to our results.

Instead, attrition could affect the prediction capacity of these variables when used together in a multivariate forms in models that include our key variables: expenditure per capita and risk aversion. The variables considered in Table 3 enter the models used to predict expenditure (equations 8 and 9) and to study the association between migration and risk aversion (equation 10) in a multivariate form. To test for non random attrition on per capita expenditure and risk-aversion respectively, we compare the Cumulative Distribution Functions (CDFs) of the predicted values of these variables (based on the predictors listed in Table 3) and test for stochastic dominance of first degree between the samples with and without attrition. This is a rather simple but effective test. We want to make sure that predictors are stable with respect to these variables whether we have attrition or not. If the predictions generated by the sample with attrition are different from those generated by the sample without attrition, our results would be biased.

For per capita expenditure, we observe this variable all along the panel. We can therefore use the 2010 full sample to make predictions and then compare the two distributions of predicted values for the sample with and without attrition. Results are shown in Table 4 and Figure 5 and show that there is no difference in the distributions of predicted values between the two samples. For risk-aversion, we observe this variable only in 2016. We assume that risk-aversion is time-invariant and we use individual characteristics in 2010 to predict the probability of being risk-tolerant for the full panel based on individuals left in the 2016 panel sample with attrition. Results are shown in Table 5 and Figure 6 and show again that there is no difference in the distribution of predicted values of risk-aversion between the overall sample and the sample with attrition. We can therefore conclude that attrition does not bias our results with respect to per capita expenditure and risk-aversion.

The migration status of households is measured across the 774 Local Government Areas (LGAs), the lowest administrative subdivision available in our data. We consider a household to have migrated if it changed LGA in any of the years considered. We consider three migration statuses for Equation 10. People who have migrated from areas with no conflict, people who have migrated from areas with some conflict (conflict in at least one year of the six years considered) and people who have migrated from areas with permanent conflict (conflict in all six years considered). Overall, we have 329 migrant households (6.6 percent of the sample) and 90.5 percent of these migrants migrated within one of the six geographical regions. Therefore, most of the migration observed is short range migration.

To control for possible predictors of risk-aversion in equation 10, we include most known correlates of risk preferences. Risk-aversion has been found to be higher in women and lower in men or individuals with higher levels of testosterone (Eckel and Grossman, 2008; Apicella et al., 2008; Johnson et al., 2006), and higher for married and older individuals (Kuznets, 1964; Myrdal, 1968); it is associated with lower cognitive abilities (Dohmen et al., 2010), lower levels of skills, occupational status and income (Hartog et al., 2002). Risk preferences were also found to be genetic and transmissible through generations (Dohmen et al., 2011; Becker et al., 2016). Our models control for age, education, marital status and 12