readily to mind were concerned with the quality of life, not production. Lighting, television and reduced labor on domestic tasks usually ranked highest. Only on reflection was it argued that by increasing the length of the working day and reducing labor requirements for household chores, more time was available for production or other income generating activities.‖ This suggests that more careful empirical analysis may uncover effects not identified in the more descriptive literature, as well as providing more certainty about attribution. Including covariates in a multivariate model is a first step towards ruling out possible rival explanations related to non-random access to electricity and contemporaneous changes. Barkat‘s (2002) study of time allocation in Bangladesh, described above, is an example of this approach. Chowdhury (2010) also employs this strategy of including community and household characteristics as covariates in her models. Based on a 2004-05 survey of married couples, and 2000 – 2004 data on infrastructure in Northwest Bangladesh, she finds that village access to electricity in 2000 significantly increases the probability that women will have non-farm employment in 2004-05. After controlling for electrification status in 2000, electrification status in 2004 has no effect, suggesting that there is a time lag in the employment impact. Chowdhury interprets this impact as a result of expansion of job opportunities in the nonfarm sector, where there is less discrimination against women.<sup>37</sup> She also asserts that ―the availability of electricity reduces time spent on activities related to lighting and cooking such as collecting firewood or drying cow dung.‖ She does not compare the probability or hours worked in non-farm paid employment by women and men, and thus it is not possible to determine whether the impacts differ by gender. Dinkelman‘s forthcoming article in the _American Economic Review_ on South Africa provides more persuasive evidence of a substantial, statistically significant, gender differentiated, causal impact of electrification on employment.<sup>38</sup> Specifically, female employment increases by 9 to 9.5 percentage points, or between 30 and 35 percent from baseline, in the wake of an electrification of a village in rural KwaZulu-Natal, as compared to no significant impact on men.<sup>39</sup> This result is strongest for women in the 30-34 age group, most likely because they have fewer childcare responsibilities.

Employing a similar strategy to Dinkelman (forthcoming), Grogan and Sadanand (2011) find that ―household electrification causes rural women to be about 23% more likely to work outside the home, but that there are no such effects for men in Nicaragua.‖<sup>40</sup> They find the greatest impacts in a younger cohort of women (20-35). In Guatemala, Grogan and Sadanand (2009) find women in villages with electricity are 9 percentage points more likely to be employed (marginal effect at the mean), and they see a five-fold increase in time spent working, versus no impact on men‘s probability of employment

> 37 Chowdhury (2010) only reports coefficients from probit and poisson models, not marginal effects.  The coefficient on electricity in 2000 is not as large or as statistically significant as the effect of a public phone in the village, for example, but is more significant than the effect of a paved road in the village.  Using a fitted value for electricity in the home (predicted from village price of electricity and land owned by household), Chowdhury (2010) also finds that electricity reduces the time spent on unpaid households work (but it is unclear how or whether she accounts for pre-estimation of the electricity variable in this regression).

> 38 Employment in South Africa is determined by a census question on whether a person (age 15 – 59) works, including formal work for salary or wage, informal work such as making things for sale or rendering a service, or farm work.

39 Dinkelman (forthcoming) uses gradient of the land as an instrument for electrification, and includes as covariates 1996 household density; fraction of households living below a poverty line; distances to the grid, road and town; fraction of adults that are white or Indian to proxy for local employers; fraction of men and women with a completed high school certificate; and two standard proxies for community poverty, the share of female-headed households and the female/male sex ratio, plus district fixed effects.

40 Grogan and Sadanand (2011) use municipal population density in 1971 and the interaction of population density with mean slope gradient of municipal land as instruments for household electricity connection in 2005, and include multiple controls at municipal and household level (e.g., capturing characteristics of labor market and general infrastructure).

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