1. How data quality affects poverty and inequality measurement PovcalNet team DECPI The World Bank

2. Outline Household survey data –Sampling, questionnaire design, interview methods, income/consumption aggregates –Non-response bias –Data processing and analysis –Overtime comparison Other data –National account (NA) data –Price data –PPPs and ICP data

3. Household survey data 1.Household survey design –Sampling –daily dairy vs. recall –different recall periods –different income/consumption modules –Non-response bias 2.Common problems on data processing 3.Common mistakes when calculating poverty measures

4. Household survey data: sampling Malawi 1997 and 2004 household survey

5. Household survey data: sampling Malawi 1997 and 2004 household survey more than 4000 households drop from 1997 sample

6. Household survey data: sampling Different sample size/frame will cause comparison problems. Vietnam 2010 survey vs. previous rounds India NSS: thick and thin rounds Indonesia and other countries China 2013 national household survey – census frame vs. legal resident registration –how to compare with previous rural/urban surveys?

7. Household survey data: daily dairy vs. recall Example of China SW poverty monitoring survey 1995-1996 1995 survey: one time recall method 1996 survey: daily dairy method 1995 mean income per capita: 854.56 Yuan 1996 mean income per capita: 992.74 Yuan Is there 16% increase in per capita income in one year?

8. Household survey data: daily dairy vs. recall Example of China SW poverty monitoring survey 1995-1996 1995 survey: one time recall method 1996 survey: daily dairy method 1995 mean income per capita: 854.56 Yuan 1996 mean income per capita: 992.74 Yuan Is there 16% increase in per capita income in one year? 10-15% increase is due to the switch from recall to dairy.

9. Example of China SW poverty monitoring survey 1995-1996

10. Household survey data: different recall periods Example of India NSS 55 th round

11. Household survey data: different recall periods Example of India NSS 55 th round Result: poverty estimates from NSS 55 are incomparable with previous years

12. Household survey data: different income/consumption modules Example of Honduras 1997 and 1999 surveys

13. Household survey data: different income/consumption modules Example of Honduras:

14. Different income modules?

15. Household survey data: different income/consumption modules Example of Ethiopia 2000 surveys:

16. Household survey data: different income/consumption modules Example of Ethiopia 2000 surveys: Reason: different consumption modules

17. Nonresponse bias in measuring poverty and inequality High nonresponse rates of 10-30% are now common LSMS: 0-26% nonresponse (Scott and Steele, 2002) UK surveys: 15-30% US: 10-20% Concerns that the problem might be increasing

18. Nonresponse bias in measuring poverty and inequality Compliance is unlikely to be random: Rich people have: –higher opportunity cost of time –more to hide (tax reasons) –more likely to be away from home? –multiple earners Poorest might also not comply: –alienated from society? –homeless

19. Probability of being in UHS in 2004/05 plotted against income (n=235,000) 19

20. Common problems on data processing 1.Income/consumption aggregates 2.Valuing income in kind 3.Missing value 4.Outliers

21. Income/consumption aggregates

23. Missing, zero and outliers Never mix missing value and zero; Examples from LAC labor force surveys Outliers: check carefully and always keep original records –Income by sources –Sub components of consumptions

24. Argentina (urban) 2001-2010

25. Annual income growth of bottom 40% (circa 2005-10)

26. Annual per capita GDP growth is less than 1% during same period

27. Missing and outliers Examples from Colombia 2000 survey – 7% are zero income

31. Welfare indicator: income vs. consumption – LAC

32. More than 14% of people with zero income!

33. Welfare indicator: income vs. consumption – East Asia

34. Common mistakes on calculating poverty measures 1.Ranking variable 2.Weights Household weight Sampling weight 3.Outliers and missing 4.Adult equivalent

35. National Account data GDP Private consumption Population CPI – sample, weights change overtime Spatial price Currency change over time

36. PPPs and ICP data ICP rounds: 1985, 1993, 1996 and 2005 difference in coverage PPPs –PWT PPP and the World Bank’s PPP –GDP PPP and consumption PPP PPPP: PPP for the poor

37. Biases in 2005 ICP “Urban bias” in price surveys –China: 11 cities; reasonably representative of urban areas but not rural –Similar problems for Argentina, Brazil, Bolivia, Cambodia, Chile, Colombia, Pakistan, Peru, Thailand and Uruguay. Correction using urban/rural poverty line differentials. India: ICP surveys under-represent rural areas (only 28%) –Implicit PPPs for urban and rural India (Rs 17 and Rs 11) PPP’s for the poor: Deaton and Dupriez have re-weighted the PPPs for sub-sample of countries with the necessary data and find similar results

38. China First time China has participated in the ICP Urban bias: prices collected from 11 cities Correction using urban/rural poverty line differential

39. 0 50 100 150 200 National poverty line ($/month at 2005 PPP) 34567 Log consumption per person at 2005 PPP Note: See Figure 1 Food PPP Fisher PPPP (Deaton-Dupriez) Lower poverty line: $22.72 $1 a day line! $31.72