<!-- Start of picture text --> Policy Research<br>Poverty Analysis and Policy<br><!-- End of picture text --> <!-- Start of picture text --> WPS 1079<br><!-- End of picture text -->

- This paper- a product of the Poverty and Social Policy Division, Africa Technical Department is part of the output of the research project "Poverty and the Social Dimensions of Structural Adjustment in Cote d'Ivoire, 1985-88: A Policy-Oriented Analysis" (RPO 675-26). Copies ofthis paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Elena Vitanov, room J2-24 1, extension 38400 (January 1993, 23 pages).

Grootaert 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 CMe d'Ivoire Living Standards Survey (1985-88) to link changes in poverty and welfare to macroeconomic trends.

It is importan **t** o 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 rsulting sample as useful as possible.

Grootaert groups the lessons learned from this work around four themes.

_Survey content._ When survey data are rich, transparency of methodology is iniprtant. 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. Grootaert recommends increasing (at least doubling) 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.

_Frequency of data collection._ Grootaert recommends that an integrated survey of the CILSS type be undertaken every four or five years, to provide benchmark data and to pernit 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.

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