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

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.

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.

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. 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.

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.

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

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 funded by the World Bank Research Committee, USAID and DANIDA.

KHDS 2004 Re-interviewing all baseline respondents Individuals interviewed at least once in KHDS 1991-1994 and alive at last interview.

KHDS 2004 Goal to re-interview all respondents Consistent quantitative survey instruments www.edi-africa.com 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)

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).

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: 962 Individuals not traced: 961 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.

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

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

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

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%)

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

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

Cumulative Density Functions of Consumption per Capita

Consumption growth by move to more/less remote area

Consumption growth by move and sectoral change Considerably number move sector without actually moving location/community.

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…

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

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

Table 10: Consumption Change & Mobility

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)

Table 11: 1st Stage results

Table 12: Consumption Change & Characteristics of the Move

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)

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

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.

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.

Reasons for moving from original homestead, by location in 2004