Desastres y Desarrollo (2).

0. Introducción. 

Ya hemos tratado este asunto en otra entrada, aunque de una manera más impresionista y anecdótica que sistemática. Ya dijimos que es una cuestión sub-estudiada tanto desde el punto de vista empírico cómo teórico.

La cuestión está alineada con el estudio más general de la influencia de la geografía en la economía o geo-economía y en la política o geo-política (y en línea con nuestro interés en predecir el Estado del Mundo en 2050, sobre todo de la partición geopolítica en unidades políticas y la distribución geográfica cuantitativa de la población) sea por:

–la disponibilidad en un determinado territorio de recursos naturales “positivos” (energéticos, mineros, buenos suelos agrícolas, buen clima…) o  “negativos” (entre ellos los desastres naturales o clima adverso).

–una localización ventajosa o desventajosa.

En la serie de entradas Trade Lane Megacities estamos analizando los efectos económicos de la localización. En la serie de entradas Desastres y Desarrollo vamos a ir analizando la disponibilidad de recursos naturales negativos.  Por descontado buena localización y recursos naturales negativos no tienen porque ser independientes:

Gallup et al. (1998) analyze the impact of geography and transportation costs
on productivity and growth, and find that areas with lower transportation costs are more productive; these areas are also often more at risk from floods, because they are on the coast or next to rivers. (Extracto de un artículo recientemente publicado del que hablaremos en el último punto).   

La pregunta que vamos a explorar en esta entrada es sencilla:

¿ pueden ser los fenómenos naturales extremos causa determinante de subdesarrollo económico ?. O dicho de otra manera: ¿ afectan de manera significativa los fenómenos físicos (geo-climatológicos)  extremos (terremotos, vulcanismo, ciclones tropicales y otro tipo de tormentas, sequías) al desarrollo económico o no ?. 

Adelanto que aunque en abstracto no parece una cuestión complicada (en principio no habría más que relacionar la variable “desastre natural” con la variable “desarrollo económico”) en realidad no es una cuestión fácil de estudiar.

Pero el propio concepto de desastre natural ya es problemático, ya que combina un concepto descriptivo de un fenómeno físico (que ocurriría igual con o sin vida, con o sin seres humanos) con una valoración (desastre y esto ya si que sólo puede ocurrir habiendo seres humanos de por medio). En los extremos el asunto está claro. Pero en muchas otras ocasiones, el  mismo fenómeno natural, según sus efectos socio-económicos (que en muchas ocasiones dependen más de la sociedad sobre los que ocurren que de sus propias características físicas) se podría valorar bien cómo desastre, bien cómo bendición.

Por no hablar de cómo concretar de manera operativa estas dos variables. Una posibilidad, cómo veremos insatisfactoria,  sería  medir la frecuencia de desastres naturales en una determinada Unidad Política con renta per cápita. Los sesgos aquí emergen de manera natural y pueden ser desastrosos para los resultados.

Por otra parte a casi todos  los estudios, cómo es lógico,  les interesa más la ayuda de emergencia que los efectos sobre el desarrollo económico.

Algunas Agencias Multilaterales vinculadas a esta cuestión de las que vamos a ir hablando son:

— UNISDR (órbita ONU),

UNU-EHS, (órbita ONU).

CRED / EM-DAT (dependiente de la WHO, órbita ONU).

World Bank. Hazard Management Unit. Disaster Risk Management. y su colaboración con el Center ofHazards and Risk Research de la Universidad de Columbia. De uno de los papers que comentamos en el último punto:

In order to derive such a proxy we avail of the natural disaster global hot-spots data constructed by a joint effort from the World Bank Hazard Management Unit and the Center for Hazards and Risks Research Unit at Columbia University; see Diley et al (2005). More specifically, this research team developed a innovative summary proxy of risk exposures faced locally (within countries) across the globe, that takes account of both the likelihood of a natural disaster event as well as the local exposure (in terms of population) to it for five different natural disasters: cyclones, earthquakes, landslides, floods, and droughts. Details of their methodology underlying the construction of their multi-hazard indicator is given in the Data Appendix.

También hay Agencias Bilaterales, Institutos de Investigación o Empresas Privadas que realizan estudios sobre estos temas:

USAID / OFDA.

OFDA is the office within USAID responsible for providing non-food humanitarian assistance in response to international crises and disasters. USAID/OFDA is organized into three divisions. The Disaster Response and Mitigation (DRM) division is responsible for coordinating with other organizations for the provision of relief supplies and humanitarian assistance. DRM also devises, coordinates, and implements program strategies for the application of science and technology to prevention, mitigation, and national and international preparedness initiatives for a variety of natural and man-made disaster situations. The Operations Division (OPS). The Program Support (PS) division provides programmatic and administrative support. 

International Center for Geohazards.

Risk assessment for natural disasters becomes more and more important in a world where the population density increases and climate changes. To be able to reduce the extent and damages of natural disasters it is necessary to first map the resulting risk.  

Within the project ICG 2 practical assessment of hazard, vulnerability and risk for geohazards is developed. ICG 2 cooperates with the other ICG projects and studies within ICG 2 includes among others: Risk assessment for Rock slopes, slopes in clays, snow avalanches and rockslide generated tsunamis. The project also put emphasis on risk communication and dissemination on risk assessment.

Scientific objectives/subprojects

The project ICG 2 has the following principal objectives:

  • Develop framework for the practical assessment of hazard, vulnerability and risk associated with geohazards.
  • Provide tools and examples on how to do the analyses.
  • Be at the forefront of actors on risk analysis in the geo-profession

The project is divided into 5 subprojects, illustrated in the figure below. 
Click on each subproject to get to the description.

1.   Framework for risk assessment
2.   Case studies
3.   Hazard zonation, vulnerability and losses
4.   Communication on social aspects
5.   Dissemination on risk assessment

Maplecroft (ver más adelante).

1. Datos empíricos (brutos o más o menos elaborados). 

Desde el punto de vista empírico la mayoría de los indicadores se centran más en los efectos sobre las poblaciones (mortalidad) que en los efectos económicos de los desastres naturales.

1.1. Datos de frecuencia según EM-DAT.

EM-DAT publica un informe anual Anual Disaster Statistical Review. The numbers and trends. Puedes ver el correspondiente a 2011 aquí.

Extractos.

The Centre for Research on the Epidemiology of Disasters (CRED) has been active for more than 35 years in the fields of international disaster and conflict health studies, with research and training activities linking relief, rehabilitation and development. It was established in Brussels in 1973 at the School of Public Health of the Catholic University of Louvain (UCL) as a non-profit institution with international status under Belgian law. In 1980, CRED became a World Health Organization (WHO) collaborating centre as part of WHO’s Global Program for Emergency Preparedness and Response. Since then, CRED has increased its international network substantially and collaborates closely with numerous UN agencies, inter-governmental and governmental institutions, non–governmental organizations, research institutes and universities.

Since 1988, with the sponsorship of the United States Agency for International Development’s Office of Foreign Disaster Assistance (USAID/OFDA), CRED has maintained EM-DAT, a worldwide database on disasters. It contains essential core data on the occurrence and impacts of more than 19 500 disasters in the world dating from 1900 to the present. The data are compiled from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies. Priority is given to data from UN agencies, followed by OFDA, governments and the International Federation of Red Cross and Red Crescent Societies. This prioritization is not only a reflection of the quality or value of the data, but it also reflects the fact that most reporting sources do not cover all disasters or have political limitations that can affect the figures. The entries are constantly reviewed for redundancy, inconsistencies and incompleteness. The database’s main objectives are to assist humanitarian action at both national and international levels; to rationalize decision-making for disaster preparedness; and to provide an objective basis for vulnerability assessment and priority setting.

Regina Below has worked at CRED for over 20 years. As technical support staff, she is involved in different activities of the centre and is in charge of the International Disaster Database (EM-DAT) project.

A continuación algunos de los mapas que proporciona la base de datos EM-DAT.

Lógicamente los países grandes tienen mayor número de eventos / mayores frecuencias; cuestión de superficie. Por lo tanto la unidad territorial País / Estado parece inadecuada y en los grandes (EEUU, China, India, Rusia, Brasil, México etc…) y sería mejor utilizar unidades regionales o provinciales, para obtener tamaños más homogéneos de superficie.

Y quizás la frecuencia no sea tan importante cómo la magnitud. ¿ Cómo medir la magnitud de un desastre natural de manera objetiva, que no tenga nada que ver con sus efectos socio-económicos ?

Y de hecho todo esto se tiene en cuenta en la mejores bases de datos disponibles sobre desastres naturales (Diley):

As outlined in the Data Appendix, the multi-hazard index by Diley et al (2005) is calculated for local sub-national level grid cells. In order to arrive at a national measure of natural disaster (per capita) hazard, we summed grid cells’ multi-hazard values within countries and normalize this sum by a country’s population size (in ‘000s) in 2000 as given by the GPW data.8

Although not easily detectable from the graph, the highest hazard countries are, unsurprisingly, mostly small islands – as, for example, Vanuata, Turks and Caicos Islands, Belize etc. – although also some larger countries also feature in the very hazardous groups (ex: Somalia, Afghanistan, Chile, etc.).

1.2. Vulnerabilidad.

De nuevo aquí nos encontramos con un concepto espinoso. Me remito a parte de la literatura existente (2006). Ver también aquí, la web de UNU-EHS.

Extracto del primer enlace:

Vulnerability and many other colloquial terms (risk, hazard, resilience, resistance) found in disaster management concepts are widely used irrespective of the fact that there are still no universally agreed definitions. An array of glossaries have been published to promote the use of a common terminology, or at least to serve as dictionaries for helping experts from different disciplines and schools to understand each other.

While this book also incorporates a comparative glossary, its main objective
is to move the whole agenda forward. It documents the efforts being
made by the scientific community to address issues well beyond these
terminological concerns, by taking stock and summarising the state of the
art of measuring vulnerability at the point where scientists and professionals
have started the WCDR follow-up process.

Y de la definición general a la operativa hay también un largo recorrido. A continuación algunas fuentes de datos sobre vulnerabilidad.

a) World Bank.

The World Bank report “Natural Disaster Hotspots: A Global Risk Analysis, presents a global view of disaster risks associated with major natural hazards. It identifies high-risk geographic regions, or “hotspots”, so that development efforts can be better informed and reduce future disaster-related losses. IEG’s “Evaluation of World Bank Assistance for Natural Disasters” suggests using a related list of “hotspot” borrowing countries, that are relatively high risk from two or more hazards, to focus Bank’s interventions and planning on reducing these risks.

The maps below present: a) Hotspots across the globe, based on losses as % of GDP (see notesbelow), b) Hotspot Countries are World Bank client-countries (borrowers) particularly vulnerable to disaster risks, and c) a Combined Map presents the first two maps superimposed.

Encuentro estos tres mapas anteriores altamente informativos. Deberían haber señalizado de alguna manera desiertos y zonas frías. Estos dos tipos de zonas se podrían considerar desastres permanentes.

b) Maplecroft es una empresa privada que precisamente se dedica a realizar estudios de vulnerabilidad.

Extracto de su sitio web:  

Alyson Warhurst is CEO and Founder of risk analysis and mapping company Maplecroft. Over the last 10 years she has built Maplecroft into the leading source of extra-financial risk intelligence for the world’s largest multinational corporations; banks and asset managers; governments and NGOs. Coming from an academic background, she now advises global companies and organisations at board level on issues including: global and political risks, human rights, ethical supply chains, corporate reputation and CSR. Alyson is a consultant to the World Economic Forum, where she is also part of the faculty; she is a member of Clinton Global Initiative and on the Board of Trustees at Transparency International UK. From 1999 to 2009 Alyson was Chair of Strategy and International Development at Warwick Business School, where she won the inaugural Faculty Pioneer “Beyond Grey Pinstripes Award” (called by the FT the “Business School Oscars”), regularly won the “Outstanding Teacher Award” and was made an Honorary Professor in 2010. She is an accomplished speaker at high-level international events and has written several books and more than one hundred articles, including a regular column for Business Week. In 2010 Alyson was a recipient of a Business Insurance Magazine “Women to Watch” award.

Edita un Atlas de Vulnerabilidad a Riesgos Naturales y al Cambio Climático. Su unidad  de medida son los 25 km2 de territorio.

En el mapa anterior vulnerabilidad al cambio climático. Los países marcados en azul oscuro son los que tienen un riesgo extremo.

a) Los 10 países más vulnerables a los desastres naturales en 2011 son (ojo, no confundir vulnerabilidad con frecuencia de desastres naturales en un determinado territorio; si lo que te interesa es la frecuencis debe de ir a EM-DAT; en el enlace un ejemplo para un país), sin importar el orden ya que lo desconozco: Bangladesh, India y Birmania en el sur de Asia, Filipinas, Vietnam y Laos (los tres son los de menor renta per cápita en todo el sudeste asiático, sólo superados por Cambodia) en el sudeste asiático, Haiti, Rep. Dominicana, Nicaragua y Honduras en el Caribe.

b) Los 10 países más vulnerables al cambio climático son, por este orden de más a menos:

Haiti. Es el país con menor renta per cápita de América (según ranking del Banco Mundial con datos 2011).

Bangladesh. Idem, en primera posición. Es el cuarto país con menor renta per cápita de Asia (detrás Afganistán, Nepal y Timor Este).

Zimbabwe. No aparece entre los 10 países más vulnerables a desastres  naturales. Segundo país con menor renta per cápita del mundo, según FMI datos 2011 (el primero es la R.D. Congo).

Sierra Leona. Idem anterior. 7º país con menor renta per cápita del mundo.

Madagascar. Idem anterior. 9º mundial.

Cambodia. Idem. (país más pobre del Sudeste asiático, sin contar Timor Este)

Mozambique. Idem. 10º mundial.

R.D. Congo. Idem. 1º mundial.

Malawi. Idem. 8º mundial.

Filipinas.  4º país más pobre del sudeste asiático.

Quiero insistir de nuevo en que no se debe de confundir “vulnerabilidad a” con “frecuencia de” desastres naturales. No obstante parece que hay una correlación clara entre vulnerabilidad (no se si también frecuencia) y desarrollo económico (medido por la renta per cápita, medida claramente insatisfactoria pero es lo que hay).

Esto es así tanto en zonas subdesarrolladas cómo en zonas desarrolladas: algo que se puede comprobar fácilmente es que por ejemplo en EEUU, también existe una correlación entre Estado vulnerable a desastre natural y  menor renta per cápita.  Esto lo analizaremos otro día en más detalle.

2. Desde un punto de vista más teórico se acaba de publicar un artículo, bajo el auspicio del Banco Mundial, que es el que en parte motiva esta entrada. Antes revisamos un artículo anterior en la  misma linea teórica.

2.1. Schumacher y Strobl.

Título. Economic development and losses due to natural disasters: the role of risk  (2008).

Abstract.

We show that the relationship between wealth and economic losses due to natural disasters is strongly linked to disaster risk. We first build an analytical model that demonstrates how countries that face a low hazard of disasters are likely to see first increasing losses and then decreasing ones with increasing economic development. At the same time, countries that face a high hazard of disasters are likely to experience first decreasing losses and then increasing ones with increasing economic development. We then use a cross country panel dataset in conjunction with a risk exposure index to investigate whether the data is consistent with the predictions from the model. As suggested by our model, we generally find an inverse ushaped link between losses and wealth for low and medium hazard countries, but a u-shaped relationship for high hazard countries.

Extractos.

One of the main stylized facts that has arisen from the still relatively new academic literature on natural disasters seems to be that the economic and human losses associated with natural disasters are larger the poorer a country is.3 This was first shown by Tol and Leek  (1999) and Burton et al (1993) for a sample of 20 nations and later confirmed in more comprehensive studies covering a large panel of countries by Kahn (2005) and Toya and Skidmore (2007). Yet, surprisingly, beyond arguing, for example, that “as a country develops, it devotes greater resources to safety, including precautionary measures…” (Hideki and Skidmore, p. 20) there is, to our knowledge, no study investigating the underlying mechanics driving this link, either of an empirical or of a theoretical nature.4, 5

In this article we investigate the relationship between the losses from natural disasters, the exposure to different levels of natural hazard risk (risk exposure) and the stages of economic development. Our main contributions are the analysis of this relationship via a theoretical model as well as through an econometric analysis of a cross country panel dataset. We find that both the theoretical model and the empirical analysis predict a non-linear relationship between economic losses and the stages of economic development that crucially depends on country’s risk exposure to natural disasters. More specifically, countries that face a low or intermediate risk exposure have a bell-shaped relationship between economic losses and wealth; whereas countries that face a high risk exposure have a u-shaped relationship between losses and wealth. This stands in contrast to the current literature, which solely suggests decreasing losses with increasing wealth

2.2 Hallegatte. 

Título: An exploration of the link between development, economic growth, and natural risk.

Plataforma de publicación:

This paper—prepared as a background paper to the World Bank’s World development Report 2014: Managing Risks for Development—is a product of the Office of the Chief Economist, Sustainable Development Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at shallegatte@worldbank.org

Abstract:

This paper investigates the link between development, economic growth, and the economic losses from natural disasters in a general analytical framework, with an application to hurricane flood risks in New Orleans. It concludes that where capital accumulates through increased density of capital at risk in a given area, and the costs of protection therefore increase more slowly
than capital at risk, (i) protection improves over time and the probability of disaster occurrence decreases; (ii) capital at risk—and thus economic losses in case of disaster—increases faster than economic growth; (iii) increased risk-taking reinforces economic growth. In this context, average annual losses from disasters grow with income, and they grow faster than income at low levels of development and slower than income at high levels of development. These findings are robust to a broad range of modeling choices and parameter values, and to the inclusion of risk aversion. They show that risktaking is both a driver and a consequence of economic development, and that the world is very likely to experience fewer but more costly disasters in the future. It is therefore critical to increase economic resilience through the development of stronger recovery and reconstruction support instruments.

Enlace(PDF).

Comentarios y extractos. El autor desarrolla sobre todo un modelo económico abstracto  y lo aplica al caso de New Orleans. Por lo tanto, en general no intenta contestar a la pregunta que nos interesa.  Sin embargo si aparecen algunos comentarios relevantes:

Current trends in disaster losses appear however consistent with the prediction of fewer but larger disasters (e.g., Etkin, 1999; Nordhaus,
2010; Bouwer et al., 2007; Pielke et al., 2008; Bouwer, 2011; Schumacher and Strobl, 2011). These results are also in line with UN-ISDR (2009), which observes that poor countries suffer from frequent and low-cost events, while rich countries suffer from rare but high-cost events. This trend is illustrated by the Japanese case. Thanks to protection investments, the country can cope without any damage with frequent earthquakes and tsunamis that would cause disasters in any other place of the world. But this resilience allows for higher investments in at-risk areas, and exceptional quakes like the recent Tohoku Pacific earthquake can then lead to immense losses.

La parte que he marcado en negrita aparentemente tiene una explicación sencilla: en los países ricos sólo se consideran desastres los eventos muy destructivos que afectan a ciudades. En los pobres, con mayorías poblacionales viviendo de la agricultura, cualquier tormenta un poco fuerte es un evento destructivo. Por otra parte recordar Japón es un caso muy citado en la literatura sobre desastres y desarrollo.

The paper suggests that natural disasters will become less frequent but more costly with development and economic growth, and this result has some policy-relevant consequences. In particular, it means that development requires more resilience, i.e. an improved ability to deal with and recover from rare events, which exceed the protection capacity. The Tohoku Pacific earthquake could thus be an illustration of the type of events the world will have to deal with in the future. Such a trend toward larger disasters translates into a strong and increasing need for crisis management and post-disaster support, through (1) forecasts and early warning to mitigate human losses (e.g., Subbiah et al., 2008; Hallegatte 2012a); (2) rainy-day funds and insurance and reinsurance schemes to support reconstruction (e.g., Ghesquiere and Mahul, 2010; de Forges et al., 2011; Kunreuther and Michel-Kerjan, 2012); and (3) new international instruments for post-disaster support and solidarity (e.g., Linnerooth-Bayer et al. 2009).

So, even though it might be optimal to see fewer and larger disasters in the future, the current increase in disaster losses is very likely to exceed what
is optimal. The difference between the individuals’ and firms’ risk taking and the optimal risk level estimated in the current analysis provides a justification for public policies that regulate risk taking (e.g., zoning policies, information campaign, technological risk regulations).

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