10 significant and negative in the unweighted estimator but positive in the weighted estimator, and Pakistan where the coefficient for urban residence (which is arguably the closest proxy to availability of natural gas among the available variables) was insignificant for selection if unweighted and significant and negative if weighted.

The selection of variables in the two equations is suggested by other studies of fuel choice (ESMAP 2003a; Gupta and Köhlin 2006; Pundo and Fraser 2006; Mekonnen and Köhlin 2008) and by standard demand analysis. The independent variables investigated in all ten countries— except household-level fuel prices, which were available only in the six countries with quantity data, and a few other items indicated below—are the following:

- (1) <u>Total household expenditure. Surveys do not always provide information on income and</u> expenditure is assumed to provide an adequate proxy. Three standard corrections are made to total expenditure, as described below. The coefficient for this variable is expected to be positive in both equations.

- (2) <u>Household-level price of LPG (first six countries). That LPG is a homogeneous</u> commodity avoids the well-known problem that households may vary quality when prices change, resulting in the implicit price changes not measuring actual price changes fully. For households that did not buy LPG (and hence did not report expenditure or quantity), the price faced is set equal to the mean of the prices paid by those households reporting positive expenditure. The coefficient is expected to be negative in both equations.

- (3) <u>Household-level prices of other fuels (first six countries). The prices of other fuels where</u> available are tested in both equations: kerosene in every country; firewood in every country except Indonesia; charcoal in India, Kenya, and Pakistan; city gas and coal in Indonesia; and the aggregate of coal and charcoal in Guatemala. Again the price faced by non-purchasing households is set equal to the mean price paid by purchasing households. However, if the number of households using a potentially competing fuel is too small, most of the households will be estimated as facing an identical price and this is unlikely to be statistically significant in the Heckman modeling equations. The coefficients in the two equations should be positive.

- (4) <u>Number of rooms and house or car ownership. The total expenditure variable is unlikely</u> to capture the economic status of the household fully, especially as auto-consumption of food is included while expenditures on large durables are excluded. All surveys collected information on house and car ownership, and nine out of ten on the number of rooms or floor area (the survey in India did not ask for this information). Those households with more rooms, or who own their own house or cars, may be more asset-rich for a given level of total expenditure. These variables could be positively associated with the decision to use LPG and the amount consumed.

- (5) <u>Household size and size squared. At the same household expenditure level, the larger the</u> household, the greater the pressure on other expenditures and less willing the household may be to spend cash on LPG, but the amount consumed by those who have decided to use LPG may increase with increasing household size, although economies of scale in LPG use moderate the rate of increase with size.

- (6) <u>Engaging in agriculture. Households engaged in agriculture may have greater access to</u> biomass, and hence less likely to select LPG in the first place or consume less of it. The