T2 shows no change by 2011 , but a large reduction by 2012 . Panel shows the association with fine particulate matter. PM2.5 concentration did not change significantly in the control group between baseline and follow-up. Households in T1 show significant reduction in average PM2.5 concentration at the first and second follow-ups. Households in T2 show no reduction in PM2.5 concentration by the first follow-up, but a significant reduction in mean PM2.5 concentration by the second follow-up. This reduction is not statistically different from the average reduction experienced by households in T1. This graph shows three striking facts. First, Both PM2.5 and kerosene expenditure change when electrification status changes. Conversely, none changes if electrification status doesn’t change . Second, the average changes among households in T2 are similar to those experienced by households in T1. Third, the new levels of kerosene consumption and PM2.5 observed for T1 in 2011 are maintained in 2012. The results are presented in Table 3, briefly discussed above. Connection to the grid is associated with 53% reduction in PM2.5 concentration between 5pm and 7am. This estimate is strongly significant. The point estimate is slightly larger in the evenings , when PM2.5 concentrations are 300 µg/m3 . The estimate for late night is also high, at 51%, but PM2.5 concentration is 140 µg/m3 , around half of the evening period. The reduction in PM2.5 in the early mornings is 42%, still large but slightly lower than the other subperiods. Average PM2.5 concentration in this subperiod is the highest, at 350 µg/m3 , which could indicate cooking breakfast. As we discussed earlier,cannabis drying system cooking fuel did not change with electrification. Using the data for T2 and T3 to test for differential pre-treatment trends in PM2.5 and the 2010 EHEIPCER wave to test for differential pre-treatment trends in expenditure in kerosene or candles, use of wood, candles, we cannot reject the null hypothesis of parallel pre-treatment trends in any of the tests we performed.This subsection analyzes changes in traditional fuel use induced by electrification and suggests kerosene as the main channel through which electrification affected overnight PM2.5 concentration.
Table 6 reports the effects of electrification on energy use. Our findings conform with the stylized fact that newly electrified households use electricity first for illumination. Electrification reduces the probability of using kerosene by 60 percentage points by round 2 . The reduction by round 3 is smaller, 22 percentage points , and by round 4 is larger again, at 43 percentage points . Monthly expenditure in kerosene decreased accordingly, by $3.57 at round 2, $2.07 at round 3 and $1.94 at round 4. The use of kerosene in kWh per month also decreased with electrification, by between 50-80% of the baseline value by rounds 2, 3, and 4, respectively. Other substitutes to electricity, like candles and car battery show no significant changes, although in some cases the standard errors are too large to reject sizable effects. These sources are less important in the household’s energy budget than kerosene, so detecting an effect would require larger sample sizes. As mentioned in the preceding section, similar patterns arise in the non-experimental sample. Next, we examine whether changes in cooking practices could have also generated the observed changes in overnight PM2.5 concentration. This is unlikely since the use of wood for cooking was around 85% and did not vary during the study period. As we saw in Table 1, 60% of the population agreed that cooking with electricity was very expensive. The bottom panels of Table 6 show that electrification did not lead to statistically significant changes neither in the use of wood for cooking nor in the probability of cooking outdoors. The coefficient on electrification in the use of wood regression for round 2 is larger than desirable, but the coefficients in rounds 3 and 4 are close to zero. Similarly, the coefficient on electrification corresponding the regression on cooking outdoors in round 4 is non-significant but large, while the coefficients for rounds 2 and 3 are close to zero. Although we cannot reject moderate changes in two of the six regressions on cooking practices, it seems most of the variation in overnight PM2.5 concentration is due to the large and statistically significant changes in kerosene consumption. Lower respiratory infections cause 2.8 million deaths globally in 2010 and thus they constitute a major public health concern. In this subsection we show that the reductions in overnight PM2.5 concentration generated by household electrification had sizable effects on respiratory infections among children under six years old. The experimental sample includes 380 children in this age-range.
Despite this relatively small sample size, there are large and statistically significant reductions in the incidence of acute respiratory infections among children. The dependent variable indicates whether the child had an episode of acute respiratory infection in the four weeks prior to the survey . When the explanatory variables are voucher, round, and their interactions, we find that vouchers led to a reduction of 16 percentage points at round 3. Adding baseline characteristics and the number of vouchers within 100m results in reductions of 18 and 17 percentage points. In all cases the reductions are significant at the 90%. These figures are large, and even more impressive when compared to the baseline since they represent reductions of between 37 to 44 percent of the mean incidence among non-recipients at round 3. The coefficient on voucher at round 4 is positive across all specifications, implying increases in ARI incidence of 8 to 11 percentage points among voucher recipients, and it is significant at the 90% in the first specification. Moreover, in no specification the null hypothesis that the coefficients on voucher in both rounds have the same magnitude can be rejected. This may initially seem a puzzle but it merely reflects that the electrification rate among non-recipients caught up with that of voucher recipients by round 4. It is worth noting that it is not the case that ARIs bounced back up to their original levels, since ARI incidence reduced from 44% to 10% between rounds 3 and 4. So, consistent with the analysis of PM2.5concentration, this shows that the effects of electrification are similar irrespective of whether a household received a voucher. The time allocation data was collected for up to four household members: the household head, his or her spouse, and up to two school-age children. This allows estimating PM2.5 exposure for four “synthetic individuals”: adult female, adult male, female child, and male child. Table 8 presents average time allocation in four type of activities for each of our synthetic individuals. The male and female heads report 8.9 hours of sleep per day, while the children report 9.5-10 hours of sleep per day. Time at home during the evening is similar for all members . The starkest differences are observed in time spent in the kitchen and time spent outside the home. While female head reports 2.5 hours per day in the kitchen, the male head reports spending an average of just five minutes. On the other hand, the female head reports spending an average of 2.7 hours outside the home,cannabis vertical farming while the male head reports 7.8 hours . The differences in time allocation that arise from this analysis already suggest that adult females are more exposed to PM2.5 since they spend considerably more time in the kitchen than any other household member. On the other hand, males spend almost one third of the time outside the home.
The main activity is farming and walking to and from the farm, where it can be safely assumed that exposure to PM2.5 is negligible. Next, we make explicit the assumptions about the PM2.5 concentration in the environments where these activities were likely conducted. As shown earlier, average PM2.5 concentration in the living room during the evenings is 0.40 mg/m3 . We take this as representative of any room in the household, except the kitchen, between 1700 hours in the evening and 0700 the next morning. Based on the sub-sample of households for which we have 3-day measurements, we estimate the average PM2.5 concentration in the living room during daytime to be 0.26 mg/m3 . We take this as representative of the rooms in the household during daytime, again with the exception of the kitchen. Since we did not collect data on PM2.5 concentration in the kitchen, we use 0.90 mg/m3 , which corresponds to average PM2.5 in the kitchen in Guatemalan households . This figure seems an adequate assumption in our context since it corresponds to a neighboring region where households also rely of fuel wood for cooking. This makes our exposure estimates adequate for households that rely on wood for cooking, and even conservative given that its not uncommon to find cases where the average concentration is above 2.0 mg/m3 . We assume household members will not be exposed to PM2.5 whenever they are not home. This assumption seems not to be too restrictive for the population in our study setting, since most of the time outside the home is spent in outdoors activities like farming, and very little time is spent conducting activities outside the home that suggest exposure to PM2.5 .The third and final component in is the inhalation rate. Since inhalation rate depends on age, we estimated it for the sample averages: 43 for the female head, 47 for the male head, 11 for the female child, and 13 for the male child. Air inhalation rates per activity are based on the EPA Exposures Handbook . Most activities conducted at home can be classified as “light activity tasks” by the EPA. Light activities include cooking, washing dishes, ironing, watching TV, doing desk work, writing and typing, and walking at a speed of up to 2.5 mph . The average inhalation rate for these activities is 0.78 m3/hour, while the average air inhalation rate while sleeping is 0.30 m3/hour, again similiar for the four synthetic individuals. The inhalation rate for activities conducted outside the home will vary greatly, depending on the intensity of these activities. For instance, walking to work could be classified as light or medium intensity, depending on the speed at which the person is walking. Farming, on the other hand, could be classified as medium to high intensity, but if lunch breaks would be light activity. However, the assumption made earlier about PM2.5 concentration being zero outside the home makes the inhalation rates of these activities irrelevant for total exposure. With these three components we estimated exposure rates for the four synthetic individuals. Estimated exposure measures are highest for the female head, at 5.68 mg/day, and lowest for the male head, at 3.20 mg/day. The exposure measures for children lie in between, with females 4.23 mg of PM2.5 per day males to 3.72 mg/day. Taken plainly as units of PM2.5, these concentrations are equivalent to 8.0 cigarettes a month for the male head, 14.2 for the female head, 10.6 for the female child, and 9.3 for the male child31. The scientific evidence is yet inconclusive as to whether generated by PM2.5 cigarette is worse or not than that generated by kerosene combustion. The changes in exposure are large for all members , but these gains are unequally distributed across household members. The male head benefits the most, with a reduction in exposure of 59%, while the female head benefits the least, with a reduction of 33%. As pointed above, these differences owe to females spending more time than males in the kitchen, where pollutant concentration is highest, while males spend more time than females outside the home, where pollutant concentration is lowest. To date, there are no dose-response functions linking exposure to PM2.5 from kerosene combustion to health outcomes. However, Pope III et al. presents an estimate of a dose-response function linking PM2.5 from first- and second-hand tobacco smoking to lung cancer and cardiovascular diseases. In Panel of Table 8 we present the relative risks that would be associated to the exposure levels found in Panel if the health effects of PM2.5 from kerosene combustion were similar to those from tobacco smoking.