Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey

1. Migration and Economic Mobility in Tanzania:Evidence from a Tracking Survey Kathleen Beegle World Bank Co-authors Joachim De Weerdt, E.D.I. Tanzania Stefan Dercon, Oxford University January 2008

2. Background • Much economic analysis of the processes of development and poverty is about the long-run. • Evidence on long-term poverty dynamics remains limited to cross-sectional work, less with panel data: • Few long-term panel data sets; • Poor analysis of the evidence, usually only focusing on correlates and descriptives; • Panel data sets suffer from high attrition.

3. Background • Attrition strongly related to ‘rules’ e.g. LSMS “Blue book” manual suggests interviewing people in same dwelling; most panels go only back to original villages or communities. BUT • Life-cycle events (death, marriage, etc) make definition of ‘household’ not stable over time. • ‘Development’ usually involves spatial movement (e.g out of agriculture, but also out of village) ....does not sound like random attrition.

4. Overview of this study • Analysis of consumption growth and poverty changes among households from 1991-2004 • Households from Kagera, a region near Lake Victoria • Drawing on a unique panel data set, involving tracking of all individuals ever interviewed • With much attention to finding back everybody wherever they went.

5. Findings • Substantial consumption growth and poverty declines in this period • Extent depends on spatial movement involved, justifying ‘tracking’ of movers • Controlling for initial household fixed effects, we find a large impact of physical movement out of the community • Results remain surprisingly stable in the 2SLS estimation.

7. KHDS 1991-1994 • Kagera Health and Development Survey • 900 households, across Kagera region • 4 rounds between 1991/94 • Stratified random sample • www.worldbank.org/lsms

8. KHDS 2004Re-interviewing all baseline respondents

9. KHDS 2004 • Goal to re-interview allrespondents • Consistent quantitative survey instruments • www.edi-africa.com

10. KHDS 2004 26 Household members for one panel respondent.

11. KHDS 2004 results • 93% of the baseline households were re-interviewed; 96% of those in 1994. • 82% of surviving individuals re-interviewed (above 90 percent for those age 20+ at base). • Individuals found back: 4,432 Individuals death: 962 Individuals not traced: 961 • New sample: Living in 2,719 households

12. Tracking households...

14. Location of surviving respondents

15. Consumption and Poverty Dynamics • consumption expenditures • Challenge to convert into real (2004) value • “narrow” definition to ensure comparability • Consumption of household to which individual belongs in each period • Monetary measure of poverty • Poverty line to match poverty levels for those left in Kagera to estimates from HBS for 2001/02 for Kagera (29%)

16. Consumption per capita in KHDS sample (in TSh)

17. Poverty in KHDS sample (in TSh)

18. Cumulative Density Functions of Consumption per Capita

19. Consumption growth by move to more/less remote area

20. Consumption growth by move and sectoral change

21. Preliminary conclusions • Moving out of poverty is correlated with moving out of the village. • Sampling only those that remain in the village is bound to affect inference. • However: is migrating itself a the way out of poverty? Not clear. • It could be that a particular characteristic both affects moving out and moving out of poverty…

22. Regression analysis • Explain consumption growth based on initial characteristics (individual, household, community). Δln Cit+1,t = α + βMi + γXit + δih +εit • Resolves time-invariant sources of endogeneity (risk aversion?, ability) • Further • Address household effects (δih) using “initial household FE” (832 to 2719 households) • Controlling for individual level factors for (Xit) • Consider moving as endogenous.. The search of IVs

23. Instrumenting strategy • Migration pull factors • Being a male, age 5-15 at baseline interacted with distance to regional capital • Migration push factors • Being age 5-15 at baseline * rainfall deviation between rounds • Social relationships within the household • Relational and positional variables in the HH • Age rank * age 5-15, male/female child of head, spouse or head

24. Table 10: Consumption Change & Mobility

25. Instrumenting strategy • tests validity of instruments • F-stat of instruments • 11.70 for movement • 9.07 for distance of move • weak instrument problem once we try finer distinctions in moving out. • CDF of baseline PCE for movers and non-movers overlap: suggesting either that omitted variable bias is small or biases “balance out” (highly able leave, less able leave)

26. Table 11: 1st Stage results

27. Table 12: Consumption Change & Characteristics of the Move

28. Other findings • Moving out of agriculture associated with higher growth • Strong additional effect from migration along with this sectoral move • Table 10 consistent with adult equivalent consumption (v. per cap)

29. Conclusions • Strong consumption growth and poverty declines overall • Moving out of the village is strongly correlated with consumption growth • Education and individual characteristics matter for moving out and for growth

30. Conclusions • IHHFE results show large gains to consumption for movers. • Migration is linked with a 37 percent higher growth compared to those that stayed in the same community • 2SLS results are similar • suggesting that relevant sources of heterogeneity are controlled for using the initial household fixed effects and individual controls from baseline.

31. Conclusions • Gains are highest for movers to more connected areas, but also higher for those moving to more-remote areas. • Without tracking • We could never have identified this. • Consumption growth would have been understated.

32. Reasons for moving from original homestead, by location in 2004