9 Three unportant caveats should be noted at the outset. First, some key variables may be difficult to analyze in a houses o!d survey. For instance, price elasticities are difficult to measure because, in many cases, there is very small variation at the time of the survey. Second, household data may be inappropriate for analyzing macro-issues. Household surveys are for understanding patterrs at the micro-level, especially the relationships between different types of household characteristics. Just because one finds, for example, a relationship between expenditure and energy use at the micro-level does not mean that the same relationship will hold at the village or national level. Macro-structural changes in energy use may be better explained by migration patterns, government policy or changes in the industrial structure of the country, rather than the characteristics of a household. Third, the level of detail which a household energy survey should achieve depends on the informational requirements and implementational capabilities of policymakers and energy planners. Thus, in designing a survey, one should not lose sight of the impact which a marginal increase in valuable information may have on feasible policy decisions. Given these overal! onsiderations, one can proceed to the mechanics of actual household energy surveying.

# Sample Design

Sample design is an important procedure which usually makes use of formal probability theory. While it may be necessary to deviate from this process for practical reasons, this can result in scicction of an unrepresentative sample which may render subsequent results unreliable when extrapolated to the population as a whole. Some of the factors which may limit the application of statistical techniques include: (a) lack of a master sample or data on the overall population from which the sample is to be drawn; (b) physical inaccessibility of population sub-greups; (c) language barriers; and (d) insufficient financial or human resources. Whether or not statistically valid samples are drawn, they should be designed so that they reflect the survey's objectives, especially household characteris¶ics which are thought to be relevant to energy consumption.

Samoling Procedures Depend on Goals and Resources. There are several types of sampling procedures which can be used in surveys including random, stratified random, matched pair, selection according to a quota, and even a total census. Generally, a random, representative sample is necessary to determine total household energy consumption for a country or a region. To draw such a sample, reliable and comprehensive data must exist on the size, location and key characteristics of the population one wants to study must exist. When this is not aval able, creative alternatives may be found. For example, in the 1987 Burkina Faso urban household energy survey, census listings were unreliable so the following methodology was used: (a) cities were divided into small geographic areas; (b) a random sample of these areas was chosen for the survey, (c) a household census was conducted in the chosen areas; and (d) a random sample was drawn from the list'.d households.

Tiae focus of the survey can also help determine whether or not a random sample of the population is necessary. For example, if an evaluation of energy consumption with and without an improved stove is to be done but the stove has not significantly penetrated the market, then a representative sample selected randomly from the population may not have enough cases of the stove to permit adequate analysis. In a recent study of rural electrification in India, a matched pair design was chosen. Households with electricity were concentrated in the high income groups and those without electricity were mainly in low income groups. With a random sample, results would have been biased because there would be no control groups (poor households with electricity and rich households without it). Therefore, the sample selection matched households from 6 occupational groups (large farmers, medium farmers, small farmers, trade and landless laborers), and households with and without electricity were selected from each group. Finally, choice and definition of the sample unit is important. Definite samples can be drawn if a household is defined as family members living together or all people who live and eat under the same roof. Whichever definition is chosen, it should be clearly stated in the presentation of survey results.

Sample Stratification Should Be Considered. Stratification is a partition of the surveyed population so that data can be disaggregated and relevant factors can be analyzed in greater detail. Household size, geographical location and wealth are often used for stratification in energy surveys, meaning that within the sample a distinction is made between different income or expenditure levels, region and the number of people in each household. Information on income and/or expenditure may not be reliable because of respondent suspicions concerning official use of the data for taxation purposes. If this is the case, proxies can be used to categorize