The effects of electrification on employment in rural Peru

IZA Journal of Labor & Development

Table 4 Differences-in-differences estimates (DD) only women

Dependent	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
variable:	Participation	Employment	Hours	Log	Log hourly	Has	Wage earner	Works in	Self-employed
			of work	earnings	wage	two jobs		agriculture
Panel A: All women
PER	0.005	0.013**	0.671**	0.069	0.032	0.003	0.003	-0.000	0.007
	(0.006)	(0.006)	(0.272)	(0.049)	(0.046)	(0.005)	(0.003)	(0.007)	(0.005)
Observations	121,637	121,637	121,637	24,233	23,746	121,637	104,830	121,637	121,637
R-squared	0.108	0.310	0.216	0.321	0.275	0.072	0.124	0.263	0.178
Panel B: Excluding migrants
PER	0.005	0.015**	0.581**	0.065	0.062	-0.001	0.003	0.003	0.002
	(0.006)	(0.007)	(0.282)	(0.059)	(0.056)	(0.006)	(0.004)	(0.007)	(0.006)
Observations	89,347	89,347	89,347	16,350	16,030	89,347	77,782	89,347	89,347
R-squared	0.120	0.342	0.244	0.322	0.290	0.081	0.113	0.278	0.197

Note: Standard errors clustered at the district level are shown in parentheses. Each coefficient comes from a separate regression. All dependent variables are binary except for hours, log (weekly) earnings, and hourly wages. Individuals who do not work have zero (weekly) hours of work. Earnings and wages are only computed for those with positive values of income. PER is equal to one after the program arrives to a district, and zero otherwise. All regressions control for presence of children below 5 in the household, individuals’ maternal language, age, and education, and district and year fixed effects. Panel A includes all women, and Panel B excludes migrants. Migrants are defined as those who, by the time of the survey, live in a district different from their district of birth.
*, **, *** denote significance at the 10%, 5%, 1% level, respectively.