dirt floors, has television, has fridge, has washing machine. We additionally compile data from various administrative records that allow us to observe at the municipal level: the number of users of electricity, the number of car registrations, and the number of building licenses for both industrial and residential use.

Given the frequency with which these data are available, panel A of table 5 uses a long difference specification in which we test whether the five year log change in the number of dwellings of the various characteristics previously described is related to five year change in log night lights. In all cases columns 1 to 4, we find that the log night coefficient is positive and that it is sharply estimated. The _R_<sup>2</sup> is in the 6 to 8% range. In panel B of the same table, we turn to administrative records available in yearly frequency. In this case we use a log log specification that includes both municipal and year fixed effects. As in the previous case we find that our measure of log nights is positive albeit less precisely estimated in columns 3 and 4 where the number of observations is limited.

Next, we investigate the relationship between log night lights and state level GDP.<sup>12</sup> Given that our primary purpose is to estimate an elasticity of light to GDP that would allow us to back out the impact of Fonden on local economic activity, we restrict the sample to the 26 states that have requested Fonden funding in the 2004-2011 period. We follow the approach taken by Henderson et al. (2011) and focus primarily on determining whether night lights are able to predict year to year growth, annual fluctuations, recession and expansions, and long term growth. Panel A of table 6 uses log night lights derived from annual composites. Panel B of the same table uses log night lights derived from pixel averaged monthly composites. The benchmark specification regresses log GDP on log night lights, state fixed effects and year fixed effects. Standard errors are clustered at the state level in all cases. Column 1 presents the result of the benchmark specification. We sharply estimate an elasticity of roughly 0.24.

Next, in column 2 we test whether night lights are capable of predicting annual fluctuations by extending the previous specification to include state trends. Since we are primarily interested in short term deviations from the state growth path, this is the key specification for our analysis. As in the previous case we sharply estimate an elasticity in the order of 0.11 (t=2.6). This result is important because it suggests that night lights do a reasonably good job of predicting annual fluctuations in GDP.

In column 3 we test for ratchet effects, that is whether, relative to the state mean over time, increases and decreases in night lights are symmetrically related to increases and decreases in GDP. This calculation is preformed in two steps: (i) We demean the data by regressing GDP and lights on year and state fixed effects. (ii) We regress the GDP residuals on absolute value positive lights residuals, and absolute value negative lights residuals. We find that the coefficients are

> 12The source of state level GDP data is INEGI. GDP is measured in constant 2008 pesos.

16