the estimated coefficients _β_<sup>ˆ</sup> _nc_ are used to predict the expenditure levels that households living in conflict areas could potentially have if they migrated to non-conflict-affected areas (Equation 9). We then use Duan’s smearing re-transformation to obtain levels of expenditure starting from the predicted log expenditures (Duan, 1983).

As a second step, the observed distribution of expenditure per capita of people living in conflict areas is compared with the distribution of predicted values for the same people if they migrated to non-conflict areas. Figure 8 shows the results using the 2016 GHS data. It is clear that the two curves cross in one point splitting the samples into two parts. The left end side of the CDFs shows that the curve of predicted values (gains from leaving) dominates the curve of observed values (gains from staying). The QM model would therefore predict that maxmin (risk averse) individuals who focus on the left hand side of the distrbutions (low gains) would opt to leave. Instead, the opposite is true for the right end side of the CDFs. In this case, the QM model would predict that maxmax (risk tolerant) individuals who focus on the higher end of the distributions (high gains) would opt to stay. As shown in the right hand panel of Figure 8, these differences are statistically significant. Therefore, the QM model predicts maxmin (risk-averse) individuals to leave and maxmax (risk-tolerant) individuals to stay.

As a third step, we test whether these QM predictions match reality. We test whether risk-averse (maxmin) individuals are effectively associated with migrating and risk-tolerant (maxmax) individuals are effectively associated with staying. Recall that migrants are defined as those households who have changed LGA at some point in time during the six panel waves and that risk-aversion is assessed using a question asked in a module of the 2016 GHS data as described in the data section. We test the association between migration and risk-tolerance for household heads controlling for a set of household socio-economic characteristics.

Results are shown in Table 7.<sup>12</sup> The association between migration and risk-tolerance is negative and significant in all three specifications of the equation. The three specifications differ in the dependent variable. In all three equations, “0” corresponds to migrants and “1” corresponds to non-migrants. However, the “1” changes in the three equations to capture different degrees of conflict in the area of origin. Equation (1) captures non-migrants irrespective of whether the area of origin had conflict in previous periods (2010-2014) or not. Equation (2) uses a sub-sample of non-migrants who originates from areas that had some conflict over the previous periods and Equation (3) reduces the sample to people in areas that had always conflict over the previous periods. Results show that the coefficient camps. Moreover, migrants are generally expected to overestimate rather than underestimate the benefits of migration.

> 12Note that some of the covariates related to the quality of dwelling are dropped in Table 7 because of perfect prediction. The variable “language” is also not included in this table and the next because it almost perfectly predicts areas in conflict. For instance, people living in areas affected by the Boko Haram conflict speak almost invariably Kanun or Hausa whereas people living in violent areas in the South speak mostly Igbo. As one would expect, language is closely associated with location.

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