How useful are integrated household
            survey data for policy-oriented analysis of poverty Lessons
            from the Cote d'Ivoire living standards survey
The author reflects on the pros and cons
            of using integrated household survey data in empirical
            analysis aimed at providing a quantitative basis for policy
            decisions affecting welfare, poverty, and the fulfillment of
            basic needs. The experience examined is that of using four
            years of data from the Cote d'Ivoire Living Standards
            Survey (1985-88) to link changes in poverty and welfare to
            macroeconomic trends. The author groups the lessons learned
            from this work around four themes. Survey content: when
            survey data are rich, transparency of methodology is
            important. It is essential that analysts provide explicit
            information about how their income and spending aggregates
            were constructed. These aggregates must be deflated with a
            regional price index, but prices should be collected
            separately from household survey data. Data on household
            spending and basic needs fulfillment are the key information
            for poverty analysis. Sample size and design: bigger and
            simpler is better. The author recommends increasing (at
            least double) sample size in future living standards
            surveys; this could be done without increasing the cost of
            the survey by reducing or eliminating the income modules of
            the questionnaire. It is important to involve analysts and
            policymakers in survey design. They need to identify up
            front, using current knowledge, the important socioeconomic
            and target groups on which the survey must be able to
            report. The sample designer can then compose the sample in
            such a way that certain groups will be undersampled and
            others oversampled, to make the analysis of the resulting
            sample as useful as possible. Frequency of data collection:
            the author recommends that an integrated survey of the CILSS
            type be undertaken every four or five years, to provide
            benchmark data and to permit in-depth analysis of household
            behavior and response to policy, if the country has the
            capability to fully analyze the data. In the intervening
            years, a much simpler collection of household spending and
            basic needs data can be used to monitor changes in welfare
            and poverty. The role of panel data: to be really useful,
            panel data collection should be extended over longer periods
            than two years, although this increases the costs and
            difficulties of finding the same households. If a country
            undertakes an integrated survey every four to five years and
            a lighter monitoring survey in between, a small, parallel,
            panel survey could be conducted. The monitoring sample and
            the panel sample should be drawn from the same master sample.