Harmonize or Perish! The Living Standards Measurement Study Gero Carletto Development Research Group

W ORLD B ANK G OALS 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

WB G OALS, I MPLICATIONS 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)

WB G OALS & 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)

Comparability of existing consumption data …

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

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

InstrumentSponsor CensusesUNFPA Income Expenditure /Budget Surveys (IES/HBS) Central Banks, IMF, NSOs Labor Force Surveys (LFS) ILO Demographic and Health Surveys (DHS) USAID Multiple Indicator Cluster Surveys (MICS) UNICEF Core Welfare Indicator Questionnaires (CWIQ) UNDP, DfID WB Africa Reg. Welfare Monitoring Survey (WMS) Stat Norway Statistics on Income and Living Conditions (SILC) Eurostat Comprehensive Food Security and Vulnerability Analysis 9CFSVA) WFP Integrated, Multi-Topic Surveys [Living Standards Measurement Study (LSMS), Integrated Surveys (IS), Family Life Surveys (FLS)] World Bank RANDNSOs

The “McNamara Anecdote” Need to understand living 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 The thinking behind the LSMS survey

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

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

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

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

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

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?

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)

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

Food Consumed Away from Home Food prepared away from home (meals and snacks) Consumed at home Purchased Grocery store Take away Received in kind Food assistance Another household Consumed away from home Purchased Commercial establish- ment (restaurant, bar, street stall…) School Received in kind Employer Food assistance Another household Source: Smith and Frankenberger (2012).

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)

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

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

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!

A closer look at Y components Wage earners

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

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

#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

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

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