I presented these slides in a Webinar focused on migration for the Policy, Institutions, and Markets CGIAR Research program in May, 2019.

2. Motivation: Voluntary migration plays central role in
development
 Countries with higher GDP have lower share of labor in
agriculture
oMigrants may go to either urban or rural areas

3. Illustration: GDP and share of labor in agriculture
ALB
ARG
ARM
AUS
AUT
AZE
BEL
BGR
BIH
BLR
BLZ
BMU
BOL
BRA
BRB
BRN
BTN
CAN
CEB
CHE
CHL
CHN
CIV
COL
CRI
CUB
CYP
CZE
DEU
DNK
DOM
DZA
EAP
EASECA
ECS
ECU
EGY
EMU
ESP
EST
ETH
EUU
FINFRAGBR
GEO
GMB
GRC
GTM
HIC
HND
HRVHUN
IBD
IDN
IRL
IRN
ISL
ISR ITA
JAM
JPN
KAZ
KGZ
KOR
LAC
LCA
LCN
LKA
LTE
LTU
LUX
LVA
MAC
MAR
MDA
MDG
MDV MEA
MEX
MKD
MLT
MNA
MNE
MNG
MUS
MYS
NAC
NAM
NLD
NOR
NZLOED
PAN
PER
PHL
POL
PRT
PRY
PSE
PST
QAT
ROU
RUS
RWA
SAU
SLV
SRB
SUR
SVK
SVN
SWE
SYC
TEA
TEC
THA
TLA
TMN
TTO
TUN
TUR
TZA
UGA
UKR
UMC
URY
USA
VEN
VNM
WSMXKX
ZAF
ZMB
ZWE
468
1012
Log,GDPpercapita
0 20 40 60 80
Share of Workforce in Agriculture
Source: World Development Indicators (2016)

4. Rural population share, 1996-2015
0
10
20
30
40
50
60
70
80
90
100
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
RuralPopulationShare
Brazil
Mexico
China
Indonesia
Nigeria
Pakistan
India
Vietnam+Bangladesh
Ethiopia
Source: World
Development
Indicators (2016)

5. Motivation: Voluntary migration plays central role in
development
 Countries with higher GDP have lower share of labor in
agriculture
oMigrants may go to either urban or rural areas
 International migration more complicated (from rural
perspective), but…
oMany relatively small countries rely on remittances for a substantial
share of GDP (e.g. Nepal)
oInternational migration quite important to some large economies
(Bangladesh, Pakistan, Philippines, Mexico)
oInternational migrant origin often from rural areas (de Brauw, 2019)

6. Remittances as a Share of GDP
Country Population Est.
Rural Share of
Population
Remittances
/GDP
Nepal 28.5 m 81.4 31.7
Liberia 4.5 m 50.3 31.2
Tajikistan 8.5 m 73.2 28.8
Kyrgyz Republic 5.9 m 64.3 25.7
Haiti 10.7 m 41.4 25.0
El Salvador 6.1 m 33.3 16.6
Senegal 15.1 m 56.3 11.9
Albania 2.9 m 42.6 9.2
Bangladesh 161 m 65.7 7.9
Morocco 34.3 m 39.8 6.9
Source: World Development Indicators (2016)

7. Outline
 Why study youth migration?
 Description of Data Used in the Talk
 What factors lead to internal migration by youth?
oDo individual, household, or village characteristics matter
more in predicting different types of migration?
oHow much do youth differ from young adults (aged 25-34)?
 Summary

8. How large
is the role
rural youth
play in
migration?
 Rural youth (aged 15-
24) are in the process
of making many life
decisions:
o When to leave
school, whether to
marry (and to whom),
etc.
 Whether or not to
migrate, either for
labor or other reasons,
and where, are key
decisions
Photo: ILO in Asia and the Pacific

9.  Availability of Panel Data, some of
which that tracks migrants, much
greater than in the past
o LSMS ISA, but also
o IFPRI panel surveys in South Asia
o IFLS
 Requirement for Inclusion: Can track
individuals in panel over time
o Can track migrants as a flow
rather than as a stock
Country Surveyed Years
Bangladesh 2011-2, 2015
Indonesia 2007-8, 2014
Nigeria 2013, 2016
Pakistan 2012, 2014
Tanzania 2008, 2013
Opportunity for migration research

10. Research questions
 Are there consistent determinants of
migration among youth (aged 15-
24) across this set of quite variable
countries?
o Are there consistent individual,
household, or village level
characteristics that predict later
migration?
 How does migration among youth
differ from young adults (aged 25-
34)?
 How prevalent are various types of
migration? Do migrants go to
rural/urban/international
destinations?
Photo: Asian Development Bank

11. Data issues
 I use a consistent set of variables across surveys
o Difficult, for example, to come up with consistent asset indicators
 How to treat age?
o I follow people who “should” be aged 15-24 (or 25-34) by the final survey
used rather than examining 15-24-year-olds at baseline
o Rationale: Substantial number of 15-24-year-olds at baseline are still in
school, and may migrate after they are youth
 Similarly, how to treat education?
o Use education in final survey, since many of the younger respondents are still
in school

12. How do we define migration?
 Want to use as broad a definition as possible (not just migration for work)
 Migrating for any reason has economic consequences
oRosenzweig and Stark (1989) show economic rationale to migrate for
marriage
oBeegle et al. (2011) show migration for any reason is correlated with
higher consumption
 Two definitions used in this talk
oBroad- any reason
oNarrow- Eliminates migration reported for marriage or to join family

13. (Broad) Youth migration rates by country

14. Are youth more likely to migrate than young adults?
0
5
10
15
20
25
30
Bangladesh Indonesia Nigeria Pakistan Tanzania
PercentofSampleMigrating
15-24 25-34

15. Gender of youth migration, by broad and narrow definition
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Bangladesh Indonesia Nigeria Pakistan Tanzania
ShareofMigrants,Female
Broad Narrow

16.  Data are sparse, but
o Substantial migration that is rural-rural (typically for family reasons)
o Difficult to study flows of international migration (even in Bangladesh,
which did not capture locations for all migrants, only labor migrants)
o Substantial rural-urban migration to secondary cities
International
Migrants
Rural-Urban
Migrants
Rural-Rural
Migrant
Rural-Urban,
Major City
Indonesia 0.020 0.028 0.153 0.011
Pakistan 0.011 0.135 0.020
Tanzania - 0.098 0.108 N/A
Where do youth migrants go?

17. REGRESSION RESULTS
 Regress an indicator variable for someone who migrates by the second
period on…
oIndividual characteristics (age at baseline, gender, level of education
measured in final survey)
oHousehold characteristics (primarily baseline household consumption)
oVillage characteristics (population density, land per capita)
 Idea is to tease out the most important characteristics for specific types of
migration

18. Individual determinants, broad migration, youth
Bangladesh Indonesia Nigeria Pakistan Tanzania
Gender (1=male) -0.155*** 0.041*** -0.095*** -0.071*** -0.087***
(0.017) (0.016) (0.015) (0.015) (0.017)
Age 0.007* 0.015*** 0.014*** 0.019*** 0.010***
(0.004) (0.002) (0.002) (0.003) (0.004)
Less than Primary
School
0.012 0.168*** -0.003 -0.014 -0.075**
(0.026) (0.059) (0.022) (0.024) (0.030)
Primary School
Complete
0.062** 0.111** -0.014 -0.026 -0.027
(0.030) (0.050) (0.023) (0.018) (0.030)
Secondary School
Complete
0.185*** 0.165*** 0.027 -0.033 0.084*
(0.041) (0.049) (0.030) (0.026) (0.045)
More than
Secondary School
0.190*** -0.058 -0.122*** 0.895***
(0.054) (0.060) (0.040) (0.062)

19. Bangladesh Indonesia Nigeria Pakistan Tanzania
Gender (1=male) -0.155*** 0.041*** -0.095*** -0.071*** -0.087***
(0.017) (0.016) (0.015) (0.015) (0.017)
Age 0.007* 0.015*** 0.014*** 0.019*** 0.010***
(0.004) (0.002) (0.002) (0.003) (0.004)
Less than Primary
School
0.012 0.168*** -0.003 -0.014 -0.075**
(0.026) (0.059) (0.022) (0.024) (0.030)
Primary School
Complete
0.062** 0.111** -0.014 -0.026 -0.027
(0.030) (0.050) (0.023) (0.018) (0.030)
Secondary School
Complete
0.185*** 0.165*** 0.027 -0.033 0.084*
(0.041) (0.049) (0.030) (0.026) (0.045)
More than
Secondary School
0.190*** -0.058 -0.122*** 0.895***
(0.054) (0.060) (0.040) (0.062)
Individual determinants, broad migration, youth

20. Bangladesh Indonesia Nigeria Pakistan Tanzania
Gender (1=male) 0.123*** 0.065*** 0.021** 0.038*** -0.009
(0.011) (0.011) (0.010) (0.007) (0.013)
Age 0.009*** 0.010*** 0.008*** 0.004*** 0.010***
(0.002) (0.002) (0.002) (0.001) (0.003)
Less than Primary
School
0.001 0.055 0.020 0.010 -0.055**
(0.022) (0.044) (0.013) (0.010) (0.023)
Primary School
Complete
-0.000 0.049 0.013 0.009 -0.069***
(0.022) (0.040) (0.013) (0.006) (0.023)
Secondary School
Complete
0.074** 0.104** 0.049** 0.020* -0.050
(0.033) (0.040) (0.022) (0.011) (0.036)
More than
Secondary School
-0.003 0.000 -0.032*** 0.898***
(0.042) (0.047) (0.009) (0.036)
Individual determinants, narrow migration, youth

21. Individual determinants, narrow migration, young adults
Bangladesh Indonesia Nigeria Pakistan Tanzania
Gender (1=male) 0.100*** 0.065*** 0.058*** 0.025*** 0.001
(0.013) (0.008) (0.018) (0.007) (0.019)
Age -0.003 -0.003** -0.003 -0.001 0.005
(0.002) (0.002) (0.002) (0.001) (0.004)
Less than Primary
School
0.019* -0.000 0.008 0.003 0.016
(0.011) (0.037) (0.014) (0.012) (0.024)
Primary School
Complete
0.001 -0.008 -0.019 0.016* 0.043*
(0.015) (0.036) (0.016) (0.008) (0.022)
Secondary School
Complete
0.034 -0.011 -0.009 0.020* 0.117**
(0.022) (0.035) (0.019) (0.012) (0.048)
More than
Secondary School
0.070 -0.028 0.049 0.059 0.614***
(0.071) (0.037) (0.035) (0.073) (0.125)

22. Differences, youth versus young adults
 Migration flows heavily
influenced by gender
o Males more likely to leave
for non-family reasons
 Age matters among youth, not
among young adults
 Education matters more for
youth than for young adults
 Substantial heterogeneity
across countries
Photo: ILO in Asia and the Pacific

23. Is there a relationship between youth migration and
household consumption at baseline?

24. Summary: Household characteristics
 Not much correlation with household demographic composition or
household head characteristics
o In some countries there is a positive correlation between household size and broad
(not narrow) youth migration (Bangladesh and Indonesia)
 If anything, negative correlation between migration and consumption
expenditures
oWait, doesn’t the literature tell us about credit constraints (e.g.
McKenzie, 2017)?
 Also find no evidence of relationship between migration and relative
deprivation with this estimation framework (e.g. Stark and Taylor, 1991)

25.  No distinct patterns between
narrow migration and village
characteristics
 Potential explanation - high
returns to migration outstrip
anything possible on the farm or
near farm in these contexts
Village
Land per
Capita
Share, HHs
with Off-
Farm Work
Log,
Population
Density
Bangladesh -0.018
(0.110)
-0.063**
(0.027)
Indonesia -0.011
(0.010)
-0.021
(0.027)
-0.023**
(0.005)
Nigeria -0.002
(0.014)
0.011
(0.019)
-0.003
(0.004)
Pakistan -0.030**
(0.013)
-0.038
(0.026)
Tanzania -0.003
(0.020)
-0.028
(0.042)
Correlations with village characteristics

26. Conclusion
 Migration to secondary cities appears quite important to youth
 Individual characteristics– notably education– important determinant of
migration
o Different than among older (25-34 year old) migrants
 No (or negative!) correlations between initial household welfare and youth
migration
o Notably this talk only used representative surveys
o Credit constraints vs. migration may exist but may be more pronounced in less
densely populated areas or for rural-urban migration
o Then doesn’t show up in this type of data
 Migration is quite heterogenous across countries and more high quality
data would shed more light on migrants’ experiences

27. References
De Brauw, Alan, 2019. “Migration out of Rural Areas, and its Implications for Rural
Livelihoods,” Annual Review of Resource Economics, forthcoming.
McKenzie, David, 2017. Poverty, Inequality, and International Migration: Insights
from 10 years of migration and development conferences, Revue d'economie du
developpement 25: pp. 13-28.
Stark, Oded, and J. Edward Taylor, 1991. “Migration Incentives, Migration Types:
The Role of Relative Deprivation,” Economic Journal 101 (408): 1163-1178.
Clemens, Michael, Cindy Huang, Jimmy Graham, and Kate Gough, 2018. Migration
is What You Make It: Seven Policy Challenges That Turned Challenges into
Opportunities. Center for Global Development: Washington, DC.
Bazzi S., Gaduh A., Rothenberg A., Wong M. 2016. Skill Transferability, Migration,
and Development: Evidence from a Resettlement Program in Indonesia. American
Economic Review 106(9): 2658-2698.

28. Thank you!
Q&A
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