ruggles (1990) suggestion and uses a “smoothed” version of the poverty scale. Ruggles 

notes that a constant family size elasticity (FSE) scale of .5 appears to approximate the U.S. 

poverty thresholds.1

 Similarly, OTA’s equivalence scale approximates the U.S. poverty 

thresholds but maintains increasing returns to scale across all family sizes by using a constant 

FSE. 

Table 1 shows the U.S. poverty thresholds for 1998 (columns 1 and 2), and compares those 

thresholds to various constant FSE equivalence scales. The alternative scales vary from an FSE 

 Family size elasticity (FSE) is the percent change in income for a percent change in family size. 

E - 2
 

1
=0 (columns 3 and 4) to an FSE=1 (columns 13 and 14), where FSE=0 represents the case of 

perfect returns to scale (no adjustment to income for family size) and FSE=1 represents the case 

of no returns to scale (per capita income equivalence). The FSEs for the scales between 0 and 1 

are calculated by estimating what FSE would maintain the U.S. poverty threshold levels for each 

pair of thresholds given. Once the FSE is determined the remaining thresholds can be calculated. 

For example, maintaining the U.S. poverty threshold levels for a family of size 1 and a family of 

size 2 would require a FSE of .35. The resulting thresholds and equivalence scale would appear 

as it does in columns 5 and 6. In columns 7 and 8, the relationship between families of size 1 

and 3 is maintained and the FSE is .41. In columns 9 and 10, the relationship between families 

of size 2 and 3 is maintained and the FSE is .5. In columns 11 and 12, the relationship between 

families of size 2 and 4 is maintained and the FSE is .65. 

IV. OTA’s Equivalence Scale 

OTA’s equivalence scale is based on an FSE of .41. It will approximate the U.S. poverty 

thresholds for most people. An FSE of .5 is close to the average FSE across all poverty 

thresholds as Ruggles suggests, but the weighted average (weighted by the population) is closer 

to .41. An FSE of .41 by design exactly matches the poverty thresholds for families of size 1 and 

3, and it closely matches the poverty threshold for families of size 2. Ninety percent of all 

families in 2000 are made up of 1, 2, or 3 members. 

A constant FSE of .41 does not approximate the U.S. poverty thresholds for large families. A 

much higher FSE would be necessary to capture the large additional amounts of income allowed 

large families under the poverty thresholds. The divergence between OTA’s equivalence scale 

and the U.S. poverty thresholds is due to OTA’s scale showing smoothly increasing returns to 

scale while the U.S. poverty thresholds show a fairly constant return to scale for large families. 

V. Comparing OTA’s Scale to the Economic Literature on Equivalence Scales 

Table 2 compares OTA’s equivalence scale (constant FSE scale of .41) to other scales found in 

the literature. Some scales in the literature do not consider families of size 1. Instead they 

consider how the addition of a child affects the well-being of a couple. To facilitate comparison 

across scales, all scales have been adjusted so that they have the same 1.28 scale factor for a 

family of size 2. Scales are grouped into need-based and welfare scales, and the need-based 

scales are further subdivided according to the method used in the study. The Engel method 

considers necessity goods such as food to determine its scale. The Rothberg method considers 

the proportion of the budget devoted to adult goods such as alcohol. The surveys depend on 

subjective evaluations of well-being. Lazear and Michael assume a per capita measure for adults 

and then measure a child’s equivalence to an adult. 

OTA’s equivalence scale is quite similar to the Rothberg method scales, survey scales, and all of 

the utility based scales. The Phipps (1998) utility scale is an exact match. 

E - 3
 
VI. Distribution of Income, Tax Burdens, and Families Using a FSE of .41 

Table 3 shows the distribution of family economic income (FEI) and individual income and 

payroll taxes under OTA’s current measure (FSE=0) and under alternative scales with constant 

FSEs. As the FSE increases to 1.0, the distributions for income and tax burdens become more 

equally distributed but not dramatically so. With an FSE of 0, 56.7 percent of FEI and 64.1 

percent of income and payroll taxes accrue to the top quintile. With an FSE of 1.0, 52 percent of 

FEI and 58.4 percent of income and payroll taxes accrue to the top quintile. The difference 

between the income and tax distributions for an FSE of 0 and FSE of .41 are fairly small. 

Tables 4a and 4b show the distribution of families by size across FEI quintiles. Table 4a shows 

the distribution with no adjustment for family size, OTA’s current method. Table 4b shows the 

distribution of families adjusting the families using OTA’s new equivalence scale (constant FSE 

of .41). The tables show that although the income and tax distributions are not significantly 

different overall, there are still some important movements across quintiles. 

Under OTA’s current method, the bottom quintile is disproportionately composed of singles (one 

person families). Singles account for 52.4 percent of all families but 65.1 percent of the bottom 

quintile. Under OTA’s new scale, singles account for 54.3 percent of the bottom quintile. This 

difference in singles reflects the movement of large families lower in the income distribution. 

Under the OTA’s new scale, 36.1 percent of five person families and 19.5 percent of “six-plus” 

person families are in the top quintile in the unadjusted distribution, but only 12.0 percent of five 

person families and 2.6 percent of six-plus person families are in the top quintile in the adjusted 

distribution. 

These movements of families across income quintiles could be qualitatively important for 

distributional analyses of tax proposals that differentially affect large (or small) families. For 

example, a child-related tax benefit would likely appear more equally distributed across quintiles 

using OTA’s new equivalence scale than under OTA’s current measure. 

E - 4
 
Table 1: Poverty Thresholds for 1998 and Possible Equivalency Scales 

Family 

Size 

1998 poverty threshold OTA's current measure Maintain relation 1 & 2 Maintain relation 1 & 3 Maintain relation 2 & 3 Maintain relation 2 

Threshold 

(1) 

Scale 

(2) 

Threshold 

(3) 

Scale 

(4) 

Threshold 

(5) 

Scale 

(6) 

Threshold 

(7) 

Scale 

(8) 

Threshold 

(9) 

Scale 

(10) 

Threshold 

(11) 

S

(

1 8,316 1.00 8,316 1.00 8,316 1.00 8,316 1.00 7,540 1.00 6,788 1

2 10,634 1.28 8,316 1.00 10,634 1.28 11,025 1.33 10,634 1.41 10,634 1

3 13,003 1.56 8,316 1.00 12,279 1.48 13,003 1.56 13,003 1.72 13,828 2

4 16,660 2.00 8,316 1.00 13,598 1.64 14,618 1.76 14,997 1.99 16,660 2

5 19,680 2.37 8,316 1.00 14,718 1.77 16,007 1.92 16,753 2.22 19,251 2

6 22,228 2.67 8,316 1.00 15,702 1.89 17,240 2.07 18,339 2.43 21,664 3

7 25,257 3.04 8,316 1.00 16,584 1.99 18,355 2.21 19,796 2.63 23,938 3

8 28,166 3.39 8,316 1.00 17,388 2.09 19,380 2.33 21,151 2.81 26,101 3

9 33,339 4.01 8,316 1.00 18,130 2.18 20,332 2.44 22,424 2.97 28,170 4

Family size "elasticity" (FSE) 1 0 0.35 0.41 0.50 0.65 

1. FSE is calculated to solve the following: poverty threshold for a family of size x 

= 

poverty threshold for a family of size y 

(x) FSE (y) FSE 

E - 5
 
Table 2: Constant Family Size Elasticty of .41 Compared to Other Scales Found in the Literature1 

Need Based Utility B

Engel Method3 Rothberg Method4 Surveys5 Other6 

Danziger, 

Van der 

OTA's Gaag, 

Equivalenc Deaton, Taussig, Van der 

Family 

Size2 

e Scale 

(Constant 

FSE of .41) 

BLS 

(1982) 

Deaton and 

Muelbauer 

(1986a) 

Canadian 

(1986) 

Deaton and 

Muelbauer 

(1986b) 

Castillo and 

Thomas 

(1989) 

and 

Slomensky 

(1984) 

DeVos and 

Garner 

(1989) 

Lazear and 

Michael 

(1988) 

Muellbauer 

(1977) 

Gagg and 

Slomensky 

(1982) 

John

and Ga

(199

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12

1 1.00 0.91 0.94 0.93 0.64 0.92 1.00 0

2 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1.28 1

3 1.48 1.62 1.80 1.63 1.43 1.56 1.43 1.50 1.54 1.46 1.27 1

4 1.64 2.61 2.27 1.87 1.55 1.59 1.71 1.79 1.64 1.56 1

5 1.77 3.04 2.04 1.69 1.84 2.05 1.66 1

1. Some of the scales in the literature do not consider families of size 1. To facilitate comparison across scales, all the scales have been calibrated so fam1.28 (the level found in column 1). 2. For the studies that only considered married couples (missing scale for family size of 1), size 2 families are composed of 2 adults and additions to family 3. The Engel method uses necessities such as food to determine the basis for equivalent needs. 4. The Rothberg method uses "adult" goods such as alcohol consumption to determine equivalent needs. 5. The surveys attempt to measure indirect utility through revealed preferences. They use subjective evaluations of consumption needs for families of diffe

6. Lazear and Michael (1988) measures the share of a family's budget allotted to children. They assume an additional adult's need is the same as the originthat each additional child will need 40 percent as much as an additional adult (no returns to scale). 

E - 6
 
Table 3: Percentage Distributions of Family Economic Income (FEI) and Individual Income and Payroll 

By Alternative Equivalence Scales
 

Equivalent 

FEI 

Quintile1 

Current (fse=0) 

FEI 

Individual 

Income and 

Payroll Taxes 

fse=.35 (poverty 1&2) 

FEI 

Individual 

Income and 

Payroll Taxes 

fse=.41 (poverty 1&3) 

FEI 

Individual 

Income and 

Payroll Taxes 

fse=.5 (poverty 2&3) 

FEI 

Individual 

Income and 

Payroll Taxes 

fse=.65 (poverty 2&4

FEI 

Individual 

Income and 

Payroll Taxe

1 2.3 0.7 2.4 0.4 2.4 0.4 2.5 0.4 2.6 0.4 2 7.2 3.7 7.4 3.9 7.4 4.0 7.6 4.2 8.0 4.6 3 12.6 10.5 13.0 10.7 13.1 10.8 13.4 11.1 13.9 11.7 4 21.3 21.1 21.5 22.0 21.5 22.1 21.5 22.3 21.3 22.3 5 56.7 64.1 55.8 63.1 55.5 62.7 55.0 62.1 54.2 61.0 

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

80 to 94 27.2 30.2 26.7 29.6 26.6 29.4 26.3 29.1 25.9 28.4 95 to 99 14.7 15.9 14.4 15.7 14.4 15.6 14.2 15.5 14.0 15.2 

top 1 14.8 17.9 14.6 17.8 14.6 17.7 14.5 17.6 14.3 17.4 

1. Equivalent FEI equals (family of size x)/(x)fse, where "fse" is the family size elasticity. For example, for a family of size 4, the divisor would be 1 if fse=0; 1.64 if fse=.

2.45 if fse=.65; and 4 if fse=1. 

E - 7
 
 

Table 4a: Distribution of Family Size by FEI-- e=0 

Quintile 

Number of persons in family 

1 2 3 4 5 6+ Total 

---- Family counts in thousands ---

1 14,999 5,914 1,227 706 53 128 23,027 

2 12,669 6,773 2,122 1,115 163 186 23,028 

3 12,476 6,547 2,049 1,453 266 239 23,030 

4 10,134 6,637 2,667 2,554 724 318 23,034 

5 10,095 6,894 2,276 2,885 681 211 23,042 

Total 60,373 32,765 10,341 8,713 1,887 1,082 115,161 

top 15 7,417 4,980 1,838 2,297 549 193 17,274 

top 4 2,165 1,460 362 486 118 16 4,607 

top 1 513 454 76 102 14 2 1,161 

---- percent distribution size across quintiles ---

1 24.8 18.0 11.9 8.1 2.8 11.8 20.0 

2 21.0 20.7 20.5 12.8 8.6 17.2 20.0 

3 20.7 20.0 19.8 16.7 14.1 22.1 20.0 

4 16.8 20.3 25.8 29.3 38.4 29.4 20.0 

5 16.7 21.0 22.0 33.1 36.1 19.5 20.0 

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

top 15 12.3 15.2 17.8 26.4 29.1 17.8 15.0 

top 4 3.6 4.5 3.5 5.6 6.3 1.5 4.0 

top 1 0.8 1.4 0.7 1.2 0.7 0.2 1.0 

---- percent distribution quintiles across size ---

1 65.1 25.7 5.3 3.1 0.2 0.6 100.0 

2 55.0 29.4 9.2 4.8 0.7 0.8 100.0 

3 54.2 28.4 8.9 6.3 1.2 1.0 100.0 

4 44.0 28.8 11.6 11.1 3.1 1.4 100.0 

5 43.8 29.9 9.9 12.5 3.0 0.9 100.0 

Total 52.4 28.5 9.0 7.6 1.6 0.9 100.0 

top 15 42.9 28.8 10.6 13.3 3.2 1.1 100.0 

top 4 47.0 31.7 7.9 10.5 2.6 0.3 100.0 

top 1 44.2 39.1 6.5 8.8 1.2 0.2 100.0 

E - 8
 
 

 

Table 4b: Distribution of Family Size by FEI-- e=.41 

Quintile 

Number of persons in family 

1 2 3 4 5 6+ Total 

---- Family counts in thousands ---

1 12,510 6,807 2,012 1,261 151 286 23,027 

2 11,261 7,191 2,424 1,554 297 304 23,031 

3 11,665 6,303 2,048 2,078 647 290 23,031 

4 10,964 6,203 2,515 2,608 567 173 23,030 

5 13,973 6,261 1,345 1,212 226 28 23,045 

Total 60,373 32,765 10,344 8,713 1,888 1,081 115,164 

top 15 10,209 4,794 1,104 955 189 25 17,276 

top 4 3,076 1,094 197 206 32 3 4,608 

top 1 688 373 44 51 5 0 1,161 

---- percent distribution size across quintiles ---

1 20.7 20.8 19.5 14.5 8.0 26.4 20.0 

2 18.7 21.9 23.4 17.8 15.7 28.1 20.0 

3 19.3 19.2 19.8 23.8 34.3 26.8 20.0 

4 18.2 18.9 24.3 29.9 30.0 16.0 20.0 

5 23.1 19.1 13.0 13.9 12.0 2.6 20.0 

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 

top 15 16.9 14.6 10.7 11.0 10.0 2.3 15.0 

top 4 5.1 3.3 1.9 2.4 1.7 0.3 4.0 

top 1 1.1 1.1 0.4 0.6 0.3 0.0 1.0 

---- percent distribution quintiles across size ---

1 54.3 29.6 8.7 5.5 0.7 1.2 100.0 

2 48.9 31.2 10.5 6.7 1.3 1.3 100.0 

3 50.7 27.4 8.9 9.0 2.8 1.3 100.0 

4 47.6 26.9 10.9 11.3 2.5 0.8 100.0 

5 60.6 27.2 5.8 5.3 1.0 0.1 100.0 

Total 52.4 28.5 9.0 7.6 1.6 0.9 100.0 

top 15 59.1 27.8 6.4 5.5 1.1 0.1 100.0 

top 4 66.8 23.7 4.3 4.5 0.7 0.1 100.0 

top 1 59.3 32.1 3.8 4.4 0.4 0.0 100.0 

E - 9