Harmonize or Perish! The Living Standards Measurement Study

1. Harmonize or Perish!The Living Standards Measurement Study GeroCarletto Development Research Group

2. World Bank Goals • Jim Yong Kim announces new goals • End extreme poverty: the percentage of people living with less than US$ 1.25 a day to fall to 3 percent by 2030 • Promote shared prosperity: foster income growth of the bottom 40 percent of the population in every country • Recognition of large data gap • 75 percent of countries with “updated” household survey

3. WB Goals,Implications • Some key current data gaps: • Household income or consumption distribution data is up-to-date in about 50% of developing countries The stated goal: halve the poverty data gaps in developing countries by 2017, ensuring up-to-date data for 75% of all countries (containing virtually all the poor)

4. WB Goals & LSMS • More data, but just as important … • quality data • policy-relevant data • comparable data • cross-country and over time • Not only averages, but distributions (shared prosperity)

5. Comparability of existing consumption data … Reference period Mode of acquisition Source: “Assessment of the reliability and relevance of food data …”, Smith et al.

6. Instrument design & implicationsfor poverty • Beegle, Kathleen, Joachim De Weerdt, Jed Friedman, and John Gibson. 2012. “Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania.” J of Development Economics 98: 3-18

7. Heterogeneity in Surveys • Initial purpose of the survey drives the way survey is designed and implemented • Different agenda  Different instrument • An increasingly crowded field…

9. The thinking behind the LSMS survey • The “McNamara Anecdote” • Need to understandliving standards, poverty, inequality and the correlates and determinants of these- not just monitor. • Unit of analysis is the household, as both a consuming and producing unit • One survey collecting data on a range of topics is a more powerful tool for policy formulation than a series of single purpose surveys: the sum is greater than the parts • Farmers are diversified • Poverty and FS are multidimensional

10. The thinking behind the LSMS survey (cont’d) • Demand driven and country-owned • Priority often given to meeting the policy needs of each country, but with an eye to x-country comparability and accepted standards • Implications • no standard set of LSMS questionnaires: content, length and complexity varies by country • Questionnaire development- lengthy process linking data users, stakeholders and data producers • Capacity building, sustainability

11. A “typical” LSMS • Consumption-based welfare measure • Multi-dimensional poverty • Multiple instruments • HH, Agriculture, Community, Price, Facilities • Strict data quality control • “Intelligent” data entry, CAPI • Small sample • Pre-coded, closed-ended questions • Training, supervision • Documentation and Dissemination • Basic Information Document • Consumption aggregate .do files • Publicly available microdata

12. The LSMS today • Goal: ensure that the LSMS meets new demands for data and remains at the forefront of survey methodology • New demand -- new topics • Old topics with new focus (agriculture) • New technologies • Increased standardization • Four areas of focus • Data collection • Methodological Work • Tools, Resources for researchers/survey practitioners • Training and Dissemination

13. LSMS OUTREACH DATA RESEARCH LSMS-ISA Tanzania Uganda Ethiopia Malawi Nigeria Niger Mali Burkina Faso Non-LSMS-ISA Serbia Haiti Tajikistan … SURVEY METHODS ANALYSIS Training (LSMS course, e-learning) Survey Clinics Sourcebooks Technical Assistance Tools (CLSP, ADePT) *Gender *AgNut *Facts+Myths *Migration *Subsidies *Tracking poverty AG POVERTY/FS OTHER *Mig *Labor *Income *Credit Land Soil Inputs Skills Crops Lvstck SHWALITA Subj. Pov. ADVOCACY/DISSEMINATION

14. Lack of standards result in poor comparability! • Take Food Consumption … • Diary vs. recall • Household vs. individual • Reference period • Nomenclature (COICOP) • Bulk purchases • Non-standard units of measure • Food consumed away from home (FCAH) • Valuation of consumed own-production

15. Take diary vs. recall … • Diary often considered …. • Unfeasible (low literacy rate) • Too onerous for respondents • Too costly • Often, diary converts into short (2-3 day) recall … but no metadata! • Recall considered imprecise (telescoping, recall bias) • 7-day recall most frequent. Most feasible?

16. Can we improve on 7-day recall? • Creating a continuum between diary and recall: “SHWALITA, the sequel” • Bounding reference period • Assisting households to recall • Accounting for bulk purchases (annualization)

17. Can we improve on 7-day recall? • Creating a continuum between diary and recall: “SHWALITA, the sequel” • Bounding reference period • Assisting households to recall • Accounting for bulk purchases (annualization) • Food Consumed Away from Home • Increasing share of total food consumption

18. Can we improve on 7-day recall? • Creating a continuum between diary and recall: “SHWALITA, the sequel” • Bounding reference period • Assisting households to recall • Accounting for bulk purchases (annualization) • Food Consumed Away from Home • Increasing share of total food consumption • Non-standard Units of Measure (CAPI)

19. Non-Standard Units

20. What about non-food? • Lack of consistency even in number of components • Imputed rents • User value of durables • Health expenditures • List of 12-month items

21. What about income? “The practical and conceptual difficulties of collecting good income data are severe enough to raise doubts about the value of trying” A. Deaton (1997), p. 30

22. Income and Consumption • Income (Y) information important for other uses (besides poverty & inequality) • Livelihood strategies (income shares) • Productivity/efficiency analysis • Net buyer/net sellers, impact of high food prices • Consumption (C) preferred welfare measure in developing countries • More stable (short-term fluctuations) • Income harder to measure (self-employment) • Less incentive to mis-report • Y relatively neglected • Some components more troubling than others!

23. A closer look at Y componentsWage earners

24. A closer look at Y components (cont’d)Farm households

25. Measuring crop production

26. Measuring crop production • Farmers/HHs don’t keep records • Crops often harvested in small quantities over several months • Mostly consumed • Recall widely used but does not always work • Measured in non-standard units of varying size • Different units along the value chain, different states • Standards do not exist or not feasible • Need validation

27. #whatwillittake … to harmonize? • Friction bet/w country ownership/temporal comparability and x-country harmonization? • Not a DHS but more standardization is possible • Start with inventory of surveys (Olivier) • Mapping and influencing “pipeline” • Agreement on current standards • Consumption vs. Income • Food consumption • Method • Reference period • Disaggregation • Nomenclature (COICOP) • Non-food expenditure components • Imputations

28. #whatwillittake … to harmonize? • Enhanced coordination • Some “unflattering” examples … • Clearly defined mandates and responsibilities • Establish forum (IHSN, UNSC,…) • Methodological research to establish future, improved standards

29. “If you want to make enemies, try to change something”  Woodrow Wilson