line. BPL households are APL energy and fixed charges if their consumption exceeds 50 kWh of consumption during a billing cycle.

The IBT schedule seeks to provide a minimum amount of electricity at an affordable cost to low-income/low-consumption households. IBTs also enable a utility to achieve its revenue goals, by deriving a larger share of its revenue from high-income and high-consumption households. From the perspective of balancing affordability and revenue sufficiency goals, designing an efficient IBT schedule amounts to separating households into distinct groups based on their willingness and ability to pay for electricity and using observable household characteristics to optimally setting consumption blocks at an appropriate marginal price.

IBTs are not unique to India and the estimation of residential electricity demand under IBT schedules has been studied extensively in developed country settings. Reiss and White (2001) use a representative sample of California households, and summarize how the structure of electricity demand varies across customers.<sup>6</sup> The model is then used to analyze the effect of tariff changes on changes in consumption and the share total monthly expenditure a household spends on electricity.

McRae (2015) is one of the few papers that conducts a similar exercise in a developing country setting,<sup>7</sup> by building an asset ownership model using Colombian census data and pairing it with a utility’s administrative billing data, to show that government subsidies for electricity programs disincentivizes greater investments in electricity infrastructure and ensnares poorer households in a low-level subsidy trap. McRae (2015) uses the demand estimation under non-linear pricing econometric modeling framework developed by Hanemann (1984)<sup>8</sup> to recover the parameters of household-level preference functions.

More recently, Wolak (2016) applies an enriched version of this modeling framework to water utility customers in California and uses it to find price schedules that ‘’optimally’’ balance the revenue and conservation goals of water utilities.<sup>9</sup> Households choose their consumption level to maximize a utility function which depends on their demographic characteristics. Despite the household’s best intentions to use only utility a maximizing level of water, each water service demand has technological uncertainty in the exact amount of water consumed. For example, running water for a hot shower on a cold day takes longer than on a warm day and therefore uses more water. Therefore, it is practically impossible for a household to precisely adjust their consumption to arrive at the utility-maximizing consumption, i.e., the kink points on their piecewise linear budget set. For this reason, in addition to the usual error term capturing the unobservable household characteristics in econometric models, a second stochastic unobservable called the optimization error is introduced into the demand model in Wolak (2016). This term accounts for uncertainty in the actual amount of water consumed by the household relative to their intended water service consumption level.

In the Indian context, two data sources that have been previously used to study electricity demand are the National Sample Survey Office’s (NSSO) consumption expenditure rounds and panels

> 6 Household Electricity Demand, Revisited. Reiss and White. 2001. http://www.nber.org/papers/w8687.pdf

> 7 Infrastructure quality and Subsidy Trap. Shawn McRae. 2015. https://www.aeaweb.org/articles?id=10.1257/aer.20110572

> 8 Hanemann, W. Michael. "Discrete/continuous models of consumer demand." Econometrica: Journal of the Econometric Society (1984): 541-561.

> 9 Wolak, Frank. 2016. Designing Non-linear Price Schedules for Urban Water Utilities to Balance Revenue and Conservation Goals. - - <u>http://web.stanford.edu/group/fwolak/cgi bin/sites/default/files/water_paper_wolak_draft 9.pdf</u>

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