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Climate, Urbanization, and Conflict: The Effects of Weather Shocks and Floods on Urban Social Disorder

Floods regularly displace large numbers of people in developing countries, leading to increased population movement from rural areas to the largest cities. This paper finds evidence that people’s displacement by flooding is associated with a higher risk of social disorder in large cities in developing countries. The evidence suggests that the effects of floods on urban social disorder occur mainly through displaced people being pushed into large cities.

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CLIMATE, URBANIZATION, AND CONFLICT: THE EFFECTS OF WEATHER SHOCKS AND FLOODS ON URBAN SOCIAL DISORDERDavid Castells-Quintana and Thomas K.J. McDermott

ADB ECONOMICSWORKING PAPER SERIES

NO. 588

July 2019

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ADB Economics Working Paper Series

Climate, Urbanization, and Conflict: The Effects of Weather Shocks and Floods on Urban Social Disorder David Castells-Quintana and Thomas K.J. McDermott

No. 588 | July 2019

David Castells-Quintana ([email protected]) is a post-doctoral researcher at the Applied Economics Department of the Universidad Autonoma de Barcelona. Thomas K.J. McDermott ([email protected]) is a Government of Ireland Research Fellow at the Socio-Economic Marine Research Unit (SEMRU) of the National University of Ireland Galway. This paper was prepared as background material for the Asian Development Outlook 2019 theme chapter on “Strengthening Disaster Resilience.” We are very grateful to Maria del Pilar Lopez-Uribe, Joshua Hallwright, and participants at the Asian Development Bank workshop on Disasters and Development held in Manila in December 2018 for helpful discussion and comments. McDermott gratefully acknowledges the support of the Irish Research Council.

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ISSN 2313-6537 (print), 2313-6545 (electronic)Publication Stock No. WPS190254-2DOI: http://dx.doi.org/10.22617/WPS190254-2

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CONTENTS

TABLES AND FIGURES iv ABSTRACT v ABBREVIATIONS vi I. INTRODUCTION 1 II. CLIMATE, URBANIZATION AND CONFLICT: A REVIEW OF THE LITERATURE 3 A. Weather Shocks and Conflict 3 B. Weather Shocks, Urbanization, and City Growth 4 C. Migration to Cities, Urbanization, and Conflict 4 III. DATA AND DESCRIPTIVE ANALYSIS 5 A. Data Sources and Main Variables 5 B. Descriptive Analysis and Stylized Facts 7 IV. EMPIRICAL ANALYSIS 10 A. Estimation Strategy 11 B. Results 13 V. CONCLUSIONS, DISCUSSION, AND NEXT STEPS 18 REFERENCES 21

TABLES AND FIGURES

TABLES

1 Summary Statistics 7 2 Effect of Weather Shocks on City Population 13 3 Effect of Weather Shocks on Urbanization Rate 14 4 Effect of Floods on City Population 16 5 Effect of Floods on Urban Conflict Events, Intensive Margin 17 6 Effect of Floods on Urban Conflict Events, Extensive Margin 17 7 Effect of City Population on Urban Conflict Events, Intensive, and Extensive Margins 18 FIGURES

1 Evolution of Main Variables 8 2 Association between Main Variables 10

ABSTRACT In this paper, we test the effect of weather shocks and floods on urban social disorder for a panel of large cities in developing countries. We focus on a particular mechanism, namely the displacement of population into (large) cities. We test this hypothesis using a novel dataset on floods—distinguishing those that affected large cities directly from those that occurred outside of our sample of large cities. Floods are found to be associated with faster growth of the population in the city, and in turn with a higher likelihood (and frequency) of urban social disorder events. Our evidence suggests that the effects of floods on urban social disorder occur (mainly) through the displacement of population, and the “push” of people into large cities. Our findings have important implications for evaluating future climate change, as well as for policies regarding adaptation to climate change and disaster resilience. Keywords: climate change, conflict, floods, migration, rainfall, social disorder, urbanization

JEL codes: D90, I30, J60, O10, Q00, Q01, Q50

I. INTRODUCTION

In this paper, we test the effect of weather shocks on urban social disorder, with a particular focus on the role of floods that occur outside of large cities, in “pushing” population into these cites, potentially resulting in social tensions and conflict in urban areas. Disasters related to natural hazards and weather shocks regularly cause displacement of population from rural to urban areas (see, for example, Barrios, Bertinelli, and Strobl 2006; Marchiori, Maystadt, and Schumacher 2012; Henderson, Storeygard, and Deichmann 2014). Floods, in particular, displace large numbers of people every year. In the past 30 years, they have displaced 650 million people worldwide, according to data from the Dartmouth Flood Observatory (Brakenridge 1985–present). While floods impact on all world regions, the magnitude of effects is particularly pronounced for countries in developing Asia (see section III.B, Figure 1). The risk of flooding is also anticipated to increase rapidly in the coming decades, due to a combination of increasing exposure—due to ongoing socioeconomic trends (including population and economic growth, and continuing urbanization)—and increasing hazard because of climate change effects on rainfall patterns and continuing sea level rise (Hallegatte et al. 2013, Jongman et al. 2014).

This displacement of people has potentially important economic and social consequences. A changing configuration of the spatial distribution of population and economic activity has direct effects on economic performance and development, as now widely acknowledged by the literature (Henderson 2003, Brülhart and Sbergami 2009, Castells-Quintana 2017). Migrations—both temporary and permanent—have also long been used as risk-coping mechanisms in the face of weather shocks, disasters, and other income or productivity shocks (Gray and Mueller 2012; Henderson, Storeygard, and Deichmann 2017).

At the same time, the displacement of people may create social tensions where resources are scarce and overcrowding occurs, or when there is excessive demand for public services (Castells-Quintana, Lopez-Uribe, and McDermott 2018). As an example, Collier, Conway, and Venables (2008) point to problems with access to land for newly arrived rural-to-urban migrants. Disorderly or reactive migration in response to weather shocks may lead to disruptions in economic activity and even social disorder (see Waldinger 2016, and citations therein, for further discussion).

While there is a growing literature on urbanization and conflict (for example, Buhaug and Urdal 2013, Ostby 2016), to date the findings in this literature have been mixed. In this paper, we test the effect of flooding that occurred outside of our sample of large cities (an exogenous source of variation in city population) on urban disorder in major cities across the developing world. Arguably, not all urbanization is equal. Urbanization is mainly determined by rural–urban migration and natural population growth. Traditionally, rural–urban migration has been associated with a process of structural change, higher productivity growth, and economic development. However, nowadays in many poor countries, urbanization is not necessarily associated with economic development. Rural–urban migration in many poor countries seems more the outcome of “push” rather than “pull” factors: deteriorating agricultural conditions worsened by climate change, high volatility in agricultural prices, disasters, and even violent conflict in rural areas. The “push” of people into urban areas does not necessarily increase productivity (see, for instance, Lipton 1977; Bates 1981; Bairoch 1988; Barrios, Bertinelli, and Strobl 2006).

Furthermore, a particular methodological issue with this literature is to identify a causal relationship between variation in urban population and urban disorder. We propose to use weather shocks, and in particular flood events that occur outside of major cities, as an exogenous push factor

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driving rural–urban migration, and potentially leading to social disorder in adjacent urban areas. Weather shocks and floods can impact conflict directly by increasing the competition for limited resources, but also by altering other socioeconomic dynamics. One of these is the concentration of population in urban areas. Developing countries experience today a very rapid process of urbanization. In many cases, this translates to a growth of urban agglomerations (cities) that can now reach 10, 20, or even 30 million inhabitants. Disasters related to natural hazards and changes in climatic conditions increasingly represent an important “push” factor. As more people get “pushed” to (large) cities, unplanned urbanization and city growth bring important challenges for sustainable development, straining basic urban services like sanitation, electricity, and transportation, as well as the job market (Collier, Conway, and Venables 2008; Castells-Quintana, Lopez-Uribe, and McDermott 2018). These challenges could fuel conflict in urban areas.

The aim of this paper is to analyze the potential effects that weather shocks, in particular floods, can have on patterns of economic development through the displacement of population from rural to urban areas. In particular, we aim to test the effects of rural–urban migration on social disorder within cities, with weather shocks as the impulse for this population movement. We do this by analyzing data for more than 138 (large) cities in 138 countries (one city per country), from 1960 to 2015.

Our paper relates to at least three strands in the literature: i) papers studying the connection between weather shocks and conflict, ii) papers studying the connection between weather shocks and urbanization, and iii) papers focusing on conflict in urban areas (see section II for a brief overview of this literature). While the literature suggests a potential connection between weather shocks, urbanization, and conflict in urban areas, to the best of our knowledge, there is no paper empirically testing this connection in a global panel of countries and/or cities. Our paper aims to fill this gap.

In line with previous literature, we find that rainfall anomalies affect the rate of urbanization in developing countries. However, we also identify diverse effects across two different world regions; in sub-Saharan Africa (SSA), lower-than-expected rainfall is associated with more urbanization, while in Asia, higher than expected rainfall leads to higher growth of (large) cities. We explain this latter finding with respect to the effects of flooding in Asia. We test this hypothesis explicitly using a novel dataset on floods—distinguishing those that affected large cities directly from those that occurred outside of our sample of large cities. The latter events are strong predictors of population in the city, and in turn of the likelihood (and frequency) of urban social disorder events. Our evidence suggests a positive correlation between floods and urban social disorder that occurs (mainly) through the displacement of population, and the “push” into large cities. Our findings have important implications for evaluating future climate change, as well as for policies regarding climate and disaster resilience.

The remainder of our paper proceeds as follows. In section II, we briefly review the three strands of related literature already mentioned. In section III, we present our data, providing a descriptive analysis of the coevolution of weather shocks and floods, urbanization and city growth, and social conflict in urban areas. Section IV performs some econometric analysis and presents our main results. Finally, section V concludes, highlighting policy implications from our results and avenues for further research.

Climate, Urbanization, and Conflict: The Effects of Weather Shocks and Floods on Urban Social Disorder | 3

II. CLIMATE, URBANIZATION AND CONFLICT: A REVIEW OF THE LITERATURE

A. Weather Shocks and Conflict

Recent decades have seen an increase in the interest in the relationship between natural resources and conflict. A body of literature argues that the abundance of natural resources, in particular minerals and oil, is positively correlated to violence and conflict. The abundance and easy access to these resources can be used to fund rebel organizations and can lead to frictions over their allocations. At the same time, the state dependence on these commodities can weaken state capacity (Hendrix 2010). Other authors have supported the idea that it is the scarcity of vital resources, like water and food, that can lead to conflict (see below). In this context, the scarcity of resources can generate grievances and fuel uprisings and disorders over distribution.

Several researchers have tried to test the causal relationship between income, natural resources, disasters, and social disorder (or conflict). They have relied on the consensus that low levels of income are correlated with high levels of uprisings, protests and riots, and look at land and water resources to determine the link between scarcity and this type of conflict. As income is endogenous to conflict, they have used rainfall as a source of exogenous variation of income. For example, Miguel, Satyanath, and Sergenti (2004) use rainfall growth as an instrument for gross domestic product (GDP) growth in SSA and find that lower economic growth increases the probability of conflict, in particular civil war. Bohlken and Sergenti (2010) run a similar analysis for India, instrumenting for state-level GDP with rainfall. They also find that low rainfall growth increases the number of riots that a state experiences in a given year. The idea behind using rainfall as a plausible candidate instrument is that low levels of rainfall result in crop failure, thereby depressing rural income. However, the critical assumption underlying the use of rainfall as an instrumental variable is that rainfall affects conflict only through its impact on income.

Other authors have found a weaker relationship. Couttenier and Soubeyran (2014) argue that the relationship between rainfall and conflict (i.e., civil war) is driven by aggregate shocks, in particular global climate shocks. Using a country-specific drought measure and a difference in difference specification, the authors find a weaker relationship between droughts and civil war. Ciccone (2011) revisits Miguel, Satyanath, and Sergenti’s (2004) analysis of civil war in SSA and shows that there is actually a positive correlation between twice-lagged rainfall levels and current conflict. This author develops this point later (Ciccone 2013) and looks at the effect of transitory shocks on conflict risk to test whether income shocks lower the opportunity cost of participating in violence, and finds again that negative income shocks lower the risk of civil conflict. In contrast with these two groups of authors, Hendrix and Salehyan (2012) demonstrate the existence of a curvilinear relationship between rainfall and social conflict for African countries: very high and very low rainfall years increase the likelihood of all types of political and social conflict.

In terms of the mechanisms that relate weather shocks, and in particular rainfall deviations, with conflict, the literature has suggested at least five different mechanisms.1 A first body of literature focuses on access to water. During rainfall shortages water stores decline, and consumers may come into conflict within their own society over access to wells and riverbeds (Kahl 2006) or over water rights and access

1 The literature emphasizes the importance of extreme events, since deviations from normal rainfall disrupt the

expectations societies develop about normal rainfall patterns, and accordingly, their plans for crops and coping strategies (Reardon and Taylor 1996).


(Eriksen and Lind 2009). A second group of authors focuses on the effect of droughts and floods on food prices, leading to disputes between rural producers and urban consumers. Alexandratos (2008), for example, has shown that the rising price of staple crops in 2008 and 2011 led to massive protests and riots when urban consumers started to demand relief from food price inflation. A third mechanism relates to the effect on government revenues through a reduction of the tax base or an increase in the demand for services and assistance to respond to weather shocks. This is particularly true in countries where agriculture and other water-intensive sectors are central for the national economy, as is the case in many African countries. As Benson and Clay (1998) have shown for African economies, extreme weather events have had a particularly pronounced effect on public finances. A fourth mechanism relates to the effects on overall growth (Miguel, Satyanath, and Sergenti 2004; Barrios, Bertinelli, and Strobl 2010; Jensen and Gleditsch 2009). Rainfall anomalies can lead to displacement or crop failure, which at the same time affect economic productivity. General economic discontent may in turn lead to public disorder and conflict. In what we have identified as a fifth mechanism, rainfall deviations may also affect livelihoods, forcing many to migrate to urban areas in search of better opportunities. Migration to cities increases the supply of labor, leading to more intense competition over jobs. Similarly, more migrants in cities affect the demand for housing and other basic services such as sanitation, electricity, police protection, and roads (Neuwirth 2005). Our paper focuses on this last mechanism, which to the best of our knowledge, has not been explicitly tested in the literature to date.

B. Weather Shocks, Urbanization, and City Growth

The literature on climate change and urbanization has focused on the effect of rainfall on urbanization, mainly in Africa. For example, Barrios, Bertinelli, and Strobl (2006) estimate that shortages in rainfall have acted to increase rates of urbanization on SSA countries, but they have not found evidence of a similar effect for the rest of the developing world. Moreover, they find that this effect has been reinforced after the colonial independence of SSA, which in many cases has been associated with legislation that lifted the prohibition of the free internal movement of native Africans. Henderson, Storeygard, and Deichmann (2014; 2017) confirm the strong link between weather shocks—in particular, rainfall—and urbanization and city growth. More severe and persistent climate changes, measured with respect to rainfall, will likely increase the challenges faced by farmers and further accelerate migration to cities. This occurs primarily in arid countries in SSA. By lowering farm incomes, shortages in rainfall encourages migration to nearby cities, while wetter conditions slow it. Similarly, Bruckner (2012) uses rainfall as an instrument for agricultural GDP share in Africa and finds that a decrease in this share leads to increased urbanization.

C. Migration to Cities, Urbanization, and Conflict

The literature that links migration and conflict has focused mainly on rural–urban migration as a potential driver of grievances and opportunities for violent mobilization. The explanations that the literature has used include relative deprivation and radicalization of migrants, reduced opportunity costs, enhanced social communication, and ethnic frictions. The influx of rural–urban migrants tends not to be accommodated by public or private sectors, and migrants are likely to experience rising relative deprivation, which in turn increases the likelihood of their engaging in social disorder (Gizewski and Homer-Dixon 1995). Migrants may also have problems adjusting to life in the cities, in particular, because of disruption to their old customs and habits. As a result, migrants may tend to be more easily recruited into radical movements (Gizewski and Homer-Dixon 1995). In urban environments, there is also a more intense competition for access to services and jobs among migrants and local urbanites (Reuveny 2007). Cities can also be ethnically and religiously diverse, and this mixing may represent a further destabilizing factor (Beall, Guha-Khasnobis, and Kanbur 2010).


Despite the several theoretical reasons to expect a link between rapid urbanization (and city growth) and conflict in urban areas, there is a relatively limited literature that has empirically tested the link between the two. Buhaug and Urdal (2013) find no support for the “urbanization bomb”—the idea that urban population growth should lead to an increase in political violence. Ostby (2016) investigates the relationship between migration into cities and political violence with city-level data in Africa and Asia, and finds that the movement of rural people into the cities creates social disorder through mechanisms such as poverty, unequal educational opportunities, and the socioeconomic marginalization of rural–urban migrants. Our analysis is in line with research that argues there may be a link between climate, urbanization, and (urban) conflict. In this article, we examine the effects of flooding on social disorders in urban areas, testing more explicitly city growth as the mechanism between these links. In addition, while most of the literature has focused on one region, mainly Africa, or short periods, we test our hypothesis over a period of 50 years for a large world sample. This allows us to study differentiated effects across world regions (mainly differences between Africa and Asia). Our analysis is also in line with the work of Bhavnani and Lacina (2015) in India, who test the causal effect of migration on social disorder, using abnormal rainfall in migrant-sending states as an instrument for migration, and conclude that migration, on average, leads to rioting in the host area.

III. DATA AND DESCRIPTIVE ANALYSIS

To study the potential effects of weather shocks on urbanization and city growth, and through these on social disorder in urban areas, we build a large dataset considering 138 countries over the 1960–2015 period. Our dataset includes several variables for our three key dimensions, namely weather shocks, urbanization and city growth, and social disorder in urban areas. For weather shocks, we consider rainfall anomalies, average temperatures (constructed from monthly gridded data), and the number of people displaced by floods. For urbanization and city growth, we consider urbanization rates and population (in logs) in largest cities. For social disorder in urban areas, we consider different measures. Below we explain our key variables and sources.

A. Data Sources and Main Variables

1. Weather Shocks Data

Our variables to capture weather shocks come from two main sources. Historical weather data, including temperature and rainfall observations, are derived from monthly global gridded data, which have been aggregated to country means.2 Our main measure of rainfall, rain_anom, captures annual deviations in rainfall for a particular country, relative to the variability of year-to-year rainfall for that country (as used in, for example, Barrios, Bertinelli, and Strobl 2006 and Hendrix and Salehyan 2012), and is defined as Rain_anomit=(ann_rainit–mean_raini)/sd_raini .3 We also include observations of annual average temperatures from the same source.

2 The country-level datasets that we use were obtained from the World Bank’s Climate Change Knowledge Portal at

http://sdwebx.worldbank.org/climateportal/index.cfm?page=downscaled_data_download&menu=historical (accessed 7 November 2018). These data are derived from the University of East Anglia’s Climate Research Unit time series dataset of high resolution gridded monthly climatic observations (see Harris et al. 2014).

3 The rain_anom variable is standardized, such that the mean in our sample is close to 0, the standard deviation is approximately 0.5, and the range of values in the sample is from roughly –2 to +2 (see Table 1, which includes summary statistics for our main variables).


Our data on the number of people displaced by floods come from the Dartmouth Flood Observatory (DFO) archive (Brakenridge 1985–present). The DFO database includes information by event on the location, timing, duration, damage, and the number of people killed and displaced, for thousands of flood events worldwide from 1985–2015, compiled from media estimates and government reports. Particularly useful for our purposes are a set of shapefiles that define the areas affected by each flood event in the archive. While these shapefiles often cover fairly broad areas (these are not inundation maps), for the purposes of our city-level analysis, they allow us to distinguish floods that have impacted directly on the cities in our data from those that have affected other areas within the same country.4 This is particularly useful for our purposes. Many flood events impact directly on large cities, displacing people already living in cities. These events would not therefore be expected to increase the population of those cities. By contrast, flood events occurring outside of major cities, and displacing population, may cause (some of) those displaced to move to the city. Using geographic information system software, we overlap the flood shapefiles obtained from DFO, with coordinates of the cities in our data (one city per country), to distinguish flood events that overlap with the city from those that affect other areas within the same country. Based on this categorization, we are able to construct our main flood variable, which is the sum of the number of people displaced by flood events occurring in country i in period t, which did not overlap with the largest city in that country.5

2. Urban Data

For urbanization and city growth we focus on two sets of variables. On the one hand, we use urbanization rates, defined as population living in urban areas as a percentage of total population. On the other hand, we use population in the largest city. The focus on the largest cities rests on the fact that the pattern of urbanization in many developing countries shows a high degree of urban concentration, with one or few cities of disproportionate size. For urban population, we use data from the United Nations World Urbanization Prospects, which includes observations of city population every 5 years from 1950 to 2015 (United Nations 1950-2050). In line with the urban economics literature, cities are considered not as administrative units but as functional urban areas. This means that we consider population in the whole urban agglomeration, which is not only more appropriate to capture the reality of urban residents, but also make the comparability across cities in different countries easier.

3. Urban Conflict Data

For conflict in urban areas, we use data from the Urban Social Disorder (USD) dataset.6 This dataset gives information on the number of disorder events in urban areas, including demonstrations, riots, and armed conflict (battles or terrorist events), a proxy of the intensity by giving an estimate of the number

4 As per the DFO website, Archive Notes: “Polygons representing the areas affected by flooding are drawn in a geographic

information system program based upon information acquired from news sources. Note: These are not actual flooded areas but rather the extent of geographic regions affected by flooding.” (accessed November 2018).

5 The definition of “displaced,” as used in the DFO archive is as follows: “Number Displaced—This number is sometimes the total number of people left homeless after the incident, and sometimes it is the number evacuated during the flood. News reports will often mention a number of people that are ‘affected,’ but we do not use this. If the only information is the number of houses destroyed or damaged, then DFO assumes that 4 people live in each house. If the news report only mentions that ‘thousands were evacuated,’ the number is estimated at 3000. If the news reports mention that ‘more than 10,000’ were displaced then the DFO number is 11,000 (number plus 10%). If the only information is the number of families left homeless, then DFO assumes that there are 4 people in each family.” (DFO website, Archive Notes at http://floodobservatory.colorado.edu/Archives/ArchiveNotes.html, accessed November 2018).

6 Available from https://www.prio.org/Data/Armed-Conflict/Urban-Social-Disorder/ (accessed November 2018).


of people involved in each incident and the number of fatalities (if any). We code this information, for integration with our climate and urban data, by counting the number of events per city-period (summing over 5-year intervals from 1960 to 2015), taking one city per country (as discussed above)—as our measure of the intensive margin of urban events. We also create a binary indicator for whether a given city experienced any disorder events in a 5-year period—our measure of the extensive margin of urban disorder. Both measures are generated for all events, and separately for events that involved fatalities and those that did not involve any fatalities, according to the USD data.

B. Descriptive Analysis and Stylized Facts

Before performing econometric analysis, we can look at our data for our three key dimensions (weather shocks, urbanization and city growth, and conflict in urban areas). Table 1 presents summary statistics for our main variables for our world sample. Figures 1a–1f present the evolution of rainfall, floods, urban growth, and conflict in urban areas. The figures show the average across countries for our world sample, for developing Asian countries only, and for SSA countries only.

Table 1: Summary Statistics

Variable Obs. Mean Std. Dev. Min Max

Rainfall anomalies 1,781 0.035 0.547 –2.257 2.111

Annual rainfall 1,781 1,038.10 760.14 27.35 3,331.46

Annual temperature 1,781 18.55 8.12 –7.38 29.14

Number displaced by floods (total)

834 744,473 5,235,825 0 9.91E+07

Number displaced by floods (no city)

834 481,368 3,194,748 0 4.87E+07

Number displaced by floods (city)

834 263,105 2,430,352 0 5.50E+07

Urbanization rate 1,617 48.77 24.85 2.08 100

City population 2,114 2,286.71 3,887.20 2.70 38,001.02

Urban social disorder (all events) 946 8.46 14.39 0 208

Fatal disorder events 946 3.71 10.04 0 176

Nonfatal disorder events 946 4.74 6.75 0 52

Min = minimum, Max = maximum, Obs. = observations, Std. Dev. = standard deviation. Notes: Rainfall anomalies are measured as annual deviations from rainfall relative to the variability of year-to-year rainfall; annual rainfall is measured in millimeters; annual temperature is measured in Celsius degrees; numbers displaced by floods measured in number of people; we define separately floods that did not overlap with the largest city in each country (no city), and those that did overlap the largest city (city); urbanization rate is defined as the population living in urban areas as a percentage of total population; city population is the population in the largest city in thousands; urban social disorder is measured in number of events observed per city; we define separately a measure that counts the number of events where at least one fatality was recorded, and the count of events where no fatalities were recorded. The data are observed for the period 1960–2015, with the exception of floods, which are observed for the period 1985–2015. Source: Authors’ calculations.


Figure 1: Evolution of Main Variables

Notes: Trends show the average across countries. Data are aggregated to 5-year periods, except for urban populations, which are observed once every 5 years. Developing Asia is as defined by the Asian Development Bank. The sample of countries included in each category is limited by data availability. In Figure 1a, annual rainfall is measured in millimeters per year. In Figure 1b, rainfall anomalies are deviations from average rainfall, relative to country-specific year-to-year variations in rainfall. In Figure 1c, people displaced by floods are expressed per year as a percentage of the population in the largest city, relative to the size of the largest city. Note that data on floods are only observed for the period 1985–2015. In Figure 1e, population is measured in millions. In Figure 1f, urban disorders refer to the number of events, averaged across countries per period. Source: Authors’ calculations.

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(e) Population in largest city (f) Urban disorder events


Looking at Figure 1, we can highlight some relevant stylized facts. In terms of weather shocks, our data show a clear decreasing trend from 1960 to 1990 in SSA, both in terms of annual rainfall and rainfall anomalies (Figure 1a and 1b). Decreasing, and more erratic, rainfall in many SSA countries has been highlighted as a cause of rapid urbanization and lower economic performance in the last decades of the 20th century (for example, Barrios, Bertinelli, and Strobl 2006; 2010). In developing Asia, by contrast, rainfall levels are not only significantly higher than in SSA (and our world sample average), but also show lower anomalies. When looking at data on people displaced by floods, substantial differences between SSA and developing Asia are also evident; in developing Asia, the average across countries in the number of people displaced by floods is much higher than in SSA or any other region of the world (even relative to the population of the largest city, as shown in Figure 1c). The number of people displaced by floods in developing Asia shows a substantial increase between 1990 and 2000, when, on average, the number displaced by flooding per year per country was more than 30% of the population in the largest city in that country.

In terms of urban growth (Figure 1d), there is a clear upward trend in both developing Asia and SSA. However, the urban rate in SSA countries remains lower than in the rest of the world. On average, there is still at least a 10 percentage point difference between SSA and Asian countries. Something similar is evident when we look at the size of the largest cities; while in SSA, the average across countries in size of the largest city was still below 2 million inhabitants in 2010, in developing Asia, that figure was almost 6 million.

Finally, in terms of conflict in urban areas (Figure 1f), there is also a clear upward trend in the number of urban disorder events. In developing Asia, the number of events was above the world average since 1995, and it shows a significant rise between the year 2000 and 2005. Interestingly, for developing Asia, the trends of our key variables show i) a substantial increase in the number of displaced people by floods from 1990 to 2000, ii) an acceleration in the size of the largest cities right after (between 1995 and 2005), and iii) a significant increase in the number of urban disorder events after 2000.

To (descriptively) explore the potential connection between our key dimensions (weather shocks, urbanization and city growth, and conflict in urban areas), Figures 2a–2d show the association between some of our main variables. People displaced by floods shows a positive association with urban disorder events, especially in developing Asia. It also shows a positive association with the size of the largest city. This association is stronger when considering only floods that have not affected the largest city.7 Finally, the size of the largest city is also positively associated with the number of urban disorder events.

7 By contrast, rainfall anomalies show no association with urbanization (and size of the largest city) in the whole sample, but

are negatively associated in SSA (in line with the literature).


Figure 2: Association between Main Variables

Notes: Figures show scatters using all available observations for each pair of variables. Figures 2a and 2b show the number of urban social disorder events and population displaced by flooding, for the full sample (a) and for developing Asia only (b). Figure 2c excludes Japan as an outlier in the population of the largest agglomeration (Tokyo). Figure 2d excludes Iraq as an outlier in urban disorder events. Source: Authors’ calculations.

IV. EMPIRICAL ANALYSIS

Our analysis involves a panel dataset covering the period 1960-2015 (1985-2015 for the floods data), for 138 (large) cities in 138 countries (one city per country). At the city level, we observe population and urban disorder events. Our main explanatory variables of interest—weather shocks and floods—are defined at the country level, given that we expect weather shocks outside of cities to “push” population into (large) cities.8 Similarly, (most of) our control variables —for example, GDP per capita and total population—are observed at the national level.

8 Population movement inside a country is much more likely (and easier) than across a border. For that reason, and for

simplicity of the analysis, we do not consider the possibility of cross-border movements of population.

HTI1995DZA2010

SAU2010ZWE1990ZMB1995

MYA2005

MON2005GEO1995RWA2010

TGO2000MON2000CMR1990

AZE2010

ARM1990TUN1985

HND2010

SAU2000

DZA1995

SAU2005EGY2000TAJ2010

HTI1985TUN2010

PRY2000

CHL2010

EGY1995SEN2010PAN2005GEO2010

TUR1990

NEP1990KAZ2000

SEN1985

DZA1990

DZA1985

AFG1985

DOM1985

PAN2010

ZAF1990

BFA1990

GTM2000

ZWE2005

ECU2010TZA2000UGA1985LBR1995BOL1990ZAF2010

TUR1985TUR1995

SEN2005NEP1985BFA1985COG2005

GEO2005

HND2000CRI1985

EGY2010

GTM1995

TUR2000

SLV1990

ETH1985SYR2000ZWE2010RWA2005TAJ2000GTM1985TAJ1990

KEN1985

IRN2005

KHM1995

URY2000ECU1995NEP1995

DOM2000KAZ2010DOM1995SLV1995

ARG2010

AFG2000

ECU2000

BOL1985

URY1985

ETH2010MAL1995

CRI2000AGO2010LBR2005CUB1995TAJ2005ZAF1995

ZWE2000ECU1985

NEP2010UGA2000

NER1990

ZMB1985GEO2000BFA2010

MYA1995

SDN1990PAN2000CUB2005

TAJ1995VEN2005HTI2010

KAZ1990GEO1985DZA2000

CHL1990

BOL1995

IRN2000IRN1995

HND1990

AFG2005

ECU1990ZMB2000ZAF2000UGA1995

HTI1990

TCD2000MDG1985CRI1990NER1995KAZ2005

JPN1985BRA1990

VEN1990

MDG1990

AFG2010

CUB2000

CHL1995UGA2010

KHM2005VEN1985

SDN2000TCD1985

VEN2010DZA2005

COL1990CHL2005MEX2000

SOM2005

GHA1995

VEN2000

TUR2005

JPN1995

GHA2000SEN2000TCD2010

SOM2010

ARG2005

AGO2000PER2010

ZAF1985

GTM2010

BOL2010ZAF2005

AFG1990EGY1990

PER2005GTM2005CHL2000PER2000JPN1990

MYA2010

PRY1990NGA2010TZA1995

AFG1995

MAL2000NER2010TUN1990

CHL1985

NGA2005ETH2000CUB1990

SOM2000INO2010

MOZ2005DOM2005

JPN2000

HTI2005

TGO1995

BOL2005

INO1995

MEX1990BRA1995

ARG2000

ETH1990SDN2010

KEN2010

INO1985JPN2010

ARG1985

PRY1995NGA1985MYA1990

SRI1995

ZMB2005

MDG2005

SLV2005

PER1985

NER1985

ARG1990

ECU2005ETH1995HND2005MAL2010KHM2010HTI2000

IRN1985

NGA2000

NEP2000

KEN1995PAK1985

PER1995

TCD2005MOZ1995MOZ2010

SRI1985

VEN1995

IRN2010

JPN2005SDN2005

PER1990

THA1985

SOM1995MAL2005

NGA1990

COL2000MEX2010

NGA1995

BRA2005UGA2005

COL1985

BRA2010BOL2000

IRN1990

BRA2000ARG1995INO1990KHM1990ETH2005

COL1995KEN2005

SDN1995

PAK2000

COL2010MEX1985KEN2000PHI2010MEX2005SRI1990

THA2010

MDG2000VIE2005MEX1995

PAK2005

INO2000

AZE1995

INO2005

BRA1985MOZ2000VIE2000

SRI2000

SRI2005

PAK1995

SDN1985THA1990HND1995

SRI2010

COL2005

PHI1985

PHI1995

BAN2010

THA2005

PHI1990

TZA1990

PAK1990

THA1995

PHI2000

THA2000

IND2010

BAN1985

KHM2000PHI2005

BAN2005

PRC19850

20

40

60

80

6 8 10 12 14 16lnflood_dis_total

(a) Floods and conflict (b) Floods and conflict, developing Asia

(c) Floods and population in the largest city (d) Population in the largest city and urban disorder events

NEVENTS_5Y Fitted values

MON2005

MYA2005

GEO1995ARM1990

MON2000

AZE2010

TAJ2010

GEO2010

KAZ2000

NEP1990

AFG1985

NEP1985

GEO2005TAJ2000TAJ1990

NEP1995

KAZ2010

AFG2000

MAL1995

TAJ2005

NEP2010GEO2000

MYA1995TAJ1995

GEO1985

KAZ1990

AFG2005

KAZ2005

AFG2010

AFG1990

MYA2010

AFG1995

MAL2000INO2010

INO1995

INO1985MYA1990

SRI1995

MAL2010

NEP2000

PAK1985SRI1985

THA1985

MAL2005INO1990

PAK2000

PHI2010

SRI1990

THA2010

VIE2005

PAK2005

INO2000

AZE1995

INO2005

VIE2000

SRI2000

SRI2005

PAK1995

THA1990

SRI2010

PHI1985

PHI1995

BAN2010

THA2005

PHI1990

PAK1990

THA1995

PHI2000

THA2000

IND2010

BAN1985

PHI2005

BAN2005

PRC1985

NEP2005

IND1985

PRC2010

PAK2010

BAN2000

IND1990BAN1990

PRC2005

IND1995

IND2005BAN1995

IND2000

0

20

40

60

5 10 15 20lnflood_dis_total


AUT2000SWE1995BGR2000

ISR2005POR2010

NZL1995

GMB2010

POR1995

SAU2005DEU1990

NIC2010

NEP1995

SAU2010

DEU2005

OMN2000

UKR2000HUN2000HTI1995NZL2010

DZA2010

CAF1995

NOR2005

GRC2010

IRL2000

ZAR2010

LBN1985

ZWE1990NZL1985ZMB1995

FRA1985

GEO1995MON2005RWA2010

MAR2000ITA1985

CUB2010

JAM2000

AZE2010

SLV2000

BEL1995

FIN2005CMR1990ARM1990

ISR1990MAR1995

ALB1995

PAN1995HND2010

SPA2000

PNG2010

POL1985

ITA2005POR2000

NOR2010

GRC1995PRI1990

SRB2000GEO2005

GTM2000

SLE2000

DZA1995

SAU2000ISR2000

EGY2000

LAO1990

HTI1985

PRY2000TUN2010

GRC1990

CHL2010

JAM1985TAJ1985

EGY1995

MAR2005

BIH2000MKD2010

SRB2005PAN2005

MDA1990

AUS1985

CAF2000

CMR2000

TUR1990

TCD1985

POL2005

GBR1995SPA2010

BLR1995

TAJ2005

KAZ2000

ALB1990

HRV2000

NEP1990

BEN2010

BFA2005

MDA2010

DJI2000

DZA1990

NZL2005

MKD2000

SEN1985BOL1995CRI2010

SPA2005

HUN2010

ECU1985MWI2010HND2000

BGR2010AFG1985

PER1985SPA1995

BEN2005

DOM1985PAN2010DOM1995

TAJ2000

ZAF1990

GHA1985

DEU1985CAN1985GRC2005ITA2010SPA1985

ZAF2010

NOR1995

ARE1995

CUB1995UKR2010

MRT2010

TUR2000

ALB2010

ZWE2005ECU2010

NEP1985

TZA2000

ITA1995CAN1990

GHA2005

UGA1985LBR1995

BOL1990

AZE1995

TAJ1995

NZL2000

TUR1985TUR1995

CHE2005

FRA2010

AUS1990

GBR1990

CAN2000

BFA1985

COG2005

DEU2010

DZA2000

RWA2005

ROM2000

CRI1985

DEU1995

GBR2010

ETH1985SYR2000

ARG1995

ZWE2010

SLV1990

GBR2000

TZA2010

TAJ1990

KAZ2010GHA1995ECU2000

AUS1995

KHM1995

AUS2000

GTM1985

ALB2000

TZA1990

RWA2000

PRI1995ROM1990

IRN2005

AGO2005

PAN2000URY2000ECU1995KEN1995

SDN1995AFG2005

CZE2005

ITA1990

ARG2010

BGR2005

VEN1985DOM2000

ZAR2000

PNG1990

FRA2005ARG1990

CAN2005ZAF1995

AFG2000DOM2010

BOL1985ZMB2005

SLE2005

URY1985

ETH2010MAL1995

CRI2000LBR2005

CHL2000AGO2010

UGA2000ZWE2000

SDN1990

PNG2000

NEP2010

POR1985

SDN2005MYA1995

BDI2010MDA1995MRT2005

ROM2010

NIC1995

NER1995CAF2005

POL2000CUB2005

KHM2005

TZA2005UKR1990

NIC1985

KEN2000

HND1995

HTI2010TUN2000

BDI2005

BRA1990

KAZ1990

TGO1995

GEO1985

PNG1995

CHL2005CHL1990

ZMB2000

CAN1995

IRN2000IRN1995

JPN1985

URY2005

HND1990

UKR1995

NAM2010

FRA2000

ECU1990

PNG2005

MWI1985

ZAF2000

UGA1995TCD2000

NAM2000

CZE1995

CRI1990

BLR1990

NAM2005

KAZ2005SRB2010

PAK2000GBR2005

IRQ2005

MDG2005HTI2005MYA1990VEN1990

BEN1985

AFG2010

CUB2000

JPN1990

FRA1990

UGA2010

ITA2000

COL2010

GHA2010ROM2005VEN2000

KHM2010

VEN2010

OMN2005

DZA2005

COL1990

BOL2005

MEX2000

MDG1995

BEN1995

SOM2005CUB1985

TUR2005

JPN1995

ZAF1985

CRI2005

INO2010

ZAR1995

MWI2005

DOM2005

PER2010INO1995

SEN2000

ARG2005

TCD2010SOM2010

AGO2000

PER2000

KEN2005

MRT1995

ROM1995BOL2010

ZAF2005

JAM2005NEP2000

AFG1990

THA2010CAN2010

EGY1990PER2005

SDN2010

ARG1985

NIC2005

TZA1995

NGA1995MYA2010

PRY1990CRI1995

NGA2010

MAL2000COL1985

TCD1995

AFG1995

JPN2005

NER2010

DJI1985MWI1990

CUB1990

NGA2005

TUN1990

BRA1995

ETH2000

SOM2000POL1995

NIC1990

UKR2005

MOZ2005

JPN2000

PRY1995

ARG2000MEX1990

BEN1990

DJI1990

ETH1990

MOZ1995

MEX1985

KEN2010AUS2010

INO1985

JPN2010

MOZ2010

BRA2000

NGA1985

SOM1995

SRI1995

PRY2010

MAL2005AUS2005

ETH1995

MDG2000

SSD2010

MAL2010PER1995FRA1995

MWI1995

ECU2005

JAM1990

HND2005

ETH2005

HTI2000

IRN1985NGA2000

DEU2000

PER1990PAK1985

TCD2005

USA2000PAK2005

MWI2000

VEN1995

COL2000

MEX2010

SRI1985

IRN2010

THA1985

BRA2005

NGA1990

BRA2010

IRN1990

UGA2005

MEX1995USA2010

BOL2000

VIE1990

INO1990

USA1995

COL1995PHI1985

SRI1990

INO2000

VIE2005THA1995INO2005

MOZ2000

BAN1985

PHI2010

THA2005

VIE1985

MEX2005BAN2010

VIE2010

PHI1995

USA2005

VIE2000

BRA1985

SRI2000SRI2005

USA1990

PAK1995THA1990

SRI2010

USA1985

COL2005PHI2000PAK1990PHI1990

BAN2005

VIE1995

PHI2005

THA2000

PRC2000

IND2010

PRC1985

12

14

16

18

0 5 10 15lnflood_dis_nocity

lnpoplargest Fitted values

RWA1960RWA1970YEM1960LAO1960BFA1965NER1965

YEM1965

ZMB1960SOM1960TGO1960KHM1975YEM1970GIN1960ARE1975LAO1965COG1960HND1960NER1970MLI1960NEP1965UGA1960TGO1965YEM1975SOM1965NEP1970CMR1960LAO1970MLI1965ZMB1965TZA1960HND1965

COG1965LBY1960NEP1975

LAO1975

CIV1960NER1975GIN1965LAO1980JOR1960

RWA1990

AGO1960UGA1965HND1970LBR1975SAU1965ARE1980

TCD1975

TZA1965LBY1965YEM1980KHM1980COG1970LAO1985

ZWE1960

MON1965MOZ1965

BFA1980

NER1980ZMB1970

ARE1985PAN1960HND1975KEN1960NEP1985JOR1965CIV1965SLV1960AGO1965

ZWE1965ECU1960RWA1995TCD1980LBR1980COG1975HTI1965UGA1970SDN1960NER1985SEN1960PAN1965MDG1970MLI1975TKM1985DOM1960AFG1965

HND1980MOZ1970

ZMB1975GIN1970

JOR1970GHA1960

TCD1985MAL1965SLV1965UGA1975NEP1990LBY1970ECU1965YEM1985KEN1965TGO1985ZWE1970BFA1985BOL1960NER1990LAO2000SOM1975COG1980MAL1970MDG1975MOZ1975AGO1970HTI1970PRY1965LBR1995

UGA1980

HND1985AFG1970SEN1965

TGO1990

TCD1990SDN1965

SRI1960MLI1980MON1985GHA1965SLV1970JOR1975ECU1970

DOM1965

BAN1960NEP1995BOL1965

LBR1985ETH1960SRI1965TKM2000

CRI1980PAN1975ZWE1975

ZMB1980BFA1990TGO1995MOZ1980SOM1980PRY1970NER1995KWT1970SRI1970LBN1960TAJ2000GIN1975CMR1980TZA1975MON1990SRI1975HND1990TAJ1995HTI1975MDG1980LBY1975SYR1960SRI1980TAJ1990

TKM2005UGA1985COG1985

SLV1975

ETH1965AGO1975

SRI1985BOL1970

GTM1960PAN1980SRI1990KHM1990ZWE1980

CRI1985ECU1975SRI1995

TGO2000GHA1970TAJ2005KGZ1990JOR1980ZMB1985

NEP2000

SRI2000MOZ1985YEM1990PRY1975TUN1965HND1995SDN1970GIN1980MAL1975LBY1980

SRI2005

BFA1995

AFG1975

KEN1975

SRI2010

DOM1970KWT1975NER2000HTI1980

SLV1980

BOL1975

KGZ1995TCD2000COG1990

SAU1975TAJ2010

PAN1985

TGO2005SYR1965

ETH1970

LBN1965JOR1985GHA1975CRI1990MDG1985MLI1990SOM1985HND2000LBY1985ZMB1990TUN1970NGA1960MON2000GIN1985KGZ2000GTM1965PRY1980AGO1980ZWE1985ECU1980

NEP2005

KGZ2005BOL1980

NER2005BAN1965

SLV1985

COG1995KGZ2010TGO2010LBR2000KHM1995PAN1990JOR1990TCD2005TZA1980RWA2005HND2005KEN1980LBY1990GHA1980

DZA1960

HTI1985

SDN1975KWT1980TUN1975GIN1990

KHM1970

ZMB1995MLI1995DOM1975UGA1995SYR1970

PRY1985GTM1970

MOZ1995

LBN1970

ETH1975

BOL1985MON2005ECU1985

CMR1990NER2010MDG1990SEN1980SLV1990NEP2010JOR1995

AFG1980

LBY1995HND2010JOR2000AGO1985MOZ2000COG2000LBY2000KAZ1985CRI2000YEM1995

SOM1990

TCD2010

LBR1990

RWA2010GIN1995TUN1980ZWE1990SLV1995

GTM1975

LBR2010BOL1990JOR2005

ARM2010

MOZ2005ZMB2000SLV2005KAZ1990

DZA1965ARM2005ECU1990KEN1985PRY1990

LBY2010

UGA2000GEO2000GEO2005

KAZ1995

JOR2010ARM2000KWT1985SYR1975

ARM1985

GEO2010

MOZ2010

HTI1990

NGA1965MON2010ARM1995SOM1995KHM2000

GTM1980

KAZ2000GEO1995SEN1985SDN1980ARM1990GEO1985GHA1990

SOM2000

LBR2005DOM1980PAN2000ECU1995

GEO1990

TUN1985

AFG1985

ZWE1995BOL1995LBN1995

COG2005KAZ2005URY1960PRY1995GTM1985

LBN1990

VEN1960

KHM2005UGA2005URY1965YEM2000PAN2005ZMB2005ECU2000MDG2000

URY1970BAN1970

SYR1980ZWE2000KEN1990CIV1980BOL2000AGO1990KWT1990DOM1985

VIE1960

KAZ2010URY1975SEN1990NGA1970

SOM2005

GHA1995

SOM2010

GIN2005

HTI1995

MAL1985

CUB1960GTM1990

ZWE2005

TUR1960

URY1980

TUN1990TZA1990ZWE2010ETH1985ECU2005LBN2000

PRY2000

LBN1975

PAN2010DZA1975

URY1985

KHM2010DOM1990BOL2005SYR1985URY1990

AFG1990

KWT2005

MDG2005

LBN1985

URY1995MYA1960

UGA2010

ECU2010URY2000

SDN1985

DZA1980

LBN1980

GTM1995

VEN1965

URY2010

AZE1985

VIE1965

GIN2010

GHA2000

BOL2010

DZA1985SYR1990

HTI2000DOM1995CIV1985

AZE1990

PRY2005

KEN1995

PER1960YEM2005

MYA1965AZE1995

LBN2005

COL1965ETH1990

DZA1990

AZE2000TZA1995SYR1995PAK1960AZE2005IRN1960

CMR2005NGA1975AGO1995CUB1980BFA2010

TUN2010AFG1995

MLI2010MYA1970UZB1985

VIE1970

GTM2000

DZA1995

CHL1960

LBN2010

DOM2000

TUR1965SYR2000MDG2010SEN2000VEN1970

AZE2010

UZB1990KWT2010

CIV1990

CUB1990UZB1995UZB2000ZAF1960

DZA2000HTI2010ETH1995CUB1995MYA1975UZB2005

HTI2005

CUB2000CUB2005SYR2005KEN2000BAN1975

TZA2000DZA2005IND1960PER1965GTM2005

YEM2010

CHL1965DOM2005SAU1990

VIE1975

SDN1990KOR1960

ETH2000MYA1980COL1970

SYR2010

AFG2000PAK1965DZA2010

ZAF1965SEN2005IRN1965CIV1995NGA1980VEN1980GTM2010AGO2000MYA1985ETH2005

CHL1970

KEN2005INO1960VEN1985

VIE1980ZAF1970

VEN1990TUR1970

VEN1995PHI1965

IND1965

VEN2000

VEN2005VEN2010MYA1990ETH2010SEN2010

ZAF1975

MAL1995PER1970

AFG2005

CIV2000

SAU1995

COL1975

SDN1995THA1970PAK1970CHL1975

ZAF1980MYA1995

KEN2010

BAN1980IRN1970

INO1965ZAF1985

KOR1965NGA1985SDN2000

COL1980IND1970PHI1970

CIV2005MYA2000

SAU2000

TUR1975

EGY1960

PER1975

ZAF1990

CHL1980

AFG2010

THA1975

INO1970

MYA2005

BRA1960SDN2005

PAK1975

COL1985

CIV2010MAL2000

CHL1985

SAU2005

IRN1975

MYA2010

VIE2000

TUR1980IND1975PER1980

SGP2005AGO2010

SDN2010

ZAF1995

CHL1990

BAN1985

THA1980EGY1965COL1990NGA1990

INO1975MAL2005

PHI1975PAK1980

SGP2010

IRN1980

CHL1995

PER1985

VIE2005SAU2010THA1985

KOR1970

TUR1985

MEX1960

COL1995

BRA1965

IND1980

EGY1970ZAF2000PRC1975CHL2000MAL2010

PER1990

IRN1985

THA1990

PHI1980

PRC1980

NGA1995

INO1980CHL2005

PAK1985

THA1995

CHL2010

COL2000THA2000

IRN1990PRC1965EGY1975

TUR1990

PER1995ARG1960BAN1990IRN1995

ZAF2005KOR1975PRC1960PRC1985

PHI1985

MEX1965INO1985

IRN2000

PAK1990

THA2005

NGA2000PER2000

ARG1965

IND1985

EGY1980COL2005BRA1970

IRN2005

TUR1995PHI1990ZAF2010

IRN2010

PER2005

ARG1970

INO1990

THA2010

KOR1980

INO1995

EGY1985

BAN1995INO2000PAK1995

COL2010

TUR2000

ARG1975

MEX1970NGA2005PER2010

INO2005

KOR1985

PHI1995

ARG1980

BRA1975INO2010IND1990

KOR2010

KOR2005

KOR2000

EGY1990ARG1985

PHI2000

PAK2000

KOR1995

BAN2000ARG1990

TUR2005

KOR1990

MEX1975

NGA2010

PHI2005ARG1995

PAK2005

PHI2010EGY1995BRA1980

BAN2005

ARG2000IND1995

TUR2010

MEX1980

ARG2005

BRA1985EGY2000

PAK2010

ARG2010MEX1985

BAN2010

BRA1990

EGY2005

MEX1990IND2000BRA1995JPN1960PRC2005

EGY2010

BRA2000MEX1995BRA2005MEX2000

IND2005

MEX2005BRA2010

PRC2010MEX2010JPN1965IND2010

JPN1970JPN1980

JPN1985

JPN1990JPN1995JPN2000JPN2005JPN2010

0

20

40

60

80

100

10 12 14 16 18lnpoplargest



Our sample of cities represents the largest city in each country (in terms of population in the urban agglomeration) and is based on the availability of the USD conflict data, which is generally only observed for one city per country. Where USD data for more than one city in a given country is available, we restrict our attention to the single largest city (usually, but not always, the capital city).

In estimation, our sample size is sometimes restricted due to the availability of our key variables. For example, while we have 138 cities in our sample, in specifications with urban disorder events as the outcome, the sample is restricted to those countries in the USD data, leaving us with a sample of 85 cities with data on urban social disorder events. Similarly, in terms of time coverage, our data spans from 1960 to 2015 (based on availability of the USD data). However, in regressions using flood data, we are restricted to the period 1985–2015, given the coverage of the flood events for which we have obtained maps (shapefiles) of affected areas, from the DFO archive (as detailed above).

A. Estimation Strategy

As our focus is on how weather shocks can “push” people into cities and therefore drive urban disorders there, we begin by testing the association between weather shocks—rainfall anomalies, mean temperature, and people displaced by floods—on the evolution of city size. We run regressions of the following form:

𝐶𝑖𝑡𝑦𝑆𝑖𝑧𝑒 = 𝛼 + 𝛽 𝑊𝑒𝑎𝑡ℎ𝑒𝑟𝑆ℎ𝑜𝑐𝑘𝑠 + 𝛽 ′𝑿𝒊𝒕 + 𝛾 + 𝜃 + 𝜖 (1)

where CitySizeit is the total population of the largest city (in logs) in country i in period t (where t = 1960, 1965, 1970…etc.). Alternatively, we also consider the log of the urbanization rate (the population living in urban areas as a percentage of the total population) in country i in period t.9 City size and urbanization rates are observed every 5 years (i.e., in 1960, 1965, 1970… etc.). Our main explanatory variables of interest are WeatherShocksit. Depending on the specification, these are either rainfall anomalies (as defined above) and annual temperatures, or the log of the number of people displaced by floods (again, as discussed above). The Xit are a set of time-varying country-level controls, including the log of GDP per capita and total country population.10 We also include year fixed effects, t , and city (country) fixed effects, i . The standard errors, it, are clustered at the city (country) level.

Our second step is to test the “reduced-form” of our main hypothesis: that weather shocks, in particular, the number of people displaced by flooding outside of major cities, are associated with urban disorder within those cities. For this, we estimate regressions of the following form:

𝐷𝑖𝑠𝑜𝑟𝑑𝑒𝑟 = 𝛼 + 𝛽 𝐹𝑙𝑜𝑜𝑑𝑠 + 𝛽 𝐶𝑖𝑡𝑦𝑆𝑖𝑧𝑒 + 𝛽 ′𝑿𝒊𝒕 + 𝛾 + 𝜃 + 𝜖 (2)

where Disorderijt represents a measure of the number of events, of type jwhere j={all, fatal, nonfatal}, in city i in period t, where t aggregates the number of events every 5 years for the period 1960–2015 (as observed in the USD data). We distinguish in the data between events that involved fatalities and those that did not. Floodsit is defined, as discussed above, as the log of the number of people displaced by flood events that occurred in country i in period t (1985–2015), which did not overlap with the largest city (where the disorder events are observed).

9 We take the log of the urbanization rate to replicate the empirical set-up in Barrios, Bertinelli, and Strobl (2006). 10 Where these controls are included alongside WeatherShocksit , the controls are lagged in order to avoid weather shocks

(or floods) having a contemporaneous effect on these controls.


Equation (2) is estimated using count models when Disorderijt is measured as a count of events for a given city-period observation.11 This approach tests the intensive margin of the effect of floods (and city population) on the frequency of disorder events. Alternatively, equation (2) can be estimated using a probability model when Disorderijt is measured as a binary indicator for city-period observations with zero or nonzero counts of disorder events of a particular type.12 In this case, equation (2) would test the extensive margin of the effect of floods (and city population) on the likelihood of (a particular type of) urban disorder. The remaining variables on the right-hand side of equation (2) are defined as in equation (1), with the difference that here we include CitySizeit alongside our main explanatory variable. We also include additional control variables relevant for explaining social disorder in urban areas, such as an indicator for country-periods with ongoing armed conflict, and an indicator of democracy.13 We first estimate versions of equation (2) without including city size on the right-hand side, to test for any significant association between people displaced by floods and urban disorder. We then include city size, to test our idea that the association between people displaced by floods on urban disorder is operating (primarily) through the displacement of population into (large) cities.

Our primary hypothesis is therefore as follows: floods occurring outside of cities displace people, leading to an increase in city population, resulting in increased urban social disorder. This general hypothesis leads to two testable hypotheses, based on estimating equation (2):

H1: 1 > 0, where CitySizeit is not included in equation (2)

H2: 2 > 0 and 1 = 0, where CitySizeit is included in equation (2)

H1 predicts that the number of people displaced by floods that occur outside a city is positively associated with urban social disorder events in the city. H2 predicts that this effect is mediated via the effect of floods on city population, and the consequent effect of city population on urban social disorder. H2 predicts that the only effect of floods on urban social disorder is via the effect of floods on city population (i.e., city size). A weaker version of H2 would be that the magnitude of the coefficient on floods (1) would decrease (and/or become less statistically significant), when CitySizeit is added to equation (2), indicating that some (but not all) of the effect of floods on urban social disorder is happening via the effects on city population.

11 Given overdispersion in the urban disorder events data, we report the results of estimation using a negative binomial

model as our baseline, although we have also experimented with a Poisson model, with (qualitatively) similar findings. 12 Where we estimate equation (2) using a probit model, the specification includes random effects, as opposed to fixed

effects. If we include country fixed effects (or country dummies), the relative lack of variation in the binary outcome at the country level results in many countries being dropped (due to collinearity) with a substantial decrease in the number of observations and precision of the findings. This is particularly an issue for the shorter panel where we use floods data (1985–2015).

13 These additional controls are based on the UCDP/PRIO Armed Conflict Data Set (Uppsala Conflict Data Program and Peace Research Institute Oslo 1946–2008), and the Polity IV Project: Political Regime Characteristics and Transitions data on democracy (Marshall and Gurr, 1800–2013), respectively.


Finally, we test the effects of city size on urban social disorder directly, by running regressions of the following form:

𝐷𝑖𝑠𝑜𝑟𝑑𝑒𝑟 = 𝛼 + 𝛽 𝐶𝑖𝑡𝑦𝑆𝑖𝑧𝑒 + 𝛽 𝑿𝒊𝒕 + 𝛾 + 𝜃 + 𝜖 (3)

where variables are defined as in equations (1) and (2) above. Equation (3) is also estimated for both the extensive and intensive margins, as discussed above for equation (2).

B. Results

We start with regressions of city size and urbanization rates on weather shocks (as in equation [1]). The first set of results, presented in Table 2, shows the effect of rainfall anomalies on city size. In general, we find a strong negative association between rainfall anomalies and city size—periods with low rainfall are associated with higher city size. This is particularly the case in SSA. However, in Asia, we find the opposite pattern—higher rainfall is associated with city growth.

Table 2: Effect of Weather Shocks on City Population (Log)

Variables (1) (2) (3) (4) (5) (6)

Rainfall anomalies (t–1) –0.049*** (0.016)

0.002(0.015)

–0.062***(0.016)

–0.062***(0.021)

–0.008 (0.020)

–0.086***(0.022)

Rainfall anomalies (t–1) x SSA –0.162***(0.037)

–0.143*** (0.042)

Rainfall anomalies (t –1) x devAsia

0.072*(0.043)

0.119***(0.043)

Temperature (t –1) –0.045 (0.032)

–0.122***(0.037)

–0.063*(0.036)

–0.015(0.041)

–0.129** (0.052)

–0.046(0.044)

Temperature (t –1) x SSA 0.355***(0.109)

0.325*** (0.106)

Temperature (t –1) x devAsia 0.215**(0.108)

0.203*(0.111)

Observations 1,294 1,294 1,294 884 884 884

R-squared 0.839 0.856 0.842 0.846 0.860 0.849

Number of countries 134 134 134 101 101 101

Controls

devAsia = developing Asia, SSA = sub-Saharan Africa. Notes: Robust standard errors, clustered by country, in parentheses. Country and time period fixed effects included in all models. *** p<0.01, ** p<0.05, * p<0.1. Rainfall anomalies are measured as annual deviations from rainfall relative to the variability of year-to-year rainfall; annual temperature is measured in Celsius degrees. The outcome variable in each model is log (city population) for the largest city in each country. Additional controls included: log (gross domestic product per capita) and log (total population). Source: Authors’ calculations.


Table 3: Effect of Weather Shocks on Urbanization Rate (Log)

Variables (1) (2) (3) (4) (5) (6)

Rainfall anomalies (t–1) –0.034*** (0.010)

–0.001(0.008)

–0.044***(0.011)

–0.027**(0.011)

0.004 (0.011)

–0.041***(0.012)

Rainfall anomalies (t –1) x SSA –0.098***(0.024)

–0.084*** (0.025)

Rainfall anomalies (t –1) x devAsia

0.053*(0.028)

0.067**(0.026)

Temperature (t –1) –0.008 (0.024)

–0.067***(0.024)

–0.021(0.025)

0.045(0.034)

–0.041 (0.034)

0.028(0.036)

Temperature (t –1) x SSA 0.242***(0.078)

0.243*** (0.079)

Temperature (t –1) x devAsia 0.131(0.104)

0.110(0.102)

Observations 1,160 1,160 1,160 884 884 884

R-squared 0.648 0.683 0.653 0.692 0.723 0.696

Number of iso num 134 134 134 101 101 101

Controls

devAsia = developing Asia, SSA = sub-Saharan Africa. Notes: Robust standard errors, clustered by country, in parentheses. Country and time period fixed effects included in all models. *** p<0.01, ** p<0.05, * p<0.1. Rainfall anomalies are measured as annual deviations from rainfall relative to the variability of year-to-year rainfall; annual temperature is measured in Celsius degrees. The outcome variable in each model is log (urbanization rate). Additional controls included: log (gross domestic product per capita) and log (total population). Source: Authors’ calculations.

In column (1) of Table 2, which includes the full sample, we find a significant negative

association between rainfall anomalies and city size. Column (2) repeats this specification allowing for differentiated coefficients for SSA countries (cities). Column (3) allows for differentiated coefficients for developing Asia. While for SSA, the coefficient for rainfall is negative, for developing Asia, it is positive, reversing the overall effect of rainfall anomalies on city population. Columns (4), (5), and (6) repeat column (1), (2), and (3) restricting the sample to developing countries only, showing similar results, with more precision on the interaction term between rainfall anomalies and developing Asia.14

We also run regressions for equation (1) but considering urbanization rates, reported in Table 3, rather than the size of the largest city as reported in Table 2. Like the results in Table 2, we find that lower-than-expected rainfall leads to higher urbanization in SSA, but the opposite pattern for developing Asia.15 While the (negative) association between rainfall anomalies and urbanization

14 In further robustness checks not reported here, we repeated the specifications in Table 2 excluding outliers in terms of

city population (Tokyo). The results remain essentially unaffected. Results available on request. 15 As with Table 2, we also run further robustness checks not reported here, repeating the specifications in Table 3,

excluding outliers in terms of city population (Tokyo), and including both interaction terms (with SSA and developing Asia) in the same regression. In all cases, the results remain essentially unaffected. Results available on request.


has been observed previously for Africa (for example, Barrios, Bertinelli, and Strobl 2006), the observation of a positive association between rainfall anomalies and city growth for Asia is relatively novel. The reversal of the sign on rainfall anomalies for Asia may appear confusing. One potential explanation is that higher rainfall is associated with more economic growth and therefore faster city growth in developing Asia. However, we do not find such an association—in fact, in results not reported here, we find that if anything, rainfall anomalies are negatively associated with economic growth in developing Asia.16 Instead, we believe our finding may come from what rainfall anomalies mean in each region, and hint to differential effects of climate change in different world regions. In (most of) Africa, rainfall is already scarce, and shows a clear decreasing trend over the last decades. A negative anomaly in our data means lower-than-expected rainfall—a negative productivity shock to agriculture—potentially pushing people from rural to urban areas. By contrast, in developing Asia, a positive rainfall anomaly in our data may be associated with too much rain (and therefore the risk of floods). In other specifications, not reported here, we also experimented with interactions between rainfall anomalies and an indicator for countries with high average annual rainfall. The findings from this exercise also suggest that “too much” rainfall is associated with faster growth of large cities in countries with already high levels of rainfall.17

Where flooding occurs in rural areas, the displaced population may be pushed toward cities. We test this hypothesis more explicitly in the next set of results.

In Table 4, we test the effect of flooding on city population.18 We limit our attention to people displaced by flood events that did not occur in a country’s largest city (see discussion of this in the data section). Here, we find that the numbers of people displaced by floods occurring outside of our large cities are associated with (changes in) the population in the city—indicating that people displaced by floods are “pushed” toward the largest urban areas. We find no evidence of significant regional variation in this case—the interactions with the SSA or developing Asia dummies are not significant. The effects of flooding on population in cities is similar for the full sample (in columns 1–3) and when we restrict to “developing” countries only (columns 4–6). As both people displaced by floods and city population are in logs, we can interpret our coefficient as elasticities. According to our coefficients, a 1% increase in people displaced by flood leads to around a 0.004% increase in the population of the largest city. This may look like a small number, but in absolute terms, the number is not negligible. For example, according to our data, in Thailand, between 2000 and 2005, the period of the big tsunami of 2004, around 5 million people were displaced by floods outside Bangkok. According to our estimates, this translates into around 128,000 more residents in Bangkok attributable only to the effect of flooding outside Bangkok.

16 We perform growth regressions in a similar spirit to the analysis reported in Barrios, Bertinelli, and Strobl (2010). For

Africa, we find something similar to Barrios, Bertinelli, and Strobl (2010): more rainfall is associated with higher economic growth. For developing Asia, we found the opposite: more rainfall, if anything, is associated with less growth. Results available on request.

17 Results available on request. 18 Note the sample size drops considerably here—mainly due to the more limited time coverage in the flooding data, which

is only available since 1985.


Table 4: Effect of Floods on City Population (Log)

Variables (1) (2) (3) (4) (5) (6)

Floods (t–1) 0.004*** (0.002)

0.003**(0.001)

0.004**(0.002)

0.005***(0.002)

0.003 (0.002)

0.004**(0.002)

Floods (t –1) x SSA 0.004(0.004)

0.005 (0.004)

Floods (t –1) x devAsia 0.006(0.006)

0.006(0.006)

Observations 778 778 778 486 486 486

R-squared 0.812 0.812 0.812 0.799 0.801 0.800


Controls

devAsia = developing Asia, SSA = sub-Saharan Africa. Notes: Robust standard errors, clustered by country, in parentheses. City and time period fixed effects included in all models. *** p<0.01, ** p<0.05, * p<0.1. Floods is in logs and is measured as the people displaced by flood events which did not overlap with a country's largest city. The outcome variable in each model is log (city population) for the largest city in each country. Columns 1–3 include the full sample, columns 4-6 include only developing countries. Additional controls included: log (gross domestic product per capita) and log (total population), both lagged. Source: Authors’ calculations.

Next, we turn to estimating equation (2)—the core of our empirical analysis—for the reduced form effects of floods on urban disorder. The results in Tables 5 and 6 suggest that the numbers displaced by floods occurring outside major cities are strongly associated with (the evolution of) the number of disorder events occurring in those cities (the “intensive margin”—Table 5), and with the probability of observing disorder events in a given city (the “extensive margin”—Table 6), as anticipated in H1. In Table 5, the results show that floods are positively related to the count of all disorder events at the city level (column 1), and the count of fatal events (column 3), while the estimated coefficient on floods is positive but not statistically significant for nonfatal events (in column 5). Similarly, the results in Table 6 show that floods are positively associated with the probability of observing urban disorder events (total—column 1; fatal—column 3; and nonfatal—column 5).

In Tables 5 and 6 we also test H2—that the effects of floods on urban disorder operate (mainly) via city population—by adding it as an additional control (in columns 2, 4, and 6 of Tables 5 and 6). In each case, we see the magnitude of the coefficient on floods declines, and becomes less statistically significant, when we add city population, indicating that (most of) the effect of floods on urban disorder is indeed operating via the effects of flooding on city population. The coefficient on city population is positive and significant in all cases. These findings would seem to support the expectation expressed in H2. The findings reported in Tables 5 and 6 are robust to the inclusion of indicators for country-periods with ongoing armed conflicts, and for democracy.19

19 The results are also robust to the exclusion of outliers in terms of city population (Japan), numbers displaced by flooding

(India and Indonesia) and frequency of urban disorder events (Iraq). Results available on request.


Table 5: Effect of Floods on Urban Conflict Events, Intensive Margin

Variables (1) All

(2)All

(3)Fatal

(4)Fatal

(5) Nonfatal

(6)Nonfatal

Floods (t-1) 0.033*** (0.012)

0.025*(0.013)

0.030**(0.015)

0.021(0.016)

0.017 (0.013)

0.008(0.014)

City population 0.232**(0.117)

0.235*(0.143)

0.280**(0.138)

Observations 384 384 359 359 384 384


Controls

Notes: All models estimated using a negative binomial model with country and time period fixed effects. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Floods is in logs and is measured as the people displaced by flood events which did not overlap with a country's largest city; city population is the log of the population in the largest city. The outcome variable in columns (1) and (2) is the count of all urban social disorder events observed for each city; in columns (3) and (4) the count of disorder events that involved fatalities; and in columns (5) and (6) the count of disorder events that did not involve fatalities. Additional control included: log (gross domestic product per capita). Source: Authors’ calculations.

Table 6: Effect of Floods on Urban Conflict Events, Extensive Margin

Variables (1) All

(2)All

(3)Fatal

(4)Fatal

(5) Nonfatal

(6)Nonfatal

Floods (t-1) 0.063*** (0.024)

0.038(0.025)

0.050***(0.019)

0.035*(0.020)

0.058** (0.023)

0.031(0.024)

City population 0.416**(0.191)

0.255*(0.137)

0.521***(0.182)

Observations 389 389 389 389 389 389


Controls

Notes: Probit random effects model, with time period fixed effects included in all models. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Floods is in logs and is measured as the people displaced by flood events which did not overlap with a country's largest city; city population is the log of the population in the largest city. The outcome variable in columns (1) and (2) is a binary indicator for whether or not urban social disorder events of any type are observed for each city; in columns (3) and (4) a similar indicator but for events that involved fatalities only; and in columns (5) and (6) a similar indicator for events that did not involve fatalities. Additional control included: log (gross domestic product per capita). Source: Authors’ calculations.


Finally, we test directly for association between city population and the frequency (and probability) of urban disorder events. In Table 7, we report the estimated effect of (log) city population on the count of disorder events at the city level (in columns 1–3) and on the probability of observing disorder events (in columns 4–6). We find that increases in city population are significantly associated with higher counts of disorder events (column 1), of fatal events (column 2), as well as nonfatal events (column 3). Turning to the extensive margin, results in columns 4–6 suggest that city population is associated with a higher probability of urban disorder events (again, total, fatal, and nonfatal).20

Table 7: Effect of City Population on Urban Conflict Events, Intensive, and Extensive Margins

Variables

(1) All Count

Model

(2) Fatal Count

Model

(3) Nonfatal

Count Model

(4) All

Prob Model

(5) Fatal Prob

Model

(6) Nonfatal Prob

Model

City population 0.316*** (0.059)

0.255*** (0.073)

0.312*** (0.066)

0.417*** (0.106)

0.288*** (0.096)

0.415*** (0.090)

Observations 820 798 820 825 825 825

Number of countries

84 82 84 85 85 85

Controls

Notes: Columns (1)–(3) were estimated using a negative binomial model with unit and time period fixed effects. Columns (4)–(6) were estimated using a probit model with random effects and time period fixed effects. *** p<0.01, ** p<0.05, * p<0.1. City population is the log of the population in the largest city. The outcome variable in columns (1)–(3) is a count of all urban social disorder events (1), fatal events (2) and nonfatal events (3). In columns (4)–(6), the outcome is a binary indicatory for city-periods with at least one disorder event of any type (4), involving a fatality (5), and not involving any fatalities (6). Additional control included: log (gross domestic product per capita). Source: Authors’ calculations.

V. CONCLUSIONS, DISCUSSION, AND NEXT STEPS

In the analysis presented in this paper, we begin by replicating an existing finding from the literature—that lower-than-expected rainfall is associated with higher urbanization and city growth in countries in sub-Saharan Africa. However, we also establish a new finding related to rainfall anomalies: in developing Asia, higher- (rather than lower-) than-expected rainfall appears to be associated with faster growth of large cities and urbanization. We interpret this finding with reference to an overabundance of rainfall in developing Asia. For arid countries—including many in sub-Saharan Africa—low rainfall likely represents a negative productivity shock to agriculture, potentially pushing people from rural to urban areas. For wetter regions, including many developing Asian countries, the opposite may be the case—too much rainfall results in a negative productivity shock for agriculture (possibly associated with flooding), driving people from rural to urban areas.

20 The coefficients on city population are similar to those in Tables 5 and 6, but are estimated more precisely here, given the

larger number of observations (we can take advantage of the full sample period, 1960–2015, in Table 7, since we are not including floods data in these regressions).


We test this hypothesis explicitly by drawing on novel data related to flooding. In particular, we distinguish between floods that impact directly on major cities, and those that occur outside of our sample of large cities. We find that the latter are strongly associated with the evolution of population in the major (developing) world cities that we study. We next turn to the central hypothesis of our paper, that the displacement of population by flooding leads to increased risk of urban disorder due to increasing congestion in urban areas as the population is “pushed” into cities as a result of floods elsewhere. We find evidence to support the direct effect of floods on urban disorder (both the count of events, the “intensive” margin, and the probability of observing a disorder event, the “extensive” margin), and evidence to support the hypothesized mechanism.

Our findings have important policy implications, especially for evaluating future climate change, as well as for policies regarding climate and disaster resilience, considering the connection between weather shocks and disasters, the spatial reallocation of population, and the tensions and conflict that can come with it.

Our findings also highlight the need for further research. As suggested by our differential findings for Africa and Asia, heterogeneities across countries with regards to baseline climate (arid versus humid countries), type of weather shocks expected, and potentially other country and/or city characteristics—for example, the share of agriculture in GDP—should be studied in more detail to understand the potential impact of climate change specific to each country. Exploring alternative datasets could also allow for more localized (spatially refined) analysis. Finally, a more explicit causal analysis of the different mechanisms linking weather shocks, urbanization and city growth, and disorder in urban areas could be explored, for instance by looking at data on local labor market outcomes, development of local infrastructure, income distribution, and other urban dynamics.

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Climate, Urbanization, and Conflict: The Effects of Weather Shocks and Floods on Urban Social Disorder

Floods regularly displace large numbers of people in developing countries, leading to increased population movement from rural areas to the largest cities. This paper finds evidence that people’s displacement by flooding is associated with a higher risk of social disorder in large cities in developing countries. The evidence suggests that the effects of floods on urban social disorder occur mainly through displaced people being pushed into large cities.

About the Asian Development Bank

ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.


CLIMATE, URBANIZATION, AND CONFLICT: THE EFFECTS OF WEATHER SHOCKS AND FLOODS ON URBAN SOCIAL DISORDERDavid Castells-Quintana and Thomas K.J. McDermott

ADB ECONOMICSWORKING PAPER SERIES

NO. 588

July 2019

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