Disaster risk, inequality, and fiscal sustainability

Le, Anh H.; Park, Donghyun; Beirne, John; Uddin, Mohammed Gazi Salah Working Paper Disaster risk, inequality, and fiscal sustainability ADB Economics Working Paper Series, No. 750 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Le, Anh H.; Park, Donghyun; Beirne, John; Uddin, Mohammed Gazi Salah (2024) : Disaster risk, inequality, and fiscal sustainability, ADB Economics Working Paper Series, No. 750, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240514-2 This Version is available at: https://hdl.handle.net/10419/310380 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org DISASTER RISK, INEQUALITY, AND FISCAL SUSTAINABILITY Anh H. Le, Donghyun Park, John Beirne, and Gazi Salah Uddin ADB ECONOMICS WORKING PAPER SERIES NO. 750 November 2024 Disaster Risk, Inequality, and Fiscal Sustainability This paper analyzes the effects of climate change on budgetary sustainability and inequality. Using panel data, the findings suggest that rising climate-related disaster risks raise government debt and undermine fiscal sustainability, with low-income households bearing the brunt of the impact. According to a New Keynesian Dynamic Stochastic General Equilibrium model, disaster risk generates recessions and increases inequality, particularly among “hand-to-mouth” agents. The paper also shows a considerable increase in sovereign debt due to disaster risk, and it recommends targeted transfers while cautioning against the fiscal cost of progressive taxes. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Disaster Risk, Inequality, and Fiscal Sustainability Anh H. Le, Donghyun Park, John Beirne, and Gazi Salah Uddin No. 750 | November 2024 Anh H. Le ([email protected]) is a PhD student at Goethe University Frankfurt. Donghyun Park ([email protected]) is an economic advisor and John Beirne ([email protected]) is a principal economist at the Economic Research and Development Impact Department, Asian Development Bank. Gazi Salah Uddin ([email protected]) is an associate professor at Linköping University. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS240514-2 DOI: http://dx.doi.org/10.22617/WPS240514-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area inthis document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Note: In this publication, “$” refers to United States dollars. ABSTRACT In this paper, we study the implications of climate change on fiscal sustainability and inequality. First, using rich panel data, we show that rising climate-related disaster risks increase government debt and harm fiscal sustainability. We also find that the adverse effect of disaster risks is larger for low-income households, exacerbating inequality. Second, we construct a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) model to examine the implications of the distributional effect of disaster risk. The model features two kinds of households and a fiscal authority. We show that disaster risk has recessionary effects and also causes inequality among households to widen. More specifically, the model indicates that “hand-to-mouth” agents suffer a drop in consumption that is three times larger than that of the Ricardian households. Importantly, we observe a significant rise in sovereign debt due to disaster risk, which poses a challenge to policymakers. Lastly, targeted transfers are recommended but progressive taxes entail a significant fiscal cost. Keywords: climate change, disaster risk, physical risk, heterogeneous agent, fiscal policy JEL codes: E20, E31, E32, E44, G12, Q54 1 Introduction The rise in global temperature in the last decade has increased the frequency of extreme climatic events. These events include extreme temperatures, extreme wind, extreme rainfall, and land resource depreciation, as well as disasters such as floods, droughts, earthquakes, and tropical cyclones. In light of the huge economic cost associated with such events, climate-related disaster risk has emerged as a high priority for policymakers. In this paper, we examine the effect of climate-related disaster risks on fiscal sustainability and inequality. By way of context, Figure 1 shows the total number of disaster events in the last 124 years. It is clear that the frequency of disaster events has increased significantly over time, especially in the last 30 years. The rising frequency of disasters highlights the growing threat of climate change, which presents a serious challenge for policymakers. Figure 1: Total Number of Disaster Events from 1990–2024 0 10 20 30 40 Number of Events 1900 1920 1940 1960 1980 2000 2020 Year Advanced EMEs EMEs = emerging market economies. Note: We consider droughts, earthquakes, extreme temperatures, floods, mass movements, storms, volcanic activities, and wildfires. Source: The International Disaster Database (EM-DAT). Figure 2 shows the median cyclically-adjusted fiscal balance of 192 countries around the world from 1990. The figure clearly shows that fiscal balance declined significantly and reached a record low level during the coronavirus disease (COVID-19) pandemic, when many governments unleashed significant fiscal stimulus in an effort to prevent sharp downturns. The consequent reduction of fiscal space in the post-pandemic era means that policymakers must address disaster risk with relatively limited fiscal space. Figure 2: Cyclically-Adjusted Balance as % of Potential GDP, Median of All Countries -6 -4 -2 0 2 Percent of GDP 1990 2000 2010 2020 2030 Year GDP = gross domestic product. Source: The World Bank, Kose et al. (2022). It is interesting to see if there are any meaningful differences between the advanced and emerging market economies. Figure 1 shows that the increase in the frequency of disasters is a global phenomenon that affects both groups of countries. However, advanced economies with stronger sovereign debt markets can deal better with climate risk even when they are burdened with a high debt level. In Figure 3, to check whether this is true, we used data from the Notre Dame Global Adaptation Initiative (ND-GAIN) on vulnerability to climate change. It turns out that EMEs are indeed more vulnerable to climate change, and their greater vulnerability may be due to having fewer assets such as deep financial markets for mitigating climate risk. 2 Figure 3: Vulnerability Index 20 30 40 50 60 70 Vulnerability Advanced EMEs EMEs = emerging market economies. Source: ND-GAIN, Chen et al. (2015). The massive destruction resulting from the realization of disaster risk affects the economy by destroying capital and productivity levels, in addition to raising uncertainty for households. Fiscal policy, in particular countercyclical public spending and social transfers to affected households and firms, stands out as the most effective policy response to cushion the large negative impact of disasters. However, the scope for fiscal policy response is limited by the need to restore fiscal sustainability, especially following the reduction of fiscal space that occurred as a result of the global COVID-19 pandemic, the effects of which are still being felt today. Disasters are likely to have an especially pronounced impact on developing countries and emerging markets for two reasons. First, disasters likely have a bigger impact on low-income households who lack savings to smooth their consumption in the face of a negative shock. Given the larger share of poor “hand-to-mouth” (HtM) agents in developing countries, the impact of disasters is thus likely to be bigger in those countries. Second, the large fiscal policy responses required to adequately cope with disasters creates a challenge for fiscal authorities, especially in countries with limited fiscal space and high debt levels. In particular, developing and emerging market economies have to 3 pay higher premiums on sovereign bonds, which further limits their ability to deal with disaster risks. This paper aims to shed light on the implications of disaster risk on both inequality and fiscal sustainability. We seek to substantiate our conceptual insights with empirical evidence. First, we provide evidence of climate change affecting both fiscal sustainability and household inequality. We demonstrate that the rise in climate vulnerability increases government debt-to-output ratios and reduces the fiscal sustainability gap. Importantly, we find that climate risk exacerbates income inequality by shifting a higher share of total income to wealthier households. This suggests that lower-income individuals might suffer more from the risks associated with climate change. In the second part, we enrich a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) model with disaster risk that reduces productivity and capital quality. Additionally, we incorporate the HtM agent and a government budget constraint that finances spending through debt, lump-sum tax, and income tax from two types of households. The NK-DSGE model generates two significant and interesting findings. First, disaster risk causes recessionary effects in line with the literature but also creates unequal impacts on households. Our findings reveal that the HtM agent suffers a drop in consumption that is three times larger than the Ricardian household due to a lack of consumption smoothing. Second, we observe a substantial increase in sovereign debt due to heightened household demand for bonds (i.e., a ‘flight-to-safety’) and reduced income tax revenue stemming from significant declines in wages and labor hours. These results are consistent with our empirical evidence. Regarding policy responses, we recommend implementing targeted transfers to mitigate the inequality arising from disaster risk. Importantly, the fiscal cost is manageable because these targeted transfers can boost gross domestic product (GDP). 2 Literature Review In this section, we review some studies that are relevant to our paper. Our work contributes to an emerging literature studying the implications of disaster events. Barro (2006, 2009) are pioneers in studying the effects of rare events on asset prices. Recent works focus on how to model disaster risk in macroeconomic models. Fernández-Villaverde and Levintal (2018) and Cantelmo et al. (2024, 2023) use Taylor 4 Figure 7: Impact of Climate Change Vulnerability for Countries with High Vulnerability and Sustainability Risk -2 0 2 4 -101234 Period Top 20% Lowest 20% Income Share Note: The horizon is yearly. The unit is in percentage with 95% confidence bands. The shock hits in t=0. Source: Authors’ calculations. Compared to Figure 5, the gap in income share from top-income and low-income households is significantly wider and longer lasting. As mentioned earlier, countries with limited fiscal space encounter challenges in adequately protecting the most vulnerable households from climate change risks. At the same time, from Figure 6, we observe a more pronounced effect on the government debt and the Gini index. Therefore, high vulnerability and sustainability risks amplify the inequality effect of climate change. Our empirical findings indicate a deterioration in fiscal balances and inequality due to climate-related disaster risk exposure, especially for those economies most vulnerable to climate change. We employ a NK-DSGE framework to explore further, also facilitating interactions across different markets and sectors of the economy simultaneously. 4 A Model with Disaster Risk To study the fiscal implications of disaster risk, we model how disasters affect the economy and the fiscal authority. We begin with a standard NK-DSGE model featuring 11 capital and a fiscal authority with distorted income tax. Additionally, to reflect the significant presence of low-income households in developing economies, we introduce a second type of household that lacks saving ability. This departure from the Ricardian household assumption is crucial for our analysis of government spending responses. Apart from these adjustments, the primary innovation compared to a standard New Keynesian model lies in incorporating the destructive impact of disaster risk on productivity and capital, necessitating a higher-order approximation akin to an asset pricing model. The subsequent model features two types of households. There is a fraction (1 −η)of Ricardian households and a fraction ηof the HtM agents. HtM agents lack saving ability by assumption and rely solely on labor income. The government finances public expenditure Gtby raising lump-sum taxes TO t, TR t, income tax, τn, and public debt, Bt. Disaster risk dampens productivity growth, destroys capital, and introduces second-moment effects on agents’ preferences. To better understand how we incorporate disaster risk into the model, we adopt the approach of Gourio (2012). First, we define productivity, zt, which constitutes the primary source of long-term growth. Thus, we express the standard growth rate of the laboraugmenting total factor productivity (TFP) as follows: zt+1 zt =eµ+εz,t+1+ϑt+1 log(1−∆) (2) with µas the trend and εz,t+1 following a normal distribution. To incorporate the disaster risk, we assume it will destroy the productivity level of the economy. We will treat ϑas a variable that indicates the event of a “disaster” and this is the non-standard term compared to a standard macroeconomic model. Specifically, ϑt+1 = 1 with probability Ξt, in which it causes damage with the share ∆to the productivity. Precisely, ϑt+1 = 1 with probability Ξt, indicating the destruction of a significant portion ∆of the current capital stock. Conversely, ϑt+1 = 0 signifies the absence of a disaster event. The probability Ξtof such disasters varies over time: log(Ξt) = (1 −ρΞ)log(˜ Ξ) + ρΞlog(Ξt−1) + σΞεΞt(3) 12 where ˜ Ξdenotes the mean, ρΞrepresents the persistence, and εΞtdenotes the independent and identically distributed innovations. After taking expectations, we end up with: Et(eϑt+1log(1−∆)) = 1 −Ξt∆(4) 4.1 Ricardian Households As mentioned in the beginning, we need an asset pricing model to capture the volatility in asset prices from disaster risk. Therefore, our Ricardian households maximize the following Epstein-Zin-Weil preferences: ˜ Ut=CO t(1 −LO t)ζ1−ψ+β(Ξ) Et˜ U1−γ t+1 1−ψ 1−γ1 1−ψ (5) with respect to the budget constraint: CO t+It+TO t+Bt pt = (1 −τN)WtLO t+Pk tutKt+Divt+BtRt−1 pt (6) where CO trepresents Ricardian household consumption, Itdesignates household investment, Btdenotes government bonds, Wtis the hourly wage, and LO tdenotes total labor supply. Pk trepresents the real return on capital, Ktrepresents physical capital at time t,Rtis the risk-free policy rate on government bonds, and Divtis the dividend from firms. Lastly, ptis the price level. Different from Cantelmo et al. (2023), we also consider the effect of disaster risk on the demand side. Following Gourio (2012) and Isoré and Szczerbowicz (2017), we assume the discount factor, β, is a function of disaster probability and make it time-varying to capture the effects of disaster risk on the demand side.1Lastly, τnis the real income tax. Following Gourio (2012), we assume that disasters will influence both productivity growth and physical capital. The capital law of motion is subject to an adjustment cost in changes in investment to capital ratio, as in Christiano et al. (2005) but with the chance that disaster shock can cause the loss of ∆to the physical capital. Hence, when the disaster materializes, it destroys the capital of the current period and affects the 1This might be important for the dynamic of inflation. If the disaster only dampens the production, it has an inflationary effect. However, if the household also anticipates it, the effect on inflation is ambiguous. 13 accumulation of the next period’s capital as well as the investment decision of the household. Kt+1 =(1 −δt)Kt+ Γ It KtKteϑt+1ln(1−∆) (7) We define the stochastic discount factor as follows: Qt,t+1 =∂˜ Ut/∂CO t+1 ∂˜ Ut/∂CO t =β(Ξ) CO t+1 CO t−ψ1−LO t+1 1−LO tζ(1−ψ)˜ U−X t+1 Et˜ U1−X t+1  −X 1−X (8) where X=γ−ψ 1−ψ. We can define the Euler equation takes the following form. EtQt,t+1 Rt πt+1 = 1 (9) where πt≡pt pt−1is the inflation rate. Similarly, the first-order conditions with respect to capital takes the following form: Rk t+1 =eϑt+1 ln(1−∆) pk t+1ut+1 qt +qt+1 qt1−δt+1 + Γ0 Kt+1 Kt+1 + Γt+1(10) which relies on the standard Tobin’s q, depreciation and utilization rates, and investment adjustment costs, as well as on the possibility of a disaster event, ϑ. Because of the asset pricing mechanism, a third-order approximation is required for the simulation. We define the risk premium as follows: Et(Premt+1)≡EtRk t+1πt+1 Rt(11) 14 4.2 “Hand-to-Mouth” Households There is a fraction ηof HtM households. It is much simpler compared to our Ricardian household as HtM agents have no assets and saving ability. Hence, they solve the following problem: max Et ∞ X t=0 βt Cr(1−σ) t 1−σ −κL Lr(1+ϕ) t 1 + ϕ!(12) s.t. Cr t= (1 −τN)WtLr t−Tr t(13) where Cr tis consumption and Lr tis the labor of HtM agents. First-order conditions with respect to labor and consumption are as follows: (1 −τN)WtLr t−Tr t1−σ 1−σ −κL Lr(1+ϕ) t 1 + ϕ= 0 (14) κL(Lr t)ϕ(Cr t)σ= (1 −τN)Wt(15) 4.3 Production Sectors ytis the aggregation of final goods using a standard constant elasticity of substitution aggregator. yt=Z1 0 yt(j)ε−1 εdjε ε−1 (16) The aggregate price and the demand curve follow: pt=Z1 0 pt(j)1−εdi1 1−ε (17) yt(j) = ytpt(j) pt−ε (18) The intermediate goods sector is quite standard for a NK-DSGE model. The only innovation belongs to the TFP growth that can be destroyed by the disaster risk. yt(j) = At(utKj,t)α(ztLj,t)1−α(19) 15 where ztis the productivity level that is affected by the disaster risk2and utKj,t represents effective capital, households determined by the utilization rate utof capitals. Lastly, Atis the exogenous TFP shock process. Intermediate producers can adjust their prices in period twith a Calvo probability of θ. The optimal price is set with respect to the expected value of future profits. However, to solve the model with a third order approximation, we derive it under a non-linearity setup. max pj,t Et ∞ X s=0 (θ)sQt+s(pj,tYj,t+s−Wt+sLj,t+s−Pk j,t+sut+sKt+s)(20) The optimal reset price does not depend on jand is the same for aggregation, p∗ t=p∗ j,t. Hence, we have the inflation definition: π∗ t=p p−1 X1,t X2,t πt(21) where X1and X2are recursive auxiliary variables and pw,t is interpretable as real marginal cost at time t. X1,t =Ytpw,t +θEt[Qt,t+1πp t+1X1t+1 ](22) X2,t =Yt+θEt[Qt,t+1πp−1 t+1 X2t+1 ](23) 4.4 Central Bank, Government, and Market Clearing The central bank controls the standard Taylor rule. ln Rt Rss =ρrln Rt−1 Rss + (1 −ρr)ρyln Yt Yt−1+ρπln πt πss  (24) In equilibrium, all markets are clear. Hence, the final goods market clearing condition follows: Yt=Ct+It+Gt(25) 2The setup with the labor augmenting TFP and capital destruction also helps to bring the effect of disaster risk into the capital market rather only to the total TFP which can be referred to as macroeconomic uncertainty (see Basu and Bundick (2017)). 16 where Gtstands for autonomous government spending. Total consumption and labor are the combinations of ”HtM” and Ricardian households. Ct= (1 −η)CO t+ηCr t,(26) Lt= (1 −η)LO t+ηLr t(27) The government finances public expenditure Gtby raising lump-sum taxes TO t, TR t, income tax, τn, and public debt Bt. For the baseline model, we fix the income tax and set the identical lump-sum transfer (tax) rule following Galí et al. (2007) for both types of households. Gt+Rt−1 πt Bt−1= (1 −η)tO t+ηtr t+ (1 −η)τN tWtLO t+ητN tWtLR t+Bt(28) log (Gt) = (1 −ρG)log G+ρGlog (Gt−1) + vG t(29) tr t−¯ tr=φBBt−¯ B+φGGt−¯ G(30) tO t−¯ tO=φBBt−¯ B+φGGt−¯ G(31) τN t=τN ss (32) 4.5 Calibration and Solution Method The calibration of the macroeconomic part is for emerging market economies following the workhorse model from the International Monetary Fund, henceforth, the Integrated Policy Framework or IPF (Adrian et al., 2020). We set the inflation target at 4% annually and the capital depreciation rate at 3.5% following Chinese calibration from Chang et al. (2019). The disaster-related parameters will follow the literature on disaster risk (Barro et al., 2022; Cantelmo et al., 2024, 2023; Fernández-Villaverde and Levintal, 2018; Gourio, 2012; Isoré and Szczerbowicz, 2017). Lastly, we adopt the approach of Isoré and Szczerbowicz (2017) to detrend the system, ensuring that the binary disaster variable disappears. After detrending, we solve the system using the perturbation 17 method with a third-order approximation.3This ensures that we can have a quite accurate solution with the third-order approximation.4We put all of the key parameters in Table 2. Table 2: Calibration for Emerging Market Economies Parameter Description Value Notes βDiscount factor 0.9945 Real rate of 1.9% annually (IPF) pElas. of subst. differentiated goods 6 Christiano et al. (2005) αShare of capital in production 0.33 i y= 0.263 θCalvo probability 0.75 IPF δDepreciation rate 3.5% Chang et al. (2019) κIInvestment adjustment cost 1 Chang et al. (2019) πTrend Inflation 1.01 IPF, 4% annually ˜gPublic spending 0.1291 g/y= 0.14 (IPF) τNIncome tax 0.15 IPF ρπTaylor rule reaction for inflation 1.5 IPF ρyTaylor rule reaction for output 0.0625 IPF ρrPersistence of interest rate 0.85 IPF ˜ ΞMean probability of disaster 0.0688 Cantelmo et al. (2023) ∆Size of disaster 0.22 Isoré and Szczerbowicz (2017) σΞDisaster risk probability (StD) 0.6 Cantelmo et al. (2023) ρΞDisaster risk probability (Persistence) 0.9 Isoré and Szczerbowicz (2017) γRisk aversion coefficient 3.8 Isoré and Szczerbowicz (2017) ηShare of HtM agent 0.42 Bracco et al. (2021), average for EMEs µGrowth rate of productivity 0.005 Isoré and Szczerbowicz (2017), 2% annually b yDebt-to-GDP 0.6 IMF WEO EMEs = emerging market economies, GDP = gross domestic product, HtM = hand-to-mouth, IMF = International Monetary Fund, IPF = Integrated Policy Framework, WEO = World Economic Outlook. Source: Authors’ calculations. 5 Numerical Simulation 5.1 Baseline Results First, the disaster risk creates a recessionary effect. When disaster risk increases, agents become more “patient,” leading to a higher propensity to save and a corresponding drop in consumption. This results in a deflationary effect as the demand decreases as well. However, the increase in savings does not lead to higher investment 3The detailed solution method can be found in Isoré and Szczerbowicz (2017) and Gourio (2012). 4Fernández-Villaverde and Levintal (2018) use Taylor projection to show that it can outweigh the solution with perturbation methods. However, their approach is about changing the disaster size, not the probability of disaster. Notably, both approaches provide similar effects. 18 but rather to increased holding of government bonds, causing output to decline. This occurs because capital accumulation becomes less profitable for households as firms demand fewer factors of production. Consequently, the rental rate of capital decreases, further reducing productivity levels. Firms demand fewer production factors due to price stickiness. Since they cannot raise prices to offset the loss in productivity, they cut back on production and also their demand for labor and capital. With the baseline model, we observe two highly interesting results. First, we see that there is inequality in the effect of disaster risk. The HtM agent’s consumption drops around four times more than that of the Ricardian one due to a lack of consumption smoothing motives. When the disaster risk hits the economy, the income of all the households decreases following the diminishing productivity level and the loss of capital. However, the HtM agents cannot smooth their consumption with their savings and suffer more compared to Ricardian households. Hence, they reduce their working hours much less compared to Ricardian households, but this cannot compensate for the damage from the disaster. 19 Figure 8: The Impulse Response of 10% Increase in Disaster Risk 5 10 15 Quarters -6 -4 -2 0 % Dev Output 5 10 15 Quarters -6 -4 -2 0 % Dev Consumption Total Hand-to-mouth Ricardian 5 10 15 -15 -10 -5 0 % Dev Investment 5 10 15 Quarters -2.5 -2 -1.5 -1 -0.5 0 % Point Dev Inflation (Annualized) 5 10 15 Quarters -1 -0.8 -0.6 -0.4 -0.2 0 % Point Dev Policy Rate (Annualized) 5 10 15 0 2 4 6 8 % Dev Quarters Government Debt-to-GDP 5 10 15 Quarters 0 0.1 0.2 0.3 Level Dev Lump-sum Taxes 5 10 15 Quarters -8 -6 -4 -2 0 % Dev Labor Total Hand-to-mouth Ricardian 5 10 15 Quarters 0 1 2 3 4 % Point Dev Risk Premium (Annualized) Quarters Dev= deviation, GDP = gross domestic product. Source: Authors’ calculations. Second, government debt rises significantly. 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Climatic Change 172 (3-4): 30. 29 ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org DISASTER RISK, INEQUALITY, AND FISCAL SUSTAINABILITY Anh H. Le, Donghyun Park, John Beirne, and Gazi Salah Uddin ADB ECONOMICS WORKING PAPER SERIES NO. 750 November 2024 Disaster Risk, Inequality, and Fiscal Sustainability This paper analyzes the effects of climate change on budgetary sustainability and inequality. Using panel data, the findings suggest that rising climate-related disaster risks raise government debt and undermine fiscal sustainability, with low-income households bearing the brunt of the impact. According to a New Keynesian Dynamic Stochastic General Equilibrium model, disaster risk generates recessions and increases inequality, particularly among “hand-to-mouth” agents. The paper also shows a considerable increase in sovereign debt due to disaster risk, and it recommends targeted transfers while cautioning against the fiscal cost of progressive taxes. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance.