Table 4 presents the regression analog of figures 1a and 1b. Panel A presents OLS estimates of equation 1. Panel B presents OLS estimates of equation 2, and 2SLS estimates of the impact of Fonden on local economic activity _π_ 1. Columns 1 to 4 present various specifications of the functions _g_ ( _·_ ). Specifically, in columns 1 and 2 we estimate using the full sample and assume that function _g_ ( _·_ ) is a second or a third order polynomial. In columns 3 and 4, we assume that _g_ ( _·_ ) is linear and restrict the sample to within 50.5 mm and 57.3 mm as determined by two optimal bandwidth calculations. Standard errors are clustered at the municipal level.

Consistent with figure 1a, the estimates of panel A reveal that crossing the threshold increases the probability of receiving Fonden between 13% to 19% and that this jump is statistically significant in all cases. Similarly, the reduced form estimates in panel B reveal that municipalities above the threshold grew by roughly 0.04 log points more than municipalities below the threshold. Once we rescale these reduced form coefficients, we find that Fonden led to an increase in the range of 0.196 to 0.37 log points. While these coefficients are only statistically significant at the margin, the estimated gains in night lights imply considerable increases in local economic activity. Specifically, in the first row of panel C, we calculate the impact of Fonden on local economic activity by multiplying the IV estimates with the elasticity of log night lights to GDP from the annual fluctuations specification, table 6 panel A column 2. Our estimates indicate that municipalities with access to Fonden grew between 2 to 4% more than those without Fonden in the year following the disaster.<sup>6</sup> In order to get a better sense of the magnitude of the changes in economic activity we additionally derive Fonden’s heavy rainfall benefit cost ratios. This calculation is preformed in four steps. First, we restrict the sample to 790 municipalities that received Fonden funds between 2004 and 2011 and for whom we have complete information on the resources allocated by the program. Second, we proxy municipal GDP before the introduction of Fonden, by multiplying UNDP estimates of municipal GDP per capita in 2000 by municipal population. Third, we calculate the dollar value of the economic activity generated by FONDEN by multiplying municipal GDP by the product of the elasticity of light to GDP and our IV estimate of the impact of Fonden, that is, we multiply the level of GDP by the additional growth created by Fonden. Fourth, we sum across municipalities to derive the total benefit and cost of the program.<sup>7</sup> To account for the uncertainty in estimating the elasticity of light to GDP, and of estimating the impact of Fonden on night lights, the third step of the calculation is preformed using coefficients drawn from two normal distributions. In each case the mean of the distribution is set to the point estimate, and the standard deviation is set to the standard error. Steps three to four are then repeated 5,000 times using random draws of coefficients described on step three. We then

> 6Note that the counter-factual is not no reconstruction response at all, but rather non-Fonden discretionary reconstruction response.

> 7Since UNDP GDP estimates are in 2005 PPP USD we convert Fonden reconstruction expenditures to the same units.

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