metadata: authors: 0: author: Rozenberg,Julie
1: author: Hallegatte,Stephane
count: World
docna: 0: docna: Model and methods for estimating the number
            of people living in extreme poverty because of the direct
            impacts of natural disasters
docty: Policy Research Working Paper
subtopic: Science of Climate Change,Climate Change and Health,Economic Theory & Research,Labor & Employment Law,Industrial Economics,Climate Change and Environment,Economic Growth,Inequality,Natural Disasters
teratopic: Poverty Reduction
abstracts: cdata!: Natural disasters have an impact on
            poverty through many different channels -- economic growth,
            health, schooling, behaviors -- that are difficult to
            quantify. It is nonetheless possible to assess the
            short-term impacts of income losses. A counterfactual
            scenario is built of what people's income would be in
            developing countries in the absence of natural disasters.
            This scenario uses surveys of 1.4 million households in 89
            countries. Depending on where they live and work, what they
            consume, and the nature of their vulnerability, the
            additional income that each household in the survey could
            earn every year on average in the absence of natural
            disasters is calculated. The analysis concludes that if all
            disasters could be prevented next year, 26 million fewer
            people would be in extreme poverty—that is, living on less
            than $1.90 a day. A systematic analysis of the uncertainty
            suggests that this impact could lie between 7 million if all
            the most optimistic assumptions are combined, and 77 million
            if we retain only the most pessimistic assumptions.
repnme: repnme: Model and methods for estimating the number
            of people living in extreme poverty because of the direct
            impacts of natural disasters
display_title: Model and methods for estimating the
            number of people living in extreme poverty because of the
            direct impacts of natural disasters
listing_relative_url: /research/2016/11/26933182/model-methods-estimating-number-people-living-extreme-poverty-direct-impacts-natural-disasters
topicv3: Economic Growth m1326241 471,Poverty m1326687 695,Climate Change m1326829 567,Drought m1503313 3162,Household Surveys m1504071 2761
publishtoextweb_dt: 2016-11-14T00:00:00Z
historic_topic: Poverty Reduction
no_of_pages: 10
author_keys: 0: author_key: 1296276
1: author_key: 1103816
model_extractions: WPS7887 Policy Research Working Paper 7887 Model and Methods for Estimating the Number of People Living in Extreme Poverty Because of the Direct Impacts of Natural Disasters _Julie Rozenberg Stephane Hallegatte_ Global Facility for Disaster Reduction and Recovery & Sustainable Development Global Practice Group Office of the Chief Economist November 2016
Policy Research Working Paper 7887

# **Abstract**

Natural disasters have an impact on poverty through many different channels—economic growth, health, schooling, behaviors—that are difficult to quantify. It is nonetheless possible to assess the short-term impacts of income losses. A counterfactual scenario is built of what people’s income would be in developing countries in the absence of natural disasters. This scenario uses surveys of 1.4 million households in 89 countries. Depending on where they live and work, what they consume, and the nature of their vulnerability, the additional income that each household in the survey could earn every year on average in the absence of natural disasters is calculated. The analysis concludes that if all disasters could be prevented next year, 26 million fewer people would be in extreme poverty—that is, living on less than $1.90 a day. A systematic analysis of the uncertainty suggests that this impact could lie between 7 million if all the most optimistic assumptions are combined, and 77 million if we retain only the most pessimistic assumptions.

This paper is a product of the Global Facility for Disaster Reduction and Recovery (GFDRR) and the Office of the Chief Economist of the Sustainable Development Global Practice Group of the World Ban, and a background paper to the GFDRR and World Bank report “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disaster.” 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 authors may be contacted at shallegatte@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Model and Methods for Estimating the Number of People Living in Extreme Poverty Because of the Direct Impacts of Natural Disasters**

# **Julie Rozenberg**

Office of the Chief Economist, Sustainable Development Practice Group The World Bank <u>jrozenberg@worldbank.org</u>

# **Stephane Hallegatte** ,

Global Facility for Disaster Reduction and Recovery (GFDRR) The World Bank <u>shallegatte@worldbank.org.</u> Keywords: development, poverty, natural disasters JEL: I30, I32, Q54, O10
# 1. Introduction

Natural disasters have an impact on poverty through many different channels (economic growth, health, schooling, behaviors) that are difficult to quantify. It is nonetheless possible to assess a fraction of the full effect. Here, we quantify the short‐term impacts that natural disasters have on poverty through people’s income when they are hit.

To do so, in this study we built a counterfactual scenario of what people’s income would be today in the developing world in the absence of natural disasters. This scenario is based on the same methodology as in Rozenberg and Hallegatte (2015) and the _Shock Waves_ report (Hallegatte et al. 2016).

It uses household surveys of 1.4 million households, which are representative of 1.2 billion households and 4.4 billion people in 89 developing countries.<sup>1</sup> The household surveys were formatted for the GIDD model, using the I2D2 database (Osorio‐Rodarte, Cruz, and Bussolo 2015). Here we used an updated version of the database, harmonized in 2012, and using 2011PPP USD for household income. Therefore, we use 1.90 USD 2011PPP per day as a threshold for extreme poverty in this study.

Depending on where they live and work, what they consume, and the nature of their vulnerability, we calculated the additional income that each household in the survey could earn every year on average in the absence of natural disasters. We then assess the average number that are living today with less than $1.90 per day only because they have been affected by a disaster.

# 2. Estimating income for farmers and nonfarmers

The model first attributes one of the following mutually exclusive categories to each person in the household surveys database: (1) agriculture worker; (2) manufacturing worker; (3) services worker; (4) adult not in the labor force; (5) elderly (above 65 years old); (6) child (under 15 years old).

These categories are of course adaptable depending on the information available in the survey and the objective of the study. Here the main objective is to differentiate farmers from the rest of the workers because some disasters will affect their income differently.

The database only gives an aggregated estimate of income at the household level, and does not provide individual income data. Since droughts affect differently the income of farmers and of consumers, we estimate the income of each category of people in the database. We calculate the � number of people in household _j_ who belong to each category ( ���� ) and we estimate current households’ revenue using these categorical predictors (with weighted linear least squares). Note that

> 1 Afghanistan, Angola, Albania, Argentina, Azerbaijan, Burundi, Burkina Faso, Bangladesh, Bosnia and Herzegovina, Belize, Bolivia, Brazil, Bhutan, Botswana, China, Côte d’Ivoire, Cameroon, Cabo Verde, Costa Rica, Dominican Republic, Ecuador, Arab Republic of Egypt, Ethiopia, Micronesia, Gabon, Georgia, Ghana, Guinea, Gambia, Guatemala, Guyana, Honduras, Haiti, Hungary, Indonesia, India, Jamaica, Kenya, Kyrgyz Republic, Cambodia, Lao People’s Democratic Republic, Lebanon, Liberia, Sri Lanka, Morocco, Moldova, Madagascar, Mexico, Macedonia, Mongolia, Mozambique, Mauritania, Mauritius, Malawi, Namibia, Niger, Nigeria, Nicaragua, Nepal, Pakistan, Panama, Peru, Philippines, Papua New Guinea, Paraguay, Rwanda, Senegal, Solomon Islands, Sierra Leone, El Salvador, Serbia, São Tomé and Príncipe, Suriname, Swaziland, Syrian Arab Republic, Chad, Togo, Thailand, Tajikistan, Turkmenistan, Tunisia, Tanzania, Uganda, Ukraine, República Bolivariana de Venezuela, Vietnam, Republic of Yemen, South Africa, Zambia.

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we exclude children ( �� �0 ) as they should not contribute to the family income, and that there is no intercept in the formula: where �� is an error term that depends on the household.

For most countries, the �� are all positive as expected and – since there is no intercept – they can be interpreted as the average per capita income brought to the household by each category of people. In some countries �� �0 or �� �0 in the regression, suggesting that people unable to work (category 7) or the elderly (category 8) may reduce the income of other household members, possibly due to caretaking obligations. In these cases, however, we force �� �0 or �� �0 and we re‐estimate the other �� .

The per‐capita revenue in each household _j_ can therefore be expressed as:

# 3. The impact of natural disasters

We model the impact of the following six natural disasters on people’s income: cyclones, storm surges, tsunamis, floods, droughts, earthquakes. The share of people affected by each event is calculated based on data from GAR (for earthquakes, tsunamis, cyclones and storm surges) and GLOFRIS (for floods and drought). The impact on people’s income is the same as for the resilience indicator, for each country. We represent the vulnerability bias between poor and non‐poor households but no exposure bias.

For each natural disaster, we model each event, in each country, independently. For floods we have data for events with return periods between 5 and 1,000 years. For droughts, we have events with return periods of 5, 25, 50 and 100 years. For cyclones, storm surges, earthquakes, and tsunamis, we model events with return periods between 2 and 1,500 years. The assumption that each event can only happen alone and once every year means that we underestimate the real impact.

All events but droughts are modeled as a shock on income for affected households, during the year of the event.

It is first necessary to select affected households. In order to avoid exposure bias, all households in the country are duplicated, i.e. affected households are “created” similar to the existing ones while the weight of existing households is reduced. The weights of affected and unaffected households are adjusted to reflect the share of people affected and to keep the total number of people in the country unchanged. This method avoids a random selection of households, which would require running the scenarios many times. The income of affected households is then changed using the vulnerabilities calculated for the resilience indicator. Those differ for the poorest 20% and the rest of the population.

The impact of droughts is modeled through two channels: a shock on the income of farmers and a shock on food prices for consumers. The vulnerability of poverty reduction to food price hikes has already been demonstrated, for instance in (Ivanic and Martin 2008; Ivanic, Martin, and Zaman 3
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For each disaster type and each event, there is uncertainty on the impacts. We therefore build three different scenarios, which are described in Table 1.

_Table 1 Low, medium and high values for the impact of disasters_

||Low|Medium|High|
|---|---|---|---|
|Vulnerability to floods,<br>storm surges,<br>cyclones, earthquakes,<br>tsunamis|Medium ‐30%|Combination of the<br>USGS PAGER building<br>inventory and simple<br>vulnerabilitycurves|Medium +30%|
|Exposure to storm<br>surges, cyclones,<br>earthquakes,tsunamis|Medium ‐30%|GAR|Medium +30%|
|Exposure to floods|Medium ‐30%|GLOFRIS|Medium +30%|
|Exposure to drought|Medium ‐30%|GLOFRIS|Medium +30%|
|Impact of droughts on<br>farmers’ income|0|10|20%|
|Number of people<br>affected by higher<br>food prices for each<br>farmer affected by a<br>drought|Medium ‐30%|50|Medium +30%|
|Impact of drought on<br>local foodprices|10%|55%|100%|

_Table 2 Reconstruction time after disasters by return period and for the three scenarios_

|||R<br>|et|urnperiod<br>||
|---|---|---|---|---|---|
|Scenarios|0‐10|10‐100||100‐500|>500|
|Low|0.5|1||2|3|
|Medium|1|2||3|5|
|High|3|3||5|10|

Note that we are building a counterfactual scenario in which disasters never hit, starting from surveys in which households’ income factors in the impacts of disasters. We therefore apply positive shocks on households, i.e. we _increase_ the income of each household when they are affected by a disaster, to reverse current disasters’ effects on people’s income.

The effect of natural disasters generally lasts more than a year. To simplify, we assume that the economy goes back to normal linearly during a “reconstruction” time that depends on the return period (Table 2). Here again we build three scenarios that account for the uncertainty around reconstruction times. For each event, therefore, we estimate the total impact (over time) by multiplying the yearly impact by N/2 (where N is the reconstruction time).

The impact of each event is represented in Figure 2, with the number of people in poverty because of an event multiplied by the probability of this event.

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# 4. Discussion

Of course, these numbers should be used with caution because they largely underestimate the total impacts of climate change on poverty. First, disaster risk data are not available in all countries, and the impact is calculated only in the countries and for the disasters for which data are available.

Second, these estimates do not include macroeconomic impacts or indirect impacts, and they assume that each disaster happens independently. As such, they disregard some of the elements discussed in Hallegatte et al. (2016, 2017).

And finally, because we rely on household surveys that report only household‐level consumption, we cannot explore the intra‐household distributional issues, and especially the question of whether children or women are disproportionally affected by natural disasters. Gender inequality has been   widely reported see, for example, Hoddinot et al. (2006) and Rose (1999) and a disproportionate   impact on children is well‐identified see Kousky (2016) but these important effects cannot be included in this analysis. The estimates proposed here are therefore a lower bound to the impacts of natural disasters on poverty.

# 5. References

- Devarajan, Shantayanan, S. Delfin Go, Maryla Maliszewska, Israel Osorio‐Rodarte, and Hans Timmer. 2013. “Stress‐Testing Africa’s Recent Growth and Poverty Performance.” Policy Research Working Paper 6517, Development Prospect Group & Africa Region, Office Of The Chief Economist, The  World Bank.

- Hallegatte, S., M. Bangalore, L. Bonzanigo, M. Fay, T. Kane, U. Narloch, J. Rozenberg et al. 2016.

- Shock Waves: Managing the Impacts of Climate Change on Poverty. Climate Change and Development Series. Washington, DC: World Bank.

- Hallegatte, S., A. Vogt‐Schilb, M. Bangalore, and J. Rozenberg. 2017. Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development Series. Washington, DC: World Bank.

- Hoddinott, J. 2006. “Shocks and Their Consequences across and within Households in Rural Zimbabwe.” Journal of Development Studies 42: 301–21.

- Hertel, Thomas W., Marshall B. Burke, and David B. Lobell. 2010. “The Poverty Implications of Climate‐Induced Crop Yield Changes by 2030.” Global Environmental Change, 20th Anniversary Special Issue, 20 (4): 577–85. doi:10.1016/j.gloenvcha.2010.07.001.

- Ivanic, Maros, and Will Martin. 2008. “Implications of Higher Global Food Prices for Poverty in Low‐ Income countries1.” Agricultural Economics 39 (November): 405–16. doi:10.1111/j.1574‐ 0862.2008.00347.x.

- Ivanic, Maros, Will Martin, and Hassan Zaman. 2012. “Estimating the Short‐Run Poverty Impacts of the 2010–11 Surge in Food Prices.” World Development 40 (11): 2302–17. doi:10.1016/j.worlddev.2012.03.024.

Kousky, C. 2016. “Impacts of Natural Disasters on Children.” Future of Children 26(1): 73–92.

- Osorio‐Rodarte, Israel, Marcio Cruz, and Maurizio Bussolo. 2015. “GIDD Sectoral Wage Bill Database by Workers ́skill Level.”.

- Rose, E. 1999. “Consumption Smoothing and Excess Female Mortality in Rural India.” Review of Economics and statistics 81 (1): 41–49.

- Rozenberg, J. and Hallegatte, S., 2015. The impacts of climate change on poverty in 2030 and the potential from rapid, inclusive, and climate‐informed development. Policy Research Working Paper 7483. Washington, DC: World Bank.

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