Evidence regarding the macroeconomic effects of natural disasters is scarce and not yet conclusive. In particular, it seems that natural events need to be “extreme” to significantly impact a country’s short or long term GDP growth (Cavallo et al. 2013
). The most commonly used database of natural disasters, EM-DAT (www.emdat.be
) includes a very large range of situations that “overwhelms local capacity and/or necessitates a request for external assistance”, from floods to earthquakes among many others. Yet, what may look like a catastrophe at the local level does not necessarily matter at aggregate levels, unless the geographical size of the country is itself very small, such as Haiti, for instance (Cavallo et al. 2010
). Therefore, while negative effects are found on specific samples or disaster types (e.g., Hsiang and Jina
) for cyclones), no significant impact emerge in meta-analyses of indirect costs (Lazzaroni and van Bergeijk 2014
).2 Skidmore and Toya
) even argue that natural disasters may even have a positive long run growth effect via the reconstruction activities in the recovery phase. From a theoretical point of view, the impact of disasters on growth is also ambiguous. Standard growth model would predict that a sudden destruction of the physical capital stock increases output growth by stimulating savings and investment. On the contrary, endogenous growth models with increasing returns to scale may find negative effects. Hence, the consequences of actual
natural disasters on dynamic macroeconomic variables are far from clear-cut, both theoretically and empirically.
Meanwhile, a recent but growing macroeconomic literature has emphasized the role of disaster risk
on economic outcomes, starting with the seminal paper by Barro
). Originally designed for replicating asset pricing features, such as the risk premium puzzle, this literature has been further developed into dynamic and stochastic macroeconomic models. Gabaix
), and Gourio
) have introduced a small but time-varying probability of disasters in real business cycle models, and find that changes in the probability
of disasters, without any arrival of the disaster itself, may suffice to trigger economic recessions. Isoré and Szczerbowicz
) further showed how to extend this approach to a New Keynesian environment, and found that disaster risk shocksn—again, absent of actual disaster realization—, generate procyclical responses of consumption, investment, labor, wage, and inflation, simultaneously to the recession and rise in equity premium. Further empirical evidence by Siriwardane
) and Marfè and Penasse
) support the relationship between changes in the probability of a disaster and macroeconomic variables.
It may seem like a paradox that changes in disaster risk affect real macroeconomic variables when natural disasters themselves generally do not. The disaster risk literature defines “disasters” as rare events destroying a large share of a country’s existing capital stock. Even though it often focuses on political or financial disasters, it also encompasses extreme natural events, by definition. Therefore, this suggests that it is not so much the arrival of actual natural disasters that affects the dynamic paths of macroeconomic variables, but the simple risk that they may occur, i.e the uncertainty component that is embedded in changes in their probability over time.
In this paper, changes in disaster risk are simulated for five Latin American countries—namely Argentina, Brazil, Chile, Colombia, and Mexico—using variants from the New Keynesian DSGE model developed by Isoré and Szczerbowicz
). This study is the first analysis of the effects of changes in disaster risk, absent of actual disaster occurrence, in the particular context of emerging economies. Here are the main results. In all five countries, a 1% increase in the probability of disaster unambiguously decrease output, consumption, investment, labor, wage, and inflation, on impact. However, a large rebound in investment follows in the period after the shock in Argentina, Brazil, and Mexico. This tends to limit the size of the macroeconomic responses for this group of countries. On the contrary, Chile and Colombia do not experience the rebound, exacerbating the magnitude and persistence of the shock. The model gives theoretical explanations to these differences. In particular, very low degrees of price stickiness make the first group of countries less vulnerable. As the probability of a disaster increases, output prices adjust quickly, such that precautionary savings are channelled quite rapidly into higher investment. On the contrary, with slightly less frequent—but still much more than e.g., in the US—price changes, Chile and Colombia’s responses in production factors, capital and labor, is much more pronounced, translating into much lower investment, and therefore output. Higher frequency and severity of natural disasters, such as in Chile, where extreme events are mostly earthquakes, also contribute to particularly strong macroeconomic responses to disaster uncertainty.
The remainder of this paper is as follows. Section 2
presents some evidence on natural disasters in the five countries of interest, regarding their frequency, type, and size in particular. Section 3
summarizes the model by Isoré and Szczerbowicz
), a New Keynesian DSGE model which embeds a small but time-varying probability of disaster. Section 4
discusses how calibration accounts for country specificities and shows the responses of macroeconomic variables to a 1% change in disaster risk. Finally, Section 5
2. Evidence on Natural Disasters in Latin America
The scope of this paper is limited to five countries, namely Argentina, Brazil, Chile, Colombia, and Mexico. This group of countries has several advantages. First, they are part of the broadly defined range of emerging economies. Toya and Skidmore
) find that high income and financially developed countries typically suffer less from natural disasters, partly because they may be located in geographic regions with disasters of lower physical intensity but also because wealth makes them less vulnerable to disasters of any given size (by larger investment in safety measures for instance). Second, among emerging economies, these five countries are geographically large enough such that any “local” natural disaster does not automatically slow down their economy, but only “extreme” ones may. Third and most importantly, empirical evidence is sufficiently important for some of the model parameters required in the theoretical simulation exercises presented here. In particular, the elasticity of intertemporal substitution and the degree of price stickiness play a key role in the responses, as explained in Isoré and Szczerbowicz
). These parameters are well documented for this group of countries, unlike for example South-Asian countries of similar income levels (see Subsection 4.1
illustrates the evolution in the number of disasters over time for these five countries, as reported in the EM-DAT database. The most striking feature—yet not limited to this particular group of countries—is the growth of disaster occurrences over time. Low figures in the early period might partly be explained by under-reporting. However, even over the 1990s onward period, the positive trend remains. Reasons for this trend are beyond the scope of this paper, but climate change could be an obvious candidate. As far as economic consequences of natural disasters are concerned, this suggests that it might be worth investigating not only the impact of actual disaster occurrences per se, but also changes in their frequency over time.
However, since only extreme events seem to matter for macroeconomic outcomes, both according to empirical evidence on natural disasters and the theoretical disaster risk literature, the scope of these events to be considered must be narrowed down from EM-DAT. Table A1
here reports only the first top-10 natural disasters per country of interest, together with their type and associated capital loss in current US dollar terms. The “
” column od the same table calculates the share of the country-specific capital stock which corresponds to this damage, using data on capital stock and deflator available from 1950 in FRED database (https://fred.stlouisfed.org/
). Finally, the long-term probability of extreme natural events is defined as the frequency of disasters with
, i.e., a destruction share of the capital stock of at least 0.1%. Averaged across the five countries, this probability is 4.5% annually. Nevertheless, heterogeneity across the five countries is substantial, partly due to the type of natural disasters they face and other geographical features. For instance, Chile’s density of capital stock in narrow geographical areas subject to earthquakes contributes to high capital damages.
Overall, these stylized facts first suggest that time variations in the probability of natural disasters may matter for economic outcomes, beyond actual occurrences of these disasters. Second, they provide information on the relative size of capital destruction in case of extreme natural disasters as well as their long-term probability, both dimensions being taken into consideration when simulating the model responses of macroeconomic variables to disaster risk shocks in Section 4
This paper generates theoretical dynamic responses of macroeconomic variables to disaster risk shocks for five emerging market economies. For this purpose, it builds on the New Keynesian DSGE model by Isoré and Szczerbowicz
) to simulate the effects of changes in the time-varying probability of disasters, absent of actual disasters. This approach follows the literature on disaster risk in business cycles, developed by Gabaix
) and Gourio
) in particular.
The five countries of interest do not differ much by their elasticity of intertemporal substitution, which is commonly low in emerging market economies with incomplete financial markets. However, they differ significantly by their degree of price stickiness. All five are much more price-flexible than the US but Argentina, Brazil, and Mexico’s frequency of price changes is almost double as compared to Colombia and Chile. This output price volatility seems to make the former group of countries much less vulnerable to changes in disaster risk than the latter. In addition, Chile’s high frequency and severity of capital destruction in case of natural disasters make it particularly responsive to time changes in the probability of disasters.
Overall, this paper suggests that analyzing the uncertainty component in natural disasters, as well as the fluctuations in this uncertainty over time, is key to economic development. Its theoretical model forms a positive approach in this direction. Yet, this paper does not draw any normative implication for practical disaster risk management as such. Further research would be needed in this direction.