# **1. Introduction**

Haiti’s geographical location and preexisting fragilities make it particularly vulnerable to natural disasters. During the last decade alone, Haiti has suffered at least 44 natural hazards,<sup>6</sup> such as hurricanes and earthquakes, with the 2010 earthquake that killed an estimated 220,000 people standing out as the most devastating of them (Kolbe et al. 2010). At the same time, preexisting vulnerabilities, including a volatile and unstable political climate with high levels of violence and crime, limit disaster preparedness, response, and recovery. Prevailing socio-economic disparities may place certain population segments in disproportionate exposure to disasters. Limited resources at the household and community levels can limit the ability of a household to prepare for disasters, making poorer households more vulnerable. At the same time, natural disasters diminish the economic capacity of households, increasing vulnerability to future catastrophic events and thereby perpetuating a cycle of poverty. Kianersi et al. (2021) found that households severely affected by Hurricane Matthew in 2016 were more likely to be food insecure in the long term. Besides income, other socio-economic factors such as education, employment, and demographic characteristics, including age and gender, may exacerbate vulnerability conditions for marginalized populations (Llorente-Marrón et al. 2020a).

This paper uses data from a mobile phone survey carried out in 2021, 2022 and 2023 to examine socioeconomic factors that may explain differences in household vulnerability to and preparedness for disasters. Understanding the risks households face, and their preparedness capacity is hampered by acute data shortages. Haiti’s last national household survey, the “Enquête sur les Conditions de Vie des Ménages après Séisme” (ECVMAS), was conducted in 2012. To overcome this data gap, this note leverages mobile phone surveys carried out in July 2021, December 2021 and 2022, and March 2023.<sup>7</sup> While these surveys are useful to capture behaviors, and cost and time efficient, they also have limitations related to low response rates, a limited reach of respondents and a higher bias due to self-declaration. Despite these

> 6 <u>https://public.emdat.be/</u>

> 7 The survey was part of the High Frequency Mobile Phone Surveys (HFS) implemented by the World Bank in several countries around the world to understand household welfare in the wake of the Covid-19 pandemic. This note uses data from the first and second waves of 2021, the third wave of 2022 and the fourth wave of 2023. The first wave of the survey included questions to estimate the wealth quintile of households (see methodological footnote on the creation of wealth quintiles below). The second, third and fourth waves of the survey included a Disaster Risk Module. A total of 2,631 households were interviewed in the second wave while 1,487 households were interviewed in both wave 1 (July 2 to August 5, 2021) and wave 2 (November 29 to December 22, 2021). For the third wave, a new sample of 1,530 respondents was interviewed (November 6 to December 11, 2022). Also, for the fourth wave, there were 1,532 new respondents. A more detailed description of the survey and the questions included can be found in the appendix.

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