Covariate shocks and deaths are not significantly associated with the likelihood of reporting depressive symptoms.

Next, we evaluate how large the effect of each type of event is on depression in monetary terms by comparing their effects alongside the coefficient of consumption expenditures (Table A1).<sup>16</sup> Using our regression specification, we can simulate how much extra consumption a respondent would need (the percentage point change in the depressive measure from consumption over the percentage change from each adverse event) in order to compensate for the effects of each incident on depression.

We find that the effects of idiosyncratic shocks and conflicts on depressive symptoms are equivalent to the loss of 3,852 and 7,914 naira, respectively. In other words, the experience of conflicts would need to be compensated with 7,914 naira (worth around US $52.27) to cancel out its impact on mental health.

# Additional correlates of depression

We also examined the correlation of some of individual characteristics with the likelihood of showing depressive symptoms. Our data set is novel in part due to the inclusion of consumption data along with that of mental health and adverse events, and as such, we can control for this. Lower lagged consumption per capita is, as expected, significantly correlated with lower levels of depressive symptomatology. This points to the persistence of well-being and its effect on mental health.

Another finding on significant covariates is notable: Married individuals are less likely to score over the CES-D cutoff than those who are single by about eight percentage points; the coefficient is similar for individuals who are married polygamously.

# Impacts of priming on reported depression

In addition to our analysis of the correlates of our depressive measure, we also look at the impacts of the survey experiment. The point estimate for the respondent priming indicator (i.e. respondents who answered questions about their adverse events before answering questions about their depressive symptoms) provides us with the causal impact of this priming. By itself, priming people by asking about their histories of conflicts, deaths, and shocks does not significantly change the likelihood of depressive symptomatology (Table 2 Panel A and B column 3).

We next evaluate the differential effects of negative incidents by the priming “intervention.” To do this, we interact the variable for experiencing any adverse event with the priming indicator. We find that the likelihood of being over the cutoff among people who experienced any type of adverse event is higher by 6.5 percentage points if they were exposed to the priming intervention than those who also experienced these incidents but were not primed (Table 2, Panel A, column 4). Priming is particularly impactful on depression for those who reported going through an idiosyncratic shock, as shown in Table 2, Panel B (column 4). The likelihood of being over the CES-D cutoff among people who have experienced an idiosyncratic shock is higher by 8.9 percentage points if they are primed than if they are not primed.

> 16 We calculate the coefficients of each adverse event divided by individualized consumption.

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