J Fam Econ Iss DOI 10.1007/s10834-014-9435-y ORIGINAL PAPER Disruption in Parental Co-habitation and its Effects on Short-Term, Medium-Term, and Long-Term Outcomes of Adolescents Andrew Hussey • Debjani Kanjilal Anil Nathan • Ó Springer Science+Business Media New York 2014 Abstract In this paper the relationships between a disruption in parental co-habitation and various categories of adolescent outcomes over multiple time horizons are explored. Using data from the National Longitudinal Study of Adolescent Health (Add Health), we estimated the effects of a change from living with both parents to just one, on academic and employment outcomes, the likelihood to indulge in risky behaviors, mental health outcomes and body mass index measures, from less than 1 year to over 14 years after the change. Propensity score matching methods were used to control for individual characteristics and pre-existing differences in the family environment that may increase the chances of separation, and the results are compared to those obtained using ordinary least squares or probit methods. Results showed evidence of adverse effects of living with one parent in the short term, medium term and long term. Adolescents living with one parent had lower academic achievement in all term lengths, poor mental health in the short to medium term, and were more likely to engage in risky behaviors in the medium to long term. A. Hussey Department of Economics, University of Memphis, 423 Fogelman Administration Building, Memphis, TN 38152, USA D. Kanjilal (&) Department of Business & Economics, Elizabeth City State University, Campus Box 781, 1704 Weeksville Rd, Elizabeth City, NC 27909, USA e-mail: [email protected] A. Nathan Department of Economics and Accounting, College of the Holy Cross, 1 College St, Worcester, MA 01610, USA Keywords Disruption in parental co-habitation  Propensity-score matching  Add health JEL Classification I21  J11  J12 Introduction Living in a single-parent family is largely perceived to have negative effects on children’s well-being, and several earlier academic studies have confirmed this perception (Amato and Booth 1997; Keith and Finlay 1988; Manski et al. 1992; Mayer 1997). Be it due to divorce, separation, or the death of a parent, the transition from a dual parent household to a single parent household may be accompanied by children’s decreased academic performance or attainment, and may alter the eventual marital and divorce probabilities of the children (Cid and Stokes 2013; Corak 2001; Ermisch and Francesconi 2001; Frisco et al. 2007; Keith and Finlay 1988). Moreover, children from these families may have increased likelihood of engaging in potentially risky behaviors, such as drinking, smoking, drug use, or sex, and may suffer from significant stress, depression, or other psychological problems (Amato 2000; Amato and Keith 1991; Brown 2006; Dronkers 1999; Schramm 2005). Adverse effects may arise through a number of channels. For example, single parents might invest less time or income in their children, or provide improper guidance or role modeling (Downey 1994; Entwisle and Alexander 1996; McLanahan and Sandefur 1994; Myers and Myers 2014). Living in a single-parent family can also reduce economic and social resources available to children (Bane and Jargowsky 1988; McLanahan and Sandefur 1994). Disruption of cohabitation can also have an effect on parents’ stress and health (Kohn and Averett 2014), which 123 J Fam Econ Iss may in turn affect parenting practices. On the other hand, if in an intact family there is friction among parents, then a divorce may not adversely affect children’s outcomes, or might even prove to be good for the child (Brown 2006; Dronkers 1999). Indeed, some studies have found no causal effect of living in a single-parent family on children’s outcomes (Entwisle and Alexander 1996; Lang and Zagorsky 2001; Marsh 1990; Sanz-De-Galdeano and Vuri 2007). Thus, despite a large and growing body of research in the area, the literature investigating the effects of parental divorce or separation on children’s outcomes remains inconclusive, or varied in its findings. This is likely due to a few reasons. First, the outcomes investigated have often been narrow and varied across studies. Second, many studies used local or non-nationally representative data. Third, studies have differed in their time frame of analysis or age of the children when parental separation occurs.1 Finally, since single-parent families are likely to be different from twoparent families, and in ways that may also independently affect children’s outcomes, the effect of living with a singleparent is likely to be confounded with these factors. Correspondingly, studies in the literature have differed methodologically, in particular with regard to how they handle this non-random assignment of changes in family structure. In this paper, we analyzed the impact of an adolescent’s transition from living with two parents to living with one parent on several key academic, behavioral and healthrelated outcomes often associated with adolescents’ future wellbeing. The academic and economic outcomes we considered include GPA, high school graduation, college attendance, college degree, employment and income. The behavior related outcomes included in our study are the propensities to engage in the potentially risky behaviors of drinking, smoking, drug use and sex. Finally, we also considered the health-related outcomes of BMI and mental health indicators. Since it has been suggested that time since the disruption of parental co-habitation is an important determinant of its effects on children (Chase-Lansdale et al.1995; Cherlin et al. 1995), we separated our analysis into short term (one year or less after parental separation), medium term (about 7 years after) and longer term (about 14 years after) effects on adolescent outcomes. We used data from the National Longitudinal Survey of Adolescent Health (Add Health), a large national survey of adolescents, to explore these relationships. The richness of the data allowed for the inclusion of detailed control variables not available to many prior researchers. Because 1 For example, Cid and Stokes (2011) looked at the role of parental separation on school drop-out behavior, while Couch and Lillard (1997) investigated much longer-term intergenerational correlation in adult earnings as a function of parental divorce. 123 we wished to isolate causal effects of living with one parent on children’s outcomes, we required changes in parental co-habitation over time. Thus, we relied on reports in each survey wave of whether or not adolescents were living with both of their parents. An individual that reported living with only one parent after initially reporting living with both served as an indicator of parental separation. We thus looked at changes in adolescent behavior due to a change in co-habitation.2 . The main estimation method used in this paper was the propensity score matching technique, which is widely used in statistics and medicine. Although this method is becoming more common in economic applications involving treatment effects, its use has been minimal in the literature on the effects of parental divorce and separation.3 The method assists in creating an appropriate control group for the treatment group of interest. In our case, we compared two groups who were otherwise observably similar, but one group receives a treatment (living with single parent) and the other group does not (remains living with both parents). Under certain assumptions, the difference in the outcomes of the two groups can be attributed solely to the treatment. In addition to multiple methods of matching on the basis of propensity score, we compared our findings to those using ordinary least squares (OLS) and probit regressions. A similar methodology was used by Frisco et al. (2007) in their estimation of the influence of parents’ union dissolution on changes in adolescents’ mathematics course work gains, overall GPA, and course failure rates. They found that academic achievement of children declined as a result of parental union dissolution. However, their study was limited to only the short-term academic performances of children. Our work can be seen as an extension of their analysis, with our addition of several categories of outcomes and multiple observations at different time periods following the dissolution of parental cohabitation. Our results suggest that adolescents who experienced parental separation had lower GPAs, poorer mental health, and increased participation in risky activities in the shortterm. They also had a lower likelihood of graduating from high school on time, lower likelihood of obtaining a college degree, and were more likely to smoke in the medium and long-terms. These results imply the advocacy of 2 Tumin et al. (2014) found that the vast majority of all marital separations end in divorce. Further, they found that separations leading to divorce are relatively short —8 to 9 months. Using samples from the National Longitudinal Survey of Youth, 1979, and the National Surveys of Family Growth, they found that between 2 and 4 % of marital disruptions among women are caused by widowhood. 3 For example, see Basu et al. (2008), Dehejia and Wahba (2002), Heckman et al. (1998), Imbens (2000) and Rosenbaum and Rubin (1983). J Fam Econ Iss programs to either keep parents together or to support families that are disrupted. Empirical Methods In analyzing the effects of change in parental co-habitation on adolescents’ outcomes, we considered two groups, one whose parents separated between the first two survey waves, Waves I and II (the treatment group), and the other whose parents did not (the control group). The main difficulty of attempting to determine causal relationships in this context is that adolescents whose parents separate are likely to differ from those whose families remain intact, and in ways that are correlated with subsequent academic, behavioral and health outcomes that can be observed in the data. Our fundamental solution to this problem was to follow that which is used by much of the literature, which was to control for selection on observables. In this case, we used the richness of the Add Health data to control for a diverse array of initial characteristics of the individual, family, school, and community. We then effectively created a counterfactual for individuals in the treatment group using individuals from the control group who were most similar in terms of these covariates. Specifically, each observation in the treatment group was matched with one or more observations in the control group. Under certain assumptions, the average difference in outcomes can then be attributed to the disruption in parental co-habitation (Rosenbaum and Rubin 1983). More concretely, we can define: Y1: outcome of an adolescent living with one parent (exposed to the treatment) Y0: outcome of an adolescent living with both parents (not exposed to the treatment) D: indicator of a change in parental co-habitation (the treatment) X: set of covariates. Matching requires the assumption that all relevant differences between the two groups will be captured by the set of covariates. That is, ðY0 ; Y1 Þ?DjXj: Children living with one parent were matched with a control group of children living with both parents, with whom the distribution of the covariates is as close as possible to the group with the treatment. Propensity score matching provides a natural method for weighting each of the covariates, thus avoiding the problem of finding an exact match for the treatment group. While finding an exact match would severely limit the number of possible covariates to be matched on, propensity score allows matching on a large number of covariates by collapsing the relevant information into a single index, or ‘‘propensity score.’’ The propensity score (PS) is defined as the probability of receiving the treatment conditional on the set of covariates. Thus, PSðXÞ ¼ PðD ¼ 1jXÞ:. In practice, we estimated the propensity score using a logit model including a large number of covariates. Using the obtained estimates, for each individual we predicted the probability of undergoing a parental separation (conditional on the observables), regardless of whether or not individuals actually experienced this disruption in parental co-habitation. We used only pre-disruption (Wave I) variables to predict the likelihood of disruption, since later variables may themselves be affected by this change in cohabitation. Individuals in the treatment group were then matched with individuals in the control group on the basis of this estimated propensity score. The first assumption thus became: ðY 0 ; Y1 ?Dj PSðXÞ:This assumption merely states that parental separation effectively happens randomly to adolescents, conditional on the rich set of covariates as embodied in the propensity score. Another necessary assumption is that the propensity score was confined to be between zero and one, which necessarily resulted from our logit specification.Under these assumptions, we estimated, in the words of the treatment effect literature, the average treatment effect on the treated (ATT), corresponding to the average effect of living with one parent on adolescents in such an environment: ( ) X 1 X ATT ¼ Yi  wði; jÞYj ND i2D J2C i where: Yi: outcome of an adolescent living with one parent Yj: outcome of an adolescent living with both parents ND: number of adolescents living with one parent Cj: set of matched control adolescents w (i, j): the weight function There are several possible different weighting methods (Imbens 2000). These include one-to-one nearest neighbor matching, k-nearest neighbors matching (k [ 1), kernel density matching and local linear regression matching. We used the simple average k-nearest neighbors matching method, specifically using two nearest neighbors. This approach created the counterfactual outcome as a simple average over the outcomes of the nearest neighbors: ^ 0i jPðXi Þ; Di ¼ 0Þ ¼ 1 EðY 2 2 X Y0j j¼1 fDj2A2 We also used a kernel density matching method, where all adolescents in the counterfactual group (living with both parents) are considered for matching and the closer observations (in terms of their propensity score) are given more weight and the further observations are given lower 123 J Fam Econ Iss Table 1 Descriptive statistics of treatment and control groups (i) Treatment Sample Mean (Std. dev.) (ii) Control (unmatched) Mean (Std. dev.) (iii) Control (matched) Mean Difference: (i) and (ii) p value Difference: (i) and (iii) p value 15.708 15.447 15.742 0.000 0.709 (1.43) (1.412) Males 0.418 (0.493) 0.457 (0.498) 0.446 0.113 0.403 Black 0.196 0.138 0.197 0.001 0.967 (0.397) (0.345) 0.484 0.992 0.876 0.106 0.694 0.286 0.075 0.535 0.507 1.356 0.000 0.887 5.141 0.001 0.668 Age American Indian 0.046 0.046 (0.021) (0.210) Asian 0.085 0.080 (0.280) (0.272) Others 0.088 0.079 (0.283) (0.271) 1.367 1.756 (1.190) (1.342) No. of siblings Mother’s education 5.209 5.617 (2.388) (2.402) Mother works full time 0.757 0.756 0.761 0.962 0.907 Father works full time (0.428) 0.907 (0.429) 0.934 0.914 0.030 0.727 (0.290) (0.247) 4.476 0.002 0.532 3.894 0.000 0.955 1.697 0.000 0.616 0.160 0.037 0.721 1.320 0.010 0.755 16.556 0.364 0.695 Closeness to mother Closeness to father Skipped school Grade repeated Attention problem TV hours 4.442 4.558 (0.845) (0.738) 3.888 4.305 (1.190) (0.922) 1.933 1.075 (7.352) (4.703) 0.169 0.134 (0.375) (0.341) 1.343 1.212 (1.133) (1.025) 16.147 15.500 (14.953) (14.483) Play computer games 2.691 2.716 3.028 0.935 0.513 Hang with friends (6.108) 2.013 (6.355) 1.966 1.991 0.331 0.744 (1.022) (0.979) 0.490 0.112 0.370 0.615 0.000 0.822 0.349 0.090 0.978 0.642 0.000 0.476 0.084 0.093 0.421 0.331 0.094 0.301 Play sports Felt depressed Felt fearful Felt sad Private school Urban 123 0.46 0.499 (0.498) (0.500) 0.627 0.447 (0.805) (0.699) 0.348 0.302 (0.588) (0.545) 0.676 0.523 (0.723) (0.644) 0.07 0.094 (0.256) (0.292) 0.299 0.263 J Fam Econ Iss Table 1 continued (i) Treatment Sample Mean (Std. dev.) (ii) Control (unmatched) Mean (Std. dev.) (iii) Control (matched) Mean Difference: (i) and (ii) p value Difference: (i) and (iii) p value 0.208 0.529 0.838 0.245 0.603 0.969 0.396 0.036 0.839 0.068 0.001 0.381 (0.458) (0.440) 0.202 0.190 (0.402) (0.392) 0.246 0.235 (0.431) (0.424) 0.389 0.340 (0.488) (0.474) North east 0.083 0.138 (0.277) (0.345) Caring neighbor 0.707 (0.455) 0.761 (0.426) 0.709 0.010 0.942 0.900 0.913 0.889 0.362 0.588 (0.299) (0.281) 3.940 0.089 0.786 2.774 0.000 0.240 101.19 0.000 0.242 0.227 0.000 0.969 0.430 0.001 0.815 Rural West South Feel safe in neighborhood Happy in neighborhood GPA PVT scores 3.958 4.038 (0.974) (0.955) 2.708 2.888 (0.853) (0.825) 100.061 102.700 (14.279) (14.165) 0.229 0.147 (0.420) (0.355) 0.422 0.347 (0.494) (0.476) Drugs 0.244 0.202 0.242 0.033 0.938 Sex (0.430) 0.385 (0.401) 0.237 0.381 0.000 0.892 (0.487) (0.425) 454 4961 Smoke Alcohol N All variables obtained from Wave I of Add Health. Sample restricted to households with cohabiting parents in Wave I. Treatment group includes individuals who transitioned from living with both parents to living with one parent between Waves I and II. Control group includes individuals for whom parental co-habitation remained intact between Waves I and II weight. Specifically, we use a biweight (quartic) distribution kernel smoothing function to match treated observations with the untreated sample, using a bandwidth of 0.6. For purposes of comparison of results, in addition to using these matching estimators, we initially carried out ordinary least squares and probit regressions. We ran OLS regressions for all outcomes that are continuous and probit regressions for all binary outcomes, where the marginal effects were reported. However the advantages of using matching estimators over OLS and probit regressions in the case of a binary treatment where there is selection into the treatment were twofold. First, any misspecification problems using OLS and probit were diminished because the functional forms in constructing counterfactuals are flexible. Secondly, the distribution of propensity scores for the treatment and control group needed to have a common support. This allowed for treatment and control groups to be essentially the same in all characteristics except for treatment itself. The comparison between the treatment and control groups was more direct than with OLS or probit, as it replicated an experimental design. Data Add Health The data used in this paper came from the National Longitudinal Survey of Adolescent Health (Add Health) that 123 J Fam Econ Iss Table 2 Wave II ordinary least squares and probit marginal effects estimates Live with one parent Age Males Black No. of siblings GPA Smoke Alcohol Drugs Sex BMI MHI Depressed -0.134*** 0.032 -0.022 0.038 0.052 0.039 1.12** 0.032* (0.008) (0.022) (0.030) (0.025) (0.031) (0.292) (0.385) (0.013) 0.008 -0.011** 0.034*** -0.010* 0.097*** -0.574*** 0.146* 0.003 (0.002) (0.009) (0.004) (0.005) (0.004) (0.005) (0.052) (0.069) -0.257*** -0.021* -0.022 0.005 -0.039** 0.586*** -1.349*** -0.013* (0.023) (0.010) (0.015) (0.011) (0.015) (0.143) (0.191) (0.006) -0.177*** -0.111*** -0.148*** -0.013 0.199*** 0.654*** -0.122 -0.001 (0.038) (0.008) (0.019) (0.017) (0.024) (0.212) (0.284) (0.008) -0.011 -0.004 0.012* -0.003 -0.018*** -0.092 0.161* 0.000 (0.009) (0.004) (0.005) (0.004) (0.005) (0.052) (0.068) (0.002) Mother’s education 0.024*** (0.006) -0.001 (0.002) 0.006 (0.003) 0.006* (0.003) -0.013*** (0.003) -0.076* (0.031) -0.098* (0.042) -0.001 (0.001) Mother works full time -0.019 0.002 0.016 -0.001 0.007 -0.019 -0.291 -0.005 (0.028) (0.011) (0.016) (0.013) (0.016) (0.158) (0.212) (0.007) Father works full time Closeness to mother Closeness to father Skipped school Grade repeated Attention problem TV hours Play computer games Hang with friends Play sports -0.027 0.001 0.017 0.005 -0.015 -0.591* -0.712* -0.012 (0.048) (0.019) (0.028) (0.021) (0.028) (0.272) (0.358) (0.011) -0.002 -0.007 -0.016 -0.009 -0.008 0.115 -0.377** -0.006 (0.020) (0.007) (0.011) (0.008) (0.011) (0.108) (0.145) (0.004) -0.005 -0.008 -0.027** -0.020** -0.020* 0.316*** -0.861*** -0.009** (0.017) (0.006) (0.010) (0.007) (0.009) (0.091) (0.121) (0.003) -0.008 0.001 -0.000 0.002* 0.004** 0.041** 0.055** 0.001* (0.004) (0.001) (0.001) (0.001) (0.002) (0.016) (0.020) (0.000) -0.198*** 0.033* -0.032 0.012 0.020 -0.0009 0.296 0.003 (0.040) (0.016) (0.021) (0.017) (0.021) (0.208) (0.282) (0.008) -0.069*** 0.005 0.032*** 0.021*** -0.006 -0.109 0.931*** 0.009*** (0.013) (0.005) (0.007) (0.006) (0.007) (0.071) (0.094) (0.002) -0.002* (0.001) 0.000 (0.000) 0.000 (0.001) -0.000 (0.000) -0.001* (0.000) 0.019*** (0.005) 0.001 (0.007) 0.000 (0.000) -0.001 -0.001 -0.000 0.001 0.002* 0.005 0.003 -0.001 (0.002) (0.001) (0.001) (0.001) (0.001) (0.012) (0.015) (0.000) --0.004 0.026*** 0.049*** 0.033*** 0.025*** -0.158* -0.112 -0.000 (0.012) (0.005) (0.007) (0.006) (0.007) (0.069) (0.093) (0.003) 0.077*** -0.025* 0.044** 0.021 0.048*** -0.137 -0.149 -0.004 (0.024) (0.010) (0.014) (0.011) (0.014) (0.137) (0.185) (0.006) Felt depressed 0.111 0.008 -0.055 0.031 0.115** -0.252 (0.070) (0.025) (0.036) (0.031) (0.042) (0.375) Felt fearful 0.037 -0.042** -0.068** -0.028 -0.014 0.050 0.555 0.011 (0.047) (0.013) (0.025) (0.019) (0.025) (0.249) (0.336) (0.010) Private school 0.100* -0.029* -0.049* 0.014 -0.053* -0.040 -0.056 -0.007 (0.043) (0.016) (0.024) (0.020) (0.024) (0.233) (0.319) (0.009) -0.043*** -0.006 -0.031*** -0.042*** -0.340*** -0.552*** -0.007* 0.09* (0.006) -0.000 (0.010) 0.003*** (0.007) 0.001 (0.010) -0.002** (0.092) 0.011* (0.124) -0.060*** (0.004) -0.001* (0.000) (0.000) (0.000) GPA PVT scores Smoke Alcohol 123 (0.001) (0.000) (0.000) (0.005) (0.007) -0.209*** 0.104*** 0.138*** 0.181*** -0.392 0.513 0.002 (0.042) (0.024) (0.020) (0.025) (0.224) (0.303) (0.008) -0.054 0.106*** 0.212*** 0.192*** 0.130 0.579** -0.000 (0.030) (0.013) (0.014) (0.017) (0.116) (0.225) (0.007) J Fam Econ Iss Table 2 continued GPA Drugs Smoke Alcohol Drugs Sex BMI MHI Depressed -0.163*** 0.134*** 0.223*** 0.172*** -0.266 0.113 0.005 (0.038) (0.017) (0.021) (0.021) (0.202) (0.275) (0.008) Sex 0.033 (0.027) 0.081*** (0.015) 0.068*** (0.020) 0.077*** (0.015) 0.123 (0.182) 0.223 (0.246) 0.017 (0.08) N 3952 4663 5288 5262 4112 5285 5285 5273 Standard errors shown in parentheses. OLS regression was performed in the cases of GPA, BMI and mental health (depressed and sad). For all other dependent variables, probit regressions were performed. Only coefficients with statistical significance in at least one regression are included in the table. Also included in each regression were controls for racial background, regional school dummies, and neighborhood variables Significance levels: p \ 0.10. * p \ 0.05. ** p \ 0.01. *** p \ 0.001 was conducted by the Carolina Population Center of the University of North Carolina at Chapel Hill. Conducted in four waves, Add Health is a longitudinal survey of a nationally representative sample of young adults who were in grades 7–12 during 1994–1995. A series of in-home interviews were conducted in 1994–95 for Wave I and were followed up in 1996 for Wave II and 2001–2002 for Wave III. Wave IV data collection was completed in 2008 with the full set of variables recently released. Add Health is one of the largest longitudinal surveys of adolescents and has a wide range of information about their personal characteristics, health, risky behaviors, daily activities, families, friends, romantic partners, peer groups, schools, neighborhoods, and communities. Because of its comprehensiveness, Add Health is now being used by many researchers in a variety of social and behavioral science disciplines for analyses of adolescents’ academic, social, physical, psychological and economic phenomena. The richness of the data and its longitudinal nature allowed us to have a relatively complete and dynamic look at the lives of these children as they transition into adulthood. The data also allowed us to consider a wide variety of individual outcomes, and to include a relatively large number of control variables in our analysis. Dependent Variables We considered academic variables (high school graduation, grade point average, standardized test scores, college attendance, and college degree), employment variables (whether employed in Wave III and personal incomes in Waves III and IV), behavioral variables (smoking, drinking, drug use and sex), and health outcomes (BMI in Waves II and III and obesity in Wave IV, and mental health in Waves II and III) as outcomes potentially affected by the change in parental co-habitation. An indicator variable for high school graduation was created primarily from high school transcript information linked to the survey data. To maximize sample size, we supplemented transcript information with students’ self-reports of their graduation status. We considered only on-time high school graduation (4 years or less) for two reasons. First, this allowed us to avoid the inclusion of GEDs, which may or may not be comparable to a regular high school diploma. More importantly, it allowed us to include the youngest cohort surveyed, initially in 7th grade, which, by the time of the third survey wave, was old enough to have graduated from high school on time. Overall high school grade point average was also obtained from student transcripts. In consideration of shortterm effects on grades, we followed Sabia (2007) in calculating Wave II grade point average. We used the average response of the student’s self-reported grades in both the most recent math class and the most recent English/language arts class. Giving equal weight to math and science classes, a 4 was assigned for each reported ‘‘A’’ grade, 3 for each ‘‘B’’ grade, 2 for each ‘‘C’’ grade and 0.5 for a ‘‘D’’ or lower grade. Wave I GPA was constructed in a similar fashion for use as a control variable for initial academic inclination. For Wave IV academic outcomes, we considered two reports, namely if individuals ever attended college and if individuals have a college degree conditional on having attended college. A unique feature of the Add Health data is that each respondent was administered the ‘‘Add Health Picture Vocabulary Test’’ (PVT), an abbreviated version of the Peabody Picture Vocabulary Test—Revised. We included the individuals’ scores on this test in Wave III as an alternative, standardized measure of academic/intellectual development. In both Waves III and IV, respondents were asked to report their annual personal income before taxes in 2001/2002 and 2006/2007 income years, respectively. In addition, in Wave III they were also asked about their employment status at that point of time. Information from these responses provided the employment variables we include in our analysis. The Add Health data contains detailed individual behavioral information. Based on student self-reports, we created dummy variables corresponding to whether the 123 J Fam Econ Iss Table 3 Wave III ordinary least squares and probit marginal effects estimates of academic and employment outcomes GPA Live with one parent Age Males Black No. of siblings Mother’s education Mother works full time Father works full time Closeness to mother Closeness to father Skipped school Grade repeated Attention problem PVT scores Graduated on time Employed Log income -0.141** -0.111 -0.056*** 0.005 -0.240 (0.046) (0.451) (0.011) (0.015) (0.144) 0.054*** 0.718*** 0.030*** 0.041*** 0.295*** (0.008) (0.114) (0.003) (0.004) (0.042) -0.294*** 1.481*** -0.026*** 0.031* 0.637*** (0.021) (0.374) (0.008) (0.012) (0.118) -0.292*** -7.014*** 0.022* -0.107*** -0.240 (0.032) (0.549) (0.010) (0.020) (0.174) -0.001 -0.783*** -0.004 0.000 0.004 (0.008) (0.132) (0.003) (0.004) (0.043) 0.043*** (0.005) 0.984*** (0.080) 0.009*** (0.002) -0.017*** (0.003) -0.060* (0.026) 0.007 0.703 0.022* 0.030* 0.067 (0.024) (0.416) (0.009) (0.014) (0.132) 0.060 -0.051 0.031* 0.073** -0.024 (0.041) (0.703) (0.016) (0.026) (0.227) -0.021 -0.277 -0.003 0.014 0.077 (0.017) (0.283) (0.006) (0.010) (0.091) -0.018 -1.24*** 0.004 -0.007 -0.051 (0.014) (0.240) (0.005) (0.008) (0.077) -0.021*** -0.084* -0.003*** -0.001 -0.007 (0.003) (0.034) (0.001) (0.001) (0.011) -0.370*** -5.044*** -0.115*** -0.031 -0.314 (0.033) (0.547) (0.015) (0.020) (0.180) -0.058*** 0.801*** 0.001 -0.000 -0.026 (0.011) (0.187) (0.004) (0.006) (0.059) TV hours -0.001 (0.001) 0.002 (0.013) -0.000 (0.000) 0.000 (0.000) -0.001 (0.004) Play computer games -0.003 -0.051 -0.001* -0.000 0.013 (0.002) (0.030) (0.000) (0.001) (0.010) -0.029** -0.465* -0.003 0.002 -0.036 (0.010) (0.182) (0.004) (0.006) (0.059) 0.101*** -0.295 0.038*** 0.017 -0.004 (0.020) (0.361) (0.008) (0.012) (0.113) 0.119* 2.06* -0.028 -0.068 -0.450 Hang with friends Play sports Felt depressed (0.058) (0.976) (0.022) (0.036) (0.323) Felt fearful 0.051 -1.93** 0.002 -0.024 0.118 (0.039) (0.654) (0.012) (0.023) (0.210) Private school 0.056 3.844*** 0.034** -0.034 -0.114 (0.028) GPA PVT scores (0.523) (0.012) (0.021) (0.190) 2.807*** 0.059*** -0.030*** -0.074 (0.239) (0.005) 0.002*** (0.008) 0.000 (0.077) 0.012** 0.014*** (0.000) Smoke Alcohol 123 (0.000) (0.000) (0.004) -0.249*** -0.410 -0.050*** -0.052* 0.254 (0.036) (0.585) (0.014) (0.021) (0.186) -0.025 1.576*** -0.005 0.018 0.076 (0.025) (0.439) (0.009) (0.015) (0.137) J Fam Econ Iss Table 3 continued GPA PVT scores Graduated on time Employed Log income -0.191*** -0.892 -0.017 -0.002 -0.129 (0.032) (0.533) (0.011) (0.018) (0.166) Sex -0.164*** (0.029) -1.472** (0.476) -0.040*** (0.011) 0.008 (0.016) 0.134 (0.150) N 3952 6005 6164 5871 4238 Drugs Standard errors shown in parentheses. OLS regression was performed in the cases of GPA, PVT scores and log of Income. For all other dependent variables, probit regressions were performed. Only coefficients with statistical significance in at least one regression are included in the table. Also included in each regression were controls for racial background, regional school dummies, and neighborhood variables Significance Levels: p \ 0.10. * p \ 0.05. ** p \ 0.01. *** p \ 0.001 individual had: (1) smoked every day for 30 days, (2) consumed alcohol when not with parents or other adults in the family, (3) used drugs (marijuana or cocaine) in their life, and (4) engaged in sexual activities ever in their life. These behavioral indicators from Waves II and III constituted our next set of dependent variables. For Wave IV behavioral indicators, we had information only on whether an individual had smoked every day for 30 days. Our final set of outcome variables was respondents’ body mass index (BMI) and obesity and indicators of mental health. Waves II and III have self-report of individuals’ weight and height. We calculated BMI using the formula: (weight in pounds times 4.88) divided by (square of height in feet). In Wave IV, individuals were asked whether they considered themselves obese or not and we used this self-report as the outcome variable for the final wave.4 We also looked at self-reports of respondents’ mental health. In particular, we relied on nineteen of the twenty questions from the Center of Epidemiological Studies Depression (CES-D) scale. Adolescents were asked to report how often in the past week they experienced each of the nineteen symptoms, and received a score between 0 and 3 for each question. A score of 0 indicated ‘‘never or rarely,’’ 1 indicates ‘‘sometimes,’’ 2 indicates ‘‘a lot of the time,’’ and 3 indicates ‘‘most of the time or all of the time.’’ The scoring of the positive items was reverse coded. The CES-D scale, constructed by adding up the values from each of the nineteen questions (thus ranging between 0 and 4 The correlation between the BMI’s calculated from self-reported weights and heights in Wave III is 0.78. Note that these waves are 5 years apart, so there may be some changes in both height and weight across these years. Also, using a rough guideline of defining obesity as having a BMI C30, binary variables on obesity were created for Waves II and III. The correlation between Waves III and IV is 0.6113. The correlation between Waves II and IV is 0.3625. Note that Waves III and IV are 7 years apart and Waves II and IV are 12 years apart. 57), has been widely used in past research to examine depression (Fletcher 2009, 2008; Roberts et al. 1991; Radloff 1977). Thus, a high CES-D score indicates poor mental well-being and we represented that as Mental Health Index (MHI). Furthermore, following Roberts et al. 1991, we created a binary variable ‘‘Depressed’’ using cutoff points from the CES-D scoring. For males, this variable equals 1 if the score was 22 and above and zero otherwise. For females, a cut-off point of 24 was used. This information was included in our analysis for Wave II.5 Disruption in Parental Co-habitation Because changes in family structure aid in identification of causal effects, we desired to observe individuals both before and after a change in parental co-habitation occured. In each wave, adolescents were asked about the presence of various household members for up to twenty such members. This lead us to create a binary variable which takes a value of one if both mother and father were present in the household in Wave I but only one was reported present in Wave II (approximately one year later).6 Since we wish to look at the effects of the change in parental co-habitation, we drop from the analysis adolescents who reported living with only one parent during Wave I. Unfortunately, a weakness of our study is that we are unsure if the disruption in parental co-habitation was due to divorce, separation, death, or other reasons, and our results need to be taken in such context. While Wave I of the survey has a parent questionnaire where parents were asked about their marital status at that point of time, later waves do not include this information. However, our variable indicating 5 All of relevant data to construct the ‘‘Depressed’’ variable is not available in Waves III and IV. 6 While constructing this variable, apart from the presence of both biological parents, we also included the presence of two foster parents or adoptive parents. 123 J Fam Econ Iss Table 4 Wave III ordinary least squares and probit marginal effects estimates of other outcomes Smoke Live with one parent Alcohol Drugs Sex BMI MHI -0.044 0.085* -0.016 0.023 0.015 0.058 (0.034) (0.024) (0.025) (0.020) (0.400) (0.225) Age -0.055*** 0.006 -0.047*** 0.002 0.432*** -0.071 (0.006) (0.004) (0.004) (0.003) (0.071) (0.040) Males 0.056*** 0.013 0.091*** -0.023* 0.245 -0.546*** Black (0.016) (0.012) (0.012) (0.001) (0.195) (0.112) -0.241*** -0.134*** -0.000 0.023 0.489 -0.012 (0.017) (0.020) (0.018) (0.014) (0.290) (0.166) No. of siblings -0.002 -0.009* -0.003 0.001 -0.119 0.031 (0.005) (0.004) (0.004) (0.004) (0.071) (0.040) Mother’s education 0.001 (0.004) 0.004 (0.003) 0.009*** (0.003) -0.003 (0.002) -0.105* (0.043) -0.012 (0.024) Mother works full time Father works full time Closeness to mother 0.009 0.020 0.010 0.001 -0.018 -0.102 (0.018) (0.013) (0.013) (0.011) (0.217) (0.124) -0.145 -0.027 0.047* 0.014 -0.026 -0.507 (0.031) (0.023) (0.022) (0.017) (0.372) (0.210) 0.005 -0.006 -0.015 -0.022* 0.188 -0.136 (0.012) (0.010) (0.010) (0.009) (0.148) (0.085) -0.007 -0.007 -0.034 -0.007 0.507*** -0.325*** (0.010) (0.008) (0.008) (0.007) (0.124) (0.071) 0.004* 0.000 0.003* 0.000 0.077*** 0.012 (0.002) (0.001) (0.001) (0.002) (0.021) (0.011) 0.056* -0.025 0.015 -0.010 -0.039 0.576*** (0.024) (0.018) (0.018) (0.016) (0.285) (0.165) Attention problem 0.010 0.008 0.016** -0.001 -0.236* 0.308*** (0.007) (0.006) (0.006) (0.005) (0.098) (0.055) TV hours 0.000 (0.001) 0.001 (0.000) -0.000 (0.000) 0.000 (0.000) 0.023*** (0.007) 0.006 (0.004) Play computer games -0.001 -0.001 -0.000 -0.001 0.030 0.011 (0.001) (0.001) (0.001) (0.001) (0.015) (0.009) -0.051 Closeness to father Skipped school Grade repeated Hang with friends Play sports Felt depressed 0.031*** 0.017** 0.025*** 0.021*** -0.261** (0.008) (0.006) (0.0106) (0.005) (0.095) (0.054) -0.027 0.053*** 0.011 0.038*** 0.150 -0.162 (0.108) (0.016) (0.022) (0.012) (0.010) (0.187) 0.006 -0.024 0.067 0.007 0.057 (0.043) (0.033) (0.036) (0.028) (0.044) -0.068* -0.055* -0.021 -0.012 0.343 0.358 (0.027) (0.022) (0.020) (0.017) (0.340) (0.197) -0.013 -0.057** 0.028 -0.017 -0.232 -0.511** (0.027) (0.022) (0.021) (0.017) (0.318) (0.187) GPA -0.062*** 0.001 -0.018* -0.033*** -0.514*** -0.115 PVT scores (0.010) 0.000 (0.007) 0.003*** (0.008) 0.001** (0.007) -0.001* (0.125) 0.011 (0.072) -0.021*** Felt fearful Private school (0.000) Smoke Alcohol 123 (0.000) (0.000) (0.000) (0.007) (0.004) 0.028 0.074*** 0.045** -0.457 0.097 (0.019) (0.020) (0.017) (0.306) (0.177) 0.150*** 0.099*** 0.094*** 0.248 -0.098 (0.019) (0.014) (0.011) (0.227) (0.132) J Fam Econ Iss Table 4 continued Smoke Alcohol 0.173*** (0.023) Sex 0.110*** (0.021) 0.031* (0.015) 0.027 (0.016) N 4663 5288 5262 Drugs Drugs Sex BMI MHI 0.058*** 0.046** -0.638* 0.072 (0.015) (0.015) (0.276) (0.161) 0.424 (0.249) -0.067 (0144) 4112 5285 5273 Standard errors shown in parentheses. OLS regression was performed in the cases of BMI and mental health (depressed and sad). For all other dependent variables, probit regressions were performed. Only coefficients with statistical significance in at least one regression are included in the table. Also included in each regression were controls for racial background, regional school dummies, and neighborhood variables Significance Levels: p \ 0.10. * p \ 0.05. ** p \ 0.01. *** p \ 0.001 living with one parent in Wave I was moderately positively correlated with the divorce variable obtained from the parent questionnaire.7 Independent Variables For our empirical strategy to be viable, we conditioned on a variety of adolescent characteristics and behaviors at or before the time of the first survey (prior to a change in parental co-habitation). Because we wished to allow the effect of a change in family structure to operate through a number of potential channels, we avoided covariates corresponding to later points in time, as subsequent behaviors or characteristics could be influenced by the prior change in family structure. In addition to Wave I values for the outcome variables described above, we included demographic variables, namely age at Wave I, gender (with females as the omitted category), and racial background (whites being the omitted category). We also controlled for the number of siblings, mother’s education level, whether mother and father work full time, and closeness to mother and father. Adolescents were asked in detail about their school activities; we included the number of days they skipped school without excuse, and indicators of whether they repeated grades and how much difficulty they had paying attention in school. They were also asked about their daily activities; we included the number of hours they spent watching television, playing video games, hanging out with friends, and whether they played sports. We also controlled for the type of school they went to (with public school being the omitted category), whether the school was in an urban, semi-urban or 7 The correlation between the parent answering as not divorced, separated, or single in Wave I and student reporting that both parents are together in Wave I was 0.594. However, the analysis is limited to students whose parents separate between Waves I and II (in order to develop a treatment group). Wave II does not have a parent questionnaire, so a switch in status from the parents’ perspective was not observed. rural location (semi-urban is the omitted category), and indicator variables corresponding to living in the West, Midwest, South or Northeast regions of the United States (Midwest being the omitted category). Finally, we controlled for neighborhood related variables, namely selfreports of whether they lived in a neighborhood where people care, whether they felt safe, and how happy they felt to be living in the neighborhood.8 . Sample Selection We drew our sample from the 20,745 adolescents who were interviewed for the initial in-home survey. For outcomes occurring in Wave II and Wave III, we relied on the 14,738 and 15,197 responses to each of these surveys, respectively. Individuals living with one parent at the time of the initial survey were dropped from the sample. We also dropped individuals who were initially aged 17 and over, to avoid including non-minors who may have moved out of their parent’s home by the time of Wave II. Finally, conditioning on non-missing values for all of our included covariates resulted in a sample size of 9,311. Table 1 displays the sample means and standard deviations of each of the covariates for three different groups. Group (i) is the treated group, comprised of individuals whose parents experienced a change in co-habitation between Waves I and II. Groups (ii) and (iii) are both made up of individuals from intact families, where Group (ii) contains the entire control sample and Group (iii) contains only those individuals who were matched to the treated group using the 2-nearest neighbors technique. Also reported are the p values of the differences in means between groups (i) and (ii) and between groups (i) and (iii). Ideally, the covariate values of the treatment group and the 8 It is acknowledged that some of these variables may have endogeneity and unobserved heterogeneity issues, which could bias the coefficients on these independent variables. However, there should be no major effect of this endogeneity on the variable of interest which is parental separation. 123 J Fam Econ Iss Table 5 Wave IV ordinary least squares and probit marginal effects estimates Ever attended college Has college degree Log Income -0.064*** -0.063*** -0.049 0.061* -0.024 (0.015) (0.018) (0.028) (0.026) (0.032) 0.031*** 0.051*** 0.080*** -0.035*** 0.021*** (0.004) (0.006) (0.008) (0.004) (0.006) -0.072*** -0.079*** 0.290*** 0.043*** 0.031 (0.012) (0.015) (0.022) (0.012) (0.016) 0.052*** 0.089*** -0.016 -0.086*** 0.065** (0.015) (0.023) (0.033) (0.012) (0.025) No. of siblings -0.010* -0.012* 0.005 0.001 -0.012* (0.004) (0.006) (0.008) (0.004) (0.006) Mother’s education 0.035*** (0.003) 0.049*** (0.003) 0.012* (0.005) 0.000 (0.003) -0.016*** (0.004) Live with one parent Age Males Black Mother works full time Father works full time Closeness to mother Closeness to father Skipped school Grade repeated Smoke Obesity 0.005 0.024 0.069** -0.005 -0.003 (0.013) (0.017) (0.025) (0.013) (0.018) 0.041 0.058* 0.068 -0.044 -0.016 (0.023) (0.030) (0.043) (0.023) (0.031) -0.006 0.002 0.035* 0.002 0.024 (0.009) (0.012) (0.017) (0.008) (0.012) 0.003 0.005 -0.023 0.010 0.017 (0.007) (0.010) (0.015) (0.007) (0.010) -0.002 -0.005* -0.001 0.003** 0.003 (0.001) (0.002) (0.002) (0.001) (0.002) -0.140*** -0.158*** -0.199*** 0.063*** -0.026 (0.019) (0.021) (0.034) (0.019) (0.023) Attention problem -0.000 0.001 -0.001 -0.001 -0.018* (0.006) (0.008) (0.011) (0.006) (0.008) TV hours 0.000 (0.00) -0.001 (0.001) -0.002* (0.001) 0.000 (0.000) 0.001** (0.000) Play computer games -0.001 -0.005*** -0.002 0.000 0.003* (0.001) (0.001) (0.002) (0.001) (0.001) Hang with friends Play sports 0.002 -0.012 0.030** 0.023*** -0.019** (0.006) (0.007) (0.011) (0.005) (0.008) 0.022 0.060*** 0.046** 0.051* -0.024* (0.011) (0.015) (0.022) (0.011) (0.016) (continued) Felt depressed 0.002 -0.032 0.106 0.012 0.001 (0.029) (0.042) (0.062) (0.030) (0.002) 0.002 -0.051 -0.023 -0.015 0.022 (0.020) (0.027) (0.040) (0.018) (0.028) 0.057** 0.081** 0.016 -0.021 -0.048 (0.021) (0.026) (0.036) (0.019) (0.026) GPA 0.112*** (0.007) 0.210*** (0.010) 0.108*** (0.015) -0.054*** (0.007) -0.051*** (0.010) PVT scores 0.005*** 0.006*** 0.002** 0.000 0.001 (0.000) (0.001) (0.001) (0.000) (0.001) Smoke -0.081*** -0.117*** -0.074* (0.020) (0.023) (0.035) Alcohol 0.004 0.033 0.030 Felt fearful Private school 123 0.004 (0.025) 0.029* -0.002 J Fam Econ Iss Table 5 continued Ever attended college Has college degree Log Income Smoke Obesity (0.019) (0.014) (0.018) (0.026) (0.013) Drugs 0.003 -0.020 0.019 0.063*** -0.056** Sex (0.016) -0.063*** (0.022) -0.097*** (0.032) -0.011 (0.017) 0.087*** (0.022) 0.11 (0.016) (0.019) (0.029) (0.016) (0.021) N 6199 6199 4238 5415 4112 Standard errors shown in parentheses. OLS regression was performed in the case of log of Income. For all other dependent variables, probit regressions were performed. Only coefficients with statistical significance in at least one regression are included in the table. Also included in each regression were controls for racial background, regional school dummies, and neighborhood variables Significance Levels: p \ 0.10. * p \ 0.05. ** p \ 0.01. *** p \ 0.001 control group should be as close as possible. For each covariate the difference in means between the treated and control groups decreased significantly for the matched sample as compared to the unmatched sample. The p values suggest that there are no statistically significant differences in means of the covariates between the treatment group and the matched control group. Correspondingly, there was a reduction in the standardized bias for nearly all of the covariates in the matched sample in comparison to the unmatched sample. Results Estimated coefficients from OLS and the marginal effects of probit regressions on short-term (Wave II) outcomes are presented in Table 2. Each column represents a separate regression corresponding to a specific outcome variable. As seen in the table, a disruption in parental co-habitation had a statistically significant negative effect on immediate outcomes in terms of scholastic performance (GPA) and mental health (MHI and Depressed), and sexual behavior. The coefficients on several of the covariates were statistically significant. In general, males were found to have higher BMI while females were more likely to be depressed and sad. An individual who reported repeating a grade tended to have a lower GPA. We also found that mother’s education had a positive effect on GPA and the closer one feels to the father, the less likely that he/she indulged in risky behavior and also less likely to feel depressed. While sports participation was associated with higher GPA, it also increased the likelihood of consuming alcohol and drugs as well as engaging in sexual activities. Indulgence in risky behaviors also significantly increased with more time spent with friends. When we allowed for more time between parental separation and the outcome measurement, living with one parent was found to be more influential on a larger number of outcomes. Our medium term (Wave III, occurring about 7 years after the original survey) OLS and probit results are presented in Tables 3 and 4. The estimates of education and employment outcomes are presented in Table 3 whereas those of risky behavior and health outcomes are presented in Table 4. We found that there was a significant negative effect of parental co-habitation disruption on academic outcomes, including both GPA and timely graduation from high school. Living with one parent was associated with a decrease in overall high school GPA of about 0.14 points and a 5.6 % lower likelihood of graduating within 4 years. Also, wages significantly decreased and smoking behavior significantly increased as well. Other covariates in Wave III regressions (Tables 3, 4) impacted outcomes as predicted.9All academic and employment outcomes increased significantly with mother’s education. Sports participation was associated with an increase in both GPA and the likelihood of on-time high school graduation, but also an increased likelihood of consuming alcohol and having sex, similar to what we found for Wave II The longer term (Wave IV) OLS and marginal effects of the probit regressions are presented in Table 5. Adolescents living in a single-parent household between Waves I and II were 6.4 % less likely to have attended college and 6.3 % less likely to have earned a college degree in the long run. They were also more likely to be earning less compared to adolescents who did not experience a change in family structure between Waves I and II, although this effect was only marginally significant. In the long run, they were also 6.1 % more likely to engage in smoking. 9 In the interest of conserving space, we do not report the coefficients of all the covariates in the OLS and probit regressions, especially the ones whose coefficients were not statistically significant in any of the regressions. However, a full list of coefficients of all covariates is available upon request. 123 J Fam Econ Iss Table 6 Propensity Score Matching Estimates of Parental Separation on multiple outcomes of adolescents Wave II 2-NN ATT Wave III K-D ATT Wave IV 2-NN ATT K-D ATT 2-NN ATT N K-D ATT Treated Untreated Academics GPA -0.202** -0.153** -0.159* -0.171** 189 3763 (0.071) (0.059) (0.076) 0.412 (0.063) 0.049 1123 4882 (0.667) (0.541) -0.052*** -0.055*** 1171 4993 (0.015) (0.013) 1215 4984 1215 4984 1112 4759 751 3487 266 4396 297 4990 299 4963 300 4973 223 3889 300 4985 300 4985 PVT scores Graduated on time Ever attended college Has college degree -0.032 -0.050*** (0.018) (0.015) -0.025 -0.046** (0.019) (0.016) Employment Employed Log income 0.009 0.009 (0.018) (0.015) -0.224 -0.175 -0.044 -0.047 (0.182) (0.151) (0.030) (0.026) 0.086* (0.038) 0.081* (0.032) 0.069* (0.032) 0.067* (0.027) Risky behavior Smokea 0.009 (0.033) Alcohola Drugs Sex 0.046 (0.027) -0.007 -0.008 -0.037 -0.024 (0.025) (0.029) (0.031) (0.026) 0.060 0.049 0.038 0.024 (0.031) (0.027) (0.030) (0.025) 0.065 0.069* 0.048 0.033 (0.036) (0.030) (0.025) (0.020) 0.394 0.115 0.508 0.164 -0.004 -0.013 (0.378) (0.322) (0.546) (0.460) (0.040) (0.033) 0.925 1.505*** 0.215 0.091 (0.580) (0.470) (0.314) (0.252) 0.035 0.050** (0.022) (0.019) Health and mental health BMI/obesity Mental health Depressed ATT corresponds to the estimate of the average treatment on the treated. Standard errors shown in parentheses. 2-NN indicates a 2-nearest neighbors weighting method and K-D indicates use of a kernel density weighting method (with a biweight distribution kernel smoothing function using a bandwidth of 0.6). Untreated sample sizes correspond to use of K-D Significance Levels: a p \ 0.10. * p \ 0.05. ** p \ 0.01. *** p \ 0.001 indicatesone observation in the treatment effects calculation was dropped due to not having propensity scores with a common support Mother’s education and sports participation had a positive significant effect on educational achievement and income. Also, adolescents were more likely to engage in smoking in the long term with more time spent with friends in Wave I. In order to investigate changes in academic achievement, risky behaviors, and mental health, we controlled for risky behavior, test scores, grades, and mental health in 123 Wave I.10Furthermore, academic achievement was significantly lowered the more one engaged in risky behavior like smoking, drinking, drug use and sex in Wave I. 10 We did not use lagged dependent variables due to attenuation bias (see Maddala and Rao 1973). However, the results using the lagged dependent variables are available upon request. J Fam Econ Iss We now turn to our results from propensity score matching. As described above, the propensity scores were estimated using a logit model, and then non-parametric estimation was carried out on all outcome variables using both the two-nearest neighbors and kernel density approaches.11 The results of the second stage are reported in Table 6. The effect on GPA using these two methods was a little bit stronger in both Waves II (ranging between 0.153 and 0.202 GPA points lost, as compared to 0.134 points lost using OLS) and III (ranging between 0.159 and 0.171 GPA points lost, as compared to 0.141 points lost using OLS). There was a similar issue with the attending college and getting a college degree variables as well, where the propensity score matching estimates were a little stronger (although not by much). On time graduation rates had similar propensity score matching estimates (between a 5.2 and 5.5 % decreased likelihood) as probit estimates. Effects on income were non-significant using propensity score matching (this effect was weakly significant using the OLS estimate). Regarding behavioral outcomes using the propensity score matching methods, the effects of separation on smoking behavior were virtually the same in Waves III and IV as before. The propensity score estimates of the effects on sexual behavior (between 6.5 and 6.9 % increased likelihood in Wave II and between a 3.2 and 5 % increased likelihood in Wave III) were a little stronger using propensity score matching. Also, the effects on mental health and depression were slightly stronger after using propensity score matching. Conclusion The effects of living with one parent on outcomes of children have been widely debated in the literature. An understanding of the potential effects of separation or divorce on short and long term outcomes of children is important for the design of new divorce laws that might combat the incidence of divorce and thus its adverse effects on children. While some researchers have concluded that living with a single parent does not have an adverse effect on children, others have found significant negative effects. Our paper underscores the importance of time frame in analyzing this issue; we found evidence of not only adverse effects of change in family structure in the immediate term (approximately one year or less following the change), but also significant medium and longer term (about 3–5 years, 11 We also checked the robustness of these two methods using onenearest and three-nearest neighbors matching, along with normal and Epanechnikov kernels, and these results were similar. These results are available upon request. and later) effects on the academic achievement of children and their propensity to engage in smoking. We used data from four waves of the National Longitudinal Survey of Adolescent Health, a large national data set involving students initially in junior high and high school. The data allowed us to control for an unusually rich set of covariates, including demographics, parents’ characteristics, home and school environment, school characteristics, daily activities, and neighborhood variables. Unlike the majority of prior research, we included a wide range of outcomes which may be related to future wellbeing, including academic performance, performance on a standardized test, timely graduation from high school, college attendance, and college degree, propensity to engage in risky behaviors like drinking, smoking, drug use and sex, and BMI/obesity and mental health outcomes. Furthermore, to our knowledge, our paper is one of the very few in the literature to use propensity score matching to uncover the effects of change in family structure on children’s outcomes. We used both two-nearest neighbor and kernel density weighting methods, and compared our results to those from OLS and probit regressions. We found evidence that parental separation negatively impacts academic outcomes (GPA, graduating on time, college attendance, and obtaining a degree) behavioral outcomes (smoking), and mental health. Furthermore, many of these effects became a little stronger after using propensity score matching, which suggests that these effects have been slightly underestimated in the past literature when using more restrictive functional forms and not using appropriate the comparison groups generated by matching methods. Our results underscore the importance of strong twoparent families for the well-being of children. Policies could aim at providing incentives to maintain stable and intact families. There could also be provision for economic security of children and certain community resources like support centers and more affordable education. Support from schools is also an important predictor of their mental well-being (Hussey et al. 2013). Since children who belong to single-parent families are more likely to suffer from emotional stress and lower academic performance, good support systems and counseling services in schools should be emphasized. Child support laws can also help to alleviate on-going disadvantages of children who undergo parental separation (Allen et al. 2011; Hofferth and Pinzon 2011). Our results also underscore the importance of addressing both short-term and longer-term implications of parental separation. In addition to policies aimed at immediate consequences of separation, such as lower grades and increased likelihood of smoking, policies affecting young adult outcomes, like college enrollment and graduation, should also be considered. 123 J Fam Econ Iss There are some limitations in our analysis. First, while the propensity score matching approach can be seen as an improvement over tradition multiple regression methods, and while our data provides a rich set of control variables, we cannot rule out the possibility of omitted variables biasing our estimates, nor the possibility that one or more of our included independent variables are endogenous. Second, our sample only included changes in family structure during a single year, when children were initially between grades 7 and 10. To the degree that parental separation may have a greater influence on younger aged children, our results may underestimate the effects of separation on children in the population as a whole. Finally, it should be emphasized that our analysis remained agnostic as to the cause for parental separation, as well as the total length of time over which adolescents remain in a single parent home. Our estimates likely reflect a mix of the effects of parental divorce, death or other separation (temporary or permanent). Given our findings of multiple negative effects of separation, we view our results as being likely lower bounds of the true effects of parental separation associated with marriage dissolution. However, our analysis leaves room for further research investigating particular causes for parental separation, and the heterogeneous effects they may have on multiple outcomes and over multiple time horizons. Additional outcomes such as grade level repetition and teenage pregnancy, as well as multi-generational effects of parental separation, including future marriage and fertility decisions of the child, should also be addressed with the availability of appropriate data. Acknowledgments The research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining Data Files from Add Health should contact Add Health, The University of North Carolina at Chapel Hill, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 ([email protected]). No direct support was received from grant P01HD31921 for this analysis. References Allen, B., Nunley, J., & Seals, A. (2011). The effect of joint-childcustody legislation on the child-support receipt of single mothers. Journal of Family and Economic Issues, 32, 124–139. doi:10.1007/s10834-010-9193-4. Amato, P. R. (2000). The consequences of divorce for adults and children. 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Applied Psychological Measurement, 1, 385–401. doi:10.1177/014662167700100306. Roberts, R., Lewinsohn, P., & Seeley, J. (1991). Screening for adolescent depression: A comparison of depression scales. Journal of the American Academy for Child Adolescent Psychiatry, 30, 58–66. doi:10.1097/00004583-199101000-00009. Rosenbaum, P. R., & Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–45. Retrieved from http://www.jstor.org/ stable/2335942. Accessed 24 Nov 2014. Sabia, J. (2007). The effect of body weight on adolescent academic performance. Southern Economic Journal, 73, 871–900. Retrieved from http://www.jstor.org/stable/20111933. Accessed 24 Nov 2014. Sanz-De-Galdeano, A., & Vuri, D. (2007). Parental divorce and students’ performance: Evidence from longitudinal data. Oxford Bulletin of Economics and Statistics, 69, 321–338. doi:10.1111/j. 1468-0084.2006.00199.x. Schramm, D. (2005). Individual and social costs of divorce in Utah. Journal of Family and Economic Issues, 27, 133–151. doi:10. 1007/s10834-005-9005-4. Tumin, D., Han, S., & Qian, Z. (2014). Meanings and measures of marital separation. Working paper. Retrieved from http:// paa2014.princeton.edu/papers/141524. Accessed 24 Nov 2014. Andrew Hussey is an associate professor at University of Memphis that specializes in labor economics, the economics of education, and applied econometrics. Dr. Hussey’s areas of research include investigating the effects of higher education on labor market outcomes, explaining income inequality and race and gender differentials in labor market and education outcomes, and adolescent obesity. Dr. Hussey received his Ph.D. from Duke University in 2006. Debjani Kanjilal is an assistant professor at Elizabeth City State University where she teaches multiple classes in Economics and Business Administration. Her primary research interest is in the area of health and labor economics, specifically adolescent behavior and outcomes. She is also engaged in inter-disciplinary research in the area of Management and Information Systems. Dr. Kanjilal received her Ph.D. in economics from the University of Memphis in 2010. Anil Nathan is an assistant professor at Holy Cross that specializes in the economics of education and health economics, along with implementing matching methods to control for selection on observables. Dr. Nathan has publications examining end of life care for dementia patients along with analyzing survival probabilities of NFL football players in relation to the rest of the general population. Dr. Nathan received his Ph.D. in economics from Duke University in 2008. 123