Presentations made at the PSSP First Annual Conference - December 13, 14, 2012 - Planning Commission, Islamabad, Pakistan

1. Population Mobility and Monsoon Anomalies in Pakistan
Presented by
Katrina Kosec
December 13, 2012

2. Main Research Questions
• What individual and household characteristics predict
migration?
– The opportunity costs of migrating vary across types of
people and households
– Ability to leave home also varies (e.g., security concerns,
gender norms)
– Who is migrating, who is not, and what predicts migration?
• How do climate shocks in particular affect the
prevalence of migration?
– Recent climate shocks (e.g. 2010 and 2011 floods) and
global warming may be changing where and how
Pakistanis live and work. In what ways?

3. Motivation: Migration Is An Important
Tool for Improving Household Welfare
• Migration can help smooth income and consumption risk
(Rosenzweig and Stark 1989)
• Migration can better match individuals with work
opportunities and motivate human capital investments
(Schultz 1961)
• Migrants generate positive income shocks that lead to
enhanced human capital accumulation and entrepreneurship
in origin households (Edwards and Ureta 2003; Yang 2005)
• This can be especially important in settings with variable
incomes (e.g., rural areas highly dependent on agriculture)

4. Rural Households in Pakistan are Highly
Dependent on Agriculture
• Only 30% of households
in rural Pakistan are
Rural Non- completely non-
Agricultural agricultural (Rural
Households Landowners
30% 38% Household Panel Survey,
2012)
• Thus, natural disasters
Agricultural and monsoon anomalies
Waged
Labor Tenants have the potential to
21% 11% have a major impact on
rural livelihoods, and to
motivate migration

5. Example of Vulnerability: Severe Floods
of 2010 and 2011
• In 2010, floods affected over 20 million people
(Pakistan Ministry of Finance 2011)
– 14 million people displaced, 3.3 million living in camps or
roadside settlements 2 months afterward (D. Walsh, The
Guardian, 2011)
– Estimated 1 billion USD of crop value destroyed (IFRC, 2011)
– Estimated 10 billion USD in total damages (Ministry of Finance,
2011)
• In 2011, floods affected 9.6 million people (Ministry
of Finance 2012)
– Almost 4 billion USD in total damages (Ministry of Finance 2012)

6. Literature on the Impacts of Climate on
Labor and Migration Patterns
• Rosenzweig and Stark (1989) show that Indian HHs exposed to higher
agricultural income risks tend to have longer-distance marriages
• Halliday (2006) shows that adverse agricultural conditions in El Salvador
increase migration
• Gray (2009) finds that international migration in rural Ecuador
decreased with agricultural and rainfall shocks, while local mobility and
internal migration increased with variation in rainfall
• Gray and Mueller (2012) find that men’s labor migration in Ethiopia
increases with drought; women’s migration decreases (revealing gender
differences in responses)
• Jayachandran (2006) finds that landless individuals experiencing a small
loss of production are more inclined to migrate in response to a shock
than are those with land (poverty level differences in responses)

7. Data Sources
• Survey Data: households are tracked over 21 years
– 1991: Data collected fpr Round 14 of IFPRI’s Pakistan Rural
Household Survey
– 2001 and 2012: Same households tracked by PIDE (2001)
and IDS/ IFPRI (2012)
– We create a person-year dataset, using all individuals ages
15-40 (“at risk for migration”)
• Weather station data from the Pakistan Metrological
Department
– Total rainfall during the monsoon (in 100s of mm)
– Date of monsoon onset (1 = June 1st, 2= June 2nd, …)

8. Migration Rates (Ages: 15-40)
Men Women
Left household, but stayed in village 1.51 2.13
Left household and village 1.34 2.25
TOTAL 2.85 4.39

9. Reasons for Migration (Ages: 15-40)
• Marriage or setting up a new household are the most
common reasons for both genders
• Men are much more likely to migrate for employment
Men Women
1%
11% Employment
20% 21%
Marriage/ new
household
Other
59% 88%

11. Factors That Predict Migration – Linear Probability
Model Analysis
Dep. variable: Individual
migrated (mean = 0.0362) Coeff. Sig. S.E. Coeff. Sig. S.E.
Male -0.0163 *** 0.0020 -0.0168 *** 0.0017
Age 0.0007 *** 0.0002 0.0009 *** 0.0002
Head or spouse -0.0386 *** 0.0045 -0.0371 *** 0.0049
Female head 0.0025 0.0069
Age of head -0.0001 0.0001
Years of education of head -0.0007 0.0005
# Children 0.0022 *** 0.0005
Owned land (10’s of hectares) -0.0008 * 0.0004
Total assets 0.0001 0.0000
% of owned land irrigated -0.0072 * 0.0040
Annual monsoon rainfall (100s mm), t-1 0.0032 *** 0.0010 0.0031 *** 9E-04
Monsoon start date (1=June 1st), t-1 0.0022 *** 0.0008 0.0021 ** 8E-04
Household FE? No Yes
Individuals 4,574 4,574
Notes: Standard errors are clustered at the village level. *** p<0.01, ** p<0.05, * p<0.1.

12. How does the Probability of Migration Vary
with Individual and HH Characteristics?
• Being female: 45%↑
• Owning an additional 10 hectares of land: 2%↓
• One year older: 2%↑
• One more dependent child in the household: 6%↑
• Not being the household head: twice as likely to migrate
• Having all land irrigated (as opposed to none): 20%↓
• Does not affect migration: Age, gender, and education level of
household head in an individual’s household; total value of
assets

13. Main Findings: Effects of Monsoon
Anomalies on Migration
• Higher rainfall during the monsoon increases migration
– 1 S.D. increase in monsoon rainfall last year (i.e. 271 mm
more rain during Jun.–Sept.)  0.9 percentage point
increase in the probability of migration (24% increase)
• A delayed monsoon onset increases migration
– 1 S.D. increase in the start date of the monsoon last year
(i.e. a 25 day delay)  0.5 percentage point increase in the
probability of migration (15% increase)
• Not shown: Income is decreasing in monsoon rainfall,
using data from 1986-1991 IFPRI Panel
– Consistent with use of migration to mitigate income risk

14. Conclusions
• There are real impacts of negative climate shocks on
migration; monsoon anomalies (more rainfall or delayed
monsoon) increase migration
• Being female, older, having less land, and having more
dependents is associated with increased migration
• Implications?
– Policymakers should view migration as a coping mechanism for
negative weather shocks
– Land/ assets may reduce access to/ use of this coping
mechanism
• Next Steps:
– More systematic analysis of migration patterns (by gender, by
distance of move, and by motivation of move)
– Incorporating more and better climate data
– Analysis of underlying factors associated with too little migration