Population Mobility and Monsoon Anomalies in Pakistan                     Presented by                    Katrina Kosec   ...
Main Research Questions• What individual and household characteristics predict  migration?   – The opportunity costs of mi...
Motivation: Migration Is An Important  Tool for Improving Household Welfare• Migration can help smooth income and consumpt...
Rural Households in Pakistan are Highly       Dependent on Agriculture                                  • Only 30% of hous...
Example of Vulnerability: Severe Floods           of 2010 and 2011• In 2010, floods affected over 20 million people  (Paki...
Literature on the Impacts of Climate on        Labor and Migration Patterns• Rosenzweig and Stark (1989) show that Indian ...
Data Sources• Survey Data: households are tracked over 21 years   – 1991: Data collected fpr Round 14 of IFPRI’s Pakistan ...
Migration Rates (Ages: 15-40)                                        Men     WomenLeft household, but stayed in village   ...
Reasons for Migration (Ages: 15-40)• Marriage or setting up a new household are the most  common reasons for both genders•...
Factors That Predict Migration – Linear Probability                 Model Analysis Dep. variable: Individual migrated (mea...
How does the Probability of Migration Vary with Individual and HH Characteristics?• Being female: 45%↑• Owning an addition...
Main Findings: Effects of Monsoon        Anomalies on Migration• Higher rainfall during the monsoon increases migration   ...
Conclusions• There are real impacts of negative climate shocks on  migration; monsoon anomalies (more rainfall or delayed ...
Population Mobility and Monsoon Anomalies in Pakistan by Katrina Kosec, IFPRI
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Population Mobility and Monsoon Anomalies in Pakistan by Katrina Kosec, IFPRI

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

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Population Mobility and Monsoon Anomalies in Pakistan by Katrina Kosec, IFPRI

  1. 1. Population Mobility and Monsoon Anomalies in Pakistan Presented by Katrina Kosec December 13, 2012
  2. 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. 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. 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. 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. 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. 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. 8. Migration Rates (Ages: 15-40) Men WomenLeft household, but stayed in village 1.51 2.13Left household and village 1.34 2.25TOTAL 2.85 4.39
  9. 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%
  10. 10. 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.
  11. 11. 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
  12. 12. 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
  13. 13. 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

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