DATA FOR EVIDENCE-BASED POLICY MAKING Dr. Tara Vishwanath, World Bank

More data, better data  More and more countries recognize the value of data collection  Moved from simply collecting data for national accounts and for understanding broad macroeconomic aggregates to detailed individual, household, community, firm, facility-level data  Enriched our understanding of how to collect good data  What is good data?  Accurate  Reliable  Relevant and timely  Useful for policy imperatives  Used for policy making

Household surveys and the World Bank Organizations like the World Bank have worked with many countries on standardization for comparability of data and tools for better measurement of data  Focus this talk on the LSMS, which is an integrated, multi-topic household survey  Started in 1980s by WB + academia + practitioners, surveys been done in over 40 countries  Collaborated with Morocco in the early sample of countries  Since then, many innovations

Why multi-topic “LSMS” survey?  Useful to study household behavior, welfare outcomes, and their interactions with government policies  Measure and monitor all relevant welfare indicators (demographic, health, education, occupation, income, expenditure and consumption)  Define poverty lines and establish poverty profiles  Explain and model the factors underlying poverty, to guide policy programming and analysis

Innovations in the LSMS  Methodological and technological experiments aimed at increasing current knowledge on survey methodologies.  improve the measurement of core indicators in LSMS surveys  develop methods for expanding the areas of policy that the LSMS surveys can cover ( country specific)  improve the quality of the data that is generated either substantively or by improving its accuracy, relevance, timeliness: self-reported health measures vs direct measurement  Experiments done or underway in: finance, labor, consumption, migration, subjective welfare, migration

Technological advances support better data collection  GPS and GIS  Much better understanding of spatial story  Facility and infrastructure maps: linking roads, schools, markets, towns to households  Web-based MIS with decentralized data collection  Computer-assisted field entry (CAFÉ)/Computer- assisted Personal Interviews (CAPI):  Increased Data Accuracy: Minimizes human error  Instant Data Access  Cell phones: Re-contact information for panels and data validation, MIS through SMS

Making the most of data: Good practice  Linking datasets through unique and consistent identifiers  Example: Linking EMIS, PETS, LSMS, test score data on schools in the country  Harmonizing sampling methods to link across surveys  DHS and LSMS in a country generally follow different sampling strategies: Linking the two and harmonizing sample gives a representative snapshot of ALL HD indicators as well as measures of household welfare

Poverty maps: Good practice  Standard practice: Use LSMS data with unit record census data to produce poverty maps  WB worked with many countries for poverty mapping including Morocco  Typically, precision of the poverty map and the level of disaggregation depends on the extent of common variables between census and LSMS.  With experience, countries are trying to improve this precision by including more common variables Example: India, China  Key tool for spatial targeting  For individual targeting, need more information

Panel data: Good practice  Panel data for understanding dynamics of growth, poverty, employment mobility, and the effects of shocks  Movements in and out of poverty  Determinants of vulnerability  Labor market dynamics  In the absence of understanding these dynamics, targeting government programs becomes a challenge  Example: With a single cross-section, we cannot predict the movers in and out of poverty

Data for impact evaluation  Typically impact evaluations require stand alone surveys  Regular, panel LSMS type surveys can be used for retrospective evaluations of policy changes, exogenous shocks….  Can build in innovative policy experiments within planned survey rounds  Oversampling target areas or populations prior to implementation of interventions  Depending on level of representation, can build in baseline and follow-up into LSMS-like surveys

Data for Policy Evaluation  Micro-data ( eg, LSMS) is useful for evaluating policy impacts on poverty and welfare outcomes  In the absence of real-time data, simulations provide a good alternative to understand policy impacts  Example: WB development a micro-simulation model to assess impact of global economic slowdown on employment, poverty across regions, demographic groups  Better survey data aids micro-simulations

Using data for policy  Good data aids evidence based policymaking  Many countries enrich debate and consensus building for policy through data dissemination  Mexico, US, are key examples of online dissemination  This has helped continuous analyses by the community of researchers and practitioners which ultimately aids good policy

Merci beaucoup pour votre attention!