conditions at the village-level in Benin, the surveys offer several proxies for village incomes. Most are based on village averages of responses to the household survey: the percentage of households that report their income as belonging to the lowest of the income brackets; the percentages of sampled households that report owning a mobile phone and a television; the percentage of households surveyed with brick walls; and the percentage with houses with more than five rooms. With 20 observations per village, these averages are only precise enough to test whether larger income differences separate villages with more and less radio access. From the village surveys, it is also possible to compare villages according to the number of private schools serving the village.

Finally, village linguistic characteristics could affect which radio markets the village is in and be independently related to cultural norms that influence health practices or to the ability of the village to attract government programs. Based on the household survey, it is possible to compare villages according to, first, the probability that any two of these households would speak the same language at home; and, second, an indicator variable for whether the most common language among the sample of 20 households in a village is different from the most common language among all the households surveyed in the commune in which that village resides.

Table 2 reveals no statistically significant association between any of these variables and either the number of community radios to which villages have access or the share of community radios as a fraction of all radios to which a village has access. The variable closest to significance is the distance of a village from the nearest urban center, in the first column, but its effects are small. A one standard deviation increase in distance (18.7 kilometers) is associated with access to approximately 0.11 fewer radio stations. Brick walls are more common in villages with a larger share of community radios but, the effect is not quite significant and, again, its magnitude is small. A one standard deviation in the fraction of households with brick walls (19 percent) is associated with only a three percent greater share of community radios. No measures of income, wealth, remoteness or linguistic diversity are significant. There is, then, no evidence that villages with access to more community radio are significantly different in other ways from villages with less access.

Table 3 reports the bivariate correlations between radio access and each of the variables. In nearly all cases, observable village characteristics are not significantly correlated with community radio access. This time, the correlation between village radio access and distance to an urban center is statistically significant, but the magnitude is small: a one standard deviation increase in distance (18.7 kilometers) is associated with access to .19 fewer stations (less than .20 of a standard deviation). This observed heterogeneity has no impact on our results. Among the 75 percent of villages that are no more than 32 kilometers from the nearest urban center, the correlation disappears. The results reported below are robust to looking only at this smaller sample of villages.

The fraction of households reporting incomes of less than 30,000 CFA francs is also marginally significantly correlated with community radio access. A one standard deviation increase in this fraction (.21) is associated with access to .12 fewer community radio stations, less than 10 percent of the standard deviation of community radio access. Again, however, these differences have no effect on the results. The correlation disappears among the 75 percent of villages where fewer than 53 percent of villages report low incomes and the results reported below are robust to looking only at this subsample.

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