# **Appendix D: Robustness checks**

To further scrutinize the robustness of our results, we conduct a battery of sensitivity analyses. These include checks with using the linear FE and IV-FE models instead of the logit FE and IV-logit-FE models, analyzing the original non-standardized mental health scores, removing workers with zero incomes, addressing panel attrition, and examining potential endogeneity of some covariates, whether the impacts are mitigated by other health channels other than mental health, alternative and disaggregate measures of mental distress, a different modeling approach, considering previous work experience, as well as implementing various stress tests on the IV.

First, we estimate the linear FE and IV-FE models for employment status and other binary outcomes variables instead of the logit FE and IV-logit-FE models and find qualitatively similar results. The results are presented in Table A3 (Appendix A). Second, our findings remain consistent when using the original non-standardized mental health scores, as shown in Table A4 (Appendix A). Third, for the labor income regressions in Table 1, refugees who do not work are assumed to have zero incomes. Alternatively, we also analyze a subsample of workers that have positive incomes and find consistent effects of mental illness (see Table A5, Appendix A).

Fourth, the panel attrition rate from wave 1 to wave 5 is approximately 22% and relatively lower than those in other surveys in Australia such as the HILDA or the Longitudinal Study of Australian Children (LSAC) (Department of Social Services, 2018). To address panel attrition issues, we create a balanced sample and replicate our main analysis in Table 1. The results confirm the negative impacts of mental illness on both employment status and labor income; furthermore, the estimates are of similar magnitudes (Appendix A, Table A6). In addition, we also account for the survey’s stratified sampling design and nonresponse rates by using the longitudinal weights provided by BNLA. Again, the results of weighted regression reaffirm our previous findings, as shown in Table A7 (Appendix A).<sup>20</sup> Fifth, while our baseline specification includes a range of covariates that are commonly employed in studies relating to labor market outcomes of refugees (e.g., Dagnelie et al., 2019; LoPalo, 2019), one may argue that language skills might be endogenous. For example, refugees who are eager to better engage in the labor market and have larger earnings might spend more effort on refining their language skills. We show that our results are robust to excluding these potential endogenous variables (Appendix A, Table A8).

Sixth, while our main interest is refugee mental illness, we also examine other (overall) health and physical health issues. In the BNLA survey, overall health is represented by an indicator denoting how refugees rated their health during the past four weeks before the interview. We recode the indicator so that it ranges from one (“excellent”) to six (“very poor”). Similarly, physical health is captured by a categorical variable with higher values representing worse physical health. We find a negative impact of overall health issue on labor outcomes, while there is little evidence of the impact of physical health on the outcome variables (Appendix A, Table A9). This lends support to our main finding that mental health issues are the main barrier affecting refugee labor outcomes.

Seventh, we construct alternative disaggregate measures of mental illness instead of the sum of the K6 items in the main analysis. Figure 1 examines each item separately to determine which dimension drives the effects of mental health. Using our preferred model specifications, we find that most of these dimensions of mental illness have a negative impact on employment status (including “nervous”, “hopeless”, “restless”, “cheer” and “worthless”) and labor income (including “hopeless”, “restless”, “effort”, “cheer”, and “worthless”). We further create a

> 20 We do not apply weights in the main analysis as the BNLA survey mainly focuses on refugees migrating to Australia within a year before the time of the first interview, and thus findings might not be generalized to all humanitarian migrants (Department of Social Services, 2017).

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