Disasters and economic growth: evidence for Argentina

ABSTRACT

Disasters pose a serious threat globally. In this paper we estimate the impact of disasters on economic growth at the district level for Argentina, for the period 1992–2013. Due to the lack of disaggregated GDP data, night light maps reported by the United States' National Oceanic and Atmospheric Administration (NOAA) are used as a proxy for economic activity. Disaster information comes from the records of the Disaster Inventory System (DesInventar), which include the full range of disasters, from mild to severe ones. A regression analysis is carried out considering a panel of districts, linking luminosity with disasters.

We find that an additional disaster -weighted by its severity- is associated with a small though statistically significant reduction in the district's economic growth rate, specifically, of 0.53 percentage points in the year of its occurrence. This result is mainly driven by the impact of hydrological disasters. However, we find no evidence of persistence of this effect over time; on the contrary there seems to be a recovery in the following period. Given the methodological limitations due to data constraints, estimates found here probably constitute a lower bound of the true macroeconomic effect. Thus, further research on the topic is recommendable.

KEYWORDS:

• Disasters
• GDP
• economic growth
• night light luminosity
• floods
• earthquakes
• Argentina

Acknowledgements

We are grateful to Dr. Marina Tortul (IIESS, UNS-CONICET) for the elaboration of georreferenced maps. Authors are grateful for funding of ANPCyT-PICT 2015-2079 and PGI-UNS 24/ZE30.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 There is also a broader literature which examines the impact of environmental shocks on different economic outcomes (Dell et al., 2012; Dell et al., 2014; Burke et al., 2015; Carleton & Hsiang, 2016; Hsiang et al., 2017).

2 The rebound is fostered by a rise in the marginal product of capital, as capital and labour become relatively scarce after the disaster.

3 Luminosity data collection began in 1970 and was only declassified in 1972 (public access was allowed). However, from 1972 to 1992 the information was only available for consultation in physical records at the University of Colorado (Elvidge et al., 2001).

4 Previously, other papers have explored the relationship between night-time luminosity and aspects such as urbanizations or energy consumption (Croft, 1978; Welch, 1980; Foster, 1983).

5 No district, in any year, reaches the maximum value on the luminosity scale (63). In any case, it is shown that the results are robust to the exclusion of the districts with greater luminosity (Table A.4 in Annex).

6 However, DesInventar contains disaster records the period 1970-2015.

7 In this way, if, on average, for example, floods tend to be more severe than hailstorms, each flood record will receive a greater weighting and the lack of data in some individual records can be partially overcome.

8 For example, if a certain disaster (of type e, occurred in district d in year t) caused damage to at least one school, then $\mathbb{I}\left(L_{edtz}^i>0\right)=1$, for i = schools.

9 The analysis of lagged effects is scarce in the literature (Klomp & Valckx, 2014; Lazzaroni & van Bergeijk, 2014).

10 Also, as suggested by Okuyama (2009), there are other methodologies -namely Input-Output (IO) models and Social Accounting Matrix (SAM) models- that allow assessing the total impact of a disaster, (considering both first-order and higher order effects), which take into account the system-wide impact of flow losses through interindustry relationships. That would require much more detailed information on the impacts of each specific event.

Additional information

Notes on contributors

Fernando Antonio Ignacio González

Fernando Antonio Ignacio González Doctoral Fellow at Instituto de Investigaciones Económicas y Sociales del Sur, Universidad Nacional del Sur (UNS)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET).

Silvia London

Silvia London Director of the Instituto de Investigaciones Económicas y Sociales del Sur, UNS-CONICET and Professor at Departamento de Economía-UNS.

Maria Emma Santos

Maria Emma Santos Researcher at Instituto de Investigaciones Económicas y Sociales del Sur, UNS-CONICET and Professor at Departamento de Economía-UNS. Research Associate to the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, UK.