THE EFFECTS OF WEATHER SHOCKS ON ECONOMIC ACTIVITY: HOW CAN LOW-INCOME COUNTRIES COPE?

Transcription

1 CHAPTER 3 THE EFFECTS OF WEATHER SHOCKS ON ECONOMIC ACTIVITY: HOW CAN LOW-INCOME COUNTRIES COPE? Global temperatures ave increased at an unprecedented pace over te past 4 years, and significant furter warming could occur, depending on our ability to restrain greenouse gas emissions. Tis capter finds tat increases in temperature ave uneven macroeconomic effects, wit adverse consequences concentrated in countries wit relatively ot climates, suc as most low-income countries. In tese countries, a rise in temperature lowers per capita output, in bot te sort and medium term, by reducing agricultural output, suppressing te productivity of workers exposed to eat, slowing investment, and damaging ealt. To some extent, sound domestic policies and development, in general, alongside investment in specific adaptation strategies, could elp reduce te adverse consequences of weater socks. But given te constraints faced by low-income countries, te international community must play a key role in supporting tese countries efforts to cope wit climate cange a global treat to wic tey ave contributed little. And wile te analysis of te capter focuses on te impact of weater socks in low-income countries, most countries will increasingly feel direct negative effects from unmitigated climate cange troug warming above optimal levels in currently cooler countries, more frequent natural disasters, rising sea levels, loss of biodiversity, and adverse spillovers from vulnerable countries. Looking aead, only continued international cooperation and a concerted effort to stem te man-made causes of global warming can limit te long-term risks of climate cange. Introduction Since te turn of te t century, te Eart s average surface temperature as increased significantly. Sizable swings in global temperatures used to appen Te main autors of tis capter are Sebastian Acevedo, Mico Mrkaic, Natalija Novta, Marcos Poplawski-Ribeiro, Evgenia Pugaceva, and Petia Topalova (lead), wit contributions from Manoj Atolia, Claudio Baccianti, and Ricardo Marto and support from Gavin Asdorian, Marina Klasnja, Olivia Ma, Fien Analbers Ribeiro, Jilun Xing, and Yuan Zeng. Te capter benefited from comments and suggestions by Edward Miguel, Benjamin Olken, and Stépane Hallegatte. over long periods, suc as fluctuations in and out of te Ice Ages. However, te speed at wic te climate as canged over te past 3 4 years appears to be unprecedented in te past, years (Figure 3.). Most scientists agree tat global temperatures are set to rise furter, at a scale and pace very muc dependent on our ability to restrain greenouse gas emissions, te central cause of global warming (IPCC 3). Extreme weater events, suc as eat waves, drougts, and floods, are likely to become more frequent, and sea levels will rise. Altoug considerable uncertainty surrounds temperature projections, te scientific consensus predicts tat witout furter action to tackle climate cange, average temperatures could rise by 4 C or more by te end of te st century. Very substantial cuts to current emissions will be needed to limit warming to less tan C. Will climate cange ave significant macroeconomic consequences, especially in low-income developing countries tat tend to be more exposed to te vagaries of te weater? And ow can tese countries cope wit te rises in temperature tey are set to experience over te coming decades? Pinning down te economic consequences of climate cange is difficult. Temperature increases of te magnitude tat could potentially occur over te next century and many oter aspects of climate cange, suc as rapid rise in sea levels, ocean acidification, and te like sit well outside recent (and relevant) istorical experience and could affect a large number of countries. Extrapolating from te istorically observed relationsip between activity and weater patterns could also be problematic as populations adapt to persistent canges in climate. Yet studying te macroeconomic effects of annual variation in weater patterns Climate refers to a distribution of weater outcomes for a given location, wile weater refers to a realization from tat distribution. Climate cange typically implies tat te wole distribution of outcomes sifts, wit a possible increase in te likeliood of extreme outcomes. As argued by Weitzman (), te fattening of te tails te increase in te probability of potentially irreversible and catastropic damages justifies aggressive policy actions to stabilize greenouse gas concentrations in te atmospere ( climate cange mitigation ) and adjust to te canging climate ( adaptation ). 7

2 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.. Average Global Temperature (Degrees Celsius) Te average global temperature as risen at an extraordinary pace over te past century, and significant furter warming could occur From, BCE to Present Early farming begins Most mammots go extinct Industrial revolution begins, ca. 76 Te Great Pyramid 6 Last glacial completed 9 period ends 5, 6,, 8, 4, Recent and st Century Projections (Deviations from 88 9 average) Observed RCP 8.5 mean prediction RCP 4.5 mean prediction Sources: Intergovernmental Panel on Climate Cange (IPCC) Coupled Model Intercomparison Project Pase Five AR5 Atlas subset; Marcott and oters (3); Matsuura and Willmott (7); National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies; Royal Neterlands Meteorological Institute Climate Cange Atlas; Sakun and oters (); and IMF staff calculations. Note: In panel, te tin lines represent eac of te 4 models in te IPCC WG AR5 Annex I Atlas, were a model wit different parametrization is treated as a separate model. Te tick lines represent te multimodel mean. Representative Concentration Patways (RCP) are scenarios of greenouse gas concentrations, constructed by te IPCC. RCP 4.5 is an intermediate scenario, wic assumes increased attention to te environment, wit emissions peaking around 5 and declining tereafter. RCP 8.5 is an unmitigated scenario in wic emissions continue to rise trougout te st century. could produce useful insigts. In an influential study, Dell, Jones, and Olken () find tat iger temperatures significantly reduce economic growt in low-income countries. Burke, Hsiang, and Miguel (5a) provide evidence tat productivity peaks at about 3 C and declines strongly at iger tempera- Dell, Jones, and Olken (4); Carleton and Hsiang (6); and Heal and Park (6) provide surveys of te new climate literature, wic explores te impact of weater fluctuations on a broad range of economic variables. tures. Since low-income countries are concentrated in geograpic areas wit otter climates, te Burke, Hsiang, and Miguel (5a) findings suggest tat a rise in temperature would be particularly armful for tis set of economies. Countries negatively affected by climate cange will need to increase teir resilience to rising temperatures and extreme weater events, bot by enancing teir ability to smoot out socks, wic could become more frequent, and by investing in adaptation strategies, suc as activity diversification, infrastructure investment, and tecnology innovation, tat reduce te arm tey do. Populations may also respond to canging climatic conditions by relocating geograpically, wic could ave important cross-border ramifications. But te evidence on wic policies may elp countries and individuals cope wit weater socks is limited. Understanding te macroeconomic effects of weater socks and te scope for policy actions to moderate tem will be crucial for low-income developing countries to acieve durable growt in te long term a precondition for convergence and implementation of te United Nations Sustainable Development Goals. Drawing from and building on te existing literature, tis capter contributes to te policy debate by examining te following questions: Wat as been te istorical relationsip between temperature and precipitation socks and economic activity in bot te sort and te medium term? Are low-income countries particularly vulnerable? Troug wat cannels do weater fluctuations affect te economy? And as te sensitivity of growt to weater socks canged over time? How can countries, particularly low-income ones, cope wit weater socks? Can policies and oter country caracteristics mitigate te macroeconomic response to weater fluctuations? Given te projected pat of temperature by te end of te st century, wat migt be te impact of climate cange on low-income countries? To address tese questions, te capter starts by documenting te istorical evolution and projected cange in temperature and precipitation patterns across broad country groups according to leading climate cange models, as well as tese groups contributions to greenouse gas emissions. It ten examines te istorical evidence on te macroeconomic effects of annual variation in temperature and precipitation 8

3 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? across a large sample of economies, igligting te cannels troug wic climatic conditions affect te macroeconomy. Te capter offers evidence on ow various policies and country caracteristics influence te sensitivity of growt to weater variations, using bot empirical analysis and model simulations, and presents case studies of various climate cange adaptation strategies. Finally, te capter incorporates te empirical estimates of economic loss from weater socks and projected canges in temperature into a dynamic general equilibrium model to trace te potential long-term effects of climate cange. Te capter s main findings are as follows: Te rise in temperature over te past century as been broad based. No country as been spared from te warming of te Eart s surface, and no country is projected to be spared furter temperature increases, wit te largest increases in temperature expected in countries wit relatively colder climates. Te contribution of low-income developing countries wic tend to be situated in some of te ottest geograpic areas on te planet to atmosperic greenouse gas concentrations is negligible, bot in absolute terms and on a per capita basis. Te macroeconomic effect of temperature socks is uneven across countries. Confirming te global nonlinear relationsip between annual temperature and growt uncovered by Burke, Hsiang, and Miguel (5a) using an expanded data set, te empirical analysis suggests tat rising temperatures lower per capita output in countries wit relatively ig annual average temperature, suc as most low-income countries. In tese economies, te adverse effect is long-lasting and operates troug several cannels: lower agricultural output, depressed labor productivity in sectors more exposed to te weater, reduced capital accumulation, and poorer uman ealt. Moreover, data indicate tat macroeconomic outcomes ave not become any less sensitive to temperature socks in recent years, pointing to significant adaptation constraints. To some extent, sound policies and institutional frameworks, investment in infrastructure, and oter adaptation strategies can reduce te damage from temperature socks in ot countries. Altoug causal interpretation is difficult, empirical evidence suggests tat countries wit better-regulated capital markets, iger availability of infrastructure, flexible excange rates, and more democratic institutions recover somewat faster from te negative impacts of temperature socks. Higer temperatures also constrain growt in ot regions of emerging market and developing economies significantly more tan in ot regions of advanced economies, wic corroborates te importance of development in reducing vulnerability. Te temperature increase projected by under a scenario of unmitigated climate cange implies significant economic losses for most low-income countries. Under te conservative assumption tat weater socks ave permanent effects on te level, rater tan te growt rate, of per capita output, model simulations suggest tat te per capita GDP of a representative low-income country would be 9 percent lower in tan it would ave been in te absence of temperature increases, wit te present value of output losses amounting to more tan percent of current GDP wen discounted at te growt-adjusted rate of.4 percent. Taken togeter, tese findings paint a worrisome picture. Rising temperatures would ave vastly unequal effects across te world, wit te brunt of adverse consequences borne by tose wo can least afford it. In all likeliood, most countries will increasingly feel te direct impact of unmitigated climate cange, troug warming above optimal temperatures, more frequent (and more damaging) natural disasters, rising sea levels, loss of biodiversity, and many oter ard-to-quantify effects. In addition, climate cange is likely to create economic winners and losers at bot individual and sectoral levels, even in countries were te effect migt be moderate or positive on average. However, low-income countries will suffer disproportionately from furter temperature increases a global treat to wic tey ave contributed little. And witin low-income countries, te poor would likely be te most eavily affected by climate cange (Hallegatte and Rozenberg 7). Having little influence on te future course of climate, ow can tese countries cope wit te callenges tey face as temperatures rise? Te findings of tis capter suggest tat domestic policies can partially dampen te adverse effects of weater socks. Improving buffers and strengtening well-targeted social safety nets tat can deliver support wen needed would elp countries smoot some of te instantaneous effects of weater socks, wile policies and institutions tat make capital and labor markets more flexible and foster structural economic transformation could elp countries recover somewat 9

4 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges faster and reduce teir vulnerability to future socks. Adaptation strategies tat reduce specific climate cange effects and risks, suc as targeted infrastructure projects, adoption of appropriate tecnologies, and mecanisms to transfer and sare tese risks troug financial markets, could also be part of te toolkit for reducing te economic damage caused by climate cange. But putting in place te rigt policies will be particularly difficult in low-income countries, wic ave uge spending needs and limited ability to mobilize te resources necessary for adaptation in a callenging economic environment. In some cases, political uncertainty and security issues exacerbate te callenge. Moreover, even wen in place, domestic policies alone cannot fully insulate low-income countries from te adverse consequences of climate cange, as iger temperatures pus te biopysical limits of tese countries ecosystems, potentially triggering more frequent epidemics, famines, and oter natural disasters, along wit armed conflict and refugee flows. Te international spillovers from tese difficult-to-predict effects of climate cange could be very considerable. Climate cange is a negative global externality of potentially catastropic proportions, and only collective action and multilateral cooperation can effectively address its causes and consequences. Mitigating climate cange requires radically transforming te global energy system, including troug te use of fiscal instruments to better reflect environmental costs in energy prices and promote cleaner tecnologies as discussed in Box 3.6. Adapting to te consequences of climate cange necessitates vast investments, including in boosting infrastructure, reinforcing coastal zones, and strengtening water supply and flood protection (Margulis and Narain ; UNEP 6). Te international community will ave a key role to play in fostering and coordinating financial and oter types of support for affected low-income countries. Wit advanced and emerging market economies contributing te lion s sare to te warming tat as occurred so far and is projected to continue, elping low-income countries cope wit its consequences is a umanitarian imperative and sound global economic policy. In te future, only continued international cooperation and a concerted effort to stem te man-made causes of global warming can limit te long-term risks of climate cange (IPCC 4; IMF 5; Stern 5; Farid and oters 6; Hallegatte and oters 6). It is important to igligt from te outset te inerent difficulty of quantifying te potential macroeconomic consequences of climate cange. Extrapolating from istorically observed weater responses of GDP to te long-term effect of global warming is callenging for several reasons. 3 On one and, suc an extrapolation may overstate te impact as governments and oter economic agents take ameliorative actions, make investments, or develop new tecnologies tat elp populations adapt to persistent canges in climate. On te oter and, te actual impact could be larger if tere are nonlinearities in te response as te climate sifts to conditions beyond recent experience. 4 Moreover, te capter does not separately quantify te effects of natural disasters, wose iger projected frequency may amplify te damages tey cause; it does not analyze distributional impacts across sectors and ouseolds witin countries, wic may be quite sizable; nor does it sed ligt on te consequences of many aspects of climate cange, suc as a rapid rise in sea levels, ocean acidification, and te like, tat ave no istorical precedent but could ave very large macroeconomic consequences. 5 Neverteless, as long as te Eart continues to warm over te rest of te st century in te same pattern as over te past 5 years a stocastic series of annual socks along an upward trend tis capter may provide valuable guidance on climate cange vulnerabilities and adaptation needs under te current production tecnologies and geograpic distribution of populations (Dell, Jones, and Olken ). 3 Dell, Jones, and Olken (4); Carleton and Hsiang (6); Hsiang (6); and Lemoine (7) provide discussions of te conditions under wic empirical estimates of te effect of weater socks based on istorical data can sed ligt on te consequences of climate cange. 4 For example, te istorically observed natural year-to-year temperature variability for countries located in te tropics is rougly.5 C. Te projected increase in temperature for tese countries between 5 and under te extreme unmitigated climate cange scenario is 4. C in oter words, more tan 8.5 times larger tan te current natural variability, implying a totally new climatic regime (see also World Bank 3). 5 A large body of literature studies te macroeconomic impact of natural disasters (see, for example, Noy 9; Cavallo and oters 3; Acevedo 4; Felbermayr and Gröscl 4; Cabezon and oters 5; IMF 6a; IMF 6b; Gerling, fortcoming; and Gerling, Moreno Badia, and Toffano, fortcoming). Te capter focuses on direct measures of te weater because natural disaster data may suffer from reporting and mismeasurement issues. Mismeasurement could be a particular problem in low-income countries, wic typically ave lower capacity to accurately evaluate, record, and report damage (Jennings ).

5 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Temperature and Precipitation: Historical Patterns and Projections Tis section sets te context for te rest of te capter by summarizing te scientific consensus on ow climate and one of its key man-made drivers greenouse gas emissions ave evolved over te past century. Te section ten presents scientists projected canges for te rest of te st century and discusses te link between temperature, precipitation, and weater-related disasters. Historical Patterns Global temperatures ave increased by rougly C compared wit te 88 9 average (Figure 3.). Te rise started in earnest in te 97s, following a large increase in carbon dioxide (CO ) emissions. 6 Altoug natural factors explain some of te warming over te past century, according to te Intergovernmental Panel on Climate Cange (IPCC), more tan alf of te temperature increase since 95 can be attributed to uman activity (IPCC 4). Te increase in temperature as occurred in all regions, wit te same accelerating trend, starting in te 97s (Figure 3.3). 7 Te median temperature over te first 5 years of tis century, compared wit te first 5 years of te past century, was.4 C iger in advanced economies,.3 C iger in emerging market economies, and.7 C iger in low-income developing countries. Even toug most of te warming occurred in advanced economies, by 5 te temperature in te median low-income developing country (5 C) was more tan twice tat of te median advanced economy ( C). Oter aspects of te climate ave also canged appreciably. Since 9, te global mean sea level as risen by 7 centimeters. As wit temperature, tere as been an increase in te pace at wic te sea level is rising: from.7 centimeter a year trougout most of te t century to.3 centimeter a year over te past years (IPCC 4). 6 Te tree most important greenouse gases, wic are regulated under te Kyoto Protocol, are carbon dioxide (CO ), metane (CH 4 ), and nitrous oxide (N O). Among tose, CO as so far been te largest contributor to global warming. 7 Trends in precipitation are generally less clear (Figure 3.3, panels, 4, and 6). Precipitation as increased somewat in te nortern emispere since te 95s, and average precipitation in low-income developing countries as declined since te 97s. Figure 3.. Increase in Average Global Temperature and Contributions of Key Factors (Deviations from 88 9 average, degrees Celsius) According to te Intergovernmental Panel on Climate Cange, most of te increase in temperature since 95 can be attributed to uman factors Temperature GHG Human Natural Sources: Carbon Dioxide Information Analysis Center; National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies; Roston and Migliozzi (5); and IMF staff calculations. Note: Te lines present te actual increase in land and ocean surface air temperature relative to 88 9 and te increase predicted by different factors. Human factors include land use, ozone emissions, aerosol emissions, and GHG emissions. Natural factors include orbital canges, solar output, and volcanic activity. Te contribution of eac factor is estimated by ModelE by NASA Goddard Institute for Space Studies. GHG = greenouse gases. CO emissions ave grown rapidly since te 95s across all income groups, along wit rising incomes and populations (Figure 3.4). However, emissions in low-income developing countries are still a fraction of tose in advanced and emerging market economies, in bot aggregate and per capita terms. And altoug advanced economies ave managed to contain teir overall emissions over te past decade, in per capita terms tey still contribute vastly more tan te rest of te world. Projections Te overwelming majority of scientists agree tat future climate cange depends largely on te pat of CO emissions, wic in turn inges on demo-

6 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.3. Temperature and Precipitation across Broad Country Groups Temperature as risen across all country groups, wile precipitation does not exibit a clear pattern. 9. Temperature (Degrees Celsius) Advanced Economies. Precipitation (mm per year),,, Figure 3.4. Annual CO Emissions across Broad Country Groups (Billion metric tons, unless noted oterwise) CO emissions ave grown rapidly since te 95s across all income groups, but emissions by low-income developing countries are negligible in bot absolute and per capita terms Advanced Economies. Emerging Market Economies United States Germany Japan Oter AEs Cina India Russia Oter EMs Temperature (Degrees Celsius) Emerging Market Economies 4. Precipitation (mm per year),,, Low-Income Developing Countries Vietnam Nigeria Banglades Oter LIDCs Average CO Emissions per Capita, 97 4 (Metric tons) United States Russia Germany Japan Oter AEs Oter EMs Cina India Vietnam Nigeria Oter LIDCs Banglades Low-Income Developing Countries Sources: Carbon Dioxide Information Analysis Center; and IMF staff calculations. Note: AEs= advanced economies; CO = carbon dioxide; EMs = emerging markets; LIDCs = low-income developing countries Temperature (Degrees Celsius) 6. Precipitation (mm per year),6,5,4,3,, 3, Sources: Climate Researc Unit (v. 3.4); and IMF staff calculations. Note: Terrestrial median annual temperature and precipitation data at grid level are aggregated to te country-year level using 95 population weigts. See Annex 3. for data sources and country groupings. mm = millimeter. grapic canges, economic development, tecnological advances, and te vigor wit wic countries implement mitigation measures. 8 Yet, given te significant buildup and persistence of greenouse gas concentration in te atmospere, even wit immediate and substantial cuts to current greenouse gas emissions, temperatures are projected to rise for some time, albeit at a slower pace. Te IPCC constructed four possible scenarios, called Representative Concentration Patways (RCP), using alternative greenouse gas concentration assumptions to project likely ranges 8 Surveying, peer-reviewed scientific papers on climate cange, Cook and oters (3) find tat 97 percent of te studies expressing a position on te reasons beind global warming agree tat it is influenced by man-made causes. See also Cook and oters (6).

7 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? of temperatures over te st century. Te rest of te capter focuses on two of tese scenarios: an intermediate pat (RCP 4.5) and an unmitigated pat (RCP 8.5), as sown in Figure 3., panel. 9 Under te RCP 8.5 scenario of unmitigated climate cange, te average global temperature by 8 could rise by 3.7 C (wit a projected range of.6 C 4.8 C). Warming would occur all over te globe, wit larger increases over te nortern emispere, were some regions could experience temperatures almost C iger tan in 5 (Figure 3.5). Between 5 and, te increase for te median advanced economy is projected to be 4.4 C, and 4.5 C for te median emerging market economy and median low-income developing country. Increases are projected to be smaller in absolute terms closer to te equator, but are very significant wen set against te istorical year-to-year and intrayear variability in temperature observed in tose locations. Cange in precipitation will vary by region, wit dry areas generally expected to become drier and wet regions expected to experience an increase in rainfall. Under tis scenario, te global mean sea level is projected to rise by almost.8 meter by te end of te st century, exposing coastal areas, including some large population centers, to iger risk of flooding and erosion. Sea level rise will not be uniform across regions it is projected to be iger tan te global mean closer to te equator and less tan te global mean at ig latitudes (IPCC 4; World Bank 3). It is important once again to stress te large uncertainty surrounding climate cange projections. Future emissions depend on many factors tat are difficult to predict and, even for te same emission scenario, climate models differ widely in teir temperature and precipitation projections (Figure 3., panel ). However, it is precisely tis uncertainty and te possibility 9 Te Paris Agreement aims to contain te rise in temperature to less tan C (ideally to less tan.5 C) relative to te preindustrial average, wic would require policy efforts beyond tose assumed under te RCP 4.5 scenario. Under te RCP 4.5 scenario, tere is increased attention to te environment. CO emissions peak around 5 and decline tereafter, wit a resulting temperature increase of.8 C by 8 relative to (a likely range of. C to.6 C and a greater tan 5 percent cance of an increase exceeding C by ). Under te RCP 8.5 scenario, CO emissions grow trougout te st century. Under tis scenario, te average increase in population-weigted temperature between 5 and across te countries in te sample is projected to be 4.4 C, wit te median country experiencing warming of 4.5 C. of fat tails te probability tat catastropic climate cange can occur tat is beind calls for strong mitigation actions to reduce emissions and for adaptation to prepare for significant socks (Weitzman ). Weater-Related Disasters As temperatures rise, te risks of extreme weater events, suc as floods, drougts, and eat waves, will increase (IPCC 4). New statistical analysis suggests tat projected climate cange will likely bring more frequent weater-related disasters events tat cause great damage or loss of life. Tis likeliood is particularly important for low-income developing countries and small states, wic istorically ave been muc more likely, relative to teir land area, to experience natural disasters tan advanced and emerging market economies (Figure 3.6, panel ). Using montly data from 99 to 4 on 8, weater-related disasters, a statistical analysis uncovers te istorical relationsip between te occurrence of a disaster and temperature and precipitation. 3 It ten combines te estimated elasticities and te projected montly temperature and precipitation in 5 and under te RCP 8.5 scenario to forecast te likeliood of natural disasters. Te results indicate tat most disaster types will be more common by te end of te century, across all country income levels. As depicted in Figure 3.6, te frequency of disasters caused by eat waves or tropical cyclones will increase considerably (see Box 3., wic explores te effect of tropical cyclones on economic activity). 4 Similarly, Te International Disaster Database (EM-DAT) defines a natural disaster as an event in wic at least one of te following criteria is met: or more people are reported killed, or more people are reported affected, and eiter a declaration of a state of emergency or a call for international assistance is made (Gua-Sapir, Below, and Hoyois 5). Low-income developing countries and small states, respectively, are five and times more likely to be it by a weater-related natural disaster tan te rest of te world, after controlling for country size. 3 Te probability of eac disaster type (flood, tropical cyclone, and so on) is estimated using a panel logit wit country fixed effects, in wic temperature and precipitation are te main explanatory variables. Te analysis expands on Tomas and Lopez (5) by modeling eac disaster type separately and relying on montly rater tan annual data. See Annex 3. for furter details. 4 Scientists project tat te frequency of tropical cyclone storms will decrease, but teir strengt and intensity will rise in a warmer world (Knutson and oters ). Tis could lead to more natural disasters caused by more intense tropical cyclones despite te overall lower frequency of storms. 3

8 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.5. Temperature and Precipitation Projections under te RCP 8.5 Scenario Under te scenario of continued increase in greenouse gas emissions, temperatures across te globe are projected to rise significantly.. Temperature Cange between 5 and (Degrees Celsius) Precipitation Cange between 5 and (mm per year),5 +, 5 5, Sources: National Aeronautics and Space Administration (NASA) Eart Excange Global Daily Downscaled Projections (NEX-GDDP); World Bank Group Cartograpy Unit; and IMF staff calculations. Note: Te NEX-GDDP data set comprises downscaled climate scenarios for te globe tat are derived from te General Circulation Model (GCM) runs conducted under te Coupled Model Intercomparison Project Pase 5 (CMIP5) and for two Representative Concentration Patways (RCP) greenouse gas emissions scenarios (4.5 and 8.5). Te CMIP5 GCM runs were developed for te Intergovernmental Panel on Climate Cange Fift Assessment Report. Te data set includes downscaled projections from te models and scenarios for daily maximum temperature, minimum temperature, and precipitation for 95. Te spatial resolution of te data set is.5 degrees (~5 km x 5 km). mm = millimeter. 4

9 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Figure 3.6. Natural Disasters: Historical and Projected Montly Probability of Occurrence Natural disasters, wic ave istorically occurred wit greater frequency in lowincome developing countries relative to teir land area, could become more common by te end of te st century under te scenario of continued increase in greenouse gas emissions. 4 5 floods and epidemics, wic mainly affect low-income developing countries, will also become more common. More frequent weater-related disasters, witout a corresponding increase in reconstruction capabilities, could amplify te damages tey cause because economies may ave insufficient time to recover between events (Hallegatte, Hourcade, and Dumas 7).. Natural Disasters by Type, 99 4 Oter LIDCs 8,476 disasters, in LIDCs LIDCs,66 Drougt LIDCs LIDCs Tropical cyclone..8.6 LIDCs Flood Epidemic. Tropical Cyclone 3. Flood 4. Heat Wave AEs EMs LIDCs Te Macroeconomic Impact of Weater Socks Te design of appropriate policies to cope wit climate cange requires an understanding of its potential macroeconomic consequences. In te absence of istorical experience wit climate cange tat may be relevant for countries today, te analysis in tis section builds on existing literature and identifies ow annual fluctuations in temperature and precipitation affect macroeconomic performance in te sort and medium term. Te cannels troug wic macroeconomic effects occur and te canges in te sensitivity of growt to weater socks are explored, motivated by evidence tat iger temperatures constrain per capita GDP growt in countries wit ot climates AEs EMs LIDCs 5. Epidemic AEs EMs LIDCs AEs EMs LIDCs 6. Wildfire AEs EMs LIDCs Sources: International Disaster Database (EM-DAT); and IMF staff calculations. Note: In panel, te colors indicate te different types of natural disasters, wit te ligter sades of eac color specifying te portion tat as occurred in low-income developing countries (LIDCs). Panels 6 sow te predicted montly probability of a disaster in 5 and, based on te Representative Concentration Patways 8.5 scenario. Most of te predicted probabilities for individual monts are not statistically significant, terefore te results sould only be interpreted as indicative of te potential increase in te frequency of disasters wit climate cange. AEs = advanced economies; EMs = emerging markets; LIDCs = low-income developing countries. Sort- and Medium-Term Effects To measure te impact of weater socks, tis section examines te istorical relationsip between weater patterns and economic activity, using te approac of Dell, Jones, and Olken () and Burke, Hsiang, and Miguel (5a). Similar to tese studies, te analysis uses witin-country and across-country year-to-year fluctuations in temperature and precipitation to identify te causal effect of weater on aggregate outcomes, bot contemporaneously and over te medium term. It builds on tese studies by expanding te geograpic and temporal coverage of te analysis, examining te effects of weater socks on a larger set of outcome variables and establising te robustness of findings to different sources of weater data and alternative, more flexible empirical specifications. Te baseline analysis uses Jordà s (5) local projection metod to trace te impulse response function of real per capita GDP to a weater sock in a sample of more tan 8 economies during Weater is measured as te country s average annual temperature and precipitation, along wit te squared terms of temperature and precipitation to account for te global nonlinear relationsip between temperature 5

10 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.7. Effect of Temperature Increase on Real per Capita Output (Percent) In relatively ot countries, suc as most low-income developing countries, an increase in temperature as a negative, statistically significant, and long-lasting effect on per capita output Estimate 9 percent confidence interval Percent of countries (rigt scale) Median temperature Contemporaneous Effect (Temperature on x-axis). Advanced Economies Emerging Market Economies 5. Low-Income Developing Countries Effect over Time (Years on x-axis). Advanced Economies (T = C) Emerging Market Economies (T = C) Low-Income Developing Countries (T = 5 C) Source: IMF staff calculations. Note: Left-and-side panels superimpose te contemporaneous effect of a C increase in temperature on per capita output at different temperature levels computed as per equation (3.3) over te distribution of average annual temperatures recorded in 5 in advanced economies (panel ), emerging markets (panel 3), and low-income developing countries (panel 5). Te blue lines sow te point estimates and 9 percent confidence intervals, wile te ligt blue bars denote te percent of countries at eac temperature level. Te vertical red line is te median temperature for te country group. Rigt-and-side panels depict te impulse response of per capita output to a C increase in temperature estimated at te median temperature of advanced economies (panel ), emerging markets (panel 4), and low-income developing countries (panel 6). Horizon is te year of te sock. T = temperature. and growt, as demonstrated by Burke, Hsiang, and Miguel (5a). 5 Te analysis confirms te existence of a statistically significant nonlinear effect of temperature on per capita economic growt, first establised by Burke, Hsiang, and Miguel (5a), in tis capter s substantially larger sample. In countries wit ig average temperatures, an increase in temperature dampens economic activity, wereas it as te opposite effect in muc colder climates. Te tresold temperature is estimated to be about 3 C to 5 C (see Annex Table 3.3.). 6 Tese results suggest igly uneven effects of warming across te globe (Figures 3.7 and 3.8). Because most advanced economies are in colder locations, wit annual average temperatures close to te tresold, a marginal temperature increase does not materially affect teir contemporaneous growt (Figure 3.7, panel ). 7 Emerging market economies and particularly low-income developing countries tend 5 Average annual temperature and precipitation are constructed by aggregating weater data at te grid-cell level to te level of te country using te population in eac cell as weigts to account for differences in population density witin countries and capture te average weater experienced by a person in te country (see Annexes 3. and 3.3). Te empirical approac consists of regressing contemporaneous and future output growt on temperature and precipitation and te squared terms to estimate an impulse response function at various orizons, controlling for country fixed effects, region-year fixed effects, lags and forwards of weater socks, and lagged growt. See Annex 3.3 for furter details. 6 Te finding is robust to, among oter tings: () using alternative sources of raw grid-level weater data, () aggregating grid-level weater data to country averages wit population weigts from different decades, (3) estimation troug an autoregressive distributive lag specification instead of a local projection metod, (4) using country-specific linear and quadratic time trends as opposed to region-year fixed effects, and (5) controlling for te occurrence of natural disasters. Te analysis does not find a consistently significant relationsip between precipitation and per capita GDP growt, altoug it uncovers an effect of precipitation on agricultural output (Annex Tables 3.3. and 3.3.). 7 Even if te effects on overall GDP in tese countries are negligible, tis may mask large losses and gains, wit some sectors facing large investment needs to cope wit iger temperatures, rising sea levels, or more damaging disasters. Moreover, te analysis focuses on te macroeconomic effects of a limited set of weater caracteristics, namely temperature and precipitation. Te negative impact of oter aspects of te climate, suc as te rise in sea levels or te occurrence of extreme weater events, may be less unequal across broad income groups, as demonstrated in Box 3., wic documents similar output losses from tropical cyclones across advanced and emerging market economies. Te estimates also abstract from potential spillovers to advanced economies from famines, epidemics, social conflicts, and oter difficult-to-predict effects of weater socks in vulnerable economies. Moreover, under te scenario of unconstrained CO emissions, most advanced economies will cross te tresold temperature and would start suffering te negative effects of iger temperatures on economic output (Annex Figure 3.6.). 6

11 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Figure 3.8. Effect of Temperature Increase on Real per Capita Output across te Globe (Percent) An increase in temperature as a igly uneven effect across te globe, wit adverse consequences concentrated in te parts of te world were te majority of te world s population lives.. Effect of a C Increase in Temperature on Real per Capita Output at te Grid Level Effect of a C Increase in Temperature on Real per Capita Output at te Country Level, wit Countries Rescaled in Proportion to Teir Population.8 to to.8.33 to.43.5 to.3.97 to.5.3 to to.3 Insignificant effect No data Sources: Natural Eart; ScapeToad; United Nations World Population Prospects Database: te 5 Revision; World Bank Group Cartograpy Unit; and IMF staff calculations. Note: Te maps depict te contemporaneous effect of a C increase in temperature on per capita output computed as per equation (3.3). Panel uses 5 gridlevel temperature, wile panel uses te recent -year average country-level temperature togeter wit estimated coefficients in Annex Table 3.3., column (5). In te cartogram in panel, eac country is rescaled in proportion to its 5 population. Gray areas indicate te estimated impact is not statistically significant. 7

12 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges to ave muc otter climates, and a rise in temperature significantly lowers per capita GDP growt. For te median emerging market economy, a C increase from a temperature of C lowers growt in te same year by.9 percentage point. For te median low-income developing country, wit a temperature of 5 C, te effect of a C increase in temperature is even larger: growt falls by. percentage points (Figure 3.7, panels 3 and 5). 8 And even toug countries projected to be significantly affected by an increase in temperature produced only about one-fift of global GDP in 6, tey are ome to close to 6 percent of current global population and more tan 75 percent of te projected global population at te end of te century (Figure 3.8 and Annex Figure 3.3.). Does economic activity in countries wit warmer climates recover quickly after a rise in temperature? Te analysis suggests not. Even seven years after a weater sock, per capita output is percent lower for te median emerging market economy and.5 percent lower for te median low-income country (Figure 3.7, panels, 4, and 6). 9 A deepening in te sape of te estimated impulse response of output to a temperature sock ints at te possibility of a growt effect (and consequently muc larger economic losses from iger temperatures). However, statistically, it is not possible to reject te ypotesis tat te contemporaneous and medium-term effects of a temperature sock on per capita output are identical. Cannels of Impact Te weater can influence economic activity troug various cannels. Te most obvious one is agricultural output, given tat temperature and precipitation are direct inputs in crop production. However, studies sow evidence of broader impacts, including on labor productivity, mortality, ealt, and conflict. Te literature 8 Tere are also substantial differences in te estimated effects of temperature increases witin eac broad country group, wic reflect te wide distribution of average temperature across countries (Figure 3.7, panels, 3, and 5; Figure 3.8). 9 Te persistence of te estimated effects may reflect te relatively persistent nature of temperature socks. Univariate time series regression analysis sows tat temperature socks decay slowly, especially in relatively ot locations. A C degree increase in annual temperature leads to significantly iger temperatures over te subsequent eigt years. Dell, Jones, and Olken () and Burke, Hsiang, and Miguel (5a) argue in favor of a growt effect, altoug it is difficult to pin down te precise cannel troug wic weater socks persistently influence economic growt. See Dell, Jones, and Olken (4); Carleton and Hsiang (6); and Heal and Park (6) for literature reviews. Weater socks can also indirectly affect economic activity troug teir impacts Figure 3.9. Effect of Temperature Increase on Sectoral Output Estimated at te Temperature of te Median Low-Income Developing Country (Percent; years on x-axis) An increase in temperature lowers agricultural output, but also as adverse effects on manufacturing value added in ot countries Estimate. Agriculture Value Added 3. Manufacturing Value Added percent confidence interval. Crop Production Services Value Added Source: IMF staff calculations. Note: Te panels depict te effect of a C increase in temperature estimated at te median low-income developing country temperature (5 C). Horizon is te year of te sock. Crop production is an index, produced by te Food and Agriculture Organization, of price-weigted quantities of agricultural commodities produced excluding production for seeds and fodder. so far as often studied tese effects witin a specific country or troug laboratory experiments; tis capter examines weter tese cannels are also at work in a cross-country setting. Box 3. extends te analysis in tis section by examining te macroeconomic effects of anoter aspect of te weater tropical cyclones. Te main analysis begins by studying weter weater socks influence only agricultural production or also affect oter economic sectors. As sown on tird markets. See Casin, Moaddes, and Raissi (7) for an analysis of te international macroeconomic transmission of El Niño witin a dynamic multicountry framework

13 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? in Figure 3.9, at te temperatures prevailing in te median low-income developing country, agricultural value added and crop production drop wit iger temperatures, recover somewat in subsequent years, and generally remain depressed over te medium term muc as expected and as documented in a large body of work. However, te analysis also confirms findings tat industrial output is similarly urt as temperatures rise in countries wit ot climates, altoug te estimates are more imprecise (see also Dell, Jones, and Olken ; Burke, Hsiang, and Miguel 5a). Only services sector output appears to be seltered from te weater. To sed ligt on te reasons weater socks affect sectors besides agriculture, te analysis concentrates on ow key elements of te aggregate production function productivity and labor and capital inputs respond to weater socks. As in oter studies, te analysis aims to capture te net reduced-form effects of weater on various outcomes rater tan uncover te potentially complex structural relationsips tat may exist among tese variables. Productivity Evidence from surveys and oter sources sows tat exposure to eat above a certain point reduces people s performance on bot cognitive and pysical tasks. 3 Te analysis terefore examines weter iger temperatures in parts of te world tat are ot decrease labor productivity. If productivity is a cannel troug wic weater socks affect aggregate GDP, te effect sould be significantly larger See, among oters, Barrios, Bazoumana, and Strobl (); Barrios, Bertinelli, and Strobl (6); Feng, Krueger, and Oppeneimer (); Sclenker and Lobell (); Lobell, Sclenker, and Costa-Roberts (); and Lanzafame (4) for evidence from emerging market and developing economies, and Sclenker and Roberts (9), Burke and Emerick (6), and Wang and oters (7) for evidence from te United States. Unlike per capita output, agricultural value added and crop production respond to precipitation, in addition to temperature socks, wit more precipitation generally boosting production. See Annex Table Seppänen, Fisk, and Faulkner (3) report a productivity loss of about percent for every C increase in temperature above 5 C, based on a survey of laboratory experiments. See also Seppänen, Fisk, and Lei (6) for a meta-analysis of te literature, Deryugina and Hsiang (4) for evidence from te United States, and Somanatan and oters (7) for recent evidence on labor productivity from India. Heat stress may also reduce cognitive function, as captured in student performance (Wargocki and Wyon 7; Graff Zivin, Hsiang, and Neidell 5; Garg, Jagnani, and Taraz 7; Park 7). for sectors in wic workers are directly exposed to te weater. 4 Analysis of sectoral data on value added per worker reveals tat, at te temperatures prevailing in te median low-income developing country, productivity of workers in eat-exposed industries falls significantly after a rise in temperature (Figure 3., panels and ). However, labor productivity is unaffected in industries in wic work is performed mostly indoors. Overall productivity may also decline if weater socks provoke political instability, incite conflict, or undermine governing institutions in oter ways. Altoug a more detailed analysis would be beyond te scope of tis capter, numerous studies document a strong link between weater socks and tese outcomes. 5 Since conflict is one of te key triggers of refugee flows, as discussed in Capter of te April 7 World Economic Outlook (WEO), weater socks could result in substantial spillovers to neigboring countries and ultimately to advanced economies troug tis cannel. Capital Accumulation Temperature increases are largely supply-side socks, but tey could lead to persistent output losses and affect growt if tey influence te rate of factor accumulation. 6 Using national accounts data, te analysis examines te response of te main components of aggregate demand gross capital formation, consumption, exports, and imports to weater socks witin te empirical framework described above. At te tem- 4 Te analysis follows Graff Zivin and Neidell (4) and uses te National Institute for Occupational Safety and Healt definitions of eat-exposed industries. Heat-exposed industries include agriculture, forestry, fising and unting, construction, mining, transportation, and utilities, as well as manufacturing in facilities tat may not be climate controlled in low-income countries and wose production processes often generate considerable eat. 5 Burke, Hsiang, and Miguel (5b) review te literature tat links climate to conflict. Forcible removal of rulers as also been linked to fluctuations in climate (Burke and Leig ; Dell, Jones, and Olken ; Caney 3; Kim 4), and several istorical cases of societal collapse ave been compellingly attributed to climate cange (Cullen and oters ; Haug and oters 3; Buckley and oters ; Büntgen and oters ). 6 Investment may fall in response to temperature socks because tere are fewer resources to invest, because te rate of return on capital is lower, and/or because te temporary negative sock to income raises te cost of financing investment in an environment of imperfect capital markets (see, for example, Fankauser and Tol 5). Wen access to formal savings, credit, or insurance is limited, ouseolds may also sell productive assets to smoot consumption in response to weater socks. 9

14 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.. Effect of Temperature Increase on Productivity, Capital, and Labor Input Estimated at te Temperature of te Median Low-Income Developing Country (Percent; years on x-axis) In ot countries, an increase in temperature dampens labor productivity in eatexposed industries, depresses investment and imports, and as damaging ealt effects. Estimate 9 percent confidence interval perature of te median low-income country, all four components respond negatively to a C increase in temperature. However, in te medium term, te effect is most pronounced for investment, wic is estimated to be 6 percent lower seven years after te sock (Figure 3., panel 3). Imports, wic are typically closely tied to investment, also exibit a significant and long-lasting drop as temperature rises (Capter of te October 6 WEO) Labor Productivity in Heat-Exposed Industries Investment 5. Infant Mortality. Labor Productivity in Non-Heat-Exposed Industries Imports Human Development Index Labor Supply Te analysis also reveals tat, in ot climates, iger temperatures may reduce (future) labor supply because of teir influence on mortality rates (Figure 3., panel 5). A C increase in temperature raises infant mortality by. percentage point in te year of te sock. Te effect grows troug te estimation period as weater-related lower income (and potential food insecurity) reinforces te direct pysiological impact of iger temperatures in ot climates. Tis cross-country panel evidence corroborates findings of numerous studies of links between weater and mortality, prenatal ealt, and oter ealt outcomes in various countries. 8 Te adverse effects on te ealt and educational attainment of cildren could be one of te key reasons beind te long-lasting nature of weater s consequences. Effects over Time As countries repeatedly face weater fluctuations, it is reasonable to expect tem to take measures tat lessen te impact of temperature socks on te economy. However, te analysis does not find any obvious evidence of suc adaptation over te past 6 years. Estimates of te response of per capita output Source: IMF staff calculations. Note: Te panels depict te effect of a C increase in temperature estimated at te median low-income developing country temperature (5 C). Horizon is te year of te sock. Heat-exposed industries include agriculture, forestry, fising, and unting, construction, mining, transportation, utilities, and manufacturing, following Graff Zivin and Neidell (4). 7 Te negative effect of temperature socks on aggregate investment is consistent wit evidence from ouseold-level studies, wic find tat weater socks could slow or even reverse capital accumulation as ouseolds try to smoot consumption or perceive investment as too risky (Hallegatte and oters 6). 8 Descênes () and Guo and oters (4) provide compreensive reviews of te literature on te link between temperature and mortality and ealt. See, for example, Descênes and Greenstone (), Barreca (), and Barreca and oters (6) for evidence from te United States; Kudamatsu, Persson, and Strömberg () for evidence from a subset of African countries; and Burgess and oters (4) for evidence from India. Carleton (7) documents a significant increase in suicide rates wen iger temperatures treaten agricultural yields in India. Deryugina and Hsiang (4), Graff Zivin and Neidell (4), Park (6), and Somanatan and oters (7) find a direct effect of iger temperature on labor supply and productivity. 3

15 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? to temperature socks over rolling -year periods suggest tat te relationsip between te two variables as remained constant (Figure 3.). 9 Te reasons beind tis apparent lack of adaptation are not well understood, but ig costs, limited access to credit for financing adaptation, insufficient information about te benefits of adaptation, limited rationality in planning for future risks, and inadequate access to tecnology are likely constraints, as discussed in Carleton and Hsiang (6). Coping wit Weater Socks and Climate Cange Tis section examines ow policies, institutions, and oter country caracteristics can mitigate te adverse consequences of temperature socks and climate cange. It begins by discussing te toolkit available to policymakers and private agents wit wic to cope wit weater socks. It ten presents illustrative evidence of te extent to wic, istorically, some policies (along wit te overall level of development) ave saped te link between macroeconomic performance and temperature socks. Te empirical evidence is complemented in Box 3. by dynamic general equilibrium model scenarios of te response of macroeconomic aggregates to weater socks under various proxies for relevant policies. Case studies of specific adaptation strategies occupy Boxes 3.3 and 3.4. Te section also examines migration as a response to persistent canges in climate as adaptation strategies reac teir limits. Finally, te role of international cooperation in supporting countries efforts to cope wit weater socks and climate cange is discussed. A Toolkit To structure te discussion, tis subsection lays out a toolkit of possible domestic policy actions and private coices tat may elp insulate economic activity 9 Studies reveal large differences in te ability of individual sectors to adapt to specific weater socks. For example, Hsiang and Narita () and Hsiang and Jina (4) find tat countries more frequently exposed to tropical cyclones experience less damage, wic suggests tat tey ave learned to cope wit tese extreme events. Mortality caused by ig temperatures as declined significantly over time wit te introduction of air-conditioning in te United States (Barreca and oters 6). But tere is little evidence of declining sensitivity of agricultural yields (Burke and Emerick 6) or overall output (Dell, Jones, and Olken ; Deryugina and Hsiang 4; Burke, Hsiang, and Miguel 5a) to temperature fluctuations. Figure 3.. Effect of Temperature Increase on Real per Capita Output Estimated at te Temperature of te Median Low-Income Developing Country over Time (Percent; years on x-axis) Te contemporaneous effect of temperature socks on per capita output as remained relatively constant over time Estimate 9 percent confidence interval Source: IMF staff calculations. Note: Te figure depicts te effect of a C increase in temperature at orizon estimated at te median low-income developing country temperature (5 C), over a -year rolling window. Eac point estimate is for a period (t, t + ). from weater socks and from te risks tat accompany climate cange (Figure 3.). Fluctuations in weater can be viewed as one of many socks tat affect macroeconomic performance. As suc, teir consequences could be attenuated by general macroeconomic and structural policies and institutions tat enance countries ex ante and ex post resilience to socks. Wile priorities will vary depending on eac country s specific circumstances and weater-related treats, policies may include tose tat seek to limit te sort-term impact wen socks occur, elp te economy recover faster, and reduce vulnerability to future socks. Policies reinforce eac oter to acieve tese goals. For example, countries wit buffers (fiscal and monetary space, large international reserves, access to foreign aid) and well-targeted social safety nets may be better placed to deliver support to people affected by weater socks, tus smooting consumption in te sort term. Adjusting to weater socks and climate cange will likely require reallocating people and capital across sectors and regions as production and trade patterns sift. Policies and institutions tat 3

16 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.. Coping wit Weater Socks and Climate Cange: A Toolkit Macroeconomic and Structural Policies to Build Resilience to Socks Adaptation Strategies to Specific Climate Cange Risks Enance ability to smoot te impact of te socks Enance flexibility and foster structural transformation Mitigate risks by reducing exposure and vulnerability Transfer and sare risks Migration Policy buffers To enable policy response Well-targeted social safety nets To effectively support tose affected Excange rate flexibility To cusion some of te economic cost of te sock Labor market policies To facilitate labor movement across production sectors and regions Education and ealt policies To strengten uman capital, facilitate lifelong learning, and develop a flexible and resilient labor force To reduce vulnerability Financial sector policies To ensure access to credit, insurance, and oter financial services needed by ouseolds to smoot consumption To enable firms to invest, develop new tecnologies, and so fort Infrastructure investment Public information provision about climate-related risks Early warning systems and evacuation scemes Stronger building laws, land use planning, and zoning rule; and better regulation of te use of common resources (for example, water) Fiscal incentives and appropriate pricing for te development and adoption of appropriate tecnologies (for example, resistant crops, air-conditioning, ousing improvements) Climate-smart infrastructure investment (for example, irrigation, drainage, seawalls) Private and sovereign insurance (for example, parametric insurance, crop insurance, catastrope bonds) Multilateral risk-saring mecanisms Strong Institutional Framework Source: IMF staff compilation. facilitate te needed reallocation, suc as tose tat ensure access to finance, labor market flexibility, and investment in uman capital and infrastructure, could speed up recovery and foster te structural transformation necessary to reduce vulnerability. 3 Mitigating te risks associated wit climate cange will also require some very specific adaptation policies to elp countries reduce teir exposure and vulnerability to climatic events. Once te key climate cange risks are identified for a particular location, bot soft and ard adaptation measures can be applied (Hallegatte 9). Soft measures may include strengtening 3 Te classification of policies presented in Figure 3. is rater loose. Greater financial access could elp farmers bot smoot consumption wen iger temperatures damage crops and invest in te tecnology needed to prevent future damage (suc as buying eat-resistant seeds). public information provision, building codes, and land use and zoning laws, and devising warning and evacuation systems, along wit targeted incentives for climate-related tecnologies (suc as air-conditioning) and transferring and saring risks related to weater events (suc as natural disasters, wic may increase in frequency) troug financial markets. Hard measures may include investment in climate-smart infrastructure, suc as retrofitting properties and building (or upgrading) irrigation or drainage systems, building seawalls, and te like. 3 Appropriate adaptation measures are igly specific to te climate-related risks in 3 See Hallegatte (9); Hallegatte, Lecocq, and de Pertuis (); IPCC (4); Cabezon and oters (5); OECD (5a); Farid and oters (6); Hallegatte and oters (6); IMF (6a); and IMF (6b) for a compreensive discussion of various climate cange adaptation strategies. 3

17 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? eac location and national circumstances; te infrastructure requirements for a flood-prone area would be vastly different from tose of an area tat is frequently exposed to drougts. Tis specificity, togeter wit lack of comparable data on adaptation measures, precludes cross-country empirical analysis. Case studies of adaptation strategies, owever, could prove insigtful and are presented in Box 3.3. Box 3.4 discusses te role of financial markets in saring and transferring weater-related risks. Important synergies exist between general macroeconomic and structural policies and specific adaptation strategies: economic and institutional development will likely strengten a country s capacity to cope wit climate cange and to invest in specific adaptation strategies. For example, stronger institutions will make enforcement of soft measures more effective, wile fiscal space will enable te investment in needed infrastructure. Conversely, some adaptation strategies, suc as efficient water use, climate-resilient ousing, or activity diversification could facilitate development even in te absence of climate cange (Farid and oters 6). Finally, as adaptation strategies reac teir limits, economic agents could respond to persistent canges in climate and te associated loss in income by relocating geograpically. Te Role of Domestic Policies and Institutions: Empirical Evidence To study te extent to wic macroeconomic and structural policies and country caracteristics mute te effect of weater socks, te analysis extends te empirical approac described above. It does so by allowing te response of per capita output to weater socks to vary wit various proxies for tese policy and institutional settings, wic are included one at a time in te analysis. 3 It is important to empasize tat, wereas fluctuations in temperature and precipitation are truly exogenous, wic allows teir causal impact to be identified, variations in policies and institutions across countries and over time are not. Accordingly, estimated correlations sould be interpreted as being merely suggestive of causal impact. 3 More specifically, te estimated specification augments equation (3.) to include an interaction term between te weater sock and te policy variable. For simplicity, te sample is restricted to countries wit average temperature exceeding 5 C, in wic an increase in temperature as a statistically significant linear negative impact on economic activity. See Annex 3.3 for furter details. Te results suggest tat aving te rigt policies and institutions in place may elp attenuate te effects of temperature socks, to some extent. Te instantaneous effect of a temperature sock is sligtly smaller in countries wit lower public debt, iger inflows of foreign aid, and greater excange rate flexibility. Te presence of monetary buffers (proxied by aving below double-digit inflation) or international reserves makes no notable difference (Figure 3.3). However, te extent of attenuation tat buffers provide is estimated to be small and sort lived. Te evidence is somewat more compelling for structural policies and country caracteristics tat are typically deemed important for easing sectoral reallocation of factors of production and structural transformation in general. Altoug te uncertainty surrounding te empirical estimates is often very large, te medium-term adverse effect of a temperature increase appears to fade wen domestic and international financial markets are better regulated, te excange rate is flexible, infrastructure is widely available, democratic institutions are strong, and te distribution of income is fairly even tat is, in more-developed economies (Figure 3.4). Patterns uncovered in te data broadly mirror simulations of a dynamic structural general equilibrium model, wic can properly isolate te causal effects of te availability of buffers, costs of capital adjustment, quality of institutions, and investment in adaptation strategies (Box 3.). Tey are also in line wit te empirical findings tat sow less damage from extreme weater events and natural disasters in countries were excange rates are flexible, financial services are readily available, and institutions are strong. 33,34 33 See Kan (5); Noy (9); McDermott, Barry, and Tol (3); Burgess and oters (4); and Felbermayr and Gröscl (4) for te role of financial development, and Von Peter, Dalen, and Saxena (); Breckner and oters (6); and Lee, Villaruel, and Gaspar (6) for te role of insurance penetration. Kan (5), Noy (9), and Felbermayr and Gröscl (4) find evidence for te role of institutions, and Ramcaran (9) examines te role of excange rates in reducing damage from extreme weater events and natural disasters. 34 Two studies make a compelling case for te importance of sectoral reallocation in alleviating output losses from climate cange. Wen quantifying te effects of climate cange on agricultural markets using micro data from.7 million fields around te world, Costinot, Donaldson, and Smit (6) find tat te welfare losses would be tree times larger if farmers were unable to switc production in response to canging climatic conditions and comparative advantage. In an empirical study, Colmer (6) establises tat labor movements from agriculture into manufacturing in India can significantly offset te aggregate economic losses associated wit weater-driven canges in agricultural productivity. 33

18 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.3. Role of Policy Buffers (Percent; years on x-axis) Figure 3.4. Role of Structural Policies and Institutions (Percent; years on x-axis) Tere is some suggestive evidence tat te contemporaneous effect of temperature on per capita output is marginally lower in countries wit lower public debt, greater foreign aid inflows, and flexible excange rates Public Debt International Reserves 5. Remittances Low debt to GDP Hig debt to GDP More tan four monts of imports Less tan four monts of imports Hig remittances Low remittances Inflation Foreign Aid Below double digit Above double digit Hig foreign aid Low foreign aid Excange Rate Flexibility Not pegged. Pegged Source: IMF staff calculations. Note: Te panels depict ow te effect of a C increase in temperature on per capita output in te sample of countries wit average temperature exceeding 5 C varies wit te empirical proxy of a policy buffer. Horizon is te year of te sock. Gray areas indicate tat te blue and red lines are significantly different from eac oter at te 5 percent level. See Annex 3.3 for te exact definition of policy variables Tere is some suggestive evidence tat te medium-term effect of an increase in temperature on per capita output is marginally lower in countries wit betterregulated financial markets, greater pysical capital, more democratic institutions, and lower income inequality Low financial sector liberalization Domestic Financial Sector Reform Hig financial sector liberalization 3. Human Capital Hig uman capital Low uman capital 5. Political Regime Hig polity score Low polity score International Finance Low restrictions on capital account Hig restrictions on. capital account Pysical Capital Hig prevalence of paved roads Low prevalence of paved roads Inequality Low Gini Hig Gini Source: IMF staff calculations. Note: Te panels depict ow te effect of a C increase in temperature on per capita output in te sample of countries wit average temperature exceeding 5 C varies wit te empirical proxies of structural policies and institutional settings. Horizon is te year of te sock. Gray areas indicate tat te blue and red lines are significantly different from eac oter at te 5 percent level. See Annex 3.3 for te exact definition of policy variables. 34

19 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? An alternative approac to assessing weter development more broadly reduces vulnerability to weater socks takes advantage of subnational cross-country data. It is difficult to establis definitively weter advanced economies experience a smaller marginal effect of eat on macroeconomic performance, because so few of tem ave ot climates. However, some of te larger advanced economies, suc as te United States, span several climate zones. 35 Tis witin-country geograpic eterogeneity makes it possible to compare weter economic activity in te ot states or provinces of advanced economies responds to a temperature increase in te same way as economic activity does in states or provinces of emerging market and developing economies wit a similar average temperature. Indeed, analysis suggests tat temperature socks urt ot areas in emerging market and developing economies significantly more tan tose in advanced economies (Figure 3.5). Tus, economic development seems, to some extent, to insulate countries from te vagaries of te weater. 36 Te Role of Migration Migration is anoter possible adaptation strategy for ouseolds urt by weater socks and persistent canges in climate one wit important cross-border spillovers. Teoretically, te impact of weater socks on migration is ambiguous (see Dell, Jones, and Olken 4). Altoug lower incomes, safety concerns, and pysiological discomfort are powerful incentives to relocate, te adverse income effect of weater socks may undermine ouseolds ability to pay for transport and oter relocation expenses (Bryan, Cowdury, and Mobarak 4; Carleton and Hsiang 6). 37 Several empirical studies ave documented adaptation to weater socks and natural disasters troug migration 35 Average annual temperatures in te US states of Maine and Texas are about 7 C and C, respectively. 36 Data constraints prevent te identification of te precise cannels troug wic development attenuates te link between weater and overall economic performance. Economic activity in ot areas in advanced economies may be more insulated from temperature socks given tat ouseolds exposed to tese socks ave better access to ex post coping mecanisms (suc as social protection) or ave reduced teir vulnerability to socks troug ex ante adaptation strategies (suc as activity diversification, adoption of air-conditioning, and te like). 37 Lack of knowledge and uncertainty about te risks caused by slowly canging climate conditions (Lee and oters 5) as well as te provision of government assistance to disaster-prone areas may also result in minimal beavioral cange (Baez and oters 7). Figure 3.5. Role of Development: Evidence from Subnational Data (Percent; years on x-axis) Te adverse effect of an increase in temperature on output is more pronounced in non-advanced economies Advanced economies Non-advanced economies Source: IMF staff calculations. Note: Te figure depicts ow te effect of a C increase in temperature in te sample of states or provinces wit average temperature exceeding 5 C varies wit an indicator of weter te state or province is located in an advanced economy. Horizon is te year of te sock. Gray area indicates tat te blue and red lines are significantly different from eac oter at te 5 percent level. witin country borders. 38 Evidence of international migration responses is scarcer and typically focuses on flows from individual countries. 39 Te analysis builds on Cattaneo and Peri (6) and examines weter weater socks and natural 38 See Gray and Mueller (b) for evidence from Banglades; and Boustan, Kan, and Rode (); Feng, Oppeneimer, and Sclenker (); Hornbeck (); and Hornbeck and Naidu (4), among oters, for evidence from te United States. Deryugina (), on te oter and, finds no population response in te years following a urricane landfall in te United States, but documents a substantial increase in government transfer payments. 39 Munsi (3), for example, finds tat more migrants move from Mexico to te United States wen rainfall is lower in a given Mexican community a pattern also confirmed by Feng, Krueger, and Oppeneimer (). Country-specific evidence also includes Etiopia (Gray and Mueller a), Indonesia (Bora-Misra, Oppeneimer, and Hsiang 4), Pakistan (Mueller, Gray, and Kosec 4), and Syria (Kelley and oters 5). Barrios, Bertinelli, and Strobl (6) and Marciori, Maystadt, and Scumacer () provide evidence from several countries in sub-saaran Africa. 35

20 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.6. Effect of Temperature and Natural Disasters on International Migration (Percentage points of origin country s total population) Among te sample of countries wit average temperature exceeding 5 C, an increase in temperature and greater incidence of natural disasters induce migration, but only from non-low-income developing countries Temperature Emerging market economies Low-income developing countries. Number of Natural Disasters Emerging market economies Source: IMF staff calculations. Note: Estimates from a panel regression of te effects of a C increase in -year average temperature and number of natural disasters on te sare of emigrants. See Annex 3.4 for furter details on te data, specification, and estimation. Vertical lines denote 9 percent confidence intervals. disasters trigger emigration. 4 Te findings suggest tat a rise in temperature and greater incidence of weater-related disasters induce emigration, but only from countries were people can generally afford to leave, wic confirms Cattaneo and Peri s (6) results (Figure 3.6; Annex Table 3.4.). Houseolds in low-income developing countries, wic tend to ave limited access to savings and credit, appear trapped by weater-induced income socks (see Black and oters ; Cen and oters 7). Tis interpretation is consistent wit te findings of Hallegatte and oters (6) tat te poorest ouseolds in 4 Focusing on te sample of countries wit average annual temperature of at least 5 C, as in te section titled Te Role of Domestic Policies and Institutions: Empirical Evidence, te analysis relates te sare of emigrants from a country to its average temperature, precipitation, and incidence of natural disasters over a -year period, controlling for time-invariant country caracteristics and global and region-specific decadal socks. See Annex 3.4 for furter details Low-income developing countries low-income countries tend to be te most exposed and vulnerable to climate cange. Tese are also precisely te ouseolds wit te fewest resources available to finance relocation. Substantial migration flows, potentially spilling across country borders, could arise if climate cange leads to a significant rise in sea levels. Hundreds of millions of people in low-lying areas could become vulnerable to flooding, forcing tem to abandon teir omes and relocate (Usery, Coi, and Finn 7, 9). In te United States alone, more tan 4 million people living in coastal areas could be affected if oceans rise te 8 centimeters te IPCC projects by under te unmitigated climate cange scenario. If te rise in sea levels is twice as muc, te affected population would exceed 3 million (Hauer, Evans, and Misra 6). International Support Climate cange is a global externality, and countries will not be able to deal wit its causes or its consequences on teir own. Bot equity and efficiency arguments call for active support from te international community in elping low-income countries plan, fund, and implement adaptation measures to cope wit te consequences of climate cange witout compromising developmental objectives. On equity grounds, low-income countries ave contributed only marginally to greenouse gas emissions, yet tey are te most vulnerable to teir armful consequences, as tis capter demonstrates. On efficiency grounds, requiring countries tat ave and/or are currently contributing substantially to te atmosperic greenouse gas concentration to bear some of te adaptation costs of low-income countries will elp offset polluters failure to fully internalize te cost of greenouse gas emissions. And wile te benefits of adaptation are largely domestic, successfully coping wit weater socks and climate cange could avert significant cross-border spillovers, for example by stemming climate-induced population migration. Support from te international community in te form of concessional climate finance will be crucial to mobilize te resources necessary to build resilience to climate cange in low-income countries (see Box 3.6). Te commitment by advanced economies to jointly contribute $ billion a year by for mitigation and adaptation in developing economies, wic was furter strengtened by te 5 Paris Agreement, 36

21 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? is an important step in tat regard. 4 In addition to financial assistance, te transfer of appropriate adaptation and clean tecnologies to low-income countries can furter enance teir efforts to cope wit climate cange by improving access to state-of-te-art tecnology, skills, and knowledge. Several initiatives under te United Nations Framework Convention on Climate Cange ave promoted te international excange of knowledge related to good practices in adaptation (suc as te Adaptation Learning Mecanism), wic can be integrated into national and local plans. Multilateral risk-saring mecanisms, suc as te Caribbean Catastropic Risk Insurance Facility and te African Risk Capacity, can also elp countries wit emergency response in te immediate aftermat of a disaster, as discussed in Boxes 3.3 and 3.4. Cognizant of te callenges posed by climate cange, te IMF, among oter international financial institutions, offers direct tecnical and financial support to small states and oter countries tat are vulnerable to weater conditions. To foster adaptation, it provides policy advice and capacity building on ow to enance macroeconomic and risk management frameworks, determine te appropriate balance between self-insurance and risk transfer, and strengten investment and growt to build resilience. 4 Te IMF as also increased vulnerable countries annual access limits under te Rapid Credit Facility and Rapid Financing Instrument to provide rapid assistance to countries wit urgent payment needs, including as a result of natural disasters (IMF 6b). public debt for a representative small open low-income country. Te model also igligts te role tat structural transformation of low-income countries (tat is, making te transition from agriculture to a more services-based economy) could play in attenuating te impact of climate cange. Box 3.5 complements te analysis by reviewing te evidence on te long-term effects of istorical climate on economic performance. Simulations are based on te Debt, Investment, and Growt (DIG) model of Buffie and oters (), wic captures aspects pertinent to low-income countries suc as low public investment efficiency and ig capital adjustment costs and can be extended easily to incorporate te structural transformation process. 43 Tese aspects of te DIG model make it preferable for studying te impact of climate cange in low-income countries relative to te Integrated Assessment Models (IAMs) more commonly used to assess climate cange effects. 44 In te DIG model, firms combine labor, private capital, and infrastructure to produce output. Consumers supply labor and derive utility from consuming traded and nontraded goods, wile te government collects revenue, redistributes income, and invests in infrastructure, wic it funds troug domestic and external borrowing, grants, and remittances. Based on te empirical results, canges in te exogenously-given sector-specific total factor productivity (TFP) levels are modeled as quadratic functions of temperature, wile all oter parameters are calibrated broadly as in Buffie and oters (). 45 Long-Term Effects of Temperature Increase A Model-Based Approac Empirical work in tis capter so far as assessed te macroeconomic effects of weater socks in te sort and medium term. Tis section incorporates tese estimates into a dynamic general equilibrium model to sed ligt on te potential long-term effects of temperature increases on GDP, investment, and 4 Estimates vary, but tere is general agreement tat adaptation needs in developing economies are on te order of billions of dollars a year (Margulis and Narain ; UNEP 6). Te Paris Agreement reiterates and extends developed economies commitment to jointly mobilize $ billion a year by : advanced economies are strongly urged to scale up teir efforts wit a concrete road map for acieving te goal and, by 5, are expected to set a new collective, quantified goal from a floor of $ billion a year (Farid and oters 6). 4 Te IMF completed its first Climate Cange Policy Assessment in June 7 in collaboration wit te World Bank for Seycelles (IMF 7). 43 For a detailed description of te model, see Buffie and oters () and Annex Te tree best-known IAMs are te Dynamic Integrated Climate-Economy (DICE) model; te Climate Framework for Uncertainty, Negotiation, and Distribution model; and te Policy Analysis of te Greenouse Effect model. RICE is a DICE model tat includes regions and AD-DICE is a variant of DICE tat includes adaptation. Antoff and Tol (), Hope (), and Nordaus and Sztorc (3) provide descriptions of tese models. Existing IAMs are typically not geograpically granular enoug, lumping togeter economies wit different income levels and average temperatures. Tey include various feedback loops among emissions, growt, and climate tat are less relevant for low-income countries. And tey are typically not well suited to analyzing sectoral issues and structural economic transformation. 45 In particular, TFP t + TFP t = β (T t + T t ) + β (T t + T t ) T t + ΔTFP t *, in wic ΔTFP * t is te TFP growt rate tat would prevail under no climate cange, assumed to be.8 percent based on te WEO medium-term growt forecast for low-income countries. β and β are te estimated coefficients on te linear and squared temperature terms in equation (3.), as reported in column (5) of Annex Table 3.3., rescaled to matc te modeled decline of GDP wen temperature increases by C, and T t is te average annual temperature for te median low-income country at time t, were te initial temperature is set at 5 C. 37

22 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.7. Long-Term Impact of Temperature Increase for a Representative Low-Income Developing Country: Model Simulations Model simulations suggest tat te increase in temperature projected under te intermediate and te unmitigated climate cange scenarios could ave significant economic consequences for a representative low-income developing country, wit sizable downside risks Intermediate Scenario (RCP 4.5) Midpoint. GDP (Percent deviation from trend) Public Debt 54 (Percent of GDP) Investment (Percent deviation from trend investment rate) Source: IMF staff calculations. Note: RCP = Representative Concentration Patways. Unmitigated Climate Cange Scenario (RCP 8.5) 95 percent confidence interval. GDP (Percent deviation from trend) Public Debt 56 (Percent of GDP) Investment (Percent deviation from trend investment rate) Te effects of climate cange are examined troug simulations of te macroeconomic response of output, te public-debt-to-gdp ratio, and private investment to te temperature increases projected under two of te scenarios prepared by te IPCC, as discussed in te Projections subsection of tis capter. Te simulations suggest tat under bot scenarios, te representative low-income country will experience sizable economic losses relative to a baseline of no canges in temperature, wit significant downside risks (Figure 3.7). Under te milder scenario, te increase in temperature will lower output by 4 percent by and depress private investment by 5 percent as firms respond to lower productivity from rising temperatures by cutting back capital spending. Te relative decline in output implies an increase in te public-debt-to-gdp ratio of percentage points by. Under te unmitigated climate cange scenario, te macroeconomic effect would be muc larger. Output would fall sort by close to 9 percent relative to no climate cange, private investment would fall by percent, and te public-debt-to-gdp ratio would rise by 5 percentage points by. 46 Conversely, te adverse effect would be significantly smaller if te rise in temperature is successfully contained to less tan C, as stipulated in te 5 Paris Agreement, underscoring te critical importance of mitigation efforts in limiting climate cange damage. Box 3.6 discusses recent developments in climate mitigation efforts. Tere is great uncertainty surrounding tese central projections because empirical estimates of te effect of temperature socks are imprecise and temperature projections are uncertain. As a result, wide confidence intervals surround tis capter s central projections. 47 Tere is a.5 percent cance of output declining more tan 8 percent below te trend under te milder scenario and more tan 6 percent under te unmitigated climate cange scenario. In line wit lower output, public debt would increase significantly relative to output (about percent of GDP under te worst-case scenario), and te private-investment-to-gdp 46 Tese results are broadly in line wit oter model-based estimates of te impact of climate cange as discussed in Tol (9). For a survey of estimates of climate cange damage at te global level, see Tol (4) and Nordaus and Moffat (7). 47 Te construction of confidence intervals is detailed in Annex 3.5. Tese intervals do not account for stocastic variations in te weater or fat-tail events. 38

23 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? ratio could plummet by as muc as percent below te trend. An alternative way to quantify climate cange damage for a representative low-income country is to compute te present value of te sortfall in economic output relative to te baseline of no climate cange and to express tis present value as a sare of current output. 48 Using a moderate growt-adjusted discount rate of.4 percent, te present value of output losses is large, at 48 percent and percent of current output under te RCP 4.5 and RCP 8.5 scenarios, respectively. Te above simulations assume a static economic structure. However, as seen in te Cannels of Impact subsection, rising temperatures affect some economic sectors more tan oters. For example, compared wit agriculture, te services sector is relatively seltered from te adverse effects of iger temperature. Hence, structural economic transformation from a mostly agrarian to a more services-based economy could lower te economic cost of climate cange. Te analysis extends te baseline DIG model to include an exogenous process of reallocating labor from agriculture and manufacturing to services. Te pace of structural transformation is assumed to be moderate and replicates past trends for low-income countries: in te absence of socks, te employment sare of te services sector rises by.5 percentage points a decade. Simulations in tis extended model indicate tat over te long term, for te median low-income country, structural transformation can reduce te cost of climate cange by about 5 percent and 3 percent under te RCP 4.5 and RCP 8.5 scenarios, respectively. Te potential impact of climate cange quantified in tis section is subject to important caveats. First, extrapolating from te sort- to medium-term causal effects of weater socks estimated from istorical data to te long-term impact of potential global warming may overstate te case if persistent canges in climate induce agents to adapt teir economic activity to te new environment. Conversely, permanent canges in climate may ave consequences tat fluctuations in annual weater do not. Moreover, te model does not capture te effects of extreme weater events, wic inflict long-lasting macroeconomic damage, as demon- 48 In line wit Nordaus (), te real interest rate is assumed to be 4.5 percent, giving a growt-adjusted discount rate of.4 percent. A more extreme discount rate of. percent, proposed by Stern (7), would increase te present value of damage by an order of magnitude. strated in Box 3. in te case of tropical cyclones, and could increase in frequency, potentially amplifying te damage tey cause. Certain expected or possible events (suc as rising sea levels) ave no istoric precedents from wic to draw inference but may ave very significant economic consequences for many low-income countries, wic are also not quantified in te simulations. Moreover, te long-term projections do not incorporate several of te cannels troug wic temperature increases, and climate cange in general, could affect economic activity, suc as declining labor supply from iger mortality and migration. Even abstracting from tese difficulties, considerable uncertainty exists about ow to incorporate te empirical estimates of economic losses into te dynamic general equilibrium model. Te analysis in tis capter as taken a very conservative approac and assumes tat weater socks ave a permanent effect on te level of output. However, several studies ave argued tat te empirical evidence is not inconsistent wit a persistent effect on te growt rate of output (Dell, Jones, and Olken ; Burke, Hsiang, and Miguel 5a). Because even a small growt effect would ultimately dwarf a level effect, te adverse consequence of temperature increases for te median low-income country would be many times larger if rising temperatures were incorporated into te model as affecting te growt pat of output. 49 Summary and Policy Implications Coping wit climate cange is one of te fundamental callenges of te st century, and tis callenge looms particularly large for low-income developing economies. Tis capter documents te extraordinarily fast rise in temperature over te past century across advanced, emerging market, and low-income developing economies and te significant warming tat could occur by te end of tis century, depending on te international community s ability 49 Burke, Hsiang, and Miguel (5a) estimate muc larger damages from climate cange for ot countries: tey model temperature increases as aving a persistent effect on te growt rate, rater tan te level of output. Permanent growt effects could arise if weater socks scar productivity growt troug teir effects on institutions, innovation, or uman capital accumulation. Several studies ave found evidence of effects of weater socks on outcomes tat could plausibly sape productivity growt (for example, te link between weater and conflict or weater and educational attainment), but it is difficult to establis empirically ow long te growt damage troug tis cannel lasts. 39

24 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges to contain greenouse gas emissions. Low-income developing countries, wic tend to be in some of te ottest parts of te planet and are projected to experience sizable increases in temperature, ave contributed very little to te atmosperic concentration of greenouse gases. Yet te analysis suggests tat rising temperatures ave igly uneven macroeconomic effects, wit te adverse consequences borne disproportionately by countries wit ot climates, suc as most low-income developing countries. Te capter finds tat a rise in temperature lowers per capita output in countries wit ig average temperatures, in bot te sort and medium term, troug a wide array of cannels. In areas wit ot climates, iger temperatures reduce agricultural output, lower productivity of workers exposed to te eat, slow te rate of capital accumulation, and damage ealt. Tese findings reflect impacts of weater socks on average country outcomes. But weater socks could also ave sizable unfavorable distributional consequences witin a country. Poor ouseolds tend to be more vulnerable to weater fluctuations as a result of teir eavy reliance on agricultural income, iger proportion of income devoted to food items, and limited access to savings and credit (Hallegatte and oters 6; Hallegatte and Rozenberg 7; IMF 6b). Despite te significant warming tat as occurred over te past century, te sensitivity of per capita output to temperature socks as not canged materially, pointing to significant constraints to adaptation. Te negative effects of projected climate cange for low-income countries could be large. Focusing on one particular aspect of climate cange namely, te projected rise in temperature and under te conservative assumption tat temperature increases affect te level rater tan te growt pat of output, model simulations suggest tat, absent efforts to reduce global emissions, te output of a representative low-income country could be 9 percent lower tan witout an increase in temperature, wit considerable downside risks. 5 Te significant uncertainty about te magnitude and effects of climate cange not only ow muc temperatures will rise, but also ow te environment will react calls for careful consideration of tese downside risks. 5 Moreover, te negative welfare consequences of canging climate conditions will likely exceed output losses. Uncomfortably ig temperatures could spur investment as ouseolds adapt, but te increase in economic activity may not improve welfare. How can low-income countries cope wit te rise in temperatures tey are set to experience over te coming decades? Altoug causal interpretation is difficult, te capter finds tat te sensitivity of per capita output to temperature socks varies wit several mediating factors, and tese factors are fundamental to teasing out te capter s policy implications. Sound domestic policies and institutions, and development in general, could play a role in partially reducing te adverse effects of weater socks. Having policy buffers in place can elp cusion some of te negative effects of weater socks by elping sustain public investment at adequate levels. Policies and institutional settings tat facilitate te reallocation of factors of production across economic sectors and geograpic regions and tat foster development suc as better access to domestic and international financial markets, ig-quality infrastructure, and stronger institutions can increase resilience to weater socks to some extent. Tese policies and institutional settings enable countries to recover faster from te negative consequences of temperature increases and reduce teir exposure and vulnerability in te future. Investment in adaptation strategies and projects suc as, for example, well-targeted social safety nets tat can promptly deliver support were needed, climate-smart infrastructure, and appropriate tecnology could also reduce some of te damage from climate cange, as illustrated by selected case studies. But low-income countries ave uge spending needs and scarce resources to undertake te investments necessary to cope wit climate cange. According to United Nations estimates, attaining te Sustainable Development Goals would require low-income countries to increase public spending by up to 3 percent of GDP an amount tat likely exceeds te fiscal space available in most countries (Baum and oters 7; Scmidt-Traub 5). Low-income countries also often lack te institutional setting, administrative capacity, or political stability to implement appropriate macroeconomic policies or adaptation strategies (Figure 3.8). Moreover, domestic policies alone cannot fully insulate low-income countries from te consequences of climate cange as iger temperatures pus te biopysical limits of tese countries ecosystems, potentially triggering more frequent epidemics, famines, and oter natural disasters, at te same time fueling migration pressure and conflict risk. Te international spillovers from tese impacts of climate cange in vulnerable countries could be very sizable. 4

25 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Figure 3.8. Vulnerability to Temperature Increase and Adaptation Prospects Low-income developing countries, were te effect of temperature increase is estimated to ave te most pernicious effect, tend to ave muc lower climate cange adaptation capacity and readiness. Effect of a C increase in temperature on per capita output Effect of a C increase in temperature on per capita output Low-income developing countries Emerging market economies Advanced economies.. Adaptation Capacity Index (x-axis) Adaptation Readiness Index (x-axis) Sources: Notre Dame Global Adaptation Index; and IMF staff calculations. Note: Te figure depicts te estimated effect of a C increase in temperature on per capita output at orizon against countries score for adaptation readiness and adaptation capacity. A iger score indicates better adaptation capacity and more readiness. Given tat low-income countries potential to address te climate cange callenge by temselves is limited, te international community must play a key role in providing and coordinating financial and nonfinancial support to tese countries (see Box 3.6). Advanced and emerging market economies ave contributed te lion s sare to actual and projected climate cange. Hence, elping low-income developing countries cope wit te consequences of climate cange is bot a umanitarian imperative and sound global economic policy tat elps offset countries failure to fully internalize te costs of greenouse gas emissions. Wile te analysis in tis capter focused on te impact of global warming in low-income countries, it is important to note tat all countries will increasingly feel direct negative effects from unmitigated climate cange, troug more frequent (and more damaging) natural disasters (see Box 3.), rising sea levels, loss of biodiversity, and many oter difficult-to-quantify consequences. Warming will also begin to weig on growt in many advanced economies as teir temperatures rise above optimal levels (see Annex Figure 3.6.). And even in countries were te effect migt be moderate or positive on average, climate cange will create winners and losers at bot te individual and sectoral levels. Moreover, te international spillovers from te most vulnerable countries, troug depressed economic activity and potentially iger conflict and migration flows, could be considerable. Going forward, only a global effort to contain carbon emissions to levels consistent wit an acceptable increase in temperature can limit te long-term risks of climate cange (Farid and oters 6; Hallegatte and oters 6; IMF 5; Stern 5; IPCC 4). 4

26 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.. Te Growt Impact of Tropical Cyclones Tropical cyclones, commonly known as urricanes in te Atlantic and as typoons in te nortwest Pacific, are one of te most destructive forces of nature. Tey caused damage of $548 billion (constant dollars) worldwide during 4 (International Disasters Database [EM-DAT]; Gua-Sapir, Below, and Hoyois 5), almost tree-quarters of wic occurred in advanced economies. Tis box estimates te effect of tropical cyclones on economic activity and discusses te possible consequences of climate cange troug its effects on tropical cyclones under an unconstrained greenouse gas emissions scenario (Representative Concentration Patway 8.5). Measuring Tropical Cyclones and Empirical Estimation Several studies ave examined te macroeconomic impact of tropical cyclones, typically finding significant economic damage. 3 Te analysis in tis box Te autor of tis box is Sebastian Acevedo. A tropical cyclone is a rotating, organized system of clouds and tunderstorms tat originates over tropical or subtropical waters and as a closed low-level circulation (NOAA 7b). Hurricane-strengt winds (greater tan 64 knots) can extend beyond miles for te largest storms. Storms cause more absolute damage in advanced economies because teir capital stocks tend to be more valuable; owever, as a percentage of GDP, damage is generally iger in small states and low-income developing countries. Te EM-DAT reports damage for about alf of te disasters caused by storms. Acevedo (6) finds tat, in te Caribbean, economic damage caused by tropical cyclones could be.6 to 3.6 times iger tan reported. 3 Raddatz (9); Fomby, Ikeda, and Loayza (3); and Acevedo (4) use data from te EM-DAT to estimate te effects of different types of natural disasters (including storms) on growt, wile a parallel body of literature (Strobl ; Bertinelli and Strobl 3; Hsiang and Jina 4) uses wind-field models to estimate te effects of storm winds on growt. Bakkensen and Barrage (6) use maximum wind speed at landfall, wic is closer to te approac used ere. combines detailed data on maximum sustained wind speed and settlements population to construct a compreensive database of tropical storms tat took place near centers of economic activity. 4 Between 95 and 6, 4,597 storms passed witin miles of a city, affecting 3,3 cities in 3 countries or territories. Tropical cyclones affect countries of different sizes, from small islands in te Caribbean and te Pacific to large countries suc as Cina, Mexico, and te United States. Wen a storm strikes a small country, it generally affects a large portion of its territory and population, wile te impact in larger countries can be contained to relatively smaller areas. To account for tis difference, te wind variable te maximum sustained wind in knots witin miles of a country (Wind i,t ) is weigted by te sare of te population exposed to all tropical cyclones in a year (P i,t ). Storms also differ in te speed at wic tey move, wit slow-moving storms being potentially more destructive. Tus, te wind variable is also weigted by te sare of a country s time endowment exposed to all storms witin a year (TE i,t ), in wic te time endowment is given as te product of te number of ours in a year and te number of cities in a country. Table 3.. summarizes te key elements of te cyclone variables. To estimate te effect of tropical cyclones on per capita output, te analysis extends te local projection empirical approac used in te capter to include te 4 Te International Best Track Arcive for Climate Stewardsip contains data on 7,4 tropical cyclones, wit information on maximum sustained wind speed between 95 and 6 (Knapp, Applequist, and oters ; Knapp, Kruk, and oters ). Tese data are combined wit te CIESIN (6) settlements population in, wic contains data for 67,68 cities tat range in population from one person to 8.5 million people. Table 3... Caracteristics of te Average Tropical Cyclone by Country Group MSW witin Miles (knots) Exposed Population Exposed Time Endowment Distance (miles) World Advanced Economies Emerging Market Economies Low-Income Developing Countries Small States Islands Sources: CIESIN GRUMPv Settlement Points r; Ibtracs v3r9; and IMF staff calculations. Note: Maximum sustained winds (MSW) one minute average in knots per our. Exposed population as a sare of total population. Exposed time endowment as a sare of te total ours available in eac country (4 ours 365 days cities). Distance is te average distance from eac city (witin miles of te storm) to te storm position were te wind was at its maximum. 4

27 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3. (continued) Table 3... Effect of Weater and Wind Socks on Economic Activity Real GDP per Capita Growt () () (3) Temperature.347***.93***.9*** (.357) (.) (.3) Temperature.5***.38***.37*** (.) (.) (.) Precipitation (.4) (.4) (.6) Precipitation.3.. (.) (.) (.) Wind Population Time Endowment 6.75** (.9) Adjusted R Number of Countries Number of Observations 8,85 4,696 4,696 Source: IMF staff calculations. Note: All regressions control for country and region-year fixed effects; lags and forwards of temperature, precipitation, and teir squared terms; and lag of growt. Column (3) also controls for te contemporaneous wind variable, as well as its lags and forwards. Column () replicates te capter s baseline specification (column (5) in Annex Table 3.3.). Columns () and (3) include only countries exposed to tropical cyclones. Standard errors clustered at te country level. * p <.; ** p <.5; *** p <.. wind variable weigted by te sare of population and time exposed. Te specification estimated is as follows: y i,t + y i,t = α ( Wind i,t P i,t TE i,t ) + α ( Wind i,t P i,t TE i,t ) + j = α 3 ( Wind i,t + j P i,t + j TE i,t + j ) + β c i,t + β c i,t + j = β 3 c i,t + j + φ Δy i,t + µ i + θ r,t + ε i,t, (3..) in wic indexes te estimation orizon, µ i are country fixed effects, θ r,t are region-year fixed effects, y i,t is te log of GDP per capita, and c i,t refers to average annual temperature and precipitation and teir squared terms. Te results presented in Table 3.. indicate tat if te wind speed increased by one knot trougout te entire country (tat is, te entire population is exposed), and for an entire year, real GDP per capita would decline by 6.7 percent te year te storm strikes. Tis, of course, is not a very useful indicator of te effect of a typical storm on a country; a better measure is te marginal effect of increasing wind speed as captured by α P i,t TE i,t. Findings Tropical cyclones ave a significant negative effect on output, wit te biggest impact felt in small states and islands tat are generally more exposed to tis type of storm (Figure 3..). 5 By income group, advanced economies are te ardest it by tropical cyclones because tey tend to be exposed to iger wind speeds. Te estimates are not only statistically, but also economically, significant. Seven years after an average storm strikes, per capita output is almost percent lower tan if te storm ad not appened, wit.5 times larger losses experienced by small states (Figure 3..). 6 Te effects of storms are very persistent: even after years, te economy as not fully recovered from te sock. 7 Notably, te effect of tropical cyclones on economic activity is separate and in addition to te effects of temperature (Table 3..). Introducing te wind variable does not materially cange te coefficients on temperature and precipitation for te same sample of countries. Climate Cange and Tropical Cyclones Climate scientists predict tat, wit climate cange, tere will be fewer tropical cyclones tat form, but te 5 For a discussion of small states vulnerability to natural disasters and climate cange, see IMF (6b). 6 A storm strike includes any tropical cyclone tat passed witin miles of a city in a country. 7 Hsiang and Jina (4) find a similar response; in teir case, te decline in GDP is muc larger, but te partial recovery starts after 5 years. 43

28 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3. (continued) Figure 3... Effect of Tropical Cyclone Exposure on Real GDP per Capita (Percent; years on x-axis) Estimate. World. Advanced Economies Emerging Market Economies 5. Small States percent confidence interval Low-Income Developing Countries Islands Source: IMF staff calculations. Note: Cumulative impact of a one-knot increase in tropical cyclone winds on real GDP per capita. Horizon is te year of te sock. Figure 3... Cumulative Effect of Average Tropical Cyclone on Real GDP per Capita after Seven Years (Percent) World Average 95 6 RCP 8.5 Advanced economies Emerging markets Low-income developing countries Small states Islands Source: IMF staff calculations. Note: Cumulative effect after seven years on real GDP per capita of te average tropical cyclone tat eac country group is exposed to in terms of maximum wind speed, exposed population, and exposed time endowment. RCP = Representative Concentration Patways. ones tat do will be more intense and destructive (Knutson and oters ). In te unmitigated climate cange scenario (Representative Concentration Patway 8.5), sea surface temperature in 9 is expected to increase by.6 C relative to 995 5, wic suggests tat te maximum wind speed of tropical cyclones could increase by 9 percent. 8 Te analysis in tis box suggests tat te average country would suffer an additional. percent of per capita output loss every time it is it by an average tropical cyclone, wit smaller states experiencing. percent greater damage (Figure 3..). 8 Sea surface temperature is a key ingredient in te formation and development of tropical cyclones (Landsea 4). A C increase in sea surface temperature raises maximum wind speed by 3.5 percent (Knutson and Tuleya 4). 44

29 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.. Te Role of Policies in Coping wit Weater Socks: A Model-Based Analysis To illustrate ow policies can elp moderate te consequences of weater socks in low-income countries, tis box uses te Debt, Investment, and Growt (DIG) model developed by Buffie and oters () and simulates te macroeconomic effects of temperature increases under various assumptions for key policy variables. As demonstrated empirically in te capter, in ot countries, an increase in temperature reduces productivity. Moreover, a temperature increase could precipitate te loss of productive land. Consequently, te analysis calibrates te weater damage to total factor productivity and private capital to broadly matc te estimated response of GDP to a C increase in temperature in a representative low-income country wit a baseline temperature of 5 C and examines ow tis damage can be saped by macroeconomic and structural policies (Figure 3..). Policy Space and te Role of Institutions Weater socks can weig significantly on te public purse of low-income countries. Government revenues can be adversely affected by te reduction in agricultural and industry output at te same time tat spending may need to be ramped up to deliver support to affected ouseolds if weater socks compromise food security, to rebuild transport or communication infrastructure if tey are damaged by natural disasters, and potentially to retrain te workforce. Because fiscal space is often tigt in many low-income countries, expanding transfers from advanced economies for instance, troug te transfers agreed to under te Paris Agreement could strengten countries ability to reduce te impact of weater socks. Model simulations suggest tat receiving additional transfers used to build up public investment for tree years, starting a Te autors of tis box are Manoj Atolia, Claudio Baccianti, Ricardo Marto, and Mico Mrkaic. Te DIG model is a real, neoclassical, dynamic open economy framework wit two production sectors tat use public and private capital as input and many features tat are pertinent to low-income countries, suc as low public investment efficiency, limited fiscal space, and capital adjustment costs. Te model is also used to simulate te long-term effects of climate cange in te section of te capter titled Long-Term Effects of Temperature Increase A Model-Based Approac. For simplicity, te traded and nontraded sectors are assumed to react equally to weater socks. Te findings are robust to tis modeling coice. Most oter parameters are calibrated as in Buffie and oters (), except te real interest rate on public debt, wic is lower tan in te original paper because of te decline in global interest rates. See Annex 3.5 for furter details. Figure 3... Role of Policies: A Model- Based Analysis (Real GDP, deviation from steady state; years on x-axis). Baseline. Role of Fiscal Space Perfect efficiency Typical efficiency Low efficiency Role of Public Sector Efficiency Baseline 5. Role of Hysteresis Baseline Medium ysteresis Hig ysteresis Baseline Low grants Hig grants Role of Capital Adjustment Costs Baseline Hig cost Medium cost Role of Fiscal Incentives for Adaptation Baseline Wit adaptation Source: IMF staff calculations. Note: Te baseline assumes no additional grants in panels and 3, low adjustment cost in panel 4, no ysteresis in panel 5, and no adaptation in panel 6. In panel, additional grants amount to.5 percent of GDP in low grants scenario, and percent of GDP in ig grants scenario. In panel 3, all simulations, except te baseline, assume ig additional grants

30 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3. (continued) year after te weater sock, could limit te damage of weater socks to output (Figure 3.., panel ). Additional transfers of percent of te recipient country s GDP reduce te dept of te recession by about.5 percent trougout te simulation period. Encouragingly, because te transfers increase te stock of public infrastructure, tereby boosting productive capacity in bot sectors, tey increase output not only in te sort term, but also in te long term. Additional transfers benefit te recipient country, but te size of te benefit depends crucially on te efficiency of investment in public sector infrastructure, in particular, and on te quality of public sector governance in general. Efficiency of public investment is low in many low-income countries, wit estimates of te sare of expenditures on public infrastructure tat truly increase te stock of public capital ranging from percent to 6 percent (Hulten 996; Pritcett ; Foster and Briceno-Garmendia ). Te results of te simulations sow tat, in countries wit ig public investment efficiency, te receipt of additional transfers can effectively dampen te adverse consequences of a weater sock (Figure 3.., panel 3). In countries wit low public investment efficiency, owever, tere is little difference between receiving and not receiving additional transfers. In sum, te simulation sows convincingly tat low-income countries must continue to improve te efficiency of public investment and strengten teir institutional frameworks to reap te full benefit of aving buffers to counteract te effects of canging weater conditions. Policies tat Ease Factor Reallocation and Structural Transformation Weater socks disrupt production, especially in certain sectors of te economy, and adjusting to tese socks would require reallocating workers and capital across and witin sectors. Te speed and cost at wic tese factors of production can be reallocated will influence ow fast te economy can recover after adverse socks to total factor productivity or te stock of capital. In low-income countries, reallocation of capital (and factors of production in general) can be ampered by rigid economic environments and suboptimal policies, for example, limited access to financial markets, bureaucratic impediments (suc as difficulties in obtaining building permits), and legal uncertainties. 3 Simulations indicate tat iger costs of capital reallocation slow te recovery from weater socks (Figure 3.., panel 4). 4 Te speed at wic affected workers can be reallocated to alternative productive activities also matters. Unemployment can cause ysteresis or permanent scarring of productivity, given tat workers lose skills during long unemployment or underemployment episodes. Tis in turn could ave long-lasting consequences for economic performance. In te DIG model, tis cannel is captured in te sensitivity of productivity to lagged negative output gaps. 5 Te results from simulations tat vary tis sensitivity suggest tat ysteresis could significantly prolong and deepen te effects of weater socks. Hence, policies sould aim to preserve uman capital, including by instituting programs tat provide incentives to te unemployed to participate in uman-capital-preserving activities, suc as public works projects, as in te Etiopian Productive Safety Net Program, discussed in Box 3.3. Investment in Adaptation Strategies In addition to te general macroeconomic and structural policies discussed above, governments, ouseolds, and firms engage in direct investments in adaptation strategies in response to canging weater conditions (for example, by planting more-eat-resistant crops or investing in green infrastructure). Many adaptation measures, owever, ave te nature of public goods. Setting up an early-warning system for extreme eat, instituting information campaigns about water conservation, or increasing vegetation in public areas and oter green infrastructure investments all ave nonrival 3 In te DIG model, te ease of factor reallocation is captured in te cost of private capital adjustment parameter. Te cost of capital adjustment is inversely proportional to elasticity of investment wit respect to Tobin s q, in wic iger elasticity implies lower capital adjustment costs. 4 Te quantitative impact appears small, but te simulation sould be seen as a qualitative guide only. Te size of te GDP decline depends on te cost of capital adjustment as well as on te sape and timing of te sock. If te climate sock results mostly in te destruction of private capital and, to a lesser extent in lowering total factor productivity, ten te recovery is slower and damage to GDP larger because of slower rebuilding of capital. 5 Te size of te effect is calibrated by using te estimated elasticity of current wages to lagged ours worked by Altuğ and Miller (998). Teir estimated elasticity of. stands for te ig degree of ysteresis in te model specification. 46

31 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3. (continued) and nonexcludable payoffs. Because ouseolds and firms are unable to internalize te full social benefits, government involvement may be needed to provide incentives to private agents to undertake adaptation efforts toward socially optimal levels. In an extension of te DIG model, te government introduces fiscal incentives for te adoption of resilience-improving tecnologies and finances te provision of public goods related to weater risks, wic lowers te sensitivity of output to temperature increases. Assuming tat private adaptation expenditure falls percent sort of te social optimum, and tat government policy aims at restoring optimality, simulations suggest tat over years, eac $ spent on adaptation by te government reduces total weater damage by $. Te mecanism beind tis finding is private investment s response to te reduced weater-related productivity losses, wic boosts GDP in te medium and long term. Te simulation illustrates a general principle tat improving resilience troug public adaptation spending can reduce weater-driven downturns and accelerate recoveries (Figure 3.., panel 6). 47

32 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.3. Strategies for Coping wit Weater Socks and Climate Cange: Selected Case Studies Adverse effects of weater socks and climate cange ave motivated local communities and countries to adapt and counter tese unfavorable consequences. As demonstrated in Figure 3., a wide range of strategies could dampen te negative impacts of weater socks and natural disasters by reducing exposure and vulnerability or by transferring and saring weater-related risks. Te purpose of tis box is to sowcase some examples of successful coping strategies. Social Safety Nets Approximately 85 percent of te Etiopian population is employed in agriculture, mostly on small family-owned farms. Climate cange and associated drougts, delayed rains, and flooding weig on agricultural productivity and food security. Furtermore, in some areas, te land as become degraded due to overuse. Consequently, approximately percent of te rural population is cronically food insecure. To assist te at-risk population, te Etiopian government and international partners instituted te Productive Safety Net Program (PSNP) in 6. Te PSNP provides cas or food to ouseolds unable to feed temselves all year, particularly in te lean season (June August). Te aid is contingent on active participation in local productivity-enancing or environmental programs for example, land reabilitation, improvement of water sources, and construction of infrastructure suc as roads and ospitals. A complementary program, te Houseold Asset Building Program, wic targets te same ouseolds as te PSNP, elps ouseolds diversify teir income sources and increase productive assets, including by offering tecnical assistance, wit te goal of acieving lasting food security. Wit more tan 7.6 million participants (or almost 8 percent of te Etiopian population) and 47, small community projects every year, te PSNP is te largest climate cange adaptation program in Africa. Te community projects, wic are mostly devoted to environmental restoration, are offering measurably positive results. Te PSNP as reduced soil loss by more tan 4 percent and improved te quality and quantity of available water. Studies suggest tat land productivity as consequently increased by up to 4 percent. In addition, te PSNP as reduced te damage from seasonal flooding. Te program as also improved te Te autors of tis box are Claudio Baccianti and Mico Mrkaic. food security of vulnerable ouseolds beneficiaries of te PNSP experienced a 5 percent smaller drop in consumption relative to tose tat were not covered by te program in te aftermat of drougts (Porter and Wite 6). Te PSNP as also reduced te number of people in need of umanitarian intervention and te cost of suc intervention. Finally, te PSNP as increased savings of vulnerable ouseolds and as facilitated improved access to educational and ealt services. Tecnology Adoption Hig temperatures significantly lower labor productivity and could lead to adverse ealt outcomes suc as increased incidence of ypertermia and worsening cronic cardiovascular or respiratory diseases and mortality, as demonstrated in a large body of work and te analysis in tis capter. Governments and individuals ave various options for reducing tese adverse economic and ealt impacts, suc as green infrastructure (to increase te presence of vegetation in cities) and specific construction tecnologies (for example, roofs tat are igly solar reflective). Among all options, modern air-conditioning, invented at te turn of te t century, is te most common solution adopted by ouseolds and firms to deal wit excessive eat. Te benefits of climate control, bot in te workplace and for ealt outcomes, are well documented. In a 957 survey, 9 percent of American firms named cooled air as te single biggest boost to teir productivity (Cooper ), and Singapore s founding fater, Lee Kuan Yew, credited air-conditioning as te most important factor in is country s development success. Te dramatic decline in eat-related mortality over te t century in te United States as also been attributed to te adoption of residential air conditioning (Barreca and oters 6). Neverteless, te negative effects of air-conditioning cannot be ignored. Increased adoption of indoor climate control increases energy consumption and greenouse gas emissions. Exaust from air-conditioning macines and facilities can give rise to local pockets of ot air, wic can present significant negative externalities for nearby populations. Hig up-front costs and infrastructure requirements make tis tecnology out of reac for poor and vulnerable populations, especially in low-income developing countries. As of, sligtly more tan one-tird of ouseolds ad access to electricity in te median low-income developing country. 48

33 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.3 (continued) Intelligent planning and implementation of air-conditioning could reduce some of te negative spillovers of tis oterwise effective strategy for adapting to rising temperatures. A case in point is district cooling a centralized air-conditioning system wic as been adopted in major cities in advanced economies and is currently under construction in te Gujarat International Finance Tec-City, a new business district in Gujarat, India. Wit district cooling, cilled water is produced at a central source and is distributed to final consumers troug underground pipes. A centralized cooling system as clear environmental and economic advantages over decentralized air-conditioning. Te centralized production of cilled water consumes 35 to 5 percent less energy tan individual air cooling units, reducing cost and pollution. Higer energy efficiency, in turn, eases te pressure te diffusion of air-conditioning puts on te local electricity sector, wic often lags te rapidly growing demand for energy in emerging market and developing economies. Finally, district cooling eliminates te up-front cost for final users, making indoor climate control more accessible. As in te provision of oter types of infrastructure, suc as energy and water distribution, public sector involvement could speed up te development and expansion of district cooling systems, wic could be eld back by low energy prices, insufficient demand density, economic uncertainty, and oter risks related to te substantial up-front investment. Te government of Gujarat as taken direct control of te construction of te cooling distribution network, as ave te governments of te Republic of Korea, Qatar, and Singapore. Climate-Smart Public Infrastructure Investment Flas floods in Kuala Lumpur, Malaysia, ave caused considerable property damage, impassable traffic congestion, contamination of te water supply, and loss of uman life. To alleviate tese problems, te autorities embarked on an ambitious dual-purpose infrastructure project tat would elp wit bot traffic and flood water management. Te Stormwater Management and Road Tunnel (SMART Tunnel) is a dual-purpose structure designed to combat flas floods. A tree-level tunnel combines a two-level road tunnel and a storm drainage system underneat. Under normal conditions, te drainage level is closed and te tunnel is used as an ordinary road traffic tunnel. However, te tunnel is designed so tat one or bot traffic-carrying levels can be temporarily repurposed by being allowed to flood for use as storm drains. During moderate storms, te system reallocates te lower traffic level to carry storm water, wile te top level can still be used by motorists. If te rainfall is expected to be extreme, bot traffic-carrying levels can be closed to traffic, evacuated, and used as drains. Cost-benefit analysis as demonstrated te effectiveness of te tunnel system. At a cost of about $5 million, it is expected to prevent more tan $.5 billion in flood damage and reduce te costs of traffic congestion by more tan $ billion over te next 3 years. Early-Warning Systems and Evacuation Programs Situated in te Ganges delta, Banglades is one of te countries most vulnerable to climate cange. Annual floods typically inundate about one-fift of te country, leading to loss of life and property damage. Over te past 7 years, storms ave caused tousands of deats and millions of tons of crop damage, and, because of climate cange, te problems are expected to worsen. After te extraordinary damage caused by Cyclone Sidr, te autorities and international partners embarked on te Emergency Cyclone Recovery and Restoration Project (ECRRP). 3 Te goals of te ECRRP are to improve agricultural infrastructure and long-term disaster preparedness, including by building and reconstructing cyclone selters and reinforcing embankments. Te program as meaningfully reduced te risk of cyclone exposure of te vulnerable population by rebuilding about 4 cyclone selters and repairing more tan kilometers of embankments. Te ECRRP as also elped increase agricultural resilience to climate socks and elped improve te livelioods of te affected populations. In addition to providing farmers wit agricultural equipment, saline-tolerant rice seeds, and training in crop diversification for better farm management, investments in grain silos and livestock protection ave reduced te exposure of te agricultural production cain to weater-related socks. In extreme years, floods can affect up to tree-quarters of te land area in Banglades. 3 Te cyclone destroyed.5 million ouses and damaged.3 million tons of crops. 49

34 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.3 (continued) Multilateral Risk-Saring Mecanisms Caribbean Catastropic Risk Insurance Facility Caribbean countries are regularly affected by tropical storms, extreme rainfall, eartquakes, and volcanic eruptions. Because tese socks are, at least in part, uncorrelated, risk saring in te form of a regional insurance pool can offer welfare improvements relative to self-insurance or purcase of reinsurance by individual countries. Te Caribbean Catastropic Risk Insurance Facility (CCRIF) is te world s first regional risk-pooling financial institution, offering insurance for te most prevalent natural disasters in te region. It was formed in 7 and currently includes 7 members. 4 Te CCRIF insures against tropical cyclones, excessive rainfall, and eartquakes. All 7 participating countries can purcase up to $ million of coverage for eac category of risk. Te program is designed to finance emergency response, over te weeks and monts after te disaster, rater tan provide compreensive insurance against asset losses or infrastructure damages. Te insurance is parametric payouts are based on parameterized models for eac category of insured events: tropical cyclones, excessive rainfall, and eartquakes. For example, te payout after an eartquake is proportional to its intensity, location, and estimated losses. Predetermined payouts, based on publicly observable data, obviate te need for time-consuming and costly damage assessments and insurance adjustments. A downside of parametric insurance in response to te effects of basis risk; tat is, calculated payouts migt not matc te actual damage. 5 During 7 5, te CCRIF made 3 payouts to eigt members in te total amount of $38 million, most of wic was in response to te effects of tropical 4 Anguilla, Antigua and Barbuda, Te Baamas, Barbados, Belize, Bermuda, te Cayman Islands, Dominica, Grenada, Haiti, Jamaica, St. Kitts and Nevis, St. Lucia, St. Vincent and te Grenadines, Trinidad and Tobago, and te Turks and Caicos Islands joined at te inception; Nicaragua joined in 5. Te CCRIF is contemplating expanding beyond te Caribbean. 5 Indemnity insurance avoids tis problem, but suffers from costly assessments and adjustments. cyclones. Te payouts ranged from. to.3 percent of GDP for te recipient country. Wile te payouts do not cover all losses, tey offer important support to insured countries, including from te rapid disbursement of funds payouts ave been disbursed, at te latest, two weeks after te insured event. In addition, CCRIF members are given complete freedom regarding te use of te funds received. Te CCRIF as proved to be an effective risk- pooling mecanism. Its effectiveness is recognized by bot te insured countries, wic can obtain coverage at a lower cost tan tey could individually from commercial insurers, and from te participants in te reinsurance market. African Risk Capacity Te African Risk Capacity (ARC) is a mutual insurance facility wose aim is to strengten food security. Te ARC, a Specialized Agency of te African Union, was establised in to elp African Union members insure against crop failure caused by extreme weater events, suc as drougts and floods, by pooling climate-related risks. Initially, 8 African Union members signed te establisment agreement; since ten, membersip as grown to more tan 35 countries. Te ARC provides parametric insurance. Wen an insured event occurs, te payout is based on models and satellite input data to predict te extent of crop failures and te associated costs. Using parametric instead of indemnity insurance accelerates te payouts, wic is of particular importance to te most vulnerable populations. By pooling teir risks, participating countries reduce te cost of insurance by about alf, given tat drougt is very unlikely to affect te wole country pool. Evidence points to te benefits of te ARC, but callenges remain. Te ARC as reduced te volatility of food consumption for te most vulnerable ouseolds. Furtermore, it as elped reduce te need for fire sales of assets in distressed regions. However, te risk pool is still relatively small (for example, in comparison wit te CCRIF) and could be expanded furter to better diversify te risks. In addition, misallocation of insurance may decrease wit accumulated experience. 5

35 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.4. Coping wit Weater Socks: Te Role of Financial Markets Financial markets can reduce te adverse consequences of weater socks by reallocating te costs and risks of suc socks to tose most willing and able to bear tem. Insurance products, suc as weater derivatives, can elp ouseolds and firms vulnerable to sort-term fluctuations in temperature and precipitation edge teir idiosyncratic weater exposure. Catastrope (Cat) bonds can elp disperse catastropic weater risk to capital markets. However, te degree to wic financial markets can mitigate te impacts of weater socks inges on te level of insurance penetration and on te capacity to correctly price weater-related risks. Tis box reviews recent developments in te market for weater-related financial products and provides new evidence on te extent to wic stock markets efficiently price weater-related risks. Insurance Recent studies igligt te important role tat insurance markets could play in facilitating economic recovery in te aftermat of weater-related natural disasters. A iger degree of insurance penetration can limit te fiscal burden of natural disasters (Lloyd s ) and reduce teir negative macroeconomic consequences (Von Peter, Dalen, and Saxena ), especially in countries wit strong institutions (Breckner and oters 6). Parametric insurance products, developed in te early s, also old promise for providing protection from various weater-related risks to ouseolds and firms in low-income countries. Overcoming important barriers to te provision of traditional insurance to small farmers, tese products minimize transaction costs, are easy to enforce, and limit potential adverse selection and moral azard issues. Yet, insurance penetration, as captured in non-life insurance premiums as a percentage of GDP, remains low, especially in developing economies (Figure 3.4.). And despite its advantages, te take-up of parametric insurance as been disappointing (Hallegatte and Te autor of te box is Alan Xiaocen Feng. Unlike traditional indemnity insurance for natural azards, parametric insurance products offer payments tat are based on a publicly observable index, suc as rainfall or temperature. Wile teir design offers numerous advantages over traditional products, parametric insurance can leave a fair amount of residual risk uncovered ( basis risk ), given tat te actual loss may differ from te payment received by contract olders. Figure Insurance Penetration: Non-Life Insurance Premium (Percent of GDP) Nort America Western Europe Japan Africa Latin America and te Caribbean Eastern Europe Sout and East Asia Sources: Haver Analytics; Swiss Re, Sigma database; and IMF staff calculations. oters 6). Many factors ave likely contributed to te slow adoption of te novel financial instruments, including limited financial literacy or experience wit similar financial products, insufficient understanding of te product, ig cost, and residual basis risk (see, among oters, Cole and oters, 3; Karlan and oters 4). Catastrope Bonds Te market for Cat bonds, a financial instrument tat transfers catastrope risk from te issuing primary insurers and reinsurance companies to te capital markets, as grown rapidly in recent years, reacing an outstanding volume of nearly $3 billion at te end of 6 (Figure 3.4.). Cat bonds are attractive to investors because tey ave relatively iger yields and low correlation wit te returns of most oter financial assets. Te low-interest-rate environment since Cat bonds pay interest, principal, or bot during normal times, but absorb losses if a predefined catastrope occurs. Tey were first introduced in te mid-99s, in te aftermat of Hurricane Andrew. 5

36 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.4 (continued) Figure Catastrope Bond Market (Billions of US dollars) Issued Outstanding Source: Artemis Insurance -Linked Securities and Catastrope Bond Market Report ( Note: Years ending June 3. te global financial crisis, as well as new regulations tat recognize te relief of capital troug Cat bond issuance, ave potentially contributed to te growt of te Cat bond market. Cat bonds ave become an increasingly popular tool for private insurance and reinsurance companies in Europe, Japan, and te United States to transfer away teir risk exposures to eartquakes, storms, and urricanes. As discussed in te capter, low-income developing countries and small states are especially vulnerable to catastropic risks. Mexico, in 6, was te first country to issue Cat bonds; since ten, several low-income developing countries ave issued Cat bonds covering urricanes, eartquakes, and oter extreme events. Te World Bank issued its first-ever Cat bond in 4 to provide reinsurance to te Caribbean Catastrope Risk Insurance Facility, a risk-pooling facility designed to limit te financial impact on 6 Caribbean country governments after possible eartquakes and urricanes (see also Box 3.3). A similar arrangement te Extreme Climate Facility is being developed by te African Risk Capacity (see Box 3.3) to allow for te issuance of Cat bonds to alleviate te impact Figure Temperature Socks and Stock Price Predictability: Food and Beverages Sector One-year-aead standardized return 3 y =.4x +.8 R = Temperature, annual deviation from country average Sources: Datastream; Peng and Feng (fortcoming); and IMF staff calculations. Note: One-year-aead food and beverages sector returns are regressed on annual average temperature (deviation from te country average, degrees Celsius). Sample is restricted to countries wit an average annual temperature above 5 C. of extreme weater conditions on member African countries. Do Financial Markets Correctly Price Weater-Related Risks? Te optimal level of insurance against abnormal weater conditions requires accurate assessments of weater-related risk. Tere is growing evidence tat investors in financial markets do not fully understand, at least immediately, te impact of weater socks on output and productivity. Hong, Li, and Xu (6) sow tat te stock indices of te food industry in te United States and in a few oter advanced economies respond to canges in drougt indices only wit a delay. Tis finding suggests tat markets do not incorporate weater information into prices until several monts later, peraps after te losses incurred are reflected in food companies annual reports. Te 5

37 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.4 (continued) initial underreaction to weater socks may indicate te possibility of underinsurance, even in te presence of easily accessible insurance products. Te analysis in tis box examines te response of investors to temperature variations. As demonstrated in te capter, an increase in temperature in countries wit relatively ot climates as a negative effect on output and productivity, especially in certain sectors of te economy. Using data on equity market returns across 7 sectors in 4 countries and annual fluctuation in temperature, te analysis studies weter financial markets correctly price in tese adverse temperature effects. If markets are efficient, fluctuations in temperature sould ave no predictive power on equity returns because stock prices instantaneously reflect te impact of temperature socks on firm performance. Empirical analysis suggests tat tis is not te case. Higer temperature can predict negative future (-mont-aead) stock returns for te food and beverages sector, suggesting tat investors respond to temperature socks wit a delay (Figure 3.4.3). 3 Tese effects are particularly strong for countries at lower latitudes (for example, tose wit average annual temperature greater tan 5 C) and are insignificant for industrial, tecnology, utilities, and oil and gas sectors. Te predictability of stock returns in te food and beverages sector suggests tat te impact of temperature socks on productivity is not well priced by investors until several monts later (possibly only after earnings reports reflect tese losses), consistent wit te ypotesis of underreaction to tese socks. 3 Te one-year-aead equity return for te food and beverages sector is regressed on current-year average temperature in te country, controlling for country-year fixed effects as well as te dividend yield of te sector. Equity returns are normalized by te standard deviation of yearly sector returns in eac country. Results are robust to controlling for one-year-aead average temperature in te country. Similar effects are found for retail and personal goods sectors (Peng and Feng fortcoming). 53

38 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.5. Historical Climate, Economic Development, and World Income Distribution As argued in te capter, climate cange may ave very long-lived effects on economic performance, altoug te exact magnitudes depend on many factors, including economic agents adaptability and te ability of te economy to structurally adjust. Empirically, it is very difficult to disentangle weter weater socks ave permanent level or growt effects on output based on recent data (since 95); if tey reflect permanent growt rater tan level effects, ten te consequences may be many times larger tan te initial effects, but tis impact would manifest only over a very long time. Tis box reviews a relatively new and growing literature tat attempts to directly assess weter istorical climate can ave a large and permanent effect on economic performance. Enabled by te rising availability and granularity of istorical data, te literature examines te relationsip between modern outcomes and istorical climate, starting from te ypotesis tat istorical events (potentially in te very deep past) interact wit te pysical environment and can ave permanent effects on economic development and performance. Leveraging te exogeneity of istorical climate, Bluedorn, Valentinyi, and Vlassopoulos (9) estimate te reduced-form relationsip between a Te autor of tis box is Jon C. Bluedorn. Nunn (4) provides an excellent exposition of te idea, wic is central to recent empirical researc on istorical development. country s temperature over different periods from 73 to and its modern income per capita, uncovering some striking patterns. A simple bivariate regression confirms te strong negative correlation between income in and te average temperature during (Table 3.5., regression ). However, after controlling for istorical average temperature in te 8t and 9t centuries, a time-varying and nonmonotonic effect of temperature on current country incomes is revealed, wit 8t century temperature exibiting a positive and large effect wile 9t century temperature sows an even larger negative effect (Table 3.5., regression ). Interestingly, once istorical climate is introduced, t century temperature no longer sows a strong, negative association wit current income, suggesting tat it may be serving as a proxy for te combined effects of istorical climate, rater tan capturing a direct impact of te current temperature level in te simple regression. Wat migt account for te estimated nonmonotonic relationsip between temperature and income? Bluedorn, Valentinyi, and Vlassopoulos (9) postulate tat it could reflect interactions between temperature and istorical events across centuries. For example, te large negative effect of 9t century temperature on current incomes could be linked to a slower diffusion of tecnologies from te United Kingdom and Europe, wic were at te tecnological frontier ten, and generally at te cooler end of te global temperature distribution. If te tecnologies tese countries developed were more suitable for Table Effect of Historical Climate on Current Real Output Mean Temperature Mean Temperature R R N Sample () () Full Sample.6**.6.77.*.864**.7 67 (.) (.73) (.35) (.3) Visual Outliers Excluded.58** **.353**.4 6 (.) (.8) (.484) (.446) Sub-Saaran Africa Excluded.6*.4.6**.66**.55**.6 8 (.) (.47) (.6) (.57) Neo-Europes Excluded.57**.4.69*.65**.43**.5 63 (.) (.68) (.46) (.453) Source: IMF staff calculations. Note: Dependent variable is log real GDP per capita in, purcasing power parity adjusted. Robust standard errors appear underneat coefficient estimates in parenteses. Visual outliers are Australia, Bolivia, Eritrea, Etiopia, and te United States. Neo-Europes = Australia, Canada, New Zealand, and te United States. N = number of countries in te cross-sectional sample. See Bluedorn, Valentinyi, and Vlassopoulos (9). * p <.; ** p <.5; *** p <.. 54

39 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.5 (continued) cooler climates, te negative correlation between 9t century temperature and current incomes could arise from istorically slower tecnological adoption. Alternative interpretations are possible, suc as a negative relationsip between istorical temperature and te quality of institutions adopted in European colonies in te 9t century (see Acemoglu, Jonson, and Robinson ). Te positive effect of 8t century temperature on current incomes is more difficult to interpret. Fenske and Kala (5) provide a compelling ypotesis for Africa, were te level of a region s participation in te 8t century slave trade may ave been saped by climate conditions. Given te adverse effects iger temperatures ave on agricultural productivity and mortality in otter climates, as documented in te capter, Fenske and Kala (5) argue tat a region s slave supply costs fell wen temperatures were lower, leading to greater slave exports, wic, in turn, is strongly associated wit poorer incomes today (Nunn 8). Climate may ave also affected te timing of transitions along te economic development pat. Asraf and Micalopoulos (5) argue tat climatic volatility tousands of years ago affected te willingness of uman societies to experiment wit farming as a solution to unpredictable foraged food sources. Tey find a statistically significant and robust ump-saped relationsip between te standard deviation of istorically experienced temperatures in a region and te timing of te adoption of agriculture areas wit more volatile climate (assuming tat te volatility was not so large as to precipitate social collapse) tended to make te transition to farming earlier, partly accounting for differences in income today. Andersen, Dalgaard, and Selaya (6) consider anoter caracteristic of climate te istorical intensity of ultraviolet radiation (UV-R) experienced in a location. Tey argue tat iger UV-R intensity affects mortality and tereby te willingness to engage in uman capital investment. Tis, in turn, affected te time at wic a society experienced te fertility transition (te decline of fertility associated wit a rise in incomes; see Galor ). A slower fertility transition is associated wit lower incomes at te country level today. In a mix of empirical and teoretical work, tey find a positive relationsip between UV-R and te transition timing, consistent wit te link tey ypotesize. As sown by tese studies, istorical climate can ave very long-lived effects on economic development troug its interaction wit istorical events. 55

40 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.6. Mitigating Climate Cange Altoug te primary focus of te capter is te macroeconomic consequences of climate cange and potential for adaptation in low-income countries, only a concerted global effort to cut greenouse gas emissions and slow te pace of rising temperatures can limit te long-term treat of climate cange. Tis box reviews recent developments in climate cange mitigation efforts and describes te crucial role fiscal policies could play in abating climate cange and mobilizing financing for mitigation and adaptation, drawing on recent IMF work. Te 5 Paris Agreement In December 5 parties to te United Nations Framework Convention on Climate Cange agreed to te aspirational goal of containing global warming to C above preindustrial levels (and to strive to keep warming to.5 C), tus laying te foundation for meaningful progress on addressing climate cange at te global level. Mitigation pledges were submitted by 95 countries in teir Nationally Determined Contributions (NDCs) under te 5 Paris Agreement, wit many pledges aiming to reduce emissions in 3 by about 3 percent relative to emissions in some baseline year. Starting in 8 parties are required to report progress on meeting mitigation pledges every two years, and to submit updated (and preferably more stringent) NDCs every five years. Te pledges are not legally binding, owever, and tere is some risk of backtracking, given tat te United States is witdrawing from te agreement. Te Paris Agreement strengtens previous commitments by developed economies to jointly mobilize $ billion a year by for adaptation and mitigation in developing economies. By 5 te parties to te agreement are expected to set a new collective quantifiable goal from a floor of $ billion a year many developing countries more ambitious mitigation commitments are contingent on receiving external finance. Te Role of Fiscal Instruments in Climate Cange Mitigation It is widely accepted tat carbon pricing carging for te carbon emissions from fossil fuels sould be Te autor of tis box is Ian Parry. See, for example, Capter 4 of te October 8 World Economic Outlook; Parry, de Mooij, and Keen (); Parry, Morris, and Williams (5); Farid and oters (6); and Parry and oters (6). front and center in implementing mitigation pledges in bot advanced and emerging market economies. Carging for carbon emissions increases te price of energy from fossil fuels (especially carbon-intensive coal) and provides incentives for mitigation, including replacing coal wit less-carbon-intensive natural gas as well as carbon-free renewables and nuclear energy. In addition, carbon pricing stimulates improvements in energy efficiency, reduces te demand for energy-consuming products, and promotes innovation (for example, in te areas of carbon capture and storage tecnologies). Carbon pricing can be implemented troug carbon taxes or emissions trading systems. Carbon taxes are imposed on fossil fuels in proportion to te fuel s carbon content. Implementing carbon taxes is a straigtforward extension of already-establised taxes on fossil fuels and can be easily administered in most countries. Emissions trading systems put an upper limit on emissions by issuing emissions allowances. Firms are required to obtain allowances to cover teir emissions, and te trading of allowances among emitters establises te price of emissions. Emissions trading systems are typically implemented downstream on power generators and large industrial firms and need to be accompanied by oter measures to cover smaller sources of emissions, for example, from veicles and buildings. Cina Cina, te largest emitter of carbon dioxide (CO ), accounted for 9 percent of global emissions in 3. According to IMF estimates, pasing in an emissions tax of $7 a ton of CO in Cina by 3 would raise te prices of coal, electricity, and road fuels by about 7 percent, 5 percent, and 7 percent, respectively, and reduce 3 emissions by about 3 percent, relative to te no-tax scenario (Figure 3.6., panel ). An alternative wit almost equal effectiveness would simply involve te addition of a carbon carge to existing taxes on domestic and imported coal. An emissions trading system would be about 4 percent less effective tan a carbon tax. Given tat Cina is moving aead wit an emissions trading system in any case, combining it wit an up-front coal carge (peraps wit rebates for entities covered by te emissions trading system) would ensure more compreensive pricing. Despite being less effective tan carbon taxes, an emissions trading 56

41 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Box 3.6 (continued) system is noneteless muc more effective tan a variety of oter mitigation policies, suc as incentives for energy efficiency or renewables and taxes on road fuels and electricity. Coal and carbon taxes, if pased in between 7 and 3, would also substantially reduce air pollution in Cina and save almost 4 million lives. Te emissions trading system is about alf as effective in tis regard, wit about million lives saved (Figure 3.6., panel ). Te carbon tax would also raise substantial revenues of about 3 percent of GDP in 3. In oter countries, typically less coal intensive tan Cina, reduced CO emissions, lower domestic air pollution, and increased fiscal revenues would be less striking (in proportionate terms). However, te key policy lessons would remain uncanged: carbon taxes are te most effective mitigation instrument. Furtermore, carbon taxes because of teir domestic environmental and fiscal benefits can be (up to a point) in countries own interests. Easing te Transition to Carbon Pricing At te domestic level, undesirable effects of carbon pricing need to be mitigated to ease its adoption. Some carbon-intensive industries migt become uneconomical as a result of carbon pricing, and teir employees will require elp wit retraining and re allocation to oter sectors. Using a fraction of revenues from carbon pricing to enance social safety nets and to offer oter forms of fiscal relief to low-income ouseolds would smoot te transition as well. At te international level, policymakers migt consider imposing carbon price floor requirements for large emitters to reinforce te Paris Agreement and provide some reassurance against losses in competitiveness. Countries could elect to set carbon prices above te floor for fiscal or domestic environmental reasons, tus becoming environmental leaders a prototype for tis type of arrangement is te recently announced requirement tat Canadian provinces pase in a price of Can$5 a ton of CO by. Progress on Climate Mitigation Carbon pricing mecanisms ave proliferated about 4 national governments and more tan For example, Parry and oters (6) and Parry, Mylonas, and Vernon (7) sow tat, at least initially, tis assistance will require about percent or less of te carbon pricing revenues. Figure Effectiveness of Mitigation Policies in Cina 4 3. CO Reduction in 3 below Business as Usual (Percent) Energy efficiency: buildings/industry Energy efficiency: veicles Road fuel tax Energy efficiency: electricity Reducing CO intensity: power Renewable subsidy Electricity excise Emissions trading system Coal excise Carbon tax 3. Reduction in Air Pollution Deats (Millions) Emissions trading system Difference between coal excise and emissions trading system Difference between carbon tax and coal excise Source: Parry and oters (6). Note: Te price is $7 per ton of CO for emissions trading system, coal excise, and carbon tax. CO = carbon dioxide. subnational governments ave implemented, or are implementing, some form of carbon pricing. Muc more remains to be done, owever. Only percent of global greenouse gases are currently priced (altoug Cina s emissions trading system will double tis figure). Current prices are also too low. CO prices for emissions trading systems are less tan $5 a ton of CO, and carbon taxes are mostly less tan $5 a ton, wit te notable exceptions of Canada and te Scandinavian countries (World Bank, Ecofys, and Vivid Economics 6). In contrast, average global prices of about $4 $8 a ton by would be consistent wit limiting projected warming to C (Stern and Stiglitz 7). Tis sortfall in appropriate pricing could result in large-scale future climate cange and underscores te pressing need for adaptation investment. 57

42 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Box 3.6 (continued) Te Role of Fiscal Instruments in Climate Finance Financing needs for climate adaptation investment in developing economies ave been estimated at upward of $8 billion a year until 5 (Margulis and Narain ), wic greatly exceeds current finance from advanced economies. Te volume of public and private climate finance mobilized by developed economies for developing economies reaced $6 billion in 4 (of wic only 5 percent was for adaptation), compared wit te $ billion goal set in 9 and reiterated in te Paris Agreement (OECD 5b). On equity grounds, tere is some appeal in linking climate finance donations from advanced economies to teir contribution to climate cange. If te Group of Twenty economies, excluding te five members wit lowest per capita income, donated $5 for eac ton of projected CO emissions, an additional $7 billion for climate finance could be raised in. 3 Funding tese contributions from national budgets would provide a more robust source of finance tan apportioning a fraction of revenues from future (and igly uncertain) carbon pricing. Te onus, owever, is on recipient countries to carefully cost and prioritize adaption projects and to attract finance troug resilient macro-fiscal frameworks and strong governance. 3 IMF staff calculations, assuming emissions are reduced linearly over time to meet countries Paris Agreement mitigation pledges. Carbon carges for international aviation and maritime fuels are anoter promising source of climate finance a $3 a ton CO carge on tese fuels could raise revenues of $5 billion in, even wit full compensation for developing economies (Farid and oters 6). 58

43 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex 3.. Data Sources and Country Groupings Data Sources Te primary data sources for tis capter are te IMF World Economic Outlook database and te World Bank World Development Indicators database. Te main data sources on temperature and precipitation are te University of East Anglia s Climate Researc Unit (istorical data, 9 5) and National Aeronautics and Space Administration (NASA) Eart Excange Global Daily Downscaled Projections data set (forecast, present ). All data sources used in te capter s analysis are listed in Annex Table 3... For real GDP per capita, investment, and imports, te sources are listed in te order in wic tey are spliced (wic entails extending te level of a primary series using te growt rate of a secondary series). Data Definitions Te main istorical temperature and precipitation series used in te capter s analysis are constructed by aggregating grid cell data at.5.5 degree resolution (approximately 56 kilometers 56 kilometers at te equator) to te level of individual countries or subnational regions at annual or montly frequency. Annex Table 3... Data Sources Indicator Source Temperature, Historical Intergovernmental Panel on Climate Cange (IPCC) Coupled Model Intercomparison Project Pase Five AR5 Atlas subset; Marcott and oters (3); Matsuura and Willmott (7); National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS); Royal Neterlands Meteorological Institute (KNMI) Climate Cange Atlas; Sakun and oters () Temperature and Precipitation, NASA Eart Excange Global Daily Downscaled Projections data set (NEX-GDDP) Forecast (Grid level) Temperature and Precipitation, University of East Anglia, Climate Researc Unit (CRU TS v.3.4); University of Delaware (UDEL v.4.) Historical (Grid level) Population, 99, 95 (Grid level) Center for International Eart Science Information Network (CIESIN v.3 and v.4); History Database of te Global Environment (HYDE v3.); Klein and oters (6) Population 5 and Projected Population United Nations World Population Prospects database, 5 Revision CO Emissions Carbon Dioxide Information Analysis Center Temperature Forcings Carbon Dioxide Information Analysis Center; NASA GISS; Roston and Migliozzi (5) Natural Disasters Centre for Researc on te Epidemiology of Disasters, International Disaster Database (EM-DAT) Global Ocean Temperature NOAA (7a) Migration Global Bilateral Migration Database, World Bank Group; Özden and oters () Real GDP per Capita IMF, World Economic Outlook database; World Bank, World Development Indicators database Subnational GDP per Capita Gennaioli and oters (4) Crop Production Index Food and Agriculture Organization; World Bank, World Development Indicators database Sectoral Real Value Added World Bank, World Development Indicators database (Agriculture, manufacturing, services) Sectoral Labor Productivity Groningen Growt and Development Centre -Sector Database; Timmer, de Vries, and de Vries (5) Real Gross Capital Formation IMF, World Economic Outlook database; World Bank, World Development Indicators database Real Imports of Goods and Services IMF, World Economic Outlook database; World Bank, World Development Indicators database Infant Mortality Rate World Bank, World Development Indicators database Human Development Index United Nations Development Programme, Human Development Report database Consumer Price Index IMF, World Economic Outlook database Debt-to-GDP Ratio IMF, Historical Public Debt database Reserves Minus Gold Lane and Milesi-Ferretti (7); External Wealt of Nations database, updated to 5 Net Official Development Assistance World Bank, World Development Indicators database and Official Aid Received Personal Remittances Received World Bank, World Development Indicators database Excange Rate Regime Indicator Reinart and Rogoff (4); Ilzetzki, Reinart, and Rogoff (8), updated to 5 Adaptation Readiness and Capacity Notre Dame Global Adaptation Initiative; Cen and oters (5) Domestic Financial Sector Liberalization Index Abiad, Detragiace, and Tressel (8) Quinn-Toyoda Capital Control Index Quinn (997); Quinn and Toyoda (8) Human Capital Index Penn World Tables 9. Paved Roads Kilometers per Capita Calderón, Moral-Benito, and Servén (5); World Bank, World Development Indicators database; Capter 3 of te October 4 World Economic Outlook Revised Combined Polity Score (Polity) Polity IV Project Gini Coefficient Standardized World Income Inequality Database Source: IMF staff compilation. 59

44 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Te estimates are weigted by grid-level population (exploring tree alternatives: population distribution as of 95, 99, and ) to account for differences in population density (Dell, Jones, and Olken 4). Temperature and precipitation projections are from two of te four scenarios, called Representative Concentration Patways (RCP), constructed by te Intergovernmental Panel on Climate Cange. Te RCP 4.5 scenario assumes increased attention to te environment wit slow growt of carbon dioxide (CO ) emissions until 5 and a decline of emissions tereafter, resulting in a mean temperature increase of.8 C by 8 relative to (in a range of. C.6 C, wit a greater tan 5 percent cance of an increase exceeding C by ). In te RCP 8.5 scenario, CO emissions continue to grow unconstrained, and te average 8 temperature is expected to be 3.7 C iger (in a range of.6 C 4.8 C) relative to Te capter uses te average of te maximum and minimum daily temperature and total daily precipitation data from 5 and projections for 5 and at te.5 x.5 degree resolution, averaged across te models of te Coupled Model Intercomparison Project Pase 5 for eac scenario. Annual temperatures are computed as te average of te daily temperature; annual precipitation is te sum of daily precipitation. Country Groupings Annex Table 3... Country and Territory Groups Advanced Economies Emerging Market Economies Low-Income Developing Countries Countries and Territories wit Average Annual Temperature above 5 C Countries wit Province-Level Data Australia, Austria, Belgium, Canada, Cyprus, Czec Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong SAR,* Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lituania, Luxembourg, Macao SAR,* Malta, Neterlands, New Zealand, Norway, Portugal, Puerto Rico, San Marino,* Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Taiwan Province of Cina,* United Kingdom, United States Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Te Baamas,* Barain, Barbados, Belarus, Belize, Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Cabo Verde, Cile, Cina, Colombia, Costa Rica, Croatia, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Fiji, Gabon, Georgia, Grenada, Guatemala, Guyana, Hungary, India, Indonesia, Iran, Iraq, Jamaica, Jordan, Kazakstan, Kosovo,* Kuwait, Lebanon, Libya, Macedonia FYR, Malaysia, Maldives,* Marsall Islands,* Mauritius, Mexico, Micronesia,* Montenegro, Morocco, Namibia, Nauru,* Oman, Pakistan, Palau,* Panama, Paraguay, Peru, Pilippines, Poland, Qatar, Romania, Russia, Samoa, Saudi Arabia, Serbia, Seycelles,* Sout Africa, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and te Grenadines, Suriname, Swaziland, Syria, Tailand, Timor-Leste, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu,* Ukraine, United Arab Emirates, Uruguay, Vanuatu, Venezuela Afganistan, Banglades, Benin, Butan, Bolivia, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Cad, Comoros, Democratic Republic of te Congo, Republic of Congo, Côte d Ivoire, Djibouti, Eritrea, Etiopia, Te Gambia, Gana, Guinea, Guinea-Bissau, Haiti, Honduras, Kenya, Kiribati,* Kyrgyz Republic, Lao P.D.R., Lesoto, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Mozambique, Myanmar, Nepal, Nicaragua, Niger, Nigeria, Papua New Guinea, Rwanda, Senegal, Sierra Leone, Solomon Islands, Somalia,* Sout Sudan, Sudan, São Tomé and Príncipe, Tajikistan, Tanzania, Togo, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, Zimbabwe Algeria, American Samoa, Angola, Anguilla, Antigua and Barbuda, Argentina, Australia, Barain, Banglades, Barbados, Belize, Benin, Butan, Botswana, Brazil, Brunei Darussalam, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Cad, Colombia, Comoros, Democratic Republic of te Congo, Republic of Congo, Costa Rica, Cuba, Curaçao,* Cyprus, Côte d Ivoire, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Etiopia, Fiji, Gabon, Te Gambia, Gana, Grenada, Guadeloupe,* Guatemala, Frenc Guiana,* Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iraq, Israel, Jamaica, Jordan, Kenya, Kuwait, Lao P.D.R., Lebanon, Liberia, Libya, Madagascar, Malawi, Malaysia, Mali, Malta, Martinique,* Mauritania, Mauritius, Mexico, Montserrat, Morocco, Mozambique, Myanmar, Namibia, Nepal, New Caledonia, Nicaragua, Niger, Nigeria, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Pilippines, Puerto Rico, Qatar, Reunion,* Rwanda, Samoa, Saudi Arabia, Senegal, Sierra Leone, Singapore, Solomon Islands, Somalia, Sout Africa, Sout Sudan, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and te Grenadines, Sudan, Suriname, Swaziland, Syria, São Tomé and Príncipe, Tanzania, Tailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Turks and Caicos,* Uganda, United Arab Emirates, Uruguay, Vanuatu, Venezuela, Vietnam, Virgin Islands (US), West Bank and Gaza, Yemen, Zambia, Zimbabwe Albania, Argentina, Australia, Austria, Banglades, Belgium, Benin, Bolivia, Bosnia and Herzegovina, Brazil, Bulgaria, Canada, Cile, Cina, Colombia, Croatia, Czec Republic, Denmark, Ecuador, Egypt, El Salvador, Estonia, Finland, France, Germany, Greece, Guatemala, Honduras, Hungary, India, Indonesia, Iran, Ireland, Italy, Japan, Jordan, Kazakstan, Kenya, Korea, Kyrgyz Republic, Latvia, Lesoto, Lituania, Macedonia FYR, Malaysia, Mexico, Mongolia, Morocco, Mozambique, Nepal, Neterlands, Nicaragua, Nigeria, Norway, Pakistan, Panama, Paraguay, Peru, Pilippines, Poland, Portugal, Romania, Russia, Serbia, Slovak Republic, Slovenia, Sout Africa, Spain, Sri Lanka, Sweden, Switzerland, Tanzania, Tailand, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Venezuela, Vietnam Countries wit Sectoral-Level Data Source: IMF staff compilation. * Not included in te main regression analysis. Argentina, Bolivia, Botswana, Brazil, Cile, Cina, Colombia, Costa Rica, Denmark, Egypt, Etiopia, France, Germany, Gana, Hong Kong SAR,* India, Indonesia, Italy, Japan, Kenya, Korea, Malawi, Malaysia, Mauritius, Mexico, Morocco, Neterlands, Nigeria, Peru, Pilippines, Senegal, Singapore, Sout Africa, Spain, Sweden, Taiwan Province of Cina,* Tanzania, Tailand, United Kingdom, United States, Venezuela, Zambia 6

45 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex 3.. Weater Socks and Natural Disasters Altoug tere is a clear link between weater conditions and te occurrence of extreme weater events, te relationsip between weater socks and natural disasters extreme events associated wit significant economic damage and loss of life as not been studied in detail. Te analysis in tis section examines ow weater conditions influence te frequency of various types of weater-related natural disasters. A logit panel specification wit country fixed effects is used to estimate te effect of te weater variables c i,t (temperature and precipitation) on te probability of a natural disaster taking place in country i in a given mont t. Pr ( disaster i,t = ) = Φ ( β c i,t + β c i,t + γ Dev T i,t + γ Dev P i,t + γ 3 Dev Ocean i,t + δ ln (GDP ) i,t + δ ln (Pop ) i,t + µ i + ε i,t ), (3.) in wic te nonlinear function Φ( ) = exp( )/ (+exp( )) captures te effect of te regressors on te probability of a natural disaster. Country fixed effects ( µ i ) capture time-invariant country caracteristics, suc as te size and geograpic location of te country and its topology, tat may influence te exposure and vulnerability of countries to different types of disasters. 5 Te specification controls for te level of real GDP per capita and population, as well as for global weater conditions specifically te deviation in global ocean surface temperature from te 9 average tat migt affect te incidence of disasters. Te sample includes montly data during 99 4 for 8 countries and territories on more tan 8, weater-related disasters. Equation (3.) is estimated separately for eac type of natural disaster, improving on Tomas and Lopez (5), wo perform a similar exercise on annual data, but group togeter all disasters. Annex Table 3.. presents te estimation results for eac disaster type. Weater conditions ave a 5 Given te large time dimension of te sample (eac country as about 3 observations), a panel logit specification is preferred to conditional logit models because it allows for te estimation of predicted and marginal effects accounting for country fixed effects. Te results are robust to te use of conditional logit regression models developed by Camberlain (98) to avoid te incidental parameters problem tat may arise from estimating fixed effects wit a small time sample. very strong impact on te occurrence of disasters. More precipitation reduces te occurrence of disasters caused by drougts, wildfires, and eat waves, but increases te probability of disasters triggered by floods, landslides, cold waves, tropical cyclones, and oter storms. Te effects of temperature are also as expected, wit iger temperatures resulting in more disasters caused by drougts, wildfires, eat waves, tropical cyclones, and oter storms, but reducing te probability of cold waves. Te results also sow tat precipitation as nonlinear effects on te probability of most disasters. Interestingly, te estimations suggest tat te weater conditions over te preceding monts ave a significant effect on te occurrence of most types of disasters. Weater anomalies during te previous year, as captured in te cumulative deviation of temperature and precipitation from its montly -year average, are important determinants of all types of disasters, except tose caused by landslides or tropical cyclones, wic are entirely a function of sort-term weater patterns. Epidemics, owever, are not affected by sort-term weater conditions, but respond to temperature deviations in te year before te event is triggered. To quantify te likely impact of climate cange, te analysis combines te estimation results and projected temperature and precipitation in 5 and under Representative Concentration Patway 8.5 to predict te likeliood of eac type of natural disaster. Tese predicted probabilities in 5 and are compared wit te predicted incidence of natural disasters over 4 in Figure 3.6. Annex 3.3. Empirical Analysis of te Macroeconomic Effects of Weater Socks and te Role of Policies Tis annex provides furter details on te empirical model used to quantify sort- and medium-term effects of weater on economic activity to identify te cannels troug wic tese effects occur, investigate evidence or lack tereof of adaptation over time, and study te role of various policy measures in attenuating te effects of temperature socks. Te baseline analysis uses Jordà s (5) local projection metod to trace out te impulse response functions of various outcomes to weater socks based on te following equation: 6

46 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Annex Table 3... Effect of Weater Socks on Natural Disasters, 99 4 Drougt Epidemic Flood Landslide Wildfire Cold Wave Heat Wave Tropical Cyclone Oter Storms Dependent Variable () () (3) (4) (5) (6) (7) (8) (9) Precipitation.***..***.8***.3***.4***.9***.***.*** (.) (.) (.) (.3) (.4) (.5) (.3) (.3) (.4) Precipitation.***..***.***.***.**.***.*.** (.) (.) (.) (.) (.) (.) (.) (.) (.) Temperature.4*.9.5***..9***.86***.8*.68***.63*** (.3) (.) (.) (.5) (.) (.49) (.44) (.39) (.4) Temperature ***.5.. (.) (.) (.) (.) (.) (.) (.5) (.) (.) Precipitation Deviations ( monts).5***..***..*.*.3***.. (.) (.) (.) (.) (.) (.) (.) (.) (.) Temperature Deviations ( monts).37*.4** *** *** (.9) (.) (.6) (.3) (.) (.5) (.9) (.9) (.7) Global Ocean Temperature Deviations.7.4** * ***.395 (.) (.486) (.98) (.578) (.87) (.78) (.5) (.549) (.37) Log GDP per Capita t.975*.589** *** (.5) (.67) (.58) (.383) (.7) (.67) (.38) (.3) (.79) Log Population t ***.575*** ***.58 (.878) (.364) (.38) (.66) (.) (.39) (.67) (.58) (.575) Constant.48* 5.59* *** 9.4** (6.45) (3.87) (.896) (4.746) (8.55) (7.77) (4.46) (3.683) (3.35) Number of Observations 9,976 35,77 43,63 9,6 8,73 7,844,94,65 33,684 Number of Countries Source: IMF staff calculations. Note: Te dependent variable is an indicator tat takes te value of if a natural disaster of a particular type is taking place. All specifications control for country fixed effects. Standard errors are clustered at te country level. * p <.; ** p <.5; *** p <.. 6

47 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? y i,t + y i,t = β c i,t + β c i,t + γ c i,t + γ c i,t + j = δ c i,t + j + j = δ c i,t + j + φ Δy i,t + µ i + θ r,t + ε i,t, (3.) in wic i indexes countries, t indexes years, and indexes te estimation orizon (from orizon, wic represents te contemporaneous regression, up to orizon 7). Regressions for eac orizon are estimated separately. Te dependent variable is te cumulative growt rate of te outcome of interest between orizons t and t +, measured as difference in te natural logaritms (y i,t ). Following Burke, Hsiang, and Miguel (5a), te estimated regression as a quadratic specification in te weater variables c i,t, wic comprise average annual temperature (T) and precipitation (P). Te regressions control for one lag of te dependent and weater variables and for forwards of te weater variables, as suggested by Teulings and Zubanov (4). Country fixed effects ( µ i ) control for all time-invariant country differences, suc as latitude, initial macroeconomic conditions, and average growt rates, wile time fixed effects interacted wit region dummies ( θ r,t ) control for te common effect of all annual socks across countries witin a region. Te analysis also explores an alternative fixed-effects structure proposed by Burke, Hsiang, and Miguel (5a), wic includes time fixed effects ( τ i ) and country-specific linear and quadratic time trends ( θ i t + θ i t ) to account for witin-country canges over time, suc as demograpic sifts, instead of te region-year fixed effects ( θ r,t ) of te baseline specification. Standard errors are clustered at te country level. To avoid bias associated wit bad controls (or overcontrolling), te specification is purposefully parsimonious: many of te determinants of growt, typically included in standard growt regressions (for example, institutional quality, educational acievement, policies, and so fort), may temselves be saped by weater socks, as documented below, and are tus not part of te baseline estimation. Witin tis estimation framework, te effect of a C increase in temperature on te level of output at orizon can be obtained by differentiating equation (3.) wit respect to temperature: (y i,t + y i,t ) = β + β T i,t. (3.3) T i,t Evaluating equation (3.3) for eac orizon separately and using te 5 annual average temperature T i,5 allows us to obtain te impulse response functions of per capita GDP to a temperature sock for eac country. Te marginal effect of an increase in precipitation is computed analogously. Te tresold temperature at wic te effect on te outcome variable switces from positive to negative can be obtained by setting equation (3.3) to zero. Te Effect of Weater Socks on Economic Activity Annex Table 3.3. presents te key results for te effect of weater socks on per capita output, along wit numerous robustness cecks. Panel A contains te estimated coefficients for te weater variables at orizon (tat is, te contemporaneous effects of weater socks); panel B sows te effect of a C increase in temperature estimated at te median 5 temperature for advanced economies (median T = C), emerging market economies (median T = C), and low-income developing countries (median T = 5 C) on impact and after seven years. Similarly, panel C sows te effect of a millimeter increase in precipitation estimated at te median 5 precipitation for advanced economies, emerging market economies, and low-income developing countries on impact and after seven years. Annex Table 3.3. begins by replicating Burke, Hsiang, and Miguel s (5a) specification and establises its robustness to using alternative sources of weater data; alternative population weigts tat are used to aggregate gridded weater data at te country level; alternative sets of fixed effects; and alternative samples, controls, and estimation approaces. Column () estimates te specification used in Burke, Hsiang, and Miguel (5a) and includes country-specific linear and quadratic time trends, University of Delaware weater data, and 99 population weigts in te capter s substantially larger sample (te capter expands te sample bot geograpically and temporally by about 5 percent). Column () uses an alternative source of weater data, te University of East Anglia Climate Researc Unit instead of te University of Delaware, and obtains similar coefficients on te temperature and precipitation variables. Te coice of population weigts used to aggregate gridded weater data to te country level could play an important role given tat migration witin and across country borders is one of te potential strategies for coping wit adverse weater conditions. Given tat istorical data sow an increase in average annual temperatures starting in te 97s (Figure 3.3), column (3) presents results wit 95 population weigts to account for migration responses tat could ave already taken place. Following Dell, Jones, and Olken (), column (4) and column (5) (main specification for te capter) 63

48 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Annex Table Effect of Weater Socks on Output A. Real Output per Capita Growt () () (3) (4) (5) (6) (7) (8) (9) Temperature.399***.443***.48***.343***.347***.48***.34***.49***.54*** (.359) (.367) (.366) (.355) (.357) (.339) (.355) (.38) (.3) Temperature.49***.49***.48***.5***.5***.44***.5***.44*** (.) (.) (.) (.) (.) (.) (.) (.) Precipitation.56.3*.63* (.97) (.6) (.85) (.58) (.4) (.3) (.4) (.) (.34) Precipitation..**.4** (.) (.) (.) (.) (.) (.) (.) (.) Any Disaster.46** (.8) Tresold Temperature ( C) Weater Source UDEL CRU CRU CRU CRU CRU CRU CRU CRU Population Weigt Year Fixed Effects Y Y Y N N N N N N Region x Year Fixed Effects N N N Y Y Y Y Y Y Country Time Trends Y Y Y N N N N N N At Least Years of Data N N N N N Y N N N Adjusted R Number of Countries Number of Observations 8,47 9,4 8,85 9,4 8,85 8,756 8,85 8,97 6,35 B. Impact of a C Increase in Temperature on Real Output per Capita Level at Horizon AE (T= C).33*.37*.365* (.96) (.96) (.95) (.9) (.96) (.9) (.95) (.) EM (T= C).736**.73***.697***.949***.9***.687***.97***.695*** (.39) (.3) (.3) (.66) (.64) (.8) (.63) (.43) LIDC (T=5 C).7***.996***.987***.6***.9***.95***.4***.96*** (.37) (.68) (.67) (.38) (.35) (.7) (.33) (.87) Impact of a C Increase in Temperature on Real Output per Capita Level at Horizon 7 AE (T= C) (.75) (.7) (.697) (.744) (.75) (.744) (.75) (.478) EM (T= C) *.5*.88*.38*.547 (.85) (.665) (.65) (.64) (.59) (.595) (.589) (.386) LIDC (T=5 C).738*.46*.558**.547**.57**.537**.599**.7 (.) (.76) (.745) (.686) (.667) (.67) (.664) (.45) C. Impact of a mm per Year Increase in Precipitation on Real Output per Capita Level at Horizon AE (P=8 mm per year).8.66.* (.67) (.46) (.59) (.46) (.7) (.7) (.7) (.77) EM (P=9 mm per year) * (.63) (.45) (.56) (.45) (.67) (.66) (.67) (.7) LIDC (P=, mm per year) (.57) (.4) (.5) (.4) (.59) (.58) (.59) (.64) Impact of a mm per Year Increase in Precipitation on Real Output per Capita Level at Horizon 7 AE (P=8 mm per year) (.98) (.6) (.7) (.4) (.3) (.5) (.4) (.9) EM (P=9 mm per year) (.88) (.5) (.5) (.) (.9) (.) (.) (.6) LIDC (P=, mm per year) (.69) (.85) (.9) (.74) (.8) (.8) (.83) (.9) Source: IMF staff calculations. Note: Te table presents results from estimating equation (3.), wit separate regressions for eac orizon. Panel A reports te estimated coefficients on te weater variables for orizon. Panels B and C sow te marginal impact of a cange in temperature and precipitation computed as per equation (3.3) at te median temperature (T) and median precipitation (P) of advanced economies (AE), emerging markets (EM), and low-income developing countries (LIDC) contemporaneously (orizon ) and cumulatively seven years after te sock. Te specifications in columns () (8) control for country fixed effects; lags and forwards of temperature, precipitation, and teir squared terms; and lag of growt. Column (8) sows results from estimating an autoregressive distributed lag model wit seven lags of te weater variables and teir squared terms. Column (9) reports te coefficients on temperature and precipitation from a linear specification estimated on a sample of countries wit average temperature above 5 C, also including controls for country fixed effects and lag of growt. In all specifications, standard errors are clustered at te country level. CRU = University of East Anglia, Climate Researc Unit; mm = millimeter; UDEL = University of Delaware. * p <.; ** p <.5; *** p <.. 64

49 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Figure Effect of Temperature Increase on Real per Capita Output across te Globe, wit Countries Rescaled in Proportion to Teir Projected Population as of (Percent).8 to to.8.33 to.43.5 to.3.97 to.5.3 to to.3 Insignificant effect No data Sources: Natural Eart; ScapeToad; United Nations World Population Prospects database: te 5 revision; and IMF staff calculations. Note: Te map depicts te contemporaneous effect of a C increase in temperature on per capita output computed as per equation (3.3) using recent -year average country-level temperature togeter wit estimated coefficients in Annex Table 3.3., column (5). Eac country is rescaled in proportion to te projected population as of. Using projected population as of, 76 percent of world population will live in countries tat experience a negative impact from C increase. Gray areas indicate te estimated impact is not statistically significant. present results for te baseline specification wit region-year fixed effects instead of country-specific time trends. Column (6) limits te sample to countries wit at least years of data. Column (7) controls separately for te occurrence of natural disasters given tat temperature and precipitation fluctuations migt affect economic activity troug teir effect on te incidence of natural disasters, as discussed in Annex 3.. Controlling for natural disasters does not materially alter te estimated coefficients on temperature and precipitation. 5 In columns () (7), impulse responses were estimated using Jordà s (5) local projection metod. Tis approac is advocated by Stock and Watson (7), among oters, as a flexible alternative tat does not impose te dynamic restrictions embedded in vector autoregressions (autoregressive distributed lag) specifications and is particularly suited to estimating nonlinearities 5 To furter explore te robustness of tese results, weater variables were transformed using natural logaritms or normalized by subtracting te country mean and dividing by te country standard deviation. Availability of data on subnational per capita GDP and annual average temperature and precipitation allows us to estimate te same regression at a subnational level using province fixed effects. Troug all tree specifications te main finding persists: tere is a nonlinear relationsip between temperature and economic performance (results available on request). in te dynamic response. Column (8), owever, tests te robustness of te findings to using te autodistributed lag model wit seven lags of te weater variables and teir squared terms, as in Dell, Jones, and Olken (), wo test different models from no lags up to lags and find tat, across different lag specifications, results are broadly consistent in magnitude and statistical significance. Across all specifications, te estimated coefficient on temperature is positive, and te coefficient on temperature squared is negative, confirming te nonlinear relationsip between growt and temperature socks uncovered by Burke, Hsiang, and Miguel (5a). At low temperatures, an increase in temperature boosts growt, wereas at ig temperatures, an increase in temperature urts growt, wit te tresold average annual temperature estimated to be about 3 C 5 C. As an additional robustness ceck, column (9) presents results of a linear regression witout te squared terms of te weater variables in wic te sample is limited to countries wit average annual temperature above 5 C. Indeed, witin te sample of relatively ot countries, te coefficient on temperature is negative and statistically significant. Te effect of temperature increase across te globe is sown in Figure 3.8 panel at grid level; in panel, were countries are rescaled in proportion to teir 5 population; and in Annex 65

50 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Figure 3.3., were countries are rescaled in proportion to projected population. Tere is no consistently significant relationsip between precipitation and per capita GDP growt across te various specifications. Te lack of robust relationsip could reflect potentially larger measurement error in te precipitation variable, as discussed in Auffammer and oters (), wic could be furter amplified by temporal aggregation. For example, if te only cannel troug wic precipitation affects aggregate outcomes is troug its effect on agriculture, ten only precipitation during crops growing period poorly proxied by annual precipitation may be relevant. Annex Table 3.3. also reveals te very persistent effects of temperature socks. Te lower alf of panel B presents te cumulative effects of a C increase in temperature estimated at te median temperature of advanced, emerging market, and low-income developing countries seven years after te sock. All but one specification sow evidence of a long-lasting and potentially deepening adverse impact of temperature socks on per capita output at te temperatures experienced by te median low-income developing country. To examine ow widespread te effects of temperature may be, equation (3.) is estimated using sectoral value added and agricultural production as te outcomes of interest. Real value added of te agricultural, manufacturing, and services sectors from te World Bank s World Development Indicators database is complemented wit an index of crop production volume compiled by te United Nations Food and Agriculture Organization. Results are presented in Annex Table Tere is a concave relationsip between temperature and output in bot te agricultural and manufacturing sectors, wereas services value added appears to be relatively protected from te effects of iger temperature. In oter words, at te median temperature of low-income countries, an increase in temperature significantly reduces agricultural value added and crop production and lowers manufacturing output. It is important to note tat, unlike aggregate output, agricultural production is significantly affected by precipitation in addition to temperature socks. Altoug te results suggest a concave relationsip between agricultural output and precipitation, at te typical levels of precipitation of all tree country groups, an increase in precipitation unambiguously improves agricultural productivity. Te effects of precipitation are also sort lived; agricultural output seven years down te line is not affected by a precipitation sock today, wic is different from te effect of temperature. Cannels Te capter examines te potential cannels troug wic temperature socks affect te macroeconomy in a broad and long-lasting manner by studying te relationsip between temperature and eac of te main components of te aggregate production function. Investment As ypotesized by Fankauser and Tol (5), weater socks could ave long-lasting effects on output if tey influence investment decisions, and ence capital input. Equation (3.) is estimated using real gross fixed capital formation as te outcome of interest. Te analysis also examines weater s impacts on imports, given te tigt link between imports and investment. Results, presented in Annex Table 3.3.3, columns () (), confirm te idea tat temperature socks suppress investment. Altoug te uncertainty surrounding te estimated contemporaneous effects is large, seven years after a temperature increase, bot investment and imports are significantly lower in countries wit relatively ot climates (see also Figure 3.). Labor Input Te analysis also examines weter labor supply may be affected by temperature increases. Using infant mortality as te outcome of interest, equation (3.) is estimated, uncovering a convex relationsip between temperature and current (or future) labor supply (Annex Table 3.3.3, column [3]). In ot countries, an increase in temperature raises infant mortality instantaneously, wit te effect growing over time. In tese countries, iger temperatures also ave a negative effect on a broader measure of uman well-being te Human Development Index, a weigted average of per capita income, educational acievement, and life expectancy (column [4]). Productivity Motivated by te body of evidence of reduced uman cognitive and pysical performance at ig temperatures from laboratory experiments and country-specific studies, te analysis examines weter reduced labor productivity may underpin te negative temperature aggregate output relationsip in countries wit ot climates. If tis is indeed 66

51 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Table Effect of Weater Socks on Sectoral Output Agriculture Manufacturing Services Crop Production A. Dependent Variable () () (3) (4) Temperature * (.87) (.35) (.585) (.85) Temperature.43*.5*.7.5*** (.3) (.7) (.6) (.5) Precipitation.75***.8..87*** (.8) (.49) (.) (.33) Precipitation.5***...8*** (.5) (.3) (.) (.7) Adjusted R Number of Countries Number of Observations 5,847 5,5 5,73 8,836 B. Impact of a C Increase in Temperature on Dependent Variable Level at Horizon AE (T= C) (.464) (.53) (.33) (.77) EM (T= C).6***.977**.578*.767*** (.43) (.439) (.98) (.664) LIDC (T=5 C).868***.85**.6* 3.67*** (.57) (.538) (.36) (.8) Impact of a C Increase in Temperature on Dependent Variable Level at Horizon 7 AE (T= C).7*** (.753) (.798) (.445) (.889) EM (T= C) (.654) (.939) (.734) (.8) LIDC (T=5 C) (.769) (.7) (.9) (.985) C. Impact of a mm per Year Increase in Precipitation on Dependent Variable Level at Horizon AE (P=8 mm per year).458*** *** (.49) (.5) (.75) (.3) EM (P=9 mm per year).48*** *** (.39) (.) (.7) (.) LIDC (P=, mm per year).366*** *** (.) (.9) (.63) (.85) Impact of a mm per Year Increase in Precipitation on Dependent Variable Level at Horizon 7 AE (P=8 mm per year) (.57) (.39) (.86) (.84) EM (P=9 mm per year) (.43) (.37) (.69) (.67) LIDC (P=, mm per year) (.7) (.33) (.35) (.35) Source: IMF staff calculations. Note: Te table presents results from estimating equation (3.) using te same specification as in Annex Table 3.3., column (5), for different dependent variables, wit separate regressions estimated for eac orizon. In all specifications, standard errors are clustered at te country level. Panel A reports te estimated coefficients on te weater variables for orizon. Panels B and C sow te marginal impact of a cange in temperature and precipitation computed as per equation (3.3) at te median temperature (T) and median precipitation (P) of advanced economies (AE), emerging markets (EM), and low-income developing countries (LIDC) contemporaneously (orizon ) and cumulatively seven years after te sock. mm = millimeter. * p <.; ** p <.5; *** p <.. te case, sectors were workers are more exposed to eat sould see a bigger decrease in labor productivity wen temperatures rise in relatively ot countries. Te analysis uses te Groningen Growt and Development Centre -sector database, wic provides sectoral real value added and employment in 4 countries over 95, and Graff Zivin and Neidell s (4) classification of sectors into tose tat are eat-exposed and oters to estimate te following specification: According to Graff Zivin and Neidell (4), wo follow definitions from te National Institute for Occupational Safety and Healt, eat-exposed industries include agriculture, forestry, fising and unting, construction, mining, transportation, and utilities as well as manufacturing, in wic facilities may not be climate controlled in low-income countries and production processes often generate considerable eat. 67

52 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Annex Table Effect of Weater Socks on Productivity, Capital, and Labor Investment Capital Input Labor Input Labor Productivity Imports Infant Mortality A. Dependent Variable () () (3) (4) (5) Temperature ***.46.9* (.4) (.943) (.7) (.78) (.68) (.) Temperature.45.68**.5*.8***..87*** (.59) (.33) (.3) (.) (.8) (.6) Precipitation ** (.398) (.7) (.4) (.8) (.) (.95) Precipitation * (.9) (.7) (.) (.) (.5) (.4) HDI Non-Heat Exposed Adjusted R Number of Countries Number of Observations 6,93 6,866 8,685 3,864 7,848 Heat Exposed B. Impact of a C Increase in Temperature on Dependent Variable Level at Horizon AE (T= C).38.9**.8.94**.3.3 (.976) (.455) (.67) (.43) (.396) (.5) EM (T= C).6.55***.9* *** (.64) (.753) (.55) (.56) (.4) (.363) LIDC (T=5 C) ***.4*.9*.44.48*** (.33) (.99) (.63) (.67) (.478) (.456) Impact of a C Increase in Temperature on Dependent Variable Level at Horizon 7 AE (T= C) **.35.4 (.9) (.494) (.47) (.59) (.83) (.986) EM (T= C) 4.5**.439* (.83) (.33) (.375) (.75) (.4) (.9) LIDC (T=5 C) 5.87*** 3.747**.84*.467** (.74) (.56) (.46) (.95) (.36) (.365) C. Impact of a mm per Year Increase in Precipitation on Dependent Variable Level at Horizon AE (P=8 mm per year) *** (.6) (.8) (.5) (.3) (.33) (.36) EM (P=9 mm per year) *** (.46) (.7) (.5) (.) (.5) (.3) LIDC (P=, mm per year).3.53***...3. (.6) (.5) (.3) (.) (.9) (.8) Impact of a mm per Year Increase in Precipitation on Dependent Variable Level at Horizon 7 AE (P=8 mm per year) **.7.*.95.7 (.689) (.498) (.63) (.6) (.83) (.554) EM (P=9 mm per year).43.96**.74.97*.65.4 (.649) (.47) (.49) (.57) (.776) (.54) LIDC (P=, mm per year).33.94**.8.87*.6. (.573) (.4) (.3) (.5) (.666) (.467) Source: IMF staff calculations. Note: Columns ( 4) present results from estimating equation (3.) using te same specification as in Annex Table 3.3., column (5), for different dependent variables. Specification in column (5) presents results from estimating equation (3.4) were an indicator for eat exposed sectors is interacted wit temperature and precipitation, teir squared terms, and teir lags and forwards; also controlling for country-sector and region-year fixed effects, and lag of growt. Separate regressions are estimated for eac orizon. In all specifications, standard errors are clustered at te country level. Panel A reports te estimated coefficients on te weater variables for orizon. Panels B and C sow te marginal impact of a cange in temperature and precipitation computed as per equation (3.3) at te median temperature (T) and median precipitation (P) of advanced economies (AE), emerging markets (EM), and low-income developing countries (LIDC), contemporaneously (orizon ) and cumulatively seven years after te sock. HDI = Human Development Index; mm = millimeter. * p <.; ** p <.5; *** p <.. 68

53 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? y i,s,t + y i,s,t = β c i,t + β c i,t + γ c i,t + γ c i,t + j = δ c i,t + j + j = δ c i,t + j + α c i,t H s + α c i,t H s + ω c i,t H s + ω c i,t H s + j = τ c i,t + j H s + j = τ c i,t + j H s + φ Δy i,s,t + µ i,s + θ r,t + ε i,s,t, (3.4) in wic y i,s,t is te log of real sectoral value added per worker, H s is an indicator for sectors tat are eat-exposed, µ i,s are country-sector fixed effects, and θ r,t are region-year fixed effects. Standard errors are clustered at te country level. Annex Table 3.3.3, specification (5) summarizes te results of tis estimation. At iger temperatures, an increase in temperature significantly lowers labor productivity in eat-exposed industries. Temperature increases, owever, ave no discernible effect on te productivity of workers in non-eat-exposed sectors, even in countries wit ot climates. Te Role of Policies and Institutional Settings To study te extent to wic macroeconomic and structural policies and country caracteristics mediate te effect of weater socks, te analysis extends te empirical approac described above by allowing te response of per capita output to weater socks to vary wit various proxies for tese policies. Te estimated specification augments equation (3.) to include an interaction term between te weater sock and te policy variable: y i,t + y i,t = β c i,t + γ (c i,t p i,t ) + δ p i,t + β c i,t + γ (c i,t p i,t ) + δ p i,t + j = β c 3 i,t + j + φ Δy i,t + µ i + θ r,t + ε i,t. (3.5) Te sample is restricted to countries wit average annual temperature exceeding 5 C, in wic an increase in temperature as a statistically significant linear negative impact on economic activity, as in Annex Table 3.3., column (9). Consequently, te weater sock c i,t refers to average annual temperature and precipitation. Most of te policy variables p i,t are lagged to minimize reverse causality concerns and are included one at a time. As empasized in te capter, it is difficult to interpret causally te coefficients on te interaction terms, given tat te variation in policies and institutions across countries and over time is not random. Policies and institutions could also be correlated wit relevant country attributes tat are not controlled for in te regression. Moreover, policy data availability varies significantly in bot temporal and country coverage, resulting in sizable differences in te estimation sample. For ease of interpretation, in te baseline results, eac policy variable is transformed into an indicator variable depending on weter, in year t, te country is above or below te median value of tis particular policy in te estimation sample. 54 An exception to tis approac is te measurement of buffers. A country is considered to ave () fiscal buffers if public debt as a sare of GDP is less tan te 75t percentile, () monetary buffers if annual inflation is less tan percent, (3) ig international reserves if international reserves minus gold can cover at least four monts of imports, (4) ig foreign aid if foreign aid inflows as a sare of GDP are in te 75t percentile, and (5) ig remittances if per capita remittances in real dollars received are greater tan te 75t percentile. For excange rate policy, te analysis uses an indicator if te de facto excange rate regime of a country is not pegged based on te coarse classification of Reinart and Rogoff (4). Annex Tables and present te main findings. For eac policy, te tables report te estimated effect of a C increase in temperature on per capita output at orizons troug 7, were te policy is not in place and were te policy is in place. Te tables also report te p-value of a statistical test of te difference between te effect of temperature in different policy scenarios. Te sort-term negative effects of temperature socks tend to be larger in countries wit lower buffers, as evidenced by te larger estimated responses in columns (), (5), and (8) in Annex Table However, te differences are typically not statistically significant, and in te few cases in wic tey are (fiscal buffers, foreign aid, and remittances), tey tend to be very sort lived. Excange rate regime, owever, seems to be significantly associated wit te extent of damage caused by weater socks. Countries wit nonpegged excange rates tend to recover faster from tese socks. A similar pattern was documented by Ramcaran (9), wo finds tat excange rate flexibility elps economies adjust better in te aftermat of windstorms and eartquakes. 54 Results from an alternative specification in wic te policy variables are used in teir continuous forms rater tan transformed into indicators are available on request. 69

54 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Annex Table Role of Policy Buffers Impact of a C Increase in Temperature on per Capita Output () () (3) (4) (5) (6) (7) (8) (9) Public Debt Inflation International Reserves Low Hig P-value Low Hig P-value Hig Low P-value Horizon.57***.46***.9.83***.75***.4.5**.7***.5 (.387) (.35) (.95) (.3) (.44) (.34) Horizon.9**.67***.4.95***.985** **.36 (.47) (.466) (.36) (.45) (.49) (.395) Horizon.94*.695**.4.933**.97**.87.95**.3***.58 (.49) (.69) (.375) (.46) (.39) (.38) Horizon 3.597***.59***.34.79***.333***.79.8***.4***.78 (.55) (.758) (.49) (.49) (.44) (.4) Horizon 4.5**.986** **.487**.55.44***.44***.85 (.74) (.97) (.56) (.57) (.5) (.5) Horizon *.8*.46.39**.7**.66 (.758) (.936) (.583) (.68) (.69) (.63) Horizon **.57**.3.54**.36**.55 (.844) (.867) (.66) (.675) (.64) (.597) Horizon **.353**.49 (.89) (.859) (.6) (.68) (.69) (.6) Adjusted R.5..9 Number of Countries 9 7 Number of Observations 4,49 5,365 6,35 Impact of a C Increase in Temperature on per Foreign Aid Remittances Excange Rate Flexibility Not Capita Output Hig Low P-value Hig Low P-value Pegged Pegged P-value Horizon.84**.94***.6.345***.449***.34.83***.436***.6 (.38) (.334) (.337) (.3) (.3) (.35) Horizon.996**.3***.59.***.47***.3.79*.49***.8 (.448) (.396) (.389) (.4) (.46) (.45) Horizon.958**.979** *.3** **.8 (.433) (.4) (.436) (.456) (.483) (.53) Horizon 3.93*.**.74.7**.488*** **. (.55) (.475) (.53) (.499) (.574) (.6) Horizon *.3.6*.348** **.8 (.67) (.539) (.678) (.664) (.78) (.8) Horizon *.7.8*.87** *.4 (.635) (.534) (.69) (.644) (.83) (.85) Horizon *.36.57*.86** (.73) (.598) (.84) (.75) (.88) (.78) Horizon * *.5 (.677) (.499) (.749) (.73) (.87) (.85) Adjusted R.6.4. Number of Countries 5 5 Number of Observations 5,75 3,44 3,94 Source: IMF staff calculations. Note: Te table presents results from estimating equation (3.5) on a sample of countries wit average annual temperature above 5 C. In te regressions, indicators for policy measures are interacted wit temperature, precipitation, and teir lags, controlling for country and region-year fixed effects, lags of growt and policy measure, forwards of temperature and precipitation. Separate regressions are estimated for eac orizon. Regression summary statistics are reported for orizon. In all specifications, standard errors are clustered at te country level. * p <.; ** p <.5; *** p <.. 7

55 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Table Role of Structural Policies and Institutions Impact of a C Increase in Temperature on per Capita Output () () (3) (4) (5) (6) (7) (8) (9) Domestic Financial Sector Reform Index International Finance Restrictions Human Capital Hig Low P-value Low Hig P-value Hig Low P-value Horizon.54***.63*** **.39***.7.39***.5***.63 (.437) (.439) (.93) (.75) (.9) (.349) Horizon.539***.853***.7.96**.54***.5.89**.5***.5 (.58) (.598) (.39) (.367) (.4) (.4) Horizon ** **.7 (.538) (.7) (.434) (.47) (.437) (.494) Horizon **.6.89**.359***.39.65**.5**.64 (.7) (.854) (.46) (.487) (.475) (.49) Horizon ***.757*** **.686***.49 (.89) (.855) (.5) (.59) (.57) (.576) Horizon *.3.79**.8*** **.46 (.844) (.868) (.7) (.76) (.699) (.74) Horizon *..68***.868*** **.34 (.85) (.87) (.594) (.65) (.685) (.74) Horizon **.975*** *.44 (.888) (.88) (.68) (.78) (.736) (.75) Adjusted R.4.3. Number of Countries Number of Observations,455 3,434 4,58 Impact of a C Increase in Temperature on per Pysical Capital Political Regime Index Inequality Capita Output Hig Low P-value Hig Low P-value Low Hig P-value Horizon.773***.86***.66.37***.45*** ***.559***.7 (.94) (.3) (.38) (.93) (.43) (.39) Horizon.78*.777*.99.3***.39***.7.34*.4**.6 (.45) (.43) (.393) (.367) (.58) (.588) Horizon ***.79***..84.4*.35 (.44) (.459) (.46) (.433) (.584) (.59) Horizon ***.99*** *.9 (.4) (.497) (.466) (.464) (.74) (.738) Horizon **.5.599***.95*** *.6 (.464) (.573) (.566) (.6) (.87) (.8) Horizon **.7.587**.44*** *. (.65) (.755) (.67) (.75) (.899) (.877) Horizon **.9.46**.8*** **. (.586) (.69) (.679) (.74) (.93) (.93) Horizon *.4.35*.3*** (.645) (.85) (.75) (.788) (.6) (.998) Adjusted R.3..8 Number of Countries Number of Observations 3,95 5,56,798 Source: IMF staff calculations. Note: Te table presents results from estimating equation (3.5) on a sample of countries wit average annual temperature above 5 C. In te regressions, indicators for policy measures are interacted wit temperature, precipitation, and teir lags, controlling for country and region-year fixed effects, lags of growt and policy measure, forwards of temperature and precipitation. Separate regressions are estimated for eac orizon. Regression summary statistics are reported for orizon. In all specifications, standard errors are clustered at te country level. * p <.; ** p <.5; *** p <.. 7

56 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges Annex Table Role of Development: Evidence from Subnational Data Impact of a C Increase in Temperature on per Capita Output Full Sample Advanced Economies Non-Advanced Economies () () Horizon.75***.5.77***. (.74) (.59) (.) Horizon.98***.3.978***. (.63) (.3) (.35) Horizon.599**.95***.768**. (.9) (.35) (.357) Horizon ***.875**. (.34) (.339) (.49) Horizon 4.75*.736*.3**. (.386) (.385) (.499) Horizon 5.46***.485.3**.4 (.46) (.5) (.588) Horizon 6.56**.5.596**. (.478) (.56) (.646) Horizon 7.333** **.3 (.57) (.6) (.74) P-value Adjusted R.8. Number of Countries Number of Provinces Number of Observations 6,48 6,48 Source: IMF staff calculations. Note: Regression () presents results from estimating equation (3.5) using subnational data on a sample of provinces wit average annual temperature above 5 C. In te regression, te indicator for weter a province is located in an advanced economy is interacted wit temperature, precipitation, teir lags, lag of growt, and region-year fixed effects; controlling for province fixed effects and forwards of temperature and precipitation. Separate regressions are estimated for eac orizon. Regression summary statistics are reported for orizon. In all specifications, standard errors are clustered at te province level. * p <.; ** p <.5; *** p <.. Te medium-term negative effects of temperature socks tend to be smaller in countries wit better structural policies and institutions (Annex Table 3.3.5). Standard errors are again quite large, and it is often difficult to reject te ypotesis tat policies do not ave an effect, but te point estimates of te effect of temperature socks in te outer orizons are substantially larger in columns (), (5), and (8). Tis evidence is in line wit findings in te literature on te role of policies in attenuating te effects of natural disasters. See, among oters, Kan (5); Noy (9); Cavallo and oters (3); Felbermayr and Gröscl (4); and Breckner and oters (6) for te role of institutional strengt and democracy; Noy (9); Von Peter, Dalen, and Saxena (); McDermott, Barry, and Tol (3); Felbermayr and Gröscl (4); and Breckner and oters (6) for te role of financial markets; and Noy (9); Raddatz (9); and Von Peter, Dalen, and Saxena () for te role of development status. Te Role of Development Te capter examines weter te overall level of development attenuates te negative effects of temperature socks in ot countries, using subnational cross-country data. Combining subnational growt data from rougly,46 provinces and states across 79 countries from Gennaioli and oters (4) and annual temperature and precipitation data at te same level of aggregation, te analysis confirms tat tere is a nonlinear relationsip between subnational growt and temperature by estimating equation (3.). It ten zooms in on te set of provinces and states wit average temperature greater tan 5 C to examine weter economic activity in te ot states or provinces of advanced economies responds to a temperature increase in te same way as in states or provinces of emerging market and developing economies wit a similar average temperature. Equation (3.5) is estimated wit p i,t taking te value of for states or provinces located in advanced economies. p i,t is also interacted wit lag of growt, µ i denote state or province fixed effects, and region-year fixed effects, θ r,t, are allowed to vary across advanced and non-advanced economies. Standard errors are clustered at te province level. Annex Table presents te estimated effects of a C increase in temperature at orizons to 7 in all subnational regions wit temperature greater tan 5 C in column (). Te subsequent columns present te estimated effects for subnational regions in advanced and non-advanced economies, as well as te 7

57 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Table Effect of Weater Socks and Natural Disasters on Emigration, 98 5 Percent of Emigrants in Total Population () () (3) (4) (5) (6) Temperature * 8.67* 8.34* 8.7* 8.74* (.5) (4.477) (4.476) (4.357) (4.48) (4.87) Precipitation (.7) (.88) (.878) (.88) (.878) (.88) Temperature LIDC 7.475* 7.67* 7.788* 7.57* 7.634* (4.53) (4.55) (4.9) (4.49) (4.88) Precipitation LIDC (.) (.8) (.4) (.39) (.33) Number of Natural Disasters.8*.8* * (.38) (.36) (.8) (.69) War (.83) (3.77) Number of Natural Disasters LIDC (.39) (.96) War LIDC.6 (4.34) Adjusted R Number of Observations Source: IMF staff calculations. Note: All specifications include country-of-origin fixed effects, decade-region fixed effects, and decade fixed effects interacted wit a dummy for low-income developing country (LIDC). Standard errors are clustered at te country level. * p <.; ** p <.5; *** p <.. p-value of a test of teir difference. Te negative effects of temperature socks are felt muc more eavily in non-advanced economies. Annex 3.4. Te Impact of Weater Canges and Natural Disasters on International Migration Tis annex provides additional details on te empirical analysis of te effect of temperature socks and natural disasters on international migration. Te analysis relies on data from Özden and oters () on emigrant stocks for 7 economies wit average temperature greater tan 5 C between 98 and 5. Migrant stocks, wic are available at -year intervals, are differenced to compute net emigrant flows in eac decade. Building on Cattaneo and Peri (6), te analysis estimates te following specification: Emigrant i,d = α + γt i,d + β T i,d LIDC i + μp i,d + θ P i,d LIDC i + ρdisaster i,d + τ Disaster i,d LIDC i + μ i + θ r,d + φ d LIDC i + ϵ i,d, (3.6) in wic i indexes countries, d indexes decades, 55 Emigrant is te net flow of emigrants over te decade as a percentage of te total population of te origin (source) country, T is te average temperature and P te average precipitation for te decade, and Disaster is te average number of natural disasters for eac 55 Te decade includes data up to 5. country-decade. Te latter tree variables are furter interacted wit a dummy identifying low-income developing countries (LIDC) to capture potential differences in te emigration response to te weater fluctuations and natural disasters. As in Cattaneo and Peri (6), te regression furter controls for country fixed effects ( μ i ), region-decade fixed effects ( θ r,d ), and decade fixed effects interacted wit te LIDC dummy. Te random error term ϵ i,d is clustered at te country level.56 Te specification is purposefully parsimonious. Controls typically included as determinants of migrations, suc as population size, sociopolitical environment, and oters, could temselves be affected by weater fluctuations and natural disasters. In a robustness ceck, te exercise controls for te incidence of war, an important pus factor for emigration, altoug arguably tis could be yet anoter cannel troug wic weater fluctuations trigger movements of people (see Burke, Hsiang, and Miguel 5b). Annex Table 3.4. reports te main findings from estimating equation (3.6). Higer average temperatures 56 Following Dell, Jones, and Olken (), te specification includes only fixed effects as controls, since oter potential controls, suc as population size or sociopolitical environment, may temselves be affected by agricultural productivity a key cannel troug wic weater socks may influence emigration potentially producing a bias in te estimation by introducing an overcontrolling problem. Te only exception is a dummy for wars (see Beaton and oters 7), wic is included in some of te specifications and confirms te robustness of te findings. 73

58 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges over a decade do not ave a significant effect on emigration in te full sample of countries (column []). However, once te response is allowed to vary across broad groups of countries, te results suggest tat in countries tat are not classified as low income, iger temperature is indeed associated wit greater emigration flows (column []). A C increase in average decadal temperature leads to an increase in te sare of net emigrants of about 8 percentage points (wic is equivalent to one standard deviation in te sample investigated). 57 Similarly, more natural disasters over a decade also increase net emigration flows, especially in countries not classified as low income. 58 Annex 3.5. Model-Based Analysis Te model used to analyze te long-term impact of climate cange and simulate te effects of policies in Box 3. is developed and presented in Buffie and oters (). It is commonly known as te Debt, Investment, and Growt (DIG) model and as served as a workorse in many IMF studies of low-income countries. Te DIG is an optimizing intertemporal model wit perfect foresigt. It describes a two-sector small open economy model wit private and public capital, learning by doing, and endogenous fiscal policies. Public capital is productive and is used in te production function in bot sectors. Government spending can raise output directly by augmenting te stock of public capital and can crowd in and crowd out private investment. Firms operate Cobb-Douglas tecnologies to combine labor, private capital, and public capital (infrastructure) into output in te traded and nontraded sectors. Te evolution of total factor productivity (TFP) is exogenous in bot sectors. Firms face separate prices for exports, and imports and are assumed to be profit maximizing. Consumers supply labor and derive utility from consuming te domestic traded good, te foreign traded good, and te domestic nontraded good. 57 Te flow of emigrants as a sare of population in countries tat are not classified as low income in tis sample is.5 percent, on average, wit a standard deviation of 8. percentage points. For low-income countries, tese statistics are.6 percent and. percentage points, respectively. 58 Results (not sown ere and available on request) are robust to te use of oter proxies for low-income countries, suc as a dummy identifying te countries in te bottom quartile of te average GDP per capita distribution of te country sample during te full sample period analyzed. Tese goods are combined into a constant elasticity of substitution basket, and savers maximize te present value of teir lifetime utility. Te model breaks Ricardian equivalence by including bot savers and and-to-mout consumers. Te government spends on transfers, debt service, and (partially inefficient) infrastructure investment. It collects revenue from te consumption value-added tax and from user fees for infrastructure services. Te deficit is financed troug domestic borrowing, external concessional borrowing, or external commercial borrowing. Policymakers accept all concessional loans offered by official creditors. Te borrowing and amortization scedule for tese loans is fixed exogenously. Debt sustainability requires tat te value-added tax and transfers eventually adjust to cover te entire deficit, given te exogenously determined upper limit on taxes and lower limit on transfers. Te model incorporates socks to te government external debt risk premium (or world interest rates). Te majority of te model parameters are set to te same values as in Buffie and oters (), wit few exceptions, mostly to reflect te decline in global interest rates, te projection of trend GDP growt in low-income countries, and te sample median of public-debt-to-gdp ratios. Te parameters tat differ from te ones in Buffie and oters () are presented in Annex Table Simulating te Long-Term Impact of Climate Cange To trace te long-term impact of climate cange, te model incorporates te estimated relationsip between temperature and per capita output discussed in Annex 3.3 and presented in Annex Table 3.3., column (5). Te effect is assumed to occur troug temperature s effect on TFP; terefore, te estimated parameters are rescaled so tat te model matces te empirically estimated decline of GDP if temperature increases by C. 59 Te temperature during 7 is assumed to follow one of two alternative scenarios: Representative Concentration Patway (RCP) 4.5 or RCP 8.5. Te temperature increases during 7 are calculated for te median low-income country in te sample and are equal to. C and 3.9 C for RCP 4.5 and RCP 8.5, respectively. 59 Estimates of te damage to GDP cannot be used directly given tat GDP is endogenous. 74

59 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Table Parameterization of te Debt, Investment, and Growt Model Parameter Value (percent) Initial Return on Infrastructure Investment 3 Public Domestic Debt-to-GDP Ratio Public Concessional Debt-to-GDP Ratio 3 Public External Commercial Debt-to-GDP Ratio 5 Oil Revenues-to-GDP Ratio Real Interest Rate on Public Domestic Debt 7 Real Interest Rate on Public External Commercial Debt 4 Trend per Capita Growt Rate.8 Sources: Buffie and oters (); and IMF staff calculations. Tere are two sources of uncertainty in te simulation te uncertainty of RCP projections and te uncertainty of te effect of temperature on TFP. Bot sources of uncertainty are combined in te analysis as follows. Te upper-bound scenario is simulated assuming tat te temperature increase is equal to te lowest 5t percentile for eac RCP. 6 To account for te uncertainty of estimated parameters, te TFP parameters are set to te conditional expected value for te upper 5 percent of te TFP distribution. Te worst lower-bound scenario is simulated analogously. Modeling Structural Transformation Structural transformation is generated in te DIG model by introducing diverging trends in sectoral TFP growt, along te lines of Ngai and Pissarides (7). In teir model, faster productivity growt in te traded goods sector goes along wit a decline in te relative price of traded versus nontraded goods. Given complementarity in final demand, production in te former sector relative to te latter does not increase in te same proportion. Te value sare of te traded goods sector eventually srinks, even in te presence of international trade. Wile tis approac relies on only one potential driver of structural transformation, it generates te desired increase in employment and nominal-value-added sares of te nontraded goods sector, wic is mostly composed of services. Te gap in sectoral TFP growt rates is set to replicate te average increase in te service sare of value added in low-income developing countries in 99 5, wic as risen at te rate of.5 percentage points a decade. Given tis calibration, in te scenario witout rising temperatures, te employment sare of nontraded goods increases from te baseline value of 4.7 percent to 65 percent over 9 years. Modeling Optimal Adaptation Box 3. extends te original DIG model to incorporate direct investment in adaptation strategies. Te main addition is te inclusion of private adaptation and public subsidies to private adaptation, wereas damages are modeled as before. In te absence of any adaptation measure, increased temperature causes gross damage, denoted by G D jt, at time t in sector j. Te gross damage is expressed as a fraction of sectoral output: g d jt = G _ D jt q = f ( T ). jt Gross damage can be reduced by investing in adaptation. Firm i s capacity to adapt to climate cange is denoted by O i,jt. It is increasing in firm i s protection expenditures A D i,jt as well as in te total sectoral protection expenditures AD jt = A D i,jt di.6 Te residual damage for firm i in sector j is Ω i,jt = g d jt, O i,jt ( A D i,jt, ϕ AD jt ) in wic te marginal damage reduction from adaptation spending is decreasing. Te positive parameter ϕ is te elasticity of damage reduction to te level of adaptation. If te cost of a unit of protection is equal to P AD,t and te functional form for te capacity to adapt is O i,jt ( A D i,jt, AD jt ; ς ) = A D i,jt AD ς jt (wit ς ), ten cost minimization by firms in te symmetric 6 Here, te 5 95 percent confidence intervals for te temperature increases are. C to.8 C and.8 C to 5. C for RCP 4.5 and RCP 8.5, respectively. 6 Many adaptation measures ave te nature of public goods; ence, firms benefit from total sectoral protection spending. 75

60 WORLD ECONOMIC OUTLOOK: Seeking Sustainable Growt Sort-Term Recovery, Long-Term Callenges equilibrium A D i,jt = AD jt determines te optimal level of adaptation expenditure for eac firm A D i,jt = G ϕ D jt ( P AD,t ) + ϕ ( + ς ) Te optimal level of firm-specific residual damage is ten Ω jt = g d jt A ϕ ( + ς ) D jt, wic can be sown to be socially suboptimal. Te social planner s cost function, TotD i,jt, differs from tat of individual firms Tot SP D i,jt = G D jt ( A D ϕ ( + ς ) SP jt ) + P AD,t A D SP jt. Minimizing te social cost gives socially optimal adaptation expenditures G A SP D jt = ϕ + ς D jt [ ( ) P AD,t ] + ϕ ( + ς ) It can be sown tat private agents invest less tan te socially optimal amount. Te adaptation spending gap (as a fraction of te socially optimal adaptation spending) is equal to ( + ς ) + ϕ ( + ς ). It can also be sown tat te socially optimal amount of adaptation expenditures can be acieved if subsidies in te amount of υ ς,jt per unit cost of protection are paid by te government to te firms υ ς,jt = ς. ( + ς ) Annex 3.6. Reduced Form Approac to Estimating Potential Long-Term Effects of Climate Cange Indicative evidence of te potential impacts of climate cange and teir distribution across te globe could also be gleaned by combining te estimated sensitivity of per capita output to temperature increase (Annex Table 3.3., column [5]), baseline annual temperatures, and projected temperature canges for eac geograpic location. As in te modeling exercise, tis analysis takes te most conservative approac and assumes temperature increases ave a permanent level, rater tan growt, effect on per capita output. Te estimated cumulative impact on per capita GDP under te Representative Concentration Patway (RCP) 4.5 and RCP 8.5 scenarios are presented in Annex Figure It is important to note tat tis exercise captures te likely impact of one particular aspect of climate cange, namely temperature increases. Te macroeconomic effects of many expected or possible events (suc as iger incidence of natural disasters, rising sea levels, ocean acidification, and te like) are not quantified in tis exercise. Furtermore, te analysis abstracts from cross-border spillovers tat may arise if climate cange triggers more frequent epidemics, famines, and oter natural disasters along wit social unrest, armed conflict, and associated refugee flows. Te analysis suggests tat te projected warming will ave uneven effects across te globe. However, te increase in temperature, especially under te RCP 8.5 scenario, will pus many advanced economies beyond te tresold temperature level, tus triggering direct economic losses for tese countries as well. 76

61 CHAPTER 3 Te Effects of Weater Socks on Economic Activity: How Can Low-Income Countries Cope? Annex Figure Te Long-Term Impact of Temperature Increase on Real per Capita Output across te Globe (Percent). RCP 4.5 Scenario RCP 8.5 Scenario Sources: National Aeronautics and Space Administration (NASA) Eart Excange Global Daily Downscaled Projections (NEX-GDDP); World Bank Group Cartograpy Unit; and IMF staff calculations. Note: Te maps depict te effect of te projected increase in temperature between 5 and under RCP 4.5 and RCP 8.5 scenarios on real per capita output in. Gray areas indicate te estimated impact is not statistically significant. RCP = Representative Concentration Patways. References Abiad, Abdul, Enrica Detragiace, and Tierry Tressel. 8. A New Database of Financial Reforms. IMF Working Paper 8/66, International Monetary Fund, Wasington, DC. Acemoglu, Daron, Simon Jonson, and James A. Robinson.. Te Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review 9 (5): Acevedo, Sebastian. 4. Debt, Growt, and Natural Disasters: A Caribbean Trilogy. IMF Working Paper 4/5, International Monetary Fund, Wasington, DC.. 6. Gone wit te Wind: Estimating Hurricane and Climate Cange Costs in te Caribbean. IMF Working Paper 6/99, International Monetary Fund, Wasington, DC. Altuǧ, Sumru, and Robert A. Miller Te Effect of Work Experience on Female Wages and Labour Supply. Review of Economic Studies 65 (): Andersen, Tomas Barnebeck, Carl-Joan Dalgaard, and Pablo Selaya. 6. Climate and te Emergence of Global Income Differences. Review of Economic Studies 83 (4): Antoff, David, and Ricard Tol.. FUND Climate Framework for Uncertainty, Negotiation and Distribution. 77