1. Comparative Living Standards Project Kinnon Scott Diane Steele DECPI, April 27, 2010

2. Two Products Meta Data Describing Content of LSMS Surveys Meta Data Describing Content of LSMS Surveys Comparative Data Base of LSMS actual data (variables/indicators) Comparative Data Base of LSMS actual data (variables/indicators)

3. Why? Increase the use of LSMS data Increase the use of LSMS data Meet expressed demand from Meet expressed demand from Existing users Existing users Potential users Potential users

4. What are LSMS surveys? Multi-topic Household Surveys Multi-topic Household Surveys Relationships between/among topics Relationships between/among topics Strong money-metric welfare measure Strong money-metric welfare measure Demand driven Demand driven relevant to a country at given time (comparability issue) relevant to a country at given time (comparability issue) Coverage has large gaps Coverage has large gaps Timing is not consistent Timing is not consistent Designed for policy analysis and research Designed for policy analysis and research

5. Getting Data Used Document and archive the 60+ LSMS survey data bases Document and archive the 60+ LSMS survey data bases Improvements in data access policies/agreements Improvements in data access policies/agreements Provide data and documentation to researchers Provide data and documentation to researchers Each data set has Each data set has Data set (3 formats) Data set (3 formats) Basic information document Basic information document Questionnaire Questionnaire Additional Documentation Additional Documentation All in electronic format (and hardcopy) All in electronic format (and hardcopy) In-country activities (collaboration,training) In-country activities (collaboration,training)

6. LSMS Web Site

7. Key problems in further dissemination/use of data 1. No easy way to determine the content of all the surveys 1. No easy way to determine the content of all the surveys 2. Not accessible to non-specialists (trained in micro-data analysis) 2. Not accessible to non-specialists (trained in micro-data analysis) 3. Start up costs for doing cross- country analysis 3. Start up costs for doing cross- country analysis So  how to meet the needs of these users, researchers and non- researchers?

8. Problem 1: Researchers need to know which surveys have the topics they need Researchers need to know which surveys have the topics they need There is no source for this There is no source for this Need to go through all questionnaires (or consult ‘institutional memory’ Need to go through all questionnaires (or consult ‘institutional memory’

9. Solution 1: Meta Data of LSMS Surveys Create web-based tool containing meta data describing the contents of existing LSMS data sets Create web-based tool containing meta data describing the contents of existing LSMS data sets Searchable Data Base Searchable Data Base Update continually Update continually May need to add new details (LSMS- ISA) May need to add new details (LSMS- ISA)

10. Meta data search engine site

11. Key Decisions: Content Topics to include Topics to include Identify the universe Identify the universe Level of disaggregation Level of disaggregation Module (Education) Module (Education) Submodule (preschool, general, training) Submodule (preschool, general, training) Topics (preschool costs, type, distance) Topics (preschool costs, type, distance) Variables (cost of supplies, cost of transport, cost of food) Variables (cost of supplies, cost of transport, cost of food) Interlinking Interlinking (ed->level->costs) vs. (exp.->education  level (ed->level->costs) vs. (exp.->education  level

12. Key Decisions: Search Results Actual question vs Questionnaire? Actual question vs Questionnaire? Depends on purpose Depends on purpose ADP, IHSN  question banks ADP, IHSN  question banks Consistency in survey design Consistency in survey design Questionnaire development Questionnaire development LSMS- research data sets LSMS- research data sets Context matters Context matters Need to know respondent, ages, additional information Need to know respondent, ages, additional information

13. Development Path Drafted list of topics (subtopics) Drafted list of topics (subtopics) Created first web interface Created first web interface Tested Tested Substantially revised the interface Substantially revised the interface Revised and expanded the list of topics Revised and expanded the list of topics ‘Populated’ data base ‘Populated’ data base

14. Problem 2: Many potential users do not have skills to analyze micro-data Many potential users do not have skills to analyze micro-data Many potential users do not have time to analyze multiple data bases Many potential users do not have time to analyze multiple data bases Under-utilization of the data Under-utilization of the data

15. Solution 2: Comparative Data Base (CLSP) Database of a subset of variables/indicators from LSMS Surveys Database of a subset of variables/indicators from LSMS Surveys Focus is on comparability across countries Focus is on comparability across countries Detailed documentation Detailed documentation Allow ‘ on-the-fly ’ tables/statistics within and among countries Allow ‘ on-the-fly ’ tables/statistics within and among countries Respecting sampling (weights, representat.) Respecting sampling (weights, representat.) Respecting confidentiality Respecting confidentiality

16. Key Decisions: Content List of variables List of variables Needs vs Comparability Needs vs Comparability Present vs Future Present vs Future Define ‘ Comparable ’ Define ‘ Comparable ’ Standard Definitions for Indicators Standard Definitions for Indicators When not to include a survey (100% of all variables, 80%, 10%?) When not to include a survey (100% of all variables, 80%, 10%?) Test set of data- (issues in certain regions, multi-year surveys) Test set of data- (issues in certain regions, multi-year surveys)

17. Evolution Consumption Aggregates Consumption Aggregates Best possible, best comparable, existing Best possible, best comparable, existing Completely non-intuitive to users Completely non-intuitive to users Requires redefinition of poverty lines Requires redefinition of poverty lines Stick with existing consumption aggregates (well documented) Stick with existing consumption aggregates (well documented) Use existing poverty measures Use existing poverty measures

18. Evolution On-the-fly analysis On-the-fly analysis Basic statistics can be constructed by user Basic statistics can be constructed by user Need for advanced statistical ability Need for advanced statistical ability Using public domain statistical software- all on our server (Qinghua Zhao’ adaptation of R) Using public domain statistical software- all on our server (Qinghua Zhao’ adaptation of R) Need for very straightforward abilities Need for very straightforward abilities Created some ‘canned variables’ Created some ‘canned variables’ Commonly used/mis-used Commonly used/mis-used Documentation Documentation Tie to output Tie to output

19. Comparative data base site

20. Evolution Platform to build on: Platform to build on: RIGA: with FAO, collaborated in the construction of income aggregates and variables RIGA: with FAO, collaborated in the construction of income aggregates and variables LMD: with PREM and DEC integrating labor variables LMD: with PREM and DEC integrating labor variables Integrate or stand alone Integrate or stand alone

21. Development Path Built on Built on Sub-national data base Sub-national data base Africa Standardized files Africa Standardized files DDP DDP Not interactive Not interactive Costly to user Costly to user Not maintained Not maintained Created new interface completely Created new interface completely Iterative process Iterative process

22. Lessons learned Lessons learned Lessons learned Search engine for data sets very- maintaining/ updating needs to be done Search engine for data sets very- maintaining/ updating needs to be done Time and resources costs (LIS example) Time and resources costs (LIS example) Comparability/harmonized is easier said than done Comparability/harmonized is easier said than done Learning curve Learning curve Documentation of process, decisions Documentation of process, decisions Funding from KCP and GAP Funding from KCP and GAP