The Effects of Income on Health: New Evidence from the Earned Income Tax Credit

This study examines the relationship between income and health by using an expansion of the Earned Income Tax Credit (EITC) as exogenous variations of earnings. The paper adds to previous work in three ways. First, I estimate treatment effects on the treated using simulated EITC benefits and longitudinal data to remove concerns about measurement error and sample selection. Second, I test whether health effects vary across the EITC schedule. Third, I examine the role of food expenditures and health insurance as potential mechanisms. The study finds that income improves health, while food expenditures and insurance coverage are likely channels.


I. INTRODUCTION
The existence of a significant positive association between income and health, also known as the income gradient in health, has been well documented in the literature (Case et al., 2002;Deaton, 2002). Despite several contributions over the past decade in a number of fieldsall of which have found robust correlations using data from different countriesit is still not entirely clear whether such a positive association is the result of a causal relationship between income and health. There are good reasons to believe that a causal effect between income and health exists. Higher income families may have better access to care as well as more opportunities to purchase care; whereas people with lower income may be confronted with more stressful situations, which are detrimental to health. This study tests whether the well-established health gradient exists once the endogeneity of income is accounted for by using expansions in the Earned Income Tashortx Credit (EITC) in the mid-1990s as an exogenous income variation. I find that higher EITC payments lead to significant improvements in self-assessed health, while changes in food expenditures and insurance coverage are shown to be likely mechanisms underlying the relationship between income and health.
By using data from the Panel Data of Income Dynamics (PSID) for the years 1990-2003, this study exploits the expansion of the EITC, which was part of the Omnibus Reconciliation Act (OBRA) of 1993, to test for the relationship between income and health outcomes of heads of households. This approach can eliminate or significantly reduce the omitted variable bias due to shocks correlated with income and give estimates for treatment effects of receiving a boost in income on health of treated individuals. Findings for the relationship between income and health in this setting advance previous work on the gradient and provide evidence for a causal effect of income on health. Additionally, the later part of the study tests for the role of food expenditures and health insurance as potential mechanisms underlying the link between income and health.
Four recent studies on the EITC have examine whether the program is able to improve health outcomes of children (Averett and Wang, 2015), infants , mothers (Evans and Garthwaite, 2014), and low-income adults (Larrimore, 2011). This study joins this small group of papers and adds to them by making five contributions. First, by using a longitudinal data set and by estimating models with individual fixed effects, the analysis accounts for time-invariant unobserved heterogeneity, measurement error in self-assessed health and potential changes in the sample composition. Given that the EITC provides incentives for low-income individuals to enter the labor force, differences in the composition of sample before and after an expansion of the program are difficult to account for with cross-sectional data.
Furthermore, measurement error can be reduced since each individual's health is only compared to their own prior assessment, which takes into account that respondents might have their own scales in ranking their health (reference bias). To my knowledge, only one previous paper uses longitudinal data to analyze the relationship between the EITC and health (Averett and Wang, 2015).
Second, I use a tax simulator program to obtain predicted EITC payments and to examine health changes among a sample of individuals eligible to receive EITC benefits. Previous studies testing for health effects of the EITC have focused on low-educated individuals, a group most likely affected by changes to the program. Examining health changes among low-educated samples provides intent-to-treat estimates for the effects of the policy change. An analysis of health effects among people that are actually eligible to receive the increases credits can provide treatment effects on the treated. While intent-to treat effects are vital in order to obtain evidence for whether the program impacts health of the population, treatment effects on the treated can provide direct evidence for whether income in general has causal effects on health. Thus, the findings from this study complement the great work previously conducted on the relationship between the EITC and health, while specifically addressing the relationship between income and health.
Third, the study uses the imputed simulated EITC amounts which respondents are eligible to receive in order to further examine the link between income and health in more detail.
Specifically, I test whether the expansion had different health impacts for individuals falling in different parts of the EITC schedule (phase-in, plateau, and phase-out range). Previous work has established that individuals in the plateau part receives close to pure income effect (Athreya et al., 2010;Gunter, 2013), while those in the phase-in part have been found to work more on the extensive margin (Eissa and Liebman, 1996;Eissa et al., 2008;Meyer, 2010). Additionally, using the simulated EITC payments as well as the longitudinal nature of the data, I test whether the policy had different effects for individuals who experienced increases in EITC earnings of at least $500 compared to those whose benefits increased by less than $500 following the policy change. Examining different effects across the schedule and across people experiencing larger and smaller gains in benefits can provide further evidence for the effect of income on health.
Fourth, this study contributes to the remaining uncertainty regarding the mechanisms through which income can affect health outcomes by investigating the role of two potential channels. To my knowledge, this is the first study that examines the role of changes in food expenditures as a potential channel through which higher EITC benefits might affect health.
Given that there is a close link between income and food insecurity, additional income in the hands of vulnerable groups of the population could affect their levels of food security.
Furthermore, similar to work by Baughman (2005) and , this study tests for the role of changes in health insurance coverage following an expansion of the EITC.
Fifth, besides estimating DD models, I test for the robustness of the findings by additionally estimating several other specifications. These include: 1) a DDD model that accounts for the fact that other events at the time could impact health outcomes of individuals in the sample; 2) a semi-parametric DD model which loosens some assumptions about a linear relationship between income and health; 3) an alternative DD model that allows testing for the similar path assumption of the DD framework; 4) a falsification test that compares health changes of two groups that were equally affected by the expansion.
This study finds that increases in income following the expansion of the EITC leads to improvements in self-reported health status. The positive health effects are robust to variations in both sample selection and methodology and become larger when the policy change is allowed to have an adjustment period after its implementation. When examining potential mechanisms underlying the link between income and health, this paper provides evidence that increases in food expenditures and take-up rates of insurance can explain the observed health improvements.

II. PREVIOUS LITERATURE
A number of previous studies have investigated the relationship between household income and self-reported health status. Case et al. (2002) set the groundwork for this area of research by finding a significant positive relationship between family income and health of children younger than seventeen years of age in the United States. Applying similar setups as Case et al. (2002), many studies have since then investigated the existence of an income/health gradient in Canada (Currie and Stabile, 2003), England (Adda et al., 2009;Currie et al., 2007;Propper et al., 2007), Australia (Khanam et al., 2009), and Germany (Reinhold and Jürges, 2012). Based on the convincing evidence of the findings in these studies, the existence of the income gradient in health became established and widely acknowledged.
A small number of studies have so far addressed this issue by exploiting exogenous variations of income. Kuehnle (2014) uses changes in local unemployment rates as an instrument for income while examining the gradient in child health in the United Kingdom. Lindahl (2005) finds evidence for a causal link between income and health by analyzing health effects of winning the lottery, whereas no information on the timing of lottery winnings is available. Frijters et al. (2005) uses income transfers to individuals living in East Germany following the German Reunification in order to test for the causal impact of income on health. Overall, while these papers find at most small evidence for the presence of a causal link between income and health, there is still some uncertainty about the causal nature of the relationship.
The majority of previous work on the EITC has focused on the effects on economic outcomes. The existing literature has established that changes in the EITC are a successful tool in lifting families above the poverty threshold (Scholz, 1994;Neumark and Wascher, 2001;Meyer, 2010;Short, 2014;Hoynes and Patel, 2015). Based on the U.S. Census Supplemental Poverty Measure, in 2013 the EITC (and the child tax credit) lifted 4.7 million children out of poverty, which is more than any other program (Short 2014). Hoynes and Patel (2015) show that a policyinduced $1000 increase in the EITC leads a 9.4 percentage point reduction in the share of families with after-tax and transfer income below 100% poverty. Furthermore, researchers have investigated the impacts of the program on labor force participation (Eissa and Liebman, 1996;Meyer and Rosenbaum, 2001;Hotz and Scholz, 2003;Eissa et al. 2008), educational attainment (Miller and Zhang, 2009), test scores (Dahl and Lochner, 2012), marriage (Ellwood, 2000;Dickert-Conlin and Houser, 2002) and fertility (Baughman and Dickert-Conlin, 2009). Not until very recently have researchers started examining potential effects of the program on health outcomes. Expansions of the EITC have been shown to positively impact birth weight  and health (Evans and Garthwaite, 2014;Larrimore, 2011), while furthermore reducing smoking of affected mothers (Averett and Wang, 2013).

A. The Earned Income Tax Credit
The Earned Income Tax Credit (EITC) provides a refundable transfer to lower-income working families through the tax system. First enacted in 1975 as a relatively small credit capped at $400 per family to offset the growth of payroll tax payments by families with children, the program was supposed to act as a work bonus as well as a response to the 1974 recession. The implementation of the program was the outcome of vital policy discussions regarding the Negative Income Tax (NIT) as a means of reducing poverty. Following intense debates, the EITC was introduced in an attempt to reward work rather than to provide guaranteed income, while aiming at moving families beyond the poverty line. Since the original implementation, Congress has expanded the EITC several times both in terms of benefit size and eligibility requirements. The Omnibus Reconciliation Act (OBRA) of 1993, signed by President Clinton, delivered one of the most significant changes to the tax credit. The reform significantly increased differences in benefits given to eligible families with two or more children younger than nineteen years of age in the household and those with only one child. As soon as the changes of the reform were fully put in place in 1996, maximum benefits for families with two or more children more than doubled, whereas payments for families with one eligible child only slightly increased. In addition to the augmented importance of the program over the last decades, another reason for why the EITC has attracted much interest by researchers is its unique payment structure, which significantly differs from other welfare programs. The size of benefits received by eligible families depends on several factors, such as the presence and number of qualifying children in the household.2 Depending on the amount of a family's earnings and adjusted gross income, EITC payments have: 1) A phase-in range in which higher earnings yield higher credits; 2) A plateau phase in which payments remain the same even as earnings rises; and 3) A phaseout range in which higher earnings yield lower credits. Permanently disabled individuals of any age as well as full-time students up to age 24 can furthermore qualify as a filer for the EITC.
Following several expansions to the program, the plateau phase expanded from $5,000-6,000 in 1 Before the policy changes of OBRA 1993 were implemented, seven states had introduced state-level EITC payments and ten additional states adopted it until the end of the period of interest of this study in 2003. Today, twenty-five states have EITC credits at the state level in place, which further highlights the increasing importance of the program. 2 Please see Hotz and Scholz (2003) (IRS, 2002;Scholz, 1994). Despite only being offered for three years, researchers have found that the HITC increased insurance coverage of low-wage workers (Cebi and Woodbury, 2014).

B. Other Welfare Reforms during the 1990s
The late 1990s witnessed significant changes in welfare policies due to the implementation of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA). The main goal of the reforms was to make low-income families independent of welfare benefits and to provide states with flexibility in determining eligibility criteria and benefit levels. Previous literature has established that the policy changes significantly affected the lives of lower-income families who were dependent on welfare assistance at the time (Schoeni and Blank, 2000). However, there is no evidence that the welfare reforms impacted the health outcomes of affected individuals (Bitler et al., 2005). Given the framework of the study, other welfare changes that occurred in the 1990s offer a threat to the identification of the impact of the EITC on health outcomes if the other welfare changes differentially affected low-income families with two or more children compared to families compared to families with only one child.
One advantage of the timing of the EITC expansion examined in this study is that it was implemented one year before the first welfare reforms were passed, which allows me to separate the effects of the policy changes. In order to account for other economic changes and policy alterations that occurred during the period of this study, specifications that additionally control for a set of state characteristics and welfare policy variables. These controls include average annual state unemployment rates, state-level AFDC eligibility requirements (for a family of three), the presence and timing of AFDC waivers and time limits on receiving welfare, and the type of sanctions as well as indicators of whether the state expanded Medicaid coverage and implemented state-level EITC benefits. Since state dummy variables can only deal with the statelevel heterogeneity that is time-invariant, the inclusion of these additional characteristics can account for statewide variations in welfare reforms.

A. Panel Study of Income Dynamics (PSID)
The main part of this study uses data from the Panel Study of Income Dynamics (PSID), a nationally representative longitudinal sample of households and families interviewed annually since 1968 and biannually since 1997. The PSID, the longest running U.S. panel, was specifically designed to track income dynamics over time. The survey over-samples low-income families, which is advantageous for this analysis since these households are more likely to be eligible to receive EITC. Due to its detailed information on earnings, the PSID is well-suited for calculating simulated EITC benefits through the tax simulator program NBER TAXSIM (version 9; for more information see Feenberg and Coutts, 1993). Furthermore, by using state identifiers provided in the PSID, I am able to simulate both state-level and federal EITC benefits.3 In order to obtain treatment effects on the treated, I limit the sample to heads of households with at least one child who, based on the TAXSIM simulations, are eligible to receive EITC benefits.4 Consistent with findings in the literature showing that 80 to 87 percent of eligible households indeed receive the credit (IRS, 2002;Scholz, 1994), this study assumes full take-up rates (Dahl and Lochner, 2012). Individuals with missing income information (5.4 percent of the sample) are dropped from the analysis since the use of imputed values could cause a substantial measurement error and attenuate the estimates. Heads of households with missing information on their health status are removed from the analysis as well, whereas the sample is restricted to individuals less than sixty-five years of age.5 The main dependent variable is self-reported health status, which is categorized on a scale from 1 (excellent) to 5 (poor). Self-assessed health has been widely used in previous studies regarding the relationship between income and health (e.g. Case et al., 2002;Currie and Stabile, 2003;Adda et al., 2009). It has been shown to be a good predictor of other health outcomes, including mortality (Idler and Benyamini, 1997), future health care usage (van Doorslaer et al., 2000) and future hospitalizations (Nielsen, 2016). The longitudinal nature of the PSID reduces the potential measurement error in the self-reported health variable in two ways: 1) by comparing each individual's health only to their own prior assessment, and 2) by controlling for the fact that each respondent may have their own scales in ranking their health (reference bias). Additionally, the panel nature of the PSID allows me to account for potential changes in the composition of the sample following the increase of EITC benefits, a feature that cannot be addressed using cross-sectional data.
When testing for the role of food expenditures as a channel underlying the relationship between income and health, the dependent variables are the amounts of money that a household spends on food per week. Additionally, I examine whether any potential changes are driven by people purchasing more food that is eaten at home or away from home.6 Despite the fact that spending more money on food does not guarantee that individuals buy groceries with higher quality, I believe that increases in food expenditures can be viewed as a proxy for an increase in food quality. Consistent with this, a study by McGranahan and Schanzenbach (2013) provides evidence that EITC receipt increases spending on relatively healthy groceries while lowering expenditures on processed fruit and vegetables.

B. Current Population Survey (CPS):
Besides examining the role of food expenditures, this study also tests for the role of health insurance coverage as a potential mechanism underlying the relationship between income and health. For this analysis, I use data from the annual March Population Survey (March CPS).
In order to narrow the sample down to individuals who are eligible to receive EITC payments, I again use the TAXSIM program to obtain predicted amounts of EITC benefits. Using March CPS data in order to test for the role of insurance is beneficial since it provides extensive information on the health insurance coverage. More specifically, I test for the effect of the expansion of the EITC on different types of insurance (private, public, Medicaid/SCHIP).
Besides examining whether individual are more likely to have insurance coverage following an increase in income, this also allows testing whether individuals switch between different types of plans after the policy change following increases in income. Since information on insurance coverage is only available from 1992 and onwards, the period of interest is reduced to the years 1992 to 2000.

C. Descriptive Statistics
Figure 1 presents graphical motivation for using the EITC through OBRA 1993 to examine the causal link between income and health. The picture shows the amount of EITC which eligible families in the sample receive, with the sample being split into two groups: families with one child and those with two or more children. It is noticeable that the size of the benefits is very similar for both groups prior to the implementation of the expansion in 1996.
However, after the policy change, families with two or more children are receiving substantially higher payment than those with one child. By 1999, the difference between the two groups is about $900 and it remains very similar for the remaining years.7 Table 1 shows the distribution of the number of observations in the study for the FE and the non-FE sample from the PSID data. The FE sample includes 178 individuals that are eligible to receive EITC payments in every year of the study, which provides a total sample size of 1,958 observations.8 The non-FE sample, which consists of all individuals who were eligible to receive EITC benefits in a given year, has 15,189 total observations. Table 2 presents descriptive statistics for the two PSID samples used in the study. Consistent with Figure 1, it is noticeable that average EITC payments increased significantly for eligible families with two or more children compared to those with only one child. While only very small differences in EITC payments exist before the policy change (1990)(1991)(1992)(1993)(1994)(1995), the average difference in benefits increased to $795.09 and $722.13 for the FE and the non-FE sample, respectively. This effect of the policy on EITC benefits is significantly higher than the gap of $320 reported by Averett and Wang (2013). The statistics for FE sample furthermore show that a large share of credit-eligible heads of households are unmarried black women, whereas the non-FE sample appears to differ with respect to gender and race.
While the bottom of Table 2 provides descriptive statistics for health status for the entire period of the study, Figure 2 shows changes in the share of individuals who report either excellent or very good health across between 1990 and 2003 (six observations before and five observations after the policy change). The graph provides evidence that trends in health status were similar during the three years before the policy implementation between heads of households one and two or more children. After 1998, heads of households with two or more children are more likely to report either excellent or very good health. This provides suggestive evidence for positive health effects of the policy changes, while the fact that the policy had been in place for three years before differences become distinct indicates that it might take some time before the effects of income on health are noticeable.9 Figure 3 provides graphical evidence that the expansion of the EITC increased total weekly food expenditures of eligible households with two or more children. While, no differences are observable before the policy change, the graph shows that families with two or more children spend around $20 more per week on food than those with only one child after the policy change.10

A. DD Models
The study exploits the expansions of the EITC through OBRA 1993 in order to test for a causal relationship between the EITC and health outcomes. The structure of the policy changes offers the opportunity for a difference-in-differences (DD) framework to observe the average treatment effects on the treated. In the presence of changes in the composition of the sample, a cross-sectional analysis could provide inaccurate estimates if healthy individuals with two or more children choose to enter the labor force following the incentives of being eligible to higher EITC benefits after the policy change. Thus, the main specification of this paper uses the longitudinal nature of the PSID to control for individual fixed effects, and only examines individuals who are eligible to receive EITC benefits throughout the sample period. Specifically, I estimate the following equation: Yit = β0 + β1 2KIDSit + β2 Xit + δDD POSTit *2KIDSit + λ1 Year + λ2 State + αi + εit, (1) where Yit is an indicator that equals one if the EITC-eligible respondent reports to be in either excellent or very good health; 2KIDSit equals to one if there is more than one eligible child in the household; and POSTit is an indicator for the time period either before or after 1996.11 Xit 10 Given that questions on food expenditures were only included in the PSID in 1994, the sample period is reduced to the years 1994 to 1999 for this analysis. 11 The EITC expansions through OBRA 1993 were slowly phased in over the tax years 1994 and 1995. As mentioned by Evans and Garthwaite (2014), a potential misclassification of individuals who are treated in the pre-represents a set of baseline covariates that include controls for age, gender, race, and marital status of the head of household. δDD is the main parameter of interest, which captures the effect of the EITC expansion on the health status. αi captures the individual fixed effects or unobserved time-invariant heterogeneity across individuals. A set of year and state dummy variables are controlled for to accounts for differences in health patterns across time and states. The state fixed effects are important to control for existing differences across states. Given that significant other welfare reforms were passed in the late 1990s in the US, I furthermore estimate specifications that additionally net out the effects of several time-varying differences across states in labor market and welfare reforms. This approach is similar to Averett and Wang (2015). The full list of additional state-specific controls is shown in the Appendix at the end of the paper. I use linear probability methods to estimate the main specifications shown in this section.12 In an additional DD model, I increase the sample size by ignoring the longitudinal nature of the data set. Thus, all individuals who are eligible to receive EITC payments at a given time during the period of the study are included in the study. For this specification, the following specification is estimated: Yit = β0 + + β1 2KIDSit + β2 Xit + δDD POSTit *2KIDSit + λ1 Year + λ2 State + εit. (2) Differences between the specifications including and excluding individual fixed effects can provide evidence whether changes in the sample composition in cross-sectional analyses affect the estimates for expansions in the EITC on health. In order to test if changes in the estimates are driven by the inclusion of fixed effects or by changes in the sample, I re-estimate equation (2) for treatment period should bias the observed estimates in this study against finding any health impacts. For additional robustness, I find that the results remain unchanged when allowing the post-treatment period to start in 1995. 12 The results remain unchanged when estimating ordered probit models.
the fixed effect sample that is used to estimate equation (1). Furthermore, in additional specification, I examine whether the observed treatment effects change when allowing the policy change to have an adjustment period after its implementation. It seems reasonable to assume that it might take some time before health outcomes are affected by increases in income.

B. DDD Models
Like any DD model, the estimation of equation (1) makes the key assumption that trends in health outcomes over time are similar across both the treatment and control groups, implying that trends in the control group provide a good estimate of the counterfactual outcome for the treatment group in the absence of the policy change. Despite the fact that there appears to be no obvious reason to expect that this assumption is not satisfied in the given framework, a violation would lead to a bias of δDD. One way to reduce this potential bias is to explore a difference-indifference-in-differences (DDD) framework. Similar to Averett and Wang (2013) where ELIGit is an indicator for whether a family is eligible to receive any EITC benefits during the year of the survey. δDDD is now the parameter of interest, whereas the other variables remain the same as in equation (1).

C. Additional Models
This section introduces three additional models which I estimate to test whether the main results are robust to other model specifications. First, I conduct a falsification test that compares the health outcomes of heads of households that are equally affected by the policy change.
Specifically, individuals with three or more children form the treatment group, whereas those with two children are used as the control group. Given that the EITC expansions do not affect families with two and three or more children differently, I expect to find no health differences for these groups. This specification follows the approach by Averett and Wang (2013), who use a similar falsification test to examine the effects of an expansion of the EITC on maternal smoking. Besides comparing the health outcomes of two different groups (2 children vs. 3+ children), the remainder of the analysis stays the same as in equation (1): Yit = β0 + β1 POSTit + β2 3KIDSit + β3 Xit + δDD POSTit*3KIDSit + λ1 State + εit . (4) Second, I estimate a semi-parametric DD model, which was introduced by Abadie (2005) and which relaxes the assumption of a linear relationship between income and health. The method captures average treatment effects for the treated group (ATT) for the case that differences in observed characteristics create non-parallel outcome dynamics between the two observed groups, which violates the main assumption of standard DD models. The ATT is given by the following equation: where Y(1) and Y(0) represent health outcomes before and after the treatment, D is an indicator for belonging to the treatment group, P(D=1) gives the probability of receiving treatment, and P(D=1 | X) is the propensity score that equals the probability of treatment, conditional on the observed covariates X. The propensity scores for the semi-parametric analysis are obtained using probit estimation.13 The value of φ0 is obtained from the following equation: where T is a time indicator that equals one if the observation belongs to the posttreatment period and γ reflects the proportion of observations sampled in the post-treatment period. Abadie (2005) shows that the semi-parametric estimator is obtained through two steps: 1) Estimation of the propensity score and computation of fitted values for the sample; and 2) Plugging in the obtained fitted values into the sample analogue of equation (5)  Third, I estimate an alternative DD model that was introduced by Mora and Reggio (2015) and that allows statistically testing for whether the parallel path assumption of the DD setup is satisfied. Difference-in-Differences models require an assumption that trends in the variable of interest are similar for both treatment and control group in the absence of the policy intervention. This assumption implies that differences between the groups are assumed to be time-invariant without treatment. Mora and Reggio (2015) point out the fact that the identification of treatment effects does not only depend on the parallel trends assumption, but also on the trend modeling strategy applied by researchers. The authors argue that researchers often overlook this. They introduce an alternative DD estimator, which identifies the effect of the policy using a fully flexible dynamic specification as well as a number of alternative "parallel growth" assumptions. Estimating this model as an additional check for robustness allows testing for the validity of standard DD assumptions made in the baseline specification. Following Mora and Reggio (2015), who provide Stata code to implement their alternative DD specification, this alternative estimator is acquired in two steps: In the first step, standard least squares estimation of the fully flexible model is conducted. In the second step, the solution of the equation in differences identifies the treatment effects. Finding that the estimates obtained from this model are consistent with the baseline estimates can provide evidence for additional robustness of the main DD estimates.  (2), the result remains almost unchanged, which supports the claim that the health effects are not spuriously driven by the other safety net laws passed during the 1990s. As suggested by Figure 2, the effect of receiving a financial boost on health status becomes substantially larger once the DD model allows the EITC expansion to have an adjustment period shortly after its implementation. This seems reasonable since it might take some time before health impacts of the extra income become noticeable. Columns (3) and (4) show treatment effects of 10.96 and 13.28 percentage points when adjustment periods of one and three years are considered, respectively.14 The fixed effect DD estimates could be biased if individuals who are eligible to receive EITC benefits both before and after the policy change are more likely to benefit from income increases, which would be the case if their health were more susceptible to changes in income. I test for this potential bias in two ways. First, I re-estimate equation (1) with the main control variables as the outcomes. The results show that the policy change does not significantly affect observable characteristics. Second, I repeat the main analysis restricting the sample to individuals who were only eligible to receive EITC benefits during the pre-expansion period.

A. DD Estimation
The results are consistent with the main treatment effects shown in Panel A of Table 3.15 14 In additional models, I test for the effects of the policy on the likelihood of reporting fair or poor health. While finding negative effects, the estimates for the bottom two categories of health status are smaller in magnitude than the estimates for the top two health categories (reduction of 4.02 percentage points compared to an increase of 8.92 percentage points), while also being imprecisely estimated. One reason for the relatively small finding could be that only 14.91 percent of treated individuals report being in the bottom two health categories prior to the policy change. Thus, while lacking statistical significance, the observed decline of 4.02 percentage points corresponds to a 26.96 percent change, which is even larger than the change in the top two categories of health status. 15 The results are not shown in the paper, but are available upon request.
Panel B provides DD estimates without individual fixed effects, which allows testing for the effects of the policy with a larger sample size. The results again provide evidence that the increase in income increased health status of treated individuals. However, the magnitude of the effect is substantially smaller in comparison to the fixed effect estimate from Panel A. The differences in the size of the observed effects could either be the result from including fixed effects and/or the result of a change in the sample. To test for this, Panel C provides treatment effects that are obtained from estimating equation (2), while using the smaller fixed effect sample from Panel A. The results are found to be close to the fixed effect estimates, which provides evidence that the change in the sample is driving the difference between models including and excluding individual fixed effects.
The estimates without individual fixed effects could be biased if the composition of the sample changes from before to after the policy change. Again, when re-estimating the model using the control variables as outcomes, I find that the policy change did not have differential impacts on observable characteristics, such as gender, race, marital status and education.  (1999, 2001 and 2003), which confirms the presence of an adjustment period before health improvements are observable.

B. DDD Estimation
The previous estimates remain unbiased if similar health trends would have occurred for individuals in both the treatment and control groups in the absence of the policy change. Figure 2 provides suggestive evidence supporting this assumption by showing that trends in health status were almost identical for the two groups during the three years before the policy implementation (1993)(1994)(1995). To further account for potential differences in health trends between households with two or more children and those with one child, I additionally estimate Difference-in-Difference-in-Differences (DDD) models, which include heads of households with children who are not eligible to receive EITC benefits as an additional comparison group.
DDD estimates for the impact of the policy change on health are presented in Table 4.
The fixed effect models in Panel A are slightly larger in magnitude than the fixed effect DD estimates in Table 3, again providing evidence that additional income significantly improves the health status of heads of households benefiting from the EITC expansion. Consistent with the main DD results, the estimates obtained when excluding individual fixed effects are smaller.
Again, the differences in the magnitudes of the effects appear to be driven by changes in the sample rather than by differences in the estimation technique. Overall, the results in Table 4 remove concerns that the main DD effects might be biased due to different trends in health status between the treatment and control group. Table 5 presents estimates from two additional estimation techniques. In the first two columns, I estimate a semi-parametric DD model, which was introduced by Abadie (2005). The results are consistent with the main estimates shown in Table 3, with the observed effects being larger in magnitude. The policy change is shown to increase the likelihood of reporting excellent or very good health by 11.54 and 12.58 percentage points, respectively (p<0.01). The similarity of these results with the main DD estimates suggests that the treatment effects on the treated remain consistent when relaxing the assumption of a linear relationship between income and health and imposing the same distribution of covariates for both the treatment and the control group. A potential explanation for the difference in the magnitude between the parametric and semi-parametric DD estimates could be that observable characteristics impact the results and whether one controls for them in a parametric or in a semi-parametric way changes the DD estimates.

C. Alternative Models
Columns (3) and (4) provide DD results obtained by using the alternative specifications introduced by Mora and Reggio (2015), which allows testing for the validity of the parallel trends assumption of DD models. The results suggest that the policy change increased the likelihood of being in excellent or very good health by 8.37 and 9.43 percentage points, respectively (p<0.10). These estimates are very close in magnitude to the corresponding fixed effect estimates in Table 3, which provides additional evidence for the robustness of the main DD estimates of the study.

VII. MECHANISMS
After having previously established the presence of positive health impacts as a result of experiencing increases in income through the EITC expansion, this section examines potential channels explaining the observed positive link between income and health. The two mechanisms that are investigated are changes in weekly food expenditures and in insurance coverage. These mechanisms are chosen due to the availability in the data. While it appears reasonable that both these channels likely play a role underlying the link between the EITC and health outcomes, other factors such as changes in health behaviors or financial stress could furthermore explain the findings to some extent and should be examined in future work.

A. Food Expenditures
A potential mechanism that could explain the existence of a positive relationship between the EITC and health is the intake of better nutrition following increased earnings. Previous work on the EITC shows that receiving benefits positively affects consumption of relatively healthy food items like fresh fruit, vegetables, meat, poultry, and dairy products, while reducing consumption of processed fruit and vegetables (McGranahan and Schanzenbach, 2013). To examine the role of food expenditures, I test whether the policy change altered the total amount of money households spend on food per week as well as expenditures on food eaten at home and on food eaten away from home. Despite the fact that the data does not provide information on the quality of food being purchased, I believe that the total amount of money spent on food can indicate whether nutrition plays a role in explaining the observed health improvements. Similar to the results for health status, the effects become larger when allowing the policy to adjust for some time after its implementation. The estimates correspond to changes of between 20 and 30 percent compared to pre-treatment expenditures on food. It is interesting to note that these percent changes are very similar to those observed for health status in Table 3. The estimates in Panel B show that the majority of this increase is driven by changes in expenditures on food eaten at home. Given the magnitude of the findings in Table 6, the results provide suggestive evidence that food expenditures serve as a channel underlying the positive relationship between income and health.

B. Health Insurance
Previous work has established that health insurance coverage is capable of improving the health outcomes of lower-income families (Levy and Meltzer, 2008). Similar to Baughman (2005) and , this section examines whether an expansion in the EITC increases health insurance coverage of financially affected households. Moreover, the March CPS data allows testing for differences in specific types of insurance. The dependent variables for the four separate specifications are indicators of whether a household is covered by: 1) Any insurance; 2) Private insurance; 3) Public insurance; or 4) Medicaid/SCHIP.16 16 The category Medicaid/SCHIP includes all types of public insurance coverages from category 3) excluding Medicare and military insurance. Due to the magnitude of welfare reforms that were implemented during the late 1990s, all models include controls for the state-specific characteristics shown in the Appendix. Table 7 presents the DD and DDD estimates for the effects of the EITC expansion on health insurance coverage. The DD model shows that treated households are 1.21 percentage points more likely to have any type of insurance compared to those forming the control group following the law change (p<0.01). Columns (2) shows that this increase is entirely driven by increases in private insurance coverage, while columns (3) and (4) show that the expansion had small negative effects on public coverage. The DDD findings confirm that the policy change increased the likelihood with which individuals had any coverage and private insurance, even when accounting for potential differential trends between household with one or more children. The HITC, which was available during two of the four pre-treatment years of this analysis, did not have different eligibility requirement between households with one or at least two children and should therefore not affect the estimates. In an additional model that excludes the years 1992 and 1993, I find that the results remain unchanged. This confirms that the observed treatment effects are not driven by the HITC.17 Given the assumption that private insurance provides better services than public coverage, this finding provides evidence that health insurance can be viewed as a potential channel underlying the link between increases in income and improved health outcomes. The observed positive effect of expanding EITC on private health insurance coverage is smaller in magnitude than estimates by , who find a 3.6 percentage point increase in private insurance. Unlike my result, however,  do not find evidence that treated household are more likely to have any coverage since the authors find statistically significant declines in Medicaid that offset the increases in private coverage.
One disadvantage of the analysis is that the CPS data only began providing information on whether respondents purchased their own insurance coverage or whether it is sponsored by their employers starting in 1996, which could strengthens the case that health insurance is a mechanism for the link between income and health. Nevertheless, previous work has shown that income affects the likelihood with which workers are covered by employer-sponsored insurance. Cutler (2003) shows that the costs for enrolling in employer-provided insurance plans are $350 for an individual and $1,500 for a family during the late 1990s, which is twice as much as the cost in the late 1980s. Furthermore, the paper shows that these increased costs were the main reason for why workers did not take up offered insurance plans.
The results in this section provide evidence for the role of food expenditures and health insurance coverage in explaining the observed health improvements following increases in income. However, it should be considered that these two factors are by no means the only two potential mechanisms. Other aspects, such as health behaviors and financial stress, are likely to also impact the association and should be examined in future work. The availability of data regarding the quality of food that individuals consume could furthermore strengthen the evidence suggesting that nutrition explains parts of the improved health outcomes following increases in income.

VIII. ROBUSTNESS CHECKS
In order to further test for the validity of the main results of the study, estimates for five additional robustness checks are presented in Table 8. First, I use the amounts of predicted EITC dollars that are obtained from the tax simulator in order to check whether health effects as a result of the expansion were larger for individuals who received higher EITC benefits. The results in Panel A indicate that the effect of additional earnings on health status is substantially stronger for treated individuals who received larger EITC payments (p<0.05). This finding provides additional evidence for the positive link between income and health.18 In Panel B, I use family income to identify where household in sample are on the EITC schedule and to test whether the effects on health differ across the phase-in, the plateau and the phase-out region. Previous research on the program has established that households in the phasein part of the schedule increase their employment on the extensive margin following changes to the EITC (Eissa and Liebman, 1996;Eissa et al., 2008;Meyer, 2010). On the other hand, earlier work has shown that household in the middle of the schedule receiving something close to a pure income effect because of little to no change in the number of hours worked (Athreya et al., 2010;Gunter, 2013). The estimates in Panel B show that individuals in the plateau phase experienced the largest improvements in health status (p<0.10), while the effects are smaller in magnitude and imprecisely estimated in both the phase-in and phase-out part of the schedule. Again, the findings provide additional evidence that the improvements in health following the EITC expansion, which are shown in the main analysis, are the result of increases in income.
The DD estimates with individual fixed effects in Table 3 are obtained by sampling individuals who are, based on the TAXSIM simulations, eligible to receive EITC benefits throughout the sample period. In Panel C, I relax this restriction and include all heads of households who were eligible to receive EITC benefits at least twice in both the pre-and the post-treatment period, which allows me to use a larger sample size (N=2,541). While the magnitude of the treatment effect is slightly smaller than in the main model, the DD estimate 18 In an additional specification, I test for the effect of annual changes in predicted EITC benefits on health status. While the estimates suggests that higher increases in EITC have positive health effects, they are imprecisely estimated. One reason for this could be that overall there is relatively small variation in EITC payments to the two groups (on average $113 per year for the entire sample period), with substantial changes only occurring around the time of the EITC expansion.
again suggests that the policy change increases the likelihood of being in excellent or very good health by 6.91 percentage points (p<0.10).
Next, I conduct a falsification test that compares changes in health status between the two groups that are equally affected by the expansion. Eligible heads of households with at least three children form the treatment group, whereas the control group consists of eligible heads of households with two children.19 Figure 6 justifies the validity of this falsification test by confirming that EITC credits evolved identically for both groups throughout the period of study.
Consistent with the claim that the previously observed health improvements are a result of increases in income, the falsification test finds small and statistically insignificant differences the effect of the policy on health status between the two groups (Panel D).
In Panel E, I estimate an additional specification that accounts for the potential issue of reverse causality, which would exist when health status predicts the number of children living in a household and would bias the results. One example of this is if one-child families with health conditions in the pre-treatment period decide not to have a second child and are therefore unable to benefit from the program expansion. In order to test for the presence of this issue, I exclude individuals who report suffering from limiting health conditions from the analysis. The estimates from this model are larger in magnitude than the main DD estimates in Table 3, which suggests that reverse causality is not influencing the estimates.
In a final robustness check, I use the sample identification used in previous research on potential health-related impacts of the EITC expansions and examine a sample of lower-educated individuals Evans and Garthwaite, 2014;Averett and Wang, 2013). 19 Differences in EITC benefits between eligible households with two and more than three children were introduced in later years, not during the period of this study.
Consistent with the expectation that this sample restriction will increase the number of individuals since it could potentially include some people who are not eligible to receive EITC payments, the sample size increases from 1,958 to 50,195. The estimate from the low-educated sample suggests that the EITC expansion increased the likelihood of reporting the top two health categories by 1.62 percentage points (p<0.10), which corresponds to a 3.19 percent increases from the pre-treatment period. This change is substantially smaller than the observed effect of the policy change on the sample of individuals predicted to be eligible to receive EITC payments (20.02 percent).

IX. DISCUSSION AND CONCLUSION
The findings of this study advance the literature on the relationship between income and health by providing evidence for the protective health effects of exogenous increases of income to vulnerable parts of the population. When examining potential explanations for the positive health impacts of additional income, the paper finds that increased spending on food and higher take-up rates of private health insurance can serve as mechanisms. It would be interesting for future work to examine the short-and long-term effects of similar policies on health outcomes of children living in directly affected households. Since it appears likely that income affects health in several ways, a further examination of other potential channels such as the role of healthrelated behaviors, health care usage, health expenditures, and stress should be conducted to better understand the link between income and health.
Given the fact that the EITC has become the most important cash transfer program in the United States, learning more about the program's impact on the health of individuals should be important to policymakers. The analysis in this study provides additional evidence for the presence of health benefits related to the EITC. The estimates for the positive health effects for adults are consistent with findings by Evans and Garthwaite (2014) and .
Recent work on the tax credit suggests that further program expansions could help reduce existing health inequalities (Fletcher and Wolfe, 2014). Based on the success of earlier policy changes, other researchers have proposed that the program should be expanded for both families with one child as well as for childless families (Hoynes, 2014;Marr et al., 2013).
The findings of this paper furthermore suggest that governmental regulations aimed at assisting lower income families are capable of providing health benefits. As proposed by

X. DISCLOSURE STATEMENT
The author has no financial arrangements that might give rise to conflicts of interest with respect to the research reported in this paper. This picture depicts the coefficients of the interaction terms between the treatment indicator and the year dummies for the outcome "excellent or very good health status". The reference year is 1990. This picture depicts the coefficients of the interaction terms between the treatment indicator and the year dummies for the outcome "excellent or very good health status". The reference year is 1990. Figure 6: EITC Benefits to Eligible Households with 3+ Children versus 2 Children:    Robust standard errors, clustered by states, are shown in parentheses. All models control for age, gender, race, marital status as well as the number of people living in the household. Furthermore, state and year fixed effects are controlled for. * p< 0.10, ** p< 0.05, *** p < 0.01. Robust standard errors, clustered by states, are shown in parentheses. The standard errors for the semi-parametric analysis are obtained through bootstrapping using 99 replications. All models control for age, gender, race, marital status as well as the number of people living in the household. Furthermore, state and year fixed effects are controlled for. * p< 0.10, ** p< 0.05, *** p < 0.01. Robust standard errors, clustered by states, are shown in parentheses. All models control for age, gender, race, marital status as well as the number of people living in the household. Furthermore, state and year fixed effects are controlled for. * p< 0.10, ** p< 0.05, *** p < 0.01. Robust standard errors, clustered by states, are shown in parentheses. All models control for age, gender, race, marital status as well as the number of people living in the household. Furthermore, state and year fixed effects are controlled for. * p< 0.10, ** p< 0.05, *** p < 0.01.  Figure A1: Share of Eligible Heads of Households in Excellent/Very Good Health (1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003):

List of State Characteristics / Welfare Reform Controls:
(1) Average annual unemployment rate in state (2) AFDC eligibility threshold (until abolishment in 1996) (3) AFDC waiver a. In place?