1

Migration and Economic Mobility in Tanzania:

Evidence from a Tracking Survey

Kathleen BeegleWorld Bank

Co-authorsJoachim De Weerdt, E.D.I. TanzaniaStefan 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 region4 rounds between 1991/94Stratified random sample

www.worldbank.org/lsms

8

KHDS 2004Re-interviewing all baseline respondents

Age at baseline 1991-1994

N (alive end of baseline)

<10 years 2,081 10-19 1,922 20-39 1,300 40-59 618 60+ 434 Total 6,355

9

KHDS 2004

Goal to re-interview all respondents

Consistent quantitative survey instruments

www.edi-africa.com

KHDS 200426 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

912Original

Households

63Untraced*

832Recontacted

17Deceased

2,774New

Households interviewed

Tracking households...

13

2,719households

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 countr

y outsid

e Kager

a Regio

n

2%Live

outside country

:

Uganda

14

Location of surviving respondents

re-interviewed not tracedSame Village 2797Nearby Village 626elsewhere Kagera 636somewhere Kagera 545elsewhere Tanzania 314 294Other Country 59 53Don't Know 70TOTAL 4432 961

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

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

Consumption per capita in KHDS sample (in TSh)

17

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

Poverty in KHDS sample (in TSh)

Cumulative Density Functions of Consumption per Capita

0.2

.4.6

.81

0 100000 200000 300000 400000 500000conspc

1991 2004 stayed in village2004 moved within Kagera 2004 moved out of Kagera

19

Consumption growth by move to more/less remote area

Mean Median N

Did not move 0.13 0.16 2,150

Move out of community 0.52 0.49 1,088 Out of those that moved out of community:

Move to more remote area 0.25 0.19 408

Move to similar area 0.42 0.34 417

Move to less remote area 0.86 0.83 338

20

Consumption growth by move and sectoral change

Stayed in Community

Moved out of Community

All

Stay in Agriculture

0.18 (1,251)

0.28 (477)

0.22 (1,728)

Move out of Agriculture into Non-Agriculture

0.42 (201)

1.04 (207)

0.67 (408)

Stay in Non-Agriculture 0.12 (88)

0.87 (85)

0.43 (173)

Move into Agriculture from Non-Agriculture

-0.12 (157)

-0.00 (88)

-0.03 (254)

Total 0.18 (1,697)

0.49 (857)

0.27 (2,554)

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

(1) (2) (3) (4) IHHFE IHHFE 2SLS 2SLS Moved outside community 0.363*** 0.372** (0.025) (0.151) Kms moved (log of distance) 0.121*** 0.104** (0.006) (0.043)

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

(1) (2)

Moved Distance moved

Baseline covariates: excluded instruments Head or spouse -0.218*** -0.635*** (0.038) (0.147) Child of head -0.097*** -0.417*** (0.032) (0.123) Male child of head -0.115*** -0.338** (0.037) (0.144) Age rank in HH * age 5-15 12.383 58.015* (8.008) (30.894) Km from reg. capital * male * age 5-15 -0.001*** -0.002** (0.000) (0.001) Average rainfall deviation * age 5-15 0.000** 0.001**

(0.000) (0.000)

27

Table 12: Consumption Change & Characteristics of the Move

(1) (2) IHHFE IHHFE Characteristics of the move

Move to more remote area 0.177*** (0.036) Move to similar area 0.097** (0.044) Move to more connected area 0.485*** (0.047) Km moved 0.073*** (0.011) Distance moved if to similar area 0.033** (0.015) Distance moved if to more connected area 0.070*** (0.013)

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.

Same village

Nearby village

Within Kagera

Outside Kagera Total

Same village

Nearby village

Within Kagera

Outside Kagera Total

Found work 5 10 27 15 57 1.0 2.6 6.2 6.0 3.6 To look for work 10 16 59 64 149 2.0 4.2 13.6 25.6 9.5 Posted on a job 0 4 3 9 16 0.0 1.1 0.7 3.6 1.0 Looking for land 76 29 43 10 158 14.8 7.6 9.9 4.0 10.0 Schooling 8 16 23 36 83 1.6 4.2 5.3 14.4 5.3 Marriage 159 180 140 37 516 31.1 47.2 32.3 14.8 32.7 Divorce 11 11 11 3 36 2.2 2.9 2.5 1.2 2.3 Parents died 15 13 12 5 45 2.9 3.4 2.8 2.0 2.9 To care for a sick person 3 1 3 0 7 0.6 0.3 0.7 0.0 0.4 To seek medical treatment 0 2 4 3 9 0 0.5 0.9 1.2 0.6 Following inheritance 52 21 16 1 90 10.2 5.5 3.7 0.4 5.7 Other family problems 62 29 26 12 129 12.1 7.6 6.0 4.8 8.2 Follow parents 29 17 31 22 99 5.7 4.5 7.1 8.8 6.3 Follow spouse 5 1 1 4 11 1.0 0.3 0.2 1.6 0.7 Follow relatives 3 2 12 11 28 0.6 0.5 2.8 4.4 1.8 New house 20 5 0 0 25 3.9 1.3 0.0 0.0 1.6 Other (specify) 49 22 21 15 107 9.6 5.8 4.8 6.0 6.8 Unanswered 5 2 2 3 12 1.0 0.5 0.5 1.2 0.8 Missing 1,638 19 3 1 1,661 -- -- -- -- -- Total 2,150 400 437 251 3,238 100 100 100 100 100

Reasons for moving from original homestead, by location in 2004