1. Establishing Comparable Poverty Estimates in Serbia (and elsewhere…) Jill Luoto January 25, 2007 Western Balkans Poverty Analysis Course: World Bank

2. Goals Introduce an adaptation of the poverty mapping methodology that enables the prediction of new poverty estimates that are strictly comparable when otherwise incomparable welfare estimates exist Present brief summary of findings for Serbia Lead everyone in an exercise using the PovMap software on Serbian data

3. The Problem Estimating the evolution of poverty in Serbia over recent years is complicated by a change in official surveys –Living Standards Measurement Survey (LSMS) implemented in 2002 and 2003 –Household Budget Survey (HBS) implemented 2003-2006 The two survey instruments have different consumption modules. Some of the differences include: –LSMS included a list of item codes for consumption goods –HBS utilized open diary format –Different recall periods: 1 week in LSMS, 2 weeks in HBS –Different imputation procedures for housing rents and other expenditure items All in all, many differences in the way consumption and resulting poverty were estimated across surveys

4. Different Consumption Definitions Lead to… Incomparable Poverty Estimates –Lanjouw and Lanjouw (2001) offer real world examples where only slight changes in the definition of the consumption aggregate affect resulting poverty estimates dramatically For Serbia, the different consumption modules between LSMS and HBS have caused policymakers to generally consider their respective poverty estimates not to be comparable –This leaves open the question as to what happened to poverty in Serbia between 2003 and 2005

5. Possible Solution: Adaptation of the poverty-mapping methodology that aims to reconcile comparability of consumption definitions across surveys Other components of LSMS and HBS collect similar information –Geographic information –Household demographics –Asset ownership –Education and Labor Information Instead of imputing consumption definition from a survey into a census across space, impute from survey to survey across time –Necessarily ensures an identical definition of consumption across data sources –Implicit assumption that the relationship between consumption and its correlates remains stable over timeassumption

6. Methodology, In Brief Establish the completely comparable components between surveys Estimate a model of consumption in one survey using as explanatory variables only those correlates of consumption that are comparably defined across surveys Take the point estimates from that model of consumption and impute them into the other survey to estimate new consumption figures using same set of explanatory variables Derive new estimate of poverty using predicted consumption figures

7. Example: Two Surveys, Years 1 and 2 Find comparable survey components such that X 1 and X 2 have equal definitions, i.e., Examples of X variables: Household Demographics Education of HH Members Asset Ownership Housing Quality Indicators X 1 =X 2

8. X1X1 X1X1 X1X1 X2X2 Poverty Mapping typically imputes consumption definition across space from a survey to a census (within same year or short period of time) This adaptation imputes a consumption definition from one survey into another from a different year, i.e., across time

9. Implementation Gather all of the variables that collect similar information in LSMS and HBS (there are many…) –Generally 5 main categories for the types of information that are useful in describing a household’s welfare and commonly collected in surveys: »Geographic Information »Demographics »Education and Profession Variables »Asset Ownership/Wealth Indicators »Basic Health Information –Define new variable in each dataset that has same definition (and name) across datasets –Compare means, distributions of similar variables across surveys to ensure capturing same information

10. Finding common variables across surveys… HBS QuestionnaireLSMS Questionnaire

11. Restoring Comparability to Education Variables My variable definition matches exactly between surveys My Definition:

12. Importing data into PovMap We will be using subsets of pre-made Stata datasets from LSMS 2003 and HBS 2005 that have been matched and have identical variable names Go to: File  New Project  Name your project Each dataset must have a hierarchical household-level identifying variable that can be truncated to identify the cluster –Example: HID=32601Cluster=326

13. Stage 1: “Checker” Stage Compare distributions of variables across datasets If you think after this final stage of comparison that the variables are truly capturing the same information, “set” the variable to be included as a potential regressor Since we’re imputing from one survey to another from different year, it’s important to keep in mind that some variables are going to change over time, e.g., % owning cell phones

14. Summary Statistics for Comparably Defined Variables: Geographic Variables

15. Summary Statistics: Demographic Variables

16. You can also compare the entire distributions of similar variables across data sources

17. Summary Statistics: Education and Profession Variables

18. Summary Statistics: Housing Quality Indicators

19. Summary Statistics: Durables Ownership Variables

20. Stage 2: Building a Consumption Model You've chosen all of the potential explanatory variables after all phases of screening (comparing surveys, comparing distributions) and now you move on to building your model of consumption Categorical variables are translated into a sequence of dummies Build models stepwise or “intuitively” choose explanatory variables using OLS Aim for highest R 2 possible to best capture variation in household welfare levels Simultaneity and Omitted Variables Bias are not important for our purposes

21. Estimate consumption on subset of variables comparably defined across surveys; aim for highest R 2 Regression results from LSMS 2003

22. Stage 3: Cluster Effects Decompose the error term into a cluster effect and an idiosyncratic household effect: –This stage deals with modeling the cluster effect Since disturbance terms are likely to be correlated within clusters (due to unobserved geographic and other factors beyond those already included as regressors), this stage accounts for this by estimating a cluster random effect If you click on the "no locational effect" button, you take away this cluster effect from your estimation –Underestimated standard errors

23. Stage 4: Idiosyncratic Model Here, still using the base survey data, you are trying to model the heteroskedasticity of the household idiosyncratic effect to allow it a more flexible form This stage tries to model the variance in the household-specific error terms as functions of the included X variables and combinations of variables Can use stepwise modeling or basic OLS or any other method to choose the explanatory variables that best explain variation in the household idiosyncratic effect Generally very low R 2 ’s in this stage (it’s all unobserved variation); 0.01-0.02 is sufficient

24. Stage 5: Household Effects Shows you a plot of the residuals from the model of the idiosyncratic household level error terms The “Prediction Plot” generally shows that your predictions aren’t so great here Empirical distribution of residuals can be compared to the normal and t-distributions (of varying degrees of freedom)

25. Stage 6: Simulation Here, we need to simulate the residual terms (both the cluster effect and the household idiosyncratic effect) since they are necessarily unknown in the latter survey (or census) Distributional forms: You can either impose a normal distribution or allow for a more flexible semi- parametric distributional form using information from the predicted residuals from base data Choose the level of aggregation of your poverty estimates Choose poverty line, household size variable, and poverty indicators of your choosing Go!

26. Stage 7: Results Compare your resulting estimates of poverty with the baseline estimates from first survey

29. Conclusions For Serbia, this exercise suggests a gradual decline in poverty between 2003 and 2005 Resulting poverty headcount estimate of 7.5% based on models of consumption from both LSMS 2002 and LSMS 2003 –Lower than official estimate of 9.1 for 2005 based on consumption module of HBS –Nearly 30% drop in poverty from LSMS 2003 headcount estimate of 10.5 if results are believed This methodology can be used in a variety of settings to restore comparability of surveys to estimate evolution of poverty over time within a country or region Download the PovMap software at: http://iresearch.worldbank.org/PovMap/index.htm