Natural Disasters and Mortality: A Probabilistic Analysis for Argentina, 1970-2015

Revista de Estudios Andaluces (REA) | Núm. 40, julio (2020) | e-ISSN: 2340-2776. https://dx.doi.org/10.12795/rea.2020.i40 | © Editorial Universidad de Sevilla 2020 https://editorial.us.es/es/revistas/revista-de-estudios-andaluces https://revistascientificas.us.es/index.php/REA CC BY-NC-ND 4.0 78 78 https://dx.doi.org/10.12795/rea.2020.i40.05 Natural Disasters and Mortality: A Probabilistic Analysis for Argentina (1970-2015) Desastres Naturales y Mortalidad: Análisis Probabilístico de Argentina (1970-2015) Fernando Antonio Ignacio González [email protected] https://orcid.org/0000-0002-8347-9808 Instituto de Investigaciones Económicas y Sociales del Sur, Universidad Nacional del Sur (UNS)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). San Andrés 800, Bahía Blanca-Argentina. María José Vázquez-Cueto y Francisco Gutiérrez-López INFO ARTÍCULO ABSTRACT Recibido: 10-04-2020 Revisado: 01-06-2020 Aceptado: 04-06-2020 KEYWORDS Natural disasters Floods Mortality risk Information diffusion method Probabilistic analysis Argentina The frequency and intensity of natural disasters are expected to increase due to climate change. In this context, this work advances in the estimation of an absolute measure of mortality risk generated by natural disasters, in regions of Argentina. In particular, exceedance probabilities of mortality are estimated using the information diffusion method. The historical data of natural disasters provided by the records of DesInventar are used. These records cover the 1970-2015 period and are published by the Social Studies Network in Disaster Prevention in Latin America (LA RED). The results suggest that the Centro region is the one with the highest risk of absolute mortality, while the Noreste region has the lowest risk levels. In a given year there is a 0.93 probability that, at least, one death will occur due to natural disasters in the Centro region. In the Noreste region, this probability is 0.77. The results are robust and significant. PALABRAS CLAVE RESUMEN Desastres naturales Inundaciones Riesgo de mortalidad Método de difusión de la información Análisis probabilístico Argentina Se espera que la frecuencia e intensidad de los desastres naturales aumenten, a futuro, debido al cambio climático. En este contexto, este trabajo avanza en la estimación de una medida absoluta del riesgo de mortalidad generado por desastres naturales, en regiones de Argentina. En particular, se estiman probabilidades de excedencia de mortalidad utilizando el método de difusión de información. Se utilizan los datos históricos de desastres naturales proporcionados por los registros de DesInventar. Estos registros cubren el período 1970-2015 y son publicados por la Red de Estudios Sociales en Prevención de Desastres en América Latina (LA RED). Los resultados sugieren que la región Centro es la que tiene el mayor riesgo de mortalidad absoluta, mientras que la región Noreste tiene los niveles de riesgo más bajos. En un año dado hay una probabilidad de 0.93 de que, al menos, una muerte tenga lugar debido a desastres naturales en la región Centro. En la región de Noreste, esta probabilidad es 0.77. Los resultados son robustos y significativos. DOI: https://dx.doi.org/10.12795/rea.2020.i40.05 Formato de cita / Citation: González, F.A.I. (2020). Natural Disasters and Mortality: A Probabilistic Analysis for Argentina (1970-2015). Revista de Estudios Andaluces, 40, 78-95. https://dx.doi.org/10.12795/rea.2020.i40.05 Correspondencia autores: faigonzale[email protected] (Fernando Antonio Ignacio González). Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 79 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 1. INTRODUCTION A disaster is a serious disruption of the functioning of a community or society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope with using its own resources (United Nations International Strategy for Disaster Reduction [UNISDR], 2009). If the primary cause of the disaster is nature, it is called a natural disaster, while if it is directly generated by human behaviour it is called an anthropogenic or technological disaster. This work examines natural disasters and this includes geophysical, hydrological, meteorological and climatological disasters (Centre for Research on the Epidemiology of Disasters [CRED], 2009)1. Every year a large number of natural disasters of all kinds take place globally. During 2019 alone, natural disasters affected 95 million people worldwide -with an estimated mortality of more than 11,000 people- and generated losses of US$ 130 billion (CRED, 2020). In the future, an increase in the frequency and severity of natural disasters, associated with climate change, is expected (Banholzer, Kossin & Donner, 2014; Montroull, Saurral & Camilloni, 2018; IPCC, 2018). In the case of Argentina, natural disasters that took place, between 1980-2018, implied a cost of U$S 24.000 million in economic losses and generated 1037 deaths directly (Munich Re, 2019) 2, being the most frequent disasters floods (32%), tempests (21%) and forest fires (8%) (DesInventar, 2018). In 2018, Argentina was among the ten most disaster-affected countries in the world (CRED, 2019). In turn, of all registered natural disasters in Argentina, between 1970-2015, 41% occurred in the last fifteen years (DesInventar, 2018). This seems to suggest a growing trend in the frequency of disasters. However, other factors may explain this: for example, the availability of better disaster records or a greater social interest in them. Globally, a growing consensus has developed around the idea of joint consideration of the multiple types of natural disasters that occur in a country or region to enable a comprehensive analysis of the risk profile (Kousky, 2012; Liu, Siu & Mitchell, 2016). Frequently, the risk analysis associated with multiple natural disasters is conceived in a relative way: based on the construction of indexes -which consider elements of hazard, exposure and vulnerabilityqualitatives or semi-quantitatives, comparisons are made between countries or regions to detect those zones which present a greater risk of disaster (Xu et al., 2016). The relative risk approach is adopted in numerous publications at regional and global level (UNDP, 2004; World Bank, 2005; Brecht, Deichmann & Gun Wang, 2013; German Watch, 2019). The foregoing, however, does not allow to quantify joint probabilities of occurrence of disasters or losses exceedance probabilities3. In this regard, knowing -in probabilistic termsthe economic cost or human deaths associated with different natural disasters is relevant in the assessment and mitigation of risk. The estimation of probabilistic models applied to the analysis of natural disasters has many precedents (Grunthal et. al, 2006; Askan & Yucemen, 2010; Winter, Schneeberger, Huttenlau & Stotter, 2017, Stephenson, Hunter, Youngman & Cook, 2018). Frequently, probabilistic models assume a certain distribution for natural disaster losses (parametric estimation) and use historical disaster data to estimate distribution parameters (Grunthal et al., 2006; Winter et al., 2017). Given the limited availability of historical disaster data -that is, small samplesnonparametric estimation methods are used (Xu et al., 2016). This includes estimates of kernel density, histogram density and information diffusion. The use of histograms presents the difficulty of choosing the intervals, while the estimation of kernel densities implies defining an adequate bandwidth4 (Liu, Siu, Mitchell & Xu, 2013). On the other hand, Huang (1997) proposed the use of the information diffusion method to overcome the limitations of non-parametric methods in contexts where historical data on disasters are limited (Liu et al., 2013; Xu et al., 2016). In particular, this method adopts notions specific from the theory of fuzzy sets and 1. Despite this distinction between natural and technological disasters, the fact that human behaviour can influence the occurrence and impact of natural disasters is not ignored. 2. Values are expressed in constant 2018 dollars. 3. Probability for a specific value -for example, of deaths per year- to be equaled or exceeded. 4. Bandwidth is a smoothing parameter of the estimator. A discussion about its choice can be consulted in Heidenreich, Schindler & Sperlich (2013). Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 80 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 where each observation can belong -with different probabilities- to multiple subsets of the domain of the data. This allows to improve the estimation of probabilities based on the expansion of observations. In this context, the present work tries to contribute to the estimation of exceedance probabilities of losses of human lives (mortality) associated with multiple natural disasters in Argentina -that is, an absolute risk measurebetween 1970-2015. In turn, given the existence of deep regional disparities in Argentina, in terms of income, poverty, life expectancy, education, prices -among others- (Longhi & Osatinsky, 2017; González, 2020) the estimates are conducted by region, making it possible to compare them. From now on, section 2 presents the conceptual framework, section 3 describes the methodology, the source of information and the regionalization adopted for the Argentine case. Section 4 presents the results and finally, section 5 considered main conclusions. 2. CONCEPTUAL FRAMEWORK The impact of natural disasters on mortality is a topic that has been widely studied. This work focuses on those deaths directly attributable to the disaster. However, the literature suggests that after the disaster, mortality may also increase due to diseases such as hepatitis E, gastrointestinal disease or leptospirosis in the case of floods (Alderman, Turner, &Tong, 2012), respiratory diseases after extreme temperatures (Gronlund, Sullivan, Kefelegn, Cameron & O´neill, 2018) or some kind of psychological distress after earthquakes (Matsubayashi, Sawada & Ueda, 2013; Fujiwara et al., 2017). In aggregate terms, the literature has highlighted the relevance of two factors in explaining mortality from disasters. First, location at the time of the disaster determines the exposure, or not, to the disaster. Some countries are more prone to extreme events due to their location or climate. The literature has also advanced in the study of differences within the same city or region: often poor people are located in high risk areas (Hallegatte, Vogt-Schilb, Rozenberg, Bangalore & Beaudet, 2020). This means that a higher mortality from disasters is expected in developing countries and in the poorer regions within countries. The empirical evidence confirms this: despite the number of disasters is similar in developed and developing countries, most deaths occur among developing ones (Hu, Zhang, Shi, Chen & Fang, 2018; CRED, 2015). Of all the natural disasters that took place, in 2015, 44% occurred in low-income countries while 68% of all deaths from natural disasters occur in these countries. In other words, each disaster causes, on average, three times as many deaths in a low-income country than in a high-income country (CRED, 2015). Second, household conditions are of great importance during a disaster (Frankenberg, Thomas, Samuel, Bondan & Duncan, 2011; Hallegatte, Vogt-Schilb, Rozenberg, Bangalore & Beaudet, 2020). A household with precarious materials (in the ceiling or walls) is undoubtedly more exposed to potential negative consequences in the event of an extreme disaster. This risk is transferred to its occupants. Also, increasing time indoors due to extreme outdoor conditions might have indirect adverse health consequences (Gronlund et al., 2018). Lack of access to basic services, such as drinkable water, electricity, or a sewer network, also increases the risk of mortality given the simultaneousness between infectious diseases and the disaster. These material deprivations (precarious housing and lack of access to basic services), in conjunction with other factors, determine the susceptibility or predisposition of a community or individual to suffer damage in the case a disaster occurs -i.e. vulnerability to disasters- (Inter-American Development Bank, 2003). From the above, it appears that the potential damage resulting from a disaster does not depend solely on the underlying natural hazard (geophysical, meteorological, hydrological or climatic event) but also on the exposure to this hazard (location in affected areas) and on vulnerability arising from the existence of basic material deprivations. In other words, the so-called natural disaster is a complex interrelation between the natural and socio-economic dimension (Barrenechea, Gentile, González & Natenzon, 2000; Natenzon, 2016) and the disaster risk arises from the combination of these 3 elements (hazard, exposure and vulnerability). The risk of disasters in Argentina has some relevant features. First, almost a third of the Argentine population is concentrated in the Gran Buenos Aires agglomerate. Its increasing population density and urbanization patterns make this agglomerate especially susceptible to the occurrence of disasters (Ríos, 2014). Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 81 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 Wide disparities of disaster risk are observed within this agglomerate (Herrero, Natenzon & Miño, 2018). Floods are especially worrying in this case (Barros et al., 2006; Ríos & González, 2011) but also in other regions (Maldonado, Natenzon & Cóccaro, 2005; Casco, 2011). However, given the territorial breadth of Argentina, its regions present different profiles of disaster risk (Viand & Briones, 2015). The Argentine case stands out for the existence of wide territorial disparities in vulnerability to disasters. The northern provinces of Argentina are those with the highest levels of incidence of poverty, infant mortality, malnutrition, among others (Longhi & Osatinsky, 2017). These provinces include the Noreste (NEA) and Noroeste (NOA) regions (see section 3 for a detailed explanation)5. Some of these regional disparities are analysed in Table 1 in which selected indicators are presented. It can be observed that the NEA, with 9% of the population, contributes with 3.7% of GDP, 1.7% of total exports, and only 4.4% of all registered jobs. NOA shares are somewhat better, but also disproportionate in relation to their population contribution. The NEA and NOA infant mortality rates are two to three points higher than in the other regions, and the percentage of the population with Unsatisfied Basic Needs is approximately double that in the rest of the regions. Child malnutrition also has worrying prevalences in northern Argentina (Bolzan et al., 2005). Table 1. Selected indicators by region. Region PopulationaShare of population Share of GDPbShare of exportscShare of registered jobsd NEA 3,679,609 9.17 3.75 1.7 4.42 NOA 4,911,412 12.24 6.46 7 7.44 Cuyo 2,852,294 7.11 6.41 5.7 6.07 Centro 26,573,593 66.24 75.07 73.4 75.57 Patagonia 2,100,188 5.24 8.31 8.4 6.50 Argentina 40,117,096 100 100 100 100 Source: Own elaboration based on data from Ministerio de Economía, Ministerio de Trabajo and the Censo Nacional de Población, Hogares y Vivienda (2010). aOwn calculations based on the Censo Nacional de Población, Hogares y Vivienda (2010). bOwn calculations based on INDEC (2004). cInformation extracted from INDEC (2018). Exports of undetermined origin or those goods subject to a temporary admission regime are not included. dOwn calculations for the IV Quarter of 2017 based on the Ministerio de Producción y Trabajo de la Nación (2018). Such a disadvantageous position for the provinces of Northern Argentina is not recent (Cortés Conde, 1998; Díaz Orueta, González Villar, Lourés & Sintes, 2000; Gorenstein et al., 2012). It goes back to the origins of the formation of the national space (since the end of the 18th century), where the City of Buenos Aires and the Centro region tended to concentrate most of the productive structure. This was favoured by political decisions such as the closure of inland rivers to the navigation of overseas, the status of Buenos Aires as the only port, the monopoly of customs revenues, the concentration of rail lines around Buenos Aires and the free access of imported manufactures, with which the interior economies could not compete. Later stages tended to perpetuate existing disparities (González, 2019). In a context of wide regional disparities, this work provides estimates of mortality risk from natural disasters in Argentina. However, a first limitation is the relative scarcity of mortality records. For Argentina, the only complete database is DesInventar and in most of its records there is no information on the number of deaths associated with each disaster. Given this scarcity of mortality records an information diffusion method is used 5. The other regions include: Cuyo, Centro, Patagonia and Gran Buenos Aires. Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 82 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 -trying to overcome this lack of data- (Huang, 1997). This allows generating non-parametric and absolute estimates of mortality risk. Logically, this work builds on the theoretical and empirical contributions of previous works on this topic. Liu et al. (2013) develop a multi-hazard risk assessment method based on information diffusion theory to overcome the lack of historical and spatial data on natural disasters. They provide estimates for the China’s Yangtze River Delta region and find that Hangzhou and Ningbo cities are those of highest risk (measure by exceedance probabilities) while Shanghai presents the lowest risk. The estimated exceedance probabilities of one death are 0.99 and 0.20, respectively. The estimates are normalized considering population. These authors remark that the exceedance probability is the inverse of the return period, this is, the risk associated to a 0.05 (5%) exceedance probability is the same as a 20-years return period. Also for the Chinese case, Xu et al. (2016) provides estimates of exceedance probabilities for meteorological disasters. They find that southeastern provinces present a higher risk of multiple meteorological hazards as a result of their geographical location and topography. Extending the analysis of disaster risk, Bai, Zhang, Hong, Qian & Wang (2014) propose an algorithm to assess the risk of earthquakes in the Chinese province of Yunnan. They compare this algorithm with other methods (neural networks and regression analysis) and find that their algorithm based on the method of information diffusion generates a better estimate of risk. Yang, Hu & Jaeger (2016) focus on the analysis of hurricanes for the case of the Chinese province of Hainan. They explore the risk of loss in transportation network and find that the most important spatial characteristics includes location and that the high-way network of Hainan may suffer a 90% damage every 4.28 years (return period). Liu, Siu & Mitchell (2017) consider multiple natural disasters and their potential interactions in the case of the northeast region of Zhejiang (China). They show that a short period of time between natural disasters (two successive hurricanes for example) significantly increases the expected losses in the affected region. This takes place because people are particularly vulnerable after the first disaster and do not reach a full rebuild before the second event takes place. To the best of my knowledge, a few other studies exist and this includes the analysis of risk of crop loss (Balezentis, Streimikiene, Melnikiene & Yu, 2017; Xu et al., 2019) and precipitation risk (Olya & Alipour, 2015). In particular, Balezentis et al. (2017) propose a non-parametric measure of yield risk for Lithuanian crop farming and find that maize, buckwheat and winter rape presents the highest yield risk. Xu et al. (2019) focus on the Chinese case and study the crop loss risk due to heavy rain and strong wind in the Yangtze River Delta. They find that districts located in the southeast of the region are those that present a higher risk. In turn, the risk decreases, between districts, from south to north and from east to west. Finally, Olya & Alipour (2015) explore the relationship between precipitation risk and tourism for the case of North Cyprus. They find that the northern borders of the island are exposed to higher levels of precipitation, resulting in adverse effects on tourism. It should be noted that the literature on this topic (non-parametric estimates using information diffusion method) is really scarce. All the antecedents I cite date from the last 5 years and most of them provide estimates for the case of China. This method allows obtaining robust probability estimates -when historical records are scarce- and normative decisions are not required as in other non-parametric methods. 3. METHODS 3.1 Methodology The procedure for the calculation of exceedance probabilities based on the information diffusion method is detailed below, following Liu et al. (2013) and Xu et al. (2016). Equations (1) to (9) must be estimated for each of the r regions and, in turn, equations (1) to (6) for each of the d types of natural disasters considered. Equations (7) to (9) proceed with the aggregation of natural disasters by region. Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 83 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 Let mt be the deaths toll caused -in a certain region and by type of natural disasterin year t and be Ud the fuzzy set of the number of deaths -for the entire analysed period-, which is determined from the historical disasters data and is in the interval [0-r] -being r a positive integer-. So: Ud={uj |j=1,2,..,r + 1} ={u1,u2,..,ur+1} = {0,1,..,r} (1) The mortality data of each year can be diffused to the discrete domain of discourse from the following equation: (2) where h is the diffusion coefficient established from the minimum and maximum mortality values -mmin and mmax, respectively- and the sample size n -defined by the number of years in the sample-: (3) Huang (1997) details h values for different sample sizes. The information distribution -ut(uj)-, is obtained from the normalization of ft (uj ) and the result can be expressed as a continuous probability density function: (4) Then, the probability distribution p(uj) in uj can be calculated from equations (5) and (6): (5) (6) where p (uj) is the probability distribution of 0-r deaths for a given type of natural disaster. On the other hand, assuming that the occurrence of each natural disaster considered is independent of the occurrence of other disasters, the deaths toll caused by multiple natural disasters (umj) can be obtained as: (7) Therefore, the probability distribution of deaths caused by multiple natural disasters can be calculated as: (8) where ujd is the number of deaths (uj) caused by each type of natural disaster d. Finally, mortality exceedance probability due to multiple natural disasters, P (umj), is obtained as: (9) Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 84 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 In summary, the computation of exceedance probabilities allows us to know which is the probability -in a given region- of experience annually, at least, a certain death number due to multiple natural disasters. In this way, the exceedance probability of 0 deaths will be equal to 1, while for arbitrarily high values the exceedance probability tends to 0. 3.2 Stochastic dominance and comparisons between regions From the exceedance probabilities it is possible to construct loss curves relating number of deaths (X axis) with exceedance probabilities (Y axis) -by region-. The greater the slope of the curve, the higher the probability of occurrence of a given event -in this case, deaths from disasters- and the greater the area under the loss curve, the greater the uncertainty about the occurrence of a given event -that is, the probability distribution is wider-. Advantageously, the loss curves allow comparison between regions based on the use of stochastic dominance techniques, that is, they allow concluding if a region is unambiguously more or less risky than another in terms of mortality from natural disasters. In particular, if an accumulated distribution function F(x) allows knowing the non-exceedance probability for a given value b of x -that is, the probability of obtaining a value less than or equal to b-, a loss curve can be understood as the complement of an accumulated distribution function [1-F(x)], that is, computes the exceedance probability for a given value of x. Complementary cumulative distribution functions are also called survival functions in other disciplines (Alkire et al., 2015). In this way, let F1(x) be the function representing the loss curve of region 1, it is said that first order stochastically dominates F2(x) -the loss function of region 2- if F1(x) ≥ F2(x) for all x and F1(x) (x)>F2(x) for some x. This implies that, if F1(x) stochastically dominates F2(x), no matter how many deaths are chosen for comparison, the exceedance probability of region 1 will always be greater than or equal to the exceedance probability of region 2 and, therefore, it will be region 1 the one that presents -unambiguously- the highest absolute risk of mortality due to natural disasters. The use of stochastic dominance techniques -when making comparisons between regionsgives the advantage that the conclusions obtained are robust, that is, valid for any chosen mortality value. However, in case the loss curves intersect -in at least one pointthe criterion does not allow reaching a conclusion and, therefore, the ordering is incomplete. Trying to overcome the above, some applications have implemented restricted stochastic dominance techniques, that is, limited to a certain interval of the distribution function (Alkire & Santos, 2014). It should be considered that, despite the possible existence of first order stochastic dominance between any pair of regions -comparing the exceedance probabilities for all mortality valuesthis does not imply that the differences are significant. In this regard, advancing the issue, Santos & Zaratiegui (2017) test the significance of the differences between two distributions using a Kolmogorov-Smirnov type test proposed by Barrett & Donald (2003). Considering two representative functions of the loss curves of regions 1 and 2 - F1(x) and F2(x), respectively-, the hypotheses to be considered are: H0: 1(x) ≥ 2(x) for all x ϵ (0,) Ha: 1(x) < 2(x) for some x ϵ (0,) with x ϵ R++ and x <∞ If H0 is not rejected, F1(x) first order stochastically dominates F2(x). The considered statistic is: (10) where sups it is the supremum of the difference between both empirical distributions and, n and m are the sample sizes for distributions 1 and 2, respectively. Then, the values obtained for the statistic Ŝ1 must be compared with the critical values suggested by Barret & Donald (2003) -1,073, 1,2239 and 1,5174 for significance Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 85 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 levels of 10%, 5% and 1%, respectively - and the null hypothesis is rejected if Ŝ1 is greater than the critical reference value. Successively, the test can be repeated for each pair of considered distributions, evaluating the significance of the differences. For each pair considered, it is possible to test whether F1(x) stochastically dominates F2(x) or, if on the contrary, F2(x) is the one that stochastically dominates. 3.3 Data source In Argentina, the most complete historical record of natural disasters is the Disasters Inventory System (DesInventar in Spanish), developed by the Social Studies Network on Disaster Prevention in Latin America (LA RED in Spanish), in which are registered different types of natural and anthropic disasters occurred in Argentina, between 1970-2015, with a level of disaggregation up to the municipal level. For each registered event, information is provided on the number of people exposed, estimated economic losses, damages caused, duration and type of disaster. The database is constructed from reports of newspapers of national circulation. In particular, for the 1970-2015 period, DesInventar presents -for each type of natural disaster considered in this workthe following mortality records: Table 2. Deaths per type of natural disaster in Argentina 1970-2015. Natural disaster Number of deaths % Avalanche 44 1.57 Alluvium 208 7.43 Landslide 31 1.11 Hailstorm 9 0.32 Frost 317 11.33 Forestal fire 104 3.72 Flood 796 28.45 Rain 51 1.82 Fog 160 5.72 Snowstorm 118 4.22 Heat wave 14 0.50 Drought 13 0.46 Earthquake 100 3.57 Tempest 499 17.83 Electric storm 104 3.72 Twister 31 1.11 Gale 199 7.11 Total 2798 100 Source: Own elaboration based on DesInventar (2018). Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 86 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 In the examined period, 1970-2015, there were more than 2,600 deaths associated with the analyzed natural disasters according to the records of DesInventar. Floods and tempests explain almost 50% of deaths toll. 3.4. Regions in Argentina The Argentine territory is divided into regions which include two or more provinces. The National Institute of Statistics and Censuses (INDEC in Spanish) -the official statistical agency of Argentinaconsiders the existence of six regions (INDEC, 2019): ‒Noreste (NEA): covers the provinces of Misiones, Corrientes, Chaco and Formosa. ‒Noroeste (NOA): includes the provinces of Salta, Jujuy, Tucumán, Santiago del Estero, Catamarca and La Rioja. ‒Cuyo: covers the provinces of Mendoza, San Juan and San Luis ‒Centro: includes Entre Ríos, Santa Fe, Córdoba and Buenos Aires ‒Patagonia: covers Rio Negro, Neuquén, Santa Cruz, Chubut, Tierra del Fuego and La Pampa. ‒Gran Buenos Aires (GBA): includes the City of Buenos Aires and its suburbs -districts from the Province of Buenos Aires6- which constitute the Gran Buenos Aires agglomerate (INDEC, 2003). Due to the limited geographical disaggregation in DesInventar’s disaster records, it is not possible to identify -in all casesthe city of disaster occurrence. For this reason, it is not possible -for the districts of the suburbs of the City of Buenos Aires- to determine if a disaster should be attributed to Gran Buenos Aires or to the Centro region. Consequently, from now on, it is decided to include the suburbs of the City of Buenos Aires within the Centro region. That is, all the districts of the Province of Buenos Aires are part of the Centro region. Map 1 presents the location of the Argentine regions. In each case, the number of disasters occurred and their participation in the national total are indicated. It should be noted that, in 1987, the provinces that constitute the NEA and NOA regions celebrated the Norte Grande Argentino Integration Treaty (NGA in Spanish), following criteria of geographical proximity and socio-economic similarities. In this way, a region encompassing the ten provinces of northern Argentina was created. For its part, the province of La Pampa, which is frequently included in the Centro region, celebrated the Patagonian Provinces Agreement in 1996 and, therefore, opts for its inclusion among the Patagonian provinces. Table 3 presents the distribution of population and area by region in Argentina: Table 3. Area and population by region in Argentina, 2010. Region Population in % Area in % (km2) Buenos Aires City 7.20 0.007 Centro 58.25 24.62 Cuyo 7.11 11.34 NEA 9.17 10.42 NOA 12.24 20.14 Patagonia 6.03 33.47 Argentina 40117096 2780400 Source: Own elaboration based on National Population, Household and Housing Census 2010. 6. In Spanish these districts are called partidos in the Province of Buenos Aires and departamentos in other provinces. Fernando Antonio Ignacio González / REA N. 40 (2020) 78 - 95 93 © Editorial Universidad de Sevilla 2020 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2020.i40.05 German Watch (2019). 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