# 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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