Tsukuba Economics Working Papers
No. 2024-003

The Effect of Climate Change on Internal Migration: Evidence from Micro Census
Data of 16 Sub-Saharan African Countries
byChrispin Kamuikeni
Hisahiro Naito
May 2024

UNIVERSITY OF TSUKUBA
Faculty of Humanities and Social Sciences
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Climate Change on Internal
Migration: Evidence from Micro Census Data of 16
Sub-Saharan African Countries
Chrispin Kamuikeni ∗†Graduate School of Humanities and Social Sciences
University of Tsukuba

Hisahiro Naito ∗‡Graduate School of Humanities and Social Sciences
University of Tsukuba
May 15, 2024∗

This research was conducted C.K. appreciates the financial support from Reserve Bank of Malawi.
The authors appreciate the comments from Taiyo Fukai, Kennnichi Kashiwagi, Yoshinori Kurokawa,
Yuko Nakano, Mohammad Abdul Malek and Zeng Fei Yu. The authors are responsible for all remaining errors.
†
Email:cgkamuikeni@gmail.com Address: 1-1-1, Tennodai, Tsukuba City, Ibaraki Prefecture‡
Corresponding author. Email: naito@dpipe.tsukuba.ac.jp. Address: Tennodai 1-1-1, Tsukuba
City, Ibaraki Prefecture, Japan, Postal Code 305-0032

Abstract
Applying a Panel Fixed Effect model to a large dataset of migration and local
weather conditions in 16 sub-Saharan African (SSA) countries, this study estimatesthe impacts of long-term weather aberrations on within-country migration. To address
potential omitted variable bias, this study accounts for weather conditions in alternative places of residence–an aspect which has been overlooked by previous studies.
Results establish a causal link between climate change and migration, but this effectis observed primarily in a block of West SSA countries. In this region, climate-related
relocation is driven by both long-term changes in weather (specifically rainfall andtemperature) and temperature volatility. In this region, climate-related relocation isdriven by both long term changes in weather (rainfall and temperature) and temperature volatility. Quantitatively, this study finds that over the last 30 years, an averageannual rainfall decline of 120mm increased internal migration by 14 percentage points
while a sustained average temperature increase of 0.5°C resulted in an 8 percentage
point rise in internal relocation. However, temperature fluctuations are found to lowered the odds of out-migration by 22 percentage points. Additional findings reveal that
increasing temperatures force climate migrants to travel to much farther destination
areas. However, we do not find evidence that adverse rainfall outcomes increase relocation distance. Additionally, We establish that climate migrants tend to relocate fromrural districts to urban centers. Finally, We obtain evidence that climate-related mobility involves relocation of a family units, as suggested by the significance of climate
mobility of young children (less than 12 years old). Meanwhile, when the same specifications are applied on East SSA, we find weak evidence of climate-related mobility
in this region.

2

1

Introduction

There exist growing concerns that weather shocks will greatly reduce habitability andeconomic prospects of some parts of the world, and that this could trigger large scale
human displacement. The continued worsening of global climate outcomes in the formof continued rising trends in global surface temperature and increased erratic-nessof rainfall (World Meteorological Organization , WMO) justify these fears. In thedeveloping regions such as Africa where climate vulnerability is high (Wolde et al.
(2023), climate change has already heightened the risks of food insecurity and conflicts,
necessitating out-migration as one of the adaptation measures ((World MeteorologicalOrganization , WMO)). Current estimations by the Bank (2021) indicate that close to
a 100 million Africans could be forced to migrate within their own countries by 2050
due to the effects of changing weather patterns.
Understanding climate-induced mobility is critical because of its potential impacts
on both the receiving and the sending communities, as well as the migrants themselves.
Resultantly, scholarly efforts have led to a rapid growth in literature on this topic.
However, despite these efforts, uncertainty persists regarding the precise relationshipbetween climate change and migration (Bertoli et al. (2022), Wolde et al. (2023),
Helbling et al. (2023), Ofori et al. (2023)) – as existing research offers mixed results interms of direction, extent and significance of impact. This discourages policy efforts toaddress the challenges related to potential increases in global climate-related migration
flows Hoffmann et al. (2020) and to explore migration as a viable adaptation measure
to climate change. Conversely, a better understanding of the nature and extent ofclimate mobility will be important for crafting effective adaptation policies in both thesending and receiving areas (Beltran and Hadzi-Vaskov (2023)).
Meanwhile, as the quest for evidence of climate-driven mobility continues, We identify a few research gaps to address. First, existing research has not considered weatherof the potential destination areas, leading to concerns about omitted variable bias.
Indeed, climatic conditions in both the place of origin and the destination play a crucial role in explaining migration patterns. Unfavorable weather outcomes at the place3

of origin act as a push factor, compelling people to seek better prospects elsewhere.
Conversely, adverse weather conditions at the potential destination serve as a deterrent, discouraging migration. Similarly, favorable weather conditions in the source area
discourage out-migration, while desirable weather conditions in an alternative location
constitute a pull factor that attracts individuals seeking improved living conditions (seeLee (1966) and Brettell and Hollifield (2013) for push and pull theory of migration).
Additionally, weather conditions tend to be spatially correlated, particularly for shorter
distances typically involved in internal relocation relative to international migration.
This provides further justification for controlling for weather of potential destination
areas.
Second, existing studies on sub-Saharan Africa region have individually covered only
a limited geographical extent. According to (Gray and Wise, 2016) and (Wolde et al.,2023), prior to 2016, the bulk of research was country specific. Additionally, while
Bertoli et al. (2022) studied a larger sample of the 13 countries, their investigation
was confined to West Africa. Overall, the limited geographical coverage coupled withdiversity of findings has limited our ability to formulate general claims.
In view of the foregoing, we contribute to existing literature in two ways. First, we
take into account weather experiences in alternative locations. Secondly, our measureof migration–which is a dummy of the difference between place of birth and place of
current residence–assists in constructing a consistent migration variable across SSA.
As a result, We are able to include a relatively large sample of 16 SSA countries in
my analysis. Furthermore, given a geographically diverse sample, We classify thesecountry samples into West SSA (9 countries) and East SSA (7 countries), achieving abalanced representation between the western and eastern regions of SSA.
In addressing the causal impact of climate change on within-country relocation, ourstudy formulates and answers several key questions. First, we examine the causal effectof long-term changes in temperature, rainfall, and volatility on migration. Second, we
explore heterogeneous effects across different age groups and genders. Additionally, we
assess whether climate-induced migration moves people from rural area to urban cen-4

ters and whether climate-driven migration influences the choice of relocation distance.
Lastly, our study investigates whether climate-induced mobility involves the relocationof family units or individuals independently.
The outline of the rest of the study is as follows: Chapter 2 reviews related literature. Chapter 3 discusses data and construction of variables used in the analysis.
Chapter 4 lays out the identification strategy. Chapter 5 discusses estimation results.
Lastly, Chapter 6 concluded with policy implications, weaknesses of the study andareas for future research.

5

2

Literature Review

Climate change refers to persistent deviation of temperature and precipitation from
historical patterns. This is contrasted to short-term weather shocks, which are sudden
and extreme deviations from typical weather lasting for a relatively shorter period.
While weather shocks have been a common experience over the stretch of human existence, climate change is a more recent phenomenon primarily driven by anthropogenic
global warming (Pörtner et al., 2022). However, the two concepts are related in thatclimate change has been linked to the frequency and severity of extreme weather events(Hermans and McLeman (2021); Reed and Stringer (2016); Mueller et al. (2020b)).
The response of migration to climate change may differ depending on the magnitude
and suddenness of the weather shocks. Kaczan and Orgill-Meyer (2020) and Thiede
et al. (2016) distinguish between drastic changes in weather referred to as rapid on-set
shocks (such as heavy rain, tropical storms, heat waves) from broad-scale marginalchanges in the climate (specifically, in the patterns and levels of rainfall and temperature) referred to slow on-set shocks. Rapid on-set shocks may result in either migration
or not depending on whether they spare or destroy capabilities to emigrate. In contrast,
slow on-set shocks have also been found to explain migration ((Thiede et al., 2016),(Missirian and Schlenker, 2017)). Similarly, Wolde et al. (2023) identify the so-called
direct and indirect environmental changes causing migration. They define direct environmental change drivers as the prompt reason/cause for displacement while indirectdrivers as the slowly emerging or accumulated environmental changes putting pressureon already vulnerable communities.
Literature further shows that climate-related migration responses can differ widely
across regions and countries even for the same climatic conditions (Bertoli et al. (2022),
Thiede et al. (2016)). This suggests that other factors may interact with climate shocks
in producing observed migration flows. Indeed, climate change heightens migration risk
through its role an additional stressor/threat multiplier. Migration incidence has beenshown to be interconnected with the need to escape unfavorable conflict-related conditions and adverse economic/agricultural outcomes, usually to more urbanised destina6

tions (Abel et al. (2019); Missirian and Schlenker (2017); Falco et al. (2019),Marchiori
et al. (2012),Wolde et al. (2023).
Several notable studies have been conducted on developing countries.
Thiede et al. (2016) employed logistic regression models on eight South Americancountries in studying climate-induced mobility across provinces. They measured outmigration as relocation to another province over the past five years prior to census.
Regarding weather, they assessed two aspects of climate change namely, intensity ofclimate anomalies (slow-onset events) and cumulative exposure to climate extremes(sudden-onset events). To enable valid cross-sample comparisons, they normalized
weather deviations from their respective historical means using z-scores. They observed that while weather deviations measure intensity of climate change, they do not
identify exposure to extreme weather events such as droughts and floods. To measure
cumulative exposure to weather extremes, they count the number of monthly observations falling outside a 2-standard deviation threshold in a given observation window.
Their finding was that temperature extremes consistently explain migration in the region. Additionally, climate-migrants tend to move into urban. However, since theircensus data did not indicate the urban status of the place of origin, they were unable
to confirm whether climate relocation is from rural to urban. [We will address thisissue in my study]. Thirdly, they acknowledge that climate effects on migration varyby country and historical climate conditions.
Similarly, Bertoli et al. (2022) investigated the presence of a universally applicable
framework explaining climate-induced mobility in west Africa. Using data on migration intentions and localized weather shocks in a multilevel framework, over 2008-2016
period, they conducted a meta analysis on numerous competing regression specifications. They find that rainfall explains changes in migration intentions, albeit in a few
countries only. Overall, they conclude that the significance, sign and magnitude ofthe effects are neither robust nor consistent across countries, making it challenging toobtain a general link between climate change and migration.
Closely aligned with my study is notable research by Weinreb et al. (2020), who

7analyzed how changes in weather patterns affected rural-urban migration across 41 subSaharan African countries, by age and sex, over the 1980–2015 period. They combine
age- and gender-specific estimates of net rural-urban migration with historical weather
data from the Climatic Research Unit (CRU). They employ pre-existing rural-urban
migration dataset constructed from a variant of the so-called census survival approach,which utilizes population counts by age and sex for the rural and urban sectors at
two separate points in order to estimate time-specific net rural-to-urban migration
profiles. Their main deviation from prior literature is in weighting local weather bypopulation, which they argue that it more accurately reflects the rural population’s
experience of climate change. Differences in estimated impacts from weighted andunweighted measures of weather suggest the extent to which unweighted findings are
distorted by patterns in relatively underpopulated areas. Their results reveal that rural
out-migration of young adults is the most responsive to shifts in weather patterns,
with lower rainfall, lower variability in rainfall, and higher temperatures increasing
subsequent rural out-migration. Further, they document that the strength of theseeffects has grown stronger over time for 20–24 year olds, though weaker above age
30. In contrast, increasing temperature variability is associated with a higher ruralin-migration of children (0–9) and older adults (55–64). Gender differences in theseeffects are minimal and concentrated in areas which experienced heavy reductions inrainfall.
Mueller et al. (2020a) studied the response of human migration to climate vulnerability in low and middle-income countries in Africa. Employing census data on
migration from 4 million individuals from three middle-income African countries overa 22-year period within country-specific fixed-effect logistic model specifications, theylink link these individuals to climate exposures in their origins and estimate climaticeffects on migration. While they uncover climate anomaly-related mobility in all sampled countries, namely Botswana, Zambia and Kenya, the direction, magnitude andsignificance differ by country. For instance, they report a 1-standard deviation increasein temperature inhibits migration in Botswana by 19 percent. Additionally, 1-standard

8

deviation increase in precipitation reduces migration in Botswana (11%) and Kenya(10%), and increases in migration in Zambia (24%). Overall, the authors demonstratethat temperature exerted a minimal influence of migration and precipitation anomalies, though important, varied in direction of impact across the countries. Additionally,
they extended their analysis to the examination of the link between climate migration
and unemployment. Unclear patterns between weather anomalies, unemployment, andinactivity cast doubt on claims that climate change fosters urbanization in Africa (asadvanced by Marchiori et al. (2012), for example)
Employing a diverse set of analytical tools in conducting a meta-analysis of 87 studies on environmental mobility in SSA, Wolde et al. (2023) explore whether and howhydroclimatic variability has affected internal migration flows across 32 SSA. Their results reveal environmental migration is an outcome of a complex interplay between environmental factors and underlying non-environmental factors (political, economic, administrative, social, and development processes that lead to the depletion/degradationof natural resources). The impact of non-environmental factors on climate migration
is non-negligible, as they subject the population to cumulative environmental changes
and erode their resilience.
Nawrotzki and DeWaard (2018) pinpoint two sources of heterogeneity in climatic
impacts. First is differential vulnerability of populations to climate change. Theynote that some populations are too materially poor to migrate, referred to as highly
vulnerable "trapped" groups. By the same line of argument, we can also argue thatsome groups are materially well off that they can afford alternative adaptation measures. We refer these groups to as the climate resilient group. Second is the differential
vulnerability of places. The authors noted that patterns in climate-related migrationvary across regions, and sought to explore the distinguishing features of those regionsthat inhibit climate migration. Combining climate information with aggregated census
micro data, they estimate gravity models of inter-district migration flows in Zambia. They document that adverse climate conditions are linked to migration in only in
wealthier districts, while in poor districts people tend to remain despite climate-related9challenges. Additionally, they indicate that, despite the limitations posed by poverty,
having access to migrant networks empowers individuals in the poorest districts to relocate in response climate-related factors. This offers a practical avenue for overcoming
mobility limitations.
Similar to the foregoing paper, Nawrotzki et al. (2017) note that adverse climaticconditions may differentially influence human migration patterns between rural andurban areas. They then investigate the relationship between climate shocks and migration between rural and urban areas within Mexico using Mexican censuses andclimate data from Terra Populus at municipal level. They measure climate shocks asmonthly deviations from a 30-year (1961–1990) long-term climate normal period andspecify quadratic and cubic relationships. Their analysis examines four internal migration permutations: rural-urban, rural-rural, urban-urban, and urban-rural. Amongother findings, they report that each additional drought month increases the odds ofrural-urban migration by 3.6%.
...
Although the approach by Weinreb et al. (2020) is similar to my study, importantdifferences emerge. First, their measure of rural-urban migration is at national level,
implying that they conducted a macro-level analysis, with country-agegroup-gender as
unit of analysis. In contrast, We use out-migration dummy at individual level. My
approach and data allows locating precisely the respondents’ area of origin and current place of residence. This allows me to identify the more precisely the weather
conditions the individual was exposed to. Secondly and related to the first, theirweather data is aggregated at national level, which overlooks the within-country variation in weather outcomes. Thirdly, the study assumes linearity in migration response
to weather shocks. In contrast, We consider non-linear relationship.

10

3
3.1

Data
Migration and other census data

Since our goal is to regress migration incidence on weather changes, we obtained migration measure from national census data of each of the 21 SSA countries supplied by
the Integrated Public Use Microdata Series (IPUMS) International (Minnesota Population Center, 2015) Inspired by Blocher et al. (2021) we define migration as relocationfrom birthplace. Thus, migration incidence was observed by the "mismatch" between
an individual’s current place of residence and place of birth at a census day, otherwise migration did not occur (birthplace-based migration dummy). This approach waspreferred as it resolved the inconsistencies in measurement of migration across countries, which enabled us to pool more countries together. Again, since our sample islarge enough, we were able to subsample by age groups and gender, making a moredetailed analysis of climate-induced mobility possible. We defined place of origin anddestination by the administrative boundary level used for recording birthplace in thecensus. That is, if a census recorded place of birth at district level, then our measureof migration will be from district to district and if the official record is at regional level,
then our measure will also be at regional level.
4

Weather data

We obtained a rich panel of 5-degree by 5-degree gridded monthly mean temperature and mean rainfall data from Climatic Research Unit (CRU) of University of East
Anglia. The data, netCDF (CRU version 4.7) file, are have a global cross sectional coverage and 1901-2022 time span. For the purposes of this study, we cut the data to fullgeographic extents of each of the 16 countries. In addition, since the impact of climatechange on migration in SSA is likely to be manifested through impact on agriculture
as established by Missirian and Schlenker (2017) and Falco et al. (2019), we restrictedweather data only to the crop growing months in each country. The exception for thisrestriction were those countries which did not have a clearly defined growing season or
11those whose growing seasons varied substantially across space. For extracting weather
data according to location of analysis, we used appropriate administrative boundary
maps for each country, which were shapefiles obtained from IPUMS International. Additionally, since the CRU data are monthly frequency data, our calculation of averagemean temperature and mean rainfall over a growing season should be read as averagemonthly temperature and rainfall in a growing season.
Close inspection of weather data for SSA confirms on-going climate change. From
Figure 3.1, we observe that beginning around 1980, temperatures assumed a permanent
upward shift–hence climate change. Our reading is that SSA became hotter by morethan 0.5 C on average over the past 40 years.

12Figure 1: Trends in Mean Weather in SSA

Notes: In the above figure, the left hand side chart depicts trends in temperature while theright hand side graph plots the evolution of rainfall in SSA. Both temperature and rainfall
are smoothed (that is, they are 10 year moving averages) in order to depict trends more
finely. The green and red lines represent West and East SSA, respectively. All 46 SSA
countries were used for producing these charts. Source: Climatic Research Unit of UEA;
Plots: Kamuikeni & Naito (2024)

At the same time, while several authoritative sources link climate change to erratic
rainfall (World Meteorological Organization , WMO, Pörtner et al., 2022), our analysisof climate data further reveals a general and lasting downward shift in amounts ofrainfall received in SSA. From the same figure, we note that the volume of rainfall
decreased by about 10 mm a month (about 120 mm a year) on average since 1980s.
Similarly, in East SSA, rainfall amounts declined by roughly 2.5 mm a month (about
30 mm a year) beginning 1990s.
Although climate change is discussed in terms of general trends, we observe that
all countries in SSA have undergone warming. Furthermore, while temperatures arenotably cooler in East SSA compared to West SSA, eastern SSA is warming at asignificantly faster rate (as depicted in Figure 3.2, panel A). Specifically, over the past
70 years, Uganda, Kenya, and South Africa have seen a warming of 1.5°C. In contrast,
Madagascar has warmed the least.
13

In terms of rainfall, countries have experienced diverse outcomes (Figure 3.2, Panel
B). The two countries, Uganda and Kenya, that experienced the largest increases intemperature also saw the greatest increases in the amounts of rainfall received. In contrast, several of the westernmost countries in SSA experienced declines in the amountsof rainfall received. However, for most countries overall, the changes in rainfall amounts
were relatively small.

14

Figure 2: Heat Maps of Weather in SSA
Panel A: Temperature

Panel B: Rainfall

Notes: In each panel of the above figure, the first heat map depicts historical weather averages
over 1901-1950 period. The second one shows recent weather averages over 2011-2022. The
third heat map is the difference between the first and second heat maps. Thus, in the headingof the third heat map, 2020 represents averages over 2011-2022 and 1950 stands for averages
over 1901-1950 period. Source: Climatic Research Unit of UEA; Plots: Kamuikeni & Naito(2024)

5

Combining census and weather data

In integrating weather data with migration data, the challenge is that census-based
migration data are collected decennially, while CRU weather data are reported monthly.
To address this, we calculated the average monthly weather data over a twenty-year15period leading up to each census. Our objective was to create a summary measure ofweather exposure during a relevant pre-census period. While our baseline time frame
is 20 years before census, we additionally explored alternative time windows (such as
10 and 30 years) preceding each census year as shown in Table 3.2. We reserved thesealternative measures for performing robustness checks.
Table 1: Schematic Diagram of Time Spans of Weather Data used in the Analysis

20 years

10 years

10 years

10 years

30 years
Census year

Additionally, it appeared necessary to define the demographic characteristics ofa typical weather migrant. In this regard, we hypothesized that these migrants arecommonly out-of-school individuals in the age range of 22 to 49 years old. Further,migration patterns may be different across different age groups and gender. In viewof this, we constructed two age groups of ages 22-32 and 33-49 and maintained thesex variable. Additionally, we obtained migration record for young children age below
12 years old. Assessing whether the climate mobility patterns of these young children match those of adults would provide evidence on the question of whether climate
migrants relocate with their families.
Incidentally, there appears to be an advantage to constructing the age groups as22-32, 33-49 and 0-11. In developing countries, age misreporting tends to be high, inwhich individuals approximate their ages. We confirmed this by plotting histograms
16

of ages and noticed spikes on ages 10, 15, 20, 25, 30, 35, 40, 45, so on, suggestingthat respondents round off their ages to nearest multiple of 5 (Chart to be shown inAppendix).

17

Table 2: Definition of Weather Variables
Weather Variable
1. Recent past mean rainfall

6

Description(1)
Mean of total monthly
rainfall

2. Recent past mean temperature

Mean of monthly averagetemperatures

3. Standard deviation of rainfall

The standard deviation ofannualized mean monthly
rainfall

4. Standard deviation of temperature

The standard deviation ofannualized mean monthly
temperature

5. Coeﬃcient of variation of rainfall

This is the normalization ofstandard deviation of
rainfall by its mean

6. Coeﬃcient of variation oftemperatureThis is the normalization ofstandard deviation oftemperature by its mean

(2)
Calculated over the past 10, 20,
30 years or other periods prior tocensus depending on the timespan of interest. The word
“recent” distinguishes it from“historical long term” averages.
Calculated over the past 10, 20,
30 years or other periods prior tocensus. The deviation from themean is calculated in two ways:
As (1) deviation from recent past(within the data) mean or (2)deviation from historical 19011950 average.
Useful for accounting forsensitivity of standard deviation
to the sizes of the numbers in thedata set. (i.e. large number
datasets tend to have bigger
standard deviation than small
number datasets).

Key Weather Variables

From the constructed measures of weather exposure, we define several key explanatory
variables that capture exposure to long term climate variability (Table 3.2). [Explain
table 3.2]. Similar to Thiede et al. (2016), who standardized their measures of 5year temperature and rainfall conditions to allow comparability across locations andcountries, our study normalises the standard deviation of recent past temperaturesand rainfall using mean. The idea is that, standard deviation as a measure of volatilityof weather outcomes is sensitive to magnitude of the data points themselves. Thus,
a dataset with large numbers will typically give larger standard deviation compared
to data sets with small numbers. In perspective, as we observed from Figure 1 that18

temperature and rainfall levels are significantly higher in west SSA than in East SSA.
Thus, failing to account for this feature of the data may invalidate any comparisons
between East and West in terms of weather changes. We normalise standard deviation
measure by dividing by the sample mean.
7

Limitations of the data

Our migration measure lacks information on the timing of relocation. Although it
enables us to expand the country sample by providing a consistent measure acrosscountries, we cannot account for individual-specific characteristics such as schooling
due to the absence of migration timing. Understanding when migration occured is
crucial for assessing the individual characteristics that existed before the migration.
Scarcity of more recent census and migration data. Most of the census data available are from over 15 years ago. This results in loss of valuable information over aconsiderable stretch of time.
8

Estimation Strategy

This study examines the relationship between climate change and migration using
a Panel Fixed Effects (Panel FE) model. This model is preferable for addressing
unobserved time-invariant heterogeneity. As Weinreb et al. (2020) notes, a numberof unmeasured factors could influence the relationship between climate variability andmigration, including topographical variations across countries, soil quality and tree
coverage, traditional systems of agricultural land ownership, and variable standards
for defining urban and rural boundaries. Some of these vary significantly over time,
while others are fixed or at least relatively stable. They accounted for some of thesefactors (i.e. the time-stable factors) using within-country fixed effects. Additionally,
while weather shocks are fairly exogenous, controlling for fixed effects may enhance theprecision of my estimates (CITE). The model is as follows:

19

Yitjc = β0 +

m
X
h=1

βh Xtjch +nX

βk Ztjck + αj + αt + αct + εtkci

(1)

k=m+1

where Yitjc is a dummy of outmigration, which is equal to 1 if an individual i at
census year t in location j of country c lives in a location different from his/her birth
location and 0 if he/she still lives in his/her birthplace. It then follows that E[Yitjc ] =prob(Yitcj = 1), which is probability of outmigration. Xtjc is a measure of weather
corresponding to census year t in location j of country c. Ztjck is a vector of confounding
factors, particularly weather conditions in alternative places of residence, measured
in exactly the same way as in the place of origin. αj are birthplace fixed effects foraccounting for unobserved heterogeneity across birth locations, αt are time fixed effects
for accounting for time varying common shocks to all locations. αct is an interaction ofterm for country-specific fixed effect and census year fixed effect. It controls for nonuniform variation of time FE across countries. This is particularly useful for accounting
for the fact that different countries conduct censuses at different time points.
To ensure that we capture (long-term) climate change effects, we compare developments between two time periods with an interval ranging between 20 to 40 years
depending on available census data for a country. Technically, we achieve this interval
by obtaining migration and weather data consistent with the latest and oldest available
censuses for each country. Essentially, we utilize two census waves for each country.
For countries whose census data was available only as a single wave, such as Ethiopia,
we were unable to incorporate them in to our sample because of incompatibility with
our Panel FE method.
Migration status is calculated from census data, and corresponding weather data is
the average weather outcomes over the last 20 years prior to census. The specification
is nonlinear, where mean rainfall and mean temperature together with their squared
terms; standard deviations of annual mean rainfall and mean temperature are the key
explanatory variables.
The unit of analysis is individual. Thus, from census data, a large dataset is20available from which We are able to performing sub-sampling in exploring additional
research questions, including heterogeneous impacts across regions, gender, age, as wellas the preferred destination type (rural/urban) of rural climate migrants.
Several pieces of literature demonstrate that the channel for climate-driven mobility
in regions that are highly dependent on rain-fed agriculture such as SSA is agricultural
productivity (Missirian and Schlenker (2017), Falco et al. (2019)). Therefore, in thisresearch, we rely primarily on weather data that are restricted to agricultural season.
However, as the onset and closing of growing seasons can vary within the same country
and over time, we also perform robustness checks where the growing season restriction
is relaxed entirely.
Additionally, our measure of exposure to climate change is to average monthly
weather outcomes over the last 20 years before census for mean weather variable .
Similarly for standard deviation of weather, we compute average deviation of weather
over the past 20 years prior to census. This means we are linking outmigration incidence to average weather conditions in the last 20 years. Admittedly, the weaknessof this approach is that some individuals might have migrated over 20 years ago, andtherefore, were unexposed to this average weather. Similarly, other individuals might
have relocated more recently that the 20 year average weather may not be quite representative of their climatic experiences. We address this concern in the robustness
checks where we construct alternative measures of weather by splitting the 20 year
pre-census period into two 10 year periods. Therefore, we estimate additional regressions where weather is observed over the past 10 years prior to census and also overthe past 10 years before the 10 years leading up to census.
Lastly, we note that migration may respond not only to temperature and rainfall
fluctuations around their recent mean, but also around their historical mean. Thus,
we also check this possibility in the robustness checks by measuring standard deviation/coefficent of variation of weather as deviation from historical normal mean.

21

9

Estimation Results

9.1

Descriptive statistics

Tables 3 and 4 shows the summary of weather and migration experiences of young
working age adults aged 22-32 in East and West SSA, respectively. Since our benchmark specification (see Chapter 4) is one where summary measures of weather are
calculated over the growing season covering a period of 20 years before census, theweather variables presented in the two tables have been computed in this way.
Birthplace-based out-migration rates for 22-32 age group stood at 28% in East SSA,
marginally higher than 27.5% in West SSA and with the same standard deviation of
0.45.

22

Table 3: Descriptive Statistics of Variables: East SSA

VARIABLES

mean

Country
Census year
Age
Out-migration
Recent mean rain (in decimetres)
Recent mean temperature (°C)
Rainfall standard deviation
Temperature standard deviation
Rainfall coefficient of variation
Temperature coefficient of variation
N

646.639
2003.020
26.6750.279
1.251
23.302
0.231
0.326
0.188
0.014

23

sd

min

max

208.089
72.000894.000
11.063 1969.000 2018.000
3.138
22.00032.000
0.448
0.000
1.000
0.335
0.353
2.318
2.488
14.38728.897
0.091
0.066
0.578
0.072
0.193
0.666
0.068
0.063
0.387
0.003
0.008
0.029
3,949,270

Table 4: Descriptive Statistics of Variables: West SSA

VARIABLES

mean

Country
Census year
Age
Out-migration
Recent mean rain (in decimetres)
Recent mean temperature (°C)
Rainfall standard deviation
Temperature standard deviation
Rainfall coefficient of variation
Temperature coefficient of variation
N

434.428 246.210 120.000
2002.507 11.888 1974.000
26.7253.082
22.000
0.275
0.447
0.000
1.468
1.012
0.034
27.0242.169
19.258
0.150
0.081
0.004
0.296
0.074
0.145
0.125
0.054
0.027
0.011
0.002
0.005
2,698,961

24

sd

min

max
854.000
2015.000
32.0001.000
4.592
32.610
0.648
0.523
0.385
0.020

Additionally, our study is interested in climate-driven mobility of the middle aged
economically active group aged 33-49 to quantify any age group-related heterogeneity ofclimatic impacts, as well in young children aged below 12 to determine familial climaterelated mobility patterns. Summary statistics show that out-migration prevalence forage group 33-49 was the same as for 22-32 age group. Meanwhile, children relocatedfrom their birthplace at an average rate of 10.4 % in the East and 10.8% in the West(table not shown). Exposure to weather remains the same.
10

Estimation Results

10.1

West SSA

Table 5 reports coefficient estimates of the impact of climate change on within-country
migration in West SSA. Results indicate the existence of climate-induced internal mobility within West SSA countries. We establish the channel of impact to be bothchanges in mean rainfall and mean temperature, and also fluctuations in temperature.
Quantitatively, the marginal effect (β 1 + 2β 2 X) of rainfall when rainfall is at averagelevel of 1.5 dm (see summary statistics), the overall decrease in rainfall is 0.1 dm (seeFigure 1) and we use estimates from column 3 is: -0.095 -9.5 percentage points.
Similarly, marginal effect of temperature when temperature is about 27 °C (averageover the sample period) and change in temperature is 0.5 °C (see Figure 1) is: 0.125
12.5 percentage points.
However, given that the squared term of temperature is negative, it suggests that
increasing temperatures progressively limit migration. The implication is that, as areas become hotter due to climate change, people will be unable to move out. Thisis a puzzling finding. The standard hypothesis is that ever increasing temperatures
are detrimental to agriculture and as yield declines due to adverse climatic conditions,an increasing number of people would be moving out of the affected areas. However,Kaczan and Orgill-Meyer (2020) and Thiede et al. (2016) justify the observation thatwarming would eventually reduce the probability of out-migration by pointing out thatsuch adverse climatic incomes would weaken the economic well-being of populations,25

rendering migration unaffordable. This situation has been referred to as the incapability to migrate (Kaczan and Orgill-Meyer, 2020) and migration inhibitor mechanism(Thiede et al., 2016).
Table 5: Estimates of Climate-Driven Migration: West SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

-0.9957* -0.9772** -1.4238***-1.1074*(0.5487)(0.4945)(0.4857)(0.5694)
2.8245*** 2.0635***
3.6211***3.1345***(0.9049)(0.6836)(1.1685)(0.9939)0.08920.1201*0.1578**0.1099(0.0833)(0.0690)(0.0725)(0.0792)-0.0477*** -0.0386*** -0.0624*** -0.0528***(0.0154)(0.0117)(0.0202)(0.0173)
1.08650.57730.86671.3417*(0.6928)(0.6788)(1.1133)(0.8103)-69.6571** -28.9760** -102.5348*** -125.3149***(29.1282) (14.1185)(37.2313)(38.2307)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
Gender
Both
Both
Both
Both
N
2,698,961 2,698,961 2,698,961
2,698,961
R-squared
0.13900.14330.14090.1413
Notes: Clustering robust standard errors in parentheses assuming that the error terms
are correlated within each location. *** p<0.01, ** p<0.05, * p<0.126Additionally, we find that failure to account for weather of potential destinations
would understate the estimated impacts.
We also consider heterogeneous effects across age groups. Applying the specification
on age group 33-49 yielded the following result:
Table 6: Estimates of Climate-Driven Migration: West SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

-0.9832*-0.8156-1.2201**-0.9173(0.5736)(0.5114)(0.5127)(0.5568)
2.7176*** 1.8541***3.6101***3.1404***(0.8918)(0.6625)(1.1705)(0.9651)0.06430.08370.10480.0589(0.0886)(0.0735)(0.0799)(0.0821)-0.0458*** -0.0349*** -0.0615*** -0.0520***(0.0152)(0.0113)(0.0202)(0.0168)
0.93140.50980.91381.3445(0.7396)(0.7250)(1.1555)(0.8604)-76.3701** -35.9721** -114.2011*** -133.9981***(30.6791) (14.6465)(38.5203)(39.5588)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
33-49
33-49
33-49
33-49
Gender
Both
Both
Both
Both
N
2,313,731 2,313,731 2,313,731
2,313,731
R-squared
0.13450.13910.13660.1372
Notes: Clustering robust standard errors in parentheses assuming that the error terms
are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

From this table (Table 5.4), we see that for model 3, coefficients of rainfall, temperature and volatility in temperature retain their significance and signs.
27

Similarly, when we estimated for young children, we obtained consistent results thesizes of the estimated coefficients between the regressions for children and adults arecomparable (Table 5.5). This outcome confirms that adults respond to climatic shocks
by migrating away with their families.
Table 7: Estimates of Climate-Driven Migration: West SSA, Children

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

-0.9872* -1.0864*** -1.3524***-1.1605**(0.5258)(0.4114)(0.4545)(0.4486)
1.8074*0.67922.5861**2.2396**(0.9422)(0.7336)(1.1026)(1.0556)
0.09350.1239**0.1448**0.1120*(0.0766)(0.0571)(0.0633)(0.0617)-0.0299*-0.0150-0.0447**-0.0382**(0.0159)(0.0124)(0.0189)(0.0179)
0.5735
0.09330.35320.8631(0.6826)(0.6935)(1.1914)(0.8644)-79.0193** -38.7210** -114.9597*** -143.8472***(31.3544) (15.2341)(40.3630)(43.0578)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
0-11
0-11
0-11
0-11
Gender
Both
Both
Both
Both
N
6,138,663 6,138,663 6,138,663
6,138,663
R-squared
0.19310.20330.19800.2000Notes: Clustering robust standard errors in parentheses assuming that the error terms
are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

28

Comparisons between genders revealed uniform responses to climate change.
Table 8: Estimates of Climate-Driven Migration: West SSA, Male

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

-0.8843-0.8794* -1.3030***-0.9126(0.5493)(0.5206)(0.4982)(0.6105)
2.5981*** 1.8447**
3.4262***2.8825***(0.9712)(0.7569)(1.2384)(1.0717)0.08430.11020.1450**0.0876(0.0826)(0.0730)(0.0731)(0.0828)-0.0435*** -0.0347*** -0.0588*** -0.0481**(0.0166)(0.0130)(0.0214)(0.0188)
1.1868*0.58210.7762
1.2868(0.7172)(0.6905)(1.1218)(0.8261)-66.9664** -27.5430* -99.5159*** -121.6757***(29.1226) (14.4736) (37.1650)(38.4289)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
Gender
Male
Male
Male
Male
N
1,227,523 1,227,523 1,227,523 1,227,523
R-squared
0.14240.14630.14430.1446
Notes: Clustering robust standard errors in parentheses assuming that the error terms
are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

29

Table 9: Estimates of Climate-Driven Migration: West SSA, Female

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

-1.0636* -1.0393** -1.4976***-1.2327**(0.5571)(0.4809)(0.4858)(0.5454)2.9978*** 2.2275***
3.7908***3.3534***(0.8674)(0.6480)(1.1383)(0.9558)
0.08820.1241*0.1632**0.1219(0.0855)(0.0673)(0.0740)(0.0779)-0.0509*** -0.0415*** -0.0654*** -0.0567***(0.0147)(0.0111)(0.0196)(0.0166)
0.98790.56000.93181.3721*(0.6785)(0.6741)(1.1082)(0.8030)-71.3279** -29.9906** -104.4867*** -127.7530***(29.1276) (14.0505)(37.2902)(38.0927)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
GenderFemaleFemaleFemaleFemaleN
1,471,438 1,471,438 1,471,438
1,471,438
R-squared
0.14090.14590.14300.1435
Notes: Clustering robust standard errors in parentheses assuming that the error terms
are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

11

East SSA

When we applied the exact same specification to East SSA, we could not confirm thatclimate change affects migration in this region (Table 5.8).

30

While columns that take into account weather of alternative locations show someevidence that volatility in rainfall reduces likelihood to migrate, the impacts are economically too small (one standard deviation increase in rainfall fluctuations, leads toonly 4 percentage point (0.6247 x 0.068SD x 100) decrease in the odds of out-migration.
Similar to West SSA, assessment of the age group differences in climate responses
shows no material differences in climate migration behavior between age gropus 22-32
and 33-49 (Tables 5.8 and 5.9). However, young children (0-11 years old) appear to
respond by a lesser magnitude to climate variability (Table 5.10). But again, all age
group specific results do not show statistical significance in East SSA.

31

Table 10: Estimates of Climate-Driven Migration: East SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)
0.3569(0.3822)0.0069(0.0525)-0.1178(0.1261)0.0000(0.0012)-0.3240(0.2225)
3.5476(6.6057)

Out-migration(2)(3)-0.4144(0.3202)0.0214(0.0518)0.1304(0.1074)-0.0004(0.0014)-0.3888*(0.2327)
5.5861(9.1675)(4)-0.4167 -0.4785*(0.2767) (0.2435)-0.01960.0350(0.0568) (0.1054)
0.10740.1484(0.0892) (0.0923)
0.0003-0.0008(0.0014) (0.0024)-0.6247* -0.6584**(0.3202) (0.3321)
10.4752
9.7065(12.3707) (9.5620)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
Gender
Both
Both
Both
Both
N
3,949,270 3,948,565 3,949,270 3,949,270
R-squared
0.09430.0951
0.09500.0951
Notes: Clustering robust standard errors in parentheses assuming that the error
terms are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

32

Table 11: Estimates of Climate-Driven Migration: East SSA, 33-49

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)
0.1775(0.4053)
0.0414(0.0433)-0.0402(0.1314)-0.0008(0.0011)-0.3194(0.2026)
6.4279(6.9328)Out-migration(2)(3)-0.4707(0.3787)
0.0272(0.0472)0.1484(0.1248)-0.0005(0.0013)-0.3521(0.2145)9.8592(9.4281)(4)-0.7422** -0.6788**(0.2964) (0.3038)-0.00210.0501(0.0536) (0.0986)
0.2312** 0.2120*(0.0933) (0.1080)-0.0002-0.0013(0.0014) (0.0023)-0.4726* -0.4805(0.2831) (0.3019)
10.7830
9.0205(11.8301) (8.6500)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
33-49
33-49
33-49
33-49
Gender
Both
Both
Both
Both
N
3,186,135 3,185,443 3,186,135 3,186,135
R-squared
0.09870.09940.0996
0.0996
Notes: Clustering robust standard errors in parentheses assuming that the error
terms are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

33

Table 12: Estimates of Climate-Driven Migration: East SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

0.3970-0.2898-0.3185-0.2577(0.2699) (0.2528) (0.1997) (0.1665)
0.00670.00380.02720.0064(0.0360) (0.0370) (0.0417) (0.0694)-0.10220.08100.08340.0911(0.0819) (0.0768) (0.0582) (0.0622)-0.0002 -0.0004-0.0009-0.0004(0.0008) (0.0009) (0.0010) (0.0016)
0.1332-0.1723-0.3418-0.0776(0.2455) (0.3014) (0.3121) (0.4118)-1.1695 -5.4512
1.1476-2.5091(7.4794) (11.0559) (15.7765) (12.9074)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
0-11
0-11
0-11
0-11
Gender
Both
Both
Both
Both
N
6,157,092 6,156,196 6,157,092 6,157,092
R-squared
0.07010.07130.07120.0713
Notes: Clustering robust standard errors in parentheses assuming that the error
terms are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

34

Gender heterogeneity Assessment of the gender differences in climate responses
shows that35

Table 13: Estimates of Climate-Driven Migration: East SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)

Out-migration
(2)
(3)

(4)

0.3787-0.5666* -0.4301 -0.5057*(0.4133) (0.3338) (0.3430) (0.2675)-0.01780.0124-0.0452
0.0054(0.0547) (0.0528) (0.0596) (0.1128)-0.12190.16920.10210.1590*(0.1367) (0.1084) (0.1021) (0.0947)
0.0007-0.00030.0009-0.0001(0.0013) (0.0014) (0.0015) (0.0026)-0.4466* -0.6750** -0.8824** -0.9287**(0.2462) (0.2789) (0.3673) (0.3745)
4.12793.886013.11299.9811(6.7598) (9.7771) (13.5693) (12.4087)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
Gender
Male
Male
Male
Male
N
1,840,428 1,840,077 1,840,428 1,840,428
R-squared
0.10400.10500.10480.1048Notes: Clustering robust standard errors in parentheses assuming that the error
terms are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

36

12

Robustness checks

We conduct a several robustness checks to ascertain the stability of our estimatedcoefficients. First, we replace our measure of standard deviation of weather from withinsample mean with standard deviation of weather from historical (1901-1950) average.
This way, we check whether migration responds to volatility of rainfall and temperaturearound the historical mean.
The outcome of this test (reported in Appendix A) is similar to the original outcome
where standard deviation of weather was measured as dispersion around recent withinsample average. All signs are maintained. East SSA continues to yield insignificant
result while West SSA retains evidence of climate-induced mobility. However, theestimated coefficient of standard deviation of temperature declines by about half. Thisimplies that temperature fluctuations around the recent past mean are a stronger driverof climate-related migration than temperature fluctuations around the historical mean.
As mentioned in the Estimation Strategy section, we aim to determine whether
calculating weather data over the last 10 years before the census, as opposed to 20
years, has a qualitative impact on our estimates.

37

Table 14: Estimates of Climate-Driven Migration: East SSA

Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation(1)
0.3270(0.3676)0.0293(0.0514)-0.1074(0.1188)-0.0006(0.0012)-0.1825(0.2174)
2.5386(6.5344)

Out-migration(2)(3)-0.2869(0.3146)
0.0293(0.0524)0.1035(0.1059)-0.0005(0.0014)-0.0970(0.2078)
7.0722(8.4110)-0.4116(0.2608)0.0040(0.0583)
0.1192(0.0839)-0.0003(0.0014)-0.3345(0.2960)
8.1010(11.1328)(4)-0.4676*(0.2703)
0.0627(0.1006)
0.1448(0.0975)-0.0015(0.0023)-0.3808(0.3014)9.7436(7.7189)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
GenderFemale Female
FemaleFemaleN2,108,842 2,108,488 2,108,842 2,108,842
R-squared
0.09200.0927
0.0926
0.0927
Notes: Clustering robust standard errors in parentheses assuming that the error
terms are correlated within each location. *** p<0.01, ** p<0.05, * p<0.1

38

Table 15: Estimates of Climate-Driven Migration: Weather Measured over the last 10 years
before census

For another robustness check, we relax the growing season restriction and allow thesummary measures of weather to utilize data point from all calendar months.

39

Table 16: Estimates of Climate-Driven Migration: No growing season restriction for weather
data
Dependent Variable
Variables
Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, coefficient of variation
Temperature, coefficient of variation

(1)

(2)

(3)

(4)

0.0015(0.5757)
0.0485(0.0598)
0.0489(0.3176)-0.0012(0.0015)0.0868(0.2181)3.2718(7.6997)-1.2187**(0.5533)-0.0178(0.0596)
0.6628**(0.3136)0.0005(0.0015)-0.0014(0.2396)5.2153(9.9480)-1.8498***(0.5151)-0.0510(0.0529)0.9236***(0.2835)0.0009(0.0014)-0.0504(0.2535)
8.1792(12.1416)-1.5698***(0.4803)
0.0483(0.0828)0.8268***(0.2697)-0.0012(0.0020)-0.1077(0.2320)
16.9749**(8.4680)

Control Variables
Average weather of top destinations
None
Top1
Top3
Top5
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
Age Group
22-32
22-32
22-32
22-32
GenderFemaleFemaleFemaleFemaleN2,108,842
2,108,488
2,108,842
2,108,842
R-squared
0.09200.09300.0931
0.0929Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each location. *** p<0.01, ** p<0.05, * p<0.1

13Discussion

Several studies have investigated the relationship between climate change and humanmobility in Africa. Our study contributes to existing literature by addressing a conceptual gap, which was failure to account for weather conditions in potential destinations.
Secondly, our measure of migration applies consistently across countries. This allowed
us to include many countries into the analysis, which desirable for enhancing generalizability of findings.
40

Upon observing distinct differences in regression outputs between Eastern andWestern SSA countries, we divided our sample into East and West SSA. The substantial sample size provided by IPUMS census data enabled us to create precise estimatesfor each region. Overall, our findings reveal evidence of climate-related migration inWest SSA. Specifically, reduced rainfall and rising temperatures drive people out ofaffected areas. Surprisingly, we do not detect climate-induced mobility in East SSA.
Although we do not readily explain the insignificant result for East SSA, our study
rules out the possibility that inconsistencies between East and West arise from variations in definitions of migrants, measures of migration, study time frame, core explanatory variables, assumptions about the linearity of the relationship between those variables and migration, and model specifications, as suggested by Weinreb et al. (2020).
Instead, our findings point to unique differences between East and West, such as variations in adaptation measures. A new investigation could provide further insights into
these distinctions.
14

Conclusion

This study set out to examine the existence of climate-driven mobility is sub-SaharanAfrican countries. Overall, we find overwhelming evidence of climate-induced relocation in Western SSA countries. The channel is long term changes in both mean rainfall and mean temperature. Additionally, we uncover evidence that climate-induced
mobility involves relocation of family units as opposed to individuals migrating independently. Meanwhile, we are unable to uncover strong evidence from Eastern SSA
countries, despite utilizing the same methodology and variable measures as for West
SSA. The finding for east SSA necessitates further inquiry. Other researchers (Bertoli
et al. (2022); Thiede et al. (2016); Mueller et al. (2020a)) suggest that climate change
and weather shocks interact with other existing conditions and practices in intricate
ways, which may or may not result in out-migration. Understanding the unique situation of east SSA may help to explain the masked migratory effect of climate change
Our findings have important implications for policy: There is need to understand41

more clearly the impact of climate-induced migration on the welfare of host and sending
communities, as well as the migrants themselves in west SSA. This will be critical for
crafting effective policies, particularly those that safeguard the well-being of people
in both the sending and receiving communities. Further, climate adaptation policies
should focus on cushioning the vulnerable populations. Depending on impacts on thereceiving and sending communities, policies facilitating migration (as an adaptation
measure) may be instituted.
References
Abel, Guy J, Michael Brottrager, Jesus Crespo Cuaresma, and Raya Muttarak (2019)“Climate, conflict and forced migration,” Global environmental change, Vol. 54, pp.
239–249.
Bank, World (2021) “Climate Change Could Force 216 Million People to Migrate
Within Their Own Countries by 2050,” URL: \url{https://www.worldbank.
org/en/news/press-release/2021/09/13/climate-change-could-force216-million-people-to-migrate-within-their-own-countries-by-2050},
Accessed: 2021-09-13.
Beltran, Paula and Metodij Hadzi-Vaskov (2023) “Climate and Cross-Border Migration.”
Bertoli, Simone, Frédéric Docquier, Hillel Rapoport, and Ilse Ruyssen (2022) “Weather
shocks and migration intentions in Western Africa: Insights from a multilevel analysis,” Journal of Economic Geography, Vol. 22, pp. 289–323.
Blocher, Julia, Kira Vinke, Hans Joachim Schellnhuber, Stephanie Gleixner, Christoph
Gornott, Nitin Babu George, EO Kileli Leyani, Rahel Laudien, Anastasia Lobanova,
Josef Ludescher et al. (2021) “Assessing the evidence: Climate change and migrationin the United Republic of Tanzania.”
Brettell, Caroline B and James F Hollifield (2013) Migration theory: Taylor & Francis.
42

Falco, Chiara, Marzio Galeotti, and Alessandro Olper (2019) “Climate change andmigration: is agriculture the main channel?” Global Environmental Change, Vol. 59,
p. 101995.
Gray, Clark and Erika Wise (2016) “Country-specific effects of climate variability on
human migration,” Climatic change, Vol. 135, pp. 555–568.
Helbling, Marc, Diego Rybski, Jacob Schewe, Stefan Siedentop, Manon Glockmann,
Bastian Heider, Bryan Jones, Daniel Meierrieks, Albano Rikani, and Peter Stroms(2023) “Measuring the effect of climate change on migration flows: Limitations ofexisting data and analytical frameworks,” PLoS Climate, Vol. 2, p. e0000078.
Hermans, Kathleen and Robert McLeman (2021) “Climate change, drought, land degradation and migration: exploring the linkages,” Current opinion in environmental
sustainability, Vol. 50, pp. 236–244.
Hoffmann, Roman, Anna Dimitrova, Raya Muttarak, Jesus Crespo Cuaresma, andJonas Peisker (2020) “A meta-analysis of country-level studies on environmental
change and migration,” Nature Climate Change, Vol. 10, pp. 904–912.
Kaczan, David J and Jennifer Orgill-Meyer (2020) “The impact of climate change on
migration: a synthesis of recent empirical insights,” Climatic Change, Vol. 158, pp.
281–300.
Lee, E (1966) “A Theory of Migration Demography Vol. 3, No. 1.”
Marchiori, Luca, Jean-François Maystadt, and Ingmar Schumacher (2012) “The impact
of weather anomalies on migration in sub-Saharan Africa,” Journal of Environmental
Economics and Management, Vol. 63, pp. 355–374.
Missirian, Anouch and Wolfram Schlenker (2017) “Asylum applications respond totemperature fluctuations,” Science, Vol. 358, pp. 1610–1614.

43

Mueller, Valerie, Clark Gray, and Douglas Hopping (2020a) “Climate-Induced migration and unemployment in middle-income Africa,” Global Environmental Change,
Vol. 65, p. 102183.
Mueller, Valerie, Glenn Sheriff, Xiaoya Dou, and Clark Gray (2020b) “Temporary migration and climate variation in eastern Africa,” World development, Vol. 126, p.
104704.
Nawrotzki, Raphael J and Jack DeWaard (2018) “Putting trapped populations into
place: climate change and inter-district migration flows in Zambia,” Regional environmental change, Vol. 18, pp. 533–546.
Nawrotzki, Raphael J, Jack DeWaard, Maryia Bakhtsiyarava, and Jasmine Trang Ha(2017) “Climate shocks and rural-urban migration in Mexico: exploring nonlinearities
and thresholds,” Climatic change, Vol. 140, pp. 243–258.
Ofori, Desmond Oklikah, Elmond Bandauko, Senanu Kwasi Kutor, Amanda Odoi,
Akosua Boahemaa Asare, Thelma Akyea, and Godwin Arku (2023) “A Systematic
Review of International and Internal Climate-Induced Migration in Africa,” Sustainability, Vol. 15, p. 16105.
Pörtner, H.-O., D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, and B. Rama eds.
(2022) Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contributionof Working Group II to the Sixth Assessment Report of the Intergovernmental Panelon Climate Change, Cambridge, UK and New York, NY, USA: Cambridge University
Press, pp.3056, DOI: http://dx.doi.org/10.1017/9781009325844.
Reed, Mark S and Lindsay C Stringer (2016) Land degradation, desertification andclimate change: Anticipating, assessing and adapting to future change: Routledge.
Thiede, Brian, Clark Gray, and Valerie Mueller (2016) “Climate variability and interprovincial migration in South America, 1970–2011,” Global Environmental Change,
Vol. 41, pp. 228–240.
44

Weinreb, Alexander, Guy Stecklov, and Aslihan Arslan (2020) “Effects of changes inrainfall and temperature on age-and sex-specific patterns of rural-urban migrationin sub-Saharan Africa,” Population and Environment, Vol. 42, pp. 219–254.
Wolde, Sinafekesh Girma, Paolo D’Odorico, and Maria Cristina Rulli (2023) “Environmental drivers of human migration in Sub-Saharan Africa,” Global Sustainability,
Vol. 6, p. e9.
World Meteorological Organization (WMO) (2022) “The State of the Global Climate
2022,” https://wmo.int/publication-series/state-of-global-climate-202
2, Accessed: 2023-04-21.

A
B

Appendix A
Appendix B

This is my second appendix.
C

Appendix C

This is my third appendix.

45

Table 17: Robustness Check: Standard Deviation of Weather as Deviation from Historical
Mean, East and West SSA
Dependent Variable
Variables(1)

Recent mean rainfall (dm)Recent mean temperature (°C)
Recent mean rainfall square
Recent mean temperature square
Rainfall, standard deviation from historical mean
Temperature, standard deviation from historical mean

-0.0013(0.3600)-0.0517(0.0639)-0.0010(0.1190)0.0013(0.0015)0.0761(0.1424)0.2067(0.1386)

Out-migration
(2)
(3)

(4)

-0.4078 -1.4335** -2.2790***(0.3022) (0.6361) (0.6138)-0.0733
1.14682.0900**(0.0813) (0.8135) (0.8951)
0.1236
0.1969* 0.3257***(0.1012) (0.1013) (0.1003)
0.0014-0.0161 -0.0352**(0.0019) (0.0146) (0.0155)-0.08700.0180-0.3146(0.1968) (0.2666) (0.3519)
0.1975 -1.1257*** -1.5245***(0.2090) (0.3777) (0.4053)

Control Variables
Average weather of top destinations
None
Top3
None
Top3
Location FE✔
✔
✔
✔
Time FE✔
✔
✔
✔
Country x Time FE✔
✔
✔
✔
SSA Region
East
East
West
West
Age Group
22-32
22-32
22-32
22-32
Gender
Both
Both
Both
Both
N
3,949,270 3,949,270 2,698,961 2,698,961
R-squared
0.09450.09490.14140.1442
Notes: Clustering robust standard errors in parentheses assuming that the error terms arecorrelated within each location. *** p<0.01, ** p<0.05, * p<0.146