The Earned Income Tax Credit and Child Achievement
Samuel M. Lundstrom University of California, Irvine
November, 2015
Abstract:
The Earned Income Tax Credit (EITC) offers financial assistance to low income families with children while providing an incentive to work. However, only single mothers have been shown to increase their labor supply in response to the EITC. Because the EITC causes single mothers to enter the workforce and, presumably, alter their pattern of child care, the net impact of this policy on their children is unclear. I estimate the impact of the EITC on the achievement of children with single mothers using two strategies. Both are difference-in-differences approaches that exploit an increase in EITC generosity for families with two+ children relative to families with only one child. The first approach uses the interaction of group and time dummy variables in a standard difference-in-
differences framework; the second approach uses variation in a group-level simulated EITC payment.
Estimates from the first approach indicate that the EITC reduces reading achievement, but the results are not robust; estimates from the second approach are positive, though imprecise. These findings contrast with the positive, and very precise results from a recently published paper in The American Economic Review wherein the authors use the EITC instrumentally to estimate the causal effect of income on child achievement. However, I present evidence suggesting that the strategy employed in this study may not be valid. I conclude that, while it is certainly possible that the EITC boosts child achievement, we are still looking for good evidence of an effect. (JEL H24, H31, I21, I38, J13)
Lundstrom: Department of Economics, University of California-Irvine, 3151 Social Science Plaza, Irvine, CA 92697-5100, (email: slundstr@uci.edu). Acknowledgements: I am grateful to David Neumark, Scott
Barkowski, Marianne Bitler, Damon Clark, Joseph Doyle, Greg Duncan, and Lars Lefgren for helpful comments on this project. I am also grateful to Gordon Dahl and Lance Lochner for making their data and programs readily available. Finally, I am grateful to the University of California, Irvine for supporting me in my research.
The Earned Income Tax Credit (EITC) was enacted in 1975 as a means to offset the burden of Social Security payroll taxes and provide an incentive to work for low income families with children (IRS, EITC Home Page, 2012). Although the EITC has been expanded to provide a small benefit to individuals and families without children, its primary focus of assisting poor children was implicit in the original design of the program.1 It is, therefore, important to understand what impact the EITC has on the welfare of children in recipient families.
In this paper I estimate the impact of the EITC on the cognitive achievement of children with single moms. I focus on the children of single mothers for two reasons. First, kids with single moms represent a particularly disadvantaged group among low-income populations and, as such, the effect of the EITC on them is of great interest. Second, while the EITC is designed to incentivize work among recipient families, single moms are the only group that has been shown to increase its labor supply in response to the EITC (Eissa and Hoynes, 2004; Eissa and Liebman, 1996; Meyer and Rosenbaum, 2001). Because the EITC increases household income while simultaneously causing single mothers to go to work and—presumably—alter their patterns of child care, its impact on child achievement is unclear.
I estimate the impact of the EITC on child achievement—as measured by standardized test scores—using two separate identification strategies. Both are difference-in-differences regression approaches that exploit the fact that in the early 1990s the EITC became more generous to families with two+ children relative to families with only one child. If the cognitive abilities of children in EITC recipient families improve due to increased EITC generosity, we should observe an
improvement in the outcomes of children in two-child families relative to children in one-child families. The first approach uses the interaction of group and time dummy variables in a standard difference-in-differences framework. The second approach uses variation in a group-level simulated
1The congressional Omnibus Reconciliation Act of 1993 provided a small credit to individuals without children . Prior to this time, recipient families had to have children in order to qualify for the EITC.
EITC payment to better capture complex changes in EITC parameters. Estimates from the first approach indicate that the EITC reduces reading achievement, but the results are not robust;
estimates from the second approach are positive, though very imprecise.
The inconclusive results from my strategies contrast with the positive, and very precise, results from a paper by Dahl and Lochner (2012). Dahl and Lochner (2012) estimate the impact of family income on child achievement, using expansions in EITC generosity during the 1990s as an instrument for income. However, in the reduced form Dahl and Lochner's model has the same objective as do my models: to capture the effect of changes in EITC policy on child achievement.
Dahl and Lochner find that increased EITC generosity leads to large increases in child achievement, as measured by test scores. Since Dahl and Lochner's study is, to my knowledge, the only existing peer-reviewed paper looking at the relationship between the EITC and child achievement, it is important to determine whether their approach provides a credible estimate of this relationship. I present evidence suggesting that Dahl and Lochner's strategy may not be valid. Combining this finding with the results from my own strategies, the implication is that we presently do not know if the EITC boosts child achievement. There might be an effect, but we are still looking for good evidence of it.
2 History of the EITC
The federal EITC is a refundable tax credit targeted at low-income families with children.
The credit is "refundable" in the sense that if the size of the credit exceeds the filer's tax liability, the entire credit is still refunded to the filer. While the requirements for EITC eligibility have varied somewhat over the years, during the years considered in this study EITC applicants had to meet the following requirements: 1) The applicant had to have positive earned income that was below a certain threshold, 2) a qualifying child had to be below age 19 or below age 24 if he was a full time student or any age if the child was permanently disabled, 3) a qualifying child had to be a biological,
step, adopted, foster child, or the descendant of one of these children, 4) the child must have lived with the applicant for at least half of the tax year (U.S. House, 1998).
The size of the EITC is determined by a benefit schedule, as illustrated in Figure (1). In the
"phase-in" region of the schedule, the credit grows by a percentage of each additional dollar of earned income. In the "plateau" region of the schedule, the credit attains its maximum value and remains at that value over a specified income range. In the "phase-out" region of the schedule, the credit gradually diminishes to zero as it is reduced by a percentage of each additional dollar earned.
The EITC schedule is completely characterized by four parameters: (1) the phase-in rate, (2) the maximum benefit, (3) the phase-out rate, and (4) the range of qualifying incomes. During the late 1980s and early 1990s the EITC schedule went through several changes and expansions. Some of these expansions occurred as part of the Omnibus Reconciliation Act of 1990 (OBRA90). Because of OBRA90 each parameter of the EITC schedule expanded: the phase-in rate was made steeper, the maximum benefit became more generous, the phase-out rate grew steeper, and the range of
qualifying incomes was expanded. In addition, in 1991 for the first time, separate schedules were introduced for families with one child and two+ children (though the differences between the schedules were initially very slight). The OBRA90 changes were gradually phased in over three years, from 1991 through 1993. These changes are illustrated in Figure (2a) through Figure (2d).
The next—and more significant—expansions in the EITC schedule occurred as part of the Omnibus Reconciliation Act of 1993 (OBRA93). As with OBRA90, the OBRA93 changes were phased in over a three year period (from 1994 through 1996). Again, the EITC schedule grew in every dimension: the phase-in rate grew steeper, the max benefit increased, the phase-out rate grew steeper, and the range of qualifying incomes increased. These expansions were particularly large for families with two+ children. These changes are illustrated in Figure (2e) through Figure (2g). From 1996 through 2000 the EITC schedule did not change (in real terms).
3 Previous Literature
Numerous studies have examined the impact of the EITC on a variety of economic outcomes.
Much of this literature is reviewed in Hotz and Scholz (2003). The most commonly studied outcome is labor supply. In particular, a number of studies have looked at the impact of the EITC on the labor participation response of single mothers (Dickert, Houser, and Scholz 1995; Eissa and Liebman 1996; Hotz, Mullin, and Scholz 2006; Keane and Moffitt 1998; Meyer and Rosenbaum 2001). The literature has focused on single mothers because the labor participation incentives created by the EITC are unambiguously positive for this group. If a single mother is working prior to the expansion of the EITC, she has no incentive to stop working. If she is not working, the increased generosity of the EITC may induce her to enter the workforce. These studies consistently find that the EITC increases the labor force participation of single mothers. For married women, evidence suggests that the EITC slightly reduces labor force participation (Eissa and Hoynes 2004; Ellwood 2000). Though the evidence of the impact of the EITC on hours worked by women who are already working is less clear, it points toward small reductions (Eissa and Hoynes 2006; Eissa and Liebman 1996).
While labor supply has been the most frequently studied outcome, other studies have looked at the impact of the EITC on a wide array of other outcomes. Ellwood (2000) and Dickert-Conlin and Houser (2002) examine the impact of the EITC on marriage rates and find little evidence of any effect. Likewise, Baughman and Dickert-Conlin (2003 and 2009) find little evidence that the EITC impacts fertility. In more recent years, Evans and Garthwaite (2014) find evidence that the EITC positively impacts maternal health, Hoynes, Miller and Simon (2012) find evidence that the EITC reduces the incidence of low birth weight and increases mean birth weight, and Hoynes and Patel (2014) find that the EITC reduces the share of families in poverty.
Dahl and Lochner (2012) is, to my knowledge, the only existing peer-reviewed study looking at the impact of the EITC on child achievement. In their study Dahl and Lochner are not interested in the effect of the EITC on child achievement per se, but rather they use changes in the EITC
instrumentally to identify the causal effect of family income on child achievement. Even so, in the reduced form, Dahl and Lochner's estimates suggest that a $1,000 increase in simulated EITC income (their instrument) improves combined math and reading test scores by about 7.7 percent of a standard deviation.2 Their reduced form estimates for kids with single moms are slightly lower than are the estimates for kids with married moms (6.5 percent compared to 9.3 percent). However, as I shall discuss in detail in section 7, I find evidence suggesting that their instrument may not be valid.
4 Methods
In this section I present two models to estimate the impact of increased EITC generosity on the achievement of kids with single moms. Both are difference-in-differences (DD) models that take advantage of the differential growth in EITC generosity for two+ child families relative to one child families. If the cognitive abilities of children in EITC recipient families improve due to increased EITC generosity, we should observe an improvement in the outcomes of children in two-child families relative to children in one-child families. The first approach—what I call the "basic difference-in-differences" model—uses the interaction of group and time dummy variables in a standard DD framework. The second approach—what I call the "simulation" model—uses variation in group-level simulated EITC payments to better capture the complex changes in EITC parameters over the time period considered.
4.1 Basic Difference-in-Differences Strategy
This strategy has been used to estimate the effect of the EITC on a variety of outcomes including the labor force participation of single moms (Hotz, Mullin and Scholz 2006; Hoynes and Patel 2014), maternal health (Evans and Garthwaite 2014), and the share of families in poverty (Hoynes and Patel 2014). I make only minor changes to the model from how it appears in these
2 Dahl and Lochner (2012) do not report reduced form estimates. The reduced form estimate for combined math and reading is inferred from the reported IV and first stage estimates in Table 3 of their paper.
previous studies in order to take advantage of the panel nature of the NLSY. In this strategy, all years prior to 1994 are considered "pre-treatment" years. Although a small difference in the max benefit between one and two+ child families was first introduced in 1991, the difference was quite small until 1994 when the difference jumped from $91 to $557 (in 2000 dollars). The max benefit difference grew larger in 1995—to $1,142 (in 2000 dollars)—and larger still in 1996—to $1,535 (in 2000 dollars). These changes are displayed graphically in Figure (3) where a time-series plot of the maximum benefit available to one child families is overlaid on a time-series plot of the maximum benefit available to two+ child families for the years 1987 through 2000.
The Basic DD regression model is presented here:
where the subscripts i, g, and t denote the individual, the family-size group (i.e. one or two kids), and year. The outcome variable, y, is a standardized test score; and are dummy variables equal to one if the year=1994 or the year=1996 respectively; is an indicator variable equal to one if there are two children in the family in the initial sample year, t0. Holding TWO fixed to the value it takes on in the initial sample year ensures that variation does not arise due to endogenous changes in family size over time. is a vector of year fixed effects, and is a vector of individual and group level characteristics. The coefficients of interest are and , which capture the effect of increased EITC generosity on kids' test scores, recognizing that the EITC is potentially affecting kids through two channels: changes in maternal employment and the receipt of the benefit itself.
I could estimate a pooled effect for the two treatment years in this model (i.e. 1994 and 1996), but specifying the treatment years separately provides a natural robustness test: since the
difference in EITC generosity between one and two+ child families is very small in 1994 and quite large in 1996, we expect the impact on child achievement to be small in 1994 and larger in 1996. If another pattern emerges this might suggest that something other than the EITC is driving the results.
The fundamental identifying assumption in this difference-in-differences strategy is that, in the absence of the EITC expansions, the achievement gap between kids in one- and two-child families would be constant in all sample years. This assumption may be threatened if there are other policy, economic, or household changes occurring over this time period that differentially affect the achievement of kids in one and two child families. Moreover, this assumption may be threatened if achievement trends differ between the two groups in the absence of treatment. In either case, we may be attributing changes in child achievement to the EITC expansion that may be driven by other factors. After presenting the results I perform a robustness test designed to probe the validity of this assumption.
4.2 Simulation Strategy
The basic DD approach works best when there is a single policy change. However, the EITC expansions that occurred as part of OBRA90 and OBRA93 took place over a period of six years and involve simultaneous changes in multiple policy parameters. In order to more fully utilize this policy variation, I create a simulated EITC. This method of creating a simulated variable to capture changes in complex policy parameters has been used effectively in other EITC studies (Eissa and Hoynes 2004; Hoynes and Patel 2014), Medicaid studies (Cutler and Gruber 1996; Currie and Gruber 1996a;
Currie and Gruber 1996b; Gruber and Yelowitz 1999), and tax studies (Gruber and Saez 2002). In particular, the approach I use here closely mirrors the methodology that is described in Hoynes and Patel (2014), where this strategy is used to estimate the impact of the EITC on maternal employment and family poverty shares.
The strategy works in the following way. A sample is constructed for the tax year that immediately precedes the first year in my estimation sample.3 This sample consists of single mothers with one or two children. Next, a duplicate copy of this sample is created for each tax year in my estimation sample. The CPI-U is used to convert income values into current dollars for each
duplicate copy of the sample. Next, TAXSIM is used to calculate the EITC for each individual in the given tax year.4 Lastly, in each year the sample is divided into two groups according to family size (i.e. one group for families with one child, and a second group for families with two children). The average EITC is then calculated for each of these groups in each year. The result is an average
"simulated" benefit that summarizes policy changes for each of these groups without incorporating changes in benefits that might be caused by EITC-induced changes in employment. These simulated EITC payments are merged with the main estimation sample and the following model is estimated:
Note that this model is nearly identical to the basic DD model that is presented in equation (1). The only difference is that the interaction terms from equation (1) are replaced with the simulated EITC variable. In this model, captures the effect of a $1,000 increase in simulated EITC income on child achievement.
Variation in simulated EITC income comes from two sources. First, at a point in time, variation arises due to differences in between groups (i.e. one-child and two-child groups).
This cross-sectional variation is removed with the inclusion of group fixed effects. Second, for a particular group, variation arises due to real changes in the EITC schedule over time. This time- series variation is removed with year fixed effects. Identifying variation comes, therefore, from
3 The initial survey year in my estimation sample is 1992. The reason why an earlier year is not used will be explained in more detail in the data section.
4 I use TAXSIM, version 9 maintained by Daniel Feenberg and the National Bureau of Economic Research (see Feenberg and Coutts 1993 and http:// www.nber.org/taxsim).
differential changes in the EITC schedule over time for one- versus two-child families. As with the basic DD model, the fundamental identifying assumptions are: (1) that there are no coincidental policy, economic, or household changes that differentially affect the achievement of kids in one- versus two-child families, and (2) that trends in child achievement are parallel for kids from these two groups.
A minor issue of timing arises in this analysis. The vast majority of EITC recipients receive their credit after filing their taxes in the following year. Therefore, a change in the EITC schedule this year will induce labor supply changes this year in anticipation of a benefit that will be received at tax time next year. In this simulation methodology I explicitly link test scores (typically measured between March and December in my data) to the EITC schedule from the previous year, treating them as contemporaneous. In reality, the credit is received in the same year that the test is taken, but any EITC-induced changes in labor supply are from the previous year.
5 Data
The data used throughout this analysis are drawn from a compilation made available by Gordon Dahl on his website.5 This compilation consists of data drawn from the Children of the National Longitudinal Survey of Youth (NLSY) matched to their mothers in the main NLSY. The NLSY is a panel survey that originally included a nationally representative sample of 12,686 men and women who were all 14 to 21 years of age on December 31, 1978. Annual interviews were completed with most of these respondents from 1979 through 1994 at which point there was a shift to biennial interviews. Beginning in 1986, the children born to NLSY female respondents have been assessed every two years.
The NLSY reports earned income for the year that precedes the survey year, but it does not report how much income a respondent received from the EITC. Both the Internal Revenue Service
5 The data were retrieved at this website: http://econweb.ucsd.edu/~gdahl/children-and-eitc-code.html
(IRS) (2002) and Scholz (1994) estimate that about 80 to 87 percent of eligible households receive the credit. I assume full take-up, imputing each family’s federal EITC payment using the TAXSIM program. While these imputed EITC payments do not enter into the reduced form models just described, they are used as outcome variables in a first stage model that will be described later in the paper.
Cognitive achievement is measured using standardized math and reading scores from the Peabody Individual Achievement Tests (PIAT). The tests measure a child’s ability in math, reading recognition (word recognition), and reading comprehension (the ability to derive meaning from printed words). In this analysis I use the math and reading recognition scores, but not the reading comprehension scores.6 The PIAT was administered to children ages five to fourteen7 and consists of a series of 84 questions which increase in difficulty from preschool to high school levels. To make PIAT tests more easily interpretable, Dahl and Lochner create re-normalized test scores using the NLSY reported standardized scores. The scores are re-normalized by subtracting the sample mean from the NLSY random sample (i.e. excluding the poor, military, and minority oversamples) and then dividing by the sample standard deviation. This produces individual test scores with a mean of zero and standard deviation of one for the random sample of respondents. In addition, I create a combined math-reading score by taking the average of the math and reading recognition test scores for each child, then renormalizing to mean zero, standard deviation of one.
6 I exclude the reading comprehension scores because, for various reasons, reading comprehension completion rates are typically lower than for math and reading recognition. Furthermore, many younger children (ages five and six) are not given standardized reading comprehension scores because their scores are out of the range of the national PIAT sample used in the norming procedure. If I were to require that kids have valid reading comprehension scores, the sample size would be significantly reduced. For details, refer to https://www.nlsinfo.org/content/cohorts/nlsy79- children/topical-guide/assessments/piat-reading-reading-recognitionreading.
7 The PIAT tests were administered to children older than age 14 prior to 1994. Beginning in 1994 a separate survey for older children (15 and over) was created and the PIAT was not part of this survey. Because of this, about two percent of children took the PIAT tests after their 15th birthday. These observations are retained in the analysis.
Furthermore, many children ages 5-7 do not have valid scores for the reading recognition test, because their scores were out of range based on the national norming sample in 1968. See the NSLY User’s Guide for more details.
My estimation sample is restricted to kids who, 1) have single mothers in the initial sample year, 2) are in families with one or two kids in the initial sample year, 3) have valid math and reading recognition standardized test scores, and 4) are present in all sample years. In addition, families must have valid family earnings measures and valid measures of maternal employment (EITC income and maternal employment will be used as outcome variables in several first stage estimates). The
requirement that kids be present in all sample years is important since it ensures that estimates are not driven by changes in group composition over time (due to non-random attrition, for example). This requirement limits the number of consecutive surveys that can be included in the sample to five since children take the PIAT tests at most five times. Based on this limitation, I begin by restricting the sample to only include children who are present in each survey year from 1990-1998 (i.e. 1990, 1992, 1994, 1996, 1998). However, there are only 101 kids present in all five sample years for a total of 505 child-year observations. I attempt to increase the sample size by experimenting with different ranges of years (i.e. 1992-1998, 1990-1996, and 1992-1996). I find that the sample size is maximized when it is restricted to the years 1992, 1994, and 1996. There are 364 kids present in all three years for a total of 1,092 child-year observations. With this sample the basic DD model is still identified since there is one pre-treatment year (1992), and two post-treatment years (1994 and 1996).8
Summary statistics are presented separately for one and two child families, and for each survey year in Table (1). Summary statistics are presented in this manner in order to demonstrate that the groups are fixed over time. The one-child group consists of 102 children, and the two-child group consists of 262 children. Children in both groups are (on average) between 7 and 8 years of age in 1992. Kids in one child families are somewhat less likely to be black, are somewhat more likely to be non-black and non-Hispanic, have mothers that are slightly younger (about 0.3 years),
8 The decision to restrict the sample in a way that maximizes the sample size does not affect the conclusions.
Results using the largest possible range of years (i.e. 1990 through 1998) are available upon request.
and have mothers that are somewhat more likely to have a high school diploma than do kids in two child families (none of these differences are significant at the 5 percent level). Though both groups of kids have unmarried mothers in 1992, by 1996 12.1 percent of moms for the one-child group are married, and 19.5 percent of moms for the two-child group are married. Similarly, though in 1992 the groups have one or two children respectively, the family size of both groups expands over time, with two child families growing to about 2.20 children per family by 1996, and one child families growing to about 1.11 children per family in 1996.
The next rows show summary statistics for the outcome variables of interest: the PIAT test scores. These statistics reveal substantial variation across groups and over time. The next two rows show summary statistics for maternal labor force participation, and the imputed EITC benefit. These statistics will be used as outcome variables in several first stage models in order to establish that the policy is having the anticipated effect on the two channels through which we assume child
achievement might be affected.9
The final row of Table (1) shows summary statistics for the simulated EITC payment. Again, these summary statistics reveal substantial variation in the simulated EITC across groups and over time. The details of the how the simulated EITC is constructed are presented in section 4.2. The simulated EITC is created using data from the 1991 March Current Population Survey (CPS). The March CPS provides detailed income information on individuals and families for the previous tax year. Using the CPS was necessary since there are not single mothers of the appropriate ages in the NLSY in 1991. The sample that is drawn from the CPS consists of all single mothers with one or two children who are between 27 and 39 years of age. This age range corresponds with the age range of single mothers in the NLSY during the years included in the estimation sample.
9 Variables used to construct the maternal labor force participation variable and the maternal education variable are taken directly from the NLSY79 and merged with the Dahl and Lochner compilation.
6 Results
I now present results obtained from estimating the basic DD model and the simulation model.
In addition to presenting reduced form estimates of these models, I also present results from two first stage models. Recall that the working assumption is that the EITC affects child outcomes through two possible channels: additional EITC income and increased maternal labor force participation.
Although I am not performing instrumental variables estimation—with two endogenous variables and only one instrument it is not possible—, it is important to demonstrate that these models produce strong first stage estimates in order to a establish a credible basis for detecting a reduced form effect on child achievement. To obtain first stage estimates, the dependent variable in each model is replaced with either a dummy for maternal labor force participation, or a dollar amount representing the family's imputed EITC benefit.10
6.1 Basic DD Results
Estimates of the basic DD model are presented in Table (2). Reduced form estimates are presented in panel (A) and first stage estimates are presented in panel (B). The results in columns (2) and (3) come from a simple model that controls only for group and year fixed effects; the results in columns (4) and (5) come from a covariate adjusted model that also controls for a number of
demographic variables including child age and age squared, mother age and age squared, an indicator equal to one if the mother is a high school graduate, and the mother's AFQT score.
10 Recall that PIAT tests are taken in even years (i.e. 1992, 1994, and 1996) between March and December. A child taking a test in 1994, for example, might in reality be affected by two separate EITC-induced employment changes.
The receipt of the credit in 1994 is derived from the 1993 EITC schedule. Changes in maternal employment in 1993 in anticipation of a benefit that is received in 1994 may affect child achievement in 1994. In addition, the EITC schedule expands in 1994. Any change in maternal employment in 1994 in anticipation of a benefit that will be received in 1995 may also affect child achievement in 1994. I only present first stage estimates for the maternal employment change that is associated with the contemporaneous receipt of the benefit (i.e. the 1993 maternal employment, in this example, that is associated with the 1994 benefit receipt; I do not report first stage estimates for the 1994 employment change). I do this in order to simplify the discussion, recognizing that first stage estimates are not used for IV estimation, but rather they are simply used to establish a credible basis for any reduced form effects that are detected.
Looking at the first stage estimates in panel (B), the estimate for EITC income is insignificant in 1994, and large and significant (at the 1% level) in 1996. The estimates are almost identical with or without the demographic controls. Since income is measured in real $1,000s this implies that single moms with two kids receive roughly an additional $370 in EITC income relative to single moms with only one child 1996. The first stage estimate for maternal labor force participation is also insignificant in 1994 and large and significant (at the 5% level) in 1996. As before, the inclusion of the demographic control has no effect on the estimates. Since the outcome variable is binary, these results imply that single moms with two kids increase their labor force participation rate by roughly 12 percentage points relative to single moms with only one kid. This estimate is somewhat larger than are estimates of the effect of the EITC on maternal labor force participation in past literature, but the standard error is sufficiently large to not rule out a somewhat smaller effect. The key
takeaway from these first stage estimates is that the expansion in the EITC schedule resulted in large, and significant increases in both maternal labor force participation and EITC income for the two child sample compared to the one child sample in 1996, but not in 1994. The first stage estimates appear sufficiently strong to provide a credible basis for detecting an impact on child achievement in 1996, but not in 1994.
Reduced form estimates for math, reading recognition, and combined math and reading are presented in panel (A) of Table (2). Recall that test scores are standardized, so the estimates represent the percent of one standard deviation that test scores change given the expansion in the EITC schedule for two+ versus one child families in the respective years. The estimates for math achievement are statistically insignificant in 1994 and 1996, with or without the demographic
controls; the estimates for reading achievement are large, negative, and significant (at the 10% level) in 1994, but not in 1996; the estimates for combined math and reading achievement are small and very imprecise in 1994 and 1996.
It is surprising to find such a large, and negative effect of the EITC on reading test scores in 1994. If the effect of the EITC on child achievement is negative, and the EITC only affects kids via the two channels I have identified (i.e. increased maternal employment and increased EITC income), it seems likely that the negative achievement effect is due to increased maternal employment.
However, the first stage estimate for maternal labor force participation in 1994 is small and statistically insignificant. Unless there is some other channel whereby the EITC might negatively affect child achievement, this suggests that something other than the EITC must be responsible for the negative reduced form effect on reading achievement. This finding raises some questions about the validity of this identification strategy.
6.2 Simulation Results
Estimates of the simulation model are presented in Table (3). Reduced form estimates are presented in panel (A) and first stage estimates are presented in panel (B). The results in column (2) come from a model that controls only for group and year fixed effects; the results in column (3) come from a model that also controls for a variety of demographic variables. Looking at the first stage estimates in panel (B), the estimate for EITC income is large and significant (at the 5% level), with or without the demographic controls. Since income is measured in real $1,000s these estimates imply that $1,000 increase in simulated EITC income boosts real EITC income by roughly $870. The first stage estimate for maternal labor force participation is also large and significant (at the 5%
level). Since the outcome variable is binary, this result implies that a $1,000 increase in simulated EITC income boosts maternal labor force participation by about 30 percentage points. Though scaling differences make it difficult to directly compare these estimates with the estimates from the previous model, the first stage estimate for maternal labor force participation again seems quite large relative to the existing literature. Nevertheless, the first stage estimates appear sufficiently strong to provide a credible basis for detecting an impact on child achievement.
Reduced form estimates for math, reading recognition, and combined math and reading are presented in panel (A) of Table (3). With or without the demographic controls, estimates are positive, though quite imprecise in each case. Thus, reduced form estimates of this specification provide no basis for inferring that the EITC improves child achievement.
6.3 Robustness Test
It may be superfluous to perform a robustness test at this point since the effect of the EITC on reading achievement using the basic DD model has already been shown to be suspect, and estimates from the simulation model are quite imprecise. Nevertheless, for completeness I perform a simple test to probe the plausibility of the parallel trends assumption. As discussed previously, a
fundamental assumption of any DD estimation is that trends in the outcome variable of interest would be identical for treatment and control groups in the absence of treatment. While data limitations prevent me from examining pre-trends for the children that are included my estimation sample prior to 1992, I can get at this question indirectly. I construct a new sample in the identical manner that I constructed the estimation sample, but this time for the years 1988, 1990, and 1992. In 1988 and 1990 the EITC schedule is identical for families of all sizes, and in 1992 there is only a very slight difference in the schedule between one and two+ child families. I estimate the basic DD model using this new sample, treating 1988 as the pre-treatment year, and 1990 and 1992 as post- treatment years. Since the EITC schedule is almost identical for one and two+ child families over these years, any change in child achievement for kids from two-child families relative to kids in one- child families cannot be attributed to the EITC. Rather the effect must be due to differences in achievement trends for the two groups or to differential achievement shocks.
Estimates of equation (1) using this new sample are presented in Table (4). Looking first at panel (B), there are large and statistically significant (at the 5% level) effects on maternal labor force participation in both 1990 and 1992. Looking at panel (A), there is a large and statistically
significant (at the 1% level) effect on math achievement. Since the EITC schedule is virtually identical for one and two+ child families over this time period, these effects cannot be attributed to the EITC. These results provide additional evidence against the validity of the difference-in- differences methodology employed in this study.
7 Replication of Dahl and Lochner (2012)
The reduced form estimates obtained in the previous section leave open the question of whether or not the EITC aids child achievement. The inconclusiveness of my results contrasts with the sharp, and consistently positive estimates obtained by Dahl and Lochner (2012). As discussed previously, Dahl and Lochner use the EITC instrumentally to identify the causal effect of family income on child achievement. However, in the reduced form Dahl and Lochner's model has the same objective as do my models: to capture the effect of changes in EITC policy on child achievement.
Dahl and Lochner's results imply that a $1,000 increase in simulated EITC income (their instrument) raises children's combined math and reading test scores by about 7.7 percent of a standard deviation.
When the sample is restricted to children with single mothers, the estimate is slightly lower, at 6.5 percent of a standard deviation. Since Dahl and Lochner's study is, to my knowledge, the only existing peer-reviewed paper looking at the relationship between the EITC and child achievement, it is important to determine whether their approach provides a credible estimate of this relationship.
In this section I replicate DL's study and I perform several robustness tests designed to probe the credibility of DL's strategy and findings. Since my objective in this replication is to determine whether or not DL's methodology produces a credible and robust estimate of the impact of the EITC on child achievement I focus primarily on the reduced form model.
7.1 Data
As discussed in section (5), this replication relies on data that are made available by Gordon Dahl on his website. Data are drawn from the children of the NLSY79 matched to their mothers in the main NLSY79. Due to differences in DL's identification strategy from my own, the sample restrictions imposed by DL are quite different from mine. Since DL's model is estimated in differences, the estimation sample is restricted to children who are observed in at least two consecutive sample years between 1988 and 2000 and who have valid PIAT scores, family
background characteristics, and family income measures.11 The sample is limited to children whose mothers did not change marital status during two-year intervals when tests are measured, and
observations are excluded if family income levels exceeds $100,000, or if two-year changes in family income exceed $40,000. NLSY oversamples of poor white families and military families are
excluded from the sample.12
DL use a confidential version of the NLSY79 that includes state identifiers. These state identifiers are not included in the version of the data that Dahl and Lochner make available for download. There is some small variation in state EITCs that is excluded from my replication due to the omission of state identifiers. However, the impact of this omission on the estimates is,
apparently, quite small since I am able to replicate their results almost perfectly without the state identifiers. DL create a combined math and reading test score that is a normalized average of all three tests (i.e. math, reading recognition, and reading comprehension). While DL present estimates for the combined score and for each test individually, in this replication I focus exclusively on the combined score for the sake of brevity.
7.2 The Model
11 Family income is reported for the year prior to the survey. Therefore, while observations are drawn from the 1988 to 2000 surveys, income is reported for the years 1987 - 1999. In this replication I follow DL's convention in making reference to the income year, not the survey year.
12 The reason for dropping these observations is, apparently, that the interviews with the military oversamples were discontinued after 1984 and interviews with the poor white oversamples were discontinued after 1990. See the NLSY79 User Guide for details.
The reduced form model is estimated in differences, and intends to capture the
contemporaneous effect of changes in EITC generosity on child achievement. In this strategy, DL construct a "simulated" measure of changes in EITC income that is a function of lagged family income. The reduced form model is presented here (omitting Xs):
where the subscripts i and a denote child i at age a; the outcome variable, y, is a standardized test score (mean of zero, standard deviation of one); represents lagged pretax earnings;
is the instrument, representing a simulated change in EITC income, and constructed in the following way:
where is an estimate of current pretax earnings given lagged pretax earnings. In practice, they regress pretax earnings on an indicator for positive lagged pretax earnings and a 5th- order polynomial in lagged pretax earnings in the calculation of . This yields predicted changes in EITC income as a function of lagged pretax earnings, taking into account the fact that earnings evolve over time in a predictable way and that the EITC schedule changes in some years.
DL keep the type of EITC schedule (one vs. two+ children) fixed in generating the instrument, thereby ensuring that variation comes from government changes in the EITC schedule and not from changes in family structure.
Since is a function of lagged pretax earnings, and since lagged pretax earnings are likely correlated with due to such factors as serially correlated earnings shocks, regression to the mean, or measurement error, the reduced form model is augmented with a "fully flexible" function of lagged pretax earnings, . Empirically, DL use the same functional form for as is
used in calculating (i.e. an indicator for positive lagged pretax earnings and a 5th-order polynomial in lagged pretax earnings). By using a fully flexible, but time-invariant control function, they assume implicitly that the relationship between lagged earnings and shocks to child achievement does not vary over time.
Before presenting the results of this replication, it is helpful to have a clear understanding of how variation in the instrument is generated. First, over a single two-year difference for families of similar size (i.e. one or two+ child families), variation arises strictly from differences in lagged earnings levels between families. To illustrate this, suppose that the sample is restricted to the 1997- 1999 period for families who have two+ children in 1997. All families with the same lagged pretax earnings ( in 1997 will have the same predicted earnings in 1999. Moreover, all families with two+ children in 1997 face the same federal EITC schedule in 1997 and 1999 since the type of EITC schedule (i.e. one vs. two+ children) is held fixed when creating the instrument. In this case, variation in arises strictly from differences in lagged earnings levels between families.
This variation is illustrated by the solid line in Figure 4(f) where the instrument is plotted against lagged earnings for the 1997-1999 time period for families with two+ children.
Second, instrument variation arises due to differences in the EITC schedule by family size.
To see this, suppose that we augment the 1997-1999 sample of two+ child families to also include one child families. In 1997 and 1999 the EITC schedules for one child families differ from the schedules for two+ child families. Because of this, the simulated EITC change for a family with only one child differs from a simulated EITC change for a family with two+ children, even if the two families have identical levels of lagged earnings. Variation in for one child families for the 1997-1999 time period is illustrated by the dotted line in Figure 4(f).
Third, variation in arises due to differences in the EITC schedule over time. To see this, suppose that we augment the 1997-1999 sample to include all two year time periods between 1987 and 1997 (i.e. 1987-1989, 1989-1991, 1991-1993, 1993-1995, and 1995-1997). Since predicted income is a function of lagged income only, all families with the same lagged pretax income will have the same predicted income, regardless of which year the lagged year is. However, the EITC schedules in each two-year period differ from the EITC schedules in every other two year period.
Because of these schedule differences over time, families with identical lagged earnings levels and number of children will have different simulated EITCs if they are in different time periods.
Variation in the instrument over time is illustrated in Figure 4(a) through (f).
DL assume that the control function (a 5th order polynomial in lagged income and an indicator for positive lagged income) is sufficiently flexible to fit any one of the functions plotted in Figure (3) in isolation. Recall that the shape of any one of these function plots is dependent on the EITC schedule in the current year and the EITC schedule in the lagged year for each two-year period.
If the EITC schedules in the current and lagged years for one two-year period were identical to the EITC schedules in the current and lagged years for every other two-year period, then the function plots in Figure (3) would all be identical and the control function should completely eliminate any independent variation in the instrument. Identification, therefore, is assumed to be wholly dependent on differences in the EITC schedules from one two-year period to another, and from differences in the EITC schedules across differently-sized families within a two-year period. In the analysis that follows I demonstrate that, in fact, the control function is not sufficiently flexible to accomplish this task. This discovery raises some concerns about the validity of DL's strategy. I now present results from the replication and from several sensitivity analyses designed to probe the plausibility of instrument validity.
7.3 Replication Results
DL's implied reduced form estimates for the combined math and reading test score are presented for all children and for various subgroups in panel (A) of Table (5). Since DL do not present reduced form estimates, these estimates are implied by their IV and first stage results.13 The instrument is measured in $1,000s and the dependent variable is a standardized test score, so DL's implied reduced form estimates suggest that a $1,000 increase in simulated EITC income raises kids' combined math and reading test scores by 7.7 percent of a standard deviation for all children;
reduced form estimates for the various subgroups range between 6.5 and 10.3 percent of a standard deviation. The reduced form estimates from my replication are presented in panel (B). Each of my estimates is slightly larger than DL's corresponding estimate, but none of the differences are statistically significant.
7.4 Sensitivity Analysis
My objective in this sensitivity analysis is to provide evidence regarding the validity of DL's identification strategy. To do so, I perform three tests. The first two tests are motivated by the following statement made in DL's paper:
"The validity of our research design, therefore, hinges on controlling flexibly for pretax [earnings] with the control function. The fact that we use lagged pretax [earnings] is second- order."
I begin by performing a test that is designed to shed light on the first part of this statement: whether the control function is sufficiently flexible to control for lagged pretax earnings. Since the functional form of the relationship between child achievement and lagged earnings is unknown, DL's strategy is to include a "fully flexible" function of lagged earnings in the model. While there is no way to know for certain whether the included control function is sufficiently flexible, it is more likely to be so if it is at least flexible enough to remove all instrument variation arising from differences in lagged
13 The estimate for the full sample is taken from Table (3) of Dahl and Lochner (2012); estimates for the various subgroups are taken from the top panel of Table (6).
earnings levels between families. Indeed, DL explicitly claim that with the inclusion of the control function, instrument variation due to differences in lagged earnings is eliminated, leaving only variation due to government changes in the EITC schedule. This is something that I can test. Next I test the second part of DL's statement: whether the decision to specify the model as a function of lagged pretax earnings is, in fact, second-order. If it is, then it should be possible to reformulate the model as a function of current pretax earnings and obtain similar results. I test this directly. In the final test I test whether the estimates are sensitive to which time periods the instrument variation comes from. If they are, this could suggest that estimates are driven by transitory shocks to child achievement, and not by the EITC.
Sensitivity test #1: Is the control function sufficiently flexible to control for lagged pretax earnings?
As described above, the likelihood that the control function is sufficiently flexible to capture the true expected relationship between lagged earnings and child achievement is enhanced if it can be shown that the control function is sufficiently flexible to eliminate instrument variation arising from lagged earnings differences. I can test whether the control function accomplishes this task by estimating separate models for all subsamples for which the instrument varies strictly because of differences in lagged earnings levels. For each of these subgroups the control function should be sufficiently flexible to eliminate all independent variation in the instrument. The subgroups of interest can be easily identified by looking at Figure (4): each distinct function plot in this figure pertains to one such subgroup. The subgroups are: (1) all families in 1987-1989, (2) one child families in 1989-1991, (3) two+ child families in 1989-1991, (4) one child families in 1991-1993, (5) two+ child families in 1991-1993, (6) one child families in 1993-1995, (7) two+ child families in 1993-1995, (8) one child families in 1995-1997, (9) two+ child families in 1995-1997, (10) one child families in 1997-1999, and (11) two+ child families in 1997-1999.
Reduced form estimates for these eleven subsamples are presented in panel (A) of Table (6).
In panel (B) of Table (6) I present first stage estimates for the two channels whereby we assume that the EITC affects child achievement: EITC income and maternal labor force participation. While the reduced form estimates and the first stage estimates for maternal labor force participation are quite imprecise, the first stage estimates for EITC income are almost all positive and significant—ten of the eleven estimates are positive and nine of these positive estimates are significant at least at the 10% level. This is conclusive evidence that the control function is not "fully flexible". The implication is that variation in DL's instrument does not arise strictly from government changes in the EITC schedule; a substantial amount of variation derives from differences in lagged earnings levels between families. Since the control function is not sufficiently flexible to eliminate instrument variation arising from lagged earnings differences, it is less likely that the control function is
sufficiently flexible to capture the relationship between lagged earnings and child achievement. And since lagged earnings are likely endogenous due to serially correlated earnings shocks or
measurement error (for example), the reduced form estimates may be biased in an indeterminate direction.
Sensitivity test #2: Do Dahl and Lochner's estimates depend on specifying the model as a function of lagged earnings rather than current earnings?
There is no obvious advantage to specifying the model as a function of lagged earnings instead of current earnings. The key to DL's identification strategy—as DL rightly observe—is that the control function is sufficiently flexible to control for pretax earnings; the use of lagged earnings is second-order. While the endogeneity concerns posed by lagged earnings may differ from those posed by current earnings, they must be addressed in either case. Indeed, the validity of DL's results depend heavily on an assumption that lagged earnings are highly endogenous, resulting in a severe negative bias in the achievement estimates that is corrected by the inclusion of the control function.
This is illustrated in panel (B) of Table (7) where I present estimates of DL's reduced form model for all kids and for various subgroups of kids, but this time omitting the control function. When the control function is omitted the reduced form estimates are all considerably smaller—in several instances the sign is actually negative—and for all but one of the estimates the difference is
significant at the 1% level. Moreover, only when the control function is included in the model—as shown in panel (A)—are the estimates significantly different from zero.
I test whether the results are sensitive to DL's decision to specify the model as a function of lagged earnings by estimating a re-specified model that is a function of current earnings. The model expressed as a function of current earnings is presented here:
where, as before, is a standardized test score for child i at age a; is a child fixed effect; is current earnings; is current EITC income as a function of current earnings; and is a flexible function of current earnings. As with the previous model, the control function consists of a 5th order polynomial in current earnings and an indicator for positive current earnings. The model is presented here using mean-differences to remove child fixed effects but I also estimate the model in first-differences in order to more closely approximate DL's approach. Note that when the model is specified as a function of current earnings there is no longer any need to include the simulation. The identifying assumption for this model is identical to the identifying assumption for the model
specified as a function of lagged earnings: that the control function is sufficiently flexible to capture the true expected relationship between pretax earnings and child achievement. With a "fully
flexible" control function all identifying variation arises from changes in the EITC schedule over time and across family types.
Reduced form estimates of this model are presented in Table (8). Results from the model estimated in first-differences are presented in panel (A); results from the model estimated in mean- differences are presented in panel (B). The first-differences estimates are all positive and for most subgroups they are significantly different from zero at least at the ten percent level. These estimates are all somewhat smaller than are the estimates of the model specified as a function of lagged earnings and in most cases the differences are statistically significant (4 out of 7 of the differences are significant at the 5% level). Nevertheless, the estimates are generally supportive of DL's finding that the EITC boosts child achievement. This conclusion changes dramatically when the model is estimated in mean-differences, as shown in panel (B). Estimates in panel (B) are all negative and, with one exception, are significantly different from zero at the 1% level. The surprising conclusion of this test is that the estimates are not overly sensitive to whether the model is specified as a function of lagged versus current earnings, but they are very sensitive to whether child fixed effects are
removed via first- or mean-differencing. This seems to suggest that there is either a problem with DL's model or, perhaps, a problem with the estimation sample. Whatever the reason, these findings raise some doubt regarding the credibility of DL's estimates.
Sensitivity test #3: Are the estimates sensitive to which time periods variation in the instrument comes from?
If the reduced form estimates are sensitive to which time periods the instrument variation comes from, this could imply that the results are driven by transitory shocks to child achievement and not by the EITC. In order to test this, I divide the sample in two parts. In one subsample I include all first-differenced observations between 1987 and 1993; in the second subsample I include all first- differenced observations between 1993 and 1999. Estimates for the early period are presented in panel (A) of Table (9). Reduced form estimates are all negative, and for kids < 12 years of age the estimate is significant at the 1% level. First stage estimates for EITC income are all positive and
significant at the 1% level; first stage estimates for maternal labor force participation are all positive, and generally quite large. Estimates for the later period are presented in panel (B) of Table (9).
Reduced form estimates are all positive and, with the exception of the estimate for young kids, are significant at least at the 10% level. First stage estimates for EITC income are all positive and significant at the 1% level, and are nearly identical to the estimates from the early period; first stage estimates for maternal labor force participation are generally small and not significant (though they are much more precise than in the earlier period).
The fact that the first stage estimates for EITC income are almost identical in the early period and the later period indicates that there is plenty of instrument variation in either period. Moreover, since these first stage estimates are nearly identical, we might expect the reduced form estimates to also be similar. This is not what we observe however. The reduced form estimates are consistently negative in the early period, and consistently positive in the later period. It is true that the first stage estimates for labor force participation are larger in the early period, but it is difficult to imagine that this is driving the large discrepancy in the reduced form estimates between the two periods. These results suggest that the reduced form estimates are likely driven by transitory shocks to child achievement, and not by the EITC.
8 Discussion and Conclusion
In this study I analyze the impact of the EITC on child achievement, as measured by
standardized test scores. I estimate the impact of the EITC on child achievement using two separate identification strategies. In the first strategy I employ a basic difference-in-differences (DD) model that takes advantage of the differential growth in EITC generosity for two+ child families relative to one child families. The results of this estimation imply that increased EITC generosity greatly decreases child reading achievement. However, this negative effect is only significant in 1994 when the difference in EITC generosity for one versus two+ child families is very small. Moreover, a
robustness test reveals that trends in achievement may not be parallel over a time period where the EITC schedule is nearly identical for the two groups. Next, I estimate a model using a "simulated"
EITC. For the simulation model the results are all positive, but quite imprecise. I conclude that while these estimates do not rule out a relationship between the EITC and child achievement, they do not, in the end, provide any clear indication of the magnitude, or even the sign, of this relationship.
These findings contrast with the very precise, and consistently positive, estimates obtained by Dahl and Lochner (2012). Dahl and Lochner's study is, to my knowledge, the only existing peer- reviewed study looking at the relationship between the EITC and child achievement. In their study Dahl and Lochner are not interested in the effect of the EITC on child achievement per se, but rather use expansions of the EITC as an instrument for family income. However, in the reduced form, their estimates imply that a $1,000 increase in simulated EITC income (their instrument) improves
combined math and reading test scores by about 7.7 percent of a standard deviation. In this study I replicate their results and perform three robustness tests. The results of these robustness tests raise questions regarding the validity of DL's findings. First, variation in their policy instrument derives in some measure from differences in lagged earnings levels between families. Since lagged earnings are likely endogenous—as discussed by DL—the estimates are likely biased. Second, the estimates are highly sensitive to whether child fixed effects are removed via first- or mean-differencing. And third, the reduced form estimates are sensitive to which time periods instrument variation comes from, suggesting that the results might be driven by transitory shocks to child achievement and not by the EITC.
The main contribution of this paper is to demonstrate how little we know about the impact that the EITC has on child achievement. While it may in fact be true that the EITC helps kids—the evidence I present here does not rule out a positive effect—the fact is that we simply do not know at the present. Policy recommendations and decisions ought to be shaped by the most up-to-date
evidence. In addition, this study may serve as an impetus to other researchers to explore this important topic more fully. The expansion of the EITC in the early 1990s was born out of the same motivations that shaped welfare reform in the 1990s. Namely, that traditional welfare creates dependency, and that income support programs should be designed to encourage work among recipients. While this policy objective may be reasonable, the fact is—in the case of the EITC at least—the only group that has been shown to increase its labor supply is single moms. For married couples the EITC leads to traditional welfare-type incentives, leading to small reductions in labor supply. The effect of the EITC on the achievement of kids in these single mother-headed households remains an important and—for the moment—elusive topic of research.
References
Baughman, Reagan and Stacy Dickert-Conlin. 2003. "Did Expanding the EITC Promote Motherhood?", The American Economic Review, 93(2): 247-251.
Baughman, Reagan and Stacy Dickert-Conlin. 2009. "The Earned Income Tax Credit and Fertility", Journal of Population Economics, 22(3): 537-563.
Bernal, Raquel. 2008. “The Effect of Maternal Employment and Child Care on Children’s Cognitive Development.” International Economic Review 49(4):1173-1209.
Currie, Janet, and Jonathan Gruber. 1996a. “Health Insurance Eligibility, Utilization of Medical Care, and Child Health.” The Quarterly Journal of Economics, 111 (2): 431-66.
Currie, Janet, and Jonathan Gruber. 1996b. "Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women." Journal of Political Economy 106(6):1263-96.
Cutler, David M., and Jonathan Gruber . 1996. “Does Public Insurance Crowd out Private Insurance.” The Quarterly Journal of Economics, 111 (2): 391-430.
Dahl, Gordon B., and Lance Lochner. 2012. “The Impact of Family Income on Child Achievement:
Evidence from the Earned Income Tax Credit.” American Economic Review, 102(5): 1927-1956.
Dickert-Conlin, Stacy. and Scott Houser. 2002. "EITC and Marriage", National Tax Journal, vol.
55(1), pp. 25–40.
Dickert, Stacy, Scott Houser, and John Karl Scholz. 1995. “The Earned Income Tax Credit and Transfer Programs: A Study of Labor Market and Program Participation.” In Tax Policy and the Economy, ed. James M. Poterba. Cambridge, MA: National Bureau of Economic Research and the MIT Press.
Duncan, Greg J., and Jeanne Brooks-Gunn, eds. 1997. Consequences of Growing up Poor. New York: Russell Sage Foundation
Eissa, Nada, and Hilary Williamson Hoynes. 2004. “Taxes and the labor market participation of married couples: The earned income tax credit.” Journal of Public Economics 88 (9–10): 1931–58.
Eissa, Nada, and Hilary W. Hoynes. 2006. "Behavioral Responses to Taxes: Lessons from the EITC and Labor Supply." In Tax Policy and the Economy, Vol. 20, ed. James M. Poterba, 74-110. Cam bridge, MA: MIT Press.
Eissa, Nada, and Jeffrey B. Liebman. 1996. “Labor Supply Response to the Earned Income Tax Credit.” Quarterly Journal of Economics 111 (2): 605–37.
Ellwood, David T. 2000. “The Impact of the Earned Income Tax Credit and Social Policy Reforms on Work, Marriage, and Living Arrangements.” National Tax Journal 53 (4, Part 2, December):
1063–1105.
