hazard level of the administrative units within a province.<sup>18</sup> Thus our analysis may be overestimating the country-province value-at-risk of natural hazards that do not affect all provinces equally (or affect them at differing magnitudes). Therefore, our results for physical risks should be considered as an upper-bound. Comparability caveats also arise since administrative units are not necessarily proportional to countries’ surface areas when compared across countries. On the one hand, we have countries like Dominican Republic, which has 31 provinces and one national district; on the other hand, Brazil, the largest country in LAC, is 175 times as large as Dominican Republic and has a lower number of provinces: 26 states and one federal district.

In a second step, we narrow our physical risk assessment to the portion of assets that banks allocate to households for home loan purposes, i.e. mortgages. We therefore combine the flood hazard mapping information with the banking system's outstanding mortgages within each country-province. Specifically, in line with recent empirical studies (e.g., Pagliari 2021; and Bank of England 2018), we measure physical risk exposure of mortgages to floods hazard. Mortgage repayments and property values could be significantly strained by floods via repair expenses and increased insurance costs. Publicly available information allows us to dive deeper into the physical risks associated with floods by analyzing the banks’ mortgage portfolios distribution at the province level for Chile, Colombia, and Mexico.

In a third and final step, we use geographical NPL data at the country-province level and natural disaster information to understand the dynamics of banks’ balance sheets in the aftermath of large-scale natural disasters.<sup>19</sup> Data availability limits the analysis to Argentina, Brazil, Chile, Mexico, and Peru. We define large-scale natural disasters as the most economically damaging climatological, hydrological, or meteorological natural disasters occurred in our country-province sample during the past ten years. Based on EM-DAT, this identification strategy provides us with the 2018 Argentine drought that affected the north-eastern provinces during January 2018 (economic costs of US$ 3.5 billion);<sup>20</sup> the Brazilian drought that initiated in early 2014 in the northeast region (economic costs of US$ 5.4 billion); the March 2015 flood that hit northern Chile (economic costs of US$ 1.6 billion); the Hurricane Manuel that affected the southeastern coast of Mexico in September 2013 (economic costs of US$ 4.6 billion); and the March 2017 flood triggered by El Niño affecting coastal Peru (economic costs of US$ 3.2 billion).<sup>21</sup> All in all, the disasters’ time span ranges between 2011q3 and 2020q1, allowing us to observe outcomes in a window of two years before and after each event.

Our baseline empirical methodology consists of a difference-in-difference (DiD) approach that allows identifying the causal effect of natural disasters on banks’ asset quality. Specifically, we analyze the difference between affected and non-affected provinces in the change in NPLs. We

> 18 For additional details, see: https://gfdrr.github.io/thinkhazardmethods/#classification-process. 19 As in Bos et al. (2018), we focus on large-scale natural disasters only. The authors argue that only relatively large events can reasonably be assumed to cause significant loan defaults, as (i) firms and banks are to some extent prepared for common disasters, and (ii) only large-scale disasters generate a significant and regionally correlated shock that affects nearly all local firms and residents at the same time.

> 20 According to the Central Bank of Argentina, the GDP growth rate decline of 4 percent during the second quarter of 2018 was largely explained by agricultural production contraction due to the drought. That year, the production of soy and corn dropped 31.3 percent and 12.5 percent, respectively. For more details, see: <u>http://www.bcra.gob.ar/Pdfs/PoliticaMonetaria/IPOM1018.pdf.</u>

> 21 Economic damages estimates are sourced from EM-DAT and expressed in today's currency.

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