occupational status. We do not have information on testosterone levels, genetic heritage or cognitive abilities but it can be argued that gender is a good proxy for testosterone levels whereas education and employment status are good proxies for cognitive abilities whereas we measure migration across small administrative areas where genetic diversity is not expected to be very large. Instead, income (proxied by expenditure in our data) is our measure of outcomes and should not be controlled for in the migration equation by design.

The 2016 GHS also contains a battery of behavioral questions, which include questions on risk-aversion. To assess the degree of risk-aversion we used the following question: _Suppose you want to invest some money. Which option do you prefer? A) Investing in a business where I can’t lose money but has low profits; B) Investing in a business where there is a chance I can lose money but potentially brings high profits._ Empirical studies on migration and risk-aversion typically use as measures of risk-aversion a self-assessment measured on a scale from 0 to 10 (Bonin et al., 2009; Jaeger et al., 2010; Akg ̈u ̧c et al., 2016). These studies then argue that this scale is a proper measure of risk-aversion because it is closely associated with direct measurements of risk-aversion as shown by Jaeger et al. [2010] and Dohmen et al. [2011]. In our study, we do not need this extra verification as we have a question that measures risk preferences _directly_ .

The second data set we use is the The Armed Conflict Location & Event Data (ACLED). These data cover daily conflicts episodes in the country since 1997 and include information on the cause of conflict, perpetrators, casualties and fatalities. We will use the number of fatalities by LGA and match these data with the GHS panel data for the period 2010-2016. We then subdivide LGAs in areas that had no conflict (in any of the years considered), areas with some conflict (defined as areas with fatalities due to conflict in at least one of the years considered but less than six years) and areas always in conflict (defined as areas with fatalities due to conflict in all six years considered).<sup>10</sup> The ACLED data set is not perfect as many episodes of violence are not reported or detected but the map of casualties constructed with these data shows correctly all the areas where the conflict was known to be intense (see Figure 7).

# **5 Results**

We report results following the order described in the empirical strategy. As a first step we estimate the distributions of outcomes. Table 6 reports coefficients and standard errors of the predicted model for households living in non-conflict areas (Equation 8).<sup>11</sup> Next, 10Data are available on-line and include codebooks, user guides, questionnaires and other relevant material (see http://www.acleddata.com/data/).

> 11Note that the equation in Table 6 was also run without variables related to the quality of dwelling. This was to allow for the hypothesis that people may expect to live in makeshift shelters once they move. Results of this test showed that the R squared decreased by 4 percetage points whereas the following results on the dominance analysis remained intact. We also know that most migrants captured by the survey moved to accomodations not too dissimilar from their own homes with the exception of some of the IDPs hosted in

13