# Is growth at risk from natural disasters? Evidence from quantile local projections

Nabil Daher

► To cite this version:

Nabil Daher. Is growth at risk from natural disasters? Evidence from quantile local projections. Ecological Economics, 2026, 243, pp.108933. 10.1016/j.ecolecon.2026.108933. hal-05466345

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# Is growth at risk from natural disasters? Evidence from quantile local projections

Nabil Daher ID

University of Paris Nanterre, Economix UMR-CNRS 7235, France

## ARTICLE INFO

**JEL classification:**

C23

E32

O11

O57

Q54

**Keywords:**

Natural disasters

Developing economies

Quantile local projections

## ABSTRACT

This paper investigates the heterogeneous and state-dependent effects of natural disasters on GDP per capita growth in developing countries. Using a panel of 67 countries, I apply the Quantile Local Projection (QLP) method to estimate Quantile Impulse Response Functions (QIRFs) following different types of disasters—floods, droughts, storms, and earthquakes. By focusing on the conditional distribution of growth rather than its average, the analysis captures how disasters distort growth distribution at the tails. Results reveal that natural disasters represent a growing downside risk, significantly amplifying the likelihood and depth of recessions at the lower tail of the distribution. In contrast, responses at the upper tail are often expansionary in some contexts, suggesting asymmetric effects. These patterns are shaped by countries' income levels, institutional quality, and sectoral structure. Overall, the findings highlight the importance of distribution-sensitive approaches to better understand and manage the macroeconomic consequences of climate and disaster shocks.

## 1. Introduction

Over the last three decades, natural disasters have become more frequent and intense, posing a major risk to economic activity—especially in developing countries, where vulnerability is heightened by institutional fragility and limited adaptive capacity. For instance, between June and September 2022, floods in Pakistan affected 33 million people and caused over 1730 deaths. Similarly, the 2023 earthquakes in Turkey and Syria led to more than 59,000 fatalities. Estimated economic losses from these two events exceed USD 30 and 34 billion, respectively (The World Bank, 2022, 2023). These events illustrate how natural disasters can trigger widespread disruption by destroying infrastructure, displacing populations, and constraining productive capacity (IPCC, 2012; NGFS, 2024).

Although empirical and theoretical studies over the last two decades present nuanced results regarding the sign of disasters' impact on economic growth, the common consensus is that disasters have an overall negative impact on average growth. In this context, a key insight emerging from recent empirical works is that natural disasters can distort the whole conditional distribution of economic growth, shifting it to the left. In this view, the macroeconomic risk posed by disasters lies not just in average output losses, but in their capacity to increase the probability of severe adverse outcomes. This idea is consistent with the growing macro-financial literature on Growth-at-Risk (GaR hereafter), which emphasizes the importance of modeling the entire growth distribution to assess risks stemming from financial (Adrian

et al., 2019, 2022) or environmental conditions. Recent applications of this methodology have shown that increasing temperatures due to global warming (Kiley, 2024), or natural disasters (Bayoumi et al., 2021; Chavleishvili and Moench, 2025) shift the lower tail of the conditional growth distribution leftward, raising the likelihood and severity of low-growth outcomes.

GaR models effectively estimate the likelihood of low-growth outcomes under macro-financial conditions but are static and offer limited insight into the dynamics of shock transmission. To address this limitation, this study employs a Quantile Local Projection (QLP) approach to estimate Quantile Impulse Response Functions (QIRFs) to disaster shocks. This methodology combines the flexibility of local projections (Jordà, 2005) with the distributional sensitivity of quantile regressions, allowing me to trace the path of conditional quantiles of GDP growth evolve over time after a shock. It thus captures both immediate and persistent effects across the distribution, offering a dynamic complement to GaR (Han et al., 2024; Jung and Lee, 2023).

Beyond forecasting tail risks, this paper seeks to explain them. The QIRF framework reveals asymmetric, state-dependent responses without imposing arbitrary regime classifications. Unlike threshold models, which require predefined states (e.g., recession vs. expansion), QLPs enable the data-driven identification of growth distribution tails. Therefore, conditional quantiles are estimated controlling for country-specific and time-varying factors, allowing countries to move in and out
of the tails of the growth distribution over time. This dynamic structure is particularly suited to assessing how shocks propagate under evolving states of vulnerability, consistent with recent empirical insights on heterogeneity in disaster impacts.

Recent research underscores the importance of accounting for such heterogeneity. Hallegatte and Ghil (2008) show that high-growth phases may entail greater disaster exposure due to higher capital concentration. Ginn (2022), focusing on the United States, shows that the macroeconomic effects of disasters are state-dependent and amplified during economic expansions. Similarly, Atsalakis et al. (2021), using a quantile-on-quantile approach, find complex, non-linear associations between disaster severity and economic growth regimes.

While the GaR literature views natural disasters as macroeconomic tail risks that materialize in downturns, state-dependent analysis reveals a contrasting pattern: high-growth periods are also highly exposed to disaster shocks. The QLP method facilitates the estimation of the impact of disaster shocks on the lower and upper tails of the growth distribution over time, thereby capturing heterogeneous and dynamic responses. Consequently, QIRFs provide insight into the manner in which disasters distort the growth distribution, including the persistence and transmission channels of extreme outcomes (Jung and Lee, 2023).

This paper sheds light on the mechanisms behind these outcomes. I examine the differential effects of various disaster types on growth distributions and explore heterogeneity by sector, income level, and institutional strength (proxied by the ICRG corruption score). Empirically, I use a panel of 67 developing countries from 1985 to 2019, focusing on four types of disasters: droughts, floods, storms, and earthquakes. Methodologically, I shift the focus from average effects to the evolution of distributional tails following a disaster shock. I test whether disasters heighten downside risks, as in GaR as in Bayoumi et al. (2021), Chavleishvili and Moench (2025), Kiley (2024), or hinder growth during expansions, as suggested by Ginn (2022), Hallegatte and Ghil (2008).

To do so, I employ a recent estimator for panel quantile local projections: the smoothed quantile panel regression method of Chen and Huo (2021) and He et al. (2023). This method builds on Canay (2011)'s two-step approach. First, it removes fixed effects via an OLS-FE regression; then it estimates quantile regression on the purged variable. The smoothed version improves inference by using a differentiable loss function. Embedding this method in a local projection framework (Jordà, 2005) yields Quantile Impact Regression Functions that capture dynamic, quantile-specific responses while controlling for unobserved heterogeneity and time-varying factors.

To the best of my knowledge, this is the first application of QLPs to analyze disaster impacts in a panel of developing countries. This is especially relevant given the structural vulnerabilities of these countries, including limited fiscal space, weak institutions, and reduced adaptive capacity (Bos et al., 2022; Cavallo et al., 2022; Noy, 2009). Moreover, understanding how disaster effects vary by type, sector, income level, or corruption level is essential for crafting effective resilience policies. These transmission channels are explored throughout the paper.

The results show that disaster shocks amplify the likelihood and severity of growth contractions in the lower tail, particularly in low-income countries and those with weak institutional frameworks. This result is mainly driven by the vulnerability of the agricultural and manufacturing sectors. In contrast, my results suggest that the upper tail shifts upward, indicating greater probability of unusually strong expansions triggered by reconstruction and long-term growth dynamics through mechanisms such as creative destruction and sectoral reallocation.

The paper is structured as follows. Section 2 reviews the existing literature that investigates the macroeconomic impact of natural disasters, Section 3 outlines the empirical approach, Section 4 presents the data and some facts about natural disasters, while results are presented in Section 5. Section 6 concludes.

## 2. Literature review

The increasing frequency and severity of natural disasters has sparked a growing interest in understanding their impact on economic growth. This interest has created a need for policymakers to have valuable insights into the benefits of reducing and mitigating risk. The existing body of empirical macroeconomic literature offers valuable yet nuanced evidence. While some studies indicate negative effects, others find no impact, or even positive effects, of natural disasters on growth.

An early study by Skidmore and Toya (2002) suggests that disasters might boost economic growth by accelerating human capital accumulation, updating capital stock, and adopting new technologies, thereby enhancing productivity. However, Hallegatte and Dumas (2009) show that disasters do not alter long-term growth rates. Conversely, Noy (2009) shows that natural disasters can be detrimental to economic growth, particularly in developing and small economies, which experience more significant output declines than larger, developed economies for similar disaster magnitudes.<fnref n="1" /> The observed heterogeneity in the impact of natural disasters between developed and developing countries is attributed to developed countries' ability to implement countercyclical fiscal and monetary policies in response to adverse shocks, a capacity often lacking in developing economies (Beirne et al., 2021, 2024; Bos et al., 2022). Interestingly, Cavallo et al. (2013) suggest that natural disasters are unlikely to affect economic growth. However, they find that natural disasters only caused significant declines in GDP growth when followed by political unrest and institutional disorganization.

In light of the conflicting results concerning the aggregate impact of natural disasters on economic growth, certain studies have proposed more detailed analyses. For instance, Fomby et al. (2013), Loayza et al. (2012) and Panwar and Sen (2019) examine the effect of different types of disasters on economic sectors in both developed and developing countries. The findings indicate that disasters exert distinct effects across economic sectors, contingent upon the nature of the disaster. It is noteworthy that these effects on growth are not exclusively adverse; on the contrary, their findings demonstrate that the utilization of disaster-type indices yields superior statistical properties in comparison to aggregated indices.<fnref n="2" /> This body of literature suggests that floods and droughts have contrasting effects on GDP growth, primarily through their impact on agricultural production. Moderate floods can benefit crops by providing excess water and moist soil, leading to higher yields. However, severe floods can destroy capital and crops, offsetting any potential benefits. Droughts also negatively affect agricultural output by reducing water availability, which leads to lower productivity in the primary sector. Earthquakes and storms have more nuanced effects because reconstruction efforts can stimulate economic growth after a disaster. While earthquakes may boost non-agricultural sectors through rebuilding activities, storms tend to cause an initial decline in economic activity, followed by a rebound driven by recovery investments. In a recent work, Coronese et al. (2025) has emphasized the macroeconomic and distributional consequences of recurrent, localized shocks. The authors document persistent wage losses from thunderstorms in the U.S., particularly in capital-intensive sectors, and highlight how such hazards exacerbate functional income inequality.<fnref n="3" />

<footnote n="1">¹ Several theoretical macroeconomic papers consider uncertainty from time-varying disaster risk. These models suppose that disaster risk can cause a recession if the elasticity of intertemporal substitution is greater than one, leading to an increase in the equity premium. In other words, rational agents facing disaster risk would prefer to reduce current consumption to increase precautionary savings (Cantelmo et al., 2023; Isoré and Szczerbowicz, 2017).</footnote>

<footnote n="2">² Similarly, Mohan et al. (2018) find that aggregate analyses are likely to mask different responses of the components of GDP to hurricane shocks.</footnote>

<footnote n="3">³ Other empirical and theoretical studies address many economic implications of natural disasters such as employment (Barattieri et al., 2023), government revenue and consumption (Akyapi et al., 2022) and sovereign default risk (Beirne et al., 2021; Klomp, 2015; Mallucci, 2022), financial stress</footnote>
As suggested earlier, the study of the economic effects of natural disasters has evolved beyond examining average GDP growth responses. In their seminal 2008 paper, Hallegatte and Ghil found that the economy exhibits a larger adverse response to disasters during economic expansions than during economic contractions. Using an Endogenous Business Cycle Model (EnBC), the authors argue that unused resources during recessions dampens exogenous shocks. This makes high-growth periods highly vulnerable to supply shocks from natural disasters due to pre-existing disequilibria. In a recent working paper, Ginn (2022) provides compelling evidence supporting the findings of Hallegatte and Ghil (2008). Using a non-linear vector autoregression (VAR) model for the US, Ginn (2022) argues that economic activity is much more vulnerable during periods of high growth.

This cyclical heterogeneity underscores how internal dynamics and the phase of the business cycle can influence the macroeconomic impact of natural disasters. More recently, empirical approaches have expanded this research by examining the distributional effects of disasters across the growth distribution. Atsalakis et al. (2021) have acknowledged these heterogeneous distributional effects by employing a quantile-on-quantile approach to investigate the relationship between natural disasters and economic growth. Their results corroborate those of Hallegatte and Ghil (2008), suggesting that the impact of natural disasters on growth depends on the phase of the business cycle in which a disaster occurs. This reinforces the idea of a vulnerability paradox, whereby economies are more resilient to the adverse effects of natural disasters during periods of recession. However, their analysis is static and does not focus on dynamic effects over time.

On the other hand, the Growth-at-Risk (GaR) framework, introduced recently by IMF (2017b), shifts the focus from average effects to the lower tail of the growth distribution. This framework emphasizes the increased likelihood of severe economic downturns given financial conditions (Adrian et al., 2019, 2022), or on climate change (Kiley, 2024) and natural disasters (Bayoumi et al., 2021; Chavleishvili and Moench, 2025). When applying this methodology to a panel of developing countries, research demonstrates that, following extreme natural events, the probability of experiencing a severe economic downturn increases, particularly in countries with limited fiscal space or inadequate disaster preparedness.

However, GaR is a static, predictive tool with limited ability to trace dynamic causal responses. Similarly, state-dependent models capture nonlinearities by estimating responses conditional on a predefined regime (e.g., expansion or recession). To overcome these limitations, this paper uses a quantile local projection (QLP) approach, which has two main advantages. First, QLP estimates dynamic impulse responses across the entire growth distribution (e.g., at the 10th and 90th percentiles), capturing how the effects of disasters evolve over time. Second, QLP does not require specifying economic states or conditioning variables, unlike regime-switching models. Instead, QLP flexibly captures heterogeneous and asymmetric responses across quantiles. This is particularly relevant in developing countries, where disasters may deepen growth collapses or trigger sharp recoveries depending on the context.

This paper aims to reconcile two strands of literature by analyzing the impact of natural disasters on GDP growth distribution. Specifically, quantile impulse response functions (QIRFs) estimated via quantile regression (QLP) at the tails of the distribution capture how extreme outcomes evolve over time following disaster shocks. For instance, a notably negative QIRF at the tenth percentile indicates a leftward shift in the distribution, suggesting more severe recessions (Jung and Lee, 2023). Thus, QIRFs enable the analysis of the dynamic propagation of tail risks. This approach offers a structural complement to the GaR

literature by tracing the effects of identified exogenous shocks, such as natural disasters.

By applying this framework to a large panel of developing countries, the paper examines whether natural disasters amplify downside risks (left-tail deterioration) systematically or generate heterogeneous effects across the growth distribution. This question is particularly pertinent in low- and middle-income countries, where disaster exposure is high, insurance coverage is limited, and fiscal resources are often scarce. Thus, the results speak directly to macro-financial policy debates around disaster risk management, external support mechanisms, and the design of countercyclical stabilization policies.

Finally, sectoral and institutional decompositions of QIRFs can provide insights into the transmission channels of disaster impacts and the structural sources of vulnerability. These results can help distinguish whether increased economic fragility reflects direct losses or broader amplification mechanisms.

### 3. Empirical approach

Quantile regressions have gained popularity in macroeconomic applications as a tool for capturing the heterogeneity of shock effects across an outcome variable's distribution. This is particularly relevant in the context of extreme events, such as natural disasters, which disproportionately affect the tails of the GDP growth distribution, as discussed in Atsalakis et al. (2021), Bayoumi et al. (2021), Chavleishvili and Moench (2025) and Kiley (2024). Unlike traditional linear models, quantile regression enables the estimation of conditional quantiles of the response variable. This provides a more nuanced view of the distributional effects of shocks.

To translate this distributional perspective into a dynamic framework, I rely on the Quantile Local Projections (QLP) approach, which combines quantile regression with the local projections method (Jordà, 2005, 2023). This allows for the estimation of quantile impulse response functions (QIRFs), which capture the evolution of the conditional quantiles of GDP growth in response to natural disaster shocks. QLPs are particularly well-suited to this context due to their flexibility, robustness to misspecification, and ability to accommodate nonlinear dynamics or heterogeneous responses without requiring full-system estimation (Jordà and Taylor, 2024; Olea et al., 2024).<fnref n="4" />

Consider the standard local projections (LP) specification is given by:

$$ \Delta_h y_{i,t+h} = \alpha_i + \delta_{r_{xt}} + \beta_h (DS_{i,t}^k + x_{i,t}) + \varepsilon_{i,t}, \quad (1) $$

for $h = 1, ..., 10$, and where $\Delta_h y_{i,t+h} = \log y_{i,t+h} - \log y_{i,t-1}$ denotes the cumulative growth of real GDP per capita over h periods.<fnref n="5" /> Country ($\alpha_i$) and region-by-year interaction ($\delta_{r_{xt}}$) fixed effects are included to control for time-invariant country characteristics, common global shocks, and region-specific time-varying factors (See among others, Dell et al., 2012; Hsiang and Jina, 2014). This specification ensures that the estimated coefficients reflect within-country variation over time, thereby isolating the dynamic effect of disaster shocks from persistent cross-country differences in growth levels or trends. In other words, the identified responses capture how a given country's growth trajectory responds to a disaster shock, relative to its own historical baseline and conditional on a rich set of macroeconomic controls and fixed effects. All estimates are conditional on contemporaneous and one lag of the controls, and the dependent variable lagged by one year.

<footnote>and bankruptcy (Avril et al., 2025; Klomp, 2014), inflation dynamics (Beirne et al., 2022; Fratzscher et al., 2020; Kabundi et al., 2022) and monetary policy (Cantelmo et al., 2024; Hallegatte et al., 2024; Klomp, 2020).</footnote>

<footnote n="4">⁴ Li et al. (2024) show that LPs estimated via OLS exhibit lower bias than least-squares VARs but come with higher variance.</footnote>

<footnote n="5">⁵ I consider this long-difference specification to control for unobserved fixed effects, and reducing biases caused by autocorrelation or near-unit-root variables (Jordà, 2023). Hence, QIRFs are quantile paths of cumulative growth, and figures report deviations of these growth cumulations from the no-disaster path.</footnote>
The present study builds on this baseline by expanding the framework to estimate quantile local projections. This expansion is undertaken to investigate how disaster shocks affect the tails of the growth distribution. It is important to note that this specification is not designed to establish a causal link between disasters and economic expansions or recessions. Rather, the goal is to assess how the dynamic response of output to disasters differs across economic states, as proxied by conditional quantiles of the growth distribution. Quantile regression is ideal for this purpose because it models the conditional quantiles $Q_{\tau}(Y | X)$ of a response variable, which allows the marginal effect of covariates to vary across the distribution. In this context:

$$Q_{\tau}(\Delta_h y_{i,t+h} | X_{i,t}) = \alpha_i^{\tau} + \delta_{\tau x_i}^{\tau} + \beta_h^{\tau}(DS_{i,k,t} + x_{i,t}) + \epsilon_{i,t}^{\tau}, \quad (2)$$

where $Q_{\tau}(\cdot | X_{i,t})$ denotes the $\tau$-th conditional quantile of future GDP growth given contemporaneous information.

In QLPs, coefficients trace the path of cumulative GDP per capita growth relative to a no-disaster path. A negative (positive) response at quantile $\tau$ means that, holding controls and fixed effects constant, the $\tau$-th percentile of the conditional distribution shifts downward (upward), increasing the likelihood and/or severity of outcomes below (above) that quantile. These results do not imply that all economies contract or expand; they characterize distributional shifts — i.e., changes in tail risks — conditional on the same information set.

### 3.1. Panel quantile regressions

Due to the presence of unobserved heterogeneity, estimating panel quantile regression poses a significant challenge. Classical fixed-effects quantile regression (FEQR) estimates $\alpha_i^{\tau}$ jointly with $\beta^{\tau}$ under the assumption that the individual effect is additive and common to all quantiles (Koenker, 2004). However, joint estimation is computationally intensive when $N$ is large, and as Chen and Huo (2021) show, it suffers from an incidental-parameter bias of order $N/T$.

To address this issue, Canay (2011) proposes a lightweight alternative: (i) estimate $\hat{\alpha}_i$ using an OLS-FE estimator; (ii) subtract the estimated $\hat{\alpha}_i$ from $y_{it}$ and run a standard quantile regression on the purged dependent variable. This procedure is consistent when $N/T \to 0$ inference is invalid in the common empirical case where $N$ and $T$ are similar in magnitude.

Chen and Huo (2021) refine this strategy using smoothed quantile regression (SQR). They replace the non-differentiable check function with smooth approximation to enable fast computation. More importantly, they derive explicit corrections for the bias introduced in the first step. This estimator provides valid inferences without relying on restrictive asymptotic conditions.

The method proceeds as follows. First, fixed effects $\hat{\alpha}_i^h$ are estimated using the linear LP model in Eq. (1). These are then subtracted from the outcome, yielding $\Delta_h y_{i,t+h}$, the purged outcome variable. In the second stage, quantile regression is applied using a smoothed objective function:

$$\min_{\hat{\beta}_h} \sum_{i,t} \rho_{\tau}^{b} (\Delta_h y_{i,t+h} - X_{i,t} \beta_h), \quad (3)$$

where the smoothed check function is given by:

$$\rho_{\tau}^{b}(u) = u\left(\tau - \Lambda\left(\frac{u}{b}\right)\right), \quad (4)$$

with $\Lambda(\cdot)$ a smooth cumulative distribution function (e.g., standard normal), and $b$ the bandwidth parameter set to 0.05. This value is chosen pragmatically, based on a sensitivity analysis showing that impulse response functions (IRFs) stabilize for values above 0.03. It represents a trade-off between over-and under-smoothing, ensuring robust and interpretable QIRFs. Similar qualitative results are obtained with alternative values of $b$, confirming the stability of the estimates.<fnref n="6" />

This approach utilizes recent advances in smoothed quantile regression to provide stable coefficient estimates and facilitate valid inference. Specifically, I construct confidence intervals using asymptotic approximations and bootstrap procedures, as recommended by Chen and Huo (2021) and He et al. (2023).<fnref n="7" /> In my application, both inference methods (asymptotic and bootstrap) yield highly consistent results, and the choice of method does not materially affect the estimated quantile impulse response functions (QIRFs).

Although it simplifies the computation in comparison with fully nonlinear estimators, Chen and Huo (2021) demonstrate that the two-step SQR still exhibits a bias resulting from the fixed effects estimation. Notably, this bias is analytically tractable, and the authors provide corrections using both analytical bias adjustment and split-panel jackknife techniques. It is imperative to implement these corrections to ensure the validity of inferences, particularly in scenarios where the panel is imbalanced or the sample size $N$ is disproportionately large relative to $T$.<fnref n="8" />

Compared to alternative panel quantile methods, SQR offers a compelling trade-off between computational tractability and statistical robustness. It retains the two-step structure of Canay (2011), while addressing its limitations by allowing for bias corrections when the incidental parameter problem becomes significant, especially when $N/T$ is not small (Galvao and Kato, 2016; Kato et al., 2012). Unlike the fixed-effects quantile regression of Koenker (2004), SQR avoids solving high-dimensional linear programs and does not require tuning penalty parameters thanks to its smooth, and differentiable objective function.

In contrast to moment-based approaches, such as Machado and Silva (2019), SQR does not rely on location-scale assumptions or higher-order moments, thus preserving the robustness properties of the quantile loss function. Furthermore, the non-additive fixed effects approach of Powell (2022) offers important flexibility by allowing for heterogeneity and arbitrary correlation between fixed effects and instruments; however, it typically requires instrumental variables and GMM estimation. This may pose practical limitations in macroeconomic panel settings where time variation is limited, and shocks are sparse. These properties make SQR particularly appropriate for this study. Indeed, in such settings, other quantile estimators often suffer from convergence issues or bias. However, the smoothed framework enables consistent estimation and reliable inference with a manageable computational burden. The two-step smoothed quantile regression approach is appealing because it combines computational feasibility with sound inferential properties. Recent developments further emphasize this advantage (see, for instance, Galvao et al., 2024; He et al., 2023).

### 4. Data

I use annual panel data for 67 developing countries over the period 1985–2019. The panel is unbalanced because of data scarcity in some countries. Appendix A gives the list of countries considered in the study. The selection of countries is indicative of data availability on GDP per

<footnote n="6">⁶ As emphasized by Chen and Huo (2021), a fixed bandwidth is commonly used in applied work, and formal selection methods remain underdeveloped. The choice of $b = 0.05$ reflects both empirical robustness and computational tractability.</footnote>

<footnote n="7">⁷ Inference in quantile panel regressions is particularly challenging due to the presence of fixed effects and the non-differentiable nature of the check function (Galvao et al., 2024; Kato et al., 2012). Smoothing the loss function addresses these challenges by enabling standard optimization techniques and facilitating asymptotic derivations. It also improves small-sample performance by stabilizing the estimator when $T$ is limited, which is often the case in macro-panel settings.</footnote>

<footnote n="8">⁸ I tested for the presence of incidental parameter bias by applying the split-panel jackknife (SPJ) correction following Chen and Huo (2021), and only minor differences were observed suggesting that the bias is limited in my setting. While the SQR approach does not eliminate the incidental parameter bias, Chen and Huo (2021) find that it tends to be small in practice. Therefore, SQR contributes to more stable estimates and facilitates the application of formal bias corrections. The overall conclusions regarding the sign, persistence, and statistical significance of the estimated effects remain unchanged.</footnote>
capita, disaster intensity (fatalities and affected population), and key control variables, which impose substantial constraints in many low-income and small economies. Despite this, the resulting panel remains geographically diverse and encompasses a wide range of exposure levels, institutional settings, and income groups. This ensures that the QLP results are representative of structurally comparable developing economies, while the use of country fixed effects and region-year interactions mitigates concerns regarding external validity by exploiting within-country variation rather than cross-country differences.

My main dependent variable of interest is the real per capita GDP growth, which is standard in the disaster economics literature (e.g., Fomby et al., 2013; Loayza et al., 2012; Noy, 2009). This variable allows for consistent cross-country comparisons and accounts for population dynamics. It also better captures the impact of disasters on average economic well-being and avoids biases arising from demographic shocks or differences in country size. Disasters may temporarily increase investment, raising GDP levels in the short run. However, these gains are often offset by broader losses in productivity and capital stock (Marto et al., 2018), as well as setbacks in human development and poverty alleviation (Knippenberg et al., 2024; Rodriguez-Oreggia et al., 2013). Despite short-term reconstruction effects, long-run productivity and development often decline (Fomby et al., 2013) and result in long-lasting income reductions (Coronese et al., 2025; Hsiang and Jina, 2014). Focusing on per capita growth therefore allows the analysis to capture the net, medium-term economic consequences of disasters beyond transitory fluctuations in output levels.

Furthermore, in line with the literature, I include three sets of explanatory variables: (i) a natural disaster index, (ii) domestic controls that are standard determinants of economic growth, and (iii) proxies of global shocks. These variables are presented below.

## 4.1. Natural disaster index

The data on the occurrence, type and frequency of natural disasters are drawn from the EM-DAT database maintained by the Centre for Research on the Epidemiology of Disasters (CRED) that provides information on disaster-related mortality, total economic damages and number of people affected due to natural catastrophes. To be recorded in the database, a natural event must meet at least one of the following criteria: causes the death of 10 or more people, affects 100 or more people, or leads to a declaration of a state of emergency and/or a call for international assistance.<fnref n="9" />

For the construction of the natural disaster indicator, I consider the number of fatalities and the number of people affected by the disaster.<fnref n="10" /> The literature has widely used these measures to construct either a count variable (i.e, Noy, 2009; Gassebner et al., 2010; Loayza et al., 2012; Klomp, 2014), or a binary measure (i.e, Fomby et al., 2013; Panwar and Sen, 2019; Kabundi et al., 2022).<fnref n="11" />

As mentioned above, I study the impact of four types of natural disasters: droughts, earthquakes, floods, and storms. These disasters are of

particular concern due to their high frequency, significant destructive capacity, and widespread disruption they cause (Cavallo et al., 2022). Therefore, I use a combination of continuous and count measures of disaster intensity. The intensity measure used is given by:

$$intensity_{i,j,t}^k = \frac{fatalities_{i,j,t}^k + affected_{i,j,t}^k}{population_{i,t-1}}$$

where j denotes a distinct disaster of type k occurring in country i during year t. The variable $intensity_{i,j,t}^k$ thus captures the severity of an event as the ratio of its human impact (fatalities and affected individuals) to the population of a country in the previous year. Using the lagged population ensures that the intensity measure is not biased by the disaster's contemporaneous demographic impact (Noy, 2009). Second, the measure of disaster exposure, $DS_{i,j,t}^k$, for typical natural disasters is given by:

$$DS_{i,j,t}^k = \begin{cases} 1, & \text{if } intensity_{i,j,t}^k > Q3 \text{ for } intensity_{i,j,t}^k \\ 0, & \text{otherwise.} \end{cases}$$

And the aggregated yearly index by type of disaster is then calculated such as:

$$DS_{i,t}^k = \sum_{j=1}^{J} intensity_{i,j,t}^k$$

Accordingly, $DS_{i,t}^k$ serves as an exposure measure, capturing the total annual extent to which country i is affected by disasters of type k, taking into account both the number of events and their individual severity.

To identify disaster events that are likely to have an economic impact, I propose a novel, data-driven decision rule based on the upper quartile (Q3) of the disaster intensity distribution, calculated separately by disaster type. This approach implicitly accounts for the right-skewed nature of the distribution of disaster intensity (See Table B.1 for some summary statistics.) and allows for more consistent identification of economically relevant events across heterogeneous disaster categories. Since minor disasters may not have any direct macroeconomic impact (Gassebner et al., 2010; Klomp, 2014), this method filters events based on their severity and relative rarity, accounting for heterogeneity in typical intensity across disaster types. By avoiding arbitrary thresholds, the method reduces noise in identifying economically relevant events.<fnref n="12" /> Previous studies often rely on ad hoc criteria, such as a fixed cutoff of 0.01% of the population affected, to define typical disasters (e.g., Fomby et al., 2013; Panwar and Sen, 2019). Nevertheless, compared to arbitrary thresholds relying on fixed cutoffs, a data-driven threshold better accommodates the inherent heterogeneity in disaster characteristics, allowing for a more flexible and context-sensitive identification of impactful events.

Finally, in line with the view that extremely severe disasters may affect economic performance in distinct ways, both in magnitude and in dynamic responses (Fomby et al., 2013; Loayza et al., 2012), a higher threshold is employed to isolate the most extreme events. This threshold is the 99th percentile of $intensity_{it}^k$.

## 4.2. Control variables

To isolate the effect of natural disasters on extreme economic growth outcomes, I control for several macroeconomic and institutional variables. Following a common practice in the literature, the vector $x_{it}$ of control variables in Eq. (1) contains a set of domestic and global variables that account for the influence of both domestic and external factors on GDP growth.

<footnote n="9">⁹ EM-DAT classifies the following events as natural disasters: Earthquakes, Extreme temperatures, Droughts, Floods, Glacial lake outbursts, Landslides, Mass movement (Dry), Volcanic activity, Storms, and Wave action. The EM-DAT database is a comprehensive global database that includes information on all natural disasters between 1900 and 2025. The database can be accessed here.</footnote>

<footnote n="10">¹⁰ In the EM-DAT, people affected are those requiring immediate assistance, while people injured and homeless are reported separately. For my purpose, the three are combined to produce the total number of people affected.</footnote>

<footnote n="11">¹¹ In Fomby et al. (2013), the authors construct binary disaster variables based on a decision rule that weighs fatalities more heavily than non-fatal impacts. In their case, a disaster is considered if the number of fatalities plus 30% of the total number of people affected exceeds 0.01% of the country's population. Although this threshold is admittedly subjective, the authors argue that robustness checks using alternative cutoffs do not materially alter their results.</footnote>

<footnote n="12">¹² Alternative quartile thresholds (e.g., median and 60th percentile) were also considered, and the results remained consistent. Using the 10th percentile led to no significant aggregate effects, suggesting that low-intensity events introduce noise by including a large number of economically irrelevant disasters.</footnote>
### 4.2.1. Domestic controls

As domestic controls, I consider government consumption as a percentage of GDP, sourced from the WEO, to capture fiscal capacity to respond to shocks. The aftermath of disasters often requires significant government spending for recovery and adaptation efforts. Fiscal space is most constrained in economies that are highly exposed to climate risks. In line with Beirne et al. (2021, 2024), countries with limited fiscal resources to address unexpected expenditures following a major catastrophe may experience heightened macroeconomic instability and reduced policy flexibility. Additionally, the ratio of domestic credit to the private sector to GDP is used as a proxy for financial depth, reflecting the financial system's capacity to allocate resources effectively.<fnref n="13" /> As Noy (2009) argues, countries with a higher level of domestic credit are likely to reduce the costs of disasters in terms of output growth. Furthermore, the CPI inflation rate is included as another key macroeconomic control that measures price stability. This set of data comes from the World Bank's World Development Indicators database.

Finally, since my sample includes a wide range of developing countries, it is important to consider their structural differences. To this end, I include control variables that capture long-term institutional and developmental characteristics that are likely to influence disaster vulnerability and macroeconomic resilience. Specifically, I augment the set of controls with human capital (drawn from the PWT 10.01), the ratio of foreign exchange reserves to GDP (WDI), and the corruption index (ICRG). These variables mitigate omitted variable bias by capturing differences in absorptive capacity, institutional quality, and macroeconomic frameworks across countries that influence the impact of natural disasters. For example, higher levels of education and human capital are shown to increase a country's ability to adapt to shocks (Noy, 2009; Skidmore and Toya, 2002). Additionally, while central banks' holdings of international currencies serve as a precautionary tool to provide essential liquidity in times of crisis (Coulibaly et al., 2024; Jeanne and Rancière, 2011), stronger institutions tend to reduce both the human and economic toll of disasters (Mejia et al., 2019; Raschky, 2008). The corruption index is included to account for institutional quality. As Cevik and Jalles (2023) and Keefer et al. (2011) emphasize, high levels of corruption contribute to greater fatalities from natural disasters, particularly in developing countries. This highlights their vulnerability due to weak institutional quality and inadequate health and risk management systems.

### 4.2.2. Global controls

Economic growth is likely affected by not only domestic economic and climatic conditions, but also global business cycles and external shocks. First, economic openness, measured as the ratio of total trade to GDP (WDI), is included as a control variable. This variable captures both a country's exposure to trade disruptions and its potential for post-disaster recovery through integration into global markets. As highlighted by Fomby et al. (2013), Loayza et al. (2012), and Noy (2009), more open economies, particularly in developing countries, tend to benefit from more diversified adjustment channels and experience faster recoveries following natural disasters. Additionally, I account for global macroeconomic and financial conditions using the index developed by Baumeister et al. (2022).

Including these variables enables the analysis to control for time-varying global risk factors that could otherwise confound the estimation of disaster effects. These controls mitigate potential bias arising from time-varying global shocks that may independently shape growth outcomes, especially during post-disaster recovery periods. This is particularly important given that global instability can significantly alter investment decisions, and capital flows. These factors play a crucial role in shaping the economic aftermath of natural disasters in developing countries.

### 4.3. Some facts

Global climate is changing, making natural disasters more frequent and intense. Although global warming does not directly cause earthquakes (Buis, 2019), it is likely to increase the intensity and frequency of droughts, floods, and storms, as well as the vulnerability of affected countries (IMF, 2017a; IPCC, 2012, 2013, 2014). This section presents descriptive evidence on the frequency, intensity, and economic consequences of natural disasters in developing countries. The sample covers 67 countries from 1985 to 2019, providing broad representation of regions with different climates, institutions, and incomes. This heterogeneity is essential for understanding the distributional nature of disaster impacts and justifies the use of a panel-based quantile approach.

The interplay between vulnerability and the nature of the hazardous event itself plays a crucial role in determining the impact of a natural disaster. Vulnerability factors, such as economic conditions, infrastructure, and preparedness, define a country's level of risk (Cavallo et al., 2022; NGFS, 2024). Indeed, Bos et al. (2022) and Noy (2009) explain extreme losses from natural disasters as resulting from countries' structural and institutional vulnerabilities. Meanwhile, the nature and intensity of hazards, such as the magnitude of an earthquake or the strength of a hurricane, significantly influence the outcomes. Schumacher and Strobl (2011) corroborate this argument, further suggesting that the losses-development relationship exhibits a nonlinear pattern conditional on hazard intensity. This means that even better-developed countries remain vulnerable to extreme disasters. Therefore, understanding both aspects is, therefore, essential for effective disaster management and mitigation strategies.

However, advanced economies have enhanced their resilience and reduced their vulnerability by adopting countercyclical fiscal and monetary measures to address adverse shocks, such as natural disasters. Conversely, developing countries have experienced increased vulnerability primarily due to population growth over the past century (Perrow, 2011) and the poor quality of their institutions (Bos et al., 2022). This could explain why these countries tend to have a higher average population affected by natural disasters.

Fig. 1 provides a temporal overview of disaster incidence, intensity, and aggregate GDP per capita growth. The stacked bars represent the natural disaster index, $DS_{it}^k$ defined above, disaggregated by disaster type. The dashed and solid lines show average disaster intensity and GDP growth, respectively. The chart reveals a steady rise in disaster incidence over the last three decades, with storms and floods becoming particularly prominent since the early 2000s.

Notably, the correlation between disaster intensity and GDP growth is negative (-0.19), suggesting an inverse relationship between disaster severity and subsequent growth performance. This pattern was particularly visible during the late 1980s and 1990s, when increases in disaster intensity across my sample of countries were accompanied by marked slowdowns in real GDP per capita growth.

To better understand the nature of disaster shocks across event types, Table 1 summarizes the distribution of disaster intensity by category. As shown in the aforementioned Table, disaster intensities are highly skewed across all hazard types. For earthquakes, for example, the median is close to zero, reflecting the fact that no significant proportion of the population is usually affected in most country-year

<footnote n="13">¹³ It is common in empirical literature to approximate financial development by this ratio (Arcand et al., 2015; Dabla-Norris and Srivisal, 2013; Loayza et al., 2012). More recently, Svirydenka (2016) developed a more comprehensive index of financial development. While preparing this paper, I found that the domestic credit-to-GDP ratio is highly correlated with Svirydenka (2016)'s index for developing countries (correlation coefficient of 0.78). Nonetheless, I opted to use the credit-to-GDP ratio given its broader coverage and availability over time and across countries. Importantly, the core empirical results were robust to the use of either measure.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Natural Disaster Intensity and GDP Growth in Developing Countries (1985–2019).</figcaption></figure>

*Notes:* The bars represent the annual natural disaster Index $DS_{it}^k$, disaggregated by disaster type (drought, earthquake, flood, storm). The dashed line shows the average disaster $intensity_{ijt}^k$, while the solid line indicates average real GDP per capita growth across countries. The correlation between intensity and growth is reported in the upper left. *Author's calculation*.

<table><caption>**Table 1**
Summary statistics of the $intensity_{ijt}^k$ measure.</caption><thead><tr><th></th><th>N</th><th>Mean</th><th>Std-dev.</th><th>Min</th><th>P50</th><th>p75</th><th>P99</th><th>Max</th></tr></thead><tbody><tr><td>intensity<sup>Drought</sup><sub>ijt</sub></td><td>211</td><td>0.0743</td><td>0.1174</td><td>0.0000</td><td>0.0354</td><td>0.0863</td><td>0.5070</td><td>0.8058</td></tr><tr><td>intensity<sup>Earthquake</sup><sub>ijt</sub></td><td>278</td><td>0.007</td><td>0.0335</td><td>0.0000</td><td>0.0002</td><td>0.0016</td><td>0.1588</td><td>0.4031</td></tr><tr><td>intensity<sup>Flood</sup><sub>ijt</sub></td><td>1075</td><td>0.0127</td><td>0.0354</td><td>0.0000</td><td>0.0017</td><td>0.0085</td><td>0.1852</td><td>0.4478</td></tr><tr><td>intensity<sup>Storm</sup><sub>ijt</sub></td><td>545</td><td>0.0145</td><td>0.0414</td><td>0.0000</td><td>0.001</td><td>0.0082</td><td>0.2004</td><td>0.441</td></tr><tr><td>Observations</td><td>2109</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

*Notes:* Disaster intensities are reported conditional on event occurrence. *Author's calculation*.

However, the upper quantiles (p99 and maximum) are extremely high, emphasizing the rarity and potential severity of these events. By contrast, droughts typically affect broader areas and larger population shares, which explains their higher average values. Storms and floods also have low median intensities but heavy right tails, confirming that extreme events are not confined to geophysical hazards. This skewness highlights the importance of focusing on upper quantiles when identifying major shocks. These descriptive statistics reinforce the need to consider both the type and severity of the hazard in empirical analysis, a point further illustrated by the density joyplot below that offers an examination of how the GDP growth distribution evolves with disaster intensity.<fnref n="14" />

Fig. 2 below uses a joyplot to illustrate how the conditional distribution of growth shifts across intensity quantiles. This sheds light on the nonlinear and asymmetric nature of disaster impacts. Two striking patterns emerge. First, as disaster intensity increases, the entire growth distribution shifts leftward. This indicates that more severe disasters are systematically associated with weaker economic performance. Second, the left tail of the distribution becomes more pronounced, reflecting an increased probability of extreme negative growth outcomes in high-intensity episodes. Together, these shifts in level and shape provide

compelling evidence that the occurrence and intensity of natural disasters influence the macroeconomic trajectories of developing countries. However, the right tail thins out with intensity, suggesting that strong growth outcomes become less likely following severe disasters.

These distributional patterns are reflected in episodes located in the extreme tails of the growth distribution. The far-left tail of the highest disaster intensity quantile (P99) includes highly destructive events, such as the 2010 earthquake in Haiti (–6.96% of growth, intensity = 40.3), the 1988 storm in Nicaragua (–15.6% of growth, intensity = 9.1), and the 1998 flood in Zambia (–2.97% of growth, intensity = 14.2). These events triggered substantial economic contractions. Conversely, the right tail of the P99 distribution includes events such as the 1991 flood in China (7.5% of growth, intensity = 18.5), the 2002 storm in China (8.1% of growth, intensity = 7.9), and the 1987 drought in Sudan (11.6% of growth, intensity = 17.4). These cases suggest that strong growth remains possible even in the presence of high-intensity disasters, especially in countries experiencing structural booms or exhibiting strong institutional capacity. China's repeated presence in the right tail (e.g., the 1994 storm at the 75th percentile with 11.1% of growth and an intensity of 0.2) illustrates how large economies can absorb or offset the impacts of disasters. Similarly, Botswana's 2013 flood (8.6% of growth and an intensity of 0.2) shows that moderate disasters can coincide with favorable macroeconomic conditions.

These findings justify the construction of a data-driven disaster shock indicator based on the upper tail of the intensity distribution. Specifically, the 75th (99th) percentile threshold captures (major) disaster shocks while retaining sufficient variation for econometric identification. This intensity-based definition of disaster shocks complements the paper's empirical approach, which uses quantile local projections to trace the dynamic effects of disasters on future growth outcomes.

<footnote n="14">¹⁴ A boxplot comparing GDP per capita growth across disaster types is provided in Figure C.1 in Appendix C. it shows that storms and floods exhibit a significantly heavier left tail, suggesting a higher probability of sharp contractions. Furthermore, the patterns indicate a pronounced right-skewness and increased dispersion for floods and storms in comparison to droughts and earthquakes, thereby emphasizing the necessity of differentiating between disaster impacts based on both type and severity.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Distribution of GDP per capita growth across disaster intensity quantiles.</figcaption></figure>

*Notes:* This joyplot shows how the distribution of real GDP per capita growth changes with increasing disaster intensity levels among the countries in my sample from 1985 to 2019. Higher intensity levels are associated with lower median growth and more pronounced left skewness. This highlights the growing macroeconomic risks as disasters become more severe. *Author's calculation.*

The strong distributional patterns observed in Fig. 2 reinforce the relevance of this framework, which allows them to uncover tail risks and heterogeneous responses that would remain hidden under standard mean regressions.

In order to document the heterogeneity of disaster exposure across countries and over time, Appendix C provides heatmaps of the disaster shock variable, thus highlighting the uneven and clustered nature of natural hazards. Some economies, such as Indonesia, Chile, or the Philippines, experience recurrent and intense events, while others are affected only intermittently. This spatial and temporal variability is consistent with the broad composition of the sample.

These descriptive facts reveal three central features of the data: (i) the increasing and uneven occurrence of natural disasters, (ii) the heterogeneous growth effects across disaster types and intensities, and (iii) the prevalence of downside risks. These insights justify the use of a flexible empirical strategy that can capture distributional and dynamic effects, which will be developed in the next section.

# 5. Results

To accurately evaluate the dynamic and distributional effects of natural disasters on economic performance, this section first presents QIRFs based on real GDP per capita growth. These results show how disaster shock impacts vary across the conditional growth distribution, particularly in the lower and upper tails.

## 5.1. *Distributional responses to natural disasters*

I first start by showing QIRFs for the aggregate disaster index ($DS_{it}^k$). Fig. 3 below shows the QIRFs of real GDP per capita growth in response to a one-standard-deviation shock in $DS_{it}^k$ at the 10th, 50th and 90th percentiles of the growth distribution. These estimates capture how the dynamic effects of disasters differ across the distribution of economic growth, highlighting their heterogeneous and asymmetric macroeconomic consequences. The Figure reports percentage-point deviations of three conditional quantiles of cumulative growth over horizon $h$. The QIRFs reveal a significant asymmetry in the impact of natural disasters on different parts of the growth distribution.

At the lower quantile ($\tau = 0.10$), disasters clearly have an adverse impact: growth falls by around 0.9 percentage points compared to the no-disaster trajectory immediately after the disaster occurs, and remains persistently negative, with losses reaching almost 1.1 percentage points after seven years. This suggests that countries with weak growth trajectories are particularly susceptible to disasters, which exacerbate economic downturns.

At the upper quantile ($\tau = 0.90$), disasters are associated with sustained and significant growth increases of 3–4 percentage points over the medium term. These patterns suggest that, while natural disasters pose substantial downside risks for fragile economies, they can largely have a neutral or even positive impact on stronger economies. This is consistent with the idea that reconstruction investment and institutional capacity shape the growth response.

Finally, at the median ($\tau = 0.50$), the response is negative in the short term but turns positive after two to three years, with cumulative gains of 1–2 percentage points over longer periods. Though less pronounced than the effects observed at the 10th or 90th percentiles, these results imply that disasters have a persistent, albeit milder, impact on overall growth. It is important to note that my specification estimates conditional quantiles of cumulative growth, not mean effects. Mean estimates aggregate over the entire distribution and are influenced by extremes, whereas the median isolates the central tendency and is robust to outliers.<fnref n="15" />

These findings highlight the nonlinear and state-dependent effects of natural disasters. It is imperative to note that natural disasters are shown to cause a shift in growth distribution to the left in the short to medium term. However, economies witness a positive effect on the right tail of the distribution, as indicated by the substantial positive response at the 90th percentile.

<footnote n="15">¹⁵ As demonstrated in Figure D.1 of the Appendix, which presents the bias-corrected split-panel jackknife estimation, it is evident that incidental bias is minimal. Therefore, the estimations have proven to be consistently robust. In addition, Figure D.3 shows QIRFs for a reduced sample, where the countries with the highest $DS_{it}^k$-indicator concentration are excluded. As illustrated, the findings are globally robust to variations in sample size, although some minor disparities arise.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Natural disasters, responses of the 10th, 50th and 90th percentiles of real GDP per capita growth. Author's estimation.</figcaption></figure>

*Notes*: Figures show the predictive effects of a 1-SD $DS_{ijt}^k$ shock on 10th, 50th and 90th percentiles of GDP per capita growth estimated by quantile local projections. Shaded areas denote the 95% confidence interval.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Natural disasters, responses of the 10th and 90th percentiles of real GDP per capita growth. Author's estimation.</figcaption></figure>

*Notes*: Figures show the predictive effects of a 1-SD $DS_{ijt}^k$ shock on 10th and 90th percentiles of GDP per capita growth estimated by quantile local projections. Shaded areas denote the 95% confidence interval.

To further shed light on the transmission mechanisms and sources of distributional heterogeneity, I will now decompose the aggregate disaster shock by type of event. This allows me to examine whether the tail responses observed at the 10th and 90th percentiles are driven predominantly by specific categories of disasters, such as earthquakes, droughts, floods, or storms. It is crucial to distinguish between these effects, given the significant variations in frequency, intensity, and sectoral incidence among different disaster types. The subsequent set of QIRFs explores how distinct natural hazards shape downside and upside risks to growth across the distribution.

Fig. 4 above provides compelling evidence of the heterogeneity in the distributional effects of natural disasters across both disaster types and quantiles of the GDP per capita growth distribution. At the 10th percentile (panel 4(a)), disasters consistently exert adverse effects, with droughts and earthquakes inducing the most pronounced and persistent contractions. Earthquakes are shown to trigger a steep and prolonged decline in growth at the lower tail, suggesting that they significantly worsen macroeconomic fragility in already weak economic contexts, while the significant negative impact of droughts comes with a delay. Conversely, storms are shown to have a delayed yet beneficial influence over the long term, while floods have no statistically significant impact. At the 90th percentile (panel 4(b)), the results indicate asymmetric and occasionally counterintuitive responses. Despite their destructive nature, earthquakes are associated with a significant and persistent increase in upper-tail growth starting in the second year. This phenomenon may indicate positive, albeit delayed, spillovers from reconstruction. At the upper tail, droughts, floods and storms do not show significant effect, suggesting that overall positive impact on the

90-th percentile of growth distribution is mainly driven by earthquakes. The findings emphasize the importance of a quantile-based approach, as the mean effects would obscure the sharp divergence in disaster impacts across the growth distribution. Furthermore, they propose that disaster-induced downside risks predominate in low-growth environments, while high-growth episodes demonstrate resilience or even counter-cyclical rebounds, particularly following geophysical shocks.<fnref n="16" />

Taken together, the quantile local projections paint a consistent picture of how natural disasters affect the entire spectrum of growth outcomes. Disasters primarily reshape the conditional distribution of GDP growth by shifting probability mass toward the lower tail, thereby amplifying downside risks, while in some cases generating conditional improvements at the upper tail. This pattern reflects an increase in tail risk and distributional asymmetry rather than a uniform downward shift in expected growth.

<footnote n="16">¹⁶ Figure D.2 shows the results of extreme natural disasters, which are defined as events exceeding the 99th percentile of intensity$^{k}_{it}$. These disasters do not systematically generate stronger or more persistent macroeconomic effects than average events. One possible explanation is that these severe shocks drive the average patterns captured in the full-sample estimates, so isolating them provides limited new information. Furthermore, as demonstrated by Cavallo et al. (2013) and Hallegatte et al. (2024), the macroeconomic losses resulting from disasters depend not only on the physical intensity of the event, but also on institutional resilience and absorptive capacity. In many cases, stronger economies can offset the effects of severe shocks through financial inflows, reconstruction mechanisms, or effective policy responses (Noy, 2009; Yang, 2008). Finally, measurement challenges and the rarity of such events may reduce the statistical power to detect significantly distinct patterns.</footnote>
Firstly, at the 10th percentile, growth falls sharply and persistently, with the largest contractions being driven by earthquakes, droughts and storms. This highlights the vulnerability of fragile economies, where disasters exacerbate existing weaknesses and push them further into low-growth traps. Secondly, growth accelerations are observed at the upper tail, almost entirely driven by earthquakes. Although this pattern may seem counterintuitive at first, it is consistent with literature emphasizing reconstruction-led booms following large geophysical shocks (Fomby et al., 2013; Loayza et al., 2012). It reflects the capacity of stronger economies to transform disasters into opportunities for modernizing capital and infrastructure.

These results also resonate with broader evidence of cross-country heterogeneity. The contrasting outcomes observed at different points in the GDP growth distribution reflect significant disparities in macroeconomic resilience and recovery capacity. In the lower tail of the distribution, countries are typically already facing stagnation, macroeconomic fragility, or adverse structural conditions. In such contexts, natural disasters exacerbate existing vulnerabilities. Fiscal space is often limited (Beirne et al., 2024; Marto et al., 2018), access to external financing is restricted (Beirne et al., 2021; Klomp, 2015; Mallucci, 2022) and the institutional capacity to coordinate relief and reconstruction efforts is often inadequate (Bos et al., 2022; Cevik and Jalles, 2023; Noy, 2009). Consequently, capital destruction can lead to prolonged recessions accompanied by inadequate or delayed recovery initiatives. This phenomenon has been thoroughly documented in recent case studies. For example, Knippenberg et al. (2024) demonstrate that the 2022 floods in Pakistan significantly exacerbated poverty outcomes in already vulnerable communities. This highlights the compounded impact of disasters in fragile economic contexts.

Conversely, at the upper tail of the growth distribution, countries are better positioned to withstand and recover from economic shocks. In such contexts, natural disasters are more likely to manifest as temporary disruptions rather than structural breaks. As emphasized in the literature on creative destruction (e.g. Barone and Mocetti, 2014; Skidmore and Toya, 2002), post-disaster recovery can stimulate economic activity by replacing depreciated capital with more productive technologies, as well as by encouraging investment through reconstruction spending.

These findings reconcile my quantile results with the mean regression literature (e.g., Hsiang and Jina, 2014; Noy, 2009). While disasters generally depress long-term growth, the distributional perspective shows that these losses are concentrated in the lower tail, where the probability of severe downturns rises sharply. By contrast, in the upper tail, reconstruction-led rebounds — particularly following earthquakes — can sustain or even amplify expansions.

A comprehensive analysis of the QIRFs estimated in this paper, reveals a non-uniformity in the macroeconomic effects of disasters, suggesting that their impact varies across different economic contexts, and by type of disasters. Economies that are already experiencing stress are particularly vulnerable to additional shocks. In contrast, during high-growth periods countries may be able to leverage reconstruction as a short-term stimulus, thereby softening or even reversing the adverse impact. In the subsequent section, the objective is to examine the mechanisms by which these results are transmitted.

## 5.2. Heterogeneous effects of natural disasters across economic sectors

The economic structures of developing countries are typically marked by a limited diversification of economic activities and a high degree of interdependence among different sectors. Contrary to the case of advanced economies, which benefit from more balanced and resilient structures, developing countries are especially reliant on primary activities, such as agriculture. These countries are particularly vulnerable to disruptions related to climate change and disasters (Loayza et al., 2012). In such contexts, the impact of shocks to one sector

can rapidly propagate to other sectors, thereby amplifying the overall macroeconomic impact (Botzen et al., 2019; Pauw et al., 2011).

In this section, the transmission mechanisms underlying the asymmetric growth responses observed in the aggregate QIRFs are investigated. To this end, I estimate quantile impulse responses using harmonized per capita sectoral value-added data from the World Development Indicators and UN data portal. This disaggregated approach facilitates the identification of heterogeneous sectoral vulnerabilities and illuminates the factors driving asymmetric shifts in the lower and upper tails of the GDP growth distribution. While natural disasters are often geographically localized, their indirect effects can propagate across the entire economy via sectoral interconnections (Carrera et al., 2015; Felbermayr et al., 2022; Naguib et al., 2022; Pauw et al., 2011). Consequently, the sectoral QIRFs offer a more nuanced understanding of how disasters propagate through the economic structure of developing countries.

Fig. 5 presents the dynamic responses of sectoral value-added per capita growth to disaster shocks. For clarity, the main text focuses on agriculture and manufacturing — the two sectors that drive most of the tail dynamics — while the QIRFs for services, construction, and utilities are reported in Appendix D. Results indicate that agriculture is particularly vulnerable to natural disasters during periods of low growth. The response to earthquakes is notably severe after ten years. This phenomenon can be attributed primarily to the destruction of storage structures, the burial of stored seeds, and damage to agricultural lands (Gauchan et al., 2017). Furthermore, droughts are shown to exert a significant and immediate negative influence. This is an intuitive result given that droughts imply water scarcity and a reduction of soil moisture. In a recent paper, Crofils et al. (2025) employed high-frequency data to estimate the impact of weather anomalies on agricultural production. Adverse weather shocks are demonstrated to induce a delayed downturn in agricultural production in Peru.<fnref n="17" />

Floods and storms have positive responses, which, as noted in the literature, may reflect short-term benefits from increased water availability (Fomby et al., 2013). However, these events can also be highly destructive, particularly for agriculture. Their adverse effects are twofold: they can cause large-scale destruction of crops and livestock (Botzen et al., 2019; Hsiang, 2010), and they exacerbate existing socio-economic challenges (see, Knippenberg et al., 2024, for an application to Pakistan's 2022 floods). Interestingly, the positive impact tends to emerge with a delay — typically two years after the shock — suggesting that initial damages are gradually offset by improved climatic conditions that benefit agricultural production. Still, the overall effect of floods remains relatively modest in magnitude.

The agricultural sector exhibits clear asymmetry. At the lower tail ($\tau = 0.10$), it significantly amplifies downside risks following droughts and earthquakes. By contrast, at the upper tail ($\tau = 0.90$), disasters other than earthquakes do not induce significant responses. This suggests that agriculture does not contribute to upward tail risk reallocation—i.e. it remains vulnerable to severe losses but does not generate strong expansions in high-growth contexts. Specifically, Fig. 5 indicates that earthquakes have a significant positive impact, suggesting the presence of rebound mechanisms among producers, potentially facilitated by technological upgrading or favorable credit conditions. Indeed, positive externalities resulting from post-disaster reconstruction may, in the medium-to-long term, stimulate demand for agricultural goods, particularly through increased food needs, rural employment, and infrastructure investments that enhance productivity.

For the manufacturing sector, it initially experiences a severe downside risk in response to disasters, particularly earthquakes and droughts. At the 10th percentile, shocks translate into heightened probabilities of

<footnote n="17">¹⁷ See Gallic and Vermandel (2020) for a theoretical explanation for agricultural output fluctuations and overall welfare cost from weather anomalies.</footnote>
<figure><img src="image_8.png" /><figcaption>(a) Agriculture value-added</figcaption></figure>

$\tau = 0.10$

<figure><img src="image_9.png" /><figcaption>(b) Manufacture value-added</figcaption></figure>

$\tau = 0.10$

<figure><img src="image_10.png" /></figure>

$\tau = 0.90$

<figure><img src="image_11.png" /></figure>

$\tau = 0.90$

<caption>Fig. 5. QIRFs of sectoral value-added per capita growth. Author's estimation.</caption>

Notes: Figures show the predictive effects of a 1-SD $DS_{ijt}^k$ shock on the 10th percentile of sectoral value-added per capita growth estimated by quantile local projections. Shaded areas denote the 95% confidence interval.

severe contractions, reflecting the sector's capital-intensive nature and its exposure to abrupt destruction of physical infrastructure. Loayza et al. (2012) observe that seismic events primarily impact capital-intensive sectors, such as industry and construction, and abruptly diminish physical capital stock, such as infrastructure, buildings and factories, potentially impeding production. However, this decline can also stimulate new investment in reconstruction, which can yield a favorable outcome in the medium term, as can be seen by the reversal of the QIRF, starting year 3. Droughts are shown to have a slightly negative effect on the outcome at the 10th percentile, primarily due to the loss of productivity that is induced by the drought itself. Letta and Tol (2019), Mejia et al. (2019) and Tintchev and Jaramillo (2024) analyze the nexus between climate change and TFP losses. They find a negative relationship only in poor countries, where a 1 °C annual increase in temperature decreases TFP growth rates by about 1.1–1.8 percentage points.

One notable finding of this study is the significant positive response of the manufacturing sector to floods and storms. This is attributed to the surge in demand for industrial inputs, such as building materials and manufactured goods, related to reconstruction efforts (Leiter et al., 2009). Such demand can temporarily stimulate production in low-growth environments. These effects could be amplified by recovery programs funded by donors or public investment initiatives prioritizing infrastructure and urban production chains. These initiatives would direct resources toward manufacturing activities, even after destructive shocks.

It is noteworthy that, under the 90th percentile, only earthquakes generate a robust and persistent positive response in the manufacturing sector over a ten-year horizon. This upward tail shift reflects reconstruction-led dynamics, including accelerated capital replacement, surges in demand for construction materials and industrial goods, and potential investment financed by foreign aid (Cantelmo et al., 2023). Furthermore, in contexts characterized by accelerated growth, the ability to mobilize resources and orchestrate recovery initiatives can serve to amplify these effects, ultimately resulting in protracted gains in productivity.

Furthermore, at the 10th percentile, earthquakes induce a sharp and persistent downward shift in the conditional distribution, whereas other hazards have no significant impact. This downside amplification reflects the sector's structural vulnerabilities—heavy reliance on fixed capital, exposure to infrastructure destruction, supply chain breakdowns, and limited fiscal or institutional capacity to support timely reconstruction in fragile contexts.

By contrast, at the 90th percentile, the response only becomes significantly positive after year seven, indicating that rebounds led by reconstruction materialize with long delays. This delayed upward shift in the tail highlights the time it takes to mobilize resources, assess damage and implement recovery strategies. Such slow recoveries often prove to be more costly than investing in preventive resilience (Corugedo et al., 2023; Marto et al., 2018).

Manufacturing, mining, and utilities (MMU) sectors are characterized as capital-intensive and infrastructure-dependent activities that play a critical role in both economic production and disaster recovery.
These sectors encompass electricity and generation and distribution, extractive industries, and heavy manufacturing. Consequently, they are particularly exposed to physical infrastructure damage (e.g., power grids, pipelines, plants) and supply chain disruptions. At the 10th percentile (see Figure D.4), MMU sectors demonstrate some of the most severe negative responses among all sectors, particularly following earthquakes. This severe decline is indicative of numerous vulnerabilities in low-growth environments. Droughts also exert negative effects, albeit more moderate. This dynamic is reminiscent of the adverse responses previously received for the manufacturing and construction sectors. These findings suggest that MMU function as amplifiers of macroeconomic fragility when disasters strike during economic downturns. In contrast, at the 90th percentile, MMU responses are insignificant. This asymmetric pattern emphasizes the sector's importance as channels of downside vulnerability, with no offsetting gains in the upper tail of the growth distribution.

In contrast, the services sector exhibits a distinctly divergent profile. Services demonstrate minimal to no substantial response at the 10th percentile across the majority of hazard types, with the exception of a transitory contraction following earthquakes. This resilience is likely to be indicative of the sector's reduced reliance on fixed capital relative to agriculture or industry, and its capacity to reallocate activities with greater flexibility when disruptions occur. However, at the 90th percentile, services are shown to display strong and persistent positive effects, particularly in the aftermath of earthquakes. In high-growth contexts, services have the capacity to leverage reconstruction spending, rising urban demand, and structural shifts toward more modern economic activities. It is evident that as reconstruction efforts intensify, sectors such as telecommunications, transport, finance, education, and public administration tend to experience growth. This expansion in turn generates positive externalities that extend throughout the economy. These effects are further strengthened by complementarities with agriculture and manufacturing, whose recovery stimulates demand for commerce, logistics, and retail services.

The results presented in this section are consistent with those of the GaR literature, which suggests that natural disasters exacerbate economic downturns (Bayoumi et al., 2021; Chavleishvili and Moench, 2025). This paper makes a valuable contribution by shedding light on the sectoral transmission mechanisms behind these effects. It reveals that agriculture, manufacturing and utilities absorb the bulk of the initial losses, particularly during fragile growth phases. In contrast, the upper tail reveals a different, more complex dynamic. Initially, disaster shocks are insignificant at $\mu_{au} = 0.90$, but significant positive effects emerge after the third year, particularly in the case of earthquakes. This delayed response reflects the time required for the mobilization of resources, the coordination of reconstruction and the triggering of investment cycles — dynamics that cannot be captured by static or mean-based models.

These findings highlight the importance of the Quantile Local Projection (QLP) approach, which traces the temporal unfolding of quantile-specific disaster effects and reveals long-term spillovers that are invisible to mean regressions. They also nuance theoretical arguments, such as those of Hallegatte and Ghil (2008), who argue that high-growth phases may increase vulnerability due to greater capital exposure. While this mechanism applies in the short term, the evidence presented here indicates that robust pre-disaster growth can facilitate more dynamic recoveries by enhancing the capacity to mobilize resources, absorb shocks and convert reconstruction into productivity gains.

The results clearly show strong sectoral asymmetries in disaster impacts, varying in both magnitude and sign depending on macroeconomic conditions. In low-growth periods, agriculture is particularly vulnerable. However, industry, services, and infrastructure-related sectors can benefit from certain shocks. Disasters amplify sectoral imbalances in fragile economies and act as reallocation mechanisms in more resilient contexts.

These asymmetries clearly explain the observed shifts in the GDP growth distribution. The pronounced downside risk at the 10th percentile is largely due to severe contractions in agriculture, manufacturing, and utilities. These sectors are highly exposed to climatic and seismic shocks. The rightward shift at the 90th percentile is driven by positive medium-run responses to earthquakes, particularly in industry and services. Reconstruction and renewed demand offset initial losses.

### 5.3. Heterogeneity across income levels and institutional quality

To deepen the analysis, I now examine how income level and institutional quality influence disaster risk mitigation and post-shock growth in developing countries. Strong institutions promote economic stability and efficient resource allocation through social welfare programs, insurance mechanisms, effective governance, legal systems, and regulatory frameworks after a catastrophe. However, these features are more prevalent in high-income countries and frequently absent in low-income settings (Kellenberg and Mobarak, 2008; Noy, 2009). Bos et al. (2022) demonstrate that wealthier countries tend to manage disasters' consequences more efficiently. Institutional strength influences the scale and efficiency of public interventions, such as aid distribution and emergency housing, and indirectly shapes private-sector responses. This is particularly relevant for slow-onset disasters, such as droughts, where long-term support policies for affected populations — like farmers — are critical for recovery.

In this section, I estimate QIRFs allowing for heterogeneous effects by interacting disaster variable ($DS_{it}^k$) with income (dummy)<fnref n="18" /> and an institutional indicator (proxied by the corruption score from the International Country Risk Guide (ICRG)<fnref n="19" />). Higher corruption typically signals weaker governance, reduced public service delivery, and inefficient disaster response mechanisms. This makes corruption a relevant structural moderator in assessing the macroeconomic impact of disasters (see, among others, Cevik and Jalles, 2023; Keefer et al., 2011). By incorporating these interaction terms, the analysis better captures the heterogeneous macroeconomic dynamics that characterize developing economies.

In my sample of 67 developing countries, the average corruption score is 2.5. High-income countries display higher average scores and a right-skewed distribution, indicating lower levels of corruption compared to middle- and low-income economies (see Table 2). Low-income countries, by contrast, exhibit the lowest average scores, reflecting weaker institutional strength.

To better capture structural differences in macroeconomic resilience, this section examines how income levels and institutional quality shape the growth impact of natural disasters. Unlike previous estimations disaggregated by disaster type, this analysis relies on the aggregated disaster indicator ($DS_{it}$) to identify broader patterns of vulnerability and resilience. This aggregation helps abstract from disaster-specific characteristics and is particularly useful for cross-group comparisons, as it reduces sample fragmentation and yields more stable estimates of structural heterogeneity.

#### 5.3.1. Income level

Fig. 6 presents the QIRFs of real GDP per capita growth by income level, distinguishing between the lower and upper tails of the distribution. At the 10th percentile, (panel 6(a)) pronounced and persistent negative effects of disasters are observed in low-income countries.

<footnote n="18">¹⁸ Of the 67 developing countries in the sample, 17% are high-income, 11% are low-income, and 72% are middle-income.</footnote>

<footnote n="19">¹⁹ The corruption score ranges from 0 to 6, with higher scores indicating lower levels of corruption. This measure is used to proxies institutional strength because it captures the extent to which public institutions are capable, transparent, and accountable—key dimensions determining how effectively a country can prevent, absorb, and recover from natural disasters.</footnote>
<table><caption>**Table 2**
Summary statistics of corruption score.</caption><thead><tr><th></th><th>Mean</th><th>Std-dev.</th><th>Min</th><th>P10</th><th>P50</th><th>P99</th><th>Max</th></tr></thead><tbody><tr><td>All sample</td><td>2.54</td><td>0.98</td><td>0.00</td><td>1.5</td><td>2.50</td><td>5.00</td><td>6.00</td></tr><tr><td>High income</td><td>3.03</td><td>1.03</td><td>1.00</td><td>2</td><td>3.00</td><td>5.00</td><td>6.00</td></tr><tr><td>Low income</td><td>2.34</td><td>0.99</td><td>0.50</td><td>1</td><td>2.00</td><td>4.00</td><td>4.00</td></tr><tr><td>Middle income</td><td>2.44</td><td>0.91</td><td>0.00</td><td>1.5</td><td>2.42</td><td>5.00</td><td>6.00</td></tr></tbody></table>

Notes: Corruption scores are drawn from the International Country Risk Guide. Author's calculation.

<figure><img src="image_12.png" /><figcaption>**Fig. 6.** Natural disasters, QIRFs of real GDP per capita growth by income level. Author's estimation.

Notes: Figures show the predictive effects of a 1-SD $DS_{it}$ shock on 10th and 90th percentiles of GDP per capita growth estimated by quantile local projections by income level. The shaded areas denote the 95% confidence interval of the low-income countries' QIRFs, the blue dashed spikes represent the 95% confidence interval of the middle-income countries' QIRFs, and the red ones represent the 95% confidence interval of the high-income countries' QIRFs.</figcaption></figure>

The response of the middle-income group, though less substantial in magnitude, is significantly negative, between years 4 and 6. In contrast, the responses observed in high-income economies are considerably smaller in magnitude and are found to be insignificantly different from zero. This finding indicates that the adverse effects identified in Fig. 3 — particularly the leftward shift in the lower tail of the GDP growth distribution — are predominantly influenced by the most vulnerable economies, notably low-income countries.

The divergence becomes particularly pronounced at the 90th percentile, as illustrated in panel 6(b). In this context, low-income countries demonstrate statistically significant gains over time. Conversely, higher-income countries exhibit insignificant responses. The findings suggest that high- and middle-income developing countries experience reduced exposure to economic activity variability triggered by disasters. The results of this study serve to reinforce the notion that institutional and structural disparities, in this case indexed by income classification, play a pivotal role in amplifying or attenuating the impact of natural disasters on economic dynamics.

That said, it is important to consider the possibility of heterogeneous sensitivities, which may vary depending on the nature of the disaster. While the fixed effects (individual, time, and regional) included in the QLPs estimation have accurately captured these phenomena, a robustness check was conducted by estimating QIRFs by income group for each disaster type, indicating a lack of significant divergence across hazards: low-income countries continue to demonstrate the highest exposure to downside risks, while high-income developing economies exhibit minimal sensitivity. These findings confirm that the heterogeneity results are not driven by any specific category of disasters. For high-income developing countries, the QIRFs demonstrate minimal sensitivity across various disaster types, exhibiting only a modest and transient effect following floods. This is consistent with their more diversified and resilient economic structures.<fnref n="20" /> Middle-income economies

demonstrate a comparable overall pattern, albeit with somewhat amplified negative reactions to floods and storms, at the 10th percentile. The correlation between exposure to natural disasters and rapid urbanization, as well as growing inequality, has been well-documented. This correlation can amplify vulnerability and constrain recovery (Kellenberg and Mobarak, 2008; Schumacher and Strobl, 2011). The presence of significant negative effects suggests that, in contrast to high-income economies, they are not adequately protected; and in contrast to low-income economies, they may not benefit from foreign aid to the same extent.

The impact of natural disasters on low-income economies is more widespread and significant than in high-income developing countries. This phenomenon can be attributed to the fact that smaller economies, both in terms of size and income level, are more susceptible to disasters and encounter a wider extreme range of damage. Consequently, natural disasters emerge as a pivotal factor influencing the development process of low-income countries (Cantelmo et al., 2023). Noy (2009) explains the difference in average GDP growth reaction between developed and developing countries by the former's ability to implement counter-cyclical policies to mitigate the risk of recession following natural disasters and the latter's inability to do the same.²¹

Conversely, low-income countries have been shown to benefit from increased inward remittances and international financial aid following natural disasters (Bettin and Zazzaro, 2018). These financial inflows

economies in terms of both size and surface area. Consequently, the impact of natural disasters remains confined to specific localities and rarely extends to the entire economy (see, Felbermayr et al., 2022; Naguib et al., 2022, for an analysis of the impact of natural disasters on local GDP).

²¹ A notable example is New Zealand's policy response following the 2011 Canterbury earthquakes. The government implemented expansionary fiscal measures, including a NZ$5.5 billion reconstruction fund, while the central bank temporarily lowered interest rates. These counter-cyclical actions helped cushion the GDP impact and supported a swift recovery, as noted by the IMF (2011) and Treasury, New Zealand (2011).

<footnote n="20">²⁰ This observation is particularly evident among high-income countries in our sample, which are characterized by their substantial and diversified</footnote>
<figure><img src="image_13.png" /><figcaption>Fig. 7. Heatmaps of the response of GDP p.c. growth quantiles to DS<sub>i,t</sub> across corruption scores. Author's estimation.</figcaption></figure>

Notes: Heatmaps illustrate the estimated quantile impulse response functions (QIRFs) to natural disasters across various horizons and corruption scores (ICRG score). The left panel corresponds to the lower tail of the GDP growth distribution, and the right panel corresponds to the upper tail. The impact is expressed in percentage points and corresponds to a one-standard-deviation of aggregate disaster shock. The white dashed line represents the all sample average of corruption scores. Red circles denotes statistically significant responses at the 5% level of significance.

have been demonstrated to function as a fiscal buffer, thereby reducing the global negative impact on average GDP growth and on the social welfare (Cantelmo et al., 2023; Ebeke and Combes, 2013; Mejia et al., 2019). However, the downside risk to low-income growth remains significantly persistent following natural disasters. With this in mind, it would be legitimate to attribute this disparity of reaction across the tails, to institutional quality and level of development (Noy, 2009) refers to.

5.3.2. *Institutional strength*

Finally, the study examines how institutional quality shapes the macroeconomic impact of natural disasters by allowing the QIRFs to vary across the distribution of corruption scores. This is achieved through the interaction between disaster shocks and a flexible (splined-based) transformation of the corruption index, thereby allowing the marginal effects to vary smoothly across institutional quality levels. While this approach enhances the visualization and interpretability of structural non-linearities — via heatmaps — it leaves the core econometric framework unchanged, preserving the consistency of the quantile local projection estimation.<fnref n="22" /> The corresponding heatmaps are reported in Fig. 7.

Institutions are a fundamental determinant of long-term develop-
ment because they shape the incentives of economic agents, secure
property rights, and organize political power (Acemoglu et al., 2005,
2008). Bos et al. (2022) show that institutions of greater quality cor-
relate with higher disaster relief efficiencies. Further, in application to
earthquakes, Keefer et al. (2011) provide evidence that less corrupted
systems are better prepared to deal with the consequences of this seis-
mic hazard than corrupt countries, while disaster mortality is showed
to increase with corruption (Cevik and Jalles, 2023).

The left panel of the figure reveals that natural disasters have a
strongly negative effect on the 10th percentile of GDP per capita growth
in countries with high levels of corruption (score below 2.5), particu-
larly between 0 and 6 years after the shock. This lends further support

to the initial argument that low-income countries are associated with
low-quality public institutions. The red circles, which are concentrated
in the lower half of the heatmap, indicate statistically significant down-
side risks to growth under weak institutional environments. In contrast,
countries with lower corruption (score > 2.5) exhibited less pronounced
or statistically insignificant effects, indicating the presence of more
robust institutional buffers against disaster shocks (Bos et al., 2022;
Keefer et al., 2011; Noy, 2009).

This finding is further validated by panel 7(b), which demonstrates that higher corruption scores are associated with a significant positive effect on GDP growth at the upper tail. Furthermore, responses for low corruption scores are smaller in magnitude, although they are marginally positive in the short run, becoming negative afterwards. In the long term, results reveal a significant surge in the upper tail, independently of the corruption level. A comparison of these results to those presented in Fig. 6(b) reveals a consistent global pattern.

Corruption amplifies the downside risk of natural disasters, with
limited negative effects on the upper tail in the short term. This high-
lights the critical role of institutions in shaping disaster vulnerability
and recovery. As argued by Acemoglu et al. (2005, 2008), strong
institutions foster investment and efficient resource allocation, while
weak ones distort incentives and concentrate power. This logic applies
to disasters: effective governance improves preparedness and response,
reducing damages and fatalities (Bos et al., 2022; Cevik and Jalles,
2023). The use of interaction terms with institutional quality is thus
both methodologically sound and coherent and aligned with recent
literature.

6. Concluding remarks

This paper presents novel empirical evidence on the heterogeneous
macroeconomic effects of natural disasters in developing countries. By
examining a comprehensive panel dataset and distinguishing between
various categories of disasters, sectors, income levels, and institu-
tional strength, I identify significant asymmetries in the transmission
of shocks across the growth distribution and economic sectors.

The findings of this study demonstrate that the adverse effects of
disasters are highly state-dependent. Countries at the lower tail of
their growth distribution, particularly those with weak institutions and
low-income levels, experience deeper and more persistent contractions.
Conversely, upper quantiles exhibit enhanced resilience and potentially
benefit from specific types of shocks, particularly earthquakes, through

<footnote n="22">²² It is important to note that the implementation of spline-transformed interactions with corruption scores is entirely parametric and does not modify the fundamental econometric framework of the quantile local projection model. In addition, this flexible spline-based approach avoids the necessity of creating an arbitrary dummy variable to split countries by corruption level. Such a variable would rely on an exogenous and potentially debatable threshold.</footnote>
reconstruction dynamics and capital renewal. The sectoral decomposition further reveals that agriculture is the most exposed sector in downturns, while manufacturing and services tend to recover more rapidly, or even gain in high-growth contexts.

Of particular significance are the quantile impulse responses, which underscore that disasters not only reduce expected growth but also cause a shift in the entire distribution of future growth outcomes. This finding underscores the relevance of a risk-based perspective to macroeconomic vulnerability, as it captures tail risks and nonlinear effects that are often overlooked in mean-based analyses. Furthermore, the cross-country heterogeneity observed lends support to the notion that institutional quality, and sectoral composition influence the impact of shocks and determine recovery pathways.

The present study makes substantial contributions to two areas of research in the field. Firstly, the dynamic framework utilized in this study contributes to the refinement of static analyses of state-dependent effects by providing a characterization of the dynamic evolution of these effects. Secondly, it offers a more interactive framework for the GaR literature and findings, thereby facilitating a more profound comprehension of the underlying phenomena that shape the behavior of growth distribution following a disaster shock. In that sense, the study corroborates earlier findings that natural catastrophes disrupt economic growth distribution, causing severe downturns to occur more frequently. However, the advantage of the dynamic analysis presented here is that it has demonstrated that this effect has a tendency to reverse over time. Nevertheless, the findings indicate that this phenomenon is influenced by a variety of transmission mechanisms, including institutional strength and sectoral dynamics. Furthermore, the QIRF framework of the paper demonstrated that under certain conditions, the upper tail of economic growth may be positively influenced, particularly in the long term.

This latter phenomenon manifests in low-income countries and to a lesser extent in middle-income countries. This phenomenon may be analogous to the concept of creative destruction, where disasters, despite their initial disruptive nature, serve to accelerate the replacement of obsolete capital and stimulate investments that enhance productivity. In contexts where reconstruction is facilitated by external support, remittances, or institutional mobilization, disasters can induce structural transformation and modernize sectors previously constrained by inefficiencies. Furthermore, some studies (e.g., Loayza et al., 2012; Skidmore and Toya, 2002) indicate that natural disasters can also result in increases in human capital, particularly through educational opportunities and labor market adjustments. These mechanisms may offer a rationale for the upward shift in the upper tail of the distribution observed in this paper. This finding is consistent with the broader literature on endogenous growth and technological convergence (Acemoglu et al., 2005; Howitt, 2000), which suggests that shocks may indirectly enhance productivity through imitation and spillovers. In this perspective, natural disasters could be expected to function as disruptive yet ultimately developmental forces that facilitate the advancement of developing countries along their long-term growth trajectory, although recent studies argue that structural challenges still remain regarding disasters' impact on growing inequalities (Coronese et al., 2025), poverty (Knippenberg et al., 2024) and development overall (Bos et al., 2022; Kellenberg and Mobarak, 2008; Mejia et al., 2019; Schumacher and Strobl, 2011).

The present analysis suggests that targeted disaster risk management policies, which consider macroeconomic states and structural features, need to be implemented. The enhancement of institutional resilience, the investment in adaptive capacity, and the conceptualization of early-warning systems are pivotal in the mitigation of the asymmetric burden imposed by disasters. Theoretical paper by Marto et al. (2018) have stressed the importance of adopting ex-ante policies, such as building adaptation infrastructure and fiscal buffers, have been identified as instrumental in enabling countries to effectively withstand the economic ramifications of natural disasters. In a similar

vein, Cantelmo et al. (2023) and Corugedo et al. (2023) underscore that preventive investment in resilience can yield a positive economic impact, thereby underscoring the potential cost-effectiveness of such measures in comparison to financing post-disaster recovery.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.ecolecon.2026.108933.

## Data availability

Data will be made available on request.

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