The experimental sample consists of 500 households located in sub districts that were scheduled to be covered by the program during its first year. We generated experimental variation in the connection fee by offering discount vouchers to a randomly selected subsample. We randomly allocated 200 low-discount vouchers , 200 high-discount vouchers , and left the remainder households as control group . The exogenous variation in the connection fee generated by the random voucher allocation deals with self-selection in connection to the grid. Vouchers were valid for a discount towards the safety certification to be reimbursed after paying the full cost. Each voucher showed the name and address of the beneficiary, it was non-transferable, and it was valid for 9 months.The random voucher allocation also creates exogenous variation in the number of voucher recipients in a given neighborhood of household i , which generates variation in the number of new connections around household i, so we can control for the role of spillovers on grid connection. The sign of the effect is theoretically ambiguous. On the one hand, observing their neighbors connect to the grid may make households more prone to connect, through a combination of social learning and imitation effects.On the other, higher formal connection rates in a neighborhood reduce the cost of getting an informal connection, so the number of vouchers around a household may increase the number of informal connections. To estimate the role of spillovers on adoption, we use the number of household i’s neighbors that received a voucher in a given radius , marijuana drying rack controlling by the number of eligible neighbors in that radius.
Eligible households are households with no electricity at baseline. EHEIPCER, the household survey implemented for this study, is a fairly standard survey that collected data on demographic characteristics, health, education, housing characteristics, energy use, income, consumption, among others. In particular, it includes a detailed module on time allocation for up to four household members: the male head, the female head, and up to two school-age children. Strict training sessions were conducted to ensure high quality in data collection, which was conducted with handheld computers. Enumerators were trained and selected by the authors with the assistance of DIGESTYC and IFPRI staff. The indoor air pollution data described below were collected by a subset of enumerators that underwent additional special training to this end. The baseline household survey, designed using the 2007 Population Census as the sampling framework, was collected in November and December 2009. It covered 4,800 households all over northern El Salvador. Three follow-up surveys have been collected in the same months in 2010, 2011, and 2012 respectively. An additional follow-up survey is scheduled for November 2013, and a final round is scheduled to be fielded in November 2015.A central part of the project consisted in collecting data on overnight PM2.5 concentration. We obtained PM2.5 measurements in two sub-samples of households, one experimental and one non-experimental. The experimental sub-sample includes PM2.5 data on 141 randomly selected households from the 500 households that were considered for voucher allocation. The reasons for not selecting the whole sample were logistical and budgetary. Measurements for these households were collected with rounds 3 and 4 of the household survey. The non-experimental sub-sample is formed by 200 households of EHEIPCER households from neighboring sub-districts in the same departments as the experimental sample that had not connected to the grid by September 2010. Measurements in these households collected with rounds 2, 3, and 4 of the household survey.
The non-experimental sample is formed by households that had not connected to the grid by round 2. Descriptive statistics of both sub-samples are reported in Appendix Table A1. In each household we measured minute-by-minute PM2.5 concentration between 1700 hours and 0700 hours the next morning in the main evening living area. The main evening living area is defined as the room where household members spent most of their time awake during the evenings. In the majority of cases this was the living room. Measurements were conducted with the University of California at Berkeley Particle and Temperature Sensor . The UCB-PATS is a small, portable and non-intrusive datalogging particle monitor for indoor environments. It uses a photoelectric detector to measure PM2.5 concentrations down to 25 µg/m3 . The UCB-PATS records PM2.5 concentration, relative humidity and temperature at a 1 min time resolution. For details on the development and performance of the UCB-PATS see Litton et al. , Edwards et al. , and Chowdhury et al. . Experienced and meticulously trained enumerators visited the selected households, explained the purpose of the study and obtained consent to place the UCBPATS in the home. The protocol implemented to measure PM2.5 concentration is similar on the protocol applied by Northcross et al. for cookstoves. It is a standard protocol in the cookstove literature but there is no standard protocol in place for measuring indoor air pollution emitted by kerosene lanterns.The monitor was placed in the room where most household members spent most of their time awake during the evenings. In practice, this was mostly the living room. In a handful of cases it was the master bedroom. The monitor was placed on a wall 1m from the place where the lamp is usually located in the evenings, at least 1.50m away from any working doors or windows, and at a height of 1.50m above the ground. In the baseline measurement enumerators took pictures of the pictures of the placement to ensure placing the monitor in the same place in the follow-up visits.
This reduces the risk of generating artificial variation in PM2.5 concentrations by placing the monitor in different locations. In follow-up measurements, the enumerators used pictures from previous rounds to place the monitors in the same place as the baseline measurement. The enumerators filled a data sheet with exact details on the height, distance, set-up time, pick-up time, among other information. The monitors were placed in the homes be-fore 1600 hrs. If the monitor was placed in a home between Monday and Thursday, it was picked up the next morning starting around 0800 hrs. If it was placed in a home on a Friday, it was picked up the coming Monday starting around 0800 hrs. This procedure was followed to comply with labor regulations in the government sector. In a sub-sample of households the measurement took place between 5pm on a Friday and 7am the coming Monday. Following the standard practice in the environmental health literature, the resulting PM2.5 concentration for those households was averaged across the three days. According to the 2007 National Census around 80% of the El Salvadorian population had access to electricity. Although this figure is high, there are strong correlations between socioeconomic status, electrification, 4×4 grow table and use of traditional fuels for lighting or cooking. Figure 1 shows that the poorest municipalities are the ones with the lowest electrification rates and the highest use of traditional fuels for cooking and lighting. To illustrate the relationship between kerosene use and indoor air quality in our study setting, Figure 2 shows a non-parametric regression of overnight PM2.5 as a function of monthly kerosene expenditure . There is a clear positive relationship between these two variables, suggesting that reductions in kerosene use could generate important improvements in indoor air quality. Kerosene provides an important source of variation in PM2.5 even with 70% of households using wood for cooking. Table 1 shows descriptive statistics split by treatment arm. Column 1 shows the means for the control group, column 2 shows the means for the households that received a 20% discount, and column 4 shows the means for households that received a 50% discount. Columns 3 and 5 test for differences between each of the treatment arms and the control group. Household heads are on average 50 years old, 69% of them are male and have 2.4 years of schooling on average. Literacy rates among household heads are low, with only 54% of them reporting being literate.
The average age in the households is 30.8 and households are composed by 4.5 members, with a total dependency ratio of roughly 0.45. Annual income is around US$770 per head, roughly US$2.11 per person per day. The main source of energy expenditure is kerosene mainly used for lighting, and propane , mainly used for cooking, followed by candles and car battery recharging , used to power TV sets. Use of wood for cooking was reported by 70% of households. Thirty-eight percent of households had informal access to electricity at baseline. In-formal connections consist on a series of extension cables connected to each other and plugged into a neighbor’s sockets. They are at most enough for two light bulbs and some times a television set. For our purposes, households with informal connections were treated as off-grid. This can attenuate the effects of electrification on indoor air quality if we think that households with informal connections rely less on kerosene for lighting than those with no connection at all. However, since it is difficult for the government or the electric utility to determine if a household has informal access to the grid, we argue that the results from our strategy are more relevant for policy purposes. Households were also balanced regarding their ex-ante perceptions towards energy sources. The vast majority agreed that electricity illuminates better than kerosene and that wood smoke generates respiratory problems . Between 30 to 40% of respondents said that kerosene is not an expensive source of lighting, and 20-30% said it as the best way to illuminate their household. The conceptual framework in section 3 raised the possibility of heterogeneity in treatment effects, which we now turn to examine. First, we follow the stander practice of interacting treatment with the suspected source of heterogeneity, and next we estimate a CIC model to explore the distribution of treatment effects. Table 5 shows the results of these regressions. Effectively treatment effect is directly related to the number of kerosene lamps owned by the household at baseline. The effect of electrification on overnight PM2.5 concentration was not significant among households that reporting owning no kerosene lamps. The same is observed for the probability of experiencing concentrations above 67 µg/m3 . Households that reported owning one lamp experienced reductions of 53% in average PM2.5, while households with 2 lamps experience reductions of 68%. Similarly, the probability of observing concentrations above 67µg/m3 fell by 47 percentage points among households owning one lamp and by 75 percentage points among households owning two kerosene lamps. The sample size change across these regressions because of missing data on the number of kerosene lamps for some households. The CIC estimator for the average treatment on the treated is -0.70. Given random group assignment, this is also the average treatment effect. The CIC estimator is consistent with the -0.63 found among voucher recipients by round 3. This strengthens the internal validity of our findings to the extent eliminating the differences in electrification rates led to eliminating the differences in overnight PM2.5 concentration. Figure 5 analyzes the variation in magnitude of treatment effects along the distribution of overnight PM2.5 concentration. However, there seems to be variability in the treatment effect along the distribution. The reduction is significant starting roughly from the 20th percentile, and the size of the effect starts increasing starting at the 60th percentile. This is consistent with the intuition behind our study setting: treatment effects are significant above a certain threshold of indoor air pollution, and higher polluters experience larger reductions. To allow for direct comparison with respect to the each group’s baseline values, the variables in Figure 6 have been standardized by subtracting the baseline mean and dividing by the baseline standard deviation of their respective group. Panel shows the change in average monthly expenditure on kerosene in 2011 compared to the 2010 levels by treatment arm, with 95% confidence intervals. There is no change in mean between the 2009 and the 2010 measurements for any of the groups. T3 does not show any change in mean kerosene expenditures in any of the surveys compared to 2010. T1 shows a large reduction between 2010 and 2011, which is maintained by 2012.