sample of household monthly electricity billing records for the period July 2013 to June 2014 (the same period during which the PSLM data were collected).<sup>6</sup> The DISCO databases have a record of basic household characteristics and monthly electricity consumption, but they do not have any information on the household’s welfare level. To obtain a good proxy for welfare level, we merged the billing data with the National Socio‐Economic Registry (NSER), a near‐census of households in Pakistan conducted in 2011. The NSER has data for 27 million households in Pakistan on occupation of the head, household size, assets and dwelling characteristics. The government collected and used these variables to create a proxy means test (PMT) score for each household for targeting of BISP.<sup>7</sup> The National Database and Registration Authority (NADRA), which manages the NSER, generously agreed to merge the two databases for us, using the citizen’s national identity card number (CNIC) as a common identifier.<sup>8</sup> As well as looking for direct matches between CNICs in the two databases, NADRA also used its ‘family folder’ system to look for family links between CNICs in each database as illustrated in Figure 3. Around 45 percent of the 325,926 records in the DISCO database were successfully matched to the NSER data. This seems low, but is actually quite impressive considering the NSER database was put together in 2011, and many households that did not have a CNIC in 2011 may have since obtained one and recorded it in the DISCO database. Another major reason why records did not match was that many CNICs in the billing data were missing or invalid. As robustness check, we compared the matched and unmatched samples and found that they did not differ substantially (see Appendix Table A2). We are therefore relatively confident that the omission of these households does not skew the results of the analysis. Even though we have data on each month of the year July 2013 to June 2014, meter readers are often unable to read each meter every month, so billing data are often estimated in these months and balanced at the end of the year or when the meter is next read. To avoid introducing this measurement error into the analysis, in this section we report _average_ monthly consumption over the year.

Table 2 summarizes basic characteristics of the data by quintile. Again we see an increasing but very weak relationship between welfare level and electricity usage. We use quintiles of the PMT as the measure of welfare rather than per capita consumption.<sup>9</sup> It can be seen immediately that electricity consumption is relatively similar across the population and is weakly correlated with poverty.

> 6 Data provided for each household included the identity card number of the bill holder, name and address, father/guardian name, kWh consumed per month for 2013‐14 (FY14), connected load (in kWh), phase, and number of air conditioners.

> 7 The PMT score is designed to correlate closely with household per capita expenditure, enabling one to rank all households in the country roughly from poorest to richest. It is based on 23 variables related to household structure, dwelling characteristics, education, occupational status and assets.

> 8 The Citizen’s National ID Card Number (CNIC) is a national unique identification number provided to adult citizens. Further details on the matching process are provided in the Appendix, part A.

> 9 The cutoff PMT scores for each quintile are as follows: Poorest Quintile = 0 to 22, Lower‐Middle Quintile = 22 to 29.7, Middle Quintile = 29.7 to 37.4, Upper‐Middle Quintile = 37.4 to 46.4, Richest Quintile = 46.4 to 95.17.

7