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Climate Disasters and the Macroeconomy: Does State-Dependence Matter? Evidence for the US

Economics of Disasters and Climate Change Aims and scope Submit manuscript


Global climate is changing, and the occurrence of climate disasters has been rising. There is growing concern that climate change is expected to increase the frequency and intensity of weather events. Yet, the consequential effects of disasters and the ensuing implications of policymakers’ responses remain unclear. While the majority of research on climate change is ex ante, this paper explores the ex post transmission of disaster damages on economic conditions. In doing so may offer a glimpse of key, future policy options around how a disaster shock influences economic conditions, not only with regards to how a disaster affects output, as in the existing research, but also to aid policy makers and the public to further understand the influences on inflation, interest rate and economic policy uncertainty (EPU). Using a multivariate regression, we find that the impact of a natural disaster on EPU is positive and statistically significant during an expansionary phase while controlling for other determinants. Using a non-linear VAR model with local projections (LP), the aftermath of a disaster is estimated to marginally decrease output and increase inflation during an expansionary state. Accordingly, the empirical findings suggest the interest rate set by the U.S. Federal Reserve (Fed) remains relatively unchanged to a disaster shock, which is operating in a manner that is proportional to the magnitude of change in output and inflation. Consistent with the multivariate regression model, the VAR-LP demonstrates that the impact of a natural disaster magnifies the increase in EPU during periods of economic expansion.

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  1. See

  2. EPU is seasonally adjusted via ARIMA X-12 algorithm from the U.S. Census Bureau.

  3. Costly disaster data is set to \(\ln D_{t} = {\sum }_{i=1}^{n} \ln (1+cost_{i,t}^{data})\) considering the data is non-negative.

  4. While the NOAA disaster data uses a threshold of $1 Billion, these damages represent the majority of damage costs. According to the NOAA website, ”Even though $1B is an arbitrary threshold, these specific events account for the majority (> 80%) of the damage from all recorded U.S. weather and climate events.” See

  5. The hypothesis of normality is not accepted using the Jarque Bera test or Shapiro-Wilk test (p-value is 0.00 for both tests).

  6. All data used in the models are available from the authors upon request.

  7. This has the potential benefit of capturing the short-term effects that a disaster has on the economy, as opposed to using annual data as in the majority of studies. Disaster shocks, when they occur, can transmit rapidly and may also have long-term effects. The model includes disaster data defined in real cost terms, which is a preferred measure to condition on the magnitude of the disaster than count data at annual frequency.

  8. Note that Hailemariam et al. (2019) also consider the oil price, which is not done here to maintain a parsimonious model.

  9. The robust regression is based on an MM-estimator with a design adaptive scale estimate (Koller and Stahel 2011) using iteratively reweighted least squares estimation.

  10. Note that we abstract from deterministic terms with the exception of the intercept term for expositionary purposes.

  11. Recall, there were three sizable disaster events (Fig. 3), which occurred during economic expansions. Yet, Fig. 4 demonstrates that the average quarterly disaster cost (in real terms) is marginally higher in a recessionary period ($7.35 Billion) than in a non-recessionary period ($6.67 Billion).

  12. The output gap for quarterly data is based on the real GDP potential provided by the Congressional Budget Office (FRED mnemonic GDPPOT) and real GDP (FRED mnemonic GDPC1). This is consistent with the U.S. Federal Reserve, see The output gap at monthly frequency is based on industrial production via the HP filter, where the trend is based on λ = 1,600, a standard value for data at quarterly frequency (see e.g. Ravn and Uhlig 2002)

  13. See


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Correspondence to William Ginn.

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The author would like to thank the editor, Ilan Noy, and two anonymous referees for their thoughtful comments



A.1 Alternative Model 1

Fig. 8
figure 8

Disaster Shock Based on State-Dependence (Unemployment Rate). IRFs depict nonlinear responses from a disaster shock for periods of economic expansion (right panel) and periods of economic slack (left panel)

Fig. 9
figure 9

Transition Function Based on State-Dependence (Unemployment Rate). The figures shows the weighted regime (i.e., F(z)). Shaded areas indicate NBER recession dates

A.2 Alternative Model 2

Fig. 10
figure 10

Disaster Shock Based on State-Dependence (Output Growth). IRFs depicts nonlinear responses from a disaster shock for periods of economic expansion (left panel) and periods of economic slack (right panel)

Fig. 11
figure 11

Transition Function Based on State-Dependence (Output Growth). The figures shows the weighted regime (i.e., F(z)) based on the centered moving average of output growth. Shaded areas indicate NBER recession dates

A.3 Alternative Model 3

Fig. 12
figure 12

Disaster Shock Based on State-Dependence (Capital Utilization). IRFs depict nonlinear responses from a disaster shock for periods of economic expansion (right panel) and periods of economic slack (left panel)

Fig. 13
figure 13

Transition Function Based on State-Dependence (Capital Utilization). The figures shows the weighted regime (i.e., F(z)). Shaded areas indicate NBER recession dates

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Ginn, W. Climate Disasters and the Macroeconomy: Does State-Dependence Matter? Evidence for the US. EconDisCliCha 6, 141–161 (2022).

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