appliances covered in the survey is categorized into one of four energy services: heating and cooling, lighting, domestic end-use appliances, and business-end use appliances (the assignment is provided in Appendix 1, table 1). The energy demand for the four energy services is calculated by totaling the energy services demand for all appliances categorized under each group. Similarly, the total household level energy services demand is the sum of energy services for all appliances owned by the household.<sup>16</sup> The household demand for energy services based on survey data is expected to be close to the electricity consumed by the household as observed in the billing data.<sup>17</sup> To check this relationship, the (log of) household demand for energy services per day from the survey is regressed on the (log of) perday electricity consumption observed in the billing data.<sup>18</sup> Results in Table 5 show that the household demand for energy services demand calculations based on appliance ownership and usage is a better predictor of households observed daily demand at levels more than 0.67 kWhs per day (~20 kWhs per month). Therefore, we expect our demand estimation models to fit better for customers above that threshold.

The data on household income and appliance ownership is based on self-reported household surveys. To the extent that the errors in these self-reported measures are independent of the latent true variable (i.e., classical measurement error), the instrumentation approach in GMM estimation allows us to obtain consistent point estimates of parameters of interest.

# 2.3 _Temperature and precipitation data_

The daily temperature and rainfall data are at a 1x1 degree and 0.25x0.25-degree gridded resolution from Srivastava, et. al (2009) and Pai, et al. (2014), respectively. The locations of the villages and census enumeration blocks are matched to these grids to obtain household-level measures of temperature and rainfall. Figure 9 shows deviations of daily temperature and rainfall in an area from its three-year period average (2014-2017). The precipitation curve shows that there is little variation in rainfall across time and regions. The low average precipitation levels in the region also explains the high ownership rates of air coolers (a device for precipitative cooling) as observed in the survey data. The temperature curve meanwhile highlights the wide heterogeneity in climatic conditions across villages and enumeration blocks even within the same month. This heterogeneity in temperatures across

> 16 The minimum amount of household energy services demand based on appliance ownership and usage that we incorporate in our sample is 11.1 kWhs per month. This corresponds to ownership of 1 CFL light (of 20 W) and 1 mobile phone (of 6 W), used daily for 1.5 and 1 hour respectively over 30 days. Households reporting less than this subsistence level of energy services, through ownership or hours of usage, are excluded from the sample.

> 17 In section IV, we describe the actual model of energy consumed over a billing cycle (E) as a function of demand for energy services based on ownership and intensity of appliance use (s), weather and household characteristics. In this section, we are interested in checking for the average unconditional correlation between E and s, to establish some basic stylized facts about the data used in our sample.

> 18 Let , , where is household energy services demanded per day based on the survey data ( T ∗ n n n n n ) where i denotes the type of appliance and ii ii is the total annual energy demand based on the billing data setnn= αα∗cc nn ( ii nn= ∑(wwT wwnn TToo TTp( TTuuTTnnccnn uu TTwwn cc∗ n T TToo hTTn uu TTnncc nnuun) TTnnuu TToo ccTTuuTTdd nnuuTTwwnn ~~)~~ . Taking logs , , implying that if we cc(n n n uun TToo nnTTnnccTTnnuuccuuTTdd ccTTnnuun nncc TTccn n TT nnuuT uu uunn TTn ddnnTTnn ppn uuTTcc cc=) were to linearly regress the energy demand based on survey data to that of the billing data in per-day terms, and if appliance ownership TToo ccTTdduu n TTwwn nn oouunnuuTT TTnncc T uuTT nnuuT ccnnuuccnncc nndd TThnn hTTnnuunnhT cc TTccn TThnn ddnnTTnn ln(nn) = ln(αα) + ln (cc) were to capture energy demand well, we would expect the constant term of the regression to be zero ( , implying ) and beta coefficient of to be positive and close to 1. 10 ln( αα) ~0 αα~1 ln (cc)