suspicion remains that electricity is unlikely to have a causal impact on choice of cooking fuel, yet empirical analysis has not been able to rule out this possibility.

_4.2.3.4 Maternal health and fertility_ **In addition to reducing indoor air pollution from lighting and possibly cooking and heating, electricity has been hypothesized to affect health through refrigeration (improving nutrition and decreasing food poisoning) and access to information. IEG (2008) finds evidence for both of these pathways in multivariate regressions for multiple countries, but does not distinguish by gender. There is also evidence of reduced fertility, possibly via television, in that census data from across Latin America suggests a systematic negative association between birth propensities and household electrification status in rural areas.** Using secondary data on all of Argentina except Buenos Aires from 1990 and 2000, Gonzalez-Eiras and Rossi (2007) investigate the determinants of birth weight, which is partly a function of mothers‘ well-being during pregnancy (Agénor et al. 2010 summarizes evidence on this link).<sup>44</sup> They find a 2024% decrease in the proportion of children born with very low weights (and a 5-6% decrease in low birth weight) in provinces where electricity had been privatized. They argue that privatization increases both the number of households with access to electricity and the reliability of the electricity supply, which in turn mean that more households acquire refrigerators and that food stays consistently cold, which in the final step in the causal chain allows for a more varied diet and a reduction in food-borne illnesses.

Turning to fertility, Grogan (2011) documents that birth propensities are significantly higher among women in unelectrified rural households in Argentina, Bolivia, Brazil, Mexico, Panama, Peru and Venezuela. Grogan (2011) and Grogan and Sadanand (2009) provide careful analyses of the impacts of electrification on fertility in Colombia and Guatemala, arguing in both cases that it does reduce the number of children born. In Colombia, electrification reduced birth propensity (in the year prior to the 1993 census) by 6%, mostly among women who already had at least two children. 45 In Guatemala, women who were under 30 at the time electricity was installed in their communities have 28% fewer children than women who were older when electricity arrived, controlling for spatial and temporal covariates.<sup>46</sup> In Bhutan, ADB (2010) finds that 0.04 fewer children had been born within 3 years of the survey in households in communities with electricity.<sup>47</sup> While each of these studies is limited by available data, and together they only cover three developing countries, together they do strongly suggest the possibility that rural electrification has a causal impact on fertility.

> 44 Gonzalez-Eiras and Rossi (2007) use difference in difference estimates, with controls for GDP, unemployment, income inequality, public spending, political party, water privatization; and year and province fixed effects.  Their results are not robust to dropping provinces with high rates of missing values, or to standard errors clustered by province.  They therefore call for caution in drawing policy conclusions from their results.

> 45 Grogan (2011) instruments electrification of a village with historical education levels in the municipal capital (argued to be exogenous because most migration is rural to urban, and argued to reduce the cost of electrification by increasing development and hence the number of grid connections) and gradient interacted with education.  Gradient along with numerous other municipal, household, and individual characteristics are included as controls.

> 46 Grogan and Sadanand (2009) regress the number of children born on age at the time electricity was installed in the community, controlling for current age, age when improved water and sanitation introduced to community, and either community fixed effects or department fixed effects with controls for community characteristics (whether has various levels of schools, located on paved road, affected by Hurricane Mitch, and is rural), with standard errors clustered at the community level.  One weakness of this study is the high level of aggregation of many variables.

> 47The estimate reported by ADB (2010) is the average treatment effect on the treated, based on kernel matching using (i) human capital assets—household size, age of the head of the household, whether head of household is literate, number of literates in the house, gender of the head of household, marital status of the head of household, and religion of the head of household; and (ii) physical assets - household‘s holdings of land, main source of drinking water, type of house, whether household owns cows, bulls, poultry, and horse, and (iii) village-level control variables: distance from the village to district headquarter (the seat of local government), size of the village measured by the area of the village, population of the village, time taken to nearest road, and primary school.  While this is a rich set of covariates, it does not include characteristics of the women.

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