metadata: authors: 0: author: Dang,Hai-Anh H.
1: author: Nguyen,Minh Cong
2: author: Trinh,Trong-Anh
count: Africa,World
docna: 0: docna: Does Hotter Temperature Increase Poverty and
            Inequality ? Global Evidence from Subnational Data Analysis
docty: Policy Research Working Paper
abstracts: cdata!: Despite a vast literature documenting
            the harmful effects of climate change on various
            socio-economic outcomes, little evidence exists on the
            global impacts of hotter temperature on poverty and
            inequality. Analysis of a new global panel dataset of
            subnational poverty in 134 countries finds that a one-degree
            Celsius increase in temperature leads to a 9.1 percent
            increase in poverty, using the US$1.90 daily poverty
            threshold. A similar increase in temperature causes a 0.8
            percent increase in the Gini inequality index. The paper
            also finds negative effects of colder temperature on poverty
            and inequality. Yet, while poorer
            countries&#x2014;particularly those in South Asia and
            Sub-Saharan Africa&#x2014;are more affected by climate
            change, household adaptation could have mitigated some
            adverse effects in the long run. The findings provide
            relevant and timely inputs for the global fight against
            climate change as well as the current policy debate on the
            responsibilities of richer countries versus poorer countries.
display_title: Does Hotter Temperature Increase
            Poverty and Inequality ? Global Evidence from Subnational
            Data Analysis
listing_relative_url: /research/2023/06/34073204/hotter-temperature-increase-poverty-inequality-global-evidence-subnational-data-analysis
topicv3: Agriculture and Food Security m1326205 435,Poverty m1326687 695,Climate Change m1326829 567
publishtoextweb_dt: 2023-06-01T00:00:00Z
dois: 10.1596/1813-9450-10466
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 747967
1: author_key: 954025
2: author_key: NA
model_extractions: Policy Research Working Paper 10466 Does Hotter Temperature Increase Poverty and Inequality? Global Evidence from Subnational Data Analysis _Hai-Anh H. Dang Minh Cong Nguyen Trong-Anh Trinh_ Development Data Group & Poverty and Equity Global Practice June 2023
Policy Research Working Paper 10466

# **Abstract**

Despite a vast literature documenting the harmful effects of climate change on various socio-economic outcomes, little evidence exists on the global impacts of hotter temperature on poverty and inequality. Analysis of a new global panel dataset of subnational poverty in 134 countries finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty, using the US$1.90 daily poverty threshold. A similar increase in temperature causes a 0.8 percent increase in the Gini inequality index. The paper also finds negative effects of colder temperature on poverty and inequality. Yet, while poorer countries—particularly those in South Asia and Sub-Saharan Africa—are more affected by climate change, household adaptation could have mitigated some adverse effects in the long run. The findings provide relevant and timely inputs for the global fight against climate change as well as the current policy debate on the responsibilities of richer countries versus poorer countries.

This paper is a product of the Development Data Group, Development Economics and the Poverty and Equity Global Practice. . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Does Hotter Temperature Increase Poverty and Inequality? Global Evidence from Subnational Data Analysis**

Hai-Anh H. Dang, Minh Cong Nguyen, and Trong-Anh Trinh<sup>*</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _June 2023._ This version is updated on _October 2023._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

# **JEL Classification:** Q54; I32; O1

**Key words** : Climate change; temperature; poverty; inequality; subnational data

* Dang (hdang@worldbank.org; corresponding author) is a senior economist with the Living Standards Measurement Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Nguyen (mnguyen3@worldbank.org) is a senior economist with the Global Poverty Practice, World Bank; Trinh (trong-anh.trinh@monash.edu) is a consultant with the Living Standards Measurement Unit, Development Data Group, World Bank, and a research fellow at the Centre for Health Economics, Monash University, Australia. We would like to thank Carlo Azzarri, Edward Barbier, Romina Catavassi, Andrew Dabalen, Benjamin Davis, Stephane Hallegatte, David Johnston, Wojciech Kopczuk, Anke Leroux, Paulina Oliva, Paul Raschky, Russell Smyth, Jevgenijs Steinbuks, and participants at the Frontiers in Development Policy Conference (KDI) and seminars at Food and Agriculture Organization and Monash University for useful comments on earlier versions. We would like to thank Matthias Kalkuhl for helpful advice on data and Brenan Andre for assistance with the global maps. We would also like to thank the UK Foreign Commonwealth and Development Office (FCDO) for funding assistance through a Knowledge for Change (KCP) grant for the World Development Report 2021 “Data for Better Lives” and the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.
# **1. Introduction**

The increasingly prominent threats of climate change have inspired a significant body of economic research on a variety of outcomes, such as agriculture (Deschênes and Greenstone, 2007; Schlenker and Roberts, 2009), labor productivity (Somanathan _et al._ , 2021), human health (Deschênes and Greenstone, 2011), and crime and conflict (Burke _et al._ , 2015a; Heilmann _et al._ , 2021). In particular, since climate change could reduce economic growth (Dell _et al._ , 2012; Newell _et al._ , 2021), this might, in turn, translate into slower progress with poverty reduction. Furthermore, climate change may unevenly affect different countries and population groups with the most harmful consequences of income decline being borne by less affluent groups; these would likely result in increasing inequality both across and within countries (Diffenbaugh and Burke, 2019; Hsiang _et al._ , 2019).

A possible explanation for the lack of empirical evidence on the impacts of global warming is the challenge of obtaining appropriate measures of poverty and inequality. While household surveys—the main source of official poverty statistics—have become increasingly available, these surveys are still unavailable or infrequently collected in many countries, particularly in poorer regions.<sup>1</sup> Another explanation is that poverty and inequality can widely vary within (and across) countries. Consequently, ignoring subnational variations could easily mask the dynamic relationship of these outcomes with climatic conditions, which have long been known to be location specific. Indeed, recent studies suggest that analysis using spatial aggregation of data at the country level may not reveal the true effects of climate change on economic growth, which can be improved with analysis using more disaggregated data at the subnational level (Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

> 1 A recent survey by Beegle _et al._ (2016) indicates that just slightly more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Dang _et al._ (2019) find that a 10-percent increase in a country’s household consumption level is associated with almost one-third (i.e., 0.3) more surveys.

2
To further illustrate, we plot in Figure 1 poverty and inequality against temperature at the subnational level for India, a populous country with a major share of the global poor. This figure shows large degrees of subnational variation in both poverty and inequality. Poverty, as measured by the headcount poverty rate at US$ 1.90 a day, ranges from being relatively low in the Northern regions (lowest rate of 0.5 percent) to quite considerable in the Central and Eastern regions (highest rate of 52.8 percent) (Panel C). A similar pattern is seen with inequality, as measured by the Gini index, which ranges between 18.9 percent and 47 percent (Panel D). Within the country, average temperature also widely varies between 4.3<sup>◦</sup> C and 28.7<sup>◦</sup> C (Panel E). Such wide-ranging subnational variations are not revealed by simply looking at India’s country-level averages of poverty, inequality, and temperature (16 percent, 31 percent, and 22<sup>◦</sup> C, respectively), suggesting that an accurate assessment of the effects of global warming on poverty and inequality would require data analysis at the subnational level.

In this study, we find strong and statistically significant global effects of both higher and lower temperature on poverty and inequality, employing different identification strategies on a novel global database of subnational poverty and inequality. Our (preferred) subnational fixed effects model shows that a one-degree Celsius (i.e., 1<sup>◦</sup> C) annual increase in temperature causes headcount poverty increases of 0.9, 1.8, and 2.3 percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). The corresponding estimated effects using the long differences model are less pronounced at 0.5, 1.2, and 2.0 percentage point increases in poverty (which correspond to 5.3 percent, 6.1 percent, and 5.9 percent increases), suggesting household adaptation to gradual warmer temperature over time. Analysis of subnational inequality data suggests that a 1<sup>◦</sup> C rise in temperature leads to 0.8 and 1.4 percent increases in the Gini and Theil indices, respectively.

3
For both poverty and inequality, we find evidence that points to larger climate change effects at the subnational level than those estimated using more aggregated, country-level data, particularly in regions where temperature change has the largest effects. Our heterogeneity analysis further shows that countries in South Asia and Sub-Saharan Africa are more vulnerable to warmer temperature, but the effects of colder weather are also observed among countries in Europe and Central Asia.

Our study makes several new contributions to the literature. First and most importantly, we offer the first global assessment of warmer temperature on _both_ poverty and inequality, exploiting a novel global subnational panel database that we constructed based on the Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). Several attempts were made to understand the direct effects of global warming on poverty and inequality, but none examines these outcomes together.<sup>2</sup> For example, analyzing cross-sectional household survey data from 24 Sub-Saharan African countries, Azzarri and Signorelli (2020) show that a one degree increase in long-term temperature is associated with a 2.8 percentage point increase in poverty. Paglialunga _et al._ (2022) use data from 150 countries and find that a one percent temperature increase is associated with a 0.5 percentage point increase in the Gini index.<sup>3</sup> This lack of evidence poses an important, and perhaps quite urgent, challenge given the recent public debate of whether richer countries should take more responsibilities for the costs of climate change that correspond to their shares of the pollutants (Birnbaum _et al._ , 2022; Popovich and Plumer, 2021). As an example, 80 percent of global greenhouse gas emissions are currently produced by G20 economies—the world’s largest economies—but these economies can only price 49 percent of CO2 emissions from energy use (OECD, 2021).

> 2 There are also a number of studies investigating the effects of natural disasters on temporary (transient) poverty (e.g., Sawada and Takasaki, 2017). Our study, in contrast, focuses on chronic poverty as a result of climate change.

> 3 Other studies mostly focus on a country-specific context. See also Karim and Noy (2016) and Hallegatte _et al._ (2020) for recent reviews of the literatures on climate change, natural disasters, and poverty. Furthermore, since we analyze poverty and inequality using the same source of official household survey data, our estimates are consistent (and more comparable) than estimates that are based on different data sources.

4
Furthermore, examining both poverty and inequality is crucial as it provides a more comprehensive and clearer picture of the diverse impacts of climate changes. By addressing these aspects simultaneously, we can gain a deeper insight into how they interact and reinforce each other, offering a better understanding of the economic effects of climatic change.

Second, our paper adds to the broader literature on the impacts of climate change by examining how increase in temperature, driven by global warming, impact economic growth and other outcomes, and subsequently, poverty and inequality. Higher temperatures typically reduce economic growth, leading to a drop in income and employment (e.g., Dell _et al._ , 2012; Kalkuhl and Wenz, 2020; Linsenmeier, 2023). This influence is not uniform, with varying impacts observed between richer and poorer countries as well as sectors in the same economy; for example, the agriculture sector in developing economies tend to bear a disproportionate burden due to changing crop growth conditions (Mendelsohn _et al._ , 1994; Schlenker and Lobell, 2010). In addition, hotter temperature affects labor productivity and human capital, contributing to the widening gap between the rich and the poor (Barreca, 2012; Graff Zivin _et al._ , 2018; Graff Zivin _et al._ , 2020). Our paper aims to offer insights into the dynamic relationship between temperature changes, poverty, and inequality.

More specifically, we also add fresh evidence to the emerging literature on the distributional effects of climate change. While existing studies on other development outcomes mostly focus on areas with hotter temperature, far fewer studies investigate the effects of colder temperature. Yet, no study is currently available on these distributional effects for poverty and inequality. For example, Dell _et al._ (2012) show that both hot and cold deviations from the average temperature have similar effects on economic growth; Deschênes and Greenstone (2011) find more cold days to be associated with higher mortality. Most recently, Cook and Heyes (2020) find that outdoor cold temperature negatively impacts indoor cognitive performance. More evidence on the potentially adverse effects of colder temperature is 5
important since, despite global warming, unusually colder weather has become more common in many countries in the past decades. Overall, our results indicate that the distributional effects across temperature ranges (as well as across subnational regions) should be considered together with longer-term effects of temperature change as inputs for designing more effective policies aiming at fighting climate change, poverty, and inequality.

Finally, we offer new, disaggregated data on headcount poverty estimates and inequality indices for 1,594 subnational areas in 134 economies from 2003 to 2019, based on the GSAP database which is generated using household income and consumption surveys that underlie countries’ official poverty statistics. This helps distinguish our study from the few existing cross-national studies that predominantly focused on country-level datasets, which, although informative, were not able to adequately capture the intricate subnational dynamics of poverty, inequality and temperature change. As part of our analysis, we make this new dataset publicly available for the first time. Our results show that analysis based on subnational data yields more accurate estimates of the impacts of temperature on poverty and inequality; consequently, this new subnational dataset can contribute to further and better research on climate change and poverty and inequality on a global scale.

This paper consists of six sections. We discuss the data in the next section, and the analytical framework in Section 3. In Section 4, we report on the estimation results for poverty (Section 4.1), inequality (Section 4.2), their nonlinear effects (Section 4.3), and further robustness checks and heterogeneity analysis (Section 4.4). We offer further analysis on potential mechanisms, projected impacts under future climate change, and some back-of-theenvelope cost-benefit analysis in Section 5 and finally conclude in Section 6. We provide additional results in Appendix A, describe the data in more detail in Appendix B, offer more robustness checks and heterogeneity results in Appendix C and additional analysis on the potential mechanisms and projected impacts of temperature in Appendix D.

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# **2. Data**

The data used for our analysis are derived from multiple sources. We introduce a novel dataset that provides a granular perspective on poverty and inequality at the subnational level. In particular, we draw on the Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021), a collaborative effort among different teams at the World Bank over a period of time. The GSAP is built on countries’ official household income (consumption) surveys, covering over 1,594 subnational units across 134 countries, with more than 90 percent of the data ranging from 2010 to 2019. In most cases, a subnational unit refers to a province or state (i.e., firstlevel administrative boundaries – ADM1) but can also be a group of regions determined by the specific sampling strategy of household surveys.

For the main outcomes, we utilize the (headcount) poverty rate at US$1.90 a day, as estimated by the percentage of the population living on less than $1.90 a day at the 2011 purchasing power parities (PPP) prices.<sup>4</sup> For richer analysis, we also employ other poverty lines of $3.20 and $5.50 a day. As alternative sources of poverty data, we also utilize two other sources: i) country-level poverty data from the World Bank World Development Indicators (WDI), which is a widely used database for global poverty measures, and ii) subnational GDP from Kalkuhl and Wenz (2020) and Kummu _et al._ (2020), which we further convert to poverty data. Panel A of Figure 1 shows that Sub-Saharan Africa currently has the highest poverty rates and the poorest countries include Tanzania (51.3 percent), Mozambique (54.7 percent), and the Democratic Republic of Congo (72.9 percent).

For inequality, we mostly employ the Gini index and Theil index, which are the most commonly used measures of income inequality. For robustness checks, we also use the distribution of income (consumption) shares held by each decile and calculate different

> 4 The data are accessible on the Harvard Dataverse depository at <u>https://doi.org/10.7910/DVN/MLHFAF.</u>

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percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the 90/40 ratio (i.e., the Palma ratio). All income measures are converted to real terms using 2011 PPP dollars. Panel B of Figure 1 provides a global map of income inequality at the subnational level, which shows substantial variation of inequality across regions within a country.

We match our poverty and inequality data with the ERA5 satellite reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ECMWF). The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). An advantage of the ERA5 data is that it combines information from ground stations, satellites, weather balloons, and other inputs with a climate model, and therefore is less prone to station weather bias.<sup>5</sup> For robustness tests, we use the global gridded data from Climate Research Unit of the University of East Anglia (CRU) available at 0.5<sup>◦</sup> resolution. We provide a more detailed description of the data sources, including the list of the countries in each dataset and the summary statistics of the main variables in Appendix B.

# **3. Empirical Specifications**

Different identification strategies were employed to estimate the effects of climate change on economic outcomes (Burke and Emerick, 2016; Dell _et al._ , 2014; Kolstad and Moore, 2020). Early studies mostly used a cross-sectional approach utilizing spatial variation at a point of time, comparing outcomes between hot and cold areas (Mendelsohn _et al._ , 1994; Schlenker _et al._ , 2005). Yet, a key assumption when estimating the coefficients of the climate-related variable from cross-sectional models is that climate change is not correlated with other unobservable factors. Violation of this assumption could result in an omitted variables problem,

> 5 Auffhammer _et al._ (2013) find high correlations between ERA5 reanalysis data and weather station data for temperature, which further supports our study focus on temperature. However, we appear not to have similar supportive evidence for rainfall, particularly in poorer countries with limited ground station data.

8
causing the estimated coefficient of interest to be biased. Therefore, our first empirical approach identifies the effects of hotter temperature on poverty and inequality by estimating the following panel data model with fixed effects (FE): F F (1) ii,tt ii,tt ii,tt ii tt ii,tt where represents the poverty rate and inequality in location YY = ββ TT + γγ PP + αα + ππ + εε _i_ in year _t._ Depending on the ii,tt specific specification, location YY _i_ is either country in the country-level analysis or subnational unit in the subnational analysis. is the temperature variable, and the coefficient of interest ii,tt F is expected to be positive (i.e., global warming likely increases poverty and inequality). TT ββ Following previous studies’ suggestion that precipitation and temperature are historically correlated and should be included in the same regression to obtain unbiased coefficients (Auffhammer _et al._ , 2013; Dell _et al._ , 2012), we control for precipitation ( ), measured in ii,tt millimeters, in all the regressions. is the location (country or sub-national) fixed effects that PP ii controls for unobserved time-invariant factors that may be correlated with location-specific αα climate or economic patterns; is the year fixed effects that controls for unobserved temporal tt changes affecting poverty and inequality each year. We cluster the errors ππ at the specified ii,tt location level to allow for potential serial correlation over time within a region (or a country). εε For robustness, we also report Conley standard errors that allow for spatial correlation and arbitrary serial correlation in the error term (Conley, 1999). All the regressions are weighted with population weights at the subnational (country) level. While we can causally interpret F in Equation (1), it is likely derived from short-run responses to temperature change given the nature of the annual panel data analyzed in this ββ equation. Consequently, F is not necessarily representative of households’ responses to temperature change in the longer term. In other words, long-term responses to temperature ββ change may fundamentally differ from short-term responses to weather fluctuations because the former type of responses better accounts for potential household adaptation over time.

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Therefore, we address the shortcoming of Equation (1) by utilizing the long differences approach to estimate the accumulated effects of temperature change over longer periods of time (see, e.g., Burke and Emerick (2016)): (see, e.g., Burke and Emerick (2016)): L L (2) ii ii ii ii In Equation (2), ∆YY represents changes in poverty (or inequality) in the same location = ββ ∆TT + γγ ∆PP + ωω ii between two periods, and ∆YY and are the corresponding changes in temperature and ii ii precipitation. To provide more stable estimates that are robust to data fluctuations in any single ∆TT ∆PP year, we use 3-year difference averages. That is, for all the variables in Equation (2) in our study period of 2003–2019, we analyze the differences between their averages of the earliest 3-year period 2003–2005 and their averages of the latest 3-year period 2017–2019 (e.g., = ∑20192017 <u>,</u> − ∑20052003 <u>,</u> ~~)~~ . Under the long differences approach, any time-invariant 3 3 YYii tt YYii tt ii,2003−2019 ∆YY location-specific factors are differenced out. As with Equation (1), the coefficients of interest, L , is expected to be positive. ββ In both the panel FE and long differences models, we assume the effects of temperature In both the panel FE and long differences models, we assume the effects of temperature change to be in linear form. To allow for a more flexible functional form of temperature, we further employ a temperature bin approach (e.g., Chen and Gong, 2021; Mullins and White, 2020) that offers estimates of nonlinear effects: Specifically, we categorize daily temperature into 13 temperature bins, where each bin captures temperature change in increments of 3<sup>◦</sup> C (e.g., the first bin is [0<sup>◦</sup> C, less than 3<sup>◦</sup> C), the second bin is [3<sup>◦</sup> C, less than 6<sup>◦</sup> C), and so on). The two extremes of low and high temperature are respectively defined as less than 0<sup>◦</sup> C and greater than 33<sup>◦</sup> C. The temperature shock variable, , reflects the number of days when the daily average temperature in a region is within a ii,jj,tt specific bin in a particular year. We use the most thermally comfortable temperature bin, which TT is [18<sup>◦</sup> C, less than 21<sup>◦</sup> C), as the reference group. The coefficients of interest T are thus ,jj ββ 10
interpreted as the effects of exchanging a day in the 18–21<sup>◦</sup> C reference bin with a day in the other bins.

# **4. Results**

# **_4.1. Effects of temperature on poverty_**

We start examining the effects of temperature change on poverty using the country-level analysis (Panel A) and subnational level analysis (Panel B) in Table 1. For the country analysis, we aggregate our newly constructed GSAP database weighted by the subnational population. We analyze three poverty indicators using the daily poverty lines of $1.90, $3.20, and $5.50. For each outcome, we present the results of the fixed-effects panel model in Columns (1), (3), (5), followed by the results of the long differences model in Columns (2), (4), (6). In both panels, the results are strongly statistically significant and confirm the negative effects of higher temperature on poverty for all the three different poverty lines.

Yet, the estimates at the subnational-level analysis (Panel B) have stronger magnitudes than those derived from the country-level analysis (Panel A). The differences between these two sets of estimates are statistically significant, which is confirmed by the t-tests for equality of the estimated coefficients shown at the bottom of Table 1. This suggests that studies using spatial aggregation of data at the country level could mask the impacts of warmer temperature. This finding is also consistent with previous studies showing more pronounced effects of 11
temperature on economic growth at the subnational level (e.g., Damania _et al._ , 2020; Kalkuhl and Wenz, 2020).

We subsequently focus on the subnational analysis for interpreting the estimation results. In particular, Column (1) of Panel B shows that a 1<sup>◦</sup> C increase in temperature causes a 0.9 percentage point increase in poverty (at the daily $1.90 poverty line). This equals a 9.1 percent increase in poverty using the mean poverty rate of 10.1 percent. For higher poverty lines, the impact magnitudes are higher in absolute terms (i.e., 1.8 percentage point and 2.3 percentage point increases for the daily poverty lines of $3.20 and $5.50, respectively) but are somewhat weaker in relative terms (i.e., the corresponding increases in poverty for these two poverty lines are respectively 9 percent and 6.8 percent).

Using the long differences model on the same data, we show the estimated longer-term effects of temperature on poverty in Columns (2), (4), and (6). The results are qualitatively similar, indicating positive and strongly statistically significant effects of higher temperature on poverty. However, the long differences coefficient estimates are smaller in absolute value than the corresponding panel FE coefficient estimates. Specifically, a 1<sup>◦</sup> C increase in temperature is estimated to result in a poverty increase of 0.5 percentage points (5.3 percent) (using the daily poverty line $1.90) (Column 2). As shown by the t-tests at the bottom of Panel B, the differences between the panel FE estimates and the long differences estimates are statistically significant, implying that longer-run household adaptation appears to have offset the negative short-run impacts of temperature on poverty by 0.4 percentage point (or 3.8 percent). These findings are consistent with previous studies that show the role of household adaptation in mitigating the negative effects of temperature on economic production, agriculture, and human capital (e.g., Chen and Gong, 2021; Graff Zivin _et al._ , 2018; Kalkuhl and Wenz, 2020).

12
While we focus on the impacts of temperature on poverty, Table 1 also reveals significant, but mixed, effects of precipitation. We find higher rainfall to be associated with lower poverty rate in the long differences model (e.g., Column 2, Panel B), but the opposite is found in the panel FE model (e.g., Column 1, Panel B).<sup>6</sup> This ambiguity is, however, perhaps consistent with previous findings showing both negative impacts (Damania _et al._ , 2020; Kotz _et al._ , 2022) and positive impacts (Burke _et al._ , 2015b; Dell _et al._ , 2012) of rainfall on economic growth.

# **_4.2. Effects of temperature on inequality_**

We show in Table 2 the estimates on the effects of warmer temperature on income inequality at both the country-level and subnational level, which are strongly statistically significant. In particular, a 1<sup>◦</sup> C increase in temperature is estimated to result in an increase of 0.29 percentage point (0.8 percent) in the Gini index (Column 1 of Panel B), and an increase of 0.35 percentage point (1.4 percent) in the Theil index (Column 3 of Panel B). Similar to the estimation results for poverty (Table 1), the estimates at the subnational-level analysis (Panel B) are stronger than those obtained at the country-level analysis (Panel A). These results provide supportive evidence for our earlier discussion that global warming might exacerbate income inequality because poorer countries or individuals could be more vulnerable to climate change.

Indeed, our findings concur with previous studies, which find negative effects of hotter temperature on various economic outcomes. For example, recent studies by Hsiang (2010), Dell _et al._ (2014), and Deryugina and Hsiang (2014) have discovered that a 1<sup>◦</sup> C increase in temperature are associated with losses in, respectively, industrial output (2.5 percent), average country-level GDP per capita (1.0 percent), and county-average income per capita (1.7 percent). Our results are also qualitatively similar to, but offer slightly smaller

> 6 In addition, we employ alternative rainfall functions, including the quadratic term and the deviation of rainfall from the long-term mean, but still find similar results.

13
estimates than, those found in Paglialunga _et al._ (2022), which show a one percent temperature increase to be associated with 0.5 percentage point increase in the Gini index.

To investigate the potential long-run effects, we estimate Equation (2) and present the results in Columns (2) and (4) of Table 2. We document strong and statistically significant effects of hotter temperature on income inequality for both country-level analysis and subnational-level analysis and both measures of inequality. Again, we also find the effects at the subnational level to be stronger than those at the country level. Specifically, higher temperature by 1<sup>◦</sup> C is found to increase the Gini index and the Theil index by 0.35 (1 percent) and 0.59 (2.3 percent) percentage points (Panel B, Columns 2 and 4). Comparing the panel FE and long differences models, the magnitudes of effects appear larger for the latter. This may suggest inequality could accumulate over the longer term (i.e., intensification of negative effect). But we also note that the t-tests for the differences with the two models are only statistically significant for the Theil index with the subnational analysis, but not for the Gini index (and the country-level analysis).

To put our estimates into perspective, within our sample, Finland has one of the lowest Gini indexes (i.e., less inequality), with a value of 26.5, while the Central African Republic has one of the highest Gini indexes (i.e., more inequality), at 53.7. Given this, our estimated increase in the Gini index of 0.349 percentage points (Column (2) of Panel B) suggests that 1°C increase in temperature widens the gap in inequality between these two countries at the two ends of the inequality spectrum by 1.3 percent. This shift is substantial, especially when considering the cumulative and intensified impacts over time and across different countries. Another way to contextualize the impacts of temperature on inequality is by examining the progression of inequality. In our sample, the average Gini index has decreased from 37.7 in 2010 to 34.7 in 2019, showing a movement towards decreased inequality. However, our estimates suggest that a one-degree increase in temperature could counteract this positive trend, effectively offsetting 14
approximately 1.2 years of progress made in reducing inequality. This implies that the ongoing rise in global temperature could potentially halt, or even reverse, the advancements made in reducing inequality.

# **_4.3. Nonlinear effects_**

The effects of hotter temperature on poverty and income inequality discussed earlier are linear. To allow for a more flexible functional form of temperature, we assess the potential for nonlinear effects by specifying temperature as a series of indicator variables corresponding to 3<sup>◦</sup> C bins, where coefficients can be interpreted as the effects of falling into a given bin relative to the reference “comfortable” bin (i.e., 18-21<sup>◦</sup> C). We define hotter weather as temperature being in the top decile of the temperature range (i.e., greater than 27<sup>◦</sup> C), and colder weather as temperature being in the bottom decile of the temperature range (i.e., less than 6<sup>◦</sup> C). Figure 2 displays the point estimates and the 95% confidence intervals of these temperature bins, using Equation (3). Again, the results provide strong evidence for temperature effects, suggesting that one additional day of hotter temperature will lead to higher poverty and inequality, and the estimates are statistically significant at the 5 percent level. The magnitudes of the effects are generally consistent across hotter temperature bins. These results are consistent with our earlier findings of negative effects of warmer temperature.

Furthermore, the results in Figure 2 also show that colder weather worsens poverty and inequality. Our findings concur with several studies finding negative effects of colder weather on productivity, health, and economic growth (Cook and Heyes, 2020; Dell _et al._ , 2012; Deschênes and Moretti, 2009) and add fresh evidence for the impacts of colder weather on poverty and inequality. Since adaptation to colder weather differs from those to hotter weather, our results imply that the distributional effect of temperature should be considered when designing mitigation policies.

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Finally, we consider the model specification that controls for a series of lag of temperature bins (Equation 4). This approach offers insight into the cumulative effects of extreme temperature on income inequality. The estimated cumulative effects of temperature remain negative and slightly increase in magnitude compared to the contemporaneous effects as shown in Figure 2. Our results therefore suggest that when accounting for non-linearity of temperature effects, we find strong evidence of the adverse impacts of both colder temperature and hotter temperature on poverty and inequality, and such effects are documented in both the short-term and long-term. While we have identified non-linear effects of temperature, we also note that the distribution of temperature in our sample predominantly aligns with the hotter ranges, as shown in Figure B3 (Appendix B). Therefore, we focus on the linear models as presented in Tables 1 and 2, using them as the primary models for analyses in our study.

# **_4.4. Robustness tests and heterogeneity analysis_**

To investigate the robustness of the finding of negative temperature effects on poverty and inequality, we conduct a number of additional analyses. We briefly summarize the main results here and offer more detailed discussion in Appendix C.

First, we use several variants of the panel FE and long differences models, which include adding country-specific linear time trends, region-specific linear time trends, controlling for temperature change, adding a quadratic or cubic term of temperature, adding an interaction term between temperature and temperature change, and using difference choices of window. We also use alternative thresholds to define hotter and colder days in the temperature bin approach. Next, we exploit different subsamples and alternative data sources and measures of 16
temperature.<sup>7</sup> Finally, we conduct a placebo test by using within-sample randomization of temperature. Overall, the results of these exercises remain similar to our main findings.

We also offer further heterogeneity analysis across regions. We employ the temperature bin approach to consider the non-linearity of temperature effects and plot the results in Figure 3. It shows that rising temperature causes higher poverty (Panel A) and inequality (Panel B) in poorer regions such as Sub-Saharan Africa, Middle East and North Africa, and South Asia, but the effects are attenuated in the other richer regions. Furthermore, we also document negative effects of colder temperature on both outcomes, particularly in Europe and Central Asia. We further estimate temperature effects for each country. Plotting the results where each country’s marker is proportional to its real GDP per capital, Figure A5 (Appendix A) shows that countries bearing the largest effect of global warming (e.g., Uganda, Ghana, and Mozambique) also tend to be poorer or located in poorer regions.

Furthermore, we explore different country characteristics that might help mitigate temperature effects. Employing a democracy index that categorizes countries into democracies, authoritarian regimes, and hybrid regimes, we find that countries with democratic regimes appear to be less vulnerable to the impacts of global warming. Additionally, we observe that temperature effects are stronger in countries with higher shares of agriculture and are weaker in countries with higher shares of manufacturing. These findings suggest that institutions could play important roles in mitigating the effects of global warming on different countries and regions.<sup>8</sup>

> 7 These include using (i) log of temperature; (ii) temperature measured in degrees Fahrenheit; (iii) the temperature data from CRU; (iv) the number of days that temperature is above 28<sup>◦</sup> C; (v) dropping regions with temperature being above that level; and (vi) temperature shock, defined as the difference between actual temperature and longterm temperature being greater (less) than 2 (-2) standard deviations (Appendix A, Table A4).

> 8 We also investigate the role of information and communication technologies (ICTs) in poverty reduction by providing access to markets, decreasing transaction costs, and increasing income for a significant proportion of people living in developing countries (World Bank, 2016). Our results show that regions with better access to ICT are less vulnerable to the effects of higher temperature.

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# **5. Further analysis**

# **_5.1. Potential mechanisms and projected impacts under future climate change_**

Having demonstrated strong evidence of temperature effects on poverty and inequality at the subnational level, we further explore agriculture as a potential mechanism. Agriculture plays an important role in poverty reduction for various reasons. The majority of the global poor live in rural areas where agriculture is the predominant form of economic activity and agricultural growth is more effective at reducing poverty than non-agricultural growth; moreover, poorer households are more vulnerable to increases in food prices (Hertel and Rosch, 2010; Hallegate _et al._ , 2016). We analyze the global dataset of historical yields from Iizumi and Sakai (2020), which provides actual crop yields at 0.5° resolution for the period 1981-2016. Using the panel FE and long differences models as with Equations (1) and (2), we find negative effects of higher temperature on different crop yields including rice, maze, and soybean, as shown in Table A11 (Appendix A). Again, we find the long differences model estimates to be smaller than the panel FE model estimates, which are in line with previous studies showing potential adaptation in the long run (e.g., Chen and Gong, 2021). We also document the heterogenous effects of hotter temperature and discuss the results in Appendix D.<sup>9</sup> We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. We focus on different climate change scenarios taken from the recent released Coupled Model Intercomparison Project Phase 6 (CMIP6).<sup>10</sup> Table A13 (Appendix A) provides a summary of the projected changes for

> 9 As another potential mechanism, we explore subnational migration flow between the period 2005 and 2010. Using a simple OLS regression, we find some suggestive evidence that hotter temperature could lead to more migration (Appendix A, Figure A8). However, since migration could help households obtain better economic opportunities and escape poverty, our estimation results could be considered as the net impacts of hotter temperature (after factoring in the beneficial effects of migration on poverty reduction).

> 10 The CMIP6 models capture future trends in climate change under alternative scenarios of human activities. It has been bias-corrected and downscaled to ERA-5 to address deficiencies identified in earlier models like Global Meteorological Forcing Dataset (GMFD) and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). This data set aligns with the emissions scenarios of the 2021-2023 IPCC report, rendering it more current and relevant than previous models which were based on the scenarios of the 2013 IPCC report (IPCC, 2021).

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temperature for these scenarios, where temperature can increase by between 1.4<sup>◦</sup> C and 5.0<sup>◦</sup> C in 2099. These temperature increases can result in poverty increases between 0.7 and 2.7 percentage points (i.e., 7.4 and 26.6 percent increases) (Panel A) using the poverty line of $1.9/day. Similarly, the simulated effects on inequality are estimated to be between 0.5 and 1.7 percentage point increases in Gini index (i.e., 1.4 and 4.9 percent increases) (Panel B). In both cases, the largest poverty and inequality increase would occur in the scenario without any countervailing strategies based on renewable energy to address climate change between 2021 and 2099.<sup>11</sup>

# **_5.2. Back-of-the-envelope cost analysis_**

We next perform some rough calculations to help compare the costs of impeding temperature rise against that of eradicating poverty. Among the various measures for mitigating the effects of global warming, reducing carbon emissions such as carbon dioxide (CO2), methane, and nitrous oxide, is deemed crucial. The Intergovernmental Panel on Climate Change (IPCC) reports that limiting global warming to 1.5°C by 2030 would require reducing CO2 emissions by approximately 45 percent compared to 2010 levels and achieving net-zero emissions by 2050 (IPCC, 2018). This ambitious target is estimated to require a $US50 trillion investment in zero-carbon technology (Morgan Stanley, 2019).

Using the estimated effects of temperature on poverty reported in Table 1 Column (2) (i.e., 0.53 percentage point increase in poverty per one-degree increase in temperature), we estimate that the poverty headcount ratio at US$1.90 a day would increase by 0.8 percentage point (an 8 percent increase) by 2030 if the temperature increases by 1.5°C. To counteract this rise in poverty, using Lakner _et al._ ’s (2022) estimates, we calculate that the global GDP would have

> 11 We also note that our projection of future impacts might be influenced by various other factors. For example, changes in ecosystems or global food production and sea level rises may amplify or lessen these effects, rendering the task of projecting the potential consequences of climate change extremely complicated.

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to increase by approximately 1.08 percent, or around $US1.12 trillion (based on the estimated global GDP of $US103.86 trillion in 2022). Although this estimated figure only amounts to 2 percent of the investment cost of $US50 trillion, our analysis does not consider the positive externalities of poverty elimination on other welfare outcomes, such as improved health and subjective well-being, or the beneficial impacts of poverty reduction on growth in the longer term (Thorbecke and Ouyang, 2022).

To further our investigation, we estimate the allocation of costs for each country according to their respective contribution to warmer temperature. To provide a back-of-the-envelope estimate of this allocation, we use the share of CO2 emissions across countries from 1975 to 2022 (Friedlingstein et al., 2022). Table A14 (Appendix A) indicates a wide range of contribution shares for countries: while low-income countries should contribute less than 1 percent of the total costs, this figure increases to 10 percent, 29.7 percent, and 59.5 percent for lower-middle income, upper-middle income, and high-income countries, respectively. We plot in Figure A11 (Appendix A) more detailed estimates for each country.

# **6. Conclusions**

While there is growing evidence of harmful effects of climate change on macro-economic outcomes, little evidence exists regarding the impacts of warmer temperature on poverty and inequality on a global scale. A notable challenge is the absence of data at a disaggregated level that can allow for more accurate analysis of this relationship both within and across countries. Analyzing a new global panel dataset representative of subnational areas in 134 countries that we constructed, we find both hotter and colder temperature to result in higher poverty rate and inequality. We find stronger effects at the subnational level, which implies that country-level analysis does not reveal the true estimate of the global warming consequences. We also find 20
significant, but smaller, effects of temperature in the long run, which suggests that households likely adapt to permanent changes in weather conditions.

Our findings add to the ongoing discussion of richer countries’ responsibilities in mitigating the global effects of climate change. Over the last decades, poorer countries have been calling for compensation for the costs of climate change (also known as “climate reparations”) from wealthier nations, who are generally considered to be more responsible for global climate change. Our study contributes to this discussion by offering new global evidence that poorer regions are bearing the heaviest burden of global warming, and thus richer countries could provide further support in order to reduce the effects of climate change.

The availability of subnational poverty data opens other avenues for future research. While our study provides supportive evidence that agriculture is an important factor influencing climate-induced poverty and inequality, alternative channels, such as civil conflicts and labor productivity, could offer another direction. Further research on these topics would help provide policy inputs for more effective actions by different countries to address the challenge of global warming.

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# **References**

- Aghion, P., & Bolton, P. (1997). A theory of trickle-down growth and development. _Review of Economic Studies_ , _64_ (2), 151-172.

- Arouri, M., Nguyen, C., & Youssef, A. B. (2015). Natural disasters, household welfare, and resilience: evidence from rural Vietnam. _World Development_ , _70_ , 59–77.

- Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change. _Review of Environmental Economics and Policy_ , _7_ (2), 181–198.

- Azzarri, C., & Signorelli, S. (2020). Climate and poverty in Africa South of the Sahara. _World Development_ , _125_ , 104691.

- Barbier, E. B., & Hochard, J. P. (2018). Land degradation and poverty. _Nature Sustainability_ , _1_ (11), 623-631.

- Barreca, A. I. (2012). Climate change, humidity, and mortality in the United States. _Journal of Environmental Economics and Management_ , _63_ (1), 19-34.

- Beegle, K., Christiaensen, L., Dabalen, A., & Gaddis, I. (2016). _Poverty in a rising Africa_ . World Bank Publications.

- Birnbaum, M., Chiu, A., & Kaplan, S. (2022, November 7). “U.N. climate talks off to tense start as nations feud over damage.” _The Washington Post_ . Retrieved from <u>https://www.washingtonpost.com/climate-environment/2022/11/07/un-climateconference-damages/</u>

- Burke, M., Hsiang, S. M., & Miguel, E. (2015a). Climate and conflict. _Annual Review of Economics_ , _7_ (1), 577–617.

- Burke, M., Hsiang, S. M., & Miguel, E. (2015b). Global non-linear effect of temperature on economic production. _Nature_ , _527_ (7577), 235–239.

- Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture. _American Economic Journal: Economic Policy_ , _8_ (3), 106–40.

- Cappelli, F., Costantini, V., & Consoli, D. (2021). The trap of climate change-induced “natural” disasters and inequality. _Global Environmental Change_ , _70_ , 102329.

- Chen, S., & Gong, B. (2021). Response and adaptation of agriculture to climate change: Evidence from China. _Journal of Development Economics_ , _148_ , 102557.

- Conley, T. G. (1999). GMM estimation with cross sectional dependence. _Journal of Econometrics_ , _92_ (1), 1–45.

22
Cook, N., & Heyes, A. (2020). Brain freeze: outdoor cold and indoor cognitive performance. _Journal of Environmental Economics and Management_ , _101_ , 102318.

- Damania, R., Desbureaux, S., & Zaveri, E. (2020). Does rainfall matter for economic growth? Evidence from global sub-national data (1990–2014). _Journal of Environmental Economics and Management_ , _102_ , 102335.

- Dang, H. A., Jolliffe, D., & Carletto, C. (2019). Data gaps, data incomparability, and data imputation: A review of poverty measurement methods for data‐scarce environments. _Journal of Economic Surveys_ , _33_ (3), 757–797.

- Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature shocks and economic growth: Evidence from the last half century. _American Economic Journal: Macroeconomics_ , _4_ (3), 66–95.

- Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature. _Journal of Economic Literature_ , _52_ (3), 740–98.

- Deryugina, T., & Hsiang, S. M. (2014). _Does the environment still matter? Daily temperature and income in the United States_ (No. w20750). National Bureau of Economic Research.

- Deschênes, O., & Greenstone, M. (2007). The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. _American Economic Review_ , _97_ (1), 354–385.

- Deschênes, O., & Moretti, E. (2009). Extreme weather events, mortality, and migration. _Review of Economics and Statistics_ , _91_ (4), 659-681.

- Deschênes, O., & Greenstone, M. (2011). Climate change, mortality, and adaptation: Evidence from annual fluctuations in weather in the US. _American Economic Journal: Applied Economics_ , _3_ (4), 152–85.

- Diffenbaugh, N. S., & Burke, M. (2019). Global warming has increased global economic inequality. _Proceedings of the National Academy of Sciences_ , _116_ (20), 9808–9813.

- Dollar, D., & Kraay, A. (2002). Growth is Good for the Poor. _Journal of Economic Growth_ , _7_ (3), 195-225.

- Friedlingstein, P., O'sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., ... & Zheng, B. (2022). Global carbon budget 2022. _Earth System Science Data_ , _14_ (11), 48114900.

- Gazzotti, P., Emmerling, J., Marangoni, G., Castelletti, A., Wijst, K. I. V. D., Hof, A., & Tavoni, M. (2021). Persistent inequality in economically optimal climate policies. _Nature Communications_ , _12_ (1), 1-10.

23
- Graff Zivin, J., Hsiang, S. M., & Neidell, M. (2018). Temperature and human capital in the short and long run. _Journal of the Association of Environmental and Resource Economists_ , _5_ (1), 77-105.

- Graff Zivin, J., Song, Y., Tang, Q., & Zhang, P. (2020). Temperature and high-stakes cognitive performance: Evidence from the national college entrance examination in China. _Journal of Environmental Economics and Management_ , _104_ , 102365.

- Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-Schilb, A. (2016). _Shock waves: Managing the impacts of climate change on poverty_ . Washington, DC: World Bank.

- <mark>Hallegatte, S., Vogt-Schilb, A., Rozenberg, J., Bangalore, M., & Beaudet, C. (2020). From poverty to disaster and back: A review of the literature.</mark> _<mark>Economics of Disasters and Climate Change</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>(1), 223–247.</mark>

- Heilmann, K., Kahn, M. E., & Tang, C. K. (2021). The urban crime and heat gradient in high and low poverty areas. _Journal of Public Economics_ , _197_ , 104408.

- <mark>Hertel, T. W., & Rosch, S. D. (2010). Climate change, agriculture, and poverty.</mark> _<mark>Applied Economic Perspectives and Policy</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(3), 355-385.</mark>

- Hsiang, S. M. (2010). Temperatures and cyclones strongly associated with economic production in the Caribbean and Central America. _Proceedings of the National Academy of Sciences_ , _107_ (35), 15367-15372.

- Hsiang, S., Oliva, P., & Walker, R. (2019). The distribution of environmental damages. _Review of Environmental Economics and Policy, 13_ (1), 83-103.

- Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016. _Scientific Data_ , _7_ (1), 1–7.

- Intergovernmental Panel on Climate Change. (2018). _Global warming of 1.5° C: An IPCC special report on the impacts of global warming of 1.5° C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty_ . Intergovernmental Panel on Climate Change. In Masson-Delmotte _et al._ (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−24.

- Intergovernmental Panel on Climate Change (IPCC). (2021). “Technical Summary.” In _Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change_ . In Masson-

24
Delmotte _et al._ (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144.

Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions. _Journal of Environmental Economics and Management_ , _103_ , 102360.

- <mark>Karim, A., & Noy, I. (2016). Poverty and natural disasters: a regression meta-analysis.</mark> _<mark>Review of Economics and Institutions</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 26.</mark>

- Kolstad, C. D., & Moore, F. C. (2020). Estimating the economic impacts of climate change using weather observations. _Review of Environmental Economics and Policy, 14_ (1), 1–24.

- Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M., & Levermann, A. (2021). Day-to-day temperature variability reduces economic growth. _Nature Climate Change_ , _11_ (4), 319– 325.

- Kotz, M., Levermann, A., & Wenz, L. (2022). The effect of rainfall changes on economic production. _Nature_ , _601_ (7892), 223–227.

- Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. _Scientific Data_ , _5_ (1), 1–15.

- Lakner, C., Mahler, D. G., Negre, M., & Prydz, E. B. (2022). How much does reducing inequality matter for global poverty? _Journal of Economic Inequality_ , _20_ (3), 559-585.

- Linsenmeier, M. (2023). Temperature variability and long-run economic development. _Journal of Environmental Economics and Management_ , _121_ , 102840.

- Mendelsohn, R., Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on agriculture: a Ricardian analysis. _American Economic Review, 84_ (4), 753-771.

- Morgan Stanley. (2019). Decarbonization: The Race to Zero Emissions. Available at: <u>https://www.morganstanley.com/ideas/investing-indecarbonization#:~:text=The%20Race%20to%20Net%20Zero&text=Simply%20put%2C %20net%20zero%20is,balance%20between%20emissions%20and%20decarbonization.</u>

- Mullins, J. T., & White, C. (2020). Can access to health care mitigate the effects of temperature on mortality? _Journal of Public Economics_ , _191_ , 104259.

- Newell, R. G., Prest, B. C., & Sexton, S. E. (2021). The GDP-temperature relationship: implications for climate change damages. _Journal of Environmental Economics and Management_ , _108_ , 102445.

- OECD. (2021). _Carbon Pricing in Times of COVID-19: What Has Changed in G20 Economies?_ OECD, Paris.

25
- Paglialunga, E., Coveri, A., & Zanfei, A. (2022). Climate change and within-country inequality: New evidence from a global perspective. _World Development_ , _159_ , 106030.

- Popovich, N., & Plumer, B. (2021, November 12). “Who Has the Most Historical Responsibility for Climate Change?.” _The New York Times_ . Retrieved from <u>https://www.nytimes.com/interactive/2021/11/12/climate/cop26-emissionscompensation.html</u>

- Rodriguez-Oreggia, E., De La Fuente, A., De La Torre, R., & Moreno, H. A. (2013). Natural disasters, human development, and poverty at the municipal level in Mexico. _Journal of Development Studies_ , _49_ (3), 442–455.

- Sawada, Y., & Takasaki, Y. (2017). Natural disaster, poverty, and development: An introduction. _World Development_ , _94_ , 2-15.

- Schlenker, W., Hanemann, W. M., & Fisher, A. C. (2005). Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. _American Economic Review_ , _95_ (1), 395-406.

- <mark>Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to US crop yields under climate change.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>106</mark>_ <mark>(37), 15594–15598.</mark>

- <mark>Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 014010.</mark>

- <mark>Somanathan, E., Somanathan, R., Sudarshan, A., & Tewari, M. (2021). The impact of temperature on productivity and labor supply: Evidence from Indian manufacturing.</mark> _<mark>Journal of Political Economy</mark>_ <mark>,</mark> _<mark>129</mark>_ <mark>(6), 1797–1827.</mark>

- <mark>Thorbecke, E., & Ouyang, Y. (2022). Towards a virtuous spiral between poverty reduction and growth: Comparing sub Saharan Africa with the developing world.</mark> _<mark>World Development</mark>_ <mark>, 152, 105776.</mark>

- <mark>World Bank. (2016).</mark> _<mark>World development report 2016: Digital dividends</mark>_ <mark>. World Bank: Washington.</mark>

- <mark>World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington.</mark>

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<!-- Start of picture text --> i<br>I<br>i \! 1I } | d<br>0 I f | t ft<br>,<br>1 \<br>s2 \ 1:<br>z ad ;<br>\<br>nad ! \<br>I<br>6-1z Colder temperature 'I I| Hotter temperature<br><0 (0,3) (3,6) [6,9) (9,12) (12,15) [15,18) [18,21) [21,24) [24,27) (27,30) [30,33) 33+<br>‘Temperature bin<br>4 $19 4 $32 4 855<br><!-- End of picture text -->

<!-- Start of picture text --> South Asia .<br>;<br>Ss ! !!<br>2 I !<br>° z | !<br>z<br>5<br>-5 I I I<br>!<br>I I<br><1 Colder temperature | ! Hotter temperature<br><0 (0,3) [3,6) (6,9) [9,12) [12,15) 15,18) [18,21) [21,24) [24,27) [27,30) [30,33) 33+<br>‘Temperature bin<br>4 s19 4 $3.2 4 855<br><!-- End of picture text -->

<!-- Start of picture text --> 1 Sub-Saharan Africa '<br>I I<br>.2 III I\I<br>3° 1 1<br>i |<br>5<br>22 ; I<br>I I<br>i \<br>-4~ Colder temperature | ! Hotter temperature<br><0 (0,3) (3,6) (6,9) (9,12) [12,15) [15,18) [18,21) [21,24) [24,27) (27,30) [30,33) 33+<br>‘Temperaturebin<br>4 $19 4 932 4 $55<br><!-- End of picture text -->
<!-- Start of picture text --> Europe & Central Asia<br>As ‘ !<br>| !<br>! |<br>a ;I<br>' |<br>& os I I<br>%é {I I<br>g 0 ;<br>&a i] !<br>' '<br>“05 \ \<br>I I<br>1+ Colder temperature | | _ Hotter temperature<br><0 (0,3) 3,6) (6,9) (9,12) [12,15) [15,18) [18,21) (21,24) [24,27) [27,30) [30,33) 33+<br>‘Temperaturebin<br>4 Gini Theil<br><!-- End of picture text -->

<!-- Start of picture text --> East Asia & Pacific<br>J4 I I<br>I<br>I I<br>;<br>2 I I<br>i I<br>S , | \y|i et<br>3 I I<br>+ | '<br>2 I I<br>i<br>I<br>4) Colder temperature | | Hotter temperature<br><0 — (0,3) (3,6) (6,9) [9,12) [12,15) [15,18) [18,21) [21,24) (24,27) [27,30) (30,33) 33+<br>‘Temperature bin<br>‘4 Gini Theil<br><!-- End of picture text -->

<!-- Start of picture text --> Latin America & Caribbean<br>' '<br>1<br>'<br>! !<br>2 1 !<br>3 ! !<br>=i } ! ! tt i<br>3 0<br>ka ' ! 1<br>“27 I !I<br>!<br>! !<br>-A} Colder tenmpersture | |_Hotter temperature<br><0 (0,3) (3,6) 6,9) (9,12) (12,15) [15,18) [18,21) (21,24) (24,27) [27,30) (30,33) 33+<br>‘Temperature bin<br>‘— Gini Theil<br><!-- End of picture text -->

<!-- Start of picture text --> Middle East & North Africa<br>\ '<br>! I<br>! I<br>! I<br>JA ! I<br>3 ! I<br>t ! I<br>5?<br>3 I! ! I'<br>ot !\ II<br>! I<br>! I<br>-27 Colder temperature | {_Hotier temperature<br><0 (0,3) (3,6) (6,9) (9,12) [12,15)[15,18) [18,21) [21,24) [24,27) [27,30) [30,33) 33+<br>‘Temperaturebin<br>‘4 Gini Theil<br><!-- End of picture text -->

<!-- Start of picture text --> South Asia<br>\<br>!<br>A ' ! !<br>1 I<br>3i 0 ' 1 !\<br>Bo. !<br>I !!<br>+2 ' |<br>!<br>-37 Colder temperature | | _ Hotter temperature<br><0 (0,3) [3,6) (6,9) _[9,12) (12,15) [15,18) [18,21) [21,24) [24,27) [27,30) [30,33) 33+<br>‘Temperature bin<br>‘4 Gini + Theil<br><!-- End of picture text -->

<!-- Start of picture text --> Sub-Saharan Africa<br>1 I '<br>I 1<br>5 Il| | 1<br>I |<br>| | I | I z xt<br>i | !<br>5 |I |1<br>| 1<br>\ 1<br><1 Colder temperature | | _ _ Hotter temperature<br><0 (0,3) (3,6) (6,9) (9,12) (12,15) [15,18) [18,21) (21,24) [24,27) (27,30) [30,33) 33+<br>Temperature bin<br>/— Gini Theil<br><!-- End of picture text -->
# **Table 1: The effects of temperature on subnational poverty**

|Poverty:|$1.90|/day|$3.20|/day|$5.50|/day|
|---|---|---|---|---|---|---|
||Panel FE –<br>all countries<br>(1)|Long<br>differences<br>(2)|Panel FE –<br>all countries<br>(3)|Long<br>differences<br>(4)|Panel FE –<br>all countries<br>(5)|Long<br>differences<br>(6)|
|**_Panel A: Country-lev_**|**_el analysis_**||||||
|Temperature|0.366***|0.137***|0.714***|0.250***|1.024***|0.333***|
||(0.058)|(0.018)|(0.098)|(0.027)|(0.139)|(0.037)|
|Precipitation|-0.007|-0.007|0.000|-0.013|0.018|-0.015|
||(0.015)|(0.023)|(0.024)|(0.035)|(0.036)|(0.042)|
|Country FE|Yes|No|No|No|Yes|No|
|Year FE|Yes|No|No|No|Yes|No|
|Mean dependent var.|7.321|7.321|15.448|15.448|26.895|26.895|
|Observations|460|95|460|95|460|95|
|Equality test (Panel<br>vs. long differences)|p = 0|.000|p = 0|.000|p = 0|.000|
|**_Panel B: Subnation-l_**|**_evel analysis_**||||||
|Temperature|0.920***|0.535***|1.834***|1.240***|2.298***|2.008***|
||(0.160)|(0.039)|(0.214)|(0.070)|(0.308)|(0.108)|
|Precipitation|0.236***|-0.365***|0.373**|-0.486***|-0.072|-0.177|
||(0.069)|(0.092)|(0.166)|(0.142)|(0.160)|(0.248)|
|Subnational FE|Yes|No|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|Yes|No|
|Mean dependent var.|10.061|10.061|20.327|20.327|34.009|34.009|
|Observations|4,972|1,109|4,972|1,109|4,972|1,109|
|Equality test (Panel<br>vs. long differences)|p = 0|.000|p = 0|.000|p = 0|.016|
|<br>Equality test|||||||
|(country vs.|p = 0.000|p = 0.000|p = 0.000|p = 0.000|p = 0.000|p = 0.000|
|subnational)|||||||
|Number of countries|134|95|134|95|134|95|
|Number of regions|1,594|1,109|1,594|1,109|1,594|1,109|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level in Panel A and subnational level in Panel B. Regressions are weighted by region population. Poverty data are taken from the GSAP. Poverty and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the country-level analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between the panel FE results vs. the long differences results, and the country analysis results vs. the subnational analysis results. *** p<0.01, ** p<0.05, * p<0.1.

31
# **Table 2: The effects of temperature on subnational inequality**

|Inequality:|Gi|ni|The|il|
|---|---|---|---|---|
||Panel FE –<br>all countries|Long<br>differences|Panel FE –<br>all countries|Long<br>differences|
||(1)|(2)|(3)|(4)|
|**_Panel A: Country-level_**|**_analysis_**||||
|Temperature|0.154***|0.171***|0.253***|0.275***|
||(0.029)|(0.040)|(0.049)|(0.063)|
|Precipitation|0.002|0.025**|-0.002|0.031|
||(0.007)|(0.012)|(0.012)|(0.021)|
|Country FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|34.406|34.406|23.417|23.417|
|Observations|423|90|423|90|
|Equality test (Panel vs.<br>long differences)|p = 0|.726|p = 0.|604|
|**_Panel B: Subnation-lev_**|**_el analysis_**||||
|Temperature|0.285***|0.349***|0.350***|0.592***|
||(0.086)|(0.049)|(0.032)|(0.082)|
|Precipitation|-0.156**|0.474***|0.267**|0.699***|
||(0.072)|(0.100)|(0.110)|(0.170)|
|Subnational FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|35.605|35.605|25.383|25.383|
|Observations|4,129|1,019|4,129|1,019|
|Equality test (Panel vs.<br>long differences)|p = 0|.383|p = 0.|029|
|Equality test (country<br>vs. subnational)|p = 0.000|p = 0.000|p = 0.000|p = 0.000|
|Number of countries|128|90|128|90|
|Number of regions|1,484|1,019|1,484|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level in Panel A and subnational level in Panel B. Regressions are weighted by region population. Inequality data are taken from the GSAP. Inequality and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the countrylevel analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between the panel FE results vs. the long differences results, and the country analysis results vs. the subnational analysis results. *** p<0.01, ** p<0.05, * p<0.1.

32
<!-- Start of picture text --> Inequality<br>AS ' !<br>I I<br>A ii I1<br>| 1<br>Ss= fei . Ii } } 7 !‘sa! 7<br>gzag 0 i I i 1<br>“05 I| !I<br>! !<br>-.1 “Colder temperature ! ' Hotter temperature<br>a) 10.2) (2.4) (4.6) (6,8) (8,10) (10,12) (12,14) (14,16) (16,18) [18,20) (20,22) (22.24) (24.26) (26,28) (28,30) 30+<br>Temperaturebin<br>‘4 Gini Theil<br><!-- End of picture text -->

<!-- Start of picture text --> Poverty<br>!<br>1<br>2 I \\<br>g<br>8 I 1<br>Swees!2 0 1 ERRI eR<br>ga 1\ 1 11 1<br>I !1<br>!<br>-4 Colder temperature ! | Hotter temperature<br>0 (0.2) (2,4) (4,6) (6,8) [8,10) (10,12) (12,14) (14,16) (16,18) (18,20) (20,22) (22,24) (24,26) [26,28) (28,30) 30+<br>‘Temperature bin<br>4 $19 4 $32 $55<br><!-- End of picture text -->

<!-- Start of picture text --> Poverty<br>!<br>\<br>i '<br>i, \ \<br>:- . } !! ''<br>I | |<br>= 0) i'<br>\1<br>\<br>i '<br>-.1 “Colder temperature | | Hotter temperature<br><0 (0,4) [48) (8,12) (12,16) [16,20) (20,24) (24,28) (28,32) 32+<br>Temperature bin<br>4 $19 4 $32 4 5.5<br><!-- End of picture text -->

<!-- Start of picture text --> Inequality<br>! !<br>I I<br>JA ! I<br>g H<br>zS !! !I<br>eet | |<br>i ! ry } ' z t z } i<br>° 1 1<br>I I<br>! !<br>05 “Colder temperature ! ! Hotter temperature<br><0 (0,4) 14.8) [8,12) 12,16) [16,20) (20,24) 24,28) (28,32) 32+<br>Temperaturebin<br>/ Gini Theil<br><!-- End of picture text -->

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<!-- Start of picture text --> Inequality<br>a \ \<br>' !<br>08 ! !<br>| ' !<br>\ I<br>23 \ I<br>I<br>i 0 64 | 1' |<br>a '| !!<br>J<br>02 } i }<br>| t if<br>Colder temperature ; H Hotter temperature<br>)<br><0 (0,5) [5,10) (10,15) (15,20) (20,25) (25,30) 30%<br>‘Temperature bin<br>4 Gini + Theil<br><!-- End of picture text -->
<!-- Start of picture text -->
||<br>||<br>| |<br>.004 | |<br>||<br>2||<br>g| |<br>§ .002 | |<br>& | |<br>32—s | |<br>i}# | | tt<br>g ° | ]<br>aI|<br>||<br>| |<br>-.002 | |<br>||<br>||<br>I|<br>|<br>-.004 Colder temperature | Hotter temperature<br><O = 0,3) [3,6) (6,9) (9,12) [12,15) [15,18) [18,21) [21,24) [24,27) [27,30) [30,33) 33+<br>Temperaturebin<br>-— 90/10 ='— 80/20 '— Palma<br><!-- End of picture text -->

<!-- Start of picture text --> .006<br>|I<br>|I<br>|I<br>|I<br>.004 | |<br>HI§||I|<br>E| |<br>2 .002 | |<br>6||<br>3| I<br>aII<br>eS } | |<br>0 rl 1 | |T<br>|I<br>II<br>|I<br>| I<br>-.002 “Colder temperature. | | Hotter temperature<br>po<br><0 (0,3) [3,6) [6,9) [9,12) [12,15) [15,18) [18,21) [21,24) [24,27) [27,30) [30,33) 33+<br>Temperaturebin<br>-— 90/10 '— 80/20 '!— Palma<br><!-- End of picture text -->
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<!-- Start of picture text --> Europe & Central Asia<br>2<br>iita 4<br>=5°<br>«1<br>-2<br>Bin | (colder) Bin2 Bin3 Bin4 BinS Bin6 Bin7 Bin8 Bin9 Bin 10 (hotter)<br>Temperature bin<br>ro Slo rs $3.2 he $5.5<br><!-- End of picture text -->

<!-- Start of picture text --> East Asia & Pacific<br>6<br>4<br>5<br>5jz tt i} te<br>2<br>-4<br>Bin 1 (colder) Bin 2 Bin3 Bin4 Bin 5 Bin6é Bin7 Bins Bin 9 Bin 10 (hotter,<br>‘Temperature bin<br>ro S19 bet $3.2 ro $5.5<br><!-- End of picture text --> <!-- Start of picture text --> Latin America & Caribbean<br>3<br>2 ]<br>Poa i<br>= 0 + i<br>-1<br>Bin 1 (colder) Bin 2 Bin3 Bin4 BinS Bin 6 Bin7 Bins Bin 9 Bin 10 (hotter)<br>‘Temperature bin<br>ro $19 res $32 hm $5.5<br><!-- End of picture text --> <!-- Start of picture text --> Middle East & North Africa<br>3<br>2<br>its | tH i<br>«1<br>Bin | (colder) Bin2 Bin3 Bin4 Bins Bin6é Bin? Bin’ Bin9 Bin 10 (hotter}<br>Temperature bin<br>mo Slo ro $3.2 ee $5.5<br><!-- End of picture text -->

<!-- Start of picture text --> South Asia<br>4<br>2<br>;<br>i i<br>2<br>-4<br>Bin | (colder) Bin 2 Bin3 Bin4 Bin 5 Bin6é Bin7 Bins’ Bin 9 Bin 10 (hotter<br>‘Temperature bin<br>rs S19 es $3.2 mo $5.5<br><!-- End of picture text -->

<!-- Start of picture text --> Sub-Saharan Africa<br>4<br>i EE 5 z3%<br>2<br>-4<br>Bin 1 (colder) Bin 2 Bin3 Bin4 BinS Bin6é Bin7 Bin’ Bin 9 Bin 10 (hotter,<br>‘Temperature bin<br>re S19 eS $32 hn $5.5<br><!-- End of picture text -->
<!-- Start of picture text --> 45 Europe & Central Asia<br>5i 0S | }<br>05<br>,<br>Bin| (colder) Bin2 Bin 3 Bin 4 Bins Bin6 Bin? Bins Bin9 Bin 10 (hotter)<br>‘Temperature bin<br>eS Gini /— Theil<br><!-- End of picture text -->

<!-- Start of picture text --> East Asia. & Pacific.<br>4<br><i ?<br>“2<br>Bin 1 (colder) ‘Bin Bin3 Bin Bins Bin6 Bin? Bins Bin9 —_Bin 10 (hotter)<br>Temperature bin<br>-—— Gini Theil<br><!-- End of picture text --> <!-- Start of picture text --> Middle East & North Africa<br>3<br>§<br>i<br>«1<br>+2<br>Bin | (colder) Bin2 Bin3 Bint Bins Biné Bin? Bins Bind Bin 10 (hotter)<br>Temperature bin<br>-— Gini —— Theil<br><!-- End of picture text -->

<!-- Start of picture text --> Latin America & Caribbean<br>2<br>J<br>7<br>ol<br>-2<br>Bin| (colder) Bin? ‘Bind Bins Bins Bin6 Bin? Bins Bind Bin 10 (hotter)<br>Temperature bin<br>/— Gini} Theil<br><!-- End of picture text -->

<!-- Start of picture text --> South Asia<br>4<br>r<br>Eo<br>“2<br>Bin | (colder) Bin? Bind Bind Bins Bin 6 Bin? Bins Bind Bin 10 (hotter)<br>Temperature bin<br>/—— Gini Theil<br><!-- End of picture text -->

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# **Table A1: Alternative specifications of panel model**

# _Panel A: Poverty_

|Dependent variable:<br>Poverty rate at $1.90|Adding<br>country-<br>specific<br>linear time<br>trend|Adding<br>region-<br>specific<br>linear time<br>trend|Adding<br>temperature<br>change|Adding<br>temperature<br>squared<br>term|Adding<br>temperature cubic<br>term|Adding<br>temperature<br>squared term and<br>temperature<br>change|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Temperature|0.867***|0.316***|0.879***|0.873***|0.719*|0.523***|0.524***|
||(0.129)|(0.107)|(0.211)|(0.321)|(0.370)|(0.129)|(0.129)|
|∆Temperature|||-0.046|||0.044*|0.074|
||||(0.030)|||(0.024)|(0.047)|
|Temperature squared||||-0.007|-0.003|-0.002|-0.002|
|||||(0.006)|(0.012)|(0.002)|(0.002)|
|Temperature cubic|||||0.000<br>(0.001)|||
|Temperature*∆Tempearture|||||||-0.003<br>(0.005)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|No|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|No|
|Number of countries|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,972|4,972|4,972|4,972|4,972|4,972|4,972|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Poverty rate is measured by the Subnational Poverty Headcount Ratio at $1.90 a day. *** p<0.01, ** p<0.05, * p<0.1 46
_Panel B: Inequality_

|Dependent variable: Gini<br>index|Adding<br>country-<br>specific<br>linear time<br>trend|Adding<br>region-<br>specific<br>linear time<br>trend|Adding<br>temperature<br>change|Adding<br>temperature<br>squared<br>term|Adding<br>temperature cubic<br>term|Adding<br>temperature<br>squared term<br>and temperature<br>change|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Temperature|0.290***|0.175***|0.537***|0.283***|0.282***|0.533***|0.512***|
||(0.103)|(0.040)|(0.099)|(0.087)|(0.086)|(0.101)|(0.104)|
|∆Temperature|||0.209**|||0.249*|0.099|
||||(0.092)|||(0.135)|(0.164)|
|Temperature squared||||-0.001|-0.003|-0.003|-0.005|
|||||(0.004)|(0.013)|(0.007)|(0.007)|
|Temperature cubic|||||0.000<br>(0.000)|||
|Temperature*∆Tempearture|||||||0.012<br>(0.013)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of countries|128|128|128|128|128|128|128|
|Number of regions|1,484|1,484|1,484|1,484|1,484|1,484|1,484|
|Observations|4,129|4,129|4,129|4,129|4,129|4,129|4,129|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Inequality is measured by Gini index. *** p<0.01, ** p<0.05, * p<0.1 47
# **Table A2: Alternative specifications of long-difference model**

_Panel A: Poverty_

|Dependent variable:<br>Poverty rate at $1.90|4-year<br>average|5-year<br>average|Adding<br>time-<br>invariant<br>covariates|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆Temperature|0.510***|0.601***|0.708***|0.266***|
||(0.037)|(0.040)|(0.054)|(0.077)|
|Temperature||||-0.045<br>(0.127)|
|Temperature squared||||0.011**<br>(0.005)|
|Temperature*∆Tempearture||||0.155<br>(0.115)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Subnational FE|No|No|No|No|
|Year FE|No|No|No|No|
|Number of countries|95|95|95|95|
|Observations|1,109|1,109|1,109|1,109|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Poverty rate is measured by the Subnational Poverty Headcount Ratio at $1.90 a day. Control variables in Column (3) are taken from Kalkuhl and Wenz (2020) which include cumulative oil gas, distance to coast, distance to river, and altitude. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 48
_Panel B: Inequality_

|Dependent variable: Gini<br>index|4-year<br>average|5-year<br>average|Adding<br>time-<br>invariant<br>covariates|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|∆Temperature|0.309***|0.339***|0.421***|0.977**|
||(0.045)|(0.042)|(0.052)|(0.455)|
|Temperature||||0.333<br>(0.388)|
|Temperature squared||||-0.027***<br>(0.007)|
|Temperature*∆Tempearture||||-0.464***<br>(0.070)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Subnational FE|No|No|No|No|
|Year FE|No|No|No|No|
|Number of countries|90|90|90|90|
|Observations|1,019|1,019|1,019|1,019|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Inequality is measured by Gini index. Control variables in Column (9) are taken from Kalkuhl and Wenz (2020) which include cumulative oil gas, distance to coast, distance to river, and altitude. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 49
# **Table A3: Alternative measure of poverty – Multidimensional poverty**

||||Percen<br>|tage ofpopulatio|n deprived|||
|---|---|---|---|---|---|---|---|
||Monetary<br>poverty|Educational<br>attainment|Educational<br>enrolment|Electricity|Sanitation|Drinking water|Headcount<br>ratio|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Temperature|0.737***|0.726**|0.119|1.137***|0.682*|0.097|1.374***|
||(0.203)|(0.282)|(0.172)|(0.355)|(0.373)|(0.199)|(0.395)|
|Precipitation|-0.011|-0.192***|0.049|0.509**|0.461**|-0.133|-0.155|
||(0.097)|(0.064)|(0.044)|(0.210)|(0.200)|(0.117)|(0.120)|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,478|2,464|2,260|2,437|2,315|2,321|2,478|
|R-squared|0.419|0.089|0.232|0.259|0.146|0.047|0.412|
|Number of regions|1,179|1,179|1,109|1,172|1,163|1,169|1,179|
|Mean headcount<br>poverty rate (percent)|7.651|10.51|3.516|7.311|21.83|7.909|11.31|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Column (1) measures the percentage of the population living on less than $2.15 a day at 2017 international prices, Column (2) measures the percentage of population deprived of primary educational attainment; Column (3) measures the percentage of population deprived of school enrolment; Column (4) measures the percentage of population deprived of electricity; Column (5) measures the percentage of population deprived of sanitation; Column (6) measures the percentage of population deprived of drinking water; Column (7) is the share of people who are considered multidimensionally deprived. *** p<0.01, ** p<0.05, * p<0.1 50
# **Table A4: Robustness test – Alternative measures of temperature**

_Panel A: Poverty_

|||De|pendent variabl|e:Povertyrate <br>Number of<br>|at $1.90<br>Dropping<br>||
|---|---|---|---|---|---|---|
||Log<br>temperature|Temperature<br>(<sup>o</sup>F)|Temperature<br>from CRU|days<br>temperature<br>above 28|subregions with<br>temperature<br>above 28|Temperature<br>shock|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|2.218***|0.434***|0.760***|0.070***|0.666***|0.337***|
||(0.481)|(0.064)|(0.118)|(0.025)|(0.111)|(0.114)|
|Controlling for<br>rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of|||||||
|countries|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|5,090|5,090|5,059|4,209|4,856|5,090|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In Column (5), temperature shock is defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviation. *** p<0.01, ** p<0.05, * p<0.1 _Panel B: Inequality_

||||Dependent va|riable: Gini ind<br>Number of<br>|ex<br>Dropping<br>||
|---|---|---|---|---|---|---|
||Log<br>temperature|Temperature<br>(<sup>o</sup>F)|Temperature<br>from CRU|days<br>temperature<br>above 28|subregions with<br>temperature<br>above 28|Temperature<br>shock|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|1.018***|0.158***|0.248***|0.064***|0.302***|2.594***|
||(0.346)|(0.048)|(0.086)|(0.013)|(0.086)|(0.472)|
|Controlling for<br>rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of|||||||
|countries|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|5,090|5,090|5,059|4,209|4,856|5,090|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In Column (5), temperature shock is defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviation. *** p<0.01, ** p<0.05, * p<0.1 51
# **Table A5: Robustness test – Alternative samples**

# _Panel A: Poverty_

|||Dropping|De|pendent variable|:Povertyrate at $1.<br>Eldi 10|90<br>Eldi 10|S.E.||
|---|---|---|---|---|---|---|---|---|
||Balanced<br>panel|countries with<br>few<br>subregions|Excluding<br>USA|Excluding<br>India|xcung<br>percent cold<br>countries|xcung<br>percent hot<br>countries|clustered<br>country<br>level|Spatially-<br>corrected<br>Conley S.E.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|0.920***|0.922***|0.785***|0.791***|1.036***|0.526***|0.648***|0.920***|
||(0.160)|(0.179)|(0.116)|(0.113)|(0.158)|(0.100)|(0.090)|(0.279)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of countries|134|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,604|3,990|4,448|4,888|4,547|4,674|4,957|4,958|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In column (8), we use the Conley standard error (Conley, 1999) to adjust for spatial correlation based on a radius of 200 km following Graff Zivin et al. (2020). *** p<0.01, ** p<0.05, * p<0.1 52
_Panel B: Inequality_

|||Dropping||Dependent va|riable: Gini index<br>Eldi 10|Eldi 10|S.E.||
|---|---|---|---|---|---|---|---|---|
||Balanced<br>panel|countries with<br>few<br>subregions|Excluding<br>USA|Excluding<br>India|xcung<br>percent cold<br>countries|xcung<br>percent hot<br>countries|clustered<br>country<br>level|Spatially-<br>corrected<br>Conley S.E.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|0.285***|0.363**|0.288***|0.386***|0.274**|0.258***|0.213**|0.285**|
||(0.086)|(0.142)|(0.086)|(0.086)|(0.111)|(0.085)|(0.087)|(0.143)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of countries|134|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,604|3,990|4,448|4,888|4,547|4,674|4,957|4,958|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In column (8), we use the Conley standard error (Conley, 1999) to adjust for spatial correlation based on a radius of 200 km following Graff Zivin et al. (2020). *** p<0.01, ** p<0.05, * p<0.1 53
# **Table A6: The effects of temperature on poverty – Subnational GDP analysis**

||Poverty|rate $1.90<br>Lon|Poverty|rate $3.20<br>Lon|Poverty|rate $5.50<br>Lon|
|---|---|---|---|---|---|---|
||Panel FE|g<br>differences|Panel FE|g<br>differences|Panel FE|g<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|0.148**|0.057***|0.206**|0.120**|0.224**|0.105*|
||(0.064)|(0.021)|(0.084)|(0.057)|(0.095)|(0.060)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|No|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|Yes|No|
|Number of countries|74|61|74|61|74|61|
|Number of regions|3,394|1,306|3,394|1,306|3,394|1,306|
|Observations|138,060|1,306|138,060|1,306|138,060|1,306|
|Adjusted R-squared|0.334|0.204|0.350|0.369|0.385|0.254|
|Mean headcount<br>poverty rate (percent)|16.847|16.847|30.152|30.152|45.559|45.559|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Poverty rate is calculated using subnational GDP from Kalkuhl and Wenz (2020) and the poverty lines of $1.90, $3.20, and $5.50. Poverty rates and weather variables in the long-differences model are measured by the difference between averages of the earliest 10-year period (1979–1988) and averages of the latest 10-year period (2009–2018). The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 54
# **Table A7: The effects of temperature on poverty – Grid-level analysis**

|Dependent variable:|Panel|model|Long differ|ences model|
|---|---|---|---|---|
|Poverty rate at $1.90|Baseline<br>(1)|Extension<br>(2)|Baseline<br>(3)|Extension<br>(4)|
|Temperature|0.102***|-2.046***||-0.0006***|
||(0.022)|(0.060)||(0.0001)|
|∆Tempearture||0.870***|0.009***|0.005***|
|||(0.033)|(0.001)|(0.001)|
|Temperature squared||0.092***<br>(0.002)||0.0001***<br>(0.00004)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Number of countries|82|82|82|82|
|Observations|1,115,478|1,072,575|42,903|42,903|
|R-squared|0.929|0.555|0.001|0.007|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Poverty incidence is calculated using subnational GDP from Kummu _et al._ (2018) and the poverty line from WDI. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 55
# **Table A8: The effects of temperature on poverty and inequality – Country level analysis**

# **using alternative data from WDI and SWIID**

# _Panel A: Poverty data from WDI_

|Poverty:|$1.90|/day|$3.2|0/day|$5.5|0/day|
|---|---|---|---|---|---|---|
||Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|0.629***|0.217***|1.020***|0.381***|1.344***|0.501***|
||(0.098)|(0.028)|(0.153)|(0.042)|(0.220)|(0.055)|
|Precipitation|-0.019|0.047|-0.017|0.008|-0.010|0.060|
||(0.016)|(0.233)|(0.029)|(0.325)|(0.036)|(0.339)|
|Country FE|Yes|No|No|No|Yes|No|
|Year FE|Yes|No|No|No|Yes|No|
|Mean dependent var.|7.288|7.288|15.399|15.399|26.593|26.593|
|Number of countries|134|95|134|95|134|95|
|Observations|1,717|95|1,717|95|1,716|95|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Regressions are weighted by region population. Poverty data are taken from the WDI. Poverty and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1.

_Panel B: Inequality data from WDI and SWIID_

|Inequality:|Gini|– WDI|Gini –|SWIID|
|---|---|---|---|---|
|||Lon||Lon|
||Panel FE|g<br>differences|Panel FE|g<br>differences|
||(1)|(2)|(3)|(4)|
|Temperature|0.171***|0.194***|0.165***|0.255***|
||(0.023)|(0.033)|(0.037)|(0.040)|
|Precipitation|0.013**|0.022*|0.006*|0.018*|
||(0.005)|(0.013)|(0.003)|(0.010)|
|Country FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|38.295|38.295|38.445|38.445|
|Number of countries|128|90|128|90|
|Observations|1,505|90|3,781|90|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Regressions are weighted by region population. Inequality data in Columns (1)-(2) are taken from the World Development Indicators (WDI). Inequality data in Columns (3)-(4) are taken from the Standardized World Income Inequality Database (SWIID). Inequality and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on crosssectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 56
# **Table A9: The effects of temperature on poverty and inequality – Heterogeneity analysis**

|Poverty $1.90|Gini index|
|---|---|
|(1)|(2)|
|_Panel A: Regime type (Reference group: Democracy)_||
|Temperature*Hybrid regime<br>0.735*|-0.004|
|(0.444)|(0.003)|
|Temperature*Authoritarian regime<br>1.395***<br>(0.431)|0.008**<br>(0.003)|
|_Panel B: Location (Reference group: Countries near equator)_||
|Temperature* Countries near equator<br>0.943***<br>(0.293)|0.011***<br>(0.004)|
|_Panel C: Share of agriculture in GDP (Reference group: Low s_|_hare)_|
|Temperature*High agriculture share<br>0.155***<br>(0.051)|0.001***<br>(0.000)|
|_Panel D: Share of manufacturing in GDP (Reference group: Lo_|_w share)_|
|Temperature*High manufacturing share<br>-0.076**<br>(0.039)|-0.001***<br>(0.000)|
|_Panel E: Share of trade in GDP (Reference group: Low share)_||
|Temperature*High trade share<br>-0.005<br>(0.003)|0.000<br>(0.000)|
|Controlling for rainfall<br>Yes|Yes|
|Subnational FE<br>Yes|Yes|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. *** p<0.01, ** p<0.05, * p<0.1 57
# **Table A10: Role of information and communication technologies (ICTs) as mediator**

||Poverty $1.90|Gini index|
|---|---|---|
||(1)|(2)|
|_Panel A: ICT Development index_|||
|Temperature* ICT Index|-0.178***<br>(0.031)|-0.003***<br>(0.001)|
|_Panel B: Internet 2G_|||
|Temperature*Internet coverage|-2.980***<br>(0.997)|-0.034***<br>(0.013)|
|_Panel C: Internet 3G_|||
|Temperature*Internet coverage|-1.594***<br>(0.428)|-0.012***<br>(0.004)|
|_Panel D: Internet 4G_|||
|Temperature*Internet coverage|-0.762**<br>(0.297)|-0.019***<br>(0.006)|
|Controlling for rainfall|Yes|Yes|
|Subnational FE|Yes|Yes|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. *** p<0.01, ** p<0.05, * p<0.1 58
# **Table A11: Effects of temperature on agriculture**

|Crop yield|Ric|e|Ma|ize|Soy|bean|W|heat|
|---|---|---|---|---|---|---|---|---|
||Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|-0.197***|-0.111***|-0.183***|-0.115***|-0.042***|-0.041***|0.010|-0.000|
||(0.021)|(0.014)|(0.013)|(0.008)|(0.011)|(0.011)|(0.014)|(0.010)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Mean crop yield<br>(tonnes/hectare)|3.215|3.215|2.412|2.412|1.719|1.719|3.350|3.350|
|Number of countries|74|74|101|100|33|33|90|90|
|Number of regions|660|660|955|955|189|189|670|670|
|Observations|8,566|660|12,392|955|2,452|189|8,663|670|
|Equality test (Panel vs.<br>long differences)|p = 0.|000|p = 0|.000|p =|0.000|p =|0.628|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Crop yield data is provided by Iizumi and Sakai (2020). *** p<0.01, ** p<0.05, * p<0.1 59
# **Table A12: Effects of temperature on agriculture by share of agriculture in GDP**

|Crop yield|Rice|Maize|Soybean|Wheat|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Share of agriculture in GD_|_P (Reference gr_|_oup: Low sha_|_re)_||
|Temperature*High share|-0.127***|-0.005|-0.119***|-0.035***|
||(0.015)|(0.016)|(0.012)|(0.012)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Number of countries|42|70|16|68|
|Number of regions|641|915|178|634|
|Observations|9,967|14,259|2,778|9,635|
|R-squared|0.648|0.619|0.682|0.726|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Crop yield data is provided by Iizumi and Sakai (2020). *** p<0.01, ** p<0.05, * p<0.1 60
# **Table A13: Simulated effect of temperature on poverty and inequality**

**_Panel A: Poverty_**

|||SSP1-1.9|SSP1-2.6|SSP2-4.5|SSP3-7.0|SSP5-8.5|
|---|---|---|---|---|---|---|
|Temperature|Mean|1.400|2.000|3.000|4.100|5.000|
||Upper|2.200|2.900|4.300|6.200|7.400|
||Lower|0.900|1.300|2.100|3.000|3.800|
|Increase in poverty rate $1.90|Mean|0.749|1.070|1.605|2.194|2.675|
||Upper|1.177|1.552|2.301|3.317|3.959|
||Lower|0.482|0.696|1.124|1.605|2.033|
|Increase in poverty rate $3.20|Mean|1.736|2.480|3.720|5.084|6.200|
||Upper|2.728|3.596|5.332|7.688|9.176|
||Lower|1.116|1.612|2.604|3.720|4.712|
|Increase in poverty rate $5.50|Mean|2.811|4.016|6.024|8.233|10.040|
||Upper|4.418|5.823|8.634|12.450|14.859|
||Lower|1.807|2.610|4.217|6.024|7.630|

_Notes:_ Data on simulated temperature are from the Coupled Model Intercomparison Project (CMIP6) climate projections. The projection is estimated using the coefficient on the effects of temperature on inequality reported in Columns (2), (4), and (6) (Panel B) of Table 1.

**_Panel B: Inequality_**

|||SSP1-1.9|SSP1-2.6|SSP2-4.5|SSP3-7.0|SSP5-8.5|
|---|---|---|---|---|---|---|
|Temperature|Mean|1.400|2.000|3.000|4.100|5.000|
||Upper|2.200|2.900|4.300|6.200|7.400|
||Lower|0.900|1.300|2.100|3.000|3.800|
|Gini index|Mean|0.489|0.698|1.047|1.431|1.745|
||Upper|0.768|1.012|1.501|2.164|2.583|
||Lower|0.314|0.454|0.733|1.047|1.326|
|Theil index|Mean|0.8288|1.184|1.776|2.4272|2.96|
||Upper|1.3024|1.7168|2.5456|3.6704|4.3808|
||Lower|0.5328|0.7696|1.2432|1.776|2.2496|

_Notes:_ Data on simulated temperature are from the Coupled Model Intercomparison Project (CMIP6) climate projections. The projection is estimated using the coefficient on the effects of temperature on inequality reported in Columns (2), and (4) (Panel B) of Table 2.

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# **Table A14: Cost allocation by income group**

|Income group|Contribution<br>(%)|Contribution<br>($US billion)|
|---|---|---|
|High income|59.53%|29,762.56|
|Upper-middle income|29.72%|14,862.37|
|Lower-middle income|10.14%|5,070.30|
|Low income|0.61%|304.77|
|Total|100%|50,000|

Notes: The income group is identified using the World Bank country classifications 2022-2023, available at: <u>https://blogs.worldbank.org/opendata/new-world-bankcountry-classifications-income-level-2022-2023</u> 62
# **Appendix B: Data Description**

# **B1. Poverty data**

To implement the analysis, we assemble the most comprehensive data on poverty taken from the Global Subnational Atlas of Poverty (GSAP), produced by the Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in the Development Economics Data Group (DECDG). All the teams are at the World Bank.

For each survey data, the geographical area choice is based on the survey representativeness based on the sampling and sample design and survey documentation when available. For most of the database, surveys are representative at the first administrative level (ADM1) or statistical regions (areas) for the purpose of survey. On average, there are 14 subnational areas for a given country and year observation. For 18 small countries (13 percent), there is no subnational data available from the surveys, thus the national level data is used.

Subnational poverty rates are calculated using official household or income surveys for the purpose of global poverty monitoring. Poverty rates are provided at the subnational level that is representative for the associated household or income survey used. Overall, cross-sectional poverty statistics are shown for about 5,500 subnational areas based on survey representativeness and availability of matched spatial geographic boundaries.

Geographic boundaries must match the subnational regions in these surveys. In many cases, there is a one-to-one association between the regions in a household survey and the areas defined at an administrative level in the country. In cases where there is not a one-to-one association, geographic boundaries are altered to fit the representativeness of the surveys. In some cases, the geographic representation is at the level of “urban”, or “rural”. In these cases, subnational areas in the household survey are aggregated to levels that can be appropriately represented by boundaries. Several sources of geospatial files were leveraged to construct the GSAP: GADM, GAUL, NUTS, and customized spatial files. The choice of spatial files is based on more disaggregated availability and geographic alignment with household surveys. For example, NUTS spatial files are used prominently for the European countries in GSAP, since these files are developed and regulated by the EU.

Building on the cross-sectional GSAP database, we construct a new database on poverty statistics based on almost 500 available household income/expenditure survey data in the Global Monitoring Database (GMD)<sup>13</sup> for 139 economies, with more than 90 percent of the survey data ranging from 2010 to 2019. This database consists of panel data that are representative at 1,650 subnational areas. The number of countries across regions and over time are presented in Figures B1 and B2.

As both country boundary and survey representativeness can change over time, constructing a panel data of poverty at area-level is not a simple task. When there is a change

> 13 The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports.

63
in the boundary over time or survey representativeness is different, efforts are needed to maintain a long panel of data to have comparable statistics spatially and over time. Such efforts could be (1) regroup areas to a new area that matches the previous definition of areas, or (2) a higher level of geographical disaggregation over time. In this version of the panel data, on average a country has data for 14 geographical areas over the period of 3 years.

We also employ poverty data from different sources available at the country level and subnational level. The first is taken from the World Development Indicator (WDI) which provides different measures including the poverty headcount ratio, poverty gap, and number of poor at both international and national poverty lines. Our measures of interest are poverty headcount ratio at US$1.90 a day. It is calculated by the percentage of the population living on less than $1.90 a day at 2011 international prices. For richer analysis, we also use other poverty lines including the poverty headcount ratio at $3.20 and $5.50 a day.

As an alternative source of subnational poverty, we exploit the annual GRP data provided by Kalkuhl and Wenz (2020), which is available from 1981 to 2016 for more than 1,500 regions in 77 countries worldwide. The dataset, however, includes only a few countries in Africa. We calculate the incidence of poverty by assuming the poverty line of $1.90, $3.20, and $5.50 for all countries in our sample.<sup>14</sup> We also exploit annual gridded datasets for GDP per capita (PPP) from Kummu _et al._ (2020) which covers 26-year period from 1990 to 2015 for 82 countries. In this dataset, each grid cell is recorded at 5 arc-min resolution. We then apply a similar exercise as in the dataset of Kalkuhl and Wenz (2020) and measure the incidence of poverty at different thresholds. We present the list of countries in our datasets in Table B2.

# **B2. Inequality data**

The GSAP dataset is unique in terms of country-time coverage and in the breadth of the measures of inequality covered. As the main outcomes, we use the most widely accepted measures of income distribution including the Gini index and Theil index. These indices are computed on the income available to households after government taxes and transfers, excluding indirect and value-added taxes, public services, and indirect government transfers. The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus, a Gini index of zero represents perfect equality, while an index of 100 implies perfect inequality. Similarly, the Theil index measures an entropic ‘distance’ the population is away from the ‘ideal’ egalitarian state of everyone having the same income. The Theil index ranges between zero and infinity, with zero representing an equal distribution and higher values representing a higher level of inequality. As alternative measures of income inequality, we use the distribution of income share held by each decile and calculate different percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the Palma ratio (90/40 ratio). All income measures are converted to real terms using 2011 Purchasing Power Parity (PPP) dollars for comparison across survey years.

We also employ Gini data from different sources available at the country level. The first is taken from the World Bank Poverty and Inequality Platform (PIP). The data are based on primary household surveys obtained from government statistical agencies and World Bank country departments. In the case of high-income economies, they are mostly derived from the

> 14 To illustrate, we fix the poverty line for all regions in our sample and identify a region as poor if its gross income (per day) is below the poverty line.

64
Luxembourg Income Study database. As an alternative source of country-level inequality, we exploit the Standardized World Income Inequality Database (SWIID). SWIID provides standardized Gini income inequality measures for market and net outcomes based on the same concept, and thus allows the comparison of income inequality before and after redistribution by taxation and transfers over time.

# **B3. Weather data**

We match our poverty and inequality data with the ERA5 satellite reanalysis data, which is taken from ECMWF. The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). We use air temperature and precipitation, both measured as annual averages, and map the grid spacings in ERA5 to the country/region in our poverty datasets. We follow previous studies and aggregate the gridded data to the region level by computing area-weighted averages (i.e., averaging all grid cells that fall into a region) (e.g., Heyes and Saberian, 2022; Kalkuhl and Wenz, 2020). Figure B3 provides a distribution of average temperature in our sample. It shows that most regions in our sample belong to the temperature range of between 24<sup>◦</sup> C and 28<sup>◦</sup> C. Another dataset that we use in the paper is the global gridded CRU data which provides monthly estimates at 0.5<sup>◦</sup> resolution. The CRU data, however, is subject to absence of data in regions with less coverage of weather stations. Therefore, our main analysis exploits the ERA5 data which combines information from ground stations, satellites, weather balloons and other inputs with a climate model, and therefore is less prone to station weather bias (Auffhammer _et al._ , 2013).

To explore the potential impacts of future climate change on poverty and inequality, we use climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This project offers a variety of scenarios, each depicting a different trajectory for global development, energy usage, policy implementation, and climate action, thus allowing a comprehensive assessment of future climate change impacts under diverse conditions. The scenarios we consider include SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Each of these scenarios represent distinct pathways reflecting varying levels of greenhouse gas emissions, socio-economic factors, and environmental developments. For instance, SSP1-1.9 represents a scenario where strong mitigation and sustainable development lead to a temperature rise below 1.4°C by 2100, illustrating a situation where global warming is limited to well below 2°C. In contrast, SSP5-8.5 portrays a scenario of high socio-economic growth coupled with high greenhouse gas emissions, leading to a temperature rise of 5.0°C, representing a trajectory with minimal climate mitigation efforts. By examining these diverse scenarios, we aim to gain nuanced insights into how differing future conditions and policy decisions can impact poverty and inequality, thereby providing a broad perspective on the societal implications of climate change trajectories.

# **B4. Other data**

To examine the role of agriculture as the mechanism, we utilize annual production of four major crops (maize, wheat, soybean, rice) available from Iizumi and Sakai (2020). The dataset records global gridded data of annual crop yields, measured in tonnes/hectare, at 0.5<sup>◦</sup> resolution and covers the period 1982–2015. The dataset was created by combining agricultural census data, satellite remote sensing and information on crop calendar and crop harvested area. Although the data include only four main crops, thereby partly limiting our analysis, the tradeoff permits us to assemble consistent long panel data. Finally, in some specifications, we exploit data from different sources including type of regime from The Economist Intelligence, broadband internet coverage provided by Collins Bartholomew’s Mobile Coverage Explorer, and other country-level characteristics (i.e., population density, elevation, distance to the 65
nearest coast, and concentration of Particulate matter of 2.5 micrometers or smaller – PM2.5) from the NASA Socioeconomic Data and Applications Center (SEDAC). We provide descriptions and summary statistics of all variables in Table B1.

# **References**

- <mark>Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 181–198.</mark>

- <mark>Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(3), 740–98.</mark>

- <mark>Heyes, A., & Saberian, S. (2022). Hot Days, the ability to Work and climate resilience: Evidence from a representative sample of 42,152 Indian households.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>155</mark>_ <mark>, 102786.</mark>

- <mark>Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1), 1–</mark> 7.

- <mark>Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>, 102360.</mark>

- <mark>Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 1–15.</mark>

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# **Table B1: Data sources and summary statistics**

|Variable|Descriptions|Country<br>N|Obs. No.|Mean|S.D.|Min|Max|
|---|---|---|---|---|---|---|---|
|**National poverty rate (1**|**979–2019) (percent)**|o.||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|464|7.288|14.814|0.000|78.841|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a day|134|464|15.399|23.975|0.000|91.518|
|Poverty rate $5.50|Poverty Headcount Ratio at US$ 5.50 a day|134|464|26.593|32.088|0.051|97.485|
|**Subnational poverty rat**|**e (Global Subnational Atlas of Poverty – GSAP)**|||||||
|**(percent)**||||||||
|_Source: The World Bank_ (|https://datacatalog.worldbank.org/home)|||||||
|Poverty rate $1.90|Poverty Headcount Ratio at US$ 1.90 a day|134|4,972|10.061|19.389|0.000|98.010|
|Poverty rate $3.20|Poverty Headcount Ratio at US$ 3.20 a day|134|4,972|20.327|28.482|0.000|99.724|
|Poverty rate $5.50|Poverty Headcount Ratio at US$ 5.50 a day|134|4,972|34.009|34.700|0.000|100.000|
|**Subnational poverty rat**|**e**_(Source: Kalkuhl and Wenz, 2020)_|||||||
|Poverty at $1.90|Poverty rate using average gross daily income being<br>below US$ 1.90 a day|77|3,394|20.443|37.990|0.000|100.000|
|Poverty at $3.20|Poverty rate using average gross daily income being<br>below US$ 3.20 a day|77|3,394|34.075|44.185|0.000|100.000|
|Poverty at $5.50|Poverty rate using if average gross daily income<br>being below US$ 5.50 a day|77|3,394|57.450|46.434|0.000|100.000|
|**Subnational poverty rat**|**e**_(Source: Kummu et al., 2018)_|||||||
||Poverty rate using average gross daily income being|||||||
|Poverty at $1.90|<br>below US$ 1.90 a day|82|1,811,394|24.245|42.857|0.000|100.000|
|Poverty at $3.20|Poverty rate using average gross daily income being<br>below US$ 3.20 a day|82|1,811,394|32.000|46.648|0.000|100.000|
|Poverty at $5.50|Poverty rate using average gross daily income being<br>below US$ 5.50 a day|82|1,811,394|55.000|49.749|0.000|100.000|
|**National inequality**||||||||
|_WDI(Source: The World_|_Bank -_https://datacatalog.worldbank.org/home_)_|||||||

70
|Gini|Gini index (%)|128|423|34.406|6.980|22.968|59.777|
|---|---|---|---|---|---|---|---|
|Theil|Theil index (%)|128|423|23.417|10.882|8.824|70.786|
|_WDI (Source: The Wor_|_ld Bank -_https://datacatalog.worldbank.org/home_)_|||||||
|Gini|Gini index (%)|128|423|38.295|9.062|20.700|65.800|
|_SWIID (Source: Standa_|<br>_rdized World Income Inequality Database - _https://fsolt.org/swi|id/_)_||||||
|Gini|Gini index (%)|128|423|38.445|8.637|17.900|65.400|
|**Subnational inequalit**|<br>**y (GSAP)**|||||||
|_Source: The World Ban_|_k_ (https://datacatalog.worldbank.org/home)|||||||
|Gini|Gini index|128|4,129|35.627|8.045|13.371|66.448|
|Theil|Theil index|128|4,129|25.421|13.966|3.359|192.672|
|90/10 ratio|Ratio of the income of the 10% richest to that of the<br>10% poorest.|128|4,100|2.940|9.837|0.000|131.202|
|80/20 ratio|Ratio of the income of the 20% richest to that of the<br>20% poorest.|128|4,099|2.627|7.712|0.000|106.748|
|Palma ratio|Ratio of the income of the 10% richest to that of the<br>|128|4,150|0.434|1.933|0.000|64.359|
|**_Satellite weather data (_**|40% poorest.<br>**_1979_–****_2019)_**|||||||
|_Source: European Unio_|_n’s Copernicus programme_ (https://sentinels.copernicus.eu/web<br>|/sentinel/mis|sions/sentinel-5|p)||||
|Temperature|Average temperature (C)|134|5,090|18.185|7.996|-9.417|30.790|
|Rainfall|Average rainfall (mm)|134|5,090|3.880|3.178|0.006|34.882|
|_Source: Climatic Resea_|<br>_rch Unit_ (https://crudata.uea.ac.uk/cru/data/hrg/)|||||||
|Temperature|Average temperature (C)|134|5,090|18.328|8.030|-11.082|30.426|
|**_Crop yield data_**||||||||
|_Source: Iizumi and Sak_|_ai (2020)_|||||||
|Rice|Average crop yield (1981–2016)|45|10,257|3.215|3.041|0.000|22.314|
|Maize|Average crop yield (1981–2016)|76|14,870|2.412|2.480|0.000|27.743|
|Soybean|Average crop yield (1981–2016)|19|2,953|1.719|1.494|0.000|9.518|
|Wheat|Average crop yield (1981–2016)|66|10,178|3.350|3.142|0.000|15.636|
|**_Variables used in hete_**|**_rogeneity analysis_**|||||||

71
|**_Regime type in 2018_**|(_Source: The Economist_-https://www.eiu.com/n/)|||||||
|---|---|---|---|---|---|---|---|
|Democracy|=1 if democracy score more than 7|126|3,945|0.193|0.394|0.000|1.000|
|Hybrid|=1 if democracy score between 4 and 7|126|3,945|0.515|0.500|0.000|1.000|
|Authoritarian|=1 if democracy score less than 4|126|3,945|0.292|0.455|0.000|1.000|
|**_Share of agriculture_**<br>Low share|**_in GDP_**_(Source: The World Bank -https://datacatalog.worldban_<br>=1 if share of agriculture in GDP less than 10<br>percent|_k.org/home)_<br>132|4,011|0.605|0.489|0.000|1.000|
|High share|=1 if share of agriculture in GDP equal to or<br>greater than 10 percent|132|4,011|0.395|0.489|0.000|1.000|
|**_Share of manufactur_**<br>Low share|**_ing in GDP_**_(Source: The World Bank -https://datacatalog.world_<br>=1 if share of manufacturing in GDP less than 10<br>percent|_bank.org/home)_<br>132|3,911|0.692|0.462|0.000|1.000|
||=1 if share of manufacturing in GDP equal to or|132|3911|||||
|High share|greater than 10 percent||,|0.308|0.462|0.000|1.000|
|**_Share of trade in GD_**|**_P_**_(Source: The World Bank - https://datacatalog.worldbank.org/_|_home)_||||||
|Low share|=1 if share of trade in GDP less than 10 percent|132|3,924|0.632|0.482|0.000|1.000|
|High share|=1 if share of trade in GDP equal to or greater than<br>10 percent|132|3,924|0.368|0.482|0.000|1.000|
|**_Broadband internet_**(|_Source_: https://www.collinsbartholomew.com/)|||||||
|ICT|ICT Development Index|118|3,828|5.045|1.833|1.040|8.980|
|2G|Internet coverage at subnational level|130|3,955|0.913|0.161|0.000|1.000|
|3G|Internet coverage at subnational level|123|3,337|0.809|0.264|0.000|1.000|
|4G|Internet coverage at subnational level|94|1,861|0.781|0.331|0.000|1.000|

72
**Table B2: List of economies**

|No.|Region|GSAP|Kalkuhl and Wenz<br>(2020)|Kummu_et al._<br>(2018)|
|---|---|---|---|---|
|1|East Asia & Pacific|Australia|Australia|Australia|
|2|East Asia & Pacific||China|China|
|3|East Asia & Pacific|Fiji|||
|4|East Asia & Pacific|Indonesia|Indonesia|Indonesia|
|5|East Asia & Pacific|Japan|Japan|Japan|
|6|East Asia & Pacific|||Korea, Rep.|
|7|East Asia & Pacific|Lao PDR||Lao PDR|
|8|East Asia & Pacific|Malaysia|Malaysia|Malaysia|
|9|East Asia & Pacific|Mongolia|Mongolia|Mongolia|
|10|East Asia & Pacific|Myanmar|||
|11|East Asia & Pacific|Papua New G|uinea||
|12|East Asia & Pacific|Philippines|Philippines|Philippines|
|13|East Asia & Pacific|Thailand|Thailand|Thailand|
|14|East Asia & Pacific|Timor-Leste|||
|15|East Asia & Pacific|Tonga|||
|16|East Asia & Pacific|Taiwan,<br>China|||
|17|East Asia & Pacific|Vanuatu|||
|18|East Asia & Pacific|Vietnam|Vietnam|Vietnam|
|19|Europe & Central Asia|Albania|Albania|Albania|
|20|Europe & Central Asia|Armenia|||
|21|Europe & Central Asia|Austria|Austria|Austria|
|22|Europe & Central Asia|Azerbaijan|Azerbaijan||
|23|Europe & Central Asia|Belarus|Belarus||
|24|Europe & Central Asia|Belgium|Belgium|Belgium|
||||Bosnia and|Bosnia and|
|25|Europe & Central Asia||Herzegovina|Herzegovina|
|26|Europe & Central Asia|Bulgaria|Bulgaria|Bulgaria|
|27|Europe & Central Asia|Croatia|Croatia|Croatia|
|28|Europe & Central Asia|Cyprus|||
|29|Europe & Central Asia|Czechia|Czechia|Czechia|
|30|Europe & Central Asia|Denmark|Denmark|Denmark|
|31|Europe & Central Asia|Estonia|Estonia|Estonia|
|32|Europe & Central Asia|Finland|Finland|Finland|
|33|Europe & Central Asia|France|France|France|
|34|Europe & Central Asia|Georgia|Georgia|Georgia|
|35|Europe & Central Asia|Germany|Germany|Germany|
|36|Europe & Central Asia|Greece|Greece|Greece|
|37|Europe & Central Asia|Hungary|Hungary|Hungary|
|38|Europe & Central Asia|Iceland|||
|39|Europe & Central Asia|Ireland|Ireland|Ireland|

73
|40|Europe & Central Asia|Italy|Italy|Italy|
|---|---|---|---|---|
|41|Europe & Central Asia|Kazakhstan|Kazakhstan|Kazakhstan|
|42|Europe & Central Asia|Kosovo|||
|43|Europe & Central Asia|Kyrgyz Repub|lic||
|44|Europe & Central Asia|Latvia|Latvia|Latvia|
|45|Europe & Central Asia|Lithuania|Lithuania|Lithuania|
|46|Europe & Central Asia|Luxembourg|||
|47|Europe & Central Asia|Moldova|||
|48|Europe & Central Asia|Montenegro|||
|49|Europe & Central Asia|Netherlands|Netherlands|Netherlands|
|50|Europe & Central Asia|North Macedo|nia||
|51|Europe & Central Asia|Norway|Norway|Norway|
|52|Europe & Central Asia|Poland|Poland|Poland|
|53|Europe & Central Asia|Portugal|Portugal|Portugal|
|54|Europe & Central Asia|Romania|Romania|Romania|
|55|Europe & Central Asia|Russian Feder|ation||
|56|Europe & Central Asia||Serbia|Serbia|
|57|Europe & Central Asia|Slovak Repub|lic||
|58|Europe & Central Asia|Slovenia|Slovenia|Slovenia|
|59|Europe & Central Asia|Spain|Spain|Spain|
|60|Europe & Central Asia|Sweden|Sweden|Sweden|
|61|Europe & Central Asia|Switzerland|Switzerland|Switzerland|
|62|Europe & Central Asia|Tajikistan|||
|63|Europe & Central Asia||Türkiye|Türkiye|
|64|Europe & Central Asia|Ukraine|Ukraine|Ukraine|
|65|Europe & Central Asia|United Kingd|om|United Kingdom|
|66|Europe & Central Asia|Uzbekistan|Uzbekistan|Uzbekistan|
|67|Latin America & Caribbe|an|Argentina|Argentina|
|68|Latin America &<br>Caribbean|Bolivia|Bolivia|Bolivia|
|69|Latin America &<br>Caribbean|Brazil|Brazil|Brazil|
|70|Latin America &<br>Caribbean|Chile|Chile|Chile|
|71|Latin America &<br>Caribbean|Colombia|Colombia|Colombia|
|72|Latin America &<br>Caribbean|Costa Rica||Costa Rica|
|73|Latin America &<br>Caribbean|Dominican Re|public|Dominican<br>Republic|
|74|Latin America &<br>Caribbean|Ecuador|Ecuador|Ecuador|
|75|Latin America &<br>Caribbean|El Salvador|||
|76|Latin America & Caribbe|an|Guatemala|Guatemala|
|77|Latin America &<br>Caribbean|Haiti|||

74
|78|Latin America &<br>Caribbean|Honduras|Honduras|Honduras|
|---|---|---|---|---|
|79|Latin America &<br>Caribbean<br>|Mexico|Mexico|Mexico|
|80|Latin America &<br>Caribbean<br>|Nicaragua|||
|81|Latin America &<br>Caribbean<br>|Panama|Panama|Panama|
|82|Latin America &<br>Caribbean|Paraguay|Paraguay|Paraguay|
|83|Latin America &<br>Caribbean|Peru|Peru|Peru|
|84|Latin America & Caribb|ean|Uruguay|Uruguay|
|85|Middle East & North<br>Africa|Djibouti|||
|86|Middle East & North<br>Africa|Egypt, Arab|Rep.||
|87|Middle East & North<br>Africa|Iran, Islamic|Rep.||
|88|Middle East & North<br>Africa|Iraq|||
|89|Middle East & North<br>Africa|Israel||Israel|
|90|Middle East & North<br>Africa|Jordan||Jordan|
|91|Middle East & North<br>Africa|Lebanon||Lebanon|
|92|Middle East & North<br>Africa|Malta|||
|93|Middle East & North<br>Africa|Morocco|Morocco|Morocco|
|94|Middle East & North<br>Africa|Tunisia|||
|95|Middle East & North Af|rica||United Arab<br>Emirates|
|96|Middle East & North<br>Africa|West Bank an|d Gaza||
|97|Middle East & North<br>Africa|Yemen,<br>Rep.|||
|98|North America|Canada<br>|Canada|Canada|
|99|North America|United<br>States||United States|
|100|South Asia|Bangladesh||Bangladesh|
|101|South Asia|Bhutan|||
|102|South Asia|India|India|India|
|103|South Asia|Maldives|||
|104|South Asia|Nepal|||
|105|South Asia|Pakistan|Pakistan|Pakistan|
|106|South Asia|Sri Lanka|||
|107|Sub-Saharan Africa|Angola|||
|108|Sub-Saharan Africa|Benin||Benin|

75
109 Sub-Saharan Africa Botswana 110 Sub-Saharan Africa Burkina Faso 111 Sub-Saharan Africa Burundi 112 Sub-Saharan Africa Cabo Verde 113 Sub-Saharan Africa Cameroon Cameroon 114 Sub-Saharan Africa Central African Republic 115 Sub-Saharan Africa Chad 116 Sub-Saharan Africa Comoros 117 Sub-Saharan Africa Congo, Dem. Rep. 118 Sub-Saharan Africa Congo, Rep. 119 Sub-Saharan Africa Côte d'Ivoire 120 Sub-Saharan Africa Eswatini 121 Sub-Saharan Africa Ethiopia Ethiopia 122 Sub-Saharan Africa Gabon 123 Sub-Saharan Africa Gambia, The 124 Sub-Saharan Africa Ghana Ghana 125 Sub-Saharan Africa Guinea 126 Sub-Saharan Africa Guinea-Bissau 127 Sub-Saharan Africa Kenya Kenya Kenya 128 Sub-Saharan Africa Lesotho 129 Sub-Saharan Africa Liberia 130 Sub-Saharan Africa Madagascar 131 Sub-Saharan Africa Malawi Malawi 132 Sub-Saharan Africa Mali 133 Sub-Saharan Africa Mauritius Mozambiqu 134 Sub-Saharan Africa Mozambique Mozambique e 135 Sub-Saharan Africa Namibia Namibia 136 Sub-Saharan Africa Niger 137 Sub-Saharan Africa Nigeria 138 Sub-Saharan Africa Rwanda 139 Sub-Saharan Africa São Tomé and Principe 140 Sub-Saharan Africa Senegal Senegal 141 Sub-Saharan Africa Seychelles 142 Sub-Saharan Africa Sierra Leone 143 Sub-Saharan Africa South Africa South Africa South Africa 144 Sub-Saharan Africa Sudan 145 Sub-Saharan Africa Tanzania Tanzania Tanzania 146 Sub-Saharan Africa Togo 147 Sub-Saharan Africa Uganda Uganda 148 Sub-Saharan Africa Zambia Zambia 149 Sub-Saharan Africa Zimbabwe 76
# **Appendix C: Further robustness checks and heterogeneity analysis**

# **_C1. Robustness checks_**

In this section, we explore the robustness of our results in a number of different ways. We start with the results of panel model and long difference model presented in Tables 1 and 2 and show that our results are broadly consistent when using alternative model specifications. First, we estimate several alternate specifications to assuage the reader of misspecification concerns. These are presented in Tables A1 and A2 (Appendix A). Our panel model with fixed effects represents a substantial improvement over the standard cross-sectional regression, but it may also be subject to bias if there are unobservable, time-varying differences across countries or regions. We show that our estimates are insensitive to the inclusion of country (region) specific time trends (Columns 1 and 2, Table A1). Another concern is related to misspecification of the functional form of temperature. Therefore, from Columns (3) to (7) of Table A1, we employ different functional forms of temperature including controlling for temperature change, quadratic term and cubic term of temperature, and an interaction term between temperature and temperature change. Results of these exercises strengthen our main findings.

Similarly, we also apply different variants of the long differences model and present the results in Table A2. We first check whether our results remain robust when using different choices of window length (i.e., 4-year and 5-year period). The results in Columns (1) and (2) show that our findings are not sensitive to the alternative windows. In Column (3), we add a number of time-invariant covariates at the regional level including cumulative oil gas, distance to coast, distance to river, and altitude. Finally, we include an interaction term between temperature and temperature change in Column (4). In overall, the results are qualitatively similar to our main finding.

Second, we replicate the results in Figure 2 but using alternative thresholds to define hot and cold days. In Panel A of Figure A1 (Appendix A), we present the results of the temperature bin approach using the 2-degree bin, while Panels B and C show the results using the 4-degree bin and 5-degree bin, respectively. We find that when our definition of hot and cold days is less (or more) demanding, the implied effects on income inequality remain consistent.

Third, we present the results using alternative measures of poverty and income inequality at the subnational level. In Table A3 (Appendix A), we employ the multidimensional poverty indicators, which complement the traditional measure by capturing the acute deprivations in different aspects including monetary, education, electricity, sanitation, and drinking water. Similarly, we plot in Figure A2 (Appendix A) the effects of temperature using alternative measures of income including (i) the 90/10 ratio, (ii) the 80/20 ratio, and (iii) the Palma ratio. This helps address potential concern of using Gini and Theil indices as they are more sensitive to changes in the middle-income group. In overall, the results reaffirm the negative effects of higher temperature on poverty and income inequality.

Fourth, we provide further tests in Table A4 (Appendix A) to ensure that our results are not sensitive to the choice of temperature measures. We do so by using (i) log of temperature (Column 1); (ii) temperature measured in degrees Fahrenheit (Column 2); (iii) the temperature data at 0.5<sup>◦</sup> resolution from the Climate Research Unit of the University of East Anglia (CRU) (Column 3); (iv) the number of days that temperature is above 28<sup>o</sup> C (Column 4);<sup>15</sup> (v) dropping regions with temperature being above that level (Column 5); and (vi) temperature shock, defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviations (Column 6). The results show little change from the baseline specification.

> 15 We choose the temperature at 28oC as this is the most common temperature in our sample (see Figure B3, Appendix B).

77
Fifth, we replicate our main analysis to different subsamples to investigate the sensitivity of our finding, as shown in Panels A and B of Table A5 (Appendix A). First, we show that our findings remain consistent when using a balanced sample in Column (2). Second, there are countries in our samples that contain only a small number of regions. We show in Column (2) that our results remain consistent when excluding these countries. The same finding is found when we exclude large countries that may drive our results such as United States, India, and Brazil (Columns 3 and 4). We also employ subsamples of countries without extremely cold weather (Column 5) and extremely hot weather (Column 6) using the 10 percent threshold. In Column (7), we cluster standard errors at the country level, while in Column (8) we use Conley standard errors that allow for spatial correlation in the error term. In overall, we find the estimated coefficients and significance levels are largely unchanged compared to our main finding.

Sixth, we exploit poverty and inequality data from alternative sources to check the robustness of our results. We exploit the annual (subnational/grid level) GDP data coming from Kalkuhl and Wenz (2020) and Kummu _et al._ (2018) to construct poverty measures. An advantage of these datasets is that we are able to use a longer period-average (10-year) in the long differences model compared to our analysis using GSAP data. Using both panel and long differences models, Tables A6 and A7 (Appendix A) show that our findings are not sensitive to the alternative datasets, and the results are consistent across different specifications. We then conduct a similar exercise using country-level data from the World Development Indicators (WDI) and the Standardized World Income Inequality Database (SWIID). The results presented in Table A8 confirm our expectation.

Finally, we conduct a placebo test of our study design. It is motivated by the fact that if estimating our chosen specification, but replacing the true value of the regressor of interest with an alternative we know should be irrelevant, we should expect to see no evidence of the effects on poverty. We do this exercise by using a within-sample randomization. First, the ‘true’ temperature of a region is replaced by temperature from another, randomly chosen in our sample without replacement. Second, the specification from Column (1) of Table 1 and Table 2 was estimated using the resulting placebo temperature series and the resulting coefficient and _t_ -statistic on the temperature variable collected. This process is repeated with 1,000 randomizations and we present in Figure A3 (Appendix A) the coefficients and _t_ -statistics harvested. Panel A shows that none of the placebo runs generate values anywhere close to those derived under true assignment, denoted by the dashed vertical lines. In Panel B, we find that only 5 percent of these estimates are larger in magnitude than the actual coefficient. It thus provides further support to our main estimates of the effects of temperature on poverty and inequality.

# **_C2. Heterogeneity analysis_**

Consistent with the idea that warmer temperature leads to higher poverty rate and inequality, we also expect the impacts to be heterogenous across regions. We expect that countries bearing the largest effects of global warming tend to be poorer (i.e., low-income countries) or located in poor regions. This is explained by the fact that poor countries are less prepared for the effects of climate change. They are also more likely to suffer more damages, have proportionately higher material losses, and face greater obstacles during the phases of response, recovery, and reconstruction. To explore this, we split our sample into six regions and plot the coefficient estimates of temperature in Figure 3 (Panels A and B) using the temperature bin approach. As expected, hot temperatures are found to increase poverty and income inequality in most regions relative to temperature in the reference group, particularly poorer regions such as Sub-Saharan Africa, Middle East and North Africa, and South Asia. Furthermore, we also observe the negative effects of cold temperature among countries in East Asia and Pacific and Europe and 78
Central Asia. Given that the range of temperature varies across countries, we split the temperature distribution within each country into deciles and choose the 60<sup>th</sup> percentile as the baseline group. Figure A4 (Appendix A) shows that the effects of extreme weather are similar to what we observed in Figure 3.

We also provide further support to the regional heterogeneity by estimating the effect of temperature on poverty and inequality by country, adjusted by their real GDP per capital in 2018. Figure A5 (Appendix A) shows that countries bearing the largest effect of global warming are also those with the lowest income such as Uganda, Ghana, and Mozambique.

Next, we further assess the heterogeneity of the effects of temperature across different country characteristics. First, we examine whether a country’s institution may affect the impacts of temperature. This is motivated by the fact that institutions may affect adaptation to climate change through which incentives for individuals and collective action are structured. We use the democracy index from the 2020 report of the Economist Intelligence Unit and categorize countries into different types of regimes: (i) democracy; (ii) authoritarian; and (iii) hybrid. The results presented in Panel A of Table A9 (Appendix A) show evidence that countries with democracy regime appear to be less vulnerable to the impacts of global warming. We also examine the heterogenous impacts of temperature by other country characteristics. For example, countries near the equator have a higher poverty rate caused by an increase in temperature (Panel B, Table A9 in Appendix A). In addition, the effect of temperature is more pronounced in those with higher share of agriculture, while the opposite is found in countries with higher share of manufacturing (Panels C and D, Table A9 in Appendix A). Finally, we find a stronger effect among countries with lower share of trade, but our estimates are not statistically significant (Panel E, Table A9 in Appendix A).

In this paper, we are also interested in examining the role of information and communication technologies (ICTs). It is reasonable to argue that ICTs, particularly the Internet, may contribute to poverty reduction by providing access to markets, decreasing transaction costs, and increasing income for a significant proportion of people living in developing countries. Therefore, we expect that regions with better internet coverage will be less vulnerable to the effects of higher temperatures. To do this exercise, we exploit the ICT Development index from the International Telecommunication Union as well as the global expansion of mobile network (2G, 3G, and 4G) from Collins Bartholomew with the latter being available at the grid level which allows us to construct a regional index. We then present coefficients on the interaction between our ICT measures and temperature in Table A10 (Appendix A). Across all panels, we find strong and consistent evidence of the role of ICT as the mediator. Specifically, areas with better access to ICT/internet broadband are less vulnerable to the effects of higher temperature.

# **References**

Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions. _Journal of Environmental Economics and Management_ , _103_ , 102360.

Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. _Scientific Data_ , _5_ (1), 1–15.

79
# **Appendix D: Mechanism analysis and projected impacts under future climate change**

# **_D1. Potential mechanisms_**

Having demonstrated strong evidence of the effects of warming temperature on poverty at the subnational level, we further explore why impact heterogeneity exists across regions. A possible explanation is that poor countries are often located in tropical areas, where climate change occurs faster and is more intense, and their livelihoods are more dependent on the climate vulnerable agriculture sector. In fact, a growing body of evidence suggests that extreme temperature has negative effects on crop yields, particularly in poor countries (e.g., Jacoby _et al._ , 2015; Knox _et al._ , 2012; Schlenker and Lobell, 2010). We analyze the global dataset of historical yields from Iizumi and Sakai (2020), which provides actual crop yields for years from 1981 to 2016 at 0.5° resolution. Using the panel fixed effects model and long differences model as in Equations (1) and (2), we find consistent and negative effects of higher temperature on different crop yields including rice, maze, and soybean, as shown in Table A11 (Appendix A). Again, we find the long differences model estimates to be smaller than the panel model estimates, which are in line with previous studies showing potential adaptation in the long run (e.g., Chen and Gong, 2021). Similarly, we also find the effects of global warming to be more pronounced among regions with a higher share of agriculture (Appendix A, Table A12).

Given the adverse impacts of temperature on agricultural production, we further examine whether there exists any correlation between poverty and agriculture. Specifically, we plot the effects of temperature on poverty taken from the panel model specification on the _y_ -axis, and the effects of temperature on agriculture in Table A11 on the _x_ -axis in Figure A6 (Appendix A). Since the unit of analysis is different across two samples, we aggregate the data at the country level for better comparison. For all the panels, we find a negative and strongly statistically significant correlation between crop yield and poverty. Consistent with our previous findings, African countries are found to be most vulnerable to the effects of global warming. In Figure A7 (Appendix A), we further plot the effects of temperature on poverty against a country’s share of agriculture in GDP and also find the effects to be stronger among countries which rely on agriculture as the main source of income. Overall, these findings suggest that by reducing crop yield, warmer temperature may directly contribute to more poverty.<sup>16</sup> Another potential mechanism that may explain the effects of temperature is migration. Since poverty is a major driver of people’s vulnerability to climate-related shocks, it is reasonable to expect that the flow of people escaping poverty is also affected by climate change. In fact, an emerging body of literature has shown that higher temperature increases both internal and international immigration rates (e.g., <mark>Cattaneo and Peri, 2016; Missirian and Schlenker, 2017)</mark> . We reaffirm findings from the literature by using migration data available at the subnational level. The data is provided by WorldPop Open Data Repository which captures internal migration flows between 2005 and 2010.<sup>17</sup> Using a simple OLS regression, we find suggestive evidence that hotter temperature results in higher migration flow (see Figure A8, Appendix A).

# **_D2. Projected impacts under future climate change_**

We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. To do this, we combine the model estimates in Tables 1 and 2 with data on simulated weather conditions at the subnational level taken from the recent released Coupled Model Intercomparison Project Phase 6 (CMIP6). Following Burke and Emerick (2016) and Kalkuhl and Wenz (2020), we generate temperature projections as follows. First, we use annual

> 16 For simplicity, we assume land degradation to be constant, but it could play a role in the poverty and environment nexus (Barbier and Hochard, 2018). Temperature may also affect poverty via different channels such as civil conflicts and labor productivity (for a review, see Burke _et al._ , 2015 and <mark>Somanathan</mark> _<mark>et al.</mark>_ <mark>, 2021).</mark>

> 17 The data is available at https://hub.worldpop.org/.

80
temperature from ERA-5 to construct historical average temperature and probability distribution functions for the period 1979 – 2019. We then calculate the projected changes in temperature as the difference between the projected temperature, taken from CMIP6, and the historical average temperature. Finally, the temperature changes are used to calculate poverty rates (inequality) by multiplying with the baseline estimates in Columns (2), (4), and (6) of Table 1 (Columns (2) and (4) of Table 2). We select the estimates from the long differences model since it embodies any adaptations that farmers have undertaken to short-run change in climate, and thus projections of future climate change impacts would appear more trustworthy than those based on either panel or cross-sectional methods (Burke and Emerick, 2016).

Table A13 (Appendix A) provides a summary of the projected changes for temperature and poverty for different emission pathways. The temperature is predicted to rise by a range of 1.4°C to 5.0°C by 2099. This escalation in temperature may lead to an increase in poverty by 0.7 to 2.7 percentage points, equivalent to a rise of 7.4 percent to 26.6 percent, employing a poverty threshold of $1.9 per day (Panel A). Similarly, the projected impacts on inequality are expected to result in a 0.5 to 1.7 percentage point rise in the Gini index, translating to an increase of 1.4 percent to 4.9 percent (Panel B). The largest increase in poverty and inequality would occur in the scenario without any countervailing strategies based on renewable energy to address climate change. Finally, Figures A8 and A9 (Appendix A) present the projected temperature effects across regions in our sample under both emission pathways and reaffirm our previous findings that poor countries in Africa continue to be most vulnerable to hotter temperature.

# **References**

- <mark>Barbier, E. B., & Hochard, J. P. (2018b). Land degradation and poverty.</mark> _<mark>Nature Sustainability</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>(11), 623-631.</mark>

- <mark>Burke, M., Hsiang, S. M., & Miguel, E. (2015). Climate and conflict.</mark> _<mark>Annual Review of Economics</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1), 577–617.</mark>

- <mark>Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture.</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(3), 106–40.</mark>

- <mark>Cattaneo, C., & Peri, G. (2016). The migration response to increasing temperatures.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>122</mark>_ <mark>, 127-146.</mark>

- <mark>Chen, S., & Gong, B. (2021). Response and adaptation of agriculture to climate change: Evidence from China.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>148</mark>_ <mark>, 102557.</mark>

- <mark>Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(1), 1–7.</mark>

- <mark>Intergovernmental Panel on Climate Change (IPCC). (2021a). “Summary for Policymakers”. In</mark> _<mark>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</mark>_ <mark>. In Masson-Delmotte</mark> _<mark>et al.</mark>_ <mark>(eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32.</mark>

- <mark>Jacoby, H. G., Rabassa, M., & Skoufias, E. (2015). Distributional implications of climate change in rural India: a general equilibrium approach.</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>,</mark> _<mark>97</mark>_ <mark>(4), 1135–1156.</mark>

- <mark>Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>, 102360.</mark>

- <mark>Knox, J., Hess, T., Daccache, A., & Wheeler, T. (2012). Climate change impacts on crop productivity in Africa and South Asia.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(3), 034032.</mark>

- <mark>Missirian, A., & Schlenker, W. (2017). Asylum applications respond to temperature fluctuations.</mark> _<mark>Science</mark>_ <mark>,</mark> _<mark>358</mark>_ <mark>(6370), 1610-1614.</mark>

- <mark>Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 014010.</mark>

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<mark>Somanathan, E., Somanathan, R., Sudarshan, A., & Tewari, M. (2021). The impact of temperature on productivity and labor supply: Evidence from Indian manufacturing.</mark> _<mark>Journal of Political Economy</mark>_ <mark>,</mark> _<mark>129</mark>_ <mark>(6), 1797–1827.</mark> 82
# **Hai-Anh H. Dang**

# Email: hdang@worldbank.org <u>https://sites.google.com/site/haianhhdang/</u>

|https://sites.google.com/site/haianhhdang/ <br>_Septe_<br>**Employment**|_mber 2023_|
|---|---|
|_Senior Economist,_Data Production and Methods Unit and Living Standards Measurement<br>Study Unit (Development Data Group), World Bank|2020- present|
|_(Consultant) Economist,_Poverty and Inequality Unit (Development Research Group);<br>Survey Unit, Analytics and Tools Unit, Data Production and Methods Unit (Development<br>Data Group), World Bank|2004- 2020|
|**Other Affiliations**||
|Co-editor,_Review of Development Economics_|2018- present|
|Co-editor for special issue on “Measuring Income, Wealth and Well-being in Africa”,|2022- present|
|_Review of Income and Wealth_||
|Editorial Board,_International Journal of Education Development_|2018- present|
|Editorial Board,_Economic Change and Structuring_|2020- present|
|Editorial Board,_Journal of Economics and Development_|2023- present|
|IZA Research Fellow|2019- present|
|Visiting Senior Fellow, International Inequalities Institute, London School of Economics and|2023- present|
|Political Science||
|Adjunct Professor & Non-resident Research Scholar, Paul H. O’Neill School of Public and<br>Environmental Affairs, Indiana University|2018- present|
|Global Labor Organization (GLO) Fellow|2018- present|
|Research Affiliate with Household in Conflict Network (HiCN)|2022- present|
|Visiting Senior Research Fellow, International School, Vietnam National University, Hanoi|2021- 2023|
|Guest lecturer, Georgetown University|2019- 2020|
|Visiting Scholar, Monash University|Nov. 2019|
|Visiting Scholar, Singapore Management University|Nov. 2017|
|Visiting Scholar, Beijing Normal University|Dec. 2016|
|Non-resident Senior Research Fellow, Centre for Analysis and Forecast, Vietnam’s<br>Academy of Social Sciences|2016- 2022|

# **Education**

|Ph.D. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2007|
|---|---|
|MSc. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2003|
|B.A. in International Economics, Foreign Trade University, Hanoi, Vietnam|2000|

# **Selected publications in the past 3 years**

# **_Journal Articles_**

1. Dang, Hai-Anh, Trong-Anh Trinh, and Paolo Verme. (in press). "Do Refugees with Better Mental Health Better Integrate?  Evidence from the _Building a New Life in Australia_ Longitudinal Survey." _Health Economics_ .

2. Dang, Hai-Anh, Paul Glewwe, Jongwook Lee and Khoa Vu. (2023). " <mark>What Explains Vietnam’s Exceptional Performance in Education Relative to Other Countries? Analysis of the 2012, 2015 and 2018 PISA Data"</mark> . _Economics of Education Review_ , 96, 102434.

3. Dang, Hai-Anh, and Peter Lanjouw. (2023). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”, _Oxford Bulletin of Economics and Statistics_ , 85(3), 599-622.

4. <mark>Dang, Hai-Anh, John Pullinger, Umar Serajuddin, and Brian Stacy. (2023). “Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity”.</mark> 83
_Scientific Data_ , 10, 146. (media coverage: _<u>Trade for Development blog</u>_ <u>; Data blog 1, 2, 3; TReND blog; Development Asia blog)</u> 5. Dang, Hai-Anh and Paolo Verme. (2023). "Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?" _Journal of Population Economics_ , 36(2), 653– 679.

6. <mark>Nakamura, Shohei, Kseniya Abanokova, Hai-Anh Dang, Shinya Takamatsu, Chunchen Pei, Dilou Prospere. (2023). “Is Climate Change Slowing the Urban Escalator out of Poverty? Evidence from Chile, Colombia, and Indonesia”.</mark> _<mark>I</mark> nternational Journal of Environmental Research and Public Health,_ 20, 4865.

7. <mark>Dang, Hai-Anh, Cuong Viet Nguyen, and Calogero Carletto.</mark> (2023). <mark>“Did a Successful Fight against the COVID-19 Pandemic Come at a Cost? Impacts of the Outbreak on Employment Outcomes in Vietnam”.</mark> _<mark>World Development</mark>_ <mark>, 1</mark> 61, 106129.

8. <mark>Dang, Hai-Anh, Edmund Malesky, and Cuong Viet Nguyen.</mark> (2022). <mark>"Inequality and Support for Government Responses to COVID-19".</mark> _<mark>PLoS ONE</mark>_ <mark>, 1</mark> 7(9): e0272972.

9. Dang, Hai-Anh and Trong-Anh Trinh. (2022). "The Beneficial Impacts of COVID-19 Lockdowns on Air Pollution: Evidence from Vietnam". _Journal of Development Studies_ , 58(10), 1917-1933.

10. Dang, Hai-Anh, Masako Hiraga, and Cuong Viet Nguyen. (2022). "Childcare and Maternal Employment: Evidence from Vietnam". _World Development_ , 159, 106022.

11. Abanokova, Kseniya, Hai-Anh Dang, and Michael Lokshin. (2022). "Do Adjustments for Equivalence Scales Affect Poverty Dynamics? Evidence from the Russian Federation during 1994–2017". _Review of Income and Wealth_ , S167-S192.

12. Anand, Paul, Jere Behrman, Hai-Anh Dang, and Sam Jones. (2022). “Decomposing learning inequalities in East Africa: how much does sorting matter?” _World Bank Economic Review_ , 36(1), 219–243.

13. Dang, Hai-Anh, Peter Lanjouw, and Elise Vrijburg. (2021). "Poverty in India in the Face of Covid19: Diagnosis and Prospects". _Review of Development Economics_ , 25(4), 1816-1837.

14. Dang, Hai-Anh, Trung Hoang, and Ha Nguyen. (2021). "The Long-run and Gender-equalizing Impacts of School Access: Evidence from the First Indochina War". _Economic Development and Cultural Change,_ 70(1), 453-484. (media coverage: <u>Telegraph)</u> 15. Cameron, Grant, Hai-Anh Dang, Mustafa Dinc, James Foster, and Michael Lokshin. (2021). "Measuring the Statistical Capacity of Nations". _Oxford Bulletin of Economics and Statistics_ , 83(4): 870-896.

16. Dang, Hai-Anh and <mark>Cuong Viet N</mark> guyen. (2021). “Gender Inequality during the COVID-19 Pandemic: Income, Expenditure, Savings, and Job Loss”. _World Development_ , 105296 (media coverage: <u>the Hill, Jobs and Development blog).</u> 17. Dang, Hai-Anh and Trong-Anh Trinh. (2021). "Does the COVID-19 Lockdown Improve Global Air Quality? New Cross-National Evidence on Its Unintended Consequences". _Journal of Environmental Economics and Management_ , 105: 102401.

# **Some Metrics**

- Google Scholar citation: 7,311; h-index: 35

- Top 5% in terms of downloads, number of (distinct) works, and ranking in the past 10 years on REPEC

- Top 5% in general ranking, Development Economics and Welfare Economics on ResearchGate - Top 10% in terms of downloads for IZA discussion papers

(Full CV is available at https://sites.google.com/site/haianhhdang/) 84