the highest income elasticity will allow the rate designer to tailor tariffs to recover more revenues from customers that consume these services. It also allows us to unpack the heterogenous effects of price changes on household consumption across the income distribution.

Our estimate of the electricity production function implies significant price heterogeneity across customers for the same energy service. There are two main reasons for this result. First, there is heterogeneity in the rate at which appliances consume electricity across customers. For example, depending on what kind of television, washing machine, or microwave oven a household owns, the electricity consumption per hour of use of each appliance can vary across customers. How intensively the various appliances within each energy service category are used can vary depending on weather conditions. For example, when it is extremely hot outside a household might stay inside and watch more television and wash clothes, rather than use other appliances in that energy service category. Second, a given appliance can consume more electricity depending on weather conditions. For example, one hour of use of an air conditioner when it is extremely hot outside consumes more electricity than it does during milder days.

Results from our energy services demand system finds significant differences in the own-price elasticity of demand across the four energy services. The own-price elasticity of demand for these energy services vary from -2.7 for domestic end-use appliances to -0.3 for heating and cooling services. There is also considerable heterogeneity in the income elasticity of demand across these four services with the highest and lowest income elasticity for heating and cooling end-uses and for domestic appliance end-uses, respectively observed in the data.

These estimates from the structural model are then used to analyze the performance of a tariff schedule recently proposed by India’s central power ministry. The results show that the proposed tariff structure will increase consumption for all households in Rajasthan by reducing the retail cost of energy. However, consumption gains will be regressive: the average gains for households in the top income quintiles will be significantly more than the poorest ones. Moreover, the proposed tariff structure will reduce revenues for the utility by 32 percent relative to current levels.

Moving away from ad-hoc determination of tariffs, we then examine if normative principles of electricity pricing can be used to set revenue-sufficient prices that are progressive and efficient. These principles recommend a two-part tariff comprising of variable and fixed costs. The variable cost of energy should be set to the social marginal cost of electricity (the cost of producing an extra unit of energy) for all households. The fixed cost should however vary across households and should be based on their willingness to pay for power.

The social marginal cost of electricity in Rajasthan is calculated using publicly available data on energy generated by power stations at 15-minute block intervals. The marginal cost of generating power is estimated using bidding data from a segment of the wholesale electricity market. The marginal cost of transmission and distribution losses and the external cost of carbon dioxide emissions as a result of thermal power generation is also calculated to estimate the overall social marginal cost of power in Rajasthan. In calculating these costs, the paper shows that including the marginal cost of carbon emissions in the retail price of energy can increase electricity prices by approximately 50%. This result indicates that given India’s current electricity generation mix, passing the full cost of carbon externalities to the retail price of electricity may lead to an impractical rise in the price of power.

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