Does Welfare Dependency Affect Health and Health-Related Behavior? An Empirical Study

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1 Does Welfare Dependency Affect Health and Health-Related Behavior? An Empirical Study Hugo Bodory* University of St. Gallen February 5, 2015 Abstract This article investigates the effects of welfare dependency on individual health and health-related behavior. The empirical analysis uses data from the German panel survey Labor Market and Social Security (PASS) collected between 2011 and A selection on observables approach is used for identification of average treatment effects. The study applies propensity score matching for estimating these effects. Results indicate that welfare dependency can negatively affect both the general and mental health of individuals, as well as their sports-related behavior. Keywords: Welfare, health, sports, treatment effects, propensity score matching, panel data JEL Classifications I12, I38, C21 *Correspondence to: Swiss Institute for Empirical Economic Research (SEW), University of St. Gallen, Varnbüelstrasse 14, CH-9000 St. Gallen, Switzerland. hugo.bodory@unisg.ch.

2 1 Introduction Health affects the wealth of nations. Already back in medieval times, plague-ridden nations experienced severe income losses over centuries when they were hit by the deadly disease (Bernstein, 2008). This negative aspect of the relation between health and income has not changed over time. The economic literature states that poor health in individuals also reduces their income nowadays (Bloom, Canning, & Jamison, 2004; Nordhaus, 2003). For instance, workers in poor health have low productivity, which can cause reductions in income. Health and labor market outcomes such as employment and income are thus closely related to each other. Given the economic importance of health, this paper studies the health situation of welfare recipients. Many Western countries have implemented welfare programs for improving the economic situation of low-income households. Such programs provide benefits to households which otherwise would not be able to fulfill their most basic needs. Besides providing benefits, welfare programs often focus on a rapid labor market integration of welfare recipients. These programs usually impose requirements such as vocational training to lead welfare recipients out of welfare dependency. While this is one way of improving their economic situation, their health conditions often receive less consideration, despite the strong relationship between productivity and health. Moreover, welfare dependents often report being in poor health. 1 The present work thus investigates the impact of welfare dependency on health. More specifically, it investigates the health effects of dependency on the German Hartz IV welfare program. The German government introduced this program in It offers basic social security to welfare recipients. The benefits provided by the Hartz IV program are called unemployment benefits II (UB II). 2 Besides the health of UB II recipients, this work also studies their health-related behavior in order to make evident those channels that potentially affect health. This research may be policy relevant for several reasons. Policy makers could compensate potentially negative health effects of the Hartz IV welfare program by introducing appropriate countermeasures. For instance, mandatory health education could be a way to increase individual health. Such a measure could ease the burden on the social security system by reducing total health costs. Because healthier individuals may also be more productive, in the end they could dispense with welfare sooner, which is one goal of the Hartz IV program. This paper builds on two strands of the literature analyzing the health effects of welfare dependents. Firstly, it extends the current knowledge of the health effects of welfare reforms. And secondly, it adds to the existing literature that studies the effects of unemployment on health. This literature is relevant because the majority of UB II recipients do not work. 3 Previous studies examining the effects of welfare reforms on health and health-related behavior show ambiguous results. When Bitler and Hoynes (2008) reviewed the literature on 1 In this study, more than 60% of the welfare dependents reported not being in good general health. 2 UB II are commonly known as Hartz IV in Germany. Both terms are used synonymously. 3 Table 2 shows that more than 77% of the UB II recipients in the current sample had no employment. 1

3 US welfare reforms, they found that in observational studies welfare reforms had no effects on the health status and health-related behavior of welfare recipients. The health status was measured by self-reported general health and weight. The health-related behavior referred to physical activities and the consumption of cigarettes and alcohol. Considering the results of several experimental studies, Bitler and Hoynes (2008) showed that welfare reforms can positively affect mental health. However, the findings based on evidence in the US may differ from German findings because of their different health systems. In the US, health insurance is voluntary. US citizens may therefore behave differently than German citizens with compulsory health insurance. Very little is known about the health effects of welfare dependency in Germany. Eggs (2013) used the German PASS data to study the relation between health and dependence on UB II. He found a negative relation between UB II dependency and self-reported health estimated by parametric fixed effects models. The present work extends the study of Eggs (2013) by showing causal effects instead of correlation. The literature reports mixed evidence for the effect of unemployment on health. Surveys in the medical literature found a negative correlation between unemployment and health (Jin, Shah, & Svoboda, 1995; Mathers & Schofield, 1998). These results coincide with findings in the economic literature indicating positive effects of employment on mental health (Huber, Lechner, & Wunsch, 2011). However, other economic studies did not find adverse health effects due to unemployment in general (Böckerman & Ilmakunnas, 2009; Schmitz, 2011). These studies argue that their findings vary from those of previous research because of different identification strategies. For instance, they exploit exogenous events like plant closures to control for health selection. However, they show negative health effects for the subgroup of long-term unemployed, a subgroup also observed among UB II recipients. The findings of the literature are not conclusive. Reasons for ambiguous findings can be the evaluation of: (i) different welfare programs implementing different policies (regarding e.g. the generosity of benefits), (ii) different identification strategies (experimental versus observational studies), or (iii) different institutions (compulsory versus voluntary health insurance). It is therefore an empirical question if dependence on a specific welfare program affects health. To the best of my knowledge the literature shows no findings regarding the effects of UB II dependency on health and health-related behavior. This paper tries to fill this gap in the literature by empirically analyzing the effects. Newly collected data on sports activities allow one to analyze the health-related behavior in more detail. This study compares UB II recipients and non-recipients to identify and estimate the effects of UB II dependency on health outcomes. The identification of the health effects was based on a selection on observables approach. To plausibly identify these effects, UB II recipients and non-recipients have to share the same characteristics (i.e. they have to be comparable). The PASS panel survey provides data on the relevant characteristics to achieve comparability. Estimates were obtained by propensity score matching using the bias adjusted radius matching estimator of Lechner, Miquel, and Wunsch (2011). 2

4 The results of this study show that UB II dependency has negative impacts on both general and mental health. The findings also reveal large negative effects on sports-related behavior. The estimated results are robust to several sensitivity tests (Section 5.3). For example, one test uses lagged outcomes to see if missing confounding variables lead to non-zero estimates (Imbens, 2014). Other tests simulate potentially missing confounders (Ichino, Mealli, & Nannicini, 2008). All of these tests do not lead to changes in the estimated results. This evidence suggests that the Hartz IV program is a social welfare program that can worsen the health of welfare dependents. 4 The literature argues that less income, less social capital, stress, or stigma experienced while on welfare can cause such negative health effects (Eggs, 2013). Future policies taking these factors into account may improve the health of UB II recipients. Section 2 discusses the institutional setting. Section 3 provides information on the data. Section 4 outlines the methods used for identifying and estimating the causal effects. In Section 5 results and sensitivity tests are presented, and Section 6 concludes the study. 2 Institutional setting 2.1 The Hartz IV welfare program This section discusses the goals, the conditions, and the relevance of the Hartz IV welfare program. 5 In addition, the discussion sheds light on the life situation of welfare dependents. The German Social Code Book II is the legal basis of the means-tested Hartz IV welfare program. The program is tax funded. Its main task is providing social benefits (UB II) to assist needy individuals. The program additionally offers services like vocational training to UB II recipients. These services are meant to enable them to achieve financial self-sufficiency. Individuals receiving UB II have to fulfill several criteria: (i) They have to be capable of working for at least three hours a day; (ii) their earned household income has to be below the subsistence level; (iii) they may not be younger than 15 or older than the statutory retirement age; and (iv) their permanent residence must be in Germany. 67 Note that being unemployed is no criterion for receiving UB II. UB II paid to poor households enable them to meet their needs in daily life, e.g. food or clothing. The monthly maximums paid to UB II recipients and their partners are EUR 391 and 4 Only employable individuals get benefits through the Hartz IV program (see Section 2). The results presented in this study are therefore specific to this program. They cannot be generalized to other programs, such as social welfare programs for disabled individuals. 5 The Guidelines of the German Federal Employment Agency provide more information on the Hartz IV welfare program at Sammlung/SGB-II-Merkblatt-Alg-II.pdf. 6 Yearly German subsistence levels in 2014: EUR 7896 for singles; EUR for couples; and EUR 4272 for children (see 7 Statutory retirement age for Germany in 2014: years. 3

5 353 per person, respectively. 8 In addition to UB II, assistance for heating and lodging can be granted. The receipt of UB II is not restricted by any time limitations. Employed UB II recipients have more money available for themselves than unemployed. UB II recipients with employment can claim monthly allowances up to EUR 330 per person. Such incentives should encourage UB II recipients to accept low-income jobs and enter the labor market. The German social security system provides insurance coverage for unemployment, health, nursing care, retirement pensions, and accidents. UB II recipients are fully covered by compulsory health and nursing care insurances only. They are not or only partly covered by the other insurance provisions of the social security system. UB II recipients face financial sanctions if they deny, for example, participating in trainings or accepting job offers. Such sanctions can be imposed even if a job does not correspond to the vocational education of a job seeker. In case of repeated non-compliance of the program regulations, benefits can be cut by 30%, 60%, and even 100% over a period of three months. Imposing financial sanctions aims at increasing labor demand by reducing the reservations wages of UB II recipients without employment (Ochel, 2005). In 2014, 3.33 million households were qualified for receiving UB II. This accounts for approximately 8% of all German households with a total of 6.13 million household members. These large numbers indicate the importance and relevance of the Hartz IV welfare program. 9 This discussion shows that welfare dependents face difficult life situations. Besides lower income, they are partly excluded from the social insurance system and have to deal with threats of benefit cuts. 3 Data 3.1 The Labor Market and the Social Security (PASS) household panel survey The German PASS household panel survey, designed in 2007, collects data on an annual basis to examine the implications of the Hartz labor market programs (Trappmann, Gundert, Wenzing, & Gebhardt, 2010). 10 This application uses the PASS data of the 2011 and 2012 surveys. The PASS survey data is suitable for evaluating the effects of UB II dependency on health and health related behavior. The following arguments highlight this suitability. (i) The PASS data specifically focus on the life situation of UB II recipients. The data cover five topics: (a) demography, (b) income, (c) social situations and networks, (d) individual 8 Figures for See The numbers indicate stock values measured in March The German government started four labor market programs (Hartz I-IV) in the early 2000s. These programs were introduced to cut unemployment. 4

6 behaviors, and (e) trainings and benefits. This information allows one to analyze the channels for becoming independent of UB II. (ii) Approximately half of the survey respondents are dependent on UB II. 11 These individuals are still interviewed in the yearly follow-up surveys irrespective of their future labor market status. Consequently, health-related information on these individuals is available even if they become independent of UB II. (iii) The current study uses new data on the health-related behavior of survey participants. Comprehensive data on their physical activities has been available since the 2012 survey. The new data shows, for instance, information on the actual frequency and duration of sports activities, or on activities in the youth. This new information can help to identify potential paths affecting the health of UB II recipients. (iv) Receiving UB II depends on the level of household income (see Section 2.1). Analyzing effects of UB II dependency therefore requires information at the household level. The PASS data provide both information at the household level and information at the individual level. This makes the PASS data preferable to administrative data which report information at the individual level only (such as the IAB Employment Samples provided by the German Federal Employment Agency). (v) Other survey data are also available to evaluate research questions regarding UB II dependency. For example, the German Socio-Economic Panel Study (SOEP) is a survey providing data since But compared to this and other surveys, the PASS data hold more information on welfare-related characteristics. This includes employment measures such as publicly funded community service jobs (Bethmann & Gebhardt, 2011). Based on the PASS survey data, the next section describes the sample selection process and the outcome variables. 3.2 Sample selection and health-outcome variables This empirical study estimates health-related differences among individuals becoming independent and remaining dependent on UB II. It splits the data of the 2011 and 2012 surveys into three periods to identify the effects on health outcomes. In the first period from February to October 2011, only UB II recipients were sampled. In the second period in December 2011, some of them left welfare dependency (denoted as treated), while the others remained dependent on welfare (denoted as controls). Health-related outcomes were observed in the third period from February to October In the remainder of this paper, the three periods are referred to as the pre-treatment period, treatment period, and outcome period, respectively. The sample uses only individuals observed in all three periods. This enables one to build up an identification strategy for estimating the health effects. The sample is further restricted to individuals not younger than 20 years and not older than 58 years. This excludes, among others, students still attending school and individuals being close to retirement. These subgroups might 11 Yearly refreshment samples guarantee a constant share of UB II recipients in each wave. 5

7 have no interest in leaving welfare dependency and are thus not further considered. Section 7.2 in the Appendix documents detailed information on the selection process. Table 1: Self-reported health outcomes Sample means UB II non-recipients UB II recipients T-values Health Good general health (binary) *** Good mental health (binary) Health-related behavior Sports activities (binary) ** Weekly hours doing sports Friends through sports (binary) *** No daily alcohol intake (binary) Non-smoking (binary) Number of observations Note: UB II non-recipients became independent of UB II, while UB II recipients remained dependent on UB II. Significance levels for mean differences: *p<.10, **p<.05, ***p<.01. The resulting sample comprises 2750 observations. Table 1 presents descriptive statistics on the health outcomes of the treated and controls. 12 The variables are coded such that a higher value indicates better health or health-related behavior in the respective group. The controls (UB II recipients) reported poorer health than the treated (rows 1&2 of Table 1). However, the difference is statistically significant only for general health but not for mental health evaluated in the last four weeks before the survey. The health-related behavior also differs between treated and controls (rows 3-7 of Table 1). The controls were less often engaged in sports activities and found friends in their main sports activity less often. 13 Both of these differences are statistically significant. Daily alcohol consumption was (insignificantly) more often reported among the controls. Nearly no differences were found in smoking behavior and the hourly participation in the main sports activity per week. Note that the different health outcomes shown in Table 1 are descriptive statistics. They show correlation and can indicate only a possible direction of causal effects. 12 Section 7.1 of the Appendix shows the exact wording of the survey questions for the health outcomes. It also gives details about the construction of the binary variables. 13 The positive health effects of both sports activities (Pawlowski, Downward, & Rasciute, 2011; Sari, 2010) and social relationships (House, Landis, & Umberson, 1988) have been widely discussed in the literature. 6

8 3.3 Labor market status of UB II recipients The labor market status of individuals can affect their health conditions (Kroll & Lampert, 2011). Low income and economic sorrows are potential channels leading to different health conditions between unemployed and employed. The data presented in Table 2 indicate that the labor market status varied greatly among UB II recipients in the pre-treatment period. Consequently, the effects of UB II dependency on health may differ between these heterogeneous groups. Table 2: Labor market status of UB II recipients and non-recipients in the pre-treatment and treatment periods Pre-treatment period Treatment period Observations UB II recipients, not employed UB II recipients, employed 142 UB II recipients, not employed 1715 UB II non-recipients, employed 130 UB II non-recipients, not employed 133 UB II recipients, employed UB II recipients, employed 384 UB II recipients, not employed 126 UB II non-recipients, employed 113 UB II non-recipients, not employed 7 Note: Only UB II recipients were sampled in the pre-treatment period. In the treatment period, UB II recipients remained dependent on UB II, while UB II non-recipients became independent of UB II. Table 2 shows two patterns specific to dependents on UB II. Firstly, almost a quarter of the UB II recipients were employed in the pre-treatment period (23%). This statistic clearly demonstrates that unemployment was not the only cause for welfare dependency (see Section for further reasons for becoming dependent on UB II). The second pattern observed is how differently UB II recipients changed their labor market status in the transition to independence on UB II. Only half of the welfare dependents without employment started working when leaving welfare dependency (rows 3&4 of Table 2). But nearly all employed welfare dependents remained employed after becoming independent of UB II (rows 7&8 of Table 2). This empirical analysis accounts for heterogeneity regarding the labor market status of UB II recipients (Section 5.2). It presents separate results for individuals with and without employment in the pre-treatment period. 3.4 Descriptive statistics of confounding characteristics Treated and controls are not randomly selected in this observational study. They differ according to characteristics affecting welfare dependency. This paper identifies the relevant characteristics by analyzing the individual decision process of becoming and remaining dependent on UB II (Section 4.2.2). 7

9 Table 3 provides descriptive statistics on these characteristics for treated (UB II nonrecipients) and controls (UB II recipients). Columns 2&3 of Table 3 report the sample means of variables regarding demography, education, economic factors, and individual behavior. These statistics describe the selection into the treatment (i.e. becoming independent of UB II). The controls significantly differ from the treated. The controls are on average older and less educated. In addition, they are economically less successful. They work fewer hours per week and were less often employed in The controls also tend to live in states with higher unemployment rates. Both groups further differ in their sports activities during childhood. Evidence showing a positive relation between sports during childhood and cognitive skills may explain why this health-related behavior is associated with welfare dependency (Esteban- Cornejo, Tejero-Gonzalez, Sallis, & Veiga, 2014; Singh, Uijtdewilligen, Twisk, van Mechelen, & Chinapaw, 2012). The statistical significance of these differences is evaluated by a probit estimation (column 4 of Table 3). Note that the initial health conditions of both groups do not differ at any conventional significance level. Causal average health effects could simply be estimated if the confounding variables were equally distributed among treated and controls. A comparison of their average health outcomes could then show the estimands of interest. However, the sample means of the confounding variables differ between the control group and the treatment group in this study. The identification and estimation of causal effects thus requires a more complex empirical strategy. This strategy is presented in the next section. 4 Methods 4.1 Identification problem This study identifies average health effects (outcomes) after becoming independent of UB II (treatment) by comparing the health of treated and controls. Identifying causal effects requires that both groups share the same characteristics. Selection bias occurs if those characteristics that differ between these groups also affect the health outcomes. A consequence of selection bias is that estimation results do not show causation but correlation. For example, if the treated were younger (and thus healthier) than the controls, potential health effects could be caused by the age gap and not by the treatment. To avoid selection bias, this study estimates average health effects by identifying those confounding characteristics that differ between treated and controls, like age in this example. The relevant confounders were identified by analyzing the non-random selection into the treatment (Section 4.2.2). The PASS household panel survey provides data regarding these confounders. Having this data allowed us to use a selection on observables approach for identifying causal effects (Rubin, 1974). The Rubin causal effects model was applied to outline the following identification strategy, which is based on the potential outcomes framework. 8

10 Table 3: Characteristics affecting UB II dependency Sample means UB II UB II Pre-treatment confounders non-recipients recipients Probit Socio-demographic factors Age in years (20-58) *** Household size Marital status (binary) Female (binary) Migrant (binary) Education More than 9 years in school (binary) * Vocational education (binary) Socio-economic factors Monthly household income in 1000 euros, not deflated Deprivation index (weighted) Weekly working hours *** Employment (binary) Months employed in ** Unemployment rate of state (Feb.-Oct. 2011) * Participating in training measures (binary) Health and health-related behavior Good general health (binary) Good mental health (binary) Disability (binary) Doctor visits within the last 3 months Sports in childhood and adolescence (binary) ** Social factors Social engagement (binary) Friends outside the household (binary) Childcare (binary) Care of relatives (binary) Number of observations Note: UB II non-recipients became independent of UB II, while UB II recipients remained dependent on UB II. The dependent variable of the probit model is the treatment indicator (zero for UB II recipients, one for UB II non-recipients). Average marginal effects are estimated by the probit model. Significance levels for these effects: *p<.10, **p<.05, ***p<.01. The variable Deprivation index (weighted) is a direct measure of poverty. The index is based on the possession of 23 relevant household items. Each item is weighted by the share of those respondents who find it necessary (Berg et al., 2012). 9

11 4.2 Identification strategy Average treatment effect on the non-treated (ATENT) Potential outcomes Y d are outcomes that would have been realized for treated (d = 1) or nontreated (d = 0) individuals. Relating the potential outcomes framework to this application, Y d denotes the health outcomes that would have been observed if welfare dependents became independent (d = 1) or remained dependent (d = 0) on UB II. The observed outcomes can then be expressed in terms of potential outcomes: Y = Y 1 D + Y 0 (1 D), (1) where the binary treatment is denoted by D. Section 5 shows that UB II dependency can worsen health. Policies should thus aim at increasing the health conditions of those individuals who remain dependent on UB II (the nontreated). The parameter of interest is therefore the ATENT. 14 This is the average effect for individuals randomly drawn from the non-treated sub-population (that remained dependent on UB II). Formally, the ATENT θ is the average difference of potential treatment and nontreatment outcomes for non-treated individuals: θ = E[Y 1 Y 0 D = 0] (2) For the non-treated sub-population, the data provide information on Y 0 only, whereas Y 1 is never observable. This is the fundamental identification problem. To identify θ, four (partly non-testable) assumptions were imposed. These assumptions were thoroughly discussed, among others, by Imbens and Wooldridge (2009) Conditional independence assumption (CIA) The CIA was the main assumption imposed to identify the ATENT. In this application, the CIA holds if the potential outcomes Y 1 do not depend on the treatment D given the observed characteristics summarized in the covariate vector X of the covariate space χ: Y 1 D X = x x χ, (3) where denotes independence. The CIA is an identifying assumption that is not testable but has to be convincingly argued. The problem is that the independence between D and Y 1 holds only if all confounding characteristics can be observed and controlled for. Consequently, the credibility of the CIA depends on the identification of the relevant confounding variables. The following analysis reveals the individual decision process that can affect the dependence 14 Average treatment effects (ATEs) are total effects weighted by the treatment probabilities for treated and non-treated. In this study, ATEs are similar to ATENTs because of the high treatment probability for non-treated (> 85%). Results for ATEs are thus not reported. 10

12 on UB II. Based on this process, the most important confounders shown in the literature are identified. UB II recipients report that unemployment is the main reason for claiming UB II (Fuchs, 2012). Further reasons are separation of couples, births of children, health problems, poor education, and no formal vocational qualification. Individuals in such situations often find no employment or only low-income jobs that do not prevent UB II dependency despite being employed. The way out of the dependence on UB II is mainly characterized by a higher household income (Achatz & Trappmann, 2009). Employment is the leading cause for the way out of UB II dependency. But also individuals without employment can become independent of UB II, such as partners of newly employed. Analysis of the selection process shows that UB II recipients differ widely regarding both their economic situations and their life situations. Therefore, finding out the determinants of unemployment and poverty can indicate important confounding characteristics. The literature states that differences in (i) demographic factors, (ii) socioeconomic factors, (iii) social behavior, (iv) health, and (v) life style can influence labor market outcomes and poverty. Age, gender, household size, and marital status are demographic factors that are related to unemployment and poverty (Arulampalam & Stewart, 1995; Dewilde, 2008). Beside these factors, human capital in the form of education and occupational qualification can influence both the probability of becoming unemployed and the probability of experiencing poverty (Russell & O Connell, 2001; Schmillen & Möller, 2012). These studies also identify socioeconomic variables as potential sources for unemployment and poverty. Individual employment history, social class, income, and unemployment rates are such potential sources mentioned in the literature. Health, health-related behavior, and social factors are other determinants of unemployment and poverty. Herbig, Dragano, and Angerer (2013) show that mental illness is positively correlated with unemployment. An unhealthy lifestyle (e.g. through excessive alcohol consumption) may also impact the risk of becoming unemployed (Henkel, 2011). The relation between social capital and poverty is analyzed by Robinson, Siles, and Schmid (2002). The PASS data provide information on most of these variables. However, this work cannot directly control for all confounding characteristics. The PASS data does not provide information on potential confounders such as motivations (e.g. through job-search intensities), abilities, or preferences. Proxy variables were thus used to overcome this problem. Employment histories were used as a proxy variable for reflecting the motivations and abilities of UB II recipients. Preferences differing among individuals were mirrored by a deprivation index indicating the ownership of different items, for instance of a car. Using such proxy variables further helped to control for confounders. Sensitivity tests additionally justified the credibility of the CIA (Sections and 5.3.3). One test uses lagged outcomes as pseudo outcomes to exclude the presence of unobserved confounders (Imbens, 2014). Another test simulates potential confounders (Ichino, Mealli, & Nannicini, 2008). Both of these sensitivity tests did not reject the assumption of a valid CIA. 11

13 Summarizing the discussion of the CIA, this study identified the relevant confounding characteristics for uncovering causal effects. Controlling for these characteristics validated the CIA Additional identifying assumptions Three additional assumptions were imposed for identifying the ATENT: (i) the stable unit treatment value assumption (SUTVA), (ii) the assumption of exogenous covariates (characteristics), and (iii) the common support assumption. The validity of the SUTVA rules out any general equilibrium effects. This assumption implies that the welfare statuses of households did not affect each other. A possible violation of the SUTVA would occur if the job-search probabilities of UB II recipients were not independent of each other. As a consequence, the welfare state of one household receiving UB II would affect the welfare state of the others. In line with other studies using similar populations, this analysis does not consider general equilibrium effects (see Huber, Lechner, and Wunsch (2011) who investigate the health effects of German welfare recipients). The reason is that even if such effects may occur in rare cases, they would barely affect the estimates. If covariates affect the treatment, the estimated effects can become biased. Using exogenous covariates can avoid this bias. The violation of the assumption of exogenous covariates was ruled out because the control variables used were observed in the pre-treatment period. This implicitly excludes anticipation effects which could only arise if UB II recipients were able to determine the end of their dependence on UB II. The common support assumption holds if the treatment probability given observed covariates, also denoted as the propensity score, is greater than zero. The propensity score p(x) is formally defined as: p(x) = P (D = 1 X = x) > 0 x χ. (4) In the sample used, all treated were comparable to controls in terms of p(x). This validated the common support assumption. The curse of dimensionality problem is inherent in non-parametric regressions with many covariates. This is why a fully non-parametric estimation of the ATENT is not possible in this study. Rosenbaum and Rubin (1983b) showed that the ATENT can be identified by conditioning on the one-dimensional propensity score p(x) instead on the full covariate vector X: Y 1 D p(x) = p(x) x χ. (5) The finding of Rosenbaum and Rubin (1983b) was exploited for identifying causal effects and jointly solving the curse of dimensionality problem. Meeting the four identifying assumptions allows one to estimate causal effects. The estimation strategy employed is outlined in the next section. 12

14 4.3 Estimation strategy Effects were estimated by comparing the health outcomes of treated that are similar to controls in terms of observed characteristics. This study used matching for comparing the outcomes. The matching estimator for the ATENT ˆθ is formally expressed as: ˆθ = 1 N 0 D i =0 (Ŷi (1) Y i ), (6) where N 0 refers to the sample size of the controls. Equation 6 states that ˆθ equals the sample mean of the differences between an average of matched treated outcomes and the respective control outcomes. Matching is based on the propensity score p(x). Rosenbaum and Rubin (1983b) proved that p(x) is a balancing score. This score possesses the property that observations with the same value of p(x) have the same distribution of the observed characteristics. By exploiting the balancing property, it was possible to compare the health outcomes of treated and controls with similar values of p(x). The estimator used was the radius-matching estimator with bias adjustment of Lechner, Miquel, and Wunsch (2011). 15 Its finite sample properties were examined in an extensive simulation study by Huber, Lechner, and Wunsch (2013). They showed that it performed best in terms of the root mean squared error within a large number of parametric, semi-parametric, and non-parametric estimators. The advantages of this semi-parametric matching estimator compared to others are manifold. Contrary to parametric models, no tight functional form assumptions have to be imposed for the relation between Y 1 and p(x). Consequently, this relation can be estimated non-parametrically. Only p(x) is estimated parametrically by a probit model. Another advantage of the matching procedure is that it accounts for effect heterogeneity regarding observed characteristics, which is not the case for non-saturated parametric models. Two central features of the estimator are (i) weighted radius matching and (ii) bias adjustment based on a parametric regression. In a first step, treated observations within a predefined radius are weighted by their distance from the control observation they are matched with. This procedure reduces the bias compared to unweighted radius matching because it gives more weight to more similar observations. It also lowers the variance compared to nearest neighbor matching because of the higher number of treated observations. In a further step, the bias caused by mismatches is reduced. This is achieved by running a weighted parametric regression of the treated outcomes on p(x). The regression enables both to estimate the potential treatment outcome Y 1 for the non-treated more precisely and to correct for the bias due to mismatches (Huber, Lechner, & Wunsch, 2013). Inference was based on bootstrap replications. Bootstrap based inference is inconsistent if a fixed number of treated observations is matched to a control observation (Abadie & Imbens, 2008). However, bootstrap-based inference obtained by the radius matching estimator of 15 Computations were based on version 4.1 of the estimator (implemented in Gauss ). 13

15 Lechner, Miquel, and Wunsch (2011) seems to be consistent. The reason is that the number of weighted treated observations that are matched to the respective control observations varies within the pre-defined radius. Huber, Lechner, and Steinmayr (2014) provide a detailed discussion of inference and the selection of tuning parameters. They show plausible parameters for the (partly) data driven radius distance for treated observations, or for trimming procedures for a distributional overlap. The tuning parameters set in this study are listed in Section 7.3 in the Appendix. All estimates were computed using the software GAUSS. Lechner (2014) gives more information on the application of the radius matching estimator applied in this study. 5 Results 5.1 Findings for the full sample Becoming independent of UB II positively affects health (Table 4). 36% of the controls reported good general health. If the controls had been treated, 43% would have reported good general health. The estimated ATENT on good general health is thus 7 percentage points. This finding is based on the overall sample and shows a statistical significance at the 5% level. Considering the effects on mental health and the health-related behavior, no statistically significant treatment effects were found. The results presented are based on a balanced sample after matching (see Table 9 in the Appendix). Table 4: ATENTs of becoming independent on UB II on health outcomes Health outcomes Y 0 ˆθ Health Good general health (binary) ** Good mental health (binary) Health-related behavior Sports activities (binary) Weekly hours doing sports Friends through sports (binary) No daily alcohol intake (binary) Non-smoking (binary) Note: The potential non-treatment outcomes are denoted by Y 0. The estimated ATENTs are denoted by ˆθ. Inference is based on 1999 bootstrap replications. Significance levels for ˆθ: *p<.10, **p<.05, ***p<.01. In line with the results of Table 4, Eggs (2013) also showed a negative relation between UB II dependency and general health. He argued that mediators like stress, stigma, monetary problems, or the lack of social contacts may negatively affect overall health. However, his results, as well as the findings presented in Table 4, show only aggregate effects. But the mediators 14

16 mentioned by Eggs (2013) work differently for heterogeneous subgroups. For instance, employed UB II recipients face less stress due to threats of benefit cuts and earn more money than those without employment. The treatment effects may thus differ between welfare dependents with different labor market statuses. The following findings account for effect heterogeneity and may lead to a better understanding of channels affecting the health of welfare dependents. 5.2 Effect heterogeneity Table 5 shows the estimated ATENTs ˆθ on the health outcomes for two heterogeneous groups. Subgroup 1 includes UB II recipients without employment, while subgroup 2 comprises those who were employed in the pre-treatment period. Table 5: ATENTs of becoming independent on UB II on health outcomes considering effect heterogeneity Subgroup 1 Subgroup 2 (not employed in pre- (employed in pretreatment period) treatment period) Health outcomes Y 0 ˆθ Y 0 ˆθ Health Good general health (binary) * Good mental health (binary) * Health-related behavior Sports activities (binary) ** Weekly hours doing sports ** Friends through sports (binary) *** No daily alcohol intake (binary) Non-smoking (binary) Note: The potential non-treatment outcomes are denoted by Y 0. The estimated ATENTs are denoted by ˆθ. Inference is based on 1999 bootstrap replications. Significance levels for ˆθ: *p<.10, **p<.05, ***p<.01. Column 3 of Table 5 indicates the estimated effects for subgroup 1. The ATENTs on good general and mental health are positive ( 20%) and statistically significant. The results on health-related behavior indicate a significantly negative ATENT ( -29%) on the weekly duration in the main sports activity. No significant treatment effects were found on the other health-related outcomes regarding sports and lifestyle. A very different picture arises for subgroup 2. The last column of Table 5 indicates no treatment effects on health. However, large and significantly positive effects were estimated on the probabilities of being engaged in sports and finding friends in the main sports activity. If the control group had been treated, it would have taken part in sports more often by approximately 50%. In addition, it would have found friends through sports more often by nearly 80%. 15

17 The following analysis indicates that the health effects seemed to be mediated by the labor market status of UB II recipients. The findings presented in Table 5 further allow one to discuss which channels potentially lead to different health outcomes. Significantly positive health effects were found for the first subgroup, whereas no effects were observed for the second one (rows 1&2 of Table 5). The different labor market statuses of the two groups might have caused these different health effects. In subgroup 1, most of the controls (> 92%) but only half of those treated remained without employment in the treatment period (rows 1-4 of Table 2). Considering subgroup 2, more than 75% of the controls and nearly all of those treated remained employed in the treatment period (rows 5-8 of Table 2). This implies that Y 1 and Y 0 of subgroup 1 are based on individuals with very different labor market statuses, whereas that is not the case for subgroup 2. Generally speaking, individuals experienced positive health effects when they changed their labor market status from unemployed to employed. No effects were found for individuals who were employed before and after the treatment. The literature shows which channels potentially cause adverse health effects for welfare dependents. Higher income and more social contacts while working might have driven the positive effect on general health (Frijters, Haisken-DeNew, & Shields, 2005; House, Landis, & Umberson, 1988). Reasons for the positive effect on mental health might have been less stigma and stress, both of which could cause depressions (Rosenthal, Carroll-Scott, Earnshow, Santilli, & Ickovics, 2012). Some behavioral patterns related to health also differ between subgroups 1 and 2. Controls of subgroup 1 would reduce the weekly duration in their main sports activity by nearly 45 minutes if they were treated (row 4 of Table 5). This result is plausible because it accounts for less leisure time available to the formerly unemployed when starting working (rows 3&4 of Table 2). No such effects were found for subgroup 2. Individuals in subgroup 2 would more often do sports and find friends through sports if they were treated. These positive treatment effects are large and hint at a lack of social capital while being dependent on UB II (rows 3&5 of Table 5). However, only the controls of subgroup 2 would be able to increase their social networks through sports if they were treated. This might be because nearly all those treated in subgroup 2 were employed and thus better integrated into society than those treated in subgroup 1. No social network effects were estimated for subgroup 1, a group of which half of those treated remained without employment. Evidence considering effect heterogeneity shows that individuals without employment in particular would improve their health if they became independent of UB II (Table 5). To put it another way, these individuals are in poorer health only because they are on welfare. Restrictions imposed by the Hartz IV welfare program (monetary problems, stigma, stress, or low social capital) are possible reasons for poorer health. Health-improving policies may increase the physical and mental health of UB II recipients and subsequently increase their labor-market prospects. Health-related policies could be implemented in different ways. Mandatory courses for education on physical, oral, and mental health can increase the awareness of health issues. 16

18 Such courses are provided, for example, by the US Job Corps program to increase the working abilities of young adults. 16 Sports programs could also improve the health conditions of UB II recipients without employment. For instance, providing fitness-club vouchers or organizing free (group) sports events could lead to a more active life style and increase social capital, both of which can improve health. Which health-improving measures work best is a priori not clear and would have to be tested empirically. 5.3 Sensitivity analysis Common support Shifting the common support region of the propensity score p(x) away from the tails can drop potential outliers. The region of p(x) was thus restricted from (0,1) to (.02,.98) to evaluate the robustness of the estimates presented in Table 4 with regard to outliers. The effects on health newly estimated within the support region (.02,.98) are not sensitive to outliers (rows 1&2 of Table 6). The effect on general health increases by one percentage point only; this effect remains significant at the 5% level. The effect on mental health is still not different from zero at any conventional significance level. Nearly all effects on health-related behavior remain insignificant and are thus also insensitive to outliers (rows 3-7 of Table 6). Only the effect on one outcome (no daily alcohol intake) becomes significantly larger, indicating a positive treatment effect. This test shows that sensitivity regarding outliers was no at issue in the present application. The following robustness tests evaluate the plausibility of the CIA. Table 6: ATENTs using propensity scores within the region (0.02,0.98) Health outcomes Y 0 (.02,.98) ˆθ (.02,.98) Health Good general health (binary) ** Good mental health (binary) Health-related behavior Sports activities (binary) Weekly hours doing sports Friends through sports (binary) No daily alcohol intake (binary) *** Non-smoking (binary) Note: The potential non-treatment outcomes are denoted by Y 0. The estimated ATENTs are denoted by ˆθ (.02,.98). Inference is based on 1999 bootstrap replications. Significance levels for ˆθ (.02,.98) : *p<.10, **p<.05, ***p< The Job Corps welfare program provides education and vocational training to disadvantaged young US adults (see 17

19 5.3.2 Pseudo outcomes To assess the credibility of the CIA, the ATENTs on the lagged health variables (surveyed in 2011) were estimated. Finding no treatment effects can be regarded as a support for imposing the non-testable CIA (Imbens, 2014; Imbens & Wooldridge, 2009). The rationale behind this test is that a treatment can never affect an outcome observed prior to the treatment. In the case of still finding significant treatment effects, the outcomes of the treated and controls have to differ because of factors not observed in the data. Such confounders were also likely to affect the treatment D and the potential outcome Y 1 and could cause a selection bias. The pseudo ATENT θ pseudo is formally defined as: θ pseudo = E[Ypseudo 1 Y pseudo 0 D = 0] = E [ E[Y 1 pseudo Y 0 pseudo p(x), D = 0]], (7) where the outer expectation is the average over the distribution of p(x) D = 0. The pseudo potential outcome Ypseudo d denotes pre-treatment health that would be observed in the respective treatment states d {0, 1}. The covariate vector X contains all regressors shown in Table 3 except the variables on general and mental health. These health variables are used as pseudo outcomes. No statistically significant pseudo treatment effects were found in the full sample (Table 7). These findings support the credibility of the CIA. Table 7: Pseudo ATENTs of becoming independent on UB II on health Y 0 ˆθpseudo σ p-value Good general health (binary) Good mental health (binary) Note: The potential non-treatment outcomes are denoted by Y 0. The estimated pseudo ATENTs and their standard errors are denoted by ˆθ pseudo and σ, respectively. Inference is based on 1999 bootstrap replications Simulated confounding variables A simulation-based sensitivity test further assesses the robustness of the estimates with regard to violations of the CIA (Ichino, Mealli, & Nannicini, 2008). The test works by simulating a binary confounder U and including it in the estimation procedure in addition to the observed confounders X. Comparing estimates with and without U shows the sensitivity of the results to unobserved confounders similarly distributed as U. This sensitivity test does not impose any parametric assumptions on the relation between U, the treatment D, and the potential outcome Y 1. The avoidance of parametric assumptions makes this test preferable to previous ones introduced in the literature so far, for example the methods of Rosenbaum and Rubin (1983a) or Imbens (2003). 18