|_polity_<br>_d g_|0.099 / 0.000 (0.028)|0.001 / ‐0.00 (0.001)|
|---|---|---|
|_cred_<br>_dt_|0.218 / ‐0.00 (0.003)|0.038 / ‐0.00 (0.001)|
|_cred_<br>_dc_|0.090 / 0.000 (0.002)|0.011 / ‐0.00 (0.000)|
|_cred_<br>_dg_|_0.962 / ‐0.02 (0.014)_|_0.802 / ‐0.02 (0.021)_|

PIP stands for ``Posterior inclusion probability", PM is ``Posterior Mean" and PSD is ``Posterior Standard Deviation". Figures in italics correspond to entries with PIP > 0.5. Columns labelled ``Strong heredity fulfilled" perform BMA using only models where the parent variables are included when the interaction is active.

We apply both approaches to our dataset in order to evaluate the existence of subsample heterogeneity in the effects of natural disaster risk on human capital. We evaluate different model spaces, each one containing potential interactions of the disaster variable with a) the level of income per capita, b) the initial level of school enrollment, c) the political regime and d) the degree of credit constraint, as measured by _cred_ . Thus, BMA estimates are obtained for model spaces defined by the specifications in (8) with _z_ given by each one of these variables. The results are presented in Table 4, where the PIP, PM and PSD for the interaction terms are presented.<sup>10</sup> Several interesting results emerge from the analysis. On the one hand, there is basically no evidence for robust heterogenous effects of natural disasters on education depending on the variables specified. If we concentrate on the results obtained by imposing the strong heredity principle, the only interaction with PIP over 0.5 corresponds to the combined effect of geological disasters and financial depth ( _cred_ ) in the ten‐year panel. The BMA estimate corresponding to this variable, although only marginally precise, indicates that, ceteris paribus, a country which is more (geological) disaster prone tends to react less in terms of school enrollment to the alleviation of credit constraints. A similar negative effect is found in the long‐run cross‐section for the standard BMA setting, although the effect is not at all robust if we consider only models where the interacion term appears exclusively together with the parent variables. In the cross‐section setting the interaction of income and (geological) disaster risk is marginally robust, but the effect estimate is extremely imprecise and, just as in the case of credit constraints, it disappears if the strong heredity principle is imposed in the design of the BMA estimators.

# **3.5  Other robustness checks**

We also performed other robustness checks to ensure that the results are not driven by the prior structure imposed on the BMA procedure. The results shown above are robust to changing the parameter prior from the Unit Information Prior to the Risk Inflation Criterion, as well as to the use of a hyperprior on model size as proposed by Ley and Steel (2009). For the cross‐country setting, we also performed BMA on an alternative set of covariates which enlarges the group of explanatory variables in Table 1 by an extra variable that measures the percentage of mountainous terrain in the respective countries. With this variable, we control for geographical and topographical effects that may be correlated with the disaster risk variables but exert an independent effect on human capital investment (by, for instance, affecting the return of

> 10 Complete results for all other variables are available from the author upon request. The results presented in section 3.2 are not affected by the inclusion of the interaction terms as extra variables.