Elsevier

Energy Economics

Volume 30, Issue 2, March 2008, Pages 517-546
Energy Economics

Fuel demand elasticities for energy and environmental policies: Indian sample survey evidence

https://doi.org/10.1016/j.eneco.2006.10.014Get rights and content

Abstract

India has been running large-scale interventions in the energy sector over the last decades. Still, there is a dearth of reliable and readily available price and income elasticities of demand to base these on, especially for domestic use of traditional fuels. This study uses the linear approximate Almost Ideal Demand System (LA-AIDS) using micro data of more than 100,000 households sampled across India. The LA-AIDS model is expanded by specifying the intercept as a linear function of household characteristics. Marshallian and Hicksian price and expenditure elasticities of demand for four main fuels are estimated for both urban and rural areas by different income groups. These can be used to evaluate recent and current energy policies. The results can also be used for energy projections and carbon dioxide simulations given different growth rates for different segments of the Indian population.

Introduction

The main objective of this paper is to estimate the income and price elasticities of household demand for different kinds of fuels in India. There are a number of motivations for this. Energy is an important necessity for any household. In India the households need to choose not only how much but also which fuel to use. These decisions can have important consequences for the household budget, time allocation and health. They can also lead to negative environmental externalities at local, regional or global level. Price and income elasticities of demand are important for the choice of domestic energy policies. They are also useful in the context of energy policies for greenhouse gas abatement.

Given the policy importance of these elasticities, it is striking that there is such a dearth of reliable and readily available estimates.2 Of course, several studies have examined the elasticities of commercial fuels like electricity and LPG in India. However, though some studies have examined the elasticities of fuelwood and also the substitution between fuelwood and commercial fuels, surprisingly few studies have done rigorous analysis. Earlier studies that attempted to analyse fuel demand in India ranged from large-scale macro planning exercises to local household case studies. There was interest in estimates of elasticities for different kinds of fuels as part of macro planning exercise, such as the Energy Survey of India Committee (1965), The Working Group on Energy Policy (1979), The Advisory Board on Energy (1985), The Energy Demand Screening Group (1986), The Rajadhyaksha committee of power sector planning (Gadgil et al., 1989) and the Planning Commission (1998). However, the main limitation of all the studies at macro level was that the projections that were made only took into account the aggregates such as population growth rate, increase in GDP, urbanisation and technological advancements. The fundamental problem with these studies is that although macro factors can influence energy consumption patterns indirectly, the actual determinants of household energy consumption are found at the household level. Aggregate fuel demand is made up by the day-to-day decisions at the household level. These decisions are affected by budget and time constraints of the household, their opportunity costs of time, the relative accessibility of fuels (relative prices) as well as social and cultural factors. Given such a perspective, it is obvious that it is e.g. not only GDP growth that matters but also its distribution.

A second group of studies estimated the consumption of biofuels mostly for rural regions (e.g. Joshi et al., 1992). Although surveys of fuelwood consumption at the regional level are an improvement over macro level studies, as the fuel consumption mix is different for different agro-climatic zones, the estimates give only consumption per capita for rural areas. Some studies addressed the urban energy patterns and only some of these studies analysed the determinants of urban energy demand (Ray, 1980, Alam, 1985, Macauley, 1989, Dunkerley et al., 1990, ESMAP/UNDP, 1992, ESMAP (Energy Sector Management Assistance Programme), 2001). Other studies have looked into various other aspects of urban fuel usage. Reddy and Reddy (1983) made a case study of fuelwood use in Bangalore, India. The studies by Dunkerley et al. (1990) and Bowonder and Unni (1988) did not estimate the demand for fuelwood or other fuels but looked at consumption and prices of fuelwood for Indian cities in the aggregate. Soussan et al. (1990) analysed in a comprehensive study the fuelwood combustion practices in an urban context. Turare (1998) used secondary data to analyse the criteria behind choice of domestic fuel. Alam et al. (1998) too is an investigation into the efficiency aspects of urban domestic fuel choices. Barnes et al. (2005) looked at aggregated energy demand in 46 cities in 13 different countries and is the most comprehensive study of urban fuel in the developing country context to date. A more recent study by Gupta and Köhlin (2006) analysed the preferences for domestic fuel for the Indian city of Kolkata.

A third group of studies examined the consumption of fuelwood in different areas by controlling for income, size of households, landholdings, type of profession, agro-climatic zones, season, accessibility of forests etc. While some studies concentrated on the variation in consumption of fuelwood with different income and landholdings, others studied the consumption in different seasons. The studies are scattered across the country and it is very difficult to make meaningful projections for policy analysis. Some studies are based on more formal household models that have the potential to give the elasticities of interest for policy (see for instance, Amacher et al., 1993, Amacher et al., 1996, Pitt, 1985, Bluffstone, 1995, Köhlin and Amacher, 2006, Heltberg et al., 2000). However, as such studies are very few in number, and only the latter two use data from India, extrapolations cannot be made for the entire country in order to make meaningful policy analyses.

To get reasonably accurate fuel elasticities for a country as big and diverse as India, a lot of time and money need to be spent in obtaining information on fuel use and household characteristics, which is a colossal task. In India, the National Sample Survey Organisation (NSSO) collects information on quantity and expenditure on various commodities for a representative sample of the country. Expenditure and quantity of various fuels are among these commodities. One of the advantages of NSSO data is that even fuelwood collected for free is accounted for by imputing some value on it. In countries like India where majority of the rural people collect fuelwood for free, ignoring these values can result in biased assessments. This paper makes use of such a data set in order to estimate the price and income elasticities of fuelwood. Using a sample that truly reflects the whole population of India has made it possible to overcome some of the weaknesses of the previous approaches. The large sample does not only make it more representative, but it also facilitates disaggregation of the analysis to relevant sub-samples such as different income groups and for urban and rural areas separately. This gives us the opportunity to investigate energy transition in general, and the energy ladder hypothesis in particular, for the country with the highest domestic consumption of bio-energy in the world by estimation of expenditure elasticities of demand. We also analyse the own-price elasticities of different income groups and address the scope for energy substitution by estimation of cross-price elasticities of demand for various fuels.

The estimations are made using the linear approximate Almost Ideal Demand System (LA-AIDS), proposed by Deaton and Muellbauer (1980), on household data for the year 1999. Instead of income we consider the total household expenditure as a proxy. The advantage of the LA-AIDS model is that the demand system is linear in the structural parameters. The LA-AIDS model has been widely used for analysing demand for various commodities in India as well as in other countries. In this study we use a two-stage budgeting process to obtain the elasticities of different categories of fuels. In the first stage it is assumed that the household decides how much to spend on fuel and non-fuel commodities and in the second stage they allocate expenditure to different categories of fuel. Such two-stage budgeting has been used earlier to analyse demand for meat (Ealas and Unnevehr, 1988, Gao et al., 1996), fish (Cheng and Capps, 1993, Dey, 2000), demand for nondurable commodities (Carpentier and Guyomard, 2001) etc. However no study has used such an approach to estimate fuel elasticities. This study is thus an empirical contribution to the domestic energy literature.

The plan of this paper is as follows: Section 2 presents the two-stage budgeting model. Section 2 lays out the empirical specification. Section 3 discusses the issues in estimation of the model including the methodology to account for zero expenditure. Section 4 describes the data used in the study. Section 5 presents the empirical results and Section 6 concludes with the policy implications.

Section snippets

Two-stage budgeting model

In this paper we use a two-stage budgeting process. Under two-stage budgeting, expenditure decisions on fuels, and all other non-fuel goods, can be represented by a recursive structure where the household first allocate income between fuels and non-fuels and then at a second stage chose its disaggregated fuel expenditures. The theoretical framework for such a two-stage budgeting approach has been well established in the literature. The underlying theoretical model used for our empirical

Estimation of the model

We estimate the model using SAS (version 9.1). In line with the model presented in the previous section, we assume that the consumer's utility maximization decision can be decomposed into two separate stages, i.e. in the first stage, the total expenditure is allocated over broad groups of goods (here fuel and non-fuel) and in the second stage, the group expenditures are allocated over subgroups and specific commodities. For the first stage we estimate a linear relationship between the budget

Description of data

The data used for the analysis are taken from a comprehensive survey carried out by the National Sample Survey Organisation on consumption of Important Commodities in India (NSSO, 1999). The data comprises information collected from 68,961 rural households and 50,166 urban households, covering the entire country (26 states and 6 union territories). Such surveys are carried out every 5 years and the present study uses the data from the 55th round (for the year 1998–99). The survey includes

Empirical results

The estimations behave overall very well with high significance levels and expected signs. Most variables are significant at the 1% level throughout the different sub-samples. In Table 3 we present the results from the Engel estimation, corresponding to Eq. (15) in Section 3. Only a few of the explanatory variables are not significant at the 5% level. The results defy early attempts to explain energy consumption only with population and income growth.5

Conclusions and policy implications

Energy is a necessity. Given its importance for household welfare, public investments and environmental considerations it is surprising that not more formal analyses have been carried out for developing countries to analyse income, own-price and cross-price elasticities of demand for a full set of domestic energy sources, including fuelwood. The proliferation of national sample surveys might help to address this gap. In this paper we have shown that the Indian National Sample Survey

Acknowledgements

Financial support of this work from the Swedish International Development Cooperation Agency is gratefully acknowledged. The authors would like to thank Prof. Lennart Flood, Prof. Thomas Sterner, Dr. Fredrik Carlsson, Prof. Olof Johansson Stenman and Dr. Renato Aguilar, all at Department of Economics Göteborg University, for providing very valuable insights. The authors would also like to thank two anonymous reviewers and the editor Prof. Richard Tol for their valuable suggestions.

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