13 whether households not using natural gas had the option of being connected. To the extent that these variables are correlated with variables that are included in the equation—for example, better-off households may be closer to LPG outlets—the estimates of their coefficients will be biased. Another item of information not captured by household surveys is the cost of connecting to various forms of energy. For LPG, upfront costs include both the cylinder deposit fee (or cylinder purchase price) and the purchase price of an LPG stove; the connection cost is considerably higher for natural gas.

- (2) <u>Missing observations. If there is a pattern to the availability of answers, there can be a bias.</u> Appendix 2 presents information on the sample size at three levels: the total number participating in the survey; the number with complete information on expenditure items, household size, and household weights; and those included in Heckman-style modeling.

- (3) <u>Errors of measurement. Aside from recall errors, there are several other sources of errors.</u> In countries without quantity data, the greater the variation in actual prices paid from the national average prices, the larger will be the error. In the countries with quantity data, prices of fuels faced by households cannot be calculated for households that chose not to use a given fuel, and this study uses the average of the prices paid by purchasing households. This approach may be particularly problematic for biomass, for which a commercial market may not exist (dung and agricultural residues) or does not exist in some areas (firewood); even if the fuel is sold, the price may vary greatly from location to location. Finally, the problem of under-estimating the number of user households on account of non-purchase during the survey period was clearly serious in Mexico. The impact of errors of measurement depends on the extent to which the error is correlated with other variables included in the statistical modeling.

- (4) <u>Endogeneity. Where there is a feedback from the purchase of LPG to any of the</u> explanatory variables, the latter become endogenous and estimation using the Heckman model would be biased. As with conventional demand systems, income and prices are not expected to be endogenous with respect to the quantities purchased by the household, and other variables, such as those relating to household size, the gender and age of the head of household, and education are also likely to be exogenous. The decision to use another fuel, including collected biomass, would be endogenous and hence cannot be included as independent variables.

In what follows, quintiles are calculated for households ranked by total expenditures per capita and grouped so that each quintile contains the same number of _people_ . Urban and rural quintiles are drawn from the nationally based quintile groups so that, for example, the bottom quintile has far more rural people than urban and the share of urban people increases with quintile level.

# **4.2 Basic statistics by country**

The national household surveys in Guatemala, India, Indonesia, Kenya, Pakistan, and Sri Lanka had expenditure and quantity data for LPG as well as a common set of socioeconomic data. Appendix 2 provides brief descriptions of the surveys and how data are handled.

The nature of the LPG market, as well as that for competing fuels, at the time of the survey varied markedly from country to country, particularly with respect to pricing policies.