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.
1 in 9 children. In Kagera, today 15% of <18 yrs are 1 or 2 parent orphans.
Orphanhood rates: evidence of the impact of AIDS (prime-age adult mortality)
But is orphanhood a major risk factor outcomes in adulthood?
Case studies: often studies of severely affected children with no control group. Sometimes able to identify cause of parental death (AIDS v. non-AIDS orphans)
CS studies can control for pre-orhanhood characteristics. Some large cross-country studies using DHS.
Panels: (South Africa, Western Kenya, rural Kenya [adult mortality, not orphan status]) often short run.
Models of LR impact through intergenerational transmission but not much evidence for the underlying parameters.

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.
1 in 9 children. In Kagera, today 15% of <18 yrs are 1 or 2 parent orphans.
Orphanhood rates: evidence of the impact of AIDS (prime-age adult mortality)
But is orphanhood a major risk factor outcomes in adulthood?
Case studies: often studies of severely affected children with no control group. Sometimes able to identify cause of parental death (AIDS v. non-AIDS orphans)
CS studies can control for pre-orhanhood characteristics. Some large cross-country studies using DHS.
Panels: (South Africa, Western Kenya, rural Kenya [adult mortality, not orphan status]) often short run.
Models of LR impact through intergenerational transmission but not much evidence for the underlying parameters.

4. Overview of this study
Analysis of consumption growth and poverty changes among households from
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.
1 in 9 children. In Kagera, today 15% of <18 yrs are 1 or 2 parent orphans.
Orphanhood rates: evidence of the impact of AIDS (prime-age adult mortality)
But is orphanhood a major risk factor outcomes in adulthood?
Case studies: often studies of severely affected children with no control group. Sometimes able to identify cause of parental death (AIDS v. non-AIDS orphans)
CS studies can control for pre-orhanhood characteristics. Some large cross-country studies using DHS.
Panels: (South Africa, Western Kenya, rural Kenya [adult mortality, not orphan status]) often short run.
Models of LR impact through intergenerational transmission but not much evidence for the underlying parameters.

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.
1 in 9 children. In Kagera, today 15% of <18 yrs are 1 or 2 parent orphans.
Orphanhood rates: evidence of the impact of AIDS (prime-age adult mortality)
But is orphanhood a major risk factor outcomes in adulthood?
Case studies: often studies of severely affected children with no control group. Sometimes able to identify cause of parental death (AIDS v. non-AIDS orphans)
CS studies can control for pre-orhanhood characteristics. Some large cross-country studies using DHS.
Panels: (South Africa, Western Kenya, rural Kenya [adult mortality, not orphan status]) often short run.
Models of LR impact through intergenerational transmission but not much evidence for the underlying parameters.

6. Kagera region: has been affected by many different events:
HIV/AIDS (first HIV case in TZ). Epidemic has changed dramatically since late 1980s. HIV prevalence levels are now much lower.
Refugees from Rwanda/Burundi (in non-AIDS districts to the south and south west)
War with Uganda (late 1970s)
Fluctuations in coffee prices (major case crop)
Transport investments: Road to Uganda and Mwanza

7. KHDS 1991-1994 Kagera Health and Development Survey
900 households, across Kagera region
4 rounds between 1991/94
Stratified random sample
funded by the World Bank Research Committee, USAID and DANIDA.

8. KHDS 2004 Re-interviewing all baseline respondents
Individuals interviewed at least once in KHDS and alive at last interview.

9. KHDS 2004 Goal to re-interview all respondents
Consistent quantitative survey instruments
including orphans, migrants or members of households that have “dissolved”
Expectation of over 3,000 households in KHDS-2.
Collects complete “new” household information
Survey instruments adjusted to take into account the 10-year interim period. Health facility surveys dropped.
Qualitative data: of a subset of communities on shocks, risks, and poverty transitions (April 2004 and June 2005)

10. 26 Household members for one panel respondent.
KHDS 2004
26 Household members for one panel respondent.
There is more data in KHDS 2004 despite attrition of individuals. More households, more individuals.
This household contained one panel respondent (female ~19 years old) who had been residing with her parents in the baseline survey and had subsequently married a polygamist who had 5 other wives and many children (some grown).

11. KHDS 2004 results New sample: Living in 2,719 households
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:
Individuals not traced:
New sample: Living in 2,719 households
HH: 832 out of 895
60% of children 0-15 were re-interviewed in their baseline community (not necessarily in the same dwelling). 26% resided far from baseline community.
Without efforts to track children who moved out of the village (to nearby villages or elsewhere), the recontact rates would have fallen to 47% from over 80%.
Results from the CWIQ survey fielded in Kagera in 2004: hh demographics (size, educ), land, livestock.

12. New Households interviewed
Tracking households...
912
Original Households 63
Untraced*
832
Recontacted 17
Deceased
2,774
New Households interviewed

13. Stayed in the same village 19%
2,719
households
49%
Stayed in the same village
19%
Moved to a village nearby the original one
20%
Moved to another village in
Kagera Region, not nearby original village
10%
Live in country outside
Kagera Region 2%
Live outside country:
Uganda

14. Location of surviving respondents
Other country to which we tracked respondents: Uganda

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)
2004 location
mean 1991
mean 2004
difference means N
within village
155,641
186,479
30,838***
2611
nearby village
166,565
230,807
64,242***
566
elsewhere in Kagera
162,116
262,964
100,848***
571
out of Kagera
169,994
457,475
287,480***
327
Full Sample
159,217
225,099
65,882***
4075

17. Poverty in KHDS sample (in TSh)
2004 location
mean 1991
mean 2004
difference means N
within village
0.36
0.32
0.04***
2611
nearby village
0.33
0.22
0.11***
566
elsewhere in Kagera
0.37
0.24
0.13***
571
out of Kagera
0.30
0.07
0.23***
327
Full Sample
0.35
0.27
0.08***
4075

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
Considerably number move sector without actually moving location/community.

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 Δln Cit+1,t = α + βMi + γXit + δih +εit
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
X affect consumption growth but also affect migration decision
Sex
Age
Education
Relative education
Marital status
Any biological children in the household
Any biological children in the household * regional cap
Parent’s education
Double orphan

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