metadata: authors: 0: author: Milivojevic,Lazar
count: World
docna: 0: docna: Natural Disasters and Fiscal Drought
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines to what extent
            slowdowns in economic growth after natural disasters are
            accompanied by widening fiscal deficits and corresponding
            pressures on public debt. Empirical analysis based on
            exogenous measures of physical disaster intensity shows that
            natural disasters lead not only to output losses but also to
            further deterioration of countries&#x2019; fiscal
            positions. The effects are persistent and driven by
            developments in emerging markets and developing economies. A
            dynamic stochastic general equilibrium model is used to show
            the propagation mechanism of an extreme event that affects
            agricultural productivity. The model features farmers
            endowed with land with time-varying productivity subject to
            economic and weather conditions. Simulation results
            illustrate the climate-fiscal nexus existence and highlight
            the role of structural resilience in limiting the impact of
            natural disasters.
display_title: Natural Disasters and Fiscal Drought
listing_relative_url: /research/2023/02/33997256/natural-disasters-fiscal-drought
topicv3: Fiscal Aspects of Climate Change m1325995 447,Macroeconomic Vulnerability and Debt
            m1326057 1104,Climate Resilient Development m1327078 572
publishtoextweb_dt: 2023-02-07T00:00:00Z
dois: 10.1596/1813-9450-10298
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1662674
model_extractions: Policy Research Working Paper 10298 Natural Disasters and Fiscal Drought _Lazar Milivojevic_ South Asia Region Office of the Chief Economist February 2023
Policy Research Working Paper 10298

# **Abstract**

This paper examines to what extent slowdowns in economic growth after natural disasters are accompanied by widening fiscal deficits and corresponding pressures on public debt. Empirical analysis based on exogenous measures of physical disaster intensity shows that natural disasters lead not only to output losses but also to further deterioration of countries’ fiscal positions. The effects are persistent and driven by developments in emerging markets and developing economies.

A dynamic stochastic general equilibrium model is used to show the propagation mechanism of an extreme event that affects agricultural productivity. The model features farmers endowed with land with time-varying productivity subject to economic and weather conditions. Simulation results illustrate the climate-fiscal nexus existence and highlight the role of structural resilience in limiting the impact of natural disasters.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at lmilivojevic@worldbank.org.

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

## Lazar Milivojevic<sup>∗</sup>

The World Bank _Keywords:_ natural disasters, fiscal sustainability, climate change _JEL-Codes:_ C51, E62, Q54.

> ∗The author thanks Robert Beyer, Alexander Meyer-Gohde, Gauthier Vermandel, Volker Wieland, and participants at the Asian Economic Development Conference (ADB), 85th Anniversary Conference at the Faculty of Economics and Business (University of Belgrade), and South Asia Chief Economist Office internal seminar (World Bank) for helpful comments.
<!-- Start of picture text --> Sovereign risk<br>100<br>. ° .*<br>.<br>80 . * veneer<br>60 .. bd * . .*. ee,* . oweweg<br>Oe a aOR.<br>40 ‘ tee *. are = a AEs<br>en a’, * .<br>“, a * et * EMDEs<br>20 a “4 he ar<br>FY ry<br>a<br>0<br>0 2 4 6 8 10<br>Climate-driven vulnerability<br><!-- End of picture text -->
To illustrate these challenges, Figure 1 shows that countries with higher vulnerability to climate change also face higher sovereign risks. The climate vulnerability index comes from the new IMF Climate Change Dashboard and comprises climate-driven hazard and exposure, vulnerability, and lack of coping capacity. The EIU Country Risk Index indicates sovereign debt risks, based on more than 50 institutional, macrofinancial and structural variables. Climate-fiscal nexus due to the simultaneous exposure to both risks suggests that emerging market and developing economies (EMDEs) are more sensitive to physical risks of climate change and feature tighter fiscal space. When natural disasters hit an economy, a fiscal response to address economic and distributional effects may lead to a debt increase which carries downside risks of its own. Potential debt distress and limited access to international financial markets could reduce the intended fiscal support and exacerbate growth and fiscal prospects.

This paper examines to what extent the impacts on economic growth after natural disasters are accompanied by widening fiscal deficits and corresponding pressures on public debt. It relies on the ifo GAME database (Felbermayr and Groeschl, 2014), which contains exogenous measures of physical disaster intensity, well suited for causal analysis. Regression results suggest that natural disasters lead not only to output losses, but also to further deterioration of countries’ fiscal positions. A disaster of one standard deviation above the disaster index mean leads to 1.32 percent lower output per capita growth, 1.58 percent higher government debt, and 0.88 percent increase in fiscal deficit. Local projection estimates indicate that the adverse effects persist in the years after the disaster, not only on impact. Finally, the effects are significant in the subsample of EMDEs, while the advanced economies remain stable after disaster episodes.

In addition, I provide a general equilibrium interpretation of the empirical results using a Dynamic stochastic general equilibrium (DSGE) model. The model explains the propagation mechanism of a natural disaster shock that affects agricultural productivity. Based on Gallic and Vermandel (2020), it features farmers endowed with land with timevarying productivity subject to economic and weather conditions. The model is extended with a more detailed government sector, which allows considering the fiscal implications. Last but not least, counterfactual simulations highlight the role of structural resilience in limiting the impact of disasters.

# **2 Related Literature**

Empirical literature remains inconclusive on the macro-fiscal effects of natural disasters. When it comes to the impact on economic activity, Rasmussen (2004) suggests that natural disasters cause a 2.2 percent median reduction of the same-year real GDP growth rate. In a similar vein, Loayza et al. (2012) and Fomby et al. (2013) use a large panel of countries to conclude that severe disasters carry much worse effects than moderate ones, especially in developing economies. Klomp and Valckx (2014) conduct a meta-analysis based on 25 2
primary studies and find that natural disasters have a significant negative effect on growth per capita, particularly strong for climatic disasters in developing countries. Felbermayr and Groeschl (2014) propose a new panel dataset and find robust adverse nonlinear effects on economic activity. On the other hand, Skidmore and Toya (2002) and Cuaresma et al. (2008) find an expansionary effect attributable to a productivity boost from new investments and technologies.

Research on how climate change affects the fiscal sector is relatively scarce. Most studies draw on the publicly available cross-country database on disaster losses called Emergency Events Database (EM-DAT). Lis and Nickel (2009) explore the implications of climate change for fiscal policy by assessing the impact of large-scale extreme weather events on changes in public budgets and conclude that the budgetary impact of such events ranges between 0.23 and 1.1 percent of GDP depending on the country group. Melecky and Raddatz (2011) examine the economic and fiscal consequences of a natural disaster for middle and high-income countries over the period 1975 through 2008. They divide the disasters into geological, climate, and a residual category, and find that on average budget deficits increase only after climate disasters, whereas for lower-middle-income countries, the increase in deficits is widespread across all events. Gerling (2017), however, finds that after weather-related disasters government primary balances remain surprisingly stable in a sample of 19 countries in Developing Asia from 1970 to 2015.

Koetsier (2017) applies a panel synthetic control method to compare the difference in government debt between the disaster countries and their respective control groups. His dataset comprises 163 countries for the period between 1971 and 2014 and finds a considerable increase in government debt for the most damaging and deadliest disasters. Government debt, on average, increases by 11.3 percent of GDP compared to the synthetic control group, with a median effect of 6.8 percent of GDP. Feyen et al. (2020) consider a large sample of countries and conclude that the debt of the general government is 2.4 percent of GDP higher one year after the disaster. Additionally, sovereign ratings are lower two years later for countries that faced extreme weather events, consistent with the significant rise of the public debt level after one year.

Other studies look at the effects on alternative indicators of public finance soundness. Cevik and Jalles (2020a) find that vulnerability and resilience to climate change have a significant impact on the government borrowing cost. They show that countries with greater vulnerability to risks associated with climate change have higher bond yields and spreads. Additionally, they find that climate change vulnerability and resilience have significant effects on the probability of sovereign debt default (Cevik and Jalles, 2020b) and sovereign credit ratings. The adverse effect is amplified in the case of developing countries owing to their weaker capacity to adapt to the consequences of climate change (Cevik and Jalles, 2020c). Klomp (2017) empirical analysis also indicates that large-scale natural disasters considerably increase the onset probability of a sovereign debt default by about three percentage points, with particularly widespread damage by major earthquakes 3
### and storms.

Theoretical literature on the economic effects of natural disasters has been gradually evolving. Marto et al. (2018) develop a model that explores the macroeconomic impact of a major natural disaster. Their study focuses on debt sustainability concerns that arise from the need to fully rebuild public infrastructure over the medium term and compares policies, such as building adaptation infrastructure and fiscal buffers, with the post-disaster support provided by donors. Burns et al. (2021) develop a macrostructural model for Jamaica that embeds climate damages from natural disasters and considers different risk management strategies to address them. When it comes to DSGE models, Keen and Pakko (2011) develop a model to investigate the appropriate monetary policy response to natural disasters like Hurricane Katrina. Fern ́andez-Villaverde and Levintal (2018) compare different solution methods for computing the equilibrium of DSGE models with rare disasters, given large nonlinearities that could be triggered by these events. Strulik and Trimborn (2019) examine the welfare effects in the aftermath of natural disasters. Similarly, Cantelmo et al. (2019) study the macroeconomic outcomes and welfare implications in disaster-prone countries and policies to mitigate them. Most of the papers focus on the channel in which disasters destroy durable goods or productive capital. Gallic and Vermandel (2020), on the other hand, build and estimate a DSGE model with a weather-dependent agriculture sector to investigate the adverse weather implications in New Zealand.

The contribution of this paper is twofold. First, it provides evidence of the fiscal implications of natural disasters in a broad sample of countries using different specifications and the disaster measure that is based on physical intensity. As pointed out in Felbermayr and Groeschl (2014), EM-DAT and similar databases comprise disaster intensity measures that are a function of economic development. Reported monetary damages of disasters are higher in richer economies, while human costs are usually lower. As a consequence, this is likely to lead to biased estimates. Second, the paper provides a theoretical explanation of the obtained results by looking at the propagation of the shock that affects agricultural productivity. I add Gallic and Vermandel’s (2020) agriculture sector framework to an otherwise standard Real Business Cycle (RBC) model (Kydland and Prescott, 1982; King et al., 1988; King and Rebelo, 1999), extend it with a more detailed fiscal sector, and calibrate the model to resemble the economy of Bangladesh - a country particularly sensitive to frequent weather-related natural disasters.

# **3 The Empirical Framework**

## **3.1 Data**

– The main variable in the analyses the measure of disaster intensity, comes from the ifo GAME database (Felbermayr and Groeschl, 2014). It is based on primary geophysical 4
and meteorological sources, that comprise droughts, earthquakes, floods, storms, and temperature extremes in more than 100 countries for over 30 years.<sup>1</sup> The disaster index exploits physical measures of disaster intensity: precipitation, Richter scale, wind speed, and temperature. As such, it is more suitable for estimating causal relations between natural disasters and economic variables. Other databases (EM-DAT, NatCatSERVICE, and similar) contain disaster indicators that are functions of economic development and, therefore, may lead to biased estimates (Felbermayr and Groeschl, 2014).

More particularly, the weather variation represents the difference in monthly rainfall in mm, defined as the proportional deviation of total monthly rainfall from average monthly rainfall of the entire available period. Flooding events are measured by the positive difference in total monthly precipitation, while droughts are indicated with the dummy variable. It is equal to one if at least three subsequent months have rainfall below 50 percent of the long-run average monthly mean, or if at least five months within a year have rainfall below 50 percent of the long-run monthly mean, and zero otherwise. The database uses the maximum realization within a single earthquake event as the measure of the physical disaster intensity of that earthquake. Similarly, the maximum total wind speed in knots on a country basis is their disaster intensity measure for storms. Temperature extremes are measured as the percentage difference between the maximum temperature in one month from the corresponding long-run monthly mean. Strong positive deviations are interpreted as heat waves, while strong negative ones are defined as cold waves.

The overall disaster index uses the inverse of the standard deviation of a disaster type within a country over all years as precision weights to assure that no single disaster component dominates the movement of the whole index. Furthermore, as the impact of a given disaster on the economy of a country clearly depends on the disaster intensity relative to the overall size of the country, the authors scale all respective disaster variables by land area, as suggested in Skidmore and Toya (2002).

The rest of the data comes from the World Bank WDI. The exceptions are the Polity index which is taken from the Polity IV Project and gross government debt-to-GDP ratios from the IMF Historical Public Debt Database. The final dataset covers an unbalanced panel of 108 economies over the period between 1979 and 2010.

> 1The unbalanced panel covers 108 countries over the period 1979-2010: Albania, Algeria, Angola, Argentina, Armenia, Australia, Azerbaijan, Bangladesh, Belarus, Belgium, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cameroon, Canada, Chad, China, Colombia, Congo, Rep., Costa Rica, Cˆote d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Ethiopia, Finland, France, Gabon, Germany, Greece, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kenya, Korea, Rep., Kuwait, Kyrgyz Republic, Lao PDR, Liberia, Madagascar, Malaysia, Mali, Mauritania, Mexico, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, North Macedonia, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Rwanda, Senegal, Serbia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Uganda, Ukraine, United Kingdom, United States, Uruguay, Vietnam, Yemen, Rep. and Zambia.

5
# **3.2 Methodology**

The empirical analysis in this section uses fixed effect panel regressions and the local projection method. The aim is to estimate the average outcome of natural disasters on macro-fiscal variables, but also the effects in the medium run. I estimate the benchmark panel regressions first, following Felbermayr and Groeschl (2014): where _y_ denotes the dependent variable of interest – GDP per capita growth, change in government debt-to-GDP or fiscal balance. To ensure their stationarity, fiscal variables are expressed as a share of GDP and first-differenced.<sup>2</sup> _ci_ and _τt_ represent country and time fixed effects, while _D_ is a measure of disaster intensity. _X_ stands for a vector of lagged variables that control for the structural, economic policy, and external factors (Skidmore and Toya, 2002; Noy, 2009; Loayza et al., 2012; Felbermayr and Groeschl, 2014). Control variables include the level of development (lagged GDP per capita), the size of the economy (total population), an index of democratization (polity index), openness to trade, inflation, real interest rates, domestic credit, gross capital formation, current account balance, and foreign direct investment. I also consider two regressions with budgetary components as dependent variables - changes in total revenues and expenditures as a share of GDP are also considered. Finally, separate regressions are estimated for the subsamples of advanced economies and EMDEs. In all cases, country-clustered robust standard errors are used for statistical inference.

In addition, I run the local projection method (Jord`a, 2005) to estimate the dynamic effect in the years after the shock. This method provides several advantages over the traditional structural VAR methodology. It estimates sequential regressions of the dependent variable shifted several steps ahead instead of recursive use of the initial set of estimated coefficients. It is, therefore, more robust to potential misspecifications (Auerbach and Gorodnichenko, 2013; Romer and Romer, 2015). The specification is the following: where _k=0,..,4_ , that is, up to four years after the disaster. The same notation and the set of variables are used as before, while _βk_ indicates the cumulative response of _y_ in each k year after disasters. Additionally, to correct for the potential bias described in Teulings and Zubanov (2014), I include the measure of disaster intensity for each period between _t+1_ and _t+k_ . Finally, to compute confidence intervals associated with the estimated coefficients, country-clustered robust standard errors are used again.

> 2Standard F-tests reject the null hypothesis of a unit root presence in considered series, as well as Levin–Lin–Chu unit root tests on balanced subsamples.

6
|7|@||
|---|---|---|
|AlnGDPpe,|ADebt,|ABalance,|
|Disasterindex;<br>-0,0650***|O.0781***|—-0.0437***|
|<br><br>(0.0104)|<br>(0.0141)|<br>(0.00858)|
|InGDP pe:_1<br>-0.0864***|0.0806***|-).0123|
|(0.0160)|(0.0231)|(0.00896)|
|InPopulation;—;<br>-0.0683**|0.0409|-0.00685|
|(0.0325)|(0.0541)|(0.0137)|
|Polityindex,_,<br>0.0106|-0).0521|0.00730|
|<br><br>(0.0130)|(0.0318)|(0.00925)|
|Trade openness;_,<br>0.0391***|-0.0950|0.00830|
|(0.0115)|(0.0593)|(0.00759)|
|Interst rate,_1<br>0.0739|0.488|-0.159|
|(0.180)|(0.479)|(0.128)|
|Domesticcredit;_1<br>-0.0373***|O.0791***|—-0.00793*|
|<br><br>(0.00761)|<br>(0.0244)|<br>(0.00449)|
|Gross capital formation;_,<br>0.0249***|0.00649|-0.00855|
|(0.00825)|(0.0292)|(0.0129)|
|Foreign direct investment,_<br>0.0523|0.112|-0.0583|
|(0.0498)|(0.0932)|(0.0691)|
|InInflation,—;<br>-0.00370**|-0.00333|-0.000150|
|(0.00163)|(0.00433)|(0.00108)|
|Current account balance;_1<br>0.0565<br>|0.0254<br>|-0.0496<br>|
|(0.0406)|(0.0578)|(0.0309)|
|ADebt,_|-0.0299<br>(0.0400)||
|ABalance;_||-0.208***<br>(0.0762)|
|Country fixed effects<br>Yes|‘Yes|Yes|
|Time fixed effects<br>Yes|Yes|Yes|
|N<br>1902|1604|1352|
|Standard errors in parentheses<br>*p<0.10,*p<0.05,"**p<0.01|||
||(1<br>ARevenues,|(2)<br>AExpenditures;|
|---|---|---|
|Disaster index;|0.000153|0.0324***|
||(0.00624)|(0.0117)|
|Controls|||
|N|1438|1253|
|Standarderrorsin|parentheses||

* p < 0.10, ** p < 0.05, *** p< 0.01
|(1)<br>AlnGDPpc,|(2)<br>ADebt,|(3)<br>ABalance;|
|---|---|---|
|Disaster index;<br>-0.0687***<br>(0.0144)|0.0898***<br>(0.0196)|=-0.0296***<br>(0.00916)|
|Controls|||
|N<br>1382|1147|908|
|Standard errors in parentheses|||
|*op<0.10)**pi0.05;***p<0.01|||

<!-- Start of picture text --> Output growth Government debt change Fiscal balance change<br>a 6 05<br>4<br>6 4 -2<br>o 1 2 3 4 0 Ft 2 3 4 i) 1 2 3 4<br><!-- End of picture text -->
of a lagged endogenous variable on the right-hand-side of the equations (Nickell, 1981), since this bias is negligible in panels with longer time dimensions (Judson and Owen, 1999; Felbermayr and Groeschl, 2014).

# **4 The Structural Approach**

This section proposes a DSGE model that explains the transmission mechanism of disasters that affect the agriculture sector. One could think of a shock in the form of adverse weather events. I rely on the standard RBC model that features weather-dependent agricultural productivity and exogenous weather shocks, based on Gallic and Vermandel (2020). The model describes a two-sector (agricultural and non-agricultural), two-good economy, populated by households and firms. Crucially, workers from the agriculture sector face unexpected weather events that affect the productivity of their land. Finally, the government sets the countercyclical fiscal policy. The remainder of the section provides an overview of the model’s structure, its calibration, and proceeds with the simulations and comparison exercises.

## **4.1 The model**

### _Households_

There is a continuum _j ∈_ [0 _,_ 1] of identical households that maximize their lifetime utility. They consume, save, and work in the two production sectors. The lifetime utility function is separable in consumption and individually supplied labor and takes the following form: where the variables _C_ and _h_ represent the consumption index and the labor effort index. _β_ denotes the discount factor, _σ_ the relative risk aversion, _σH_ is the labor disutility coefficient, while _b_ accounts for external consumption habits. Labor supply is affected by a shift parameter _χ_ that pins down the steady state of hours worked.

As in Gallic and Vermandel (2020) and following Horvath (2000), there is a CES labor disutility index with imperfect substitutability of labor supply between the agriculture and non-agriculture sectors: The labor disutility index consists of hours worked in the non-agriculture and agriculture sectors, with a costly relocating across them governed by the substitutability parameter _ι_ .

10
The representative household faces the real budget constraint at every period t: where the income of the representative household comes from the provided labor service _h_<sup>_s_</sup> with a wage _w_<sup>_s_</sup> (in each sector s), and real risk-free domestic bonds _B_ remunerated at a rate _r_ . The household’s expenditures go to its consumption basket, bonds, and the lump-sum taxes _T_ charged by the government.

The representative household allocates total consumption between non-agricultural and agricultural consumption goods. The CES consumption bundle reads as follows: where _μ_ denotes the substitution elasticity between the two corresponding types of goods, while _φ_ is the fraction of agricultural goods in the total consumption basket. The consumption price index is determined accordingly by: Finally, demand for each type of good is a function of the total consumption index and its relative price:

# _Agriculture sector_

The economy is populated by a unit mass _i ∈_ [0 _,_ 1] of entrepreneurs, where a fraction _n_ operate in the agricultural sector (farmers).<sup>4</sup> The agricultural output is produced with the Cobb-Douglas production function: where _y_<sup>_A_</sup> is the outcome of farmers’ activity that combines an amount of land _l_ , physical capital _k_<sup>_A_</sup> , and labor _h_<sup>_A_</sup> inputs. The production is subject to a weather shock through a function Ω � _ε_<sup>_W_</sup> _t_ � described in detail below and an economy-wide technology shock _ε_<sup>_Z_</sup> that follows an AR(1) process. The parameter _ω_ is the elasticity of output to land, _α_ stands for the physical capital share in the agricultural production, whereas _κA_ is a technology parameter endogenously determined in the steady state.

The farmers’ land _l_ is subject to both economic and meteorological conditions. The agricultural production process between _t-1_ and _t_ might be affected by the unexpected realization of the weather _ε_<sup>_W_</sup> through a simple damage function:

> 4Entrepreneurs could switch from one sector to another according to a stochastic AR(1) process.

11
where _θ_ determines the elasticity of land productivity with respect to the weather, with lower values implying reduced propagation of weather-driven business cycles. To capture the implications of weather shocks as a source of aggregate fluctuations, the weather variable _ε_<sup>_W_</sup> dynamics is introduced as: where _ρW_ and _σW_ stand for the persistence of the weather shock and its standard deviation. In addition to the contemporaneous impact of weather events, agricultural production may be subject to effects that spread over time. For example, prolonged severe droughts entail early liquidation of stocks combined with a drop in the fertility rate (Kamber et al., 2013), while soil moisture deficits exhibit a persistence that is directly connected to the interaction between rainfalls and evapotranspiration, as lands require several months to recover their average productivity levels (Narasimhan and Srinivasan, 2005). Therefore, the productivity of land is assumed to be time-varying, following an endogenous law of motion: where _δl_ is the rate of decay of land productivity that reflects the potential persistence effect. The marginal product of land is increasing in the accumulation of land productivity and captured by assuming that land expenditures _x_ yield a gross output of new productive _′ ′′_ land _υ_ ( _xi,t_ ) _li,t−_ 1 with _υ >_ 0 and _υ ≥_ 0. These expenditures could be considered as agricultural spending on pesticides, herbicides, seeds, fertilizers, and water used to maintain farmland productivity. The functional form of land costs is given by: where for _φ →_ 0 land productivity exhibits constant returns, while for _φ >_ 0 returns are increasing. The parameter _τ_ allows to pin down the amount of per capita land in the steady state.

The law of motion of agriculture sector physical capital has the usual form: where _δK_ is the depreciation rate of physical capital, whereas _i_<sup>_A_</sup> accounts for investment.

The profit maximization of the price-taking representative farmer can be cast as choosing the input levels under land efficiency, capital law of motion, and technology constraint: 12
where _E_ denotes the expectation operator, Λ is the household stochastic discount factor between _t_ and _t+1_ , and _d_ represents real profits that are given by: in which _p_<sup>_A_</sup> is the relative production price of agricultural goods, whereas the function _S_ ( _x_ ) = 0 _._ 5 _κ_ ( _x −_ 1)<sup>2</sup> denotes the convex investment cost function as in Christiano et al. (2005).

# _Non-agriculture sector_

There is a continuum _i_ of perfectly competitive non-agricultural producers with the following Cobb-Douglas production function<sup>5</sup> : where _y_<sup>_N_</sup> is the production of non-agricultural output in the _i_ th firm, with the technology _ε_<sup>_Z_</sup> and input factors - physical capital _k_<sup>_N_</sup> and labor services _h_<sup>_N_</sup> . The parameters _α_ and 1 _− α_ denote output elasticity to capital and labor. Technology is assumed to be economy-wide (the same across sectors) and characterized as an AR(1) shock process: where _ρZ_ and _σZ_ represent the persistence and the standard deviation of the shock. The physical capital in the non-agricultural sector evolves in the following way: where, as before, _δK_ denotes the depreciation rate of physical capital, while _i_<sup>_N_</sup> is investment from non-agricultural firms. The representative non-agricultural firm maximizes the discounted sum of profits, under capital accumulation and technology constraints: with real profits calculated as:

> 5Non-agricultural firms do not require land inputs to produce goods and are not directly affected by weather.

13
## _Fiscal authority_

The government spends on non-agricultural output and collects lump-sum taxes. It also pays interest on the previous stock of debt and issues new debt. At every period, there is the following budget constraint: The value of government spending follows the process described below: assuming that the non-stochastic steady-state value ( _G_ ) is an exogenous fraction of steady-state output. The government sets its policy in a countercyclical manner - when output is below its steady state, the government spending increases. Given that the weather shock is the only one considered in the model, this aims to resemble fiscal support to post-disaster recovery efforts, including rebuilding damaged infrastructure and providing social assistance to affected households. The parameter _φG_ measures the strength of the government response.

Following Gali et al. (2007) and Cogan et al. (2010), the lump-sum tax reacts to respective deviations in the existing value of debt and government spending: where _ρB_ and _ρG_ are positive constants. The non-stochastic steady-state value of debt ( _YB_<sup>)issettobeinlinewiththeobserveddata.</sup>

# **4.2 Aggregation and equilibrium**

In addition to the first-order conditions from the household and entrepreneurs’ optimization problems, market clearing conditions are necessary to set the standard equilibrium equations.

The non-agricultural products market clearance is determined when the aggregate supply is equal to aggregate demand: suggesting that the total supply of non-agricultural goods is equal to the respective household and government consumption, aggregate investment, and agricultural expen14
ditures.

The equilibrium of the agricultural goods market, on the other hand, is given by: In sum, aggregate real production is determined as a combination of both: Given the presence of intermediate inputs, the GDP is calculated as: The labor market clearing condition between household labor supply and demand in each sector enables writing the total number of hours worked as:

# **4.3 Calibration**

The model is log-linearized and calibrated in quarterly frequency to resemble the economy of Bangladesh (Table 4). Some parameter values are set such that the steady-state conditions are consistent with long-run historical averages and microeconomic evidence. Other parameters are calibrated in line with the values commonly used in the literature for developing and developed countries or according to Gallic and Vermandel’s (2020) estimations. More specifically, _β_ is set to 0.98, as Ahmad et al. (2012) find similar values for the long-run discount factor in a group of developed and developing economies. The depreciation rate of physical capital ( _δK_ ) is 0.025, with the capital share in technology ( _α_ ) of 0.31, both as in Amin et al. (2018, 2019).

Turning to the agriculture sector, the share of agricultural goods in the consumption basket of households ( _φ_ ) is set to 50 percent, given the higher share of food products in developing economies’ consumption. In addition, the land-to-employment ratio is 0.12, based on the hectares of arable land per person in Bangladesh (World Bank WDI). Parameter values imply the steady-state share of the agriculture sector close to the values observed in data (World Bank WDI). The elasticity of land productivity with respect to the weather ( _θ_ ) is set to 20.59 with alternative values considered in the following subsection. Finally, an extreme weather event is proxied by a weather shock with the persistence 0 and the standard deviation of 0.58, such that it results in the contemporaneous impact of 1 percent of GDP.

When it comes to fiscal parameters, the steady-state share of spending in GDP is 0.14 (IMF WEO), while the debt-to-GDP ratio equals 30 percent (IMF WEO). Lumpsum tax function parameters ( _ρB_ and _ρG_ ) are 0.33 and 0.1, as in Gali et al. (2007).

15
|Parameters taken from the literature|||
|---|---|---|
|Discount factor|fs]|0.98|
|Capital depreciation rate|OK|0.025|
|Share of capital in output|a|0.31|
|Hours worked|H|1/3|
|Risk aversion|o|1.37|
|Labor disutility|oH|427|
|Land cost function parameter|e|3.57|
|Consumption habits|5|0.59|
|Labor sectoral cost|t|2.85|
|Land efficiency decay rate|or|0.05|
|Substitutability by type ofgoods|#|5.96|
|Investment adjustment cost|K|2.44|
|Tax function parameter|PE|0.33|
|Tax function parameter|Pe|O.1|
|Parameters matching data|||
|Steady-state share ofspending inGDP|S|0.14|
|Share of agricultural goods in consumption basket|p|0.5|
|Land per capita|T|0.12|
|Steady-state debt-to-GDP|z|0.3|
|Uncertain parameters|||
|Government spending reaction|oa|2|
|Share of land in the agricultural production function|id|0.3|
|Land-weather elasticity|é|20.59|
|Disaster (AR term)|pw|0|
|Disaster(5D)|ow|0.58|
<!-- Start of picture text --> 0 GDP 0 Agricultural output 0.8Non-agricultural output 0.4 Hours<br>-1 0.6} 0.3;<br>-0.5<br>-2 0.4 0.2<br>-3 0.2 0.1<br>-1<br>5 10 15 -4 5 10 15 0 5 10 15 0 5 10 15<br>0.4 Agricultural price 0 Land efficiency 0.02 Land cost 2 Debt-to-GDP<br>0.3 1.5<br>0.5 0.01<br>0.2 1<br>as, | 0<br>0.1 0.5<br>0 a8 -0.01 0<br>5 10 15 5 10 15 5 10 15 5 10 15<br><!-- End of picture text -->
<!-- Start of picture text --> GDP Debt-to-GDP<br>-0.4 2<br>-0.6<br>15<br>0.8<br>A<br>1<br>A2<br>4 0.5<br>26 26<br>24 16 24 16<br>22 2 =«'4 22 | (2 4<br>Land sensitivity20 18 16 0 2 4 Period6 8 10 Land sensitivity20 18 16 0 2 4 PeriodA 8<br><!-- End of picture text -->

<!-- Start of picture text --> GDP Debt-to-GDP<br>-0.7 2<br>08 15<br>-0.9<br>1<br>4<br>0.5<br>Aa<br>A2 0<br>44<br>°21 7 6 "10 >182 1 " 6 m 10 a<br>Government response o oo 2 Period Government response o 0 2 Period<br><!-- End of picture text -->
# **5 Conclusion**

The analyses in this paper show that natural disasters lead to fiscal space shrinkage along with a slowdown in economic growth. A disaster of one standard deviation above the mean of the disaster index reduces output growth per capita by 1.32 percent and increases debtto-GDP by 1.58 percentage points. The results are driven by developments in EMDEs and persist in the medium term. The observed climate-fiscal nexus implies that disasterprone countries may feature a limited capacity to respond to extreme events, but also to build up the necessary resilience. Disaster relief and infrastructure rebuilding require fiscal costs that would add implicit charges on available space, emphasizing the need to supplement future fiscal assessments with the context of climate change (Heller, 2020).

The model simulations illustrate the importance of building stronger resilience in countries vulnerable to severe natural disasters. That requires a multipronged approach (IMF, 2019; Heller, 2020): first, better structural resilience by investing in both “hard” policy measures (e.g. physical infrastructure) and “soft” measures (such as establishing early warning systems); second, building financial resilience to protect fiscal sustainability and manage recovery costs, which includes self-insurance in the form of the provision of fiscal buffers and the transfer of risk through insurance and other risk-sharing mechanisms; and finally, creating post-disaster (and social) resilience that requires contingency planning to ensure a speedy response to a disaster. Climate change is inevitable, but better policy management should help to cope with its consequences for public finances in particular and economic development in general (Feyen et al., 2020).

A comprehensive climate financing strategy will be necessary to adapt to the physical effects of climate change. Neither public money nor the private sector alone can do that job. Concessional funding, combining public and private capital through innovative financial instruments, and appropriate policies to align incentives with climate objectives must have their roles. International financial institutions will also have to step in through funding, technical assistance, and capacity development.

There are several ways to extend the analyses in the paper. One is to consider alternative propagation channels of disaster impacts, for example, through the effects on productive capital. Another possibility could be to discuss different government management strategies for disaster damages. Comparing adaptation investments, purchasing insurance from the private sector, or creating fiscal buffers in anticipation of responding to shocks, could provide valuable insights for policy makers.

19