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Household and Spatial Drivers of Migration Patterns in Africa: Evidence from Five
Countries
Valerie Mueller (IFPRI)
Emily Schmidt (IFPRI)
Nancy Lozano-Gracia (World Bank)
Urbanization and Spatial Development of Countries Research Workshop
July 13, 2015
Introduction
• Migration – key force shaping both origins and destinations
• Economic and physical mobility as forces to achieve structural change
• But SSA seems to be different - no clear agricultural or industrial revolution as in other countries to encourage rural-urban migration (Jedwab and Vollrath, 2015)
• Migration in sub-Saharan Africa characterized by labor ‘chasing’ due to distress or risk-reduction strategies (Campbell, 2011)
• Much yet to be understood…
Objectives and research questions
• Is migration in Africa different?
• What are the key factors determining migration in SSA?
• How do they compare to places that faced an industrial (China), agricultural (India) revolution?
• What are the types of flows that dominate? Is rural-urban migration the main patter of movement?
• Is migration being used to diversify employment?
Data
• LSMS-ISA Surveys• Ethiopia (2011-2, 2013-4)
• Malawi (2010-1, 2012-3)
• Nigeria (2010-1, 2012-3)
• Tanzania (2008-9, 2010-1, 2012-3)
• Uganda (2009-1, 2010-1, 2011-2)
• Detailed questions about individuals and households over time
• But… no perfect measure of migration, and short panels
Measures of Migration
• Temporary migration - All countries document if household member moved over last 12 months (but don’t know where they go or why)
• Different measures of individual permanent migration out of the household
• Tracked migrants in waves after baseline (Malawi and Tanzania)
• Self-reported migration by proxy in waves after baseline (Ethiopia and Nigeria)
• Low long-distance household migration is low over two-year horizon
• Different place than birth location (NOT used here because we don’t know anything about pre-migration characteristics)
Ethiopia requires careful interpretation – large urban areas excluded from wave 1
Rural-Urban Migration
Country Year Time Period Source Rural-Urban Migration
Ethiopia 2013-4 2 years LSMS-ISA 25.3
Malawi 2012-3 2 years LSMS-ISA 6.1
Nigeria 2012-3 2 years LSMS-ISA 11.1
Tanzania 2012-3 2 years LSMS-ISA 14.1
China 2000 5 years Census, Cai,Park, and Zhao (2008)
40.8
India 2001 9 years Census, Ministry of Home Affairs
21.1
Hypotheses – patterns of migration
• Hypothesis: Rural-rural migration patterns is driven by land scarcity, big households, and high population densities. People are NOT diversifying out of agriculture, they are moving to find a job in agriculture or access land for their own farm
• Hypothesis: Rural-rural migration patterns dominate because roads, liquidity constraints, education deficits and other factors pose barriers to moving to a city (Stark and Bloom, 1985; Stark, 1991; Rozelle et al., 1999; Wouterse and Taylor, 2008; Dillon et al., 2011). People are diversifying out of agriculture into non-agriculture in rural areas (e.g., to satisfy rise in demand for services).
• Hypothesis: Rural-urban patterns are predominantly explained by an absence of insurance mechanisms (Barrios et al., 2006; Poelhekke, 2011). People are diversifying out of agriculture into non-agriculture in cities where climate or conflict risk is low.
“Migrating for Land” Hypothesis
Rural Urban
Non-Mig. Mig. t-test Non-Mig. Mig. t-test
ETHIOPIA
Household size 5.63 6.36 0.74*** 4.92 5.95 1.04***
Owned land 4.21 5.31 1.1 0.5 0.45 -0.04
GRUMP Pop. Density 180.19 183.27 3.09 149.29 142.8 -6.49
MALAWI
Household size 5.51 6.26 0.75*** 5.3 5.88 0.58***
Owned land 3.74 1.84 -1.9 0.45 0.31 -0.13**
GRUMP Pop. Density 208.23 198.72 -9.51** 1794.59 1914.98 120.39**
NIGERIA
Household size 7.91 8.13 0.21 6.78 7.10 0.31*
Owned land 2.63 3.52 0.88* 0.35 0.66 0.31***
GRUMP Pop. Density 325.39 402.23 76.84*** 1862.38 2007.89 145.51
TANZANIA
Household size 6.24 7.62 1.38*** 5.75 6.90 1.15***
Owned land 7.48 8.34 0.86 1.34 1.56 0.22
GRUMP Pop. Density 207.28 161.18 -46.11* 1242.25 1189.02 -53.23
Constrained Migration HypothesisRural Urban
Non-Mig. Mig. t-test Non-Mig. Mig. t-testETHIOPIAPrimary Education 0.07 0.21 0.14*** 0.32 0.44 0.12***No. of rooms 1.70 1.86 0.16** 2.31 2.48 0.17Travel Time 223.44 223.54 0.10 179.27 207.02 27.75**
Distance to major road 16.44 14.61 -1.83** 14.29 16.48 2.20MALAWIPrimary Education 0.10 0.11 0.00 0.15 0.13 -0.02No. of rooms 2.70 2.83 0.12*** 3.23 3.41 0.17**Travel Time 131.68 130.85 -0.83 42.83 37.74 -5.09***
Distance to major road 9.50 9.20 -0.30 1.74 1.81 0.07NIGERIAPrimary Education 0.29 0.39 0.10*** 0.31 0.31 0.00No. of rooms 4.43 4.70 0.27** 3.57 3.75 0.17Travel Time 176.63 156.96 -19.67*** 65.99 75.44 9.45*
Distance to major road 17.76 15.69 -2.07*** 5.78 5.77 -0.00TANZANIAPrimary Education 0.51 0.57 0.06** 0.18 0.11 -0.07***No. of rooms 3.68 4.12 0.45*** 3.53 3.98 0.45***Travel Time 210.50 219.06 8.56 52.70 60.46 7.76
Distance to major road 22.89 23.84 0.96 45.76 41.47 -4.29
Migration for insurance hypothesis
Rural Urban
Non-Mig. Mig. t-test Non-Mig. Mig. t-test
ETHIOPIA
Temperature wettest Q 19.26 19.32 0.05 19.21 19.54 0.33
Rainfall wettest Q 557.61 573.15 15.54*** 601.88 616.30 14.42
Conflict fatalities 0.82 0.93 0.11 0.80 0.93 0.13
MALAWI
Temperature wettest Q 23.08 23.21 0.13* 22.01 21.86 -0.15**
Rainfall wettest Q 674.62 657.51 -17.11*** 668.74 662.09 -6.65*
Conflict fatalities 0.02 0.02 0.00 2.52 2.90 0.38
NIGERIA
Temperature wettest Q 25.24 25.09 -0.15*** 25.29 25.16 -0.13***
Rainfall wettest Q 728.83 822.37 93.54*** 723.17 766.05 42.88***
Conflict fatalities 7.68 4.70 -2.97* 39.43 21.06 -18.36***
TANZANIA
Temperature wettest Q 23.51 23.38 -0.13 25.48 25.18 -0.31*
Rainfall wettest Q 582.72 538.48 -44.24*** 607.59 573.77 -33.83***
Conflict fatalities 0.14 0.14 -0.01 2.94 3.39 0.45
Pre- and Post-Migration Employment Patterns
What’s next?
• What is rural and what is urban?
• Are dynamics of peri-urban areas masked within the rural classification?• Explore breakdown of spatial patterns
• Uncovering the patterns of migration across five African countries:• Estimate multivariate regressions of migration to examine which hypotheses
dominate
• Robustness to measure of migration (temporary versus permanent)
• Robustness to panel framework (temporary, Tanzania permanent)
Thanks!V.Mueller@cgiar.org
E.Schmidt@cgiar.org
nlozano@worldbank.org
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