Population Mobility and Monsoon Anomalies in Pakistan

Presented by Katrina Kosec

December 13, 2012

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?

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)

Rural Households in Pakistan are Highly Dependent on Agriculture

• Only 30% of households in rural Pakistan are completely non-agricultural (Rural Household Panel Survey, 2012)

• Thus, natural disasters and monsoon anomalies have the potential to have a major impact on rural livelihoods, and to motivate migration

Landowners 38%

Tenants 11%

Agricultural Waged Labor 21%

Rural Non-Agricultural Households

30%

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)

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)

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, …)

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

Reasons for Migration (Ages: 15-40)

21%

59%

20%

Men 1%

88%

11%

Women

Employment

Marriage/ newhousehold

Other

• Marriage or setting up a new household are the most common reasons for both genders

• Men are much more likely to migrate for employment

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.

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

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

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