current depressive symptoms with lagged outcomes, which we will attempt to do with prior waves of the GHS-Panel. A final method is to construct a control group via propensity score matching. However, there may be systematic differences in outcomes between those affected (by depression) and not, even after controlling for observables. In this case, this would violate the identification conditions for matching.

Unfortunately, we are unable to fully correct for the potential endogeneity. Thus, we frame our findings as a correlation between depression and the considered economic outcomes rather than a causal link.<sup>18</sup> Regression results in Table 4 show that being over the CES-D cutoff of 10 points reduces the likelihood that one is engaged in any work by 8 percentage points (column 1), in wage work by 2.5 percentage points (column 2), in agricultural work by 4.9 percentage points (column 3), and in non-farm enterprise by 3.5 percentage points (column 4).<sup>1920</sup> The effect of depressive symptoms on labor participation differs by gender (See Table A.1 in the Appendix). Being over the cutoff reduces the likelihood that men are engaged in agricultural work by 8.8 percentage points, while it is not correlated with the reduction in agricultural work among females. Instead, the indicator for depressive symptomology reduces work in non-farm enterprises by 9.6 percentage points; this correlation is not observed among males.

As expected, the experience of adverse events also has a relationship with labor force participation decisions (Table 4). Controlling for depression, the experience of covariate shocks increases the likelihood that one is engaged in any work by 4.4 percentage points, and in non-farm enterprise work by 9.5 percentage points (columns 1 and 4).<sup>21</sup> Conflict is not associated with changes in labor activities.

Next, we investigate the relationship between parents’ mental health with children's well-being. Table 4, columns 5 and 6, evaluates how parents’ depression status is correlated with investments in children's education. Parental depression is significantly negatively correlated with educational expenditure for children (who are in school) (column 6), while it does not seem to be related to the probability of their children being in school (column 5).

We observe the differential effect of parent’s depression on children’s educational outcome by children’s age. If children are young (11 years old or younger), parents’ depressive symptomatology is not associated with whether the child is enrolled in school or not, but it is significantly and negatively correlated with educational expenditures among those who go to

> 18 Despite this, we believe that the story supported by this analysis would not be further supported by the use of quasi-experimental methods.

> 19 Note that the relationships between depression and the labor-related outcome variables are negative (results not shown); these negative relationships hold and grow stronger when adding in additional covariates such as adverse events, indicating that they are not necessarily mechanisms.

> 20 We find that the measure of depression is not correlated in any significant way with measures of work (both extensive and intensive) from wave 1 of the survey.

> 21 In addition to this impact on agricultural work and participation, we find a negative correlation of depression with yields. Because the direction of causality is difficult to determine in this case, we will not discuss this extensively, but results are provided upon request.

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