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Rachel Licker, Marina Mastrorillo,
Michael Oppenheimer, Valerie Mueller,
Pratikshya Bohra-Mishra, Lyndon Estes,
and Ruohong Cai
Human Responses to Climate Variability:
The Case of South Africa
CFCC15
7 July 2015
The Case of South Africa
• Widespread and consequential impacts projected
• Significant internal migration rates
• Large levels of temporary migration – Apartheid legacy
• Laws confined residence, allowed temporary moves for work
• High poverty / vulnerability rates, unevenly distributed
Leibbrandt et al., 2010
Temp change Southern Africa (D-J-F) Temp change RCP4.5, 2081-2100 (D-J-F)
50%
IPCC WGI AR5, 2013
• No studies considering climate and migration
Research Design
New empirical, cross-scalar study
Overarching question:
How has climate variability influenced internal migration
flows in South Africa in recent history?
Approach:
(1) Top-down, province-level (origin-destination flows)
(2) Bottom-up, individual-level (individual decisions to migrate)
Research Design
• With multiple models, scales, and data sources we seek to:
• Increase lines of evidence
• Improve climate variability-migration estimates
• Better understand climate-migration system in SA
• Better define uncertainty
• Prepare a basis for projections
Province-Level Approach: Data
• SA Census 1996, 2001, 2011; Community Survey 2007
• Information on:
- Demographics
- General health and fertility
- Education and employment
- Mortality
- Housing, households and services
- Migration (previous residence* – year of move)
* Note: Different spatial disaggregation on previous residence
for each census Mastrorillo et al., submitted
Migrant Definition
An individual who in year y = 1996, 2001, 2007, 2011 was
living in district j belonging to province p, and moved there
from province i≠p within the last 4 years
General features of internal migrants:
• Migrants: account for ~3.5% per yr of total population
• Younger people (15-30 years old): most represented
among migrants
• Proportionately more white individuals in migrating
population
• ~80% of migrants choose urban areas as destinations Mastrorillo et al., submitted
Bilateral Migration Flows
Definition: Bilateral migration flows mij are the number of
migrants moving from province i to district j (not belonging to
province i) during the 4 years before the Census year
1632 obs. Mastrorillo et al., submitted
Macro Approach: Gravity Model
Focus on origin-destination flows of migration
log(mij
t ) =k +f j
t +adij +bXi
t +gCi
t* +eij
t
bilateral migration flows mij
t
bilateral variables (log of distance and contiguity dummy) dij
time-destination dummies f j
t
origin controls (e.g., pop, pc gdp, ethnic group, urbanization,
unemployment, agriculture variables); τ = lag time Xi
t
climate-related variables at origin (e.g., frequency of droughts,
rain variability, temperature anomalies, soil moisture) Ci
t*
Mastrorillo et al., submitted
Preliminary Results
Variables Sign
Demographic :
• Population
• Share of white individuals
+
+
Geographic (bilateral variables):
• Origin-destination distance
• Contiguity
-
+
Socio –
economic :
• Real per-capita GDP
• Urbanization
• Unemployment
-
+
+
Agricultural : • Percentage of agricultural GDP
• Share of people working in
agriculture
-
-
Climatic : • Frequency of droughts
• Rain variability
• Temperature anomalies
• Soil moisture
+
+
+
-
Mastrorillo et al., submitted
Preliminary Results
• Results on climate are robust to:
• Alternative definition of migration flows (1 year flows)
• Higher spatial disaggregation (2011 cross-section analysis)
• Alternative estimation technique (Poisson)
• Conditioning flows to ethnic group:
• Impact of climate (and other socio-econ. variables at origin)
almost not significant for white migrants
• Conversely, strong impact on black African migrants
Next Steps
Recap
• Influence of climate variability on province-to-district migration
flows (aggregated census data)
• Next: Influence of climate variability on probability of individual
migration (individual-level survey data)
• Corroborate province-level findings
• Not aggregating to province level: more combinations of
migrant characteristics possible
• Disaggregated, longitudinal data: better to get at migrant
motivations
• Is the climate signal more pronounced with an aggregated or
disaggregated approach, or is it the same?
Next Steps
• Discrete-time event history model
• Following Gray and Mueller (2012):
Model of log odds of migration event : no migration event
log(Prit/Psit) = χit + χdt + αp + εit
• Population: Adults (15+) at risk of migration
(people exit once moved, died, or not tracked)
• Spatial resolution: District council (n=52)
• Spatial extent: South Africa
• Time period: 2008-2012 (three waves)
• Unit of analysis: person-year
Prit = Probability of migration event for individual i at time t
Psit = Probability of no migration for individual i at time t
Χit = Vector of individuals i in year t Χdt = Climate in year t, district council d
αi = Fixed effects for individual i
εit = Error term
Next Steps
• Discrete-time event history model
• Following Gray and Mueller (2012):
Model of log odds of migration event : no migration event
log(Prit/Psit) = χit + χdt + αp + εit
• With individual fixed effects, preliminary results:
• - maximum temp extremes, - migration
• + minimum temp extremes (nighttime), + migration
• + precipitation extremes (high levels), + migration
Next Steps
• Province-level fixed effects and migrant characteristic controls (e.g.
race, education, age)
• Multinomial outcomes (long vs. short distance), separate genders
• Additional climate measures (drought, additional observations)
log(Prit/Psit) = χit + χhit + χid + αp + εit
Prit = Probability of migration event for individual i at time t
Psit = Probability of no migration for individual i at time t
Χit = Vector of predictor variables for individual i in year t Χhit = Vector of predictor variables for household h of individual i in
year t Χid = Vector of predictor variables for individual i in district council d
αp = Fixed effects for province p
εit = Error term
Next Steps
• Some similarities, some differences across models
• Explore alternative model specifications (both models) to
test comparability of results
e.g. both at province-level
Conclusions
• In South Africa, preliminary results suggest:
• Droughts, rain variability, increased minimum temperatures, lower
soil moisture: increased flows
• Questions around maximum temperature
• Relationship strengths differ across population groups
• Provinces with more GDP from agriculture: less migration
Thank you
• Migration: One possible response to climate change
• Methods relevant to study of other social responses (e.g.
conflict, labor productivity)
• Existing evidence:
• Relationships between climate variability and…
• Local & long distance moves
• Immobility
• Policy relevance: positive and negative outcomes
• Migrants
• Sending & receiving regions
• Additional research needs: e.g. more longitudinal studies,
more consideration of interactions across scales
Climate Change and Human Migration