dummy indicator to measure serious mental illness, defined as mental health score being greater than 19 (Chen et al., 2017). The results using the new measure remain qualitatively similar (Appendix A, Table A10).

Eighth, we employ as a proxy for mental distress an indicator of PTSD available from the BNLA, which is an eight-item self-reported screening measure derived from the Harvard Trauma Questionnaire. Recent evidence suggests that PTSD symptoms are common among Syrian refugees in Norway (Aarethun et al., 2021). Participants rate PTSD symptoms on a fourpoint Likert scale (from 1 for “not at all” to four for “most of the time”), reporting how much the symptoms bothered them in the past week. We convert the answers into a dummy indicator that equals one if an individual is at risk of PTSD. The results remain qualitatively consistent (Appendix A, Table A11). The size of the coefficients in Table A11 also suggests that PTSD symptoms are strongly relevant for refugee respondents, and they have negative impacts on refugee employment outcomes.

Ninth, in the main analysis, we estimate the impact of worse mental health on employment status for refugees who participate in the labor force. To address potential selfselection into LFP and subsequent employment status, we employ the alternative two-part fractional response model (Roodman, 2011). While this approach allows us to model the LFP decision and employment status separately, it does not address the endogeneity of mental health. But our estimates remain qualitatively similar (Appendix A, Table A12).

Similarly, one may argue that mental health and labor market are measured in the same wave, which might lead to the problem of reverse causality (i.e., labor market exposure affects mental health). This is less likely to be the case in our study as we measure mental health in the last four weeks prior to the interview date, while labor outcomes are measured in the last seven days. To provide further support to our main findings, we regress labor market outcomes on mental illness measured in the previous wave (i.e., wave _t-1_ ) and find qualitatively similarly results, albeit weaker statistical significance for labor incomes (Appendix A, Table A13).

Another concern one may argue is that labor outcomes of refugees in the host country may be determined by their work experience before arrival, which in turn also has an impact on the trajectory of their mental illness. In our model specification, this has been taken into account by controlling for individual fixed effects. While other time-varying experience in the past is not available in the BNLA survey, we split our sample into two groups based on whether they did any paid work in a job, business or on a farm before arrival. The results presented in Table A14 (Appendix A) reveal no difference in the effect of mental health on labor outcomes for both groups.

Finally, we conduct various checks on the IV. Firstly, we add up the total number of traumatic events experienced by refugees as an alternative instrument for mental health. In other words, we consider the intensity of past traumatic experience rather than just exposure. Using this alternative measure produces similar results (Appendix A, Table A15). In fact, every additional traumatic episode has a large impact on both employment and income. Secondly, we test the robustness of our results to deviations from the assumption of perfect exogeneity, using Conley et al.’s (2012) bounding method. This method relaxes the assumption of perfect exogeneity and assumes a flexible second-stage regression that also includes the instrument as a regressor. Assuming that the direct effect of the instrument on labor outcomes ranges from zero—perfectly exogenous—to the reduced form effect, the lower bound and upper bound estimates are both negative (Appendix A, Table A16). We conclude that the negative impacts of mental illness on labor market outcomes are robust to a large degree of instrument endogeneity.

# **References**

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