Working Paper Series

Congressional Budget Office

Washington, D.C.

The Earned Income Tax Credit and 

Expected Social Security Retirement Benefits 

Among Low-Income Women

Molly Dahl

Congressional Budget Office

(molly.dahl@cbo.gov)

Jonathan Schwabish

Congressional Budget Office

(jonathan.schwabish@cbo.gov)

Thomas DeLeire

University of Wisconsin–Madison

(deleire@wisc.edu)

Timothy Smeeding

University of Wisconsin–Madison

(smeeding@lafollette.wisc.edu)

March 2012

Working Paper 2012-06

To enhance the transparency of CBO’s work and to encourage external review of it, CBO’s working paper 

series includes both papers that provide technical descriptions of official CBO analyses and papers that 

represent original, independent research by CBO analysts. Working papers are not subject to CBO’s regular 

review and editing process. Papers in this series are available at http://go.usa.gov/ULE. This paper is 

preliminary and is circulated to stimulate discussion and critical comment.

The views in this paper are the authors’ and should not be interpreted as those of the Congressional Budget 

Office or the Institute for Research on Poverty. The authors thank Janet Holtzblatt and participants at the 

Institute’s summer workshop for helpful comments. 
The Earned Income Tax Credit and 

Expected Social Security Retirement Benefits 

Among Low-Income Women

Abstract

Expansions in the Earned Income Tax Credit (EITC) are associated with increases in 

formal employment and increases in long-term year-over-year growth in earnings for 

single mothers. In this study, we examine whether expansions in the EITC are likely to 

lead to increases in Social Security retirement benefits for less-educated women (those 

likely to be affected by the EITC) by increasing their employment and earnings when 

young. 

The increases in benefits could occur through two channels: First, as the EITC pulls 

additional less-educated women into market work, those women may accrue more 

quarters of employment and thus be more likely to qualify for Social Security retirement 

benefits. Second, to the extent that the EITC leads to increased earnings growth, lesseducated women may qualify for higher benefits. 

We rely on administrative earnings data from the Social Security Administration and 

existing estimates of the effect of the EITC on employment and earnings growth to 

simulate the impact of an EITC expansion on the future Social Security retirement 

benefits of less-educated women. The results of this simulation suggest that the EITC 

leads to an increase in the share of less-educated women that will be eligible for Social 

Security retirement benefits and leads to an increase in their monthly benefit amount. 

Thus, the existence of the EITC contributes to the financial security of affected women as 

they age and retire.
1

INTRODUCTION

Studies have found that expansions in the Earned Income Tax Credit (EITC) are 

associated with increases in formal employment (e.g., Eissa and Liebman, 1996; Meyer 

and Rosenbaum, 2000, 2001) and increases in long-term year-over-year growth in 

earnings for single mothers (Dahl, DeLeire, and Schwabish, 2009). In this study, we 

examine whether expansions in the EITC are likely to lead to increases in Social Security 

retirement benefits for less-educated women (those likely to be affected by the EITC) by 

increasing their employment and earnings when young. The increases in benefits could 

occur through two channels: First, as the EITC pulls additional less-educated women into

market work, those women may accrue more quarters of employment and thus be more 

likely to qualify for Social Security retirement benefits. Second, to the extent that the 

EITC leads to increased earnings growth, less-educated women may qualify for higher 

benefits.

Our earlier work (Dahl, DeLeire, and Schwabish, 2009; hereafter referred to as

DDS), exploited the differential expansions in the EITC between 1994 and 1997 for 

single mothers with two or more qualifying children compared with single mothers with 

only one qualifying child to estimate the effect of the EITC on employment and earnings 

growth. 

In this paper, we use the estimates from that earlier work to simulate the impact of

the EITC on future Social Security retirement benefits for a sample of less-educated 

women. To do so, we use an administrative data set from the Social Security 

Administration (SSA): the Continuous Work History Sample (CWHS), which includes 

data on earnings from 1978 to 2007 for a 1 percent sample of all issued Social Security 
2

numbers. We find that, if the EITC had increased from 1980 to 1983 in the same way that 

it increased from 1994 to 1997 for single mothers with two or more children compared 

with single mothers with one child, the EITC would have increased the share of women 

who are eligible for Social Security retirement benefits by between 2 to 3 percentage 

points and the monthly benefit amount for eligible women would have increased by 2 

percent to 7 percent. 

Although our results suggest that the EITC has a delayed effect on the financial 

security of women (beyond the immediate effects on employment and earnings growth), 

for two reasons the results are best viewed as indicative of the direction of the effect of 

the EITC on retirement benefits. First, we apply estimates of the effects of the EITC on 

the employment and growth in earnings of single mothers to a population of lesseducated women because we cannot identify single mothers in the administrative data. 

Thus we are likely overstating the effect of the EITC on Social Security retirement 

benefits. Second, Social Security retirement benefits for many women will be completely 

unaffected by changes in their own earnings history because their eligibility and benefit 

levels will be tied to a husband’s or ex-husband’s earnings history. We discuss both of 

these concerns in more detail below.

BACKGROUND

In this section we provide some background information on the EITC and on 

Social Security retirement benefits. 

The Earned Income Tax Credit

The federal EITC is a refundable income tax credit that lowers the tax liability of
3

and provides cash to some lower-income working parents.1 The credit was established in 

1975 and was greatly expanded in the 1990s. 

Today the EITC is a major component of federal efforts to reduce poverty. In 

2011 the total cost of the EITC was $78 billion; in comparison, total costs (benefits and 

administrative costs) for the Supplemental Nutrition Assistance Program (SNAP, 

formerly known as the Food Stamp Program) were $77 billion in fiscal year 2011 (CBO, 

2012).

2

The amount of credit a worker receives is based on the earnings and income of the 

people in the tax unit (in the case of joint filers, that includes income from both spouses), 

the number of children, and marital status. In 2010, the maximum EITC was $457 for 

workers without qualifying children, $3,050 for workers with one qualifying child, 

$5,036 for workers with two qualifying children, and $5,666 for workers with three or 

more qualifying children. The amount of the credit increases as earnings increase until 

the maximum credit is reached. As earnings increase beyond that point, the amount of the 

credit remains constant until a threshold is reached; then the credit decreases. 

Theoretically, the EITC, by increasing the returns to work, unambiguously 

encourages employment (that is, work on the extensive margin) for low-income single 

parents. Numerous empirical studies have found evidence that the EITC does, in fact, 

encourage work among single mothers, especially those with less education. For 

example, using a methodology similar to that used in DDS (2009), Hotz, Mullin, and 

Scholz (2010) employ a difference-in-differences strategy in which the changes in the 

 

1

In 1993, the credit was extended to workers without children. However, the maximum credit available to 

that group is small.

2 Neither SNAP nor the EITC has any direct effect on a family’s official poverty status, as defined by the 

Census Bureau, as neither is counted as income.
4

employment of single mothers with two or more children during the mid-1990s are 

compared with changes in the employment of single mothers with only one child. 

Examining welfare recipients in California, they find that the EITC expansion of the early 

1990s increased the employment of single mothers with two or more children relative to 

those with one child in the late 1990s. 

Social Security

Social Security provides retirement benefits based on average pre-retirement 

earnings. To qualify for Social Security retirement benefits based on one’s own earnings 

history, a worker must earn 40 quarters of coverage over the course of his or her lifetime. 

Beginning in 1978, the amount of earnings needed for a quarter of coverage increases

each year with the average wage index (AWI). In 2007, the amount of earnings required 

for a quarter of coverage was $1,000; thus, a worker who earned at least $4,000 that year

earned four quarters of coverage.3

A person can also qualify for Social Security retirement benefits based on the 

earnings history of a spouse. One can also qualify on the benefits of an ex-spouse if that 

marriage lasted for 10 years or longer. Effectively, a person receives his or her own 

benefit or half of a spouse’s benefit, whichever is higher.

For the purposes of calculating one’s retirement benefit, lifetime earnings are 

represented by the Average Indexed Monthly Earnings (AIME) amount. The AIME is the 

average of the highest 35 years of earnings (divided by 12 to generate a monthly amount), 

indexed to compensate both for inflation and for real growth in earnings across the 

 

3

Prior to 1978, earnings were reported to SSA on a quarterly basis. Beginning in 1978, earnings are 

reported on an annual basis and thus the qualifying amount, though still referred to as a quarter of coverage, 

is actually an annual amount.
5

United States. 

The Primary Insurance Amount (PIA) is the basic benefit that is paid to a retiree if 

he or she claims benefits at the Social Security program’s full retirement age (age 66 for 

people born between 1943 and 1954). In 2007, a retiree would have received 90 percent 

of the first $680 of average monthly earnings, 32 percent of earnings between $680 and 

$4,100, and 15 percent of earnings above $4,100. (See Figure 1.)

People may also be eligible for certain auxiliary Social Security benefits even if 

they do not have a work history sufficient to qualify for retirement benefits: survivor 

benefits may be paid to the surviving widow(er) of a qualified spouse; spousal benefits 

may be paid to the spouse of a living qualified spouse; children may be eligible for 

benefits if a qualifying parent is deceased or disabled; and, in addition, Disability 

Insurance (DI) benefits are paid to qualified disabled individuals. The availability of 

those benefits, while not the focus of this paper, bolsters the argument that the EITC may 

be associated with future economic well-being in addition to the direct effect on 

increased earnings. Any increases in employment and earnings that positively affect a 

worker’s retirement benefits would also have positive effects on their DI benefits (were 

they to become disabled) or the benefits paid to any dependents or survivors in the future.

DATA AND METHODOLOGY

We briefly describe the data and method used in DDS (2009) to estimate the 

effect of the EITC expansion in the early 1990s on the employment and long-term growth 

in earnings of single mothers. We then describe the data and method used in this study to 

determine whether those long-term earnings and employment effects lead to more 
6

quarters of coverage and higher Social Security retirement benefits for less-educated 

women.

Effects of the EITC on Employment and Earnings Growth

In our earlier work, in which we estimated the impact of expansions in the EITC

in the early 1990s on employment and earnings growth, we used the 1993 and 1996 

panels of the Survey of Income and Program Participation (SIPP)—which include data 

from 1993 to 1999—matched with longitudinal earnings records from the Social Security 

Administration’s Detailed Earnings Records (DER). Use of the matched SIPP–SSA data

allowed us to combine detailed demographic information on a large sample of single 

mothers with their earnings from 1984 to 2005.4

The sample for that analysis was unmarried (widowed, divorced, or never 

married) women, age 19 to 44, who were not disabled (according to their survey

response), were not in school, and had at least one child. The sample comprised single 

mothers in January 1993, 1994, and 1995; March 1996 (January 1996 is not available);

January 1997, 1998, and 1999; and November 1999 (January 2000 is not available).

5

Within the 1993 and the 1996 panels, some women appeared more than once. The 

standard errors were adjusted accordingly. Earnings were adjusted for inflation using the 

CPI-U-RS.

Not all women in the SIPP sample were matched to their administrative earnings 

record—the match rate varied from 83 percent to 87 percent between 1993 and 2000. 

 

4 The earnings records used in that analysis included income from self-employment. More detail can be 

found in DDS (2009).

5 March 1996 is the first month in which all four rotation groups of the 1996 SIPP panel are available, and it 

is used to represent the 1996 calendar year. November 1999 is the last month of the 1996 SIPP in which all 

four rotation groups are available, and it is used to represent the 2000 calendar year.
7

Observations that were not matched to administrative earnings records were not included 

in the analysis. Cristia and Schwabish (2009) show that the roughly 80 percent match rate 

in the 1996 panel does not introduce significant bias to the sample. 

The strategy used in DDS (2009) to identify the effects of the EITC on the

employment and earnings growth of single mothers over a five-year time frame hinges on 

the differential expansion in the EITC for single mothers with one qualifying child and 

for those with two or more qualifying children between 1994 and 1997.6Prior to the 

EITC expansions of the early 1990s—specifically, those legislated in the Omnibus 

Budget Reconciliation Act of 1993—the treatment of filers with one child was virtually 

identical to that of filers with two or more children. In 1994 the EITC increased for both 

groups, but more so for those with two or more children (see Figure 2). And between 

1994 and 1997 the EITC continued to increase for those with two or more children while 

remaining relatively unchanged (in real terms) for those with one child (see Figure 3). In 

1994 the maximum credit available to single mothers with one child was $2,819 (in 2007 

dollars), and that available to single mothers with two or more children was $3,497. By 

1997, the maximum credit available to single mothers with one child increased by $27 to 

$2,847 (in 2007 dollars), and the maximum credit available to single mothers with two or 

more children increased by $1,212 to $4,709.

DDS (2009) used that differential change in the EITC to identify the effect of the 

EITC on labor market outcomes for single mothers using two separate difference-indifferences models. Because from 1994 to 1997 the EITC did not change appreciably for 

single mothers with only one child, any observed changes in their employment or 

 

6 That is the same identification strategy used by Hotz, Mullin, and Scholz (2010).
8

earnings growth should not be attributable to changes in the EITC. And because single 

mothers with only one child and single mothers with two or more children are likely very 

similar (both on observable and unobservable characteristics), we posit that the 

employment and earnings growth of single mothers with two or more children would 

have followed the same trend as it did for those with one child if the EITC expansion had 

not occurred. To the extent that trends in the employment and growth in earnings of 

single mothers with two or more children deviated from that of single mothers with one 

child, we attribute those differences to the effect of the EITC on employment and growth 

in earnings. (This assumes that there were no other changes occurring over the 1994 to 

1997 timeframe—policy or otherwise—that were differentially affecting single mothers 

with two or more children and single mothers with only one child.)

DDS (2009) estimated models such as:

(1) ( ) ∑ ( ) 

 

 ( ) 

∑ ( ) 

 

 ( ) 

where:

 Ei,t is log annual earnings of person i in year t;

 yeari,t is an indicator variable for the year in which demographics are 

measured;

 kidsi,t2 is an indicator variable for having two or more children (versus only 

one child); and

 Xi,t is a vector of individual-level controls, including marital status (widowed, 

divorced, never married), race and ethnicity (non-Hispanic white, nonHispanic black, Hispanic, non-Hispanic other), education (less than high 
9

school, high school, beyond high school), presence of children under age 6, 

mother’s age, and mother’s age squared.

Also estimated by DDS (2009) and used in the simulations that follow is the effect of the 

EITC on employment. 

The coefficients on the interactions between the year variables and the indicator 

for having two or more children yield the difference-in-differences estimates. These 

estimates yield two quantities: the effect of the EITC on employment over a seven-year 

period and the effect of the EITC on growth in earnings over a seven-year period. (See 

Table 1).

Effect of the EITC on Social Security Retirement Benefits

In order to simulate the labor supply effect of the EITC on Social Security 

retirement benefits, we use data from the Continuous Work History Supplement 

(CWHS), a longitudinal administrative earnings data set provided by SSA. The CWHS is 

a random 1 percent sample of people who have been issued Social Security numbers; the 

sample contains longitudinal earnings records from workers’ W-2 statements. Earnings, 

as defined in the CWHS, include wage and salary earnings, tips, and some other forms of 

compensation. For this analysis, earnings exclude self-employment income and deferred 

compensation such as contributions to 401(k) accounts.7 Earnings are available from 

1978 to 2007 and are inflated to 2007 dollars using the CPI-U-RS.

Aside from age and sex, the CWHS contains very little demographic information. 

In particular, the CWHS does not include the information necessary to identify single 

 

7 The DER administrative earnings used to estimate the earnings and employment effects of the EITC in

DDS (2009) do include self-employment earnings. Because very few women in that sample report selfemployment earnings, we think that the differences in measure of earnings will not affect our results.
10

mothers (because it does not include information on marital status or on the presence of 

children). Because we cannot identify single mothers explicitly, we instead identify lesseducated women in the CWHS and apply our estimates of the effects of the EITC on the 

employment and earnings growth of single mothers to all less-educated women. 

However, the CWHS does not explicitly include education. As a result, we impute

education using a procedure that we discuss in more detail below. 

For the simulation, the CWHS is restricted to women who were born in 1953; those 

women were 25 years old in 1978 (the first year of data available) and 54 years old in 2007

(the last year of data available). Although the simulation tracks the earnings patterns of a 

single birth cohort, we are able to capture 30 years of earnings data for that cohort.

There exists the distinct possibility of recording error in administrative data, 

including the CWHS. In fact, previous research has found that some records between 

1978 and 1982 are multiples of 100 relative to other earnings fields (see Schwabish, 

2011, and Kopczuk, Saez, and Song, 2010).8 However, there are two advantages to using 

the CWHS over survey data: First, it contains a sufficiently long history to estimate close 

proxies of lifetime earnings patterns and to estimate Social Security benefits that would 

result from those earnings. Second, the CWHS contains a large number of observations—

more than 8,000 women in the less-educated group alone.

The simulation of Social Security benefits for less-educated women requires four

separate steps: First, we impute educational attainment in the CWHS; second, we 

 

8

Some of the multiples appear to be due to recording errors by the SSA, where for certain records the 

decimal place was accidentally moved two spaces to the right. We cannot fully account for these errors, so 

we adjust the sample by dropping people in the top 1% of the earnings distribution in 1978 and 1979. 

Further, after this 1 percent trim, earnings above $60,000 in both 1978 and 1979 are divided by two. That 

affects an additional 0.5 percent of the sample. With access to more data and larger samples, Kopczuk, 

Saez, and Song (2010) and, to a similar extent, Schwabish (2011) employ a more complex method to adjust 

earnings in these early years.
11

simulate the employment effects of the EITC; third, we simulate the post-employment 

earnings growth; and fourth, we estimate the Social Security retirement benefits 

associated with the simulated earnings. We then compare the calculated benefits for lesseducated women before and after the application of the employment and earnings effects

to assess the following questions: If the EITC had increased from 1980 to 1983 in the 

same way it increased from 1994 to 1997 for single mothers with two or more children 

compared with single mothers with one child, how would the lifetime earnings of the 

affected women differ from their actual and observed earnings from 1980 to 2007? And 

would those differences in earnings translate to differences in eligibility for Social 

Security retirement benefits or the amount of monthly benefit received?

Imputing Educational Attainment

First we impute educational attainment to identify the group of women potentially 

eligible for the EITC (the less-educated) and the group not likely to be eligible for the 

EITC (the more-educated). To do so, we follow the procedure that the Congressional 

Budget Office (CBO) uses in its long-term microsimulation model (Congressional 

Budget Office, 2008) and compare age-earnings profiles generated from repeated crosssections of March Current Population Survey data to the age-earnings profiles in the 

CWHS. The methodology enables us to generate less- and more-educated groups. A full 

description of the approach can be found in the Appendix.

Simulating the Employment Effects

Second, we simulate the employment effects of the EITC. Having assigned 

educational attainment in the CWHS, the employment effects estimated in DDS (2009)

are used to adjust the employment rate among the sample of less-educated women. In that 
12

paper, we generated estimates of the effects of the EITC on employment for seven years 

(1994 to 2000). In this paper, we apply those estimates to the CWHS data from 1980 to 

1986.

The employment reassignment method randomly reassigns enough unemployed 

less-educated women to employment so that the resulting employment rate matches that 

of the original employment rate in the CWHS plus the increase in employment estimated 

by DDS.

9

(See Table 2.) We assign the newly employed women earnings between the 

40th and 60th percentiles of the distribution of earnings among the less-educated women 

who moved from unemployment to employment at that same time. When targeting the 

employment rate in 1983, for example, we pull from the distribution of earnings of lesseducated women who were unemployed in 1982 and employed in 1983. The 40th

percentile of earnings is above the cutoff to obtain four quarters of coverage in each year.

Our employment reassignment method therefore forces the newly employed woman to 

have at least four quarters of coverage.

Simulating Growth in Earnings

Third, we simulate post-employment growth in earnings. As with employment 

reassignment, the effects of the EITC on growth in earnings as estimated by DDS { ,

 , ..., } are used to adjust earnings of less-educated women from 1980 to 1986, 

inclusive. The year-over-year earnings growth rates for less-educated women are derived 

from the difference-in-difference estimates calculated in DDS. The estimated earnings 

growth rate for less-educated women between 1979 and 1980 is 9.8 percentage points 

 

9 We use the point estimates as estimated, not the upper and lower bounds of the 90 percent or 95 percent 

confidence intervals. One implication of this is that we use point estimates that are not significantly 

different from zero. 
13

higher than the individual’s observed earnings growth rate. For example, if we observe a 

less-educated woman’s earnings increasing from $10,000 in 1979 to $11,000 in 1980 to 

$12,100 in 1981 (that is, at a rate of 10 percent a year), we adjust the 1980 earnings to 

$11,980. For that woman, the growth rate in earnings increased from 10 percent to 19.8 

percent (9.8 percentage points higher, corresponding to the point estimate of 0.098) 

between 1979 and 1980. We then adjust the 1981 earnings to $14,376. That growth rate 

in earnings increased from 10 percent to 20 percent (10 percentage points higher, 

corresponding to the point estimate of 0.10). Put differently, adjusted earnings from 1980 

to 1986 are calculated by multiplying the sum of the observed growth rate and the 

estimated increase in the growth rate from DDS by the earnings in the base year.

The simplified example above does not address the possibility that a woman’s 

earnings may decline from one year to the next. However, the methodology does not 

change if we observe a decline in earnings. If a woman’s earnings decline from $10,000 

in 1979 to $0 in 1980 and then increase again to $5,000 in 1981, we adjust the

-100 percent growth in earnings between 1979 and 1980 to -90.2 percent (so the adjusted 

growth rate is 9.8 percentage points higher than the observed) and earnings in 1980 are 

recoded as $980. The difficulty that arises is how to handle the observed increase from $0 

in 1980 to $5,000 in 1981 (undefined in percentage terms). One can, of course, add $1 to 

income in 1980 and then define the percentage change off of a base of a $1. But applying 

that large percentage change to a base of $980 is inappropriate (in this particular instance, 

it would mean calculated earnings of about $4.9 million in 1980). Instead adjusted 

earnings ( 

 ) in time t for 1980 to 1986 are calculated as: 
14

(2) 

 

  ( 

( )

 

)

else

 

 

The result of this formulation is that zero earnings in year t-1 are not carried through to 

replace original, positive earnings in year t. 

For year-over-year earnings growth between 1987 and 2007—that is, when the 

estimates from DDS no longer apply—we allow earnings to increase in two different 

ways. In the first, we calculate the median percent change in earnings among moreeducated women, 

. (See Table 3.) Then, for each year from 1987 to 2007, we adjust 

earnings among less-educated women such that earnings in time t are the maximum of 

her original earnings in time t or of her original earnings in time t-1 multiplied by the 

median percent change adjustment factor. That is:

(3) 

 [ 

 

 

 

]

where the superscript less refers to the level of educational attainment. In doing this, we 

force earnings growth among the less-educated to look more like those among the moreeducated. 

In the second scenario, we calculate the year-to-year percent change in each lesseducated woman’s earnings from 1987 to 2007 as observed in the original CWHS data. 

Beginning in 1987, we then apply those percent changes, effectively increasing earnings 

at the same observed year-to-year rate, but the base from which the earnings grow is now 

higher because of the application of estimates from DDS. For example, suppose a woman 
15

has actual earnings of $50 in 1986 and $75 in 1987 (a 50 percent increase in earnings 

between those two years). After applying the estimates in the first few years of the 

simulation, that woman’s estimated earnings are equal to $100 in 1986. We then apply 

the original 50 percent increase between 1986 and 1987 (the original increase from $50 to 

$75) to this new $100 base, which yields a new earnings level of $150 in 1987. 

Subsequent earnings are calculated in a similar way and thus the annual percent change in 

earnings for each woman is the same as it is originally observed in the data, but now 

generates a higher level of earnings because of the application of the estimates through 

1986. 

The path of earnings growth outside the seven-year window in which we are able 

to rely on the estimates from DDS is not obvious to us. The two methods discussed here 

are meant to be representative and to serve as an informal sensitivity analysis. In the first 

method, we force the earnings growth of less-educated women to look more like that of 

more-educated women. In the second, we force the earnings growth of less-educated 

women to remain as observed in the data. Because less-educated women tend to have 

lower earnings than more-educated women, the earnings growth rates for less-educated 

women tend to be larger (in part, because they are calculated from a smaller base) than 

rates for their more-educated counterparts. Thus, the lifetime earnings of less-educated 

women tend to be higher under the second option than under the first.

Estimating Social Security Retirement Benefits

Finally, to calculate expected Social Security retirement benefits for the women in 

our sample, we construct modified Average Indexed Monthly Earnings (AIME) amounts

and use the modified AIME to construct a Primary Insurance Amount (PIA). Because we 
16

do not have 35 full years of earnings with which to estimate the true AIME, we use a 

modified AIME calculation that uses all of the 30 years of earnings (including years of 

zero earnings) available in the CWHS (1978 to 2007). Not only does the calculation of 

the AIME require 35 years of data (which we do not have), but also, under current law, 

earnings are indexed to the average wage index in the year in which the worker turns 60. 

Our cohort turns 60 in 2013; we cannot yet know the average wage index for that year. 

Therefore, we use the average wage index to index earnings to the most recent year of 

observed earnings (2007). We then use the PIA formula in 2007 to determine the final 

monthly benefit amount (the bendpoints for that formula are at $680 and $4,100; see 

Figure 1). We do not adjust the monthly benefit amount for cost-of-living adjustments 

(COLAs). Although failing to adjust for COLAs will not result in true Social Security 

benefits, the relative values of the AIME and PIA will be maintained.

The modified AIME that we calculate probably underestimates the true AIME. 

We do not take into account five years of earnings toward the end of one’s career. Those 

are likely to be years with relatively high earnings. We do not index earnings to the wage 

index at age 60; rather, we index to the wage index at age 54, thus missing several years 

of wage growth in the indexing. And we do not adjust the benefits for COLAs. Each of 

these three choices will result in an underestimate of the AIME, compared with the actual 

AIME for the women in our sample. However, we implicitly assume that these women 

claim their Social Security retirement benefits at their full retirement age. Because more 

than half of people claim their benefits before their full retirement age, this assumption 
17

implies an overestimate of the true AIME.10 On net, we believe that the first set of factors 

dominates and that the AIME estimated here is an underestimate of the true AIME.

RESULTS

As we described above, we use the DDS (2009) estimates to simulate the impact 

of EITC expansions on a sample of less-educated women from the CWHS, all of whom

were born in 1953. We use the DDS estimates reported in Table 1 to increase the 

employment rates and earnings growth rates of these women from age 27 (in 1980) to 33 

(in 1986). Thereafter, from 1987 to 2007, we use two different methods to increase 

earnings. In the first method, we use the median growth rate of more-educated women

and in the second method, we use the observed earnings growth rate of the person.

The simulated increase in the employment rates of our sample of less-educated

women is presented in Table 2. As an example, the EITC expansions between 1994 and 

1997 are estimated to have increased the employment rate of single mothers with two or 

more children by 1.7 percentage points more than they would have otherwise increased in 

the first year of the expansion and to have increased the employment rate of single 

mothers with two or more children by 6.2 percentage points more than they would have 

otherwise increased seven years later. For our simulations, this translates to an increase in 

the employment rate from the observed rate of 44.7 percent to 46.4 percent in 1980 and 

an increase in the employment rate from the observed rate of 46.4 percent to 52.6 percent

in 1986.

The impact of the EITC on earnings growth between 1980 and 1986 is reported in 

 

10 In 2009, 61 percent of people were awarded benefits prior to age 65 (Table 6.A4 in Social Security 

Administration, 2010). 
18

Table 3. Between 1980 and 1986, we use the estimates from DDS (reported in Table 1) to 

construct a modified earnings stream. After that, we increase earnings either by the 

median increase in earnings in that year for more-educated women or by the observed 

increase in earnings for each individual woman (though applied to the new, higher level 

of earnings in 1986). 

The effects of the increases in employment and in earnings growth on average 

annual earnings over the lifetime of less-educated women are reported in Table 4. We 

report the differences at each decile as well as at the mean, and we report the observed 

distribution of average annual earnings over the lifetime for more-educated women as a 

point of comparison. The differences between using the median percent change for moreeducated women and using the observed percent change in earnings are solely due to how 

earnings growth for less-educated women is treated in the out-years—years 7 through 27. 

Using the observed earnings growth of each person tends to yield higher lifetime 

earnings. 

In Table 4, the average of annual earnings over the lifetime (hereafter called 

―average annual earnings‖) among less-educated women has a mean of $7,790, compared 

with a mean of $31,730 for more-educated women. For the 10th percentile of lesseducated women (ranked by their average annual earnings), average annual earnings over 

the lifetime are $400. They are $6,250 at the median and $17,700 at the 90th percentile for 

less-educated women (see Table 4, column 2). In column (3), we report the distribution 

of average annual earnings among less-educated women after we adjust for the effects of 

the EITC, allowing earnings to grow in the later years as they grew for more-educated 

women. In column (4) we report the percentage difference between the observed earnings 
19

and the simulated earnings. In column (5), we report the distribution of average annual 

earnings among less-educated women after we adjust for the effects of the EITC and 

allow earnings to grow in the later years as they were observed to grow for each woman. 

Column (6) reports the percentage change between that adjustment and the unadjusted 

observed results. 

Allowing earnings for less-educated women to grow at the median of the growth

rates observed for more-educated women results in a mean increase of 6.5 percent in 

average annual earnings. Those increases are largest in the bottom half of the earnings 

distribution and are substantially larger in the bottom 10 percent to 20 percent of the 

distribution. For example, the 10th percentile of average annual earnings increased by 

32.5 percent while the median increased by 9.3 percent. 

Allowing earnings for less-educated women to continue to grow at the rate 

observed results in a mean increase of 16.8 percent in average annual earnings. Again,

the increases across the distribution are larger toward the bottom of the distribution, 

though the effect is not quite as pronounced under this method as it is when the median 

growth rate for more-educated women is used.

The top panel of Table 4 examines all women; the bottom panel is restricted to 

those who have a sufficient number of quarters of coverage to qualify for Social Security 

benefits based on their own earnings history. Roughly 60 percent of less-educated women

in our sample qualify for retirement benefits on their own earnings record; 92 percent of 

more-educated women do.

In the bottom panel of Table 4, conditioning on eligibility for Social Security, the 

impact of the EITC on earnings is smaller. The mean of average annual earnings over the 
20

lifetime for less-educated women increased by 3.1 percent when we allow earnings to 

grow at the median of the growth rate for more-educated women (columns 3 and 4).

Those increases rise across the distribution from 1.5 percent at the 10th percentile to 3.6 

percent at the 90th percentile. When we allow earnings to grow at the observed rate, the 

mean of average annual earnings over the lifetime increased by 13.5 percent; again, those 

changes rise as average annual earnings increase.

Our estimate (17 percent) of the effect of the EITC on the mean of average annual 

earnings over the lifetime (Table 4, top panel) is roughly equivalent to the percent 

difference in average annual earnings of women with a high school degree ($22,468 in 

2009) and women with some college but no degree ($26,833 in 2009).11

The AIME is closely related to our measure of lifetime earnings. The lifetime 

earnings measure is an annual measure of earnings, and the AIME is a monthly measure. 

We report the impact of the EITC on the AIME in Table 5. The table shows that the 

EITC impact on the AIME is similar to the EITC impact on the average of annual 

earnings over the lifetime. 

The impact of the EITC on quarters of coverage and, therefore, on eligibility for 

Social Security is reported in Table 6. The earnings and employment effects increase the 

percentage of less-educated women who qualify for Social Security based on their own 

earnings histories by 2.3 percentage points when we allow earnings to grow at the median 

growth rate for more-educated women and by 2.6 percentage points when we allow 

earnings to grow at the observed growth rate of the individual.

The impact of the EITC on the PIA (the benefit amount) for less-educated women

 

11 See http://www.census.gov/hhes/www/cpstables/032010/perinc/new03_253.htm. 
21

is complicated slightly by the non-linear relationship between the AIME and the PIA (see 

Figure 1 and Table 7). Our results suggest that there is a 5 percent to 10 percent increase 

in the PIA as a result of the EITC for all less-educated women. Restricting the sample to 

those who qualify for retirement benefits on their own earnings record (one’s PIA is 

usually meaningless if one does not qualify for retirement benefits), there is a more 

modest increase of 1.7 percent in the mean PIA when we allow earnings to grow at the 

median growth rate for more-educated women, but a larger increase of 6.9 percent when 

we allow earnings to grow at the observed growth rate for the individual. The percentage 

increases in the PIA are roughly equal across the distribution of average annual earnings. 

CONCLUSION

The EITC contributes to the financial security of less-educated women as they age 

and retire. Our estimates and simulation show that the EITC leads to an immediate 

increase in employment coupled with relatively high earnings growth for single mothers. 

Our results suggest that those effects translate into a moderate increase in the share of 

less-educated women who qualify for Social Security retirement benefits—from about 60 

percent to about 62 percent (an increase of about 2 percentage points). Among those who 

qualify for benefits, our results suggest that the average increase in monthly benefits is 

about 2 percent to 7 percent, up from $677 to $688 or $723 (depending on how earnings 

growth is handled in the later years of the simulation). That is an extra $130 to $550 a 

year in Social Security retirement benefits. 

The study shows that the boost in employment and earnings growth from the 

EITC also results in an increase in the share of single parents (usually mothers) who will 
22

be eligible for disability or survivors’ benefits through the Social Security program. 

Estimating those effects are beyond the scope of this paper, but we do not believe that 

incorporating those types of benefits into the model would significantly change the 

results regarding retirement benefits.

As noted, we cannot identify single mothers in the administrative data. 

Furthermore, we do not explicitly model the share of the less-educated women in our 

sample that will ultimately receive a retirement benefit based on a spouse’s or exspouse’s earnings history rather than on their own earnings history. A spouse’s earnings 

history would lower the effect of the EITC on benefits, because the retirement benefit for 

women who continue to receive half of their spouse’s benefit is necessarily zero (we 

assume that the husband’s employment is not sensitive to the EITC). Thus our estimates 

likely overstate the effect of the EITC on Social Security retirement benefits. However, 

we believe the estimates indicate that the EITC is likely to have a positive effect on 

Social Security retirement benefits.
23

Appendix: Imputing Educational Attainment to the CWHS

To estimate the effect of the Earned Income Tax Credit (EITC) on Social Security 

retirement benefits, we must first identify women who are likely to be affected by the 

EITC. The Continuous Work History Sample (CWHS) contains limited demographic 

information: it does not indicate marital status, number of children, or educational 

attainment. So we use each woman’s longitudinal earnings record in the CWHS to impute 

educational attainment and then consider less-educated women as the treatment group 

and more-educated women as the control group. We do not explicitly take into account 

behaviors associated with claiming the EITC; previous research has suggested that there 

is significant volatility in EITC receipt from one year to the next (Ackerman, Holtzblatt, 

and Masken, 2009), which could be correlated with levels of educational attainment.

The method to impute educational attainment to the CWHS compares the 

longitudinal earnings patterns in the CWHS to average age-earnings profiles estimated 

from pooled March Current Population Survey (CPS) files. The procedure is similar to 

that presented in Congressional Budget Office (2008), which was used to impute 

educational attainment to CBO’s long-term microsimulation model. The imputation 

method proceeds in three steps: 

 Estimate age-earnings profiles from the CPS; 

 Compare earnings in the CWHS at each age to the CPS age-earnings 

profiles; and 

 Make a final adjustment to people not yet assigned a level of educational 

attainment.

In the first step, we estimate four regressions of earnings on a quadratic in age for 
24

women born in 1953, one for each of four education levels: less than high school, high 

school graduate, some college, and college graduate. Those regressions are based on 

pooled CPS data from calendar years 1978 to 2008 and are weighted using the CPS 

sample weights. Topcoded earnings have been adjusted using draws from a Pareto 

distribution, parameters from which are estimated in the CWHS.12 From these 

regressions, we calculate the predicted earnings and the standard error at each age for 

each level of educational attainment. These parameters enable us to generate an average 

age-earnings profile for each age and educational attainment category, and to build a 

band around each at a standard error of ±6. (See Appendix Figure 1.)

13

In the second step, we compare the earnings for each person in the CWHS to the 

predicted earnings from the CPS regressions at each age. Each time the person’s CWHS 

earnings falls within an education–earnings band, we assign them a value of 1 for less 

than high school, 2 for high school graduate, 3 for some college, and 4 for college 

graduate. We then take the mean of this series for each individual and assign ultimate 

educational attainment accordingly.14

Finally, there are some people whose earnings never fall within an educationearnings band, because their earnings are too high, are too low, or fall between the bands. 

Such people are reassigned as high school graduates, because doing so makes the 

distribution of educational attainment similar to the distribution observed in the CPS.

To illustrate how the educational attainment imputation works, consider a worker 

 

12 See Schwabish (2010).

13 We make some further adjustments to the bands for people ages 25 to 28 who had some college 

education or were college graduates because the standard error bands overlapped between those groups.

14 We also tried a variety of different estimation methods, including using the median or mode across the 

educational attainment values. We also modified whether we round the mean or median. For this sample, 

rounding the mean seems to best approximate the distribution found in the CPS.
25

who earns $6,500 at age 25; that amount falls within the range of earnings for high school 

dropouts ($6,123-$10,544) and thus, at age 25, that person is assigned the value of 1, a 

high school dropout. At age 26, her earnings rise to $13,000, which falls within the high 

school graduate range ($12,036 to $14,937); thus she is imputed to be a high school 

graduate at age 26. Continue this process for each age—notice that years of zero earnings 

are included—and then take the mean across all of those imputations. That final number 

is this worker's imputed level of educational attainment.

Overall, the imputation method generates a distribution of educational attainment 

that is fairly close to that found in the pooled March CPS file, and the average ageearnings patterns are, at least ordinally, as expected. (See Appendix Figure 2.) In 

addition, because we aggregate the two less-educated groups and the two more-educated

groups, the distribution of educational attainment is within about 2 percentage points of 

the CPS distribution. (See Appendix Table 1.)
26

REFERENCES

Ackerman, Deena, Janet Holtzblatt, and Karen Masken. 2009. ―The Pattern of EITC 

Claims Over Time: A Panel Data Analysis,‖ Internal Revenue Service, Washington, D.C. 

Congressional Budget Office. 2008. ―Assigning Education Status in CBO’s Long-Term 

Microsimulation Model,‖ Congressional Budget Office Background Paper, October.

Congressional Budget Office. 2012. ―The Budget and Economic Outlook: Fiscal Years 

2012 to 2022,‖ January.

Cristia, Julian and Jonathan A. Schwabish. 2009. ―Measurement Error in the SIPP: 

Evidence from Matched Administrative Records,‖ Journal of Economic and Social 

Measurement.

Dahl, Molly, Thomas DeLeire, and Jonathan Schwabish. 2009. ―Stepping Stone or Dead 

End? The Effect of the EITC on Earnings Growth.‖ National Tax Journal, 62(2): 329–

346. 

Eissa, Nada and Jeffrey B. Liebman. 1996. ―Labor Supply Response to the Earned 

Income Tax Credit.‖ Quarterly Journal of Economics, May: 605–637.

Hotz, V. Joseph, Charles H. Mullin, and John Karl Scholz. 2010. ―Examining the Effect 

of the Earned Income Tax Credit on the Labor Market Participation of Families on 

Welfare.‖ Unpublished paper, Duke University, http://public.econ.duke.edu/~vjh3.

Kopczuk, Wojciech, Emmanuel Saez, and Jae Song. 2010. ―Earnings Inequality and 

Mobility in the United States: Evidence from Social Security Data since 1937,‖ Quarterly 

Journal of Economics, February: 91-128.

Meyer, Bruce D. and Dan T. Rosenbaum. 2000. ―Making Single Mothers Work: Recent 

Tax and Welfare Policy and Its Effects.‖ National Tax Journal, 53(4, Part 2): 1027–1061.

Meyer, Bruce D. and Dan T. Rosenbaum. 2001. ―Welfare, the Earned Income Tax Credit, 

and the Labor Supply of Single Mothers,‖ Quarterly Journal of Economics, 116 (3): 

1063–1114.

Schwabish, Jonathan A. 2010. ―Comparing Methods of Imputing Topcoded Earnings in 

the March Current Population Survey Using Administrative Data.‖ Presented at the 

Brookings Institution, February 23, 2010.

Schwabish, Jonathan A. 2011. ―Identifying Rates of Emigration in the United States 

Using Administrative Earnings Records,‖ International Journal of Population Research,

vol. 2011. 
27

Social Security Administration. 2010. Annual Statistical Supplement, 2010. U.S. Social 

Security Administration, Office of Retirement and Disability Policy.
Year-over-Year 

Earnings Growth Employment

2 or More children in 1994 0.098 0.017

(0.094) (0.024)

2 or More children in 1995 0.100 -0.015

(0.090) (0.028)

2 or More children in 1996 0.099 0.036

(0.075) (0.027)

2 or More children in 1997 0.191 0.081

(0.072)** (0.027)**

2 or More children in 1998 0.133 0.035

(0.073)+ (0.027)

2 or More children in 1999 0.084 0.086

(0.072) (0.027)**

2 or More children in 2000 0.135 0.062

(0.072)+ (0.027)*

2 or More Qualifying Children -0.050 -0.077

(0.062) (0.023)**

Demographic controls Yes Yes

Year dummy variables Yes Yes

State dummy variables No No

Observations 10,414 12,923

R-squared 0.02 0.08

Notes: Robust standard errors are in parentheses:

Employment is defined as those with positive annual earnings.

Sample: Single women, age 19 to 44, not in school, not disabled, with a child age 

18 or younger in the household or a child age 19 to 24 and enrolled in school. 

Sample drawn in January 1993, 1994, and 1995; March 1996; January 1997, 1998, 

and 1999; and November 1999.

Demographic controls include marital status (widowed, divorced, never married 

[excluded]), race/ethnicity (non-Hispanic white [excluded], non-Hispanic black, 

Hispanic, non-Hispanic other), education (less than high school [excluded], high 

school, beyond high school), presence of children under age 6, mother's age, and 

mother's age squared.

+ significant at 10%; * significant at 5%; ** significant at 1%

Table 1

The Effects of the EITC on Earnings Growth and Employment of Single 

Mothers

Year-over-Year Earnings Growth is the difference in log annual earnings.

Source: Authors' calculations from the Survey of Income and Program Participation 

matched to the Social Security Administration Detailed Earnings Record data. See 

Dahl, DeLeire, and Schwabish (2009) for full discussion of these regression 

results.
Table 2

1 1980 0.017 44.7 46.4

2 1981 -0.015 43.1 41.6

3 1982 0.036 39.6 43.2

4 1983 0.081 41.5 49.6

5 1984 0.035 43.3 46.8

6 1985 0.086 45.8 54.4

7 1986 0.062 46.4 52.6

Sources: Authors' calculations from the Continuous Work History Sample and regression 

results from the Survey of Income and Program Participation matched to the Social 

Security Administration Detailed Earnings Record data.

Employment Rates Among Less-Educated Women Before and After 

Assignment of Employment Elasticities

Year PostExpansion in 

Simulation Year in CWHS

Estimated 

Employment 

Elasticity from 

Table 1

Original 

Employment 

Rate

Adjusted 

Employment 

Rate
Table 3

Earnings Elasticities and Median Percent Change in Earnings, 1980 to 2007

Year PostExpansion in 

Simulation Year t Year t+1

1 1979 1980 0.098 ----

2 1980 1981 0.100 ----

3 1981 1982 0.099 ----

4 1982 1983 0.191 ----

5 1983 1984 0.133 ----

6 1984 1985 0.084 ----

7 1985 1986 0.135 ----

8 1986 1987 ---- 3.04

9 1987 1988 ---- 2.33

10 1988 1989 ---- 1.83

11 1989 1990 ---- 1.47

12 1990 1991 ---- 1.85

13 1991 1992 ---- 3.20

14 1992 1993 ---- 1.39

15 1993 1994 ---- 1.95

16 1994 1995 ---- 2.17

17 1995 1996 ---- 1.55

18 1996 1997 ---- 2.31

19 1997 1998 ---- 3.28

20 1998 1999 ---- 2.02

21 1999 2000 ---- 1.25

22 2000 2001 ---- 1.31

23 2001 2002 ---- 1.85

24 2002 2003 ---- 0.92

25 2003 2004 ---- 0.48

26 2004 2005 ---- -1.09

27 2005 2006 ---- -0.02

28 2006 2007 ---- 0.43

Sources: Authors' calculations from the Continuous Work History Sample and regression 

results from the Survey of Income and Program Participation matched to the Social 

Security Administration Detailed Earnings Record data.

Median Percent 

Change in 

Earnings Among 

More-Educated 

Women

Estimated 

Earnings Elasticity 

from Table 1
Table 4

Distribution of Average Annual Earnings over the Lifetime in 2007 Average Wage Indexed Dollars

(1) (2) (3) (4) (5) (6) (7)

Percentile

Less-Educated, 

Pre-Adjustment

Less-Educated, 

Post-Adjustment

Percent 

Difference

Less-Educated, 

Post-Adjustment

Percent 

Difference More-Educated

All Workers

10 400 530 32.5 550 37.5 10,090

20 1,320 1,590 20.5 1,580 19.7 16,270

30 2,680 3,080 14.9 3,110 16.0 21,220

40 4,360 4,760 9.2 4,880 11.9 25,220

50 6,250 6,830 9.3 7,030 12.5 29,450

60 8,350 8,930 6.9 9,300 11.4 33,900

70 10,810 11,470 6.1 12,070 11.7 39,030

80 13,650 14,410 5.6 15,360 12.5 46,200

90 17,700 18,540 4.7 20,310 14.7 56,460

Mean 7,790 8,300 6.5 9,100 16.8 31,730

Workers With Sufficient Work History to Qualify for Social Security

10 5,190 5,270 1.5 5,330 2.7 14,650

20 6,640 6,810 2.6 6,950 4.7 19,460

30 7,950 8,170 2.8 8,420 5.9 23,480

40 9,370 9,610 2.6 9,980 6.5 27,200

50 10,880 11,180 2.8 11,670 7.3 31,120

60 12,470 12,910 3.5 13,580 8.9 35,370

70 14,400 14,920 3.6 15,910 10.5 40,690

80 16,750 17,410 3.9 18,960 13.2 47,460

90 19,880 20,600 3.6 23,150 16.4 57,550

Mean 11,860 12,230 3.1 13,460 13.5 33,960

Note: All earnings are adjusted to 2007 dollars using the Average Wage Index.

Sources: Authors' calculations from the Continuous Work History Sample and regression results from the Survey of Income and 

Program Participation matched to the Social Security Adminstration Detailed Earnings Record data.

Using Median Growth in Earnings for 

More-Educated Women to Increase 

Earnings in Later Years

Using Less-Educated Individual's 

Observed Earnings Growth to 

Increase Earnings in Later Years
Table 5

Distribution of Average Indexed Monthly Earnings (AIME)

(1) (2) (3) (4) (5) (6) (7)

Percentile

Less-Educated, 

Pre-Adjustment

Less-Educated, 

Post-Adjustment

Percent 

Difference

Less-Educated, 

Post-Adjustment

Percent 

Difference More-Educated

All Workers

10 30 40 33.7 50 38.9 840

25 160 190 16.8 190 17.8 1,570

50 520 570 9.3 590 12.5 2,450

75 1,010 1,070 5.5 1,130 11.4 3,530

90 1,470 1,550 4.8 1,690 14.8 4,700

Mean 650 690 6.6 760 16.9 2,640

Workers With Sufficient Work History to Qualify for Social Security

10 430 440 1.6 440 2.7 1,220

25 610 630 3.2 640 5.6 1,800

50 910 930 2.8 970 7.3 2,590

75 1,290 1,340 3.6 1,440 11.8 3,650

90 1,660 1,720 3.6 1,930 16.4 4,800

Mean 990 1,020 3.1 1,120 13.5 2,830

Note: All earnings are adjusted to 2007 dollars using the Average Wage Index.

Using Median Growth in Earnings for 

More-Educated Women to Increase 

Earnings in Later Years

Using Less-Educated Individual's 

Observed Earnings Growth to 

Increase Earnings in Later Years

Sources: Authors' calculations from the Continuous Work History Sample and regression results from the Survey of Income and 

Program Participation matched to the Social Security Adminstration Detailed Earnings Record data.
Table 6

Percent of Workers Covered by Social Security

(Percent above minimum quarters of coverage threshold)

Less-Educated More-Educated All

Before Reassignment 59.6 92.0 78.7

After Reassignment 61.9 92.0 79.6

Difference 2.3 0.0 1.0

Using Individual's Observed Earnings Growth to Increase Earnings for

Less-Educated in Later Years

Less-Educated More-Educated All

Before Reassignment 59.6 92.0 78.7

After Reassignment 62.1 92.0 79.7

Difference 2.6 0.0 1.1

Sources: Authors' calculations from the Continuous Work History Sample and regression 

results from the Survey of Income and Program Participation matched to the Social Security 

Administration Detailed Earnings Record data.

Using Median Growth in Earnings for More-Educated Women to Increase Earnings for LessEducated in Later Years
Table 7

Distribution of the Primary Insurance Amount (PIA)

(1) (2) (3) (4) (5) (6) (7)

Percentile

Less-Educated, 

Pre-Adjustment

Less-Educated, 

Post-Adjustment

Percent 

Difference

Less-Educated, 

Post-Adjustment

Percent 

Difference More-Educated

All Workers

10 30 40 33.9 41 38.9 664

25 148 173 16.8 174 17.8 897

50 469 512 9.3 527 12.5 1,180

75 719 736 2.5 756 5.1 1,525

90 866 889 2.6 936 8.1 1,991

Mean 457 480 5.1 504 10.3 1,253

Workers With Sufficient Work History to Qualify for Social Security

10 389 396 1.6 400 2.7 785

25 546 564 3.2 577 5.6 972

50 684 693 1.2 706 3.1 1,224

75 808 823 1.9 857 6.1 1,563

90 925 944 2.1 1,012 9.4 2,034

Mean 677 688 1.7 723 6.9 1,327

Sources: Authors' calculations from the Continuous Work History Sample and regression results from the Survey of Income and 

Program Participation matched to the Social Security Administration Detailed Earnings Record data.

Using Median Growth in Earnings for 

More-Educated Women to Increase 

Earnings in Later Years

Using Less-Educated Individual's 

Observed Earnings Growth to 

Increase Earnings in Later Years
Figure 1.

Average Indexed Monthly Earnings and the Primary Insurance Amount, 2007

Source: Social Security Administration. Primary Insurance Amount (PIA)

Average Indexed Monthly Earnings (AIME)

.32

.15

.90

$612

$1,706

$680 $4,100
Figure 2

EITC for Non-Joint Filers with One Child or with Two or More Children, 1993 and 1994

Source: Urban−Brookings Tax Policy Center.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Earnings per year

1993, 1 Qualifying Child

1993, 2 or More Qualifying Children

1994, 1 Qualifying Child

1994, 2 or More Qualifying Children

(2007 Dollars)
Figure 3

EITC for Non-Joint Filers with One Child or with Two or More Children, 1994 and 1997

Source: Urban-Brookings Tax Policy Center.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Earnings

1994, 1 Qualifying Child

1994, 2 or More Qualifying Children

1997, 1 Qualifying Child

1997, 2 or More Qualifying Children

(2007 Dollars)
Appendix Table 1.

Imputation to

Education Level the CWHS Difference

Less Than High School 7.4 10.3 2.9

High School Graduate 35.9 30.9 -5.0

Some College 27.6 22.6 -5.0

College Graduate 29.0 36.3 7.3

Less-Educated

43.3 41.2 -2.1

More-Educated

Some College or

College Graduate 56.6 58.8 2.2

Imputed Distribution of Educational Attainment, Current Population Survey and 

Continuous Work History Sample for Women in the 1953 Birth Cohort

Source: Authors' calculations from the Continuous Work History Sample (CWHS) and 

1979 to 2009 March Current Population Survey (CPS) data.

March CPS, 

1978 to 2008

Less Than High School or 

High School Graduate
Appendix Figure 1 

Estimated Average Earnings-Age Profiles by Educational Attainment for Women in the 1953 Birth Cohort

(2007 Dollars)

Sources: Estimated profiles from pooled 1979−2009 March Current Population Survey (CPS) data. Estimates are derived from separate earnings 

regressions for each level of educational attainment. 

Notes: All regressions are weighted using the CPS person-weights. Earnings are adjusted to 2007 CPI-U-RS dollars. Earnings bands around each 

profile represent ±5 standard errors for each estimated coefficient.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

26 28 30 32 34 36 38 40 42 44 46 48 50 52 54

Age

Less Than High School

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

26 28 30 32 34 36 38 40 42 44 46 48 50 52 54

Age

High School Graduate

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

26 28 30 32 34 36 38 40 42 44 46 48 50 52 54

Age

Some College

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

26 28 30 32 34 36 38 40 42 44 46 48 50 52 54

Age

College Graduate
Appendix Figure 2

Average Age Earnings and Imputed Level of Education Profiles

Source: Authors' calculations from the Continuous Work History Sample and 1979−2009 March Current 

Population Survey data.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

College Graduate

Some College

High School Graduate

Less Than High School 

(2007 dollars)