metadata: authors: 0: author: Wollburg,Philip Randolph
1: author: Markhof,Yannick Valentin
2: author: Bentze,Thomas Patrick
3: author: Ponzini,Giulia
count: Africa,World
docna: 0: docna: The Impacts of Disasters on African
            Agriculture: New Evidence from Micro-Data
docty: Policy Research Working Paper
abstracts: cdata!: Disasters affect millions of people each
            year and cause economic losses worth many billions of
            dollars globally. Reporting on disaster impacts in research,
            policy, and news primarily relies on macro statistics based
            on disaster inventories. The macro statistics suggest that a
            relatively small share of disaster damages accrues in
            Africa. This paper, instead, uses detailed survey micro-data
            from six African countries to quantify disaster damages in
            one key sector: crop agriculture. The micro-data reveals
            much higher damages and more people affected than the macro
            statistics would indicate. On average, 36 percent of the
            agricultural plots in the sample suffer crop losses due to
            adverse climatic events. In the countries and time period
            analyzed, these losses reduced total crop production by an
            average of 29 percent. Importantly, many of these losses are
            underreported or undetected in key disaster inventories and
            therefore elude macro statistics. In the case of droughts
            and floods, the economic losses recorded in the micro-data
            are $5.1 billion higher than in the macro statistics,
            affecting 145 million to 170 million people, more than four
            times as many as the macro statistics suggest. The
            difference stems mostly from smaller and less severe but
            frequent adverse events that are not recorded in disaster inventories.
display_title: The Impacts of Disasters on African
            Agriculture: New Evidence from Micro-Data
listing_relative_url: /research/2024/01/34234814/impacts-disasters-african-agriculture-new-evidence-micro-data
topicv3: Climate Change Impacts m1326159 1773,Agriculture and Food Security m1326205 435,Climate Related Disaster m1503636 3172,Agricultural Productivity and Markets
            m1504065 2883
publishtoextweb_dt: 2024-01-09T00:00:00Z
dois: 10.1596/1813-9450-10660
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 48
author_keys: 0: author_key: 1464872
1: author_key: 1974294
2: author_key: 2033483
3: author_key: 1487387
model_extractions: Policy Research Working Paper 10660 The Impacts of Disasters on African Agriculture New Evidence from Micro-Data _Philip Wollburg Yannick Markhof Thomas Bentze Giulia Ponzini_ Development Economics Development Data Group January 2024
Policy Research Working Paper 10660

# **Abstract**

Disasters affect millions of people each year and cause economic losses worth many billions of dollars globally. Reporting on disaster impacts in research, policy, and news primarily relies on macro statistics based on disaster inventories. The macro statistics suggest that a relatively small share of disaster damages accrues in Africa. This paper, instead, uses detailed survey micro-data from six African countries to quantify disaster damages in one key sector: crop agriculture. The micro-data reveals much higher damages and more people affected than the macro statistics would indicate. On average, 36 percent of the agricultural plots in the sample suffer crop losses due to adverse climatic events. In the countries and time period analyzed, these losses reduced total crop production by an average of 29 percent. Importantly, many of these losses are underreported or undetected in key disaster inventories and therefore elude macro statistics. In the case of droughts and floods, the economic losses recorded in the micro-data are $5.1 billion higher than in the macro statistics, affecting 145 million to 170 million people, more than four times as many as the macro statistics suggest. The difference stems mostly from smaller and less severe but frequent adverse events that are not recorded in disaster inventories.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at pwollburg@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
The Impacts of Disasters on African Agriculture: New Evidence from MicroData

**Philip Wollburg**<sup>**1,2**</sup> **, Yannick Markhof**<sup>**1,3**</sup> **, Thomas Bentze**<sup>**1**</sup> **, Giulia Ponzini**<sup>1</sup>

**JEL codes: Q54, N57, O13, Q15, C81, C83**

**Keywords: agriculture, climate change, disaster risk, survey data, Africa, loss and damage**

1 Development Economics Data Group, World Bank; 2 Wageningen University & Research; 3 United Nations University, UNU-MERIT.

This paper received funding support from the 50x2030 Initiative to Close the Agricultural Data Gap and the World Bank Research Support Budget grant “On the Measurement of Agricultural Productivity Trends in Africa”. The authors are grateful to Douglas Gollin, Erwin Bulte, Gero Carletto, Ruth Hill, Stephane Hallegatte, Talip Kilic, Travis Lybbert and participants of the CSAE 2023 Conference, the ICAS IX Conference, the EAAE 2023 Congress, and at the World Bank for their comments and feedback. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# Introduction

In 2022, natural disasters led to over $220 billion in economic losses, affecting 185 million people.<sup>1</sup> Losses in 2023 are on track to exceed the previous year’s<sup>2</sup> and large-scale disasters, such as record extreme heatwaves, the violent monsoon in India, and a prolonged severe drought in the Horn of Africa have received widespread media and public attention.<sup>3–6</sup> The frequency and intensity of disasters and their impacts has increased over the last decades, a trend that is set to continue, and likely accelerate, due to climate change and global warming.<sup>7–12</sup> Reporting on disaster impacts relies predominantly on macro statistics. A key data source is the Emergency Events Database (EM-DAT), which is a publicly available global inventory of disaster impacts that is widely used in media,<sup>13</sup> research,<sup>14</sup> and policy reports, including recently the World Bank’s 2023 ‘Atlas of the Sustainable Development Goals’ and the Food and Agriculture Organization’s (FAO) 2021 report on ‘The impact of disasters and crises on agriculture and food security.’<sup>15,16</sup> Here, we offer a different approach to studying disaster impacts, based on survey micro-data. We quantify the value of crop production losses due to adverse climatic events on more than 120,000 fields across six African countries and study the impacts of these events on African agriculture, rural populations, and the national economies. Agriculture is a key sector, on which many households in the region depend for their livelihoods, especially the poor and rural households.<sup>17</sup> Agriculture dependent households are thought to be particularly at risk of suffering the impacts of climate change and adverse shocks. Climate change and natural disasters are expected to be especially severe in rural areas in this region,<sup>18,19</sup> while smallholder agricultural production remains predominantly rainfed and the adoption of drought or heat resistant seeds or other such climate-smart technologies is limited.<sup>20</sup> We document that crop losses due to adverse shocks are common and costly both to individual farm households and to the economy at large, and that farmers often suffer multiple shocks in the same season. Taken together, production losses have a substantial aggregate impact. Importantly, these events and their impacts are underreported or undetected in common macro data sources for disaster reporting, such as EM-DAT.

Our analysis of micro-data offers an important complementary perspective to analyses based on aggregate statistics derived from disaster inventories. Aggregate statistics are critical to the study of disaster impacts, providing annual data at a global scale. They are less well-suited to capture the differential impacts of disasters on different population groups, especially poor and vulnerable people.<sup>21–</sup> 23 This is because they account primarily for damages to assets and losses in agricultural production whose value is greater and better documented among richer households and in richer countries. For instance, according to the most recent estimates of EM-DAT, about 70% of economic losses due to disasters occurred in the Americas, compared to just under 4% in Africa.<sup>24</sup> A recent study using the same data source concluded that disaster impacts do not affect poor people as much as the general population.<sup>25</sup> In contrast, evidence from survey micro-data suggests that poorer households and individuals are more exposed and less resilient to adverse climatic and environmental shocks and suffer disproportionately greater well-being losses than better-off households.<sup>18,22,26</sup> Our analysis suggests that production losses due to adverse climatic events are meaningful not only for the well-being of low-income households individually but, because of how many households are affected, they are significant also for the whole economies of our study countries and on a global scale.

2
# Results

## **Crop losses are widespread and significant**

The data used in this analysis is from the Living Standards Measurement Study-Integrated Survey on Agriculture (LSMS-ISA) in Ethiopia, Malawi, Mali, Niger, Nigeria, and Tanzania. These data were harmonized across countries and cover close to 120,000 fields on around 30,000 farms. The data show that crop losses due to disasters and adverse climatic events are widespread and significant in African smallholder agriculture. Farmers report crop losses on between 11% (Nigeria 2018/19) and 90% of plots (Niger 2011), depending on country and year (Figure 1, Panel A and Table 1). Overall, 36% of plots in our sample report a crop loss. Farmers reported losing, on average, 53% of their harvest on plots affected by crop shocks (Panel B and Table 1). Losses vary across countries and years, ranging from 48% of harvest (Ethiopia 2018/19) to 71% of harvest (Niger 2011). Disaster losses have also become more common over time (Table 3). In the 11 years from 2008 to 2019 that our dataset spans, the estimated likelihood of a plot incurring a disaster loss increased by close to 10 percentage points. This is seemingly driven by a higher prevalence of small shocks as the estimated share of harvest lost on plots with any loss decreased by on average 1.1 percentage points with every year studied.

In aggregate, crop losses due to adverse climatic events reduce the total national crop production by between 3% in Nigeria 2018-19 and 81% in Niger in 2011. A total of 29% of potential harvest value is lost across the countries and agricultural seasons observed in our dataset (Figure 1, Panel C and Table 4]).

3
<!-- Start of picture text --> Ethiopia 2015- 2016<br>2018- 2019<br>Malawi 30123945«2013<br>- 2016<br>2018- 2019<br>Mali sons<br>. 2011<br>Niger 2014<br>Nigeria 2019<br>2008<br>; 2012<br>Tanzania 2010<br>2014<br>2019<br>a ,i |<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Frequency of plots recording a shock<br><!-- End of picture text -->

<!-- Start of picture text --> Ethiopia :<br>2013 - 2014 ——<br>2015 - 2016 4<br>2018 - 2019 el<br>Malawi<br>2018 - 2019 re<br>Mali<br>2014 —_e<br>2017 jo<br>Niger<br>2011 a<br>2014 facia<br>Nigeria<br>20 30 40 50 60 70 80<br>Mean percent of harvest value lost on plot (%)<br><!-- End of picture text --> <!-- Start of picture text --> a|a<br>0 20 40 60 80 100<br>Fraction of aggregate potential harvest lost (%)<br><!-- End of picture text -->
<!-- Start of picture text --> Malawi<br>Ethiopia. 2019 2017Mali<br>2016<br>: @§ Drought<br>(© Irregular Rains<br>WS Flood<br>Tanzania CI Pests<br>Niger Nigeria 2014 (J Other<br>2011 2018 No data<br>y<br>&<br>.<br><!-- End of picture text -->
# **Crop losses differ locally and between farmers**

Not all farmers and plots are equally affected. Some are less likely to experience a loss even in the face of an adverse climatic event. Here we show that shock exposure and impacts can differ even between neighboring plots in the same area. We limit this analysis to droughts. Given the nature of droughts, all plots in the same small geographic cluster should be faced with the same drought shock – but the impacts of that drought can differ. Indeed, in 41% of the geographical clusters in our sample, some but not all plots report being affected by a drought (Table 12). This finding holds also for plots growing the same crops. In 31% of clusters, some but not all maize plots suffer drought losses (32% for sorghum plots and millet plots). The result extends to plots with the same crops cultivated by the same households (Table 13): for farms that record a drought shock on one of their maize plots, close to two-thirds of other maize plots on the same farm also record drought-related crop losses.

These findings suggest that disaster impacts are highly localized, consistent with the high spatial concentration that meteorological events can have.<sup>28</sup> Further, idiosyncratic factors, such as land characteristics and management practices, and happenstance play a role in determining whether and how much production is affected. We find that plot elevation is negatively associated with the likelihood of experiencing losses and the size of the losses incurred (an effect almost twice as strong for floods compared to other disasters), while smaller plots are less likely to suffer losses but record higher losses when they are affected (Tables 7 and 8). Losses on intercropped plots are 7.5 percentage points lower than on mono-cropped plots, though intercropped plots are more likely to experience a loss in the first place (+3.6 percentage points). Plots farmed in more input and technology intensive ways appear more resilient to crop losses due to adverse events.

Disaster exposure and impact also vary according to who manages the plot. Plots managed by women are more often affected by disaster losses (+2.2 percentage points) than plots managed by men and these losses are also larger on average (+4.4 percentage points; Table 10). These differences are likely because plots managed by women are endowed and farmed differently than plots managed by men, which in turn may follow from differential access to inputs and land between women and men.<sup>27</sup>

# **Aggregate data sources underestimate impacts of extreme events on crop production**

How do disaster impacts as captured in the survey data compare to estimates from other commonly used data sources? Here, we contrast the results from the survey microdata with publicly available estimates of disaster impacts from the Emergency Events Database (EM-DAT). EM-DAT aggregates reports from UN agencies, governments, insurance companies, research institutes and the media into a global inventory of disaster impacts.<sup>29</sup> EM-DAT is the preeminent and only publicly available data source of this kind, used widely in disaster reporting and research.<sup>30</sup> We focus on two disaster types, droughts and floods, and compare two estimates: the number of people affected and the total economic damages caused in the years which the survey micro-data covers. We create aggregate figures from the micro-data using population sampling weights.

On both metrics, and for both drought and flood impacts, the micro-data estimates on average exceed the EM-DAT estimates, that is, for years in which there is information from both sources, the survey microdata find more people affected and higher damages from droughts and floods (Figure 3 and Tables 14 and 6
16). Moreover, there are many instances in which the EM-DAT records no disaster impacts at all. This is true especially for droughts, where the microdata suggests that droughts are prevalent to some degree across every country-year combination covered, while EM-DAT records droughts affecting the population in only a third of cases. Estimates of the economic value of disaster impacts are mostly missing in the EMDAT data for the study countries, even in years when drought and flood events were recorded to affect the population in the study countries (Tables 15 and 17).

Large, salient drought and flood episodes have better coverage in the EM-DAT, such as the severe droughts in Niger in 2011<sup>31,32</sup> and Ethiopia in 2015-16,<sup>33,34</sup> or the droughts and floods Malawi in 201516,<sup>35,36</sup> which were widely covered in international media at the time. The events that go unreported in EM-DAT are smaller, on average, in terms of the population affected and the damages caused. However, we show that such smaller, under-covered events have substantial overall impacts. More than a fifth of the population suffered production and income losses in the droughts in Malawi in 2009-2010 and in Mali in 2074, according to our micro-data estimates, while there is no coverage of these events in the EM-DAT for the same years. Overall, we estimate the total number of people affected by droughts or floods in all instances covered by the microdata is between 145 million and 170 million, more than 4 times higher than what is reported in the EM-DAT for the same periods and the same shocks.

The micro-data analysis suggests that the aggregate value of the disaster impacts on crop production is substantial. For the drought in Ethiopia in 2015-2016, the micro-data crop loss estimates are much larger than the total economic damage reported in the EM-DAT. For the 2014 floods in Niger, the estimated value of crop losses exceeds the total damage reported in the EM-DAT data by almost USD 78 million (in 2022 USD values). In the other years there is no damage estimate in EM-DAT, but our survey micro-data documents even some large disaster impacts, such as in Niger in 2011 and Ethiopia in 2018-2019 with estimated losses of USD 1.6 billion and USD 1.4 billion, respectively. Taken together, we estimate that across the countries and years captured in the microdata, there were USD 5.1 billion in drought and flood damages unaccounted for in the EM-DAT data (Table 18).

What explains these discrepancies? Disaster inventories such as EM-DAT and survey microdata differ in a number of meaningful ways. Most importantly, disaster inventories do not measure shock impacts themselves but instead aggregate data from government sources, humanitarian organizations, the media, and others. They therefore rely on the comprehensiveness and accuracy with which shocks due to natural hazards are covered by one or more of these sources.<sup>30,37</sup> Less salient events as well as those affecting marginalized population groups are less likely to be reported on and less likely to have detailed information on the affected population or economic and welfare impacts.<sup>37–40</sup> This is particularly acute in LMICs where the density of information for disaster repositories to draw on is much lower and a large share of damages is uninsured.<sup>30,37</sup> Shocks in LMICs in general and smaller events (in terms of intensity or the population affected) in particular are more likely to have incomplete or inaccurate information in disaster repositories or are not covered at all.<sup>30,37,41,42</sup> Microdata such as the LSMS-ISA measure shock impacts on smallholder farmers where they occur by asking farmers directly. They therefore do not suffer from the same limitations regarding the recording of smaller, less salient, or more localized shocks and their impacts as disaster repositories.

Smaller shocks or adverse climatic events may not be considered disasters as disasters suggest a minimum level of severity. For an event to be recorded in the inventory, the EM-DAT requires a minimum of 100 people to be affected (injured, homeless, in need of immediate assistance) or an official declaration of 7
emergency or appeal for international assistance – arguably a sensible set of criteria for a disaster inventory. Not all events recorded in the micro-data meet these requirements. Importantly, the events recorded in the micro-data have substantial impacts on the livelihoods of farmers and the economies of the study countries.

At the same time, micro-data has drawbacks and limitations. First, it is rare that microdata in low- and middle-income countries are available annually, with surveys typically implemented every few years. Shock coverage and detail depend on the survey design, which typically differs from country to country. Finally, microdata does not provide the same cross-country coverage as disaster repositories. With these limitations, the microdata naturally also provides an incomplete picture (see discussion in _Appendix A_ ).

8
<!-- Start of picture text --> Ethiopia 2015 - 2016 (|===—_ :<br>Malawi 2015 - 2016 -| EE ety Niger 2011) 2011) (iis<br>Mali 2017)<br>Malawi 2009 - 2010) ==— Ethiopia 2018 2018 - 2019) 2019) Ss[iE[iE iii<br>Nee 201) ———==——<br>Tanzania 2010] Ethiopia 2013 2013 -2014-- SSS.(( [i i ii i<br>Tanzania 2010 >) ===<br>Mali 2014) Niger 2014) 2014) EEE<br>Tanzania 2012} ==<br>Ethiopia 2018-209 5 —=== — Mal 2017 ssEEEE<br>Tanzania 2008 |=a<br>Ethiopia 2013 -20147- === Nigeria 2019) 2019) [Ei ==<br>Tanzania 2014 | ===<br>Ethiopia 2013 -2014- = Mali 2014; [is- [is-s-<br>Ethiopia 2011 -2012-§ =F"<br>Malawi 2018 - 2019) == Malawi 2018 2018 - 2019 2019 - (i [i i ii ==<br>Nigeria 2019” GH<br>asSs Ss |Ss Ss | Ss | |<br>“ Share of total population rectedby drought Damages11 attributed to drought10 drought1010 shocks (Millions100 (Millions100100 of 20221,000 USD),2,000log4,000 scaled 20221,000 USD),2,000log4,000 scaled1,000 USD),2,000log4,000 scaled USD),2,000log4,000 scaled2,000log4,000 scaledlog4,000 scaled4,000 scaled scaled<br>Ea i | Ey<br>Malawi 2012 - 2013 ll<br>Nigeria 2019 _<br>Niger 2014 .<br>Niger 2014<br>Niger 2011<br>Malawi 2015<br>- 2016 -<br>0% a 10% 20%Ss30% | 1ce10 100 1,000|<br>Share of total population affected by flood Damages attributed to flood shocks (Millions of 2022 USD), log scaled<br>| by | Ey<br><!-- End of picture text -->

<!-- Start of picture text --> :<br>Niger 2011) 2011) (iis<br>Ethiopia 2018 2018 - 2019) 2019) Ss[iE[iE iii<br>Ethiopia 2013 2013 -2014-- SSS.(( [i i ii i<br>Niger 2014) 2014) EEE<br>Mal 2017 ssEEEE<br>Nigeria 2019) 2019) [Ei ==<br>Mali 2014; [is- [is-s-<br>Malawi 2018 2018 - 2019 2019 - (i [i i ii ==<br>asSs Ss |Ss Ss | Ss | |<br>Damages11 attributed to drought10 drought1010 shocks (Millions100 (Millions100100 of 20221,000 USD),2,000log4,000 scaled 20221,000 USD),2,000log4,000 scaled1,000 USD),2,000log4,000 scaled USD),2,000log4,000 scaled2,000log4,000 scaledlog4,000 scaled4,000 scaled scaled<br>| Ey<br><!-- End of picture text -->
disasters and environmental shocks.<sup>44–47</sup> These studies have mostly focused on single geographies and stopped short of quantifying the value of disaster related losses in smallholder agriculture. Other studies have relied on macro-data from disaster inventories to assess the impact of disasters on agriculture.<sup>14</sup> Here we offer a cross-country perspective using harmonized survey micro-data from six African countries. We value crop production losses to assess the economic importance of disaster impacts on rural households and African agriculture and the regional economies more broadly. The analysis shows that disaster related crop production losses among African smallholder farmers are widespread and significantly reduce the production of affected farms. In any given year, between 18% and 94% of cropfarming households suffer such crop losses and on average 53% of plots’ harvest potential is lost when they are hit by adverse shocks.

In aggregate, disaster impacts reduce the national crop production by 29% every year on average and by up to 81% in years with large-scale disasters. These results show that crop production losses due to adverse climatic events are significant both for individual farms and for the entire sector and the study countries’ economies.

We show that the EM-DAT disaster inventory misses out on a meaningful share of disaster impacts in the agricultural sector in Africa when compared to the micro-data analysis. The micro-data captures many smaller, more localized disaster events, which are less salient and therefore less likely to be reported on and register in the disaster inventory.<sup>37,48</sup> At the same time, disaster impacts are more likely to be missing entirely in lower-income settings.<sup>30</sup> Our analysis shows that less salient and underreported disaster events still have significant economic impacts.

The findings have implications for policies and interventions aimed at disaster risk reduction and resilience building. For such policies to be effective, it is important to recognize the risks from less salient and underreported adverse events and offer ways to insure households’ livelihoods against their impacts. The findings also have implications for research and measurement. Research and analysis using aggregate global data such as EM-DAT are likely missing some disaster impacts in poorer countries and among poorer people. This concerns also tracking progress towards the SDGs, in particular SDG target 1.5, which seeks to reduce the vulnerability of the poor to extreme weather events, and SDG target 13.1, which seeks to strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.<sup>15</sup> Survey micro-data such as the LSMS-ISA have more limited country and temporal coverage. Inventory and survey data offer complementary perspectives on disaster impacts and combining both sources will likely yield a more complete and nuanced understanding of the issue that will promote more effective policy design. In particular, information on disaster impacts from survey micro-data could be incorporated systematically into disaster inventories. Improving micro-data systems is key to systematically utilizing micro-data for monitoring and reporting of emergency events. More flexible and higher frequency data collection is needed to provide better temporal coverage and account for disaster impacts when they occur. Phone surveys, which have only recently become more widely adopted in lowincome settings, may provide this function, for instance as part of mixed-mode survey systems that combine traditional in-person surveys with data collection over the phone.<sup>49</sup> Integration of geospatial data can improve spatial coverage and facilitate better identification of natural hazards and disaster occurrence.<sup>50,51</sup> Sampling protocols of household surveys can be optimized to better capture disaster impacts and greater harmonization of survey methods and measurement instruments in line with best practices can benefit data quality.

10
Our study faces several limitations. The valuation of losses relies on human reporting which is subject to human error, respondents’ incentives, misreporting, and misperceptions. The data allows for a detailed analysis of disaster impacts on crop production, but other aspects of disaster impacts on agriculture are absent. This concerns, for example, damages to agricultural assets, storage losses, or impacts on livestock. Further, the data is representative of the household sector in study countries but misses commercial and larger farms. This implies that we are likely underestimating the full extent of disaster crop losses. As the analysis is focused on agriculture, we do not discuss damages incurred in other sectors.

# Methods

## A. LSMS-ISA microdata

We use plot-level survey data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) in Ethiopia, Malawi, Mali, Niger, Nigeria, and Tanzania. The LSMS-ISA comprise a series of harmonized, national, multi-topic household panel surveys with a distinct focus on agriculture. We harmonized data on agricultural outputs, inputs, and plot characteristics for close to 30,000 households over the six countries for a total of 18 survey waves collected between 2008 and 2019. The combined dataset contains over 120,000 plot observations. More specifically, the dataset includes data from the Ethiopian Social Survey (waves 1 to 4), Malawi’s Integrated Household Panel Survey (waves 1 to 4), Mali’s _Enquête Agricole de Conjoncture Intégrée_ (waves 1 and 2), Niger’s _Enquête National sur les Conditions de Vie des Ménages et Agriculture_ (waves 1 and 2), Nigeria’s General Household Survey (wave 4) and Tanzania’s National Panel Survey (waves 1 to 5). Households are selected to be representative of the population at the national and sub-national level using a two-stage stratified sampling design <mark>with census enumeration areas (EAs) as primary sampling units and households as secondary sampling units.</mark> Households are then tracked through time, except for Mali which only tracks enumeration areas (EAs). Each survey wave covers an agricultural production cycle or season.

## B. EM-DAT aggregate data

We compare survey estimates to country-level data from is the Emergency Event Database (EM-DAT). EMDAT is the preeminent public database taking stock of shocks on a global scale and widely used for research and to inform policy<sup>52</sup> . Both natural (e.g., geophysical, meteorological) and technological (e.g., industrial accidents) events are recorded, along with information on disaster damages valued in 2022 USD. The EM-DAT compiles information from a broad range of sources including insurance companies, international organizations, press agencies and governmental agencies. Disasters are recorded if they provoke 10 or more deaths, affect 100 or more people (injured/ homeless/ in need of immediate assistance) or are accompanied by an official declaration of emergency or appeal for international assistance.<sup>29</sup>

## C. Variable construction

## _Valuation of crop production_

Plot-level crop production was calculated using survey variables which were collected after the harvest in each season and country. Farmers report harvest quantities for each seasonal crop grown on each plot.

11
The harvest quantity on each plot was then valued using a set of constant crop prices. Specifically, each price corresponds to the median crop sale price calculated in one survey round in each country. Applying constant prices for each crop-country combination eliminates the effect of relative price fluctuations over time. This allows us to isolate the impact of disasters on harvest quantity rather than measuring their impact on quantity _and_ prices. An alternative approach was used to calculate and report aggregate losses, such as in Table 4 and Figure 3. In these cases, country and wave specific prices were calculated, by estimating median farmer sale prices for different crop types. This was done to reflect losses as they were perceived by farmers in the year of the shock. Whether current or constant, harvest values are initially calculated in local currency units and then converted and adjusted to 2020 USD using exchange rates and a CPI drawn from a library of the World Bank World Development Indicators.<sup>53</sup> Yields, defined as the value of production per hectare, were obtained by dividing the total harvest value on the plot by GPS-measured plot area. Finally, yields are winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentiles (while allowing full losses to be equal to 0).

# _Identification of disaster events and loss size_

Identification of disaster events is based on farmers reporting crop production losses before harvest for each crop on each of their plots. Specifically, for each crop cultivated on each plot, farmers are asked whether the area harvested was less than the area planted, i.e. if some of their crop has been lost, along with the cause of the loss in harvested area. In Ethiopia, farmers are further asked whether the crops they harvested had any damage on them and what the cause of damage was. We define disaster crop losses as any loss in crop area or any damage on the crops harvested due to climatological (drought, irregular rain, hail, wildfire), hydrological (flood) or biological (insect infestation, disease) reasons. This does not include losses due conflict, unavailability of inputs or other household or socio-economic events. We denote a plot with a disaster loss as any plot for which at least one crop on the plot had a disaster-induced loss.

To calculate the size of disaster losses, we use farmers’ reports of the share of the planted area lost due to disasters and, where available, the percent of damage on crops that were harvested. To determine the potential harvest that could have been achieved in the absence of disaster losses, we employ the methodology proposed by the Food and Agriculture Organization (FAO).<sup>54,55</sup> This means determining the realized harvest for each crop on the plot, as described in the preceding section, and scaling it up in proportion to the planted area lost and degree of damage on the crop that is attributed to disasters. The quantity lost equates to the difference between the potential harvest without disaster losses and the realized harvest. To aggregate losses across crops, we value loss quantity of each crop on each plot using a constant set of prices, which we then convert to 2020 US dollars. In order to correct for outliers in the self-reported data, we winsorize these loss values at the 99<sup>th</sup> percentile.

Equation (1) formalizes the construction of the plot-level loss aggregate.

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-> x (Y ) x ~~—*)~~ x ( ~~-(——-~~ )
by aggregating across plots belonging to the same farm, are weighted using the probability weights described in a separate section below ( _Sampling weights_ ).

# ii. _Disaster type and frequency_

Similarly, our estimates of the prevalence of different shock types is conducted at the plot-level and involves the estimation of frequencies at the national level and pooled across countries using the household sampling weights. Our estimates of the most common shock type within enumeration areas are based on simple, unweighted frequencies.

# iii. _Heterogeneity in disaster impacts across farms_

Our multivariate analysis focuses on two main outcome variables: a binary variable indicating any disaster crop loss and a continuous variable denoting the percentage share of the total potential harvest that was lost to disasters. We estimate all models with the binary crop loss indicator as outcome variable <mark>via maximum likelihood using logistic regression. Models with the percent share of harvest lost as outcome variable are estimated via ordinary least squares regression. Our independent variables for these multivariate regressions are comprised of plot characteristics (plot size, elevation, a topographical wetness index, an indicator for ownership of the plot, and main crop fixed effects), as well as plot management (hired labor and fertilizer input use, irrigation, intercropping), plot manager (age, gender, and education), and household characteristics (urban/rural residence, an indicator for livestock farming, and electricity access). Models pooling the sample across countries further include country fixed effects.</mark> We also conduct multivariate analysis using a binary variable capturing the gender of the plot manager as outcome variable and plot- and plot-management characteristics as independent variables. As before, all multivariate regressions are weighted using the sampling weights.

# **iv.** _Different disaster impacts on neighboring plots_

Our analysis of differences in drought impact within the same enumeration areas first determines whether some but not all plots belonging to the same enumeration area recorded a drought loss and then estimates the simple, unweighted proportion of enumeration areas for which this is the case. Our analysis of within-household differences in shock impacts first limits the sample to households with multiple maize plots and where at least one of the household’s maize plots recorded a drought shock. We then calculate the share of remaining maize plots belonging to the same household that also record a drought loss and report a simple, unweighted average of this share across households for each country and year.

# v. _Comparison of survey micro data with aggregate sources_

Our estimates of the number of people affected by disasters and aggregate economic losses are totals at the national level and employ the household-level sampling weights.

14
In order to the compare drought and flood impacts using LSMS-ISA data with those using EM-DAT data, we use two metrics: the share of individuals “affected” by the shock and the estimated total value of damages. Since the LSMS-ISA surveys run every two to three years, we only retain events in the EM-DAT database for which the start or end date is within a year containing LSMS-ISA data. The comparison for flood shocks is possible in fewer countries and years due to limitations in the microdata questionnaire’s scope in some cases (Table 18).

In order to compute the total number of individuals impacted by a shock within a specified period in the EM-DAT database, we aggregate the total number of people “affected” by the shock in the macro data. Affected persons are those that are reportedly injured, homeless, or otherwise in need of “immediate assistance.”<sup>29</sup> To estimate the total number of individuals affected by a shock in the LSMS-ISA microdata, we construct population weights by multiplying household weights by household size. These weights are then used as expansion factors, which we multiply by a dummy variable equal to one in the household report a shock on any of its cultivated plots. We then add up this product to calculate an expansion estimator.<sup>57,58</sup> To obtain shares of the total population, the numbers of individuals affected in both EM-DAT and the LSMS-ISA data are divided by total yearly population estimates drawn from a library of the World Bank World Development Indicators.<sup>53</sup> We then compute the estimated damages from both droughts and floods in the periods and years covered by LSMS-ISA data. We first aggregate the “total damages” estimated in the EM-DAT database, defined as the values of total losses “directly or indirectly related to the disaster”, in 2022 USD values. Using LSMSISA microdata, we aggregate the estimated value of crop losses for each household. In this case, the value of losses is calculated by multiplying the potential value of total output using _current_ prices by the estimated percentage of output lost at the plot level. Contrary to the rest of our analysis, we do not apply a common set of prices to value output and losses, but rather median crop prices faced by farmers at the time of their crop production. The loss values are then converted to 2022 USD values, in order to allow comparison with EM-DAT data. As above, we use population weights as expansion factors, that we multiply with our loss value estimates.<sup>57,58</sup>

# _Sampling weights_

In the survey data, household sampling weights are used to compute estimates that are representative of the national or subnational population. These reflect the inverse probability of selection into the sample, are adjusted to account for non-response and survey design choices, and are post-stratified to ensure that they sum to known household population totals.<sup>59</sup> Moreover, a further adjustment was made to ensure that the weights in the study sample sum to the total population of households, because only a subset of LSMS-ISA households report cultivating seasonal crops. More formally, <mark>we can define a set of households indexed by h (where h = 1, ... , N</mark> 1 <mark>) within a country-wave, where each household is associated with a sample weight W</mark> h<sup><mark>. After restricting the dataset</mark></sup> <mark>to households that cultivate crops, the total number of households in the country-wave drops from N</mark> 1 <mark>to N</mark> 2 <mark>, and weights are adjusted such that:</mark> 15
h ∑h=1 <mark>Wh Where p</mark> h<sup><mark>denotes the number of plots in household</mark></sup> <mark>h on which seasonal crops are grown, and w</mark> h<sup><mark>is the</mark></sup> <mark>final adjusted weight.</mark> <mark>The resulting weights were used to compute estimates in our analysis. Wherever population means are estimated, the standard errors provided with the estimate takes into account the clustered and stratified sampling design.</mark><sup>57</sup> <mark>All regression models with continuous variables as outcomes are estimated using ordinary least squares regression, while regressions with binary outcomes are estimated via maximum likelihood using a logistic regression model.</mark>

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21
# **Appendix**

_Table 1: Frequency of plots recording a shock, by country-year_

|Country|Year|Frequency of plots<br>recording a disaster<br>shock|Frequency of p<br>a drough|lots recording<br>t shock|Frequenc<br>recording a|y of plots<br>flood shock|Frequency of<br>irregula|plots recording an<br>r rain shock|Frequ<br>recordin|ency of plots<br>g a pest shock|
|---|---|---|---|---|---|---|---|---|---|---|
|||||Disaster||Disaster|||||
||||All plots|<br>plots|All plots|<br>plots|All plots|Disaster plots|All plots|Disaster plots|
||2011 - 2012|31.19 %|16.96 %|55.16 %|missing|missing|11.39 %|37.41 %|4.89 %|16.06 %|
|Ethii|2013 - 2014|28.32 %|11.14 %|40.14 %|missing|missing|9.94 %|36.60 %|7.35 %|30.52 %|
|opa|2015 - 2016|53.83 %|41.89 %|78.29 %|missing|missing|17.55 %|34.88 %|3.48 %|7.97 %|
||2018 - 2019|39.76 %|19.81 %|50.63 %|missing|missing|14.15 %|37.17 %|6.95 %|21.12 %|
||2009 - 2010|40.40 %|24.34 %|60.26 %|missing|missing|missing|missing|1.05 %|2.61 %|
|Mli|2012 - 2013|36.82 %|8.18 %|22.22 %|8.64 %|23.47 %|17.80 %|48.33 %|2.85 %|7.75 %|
|aaw|2015 - 2016|64.54 %|30.77 %|47.68 %|1.06 %|1.56 %|40.61 %|62.92 %|2.21 %|3.42 %|
||2018 - 2019|47.18 %|5.60 %|11.86 %|21.65 %|45.81 %|19.77 %|41.91 %|7.97 %|16.76 %|
|Mli|2014|18.59 %|14.44 %|77.71 %|missing|missing|1.75 %|9.41 %|1.05 %|5.63 %|
|a|2017|24.07 %|22.78 %|94.64 %|missing|missing|0.45 %|1.89 %|3.08 %|1.97 %|
|Ni|2011|89.88 %|65.81 %|73.22 %|0.64 %|0.71 %|missing|missing|27.69 %|30.81 %|
|ger|2014|40.11 %|23.43 %|58.41 %|2.75 %|6.86 %|missing|missing|11.08 %|27.63 %|
|Nigeria|2019|11.39 %|2.59 %|26.06 %|4.88 %|50.72 %|missing|missing|5.49 %|48.85 %|
||2008|32.21 %|14.52 %|47.11 %|missing|missing|missing|missing|13.99 %|45.45 %|
||2010|36.70 %|21.52 %|60.74 %|missing|missing|missing|missing|10.00 %|28.29 %|
|Tanzania|2012|32.78 %|19.91 %|62.79 %|missing|missing|missing|missing|9.86 %|31.05 %|
||2014|23.96 %|11.38 %|49.72 %|missing|missing|missing|missing|8.05 %|35.00 %|
||2019|40.89 %|25.80 %|65.03 %|missing|missing|missing|missing|12.39 %|31.21 %|
|All<br>countries|2008 – 2019|36.34 %|22.17 %|61.66 %|6.80 %|14.44 %|10.49 %|31.31 %|6.30 %|16.83 %|

Note: some questionnaires (the IHPS, for example) allow respondents to report multiple shock types on a single plot.

22
_Table 2: Mean fraction of potential harvest lost at the plot level, by country-year_

|Country|Year|Mean fraction of<br>potential harvest<br>lost, all plots|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>drought|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>floods|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>irregular rains|Mean fraction<br>of potential<br>harvest lost,<br>on plots<br>affected by<br>pests|
|---|---|---|---|---|---|---|
||2013-2014|48.66%|53.46 %|missing|49.36 %|45.22 %|
|Ethiopia|2015-2016|54.84%|59.71 %|missing|55.90 %|47.35 %|
||2018-2019|47.63%|46.91 %|missing|47.65 %|42.36 %|
|Malawi|2018-2019|55.00%|57.17 %|62.65 %|49.79 %|49.26 %|
|Mli|2014|48.08%|48.37 %|missing|38.11 %|40.73 %|
|a|2017|47.96%|47.50 %|missing|35.75 %|66.73 %|
|Nir|2011|71.09%|71.79 %|77.53 %|missing|66.96 %|
|ge|2014|49.33%|52.90 %|56.79 %|missing|40.97 %|
|Nigeria|2019|56.38%|36.38 %|61.87 %|missing|30.71 %|
|All countries|2011 – 2019|53.49%|56.75 %|62.28 %|51.52 %|46.52 %|

_Note: only point estimates are reported. Plots with no losses are excluded. Sample weights are used to calculate estimates._ 23
_Table 3: Linear trend in shock exposure and size_

||(1)<br>Any loss<br>Full sample|(2)<br>Percent lost<br>With any loss|(3)<br>Percent lost<br>Full sample|
|---|---|---|---|
|Year|0.00829***|-1.137**|0.0986|
||(0.00231)|(0.519)|(0.368)|
|Constant||2,344**|-178.4|
|||(1,047)|(741.5)|
|Observations|121,983|31,746|91,280|
|Country FE<br>Note: Marginal effects from logit|YES<br>regression (Column 1) and results fro|YES<br>m OLS regressions (Columns|YES<br>2 and 3). Columns 1 and|

Note: Marginal effects from logit regression (Column 1) and results from OLS regressions (Columns 2 and 3). Columns 1 and 3 are unconditional, column 2 is conditional on any loss on the plot. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 24
_Table 4: Total fraction of aggregate potential harvest lost, by country-year_

|Country|Year|Fraction of total potential harvest<br>lost in shocks|
|---|---|---|
||2013-2014|15.14 %|
|Ethiopia|2015-2016|34.63 %|
||2018-2019|31.52 %|
|Malawi|2018-2019|46.53 %|
|Mli|2014|7.59 %|
|a|2017|8.22 %|
|Nier|2011|81.12 %|
|g|2014|29.24 %|
|Nigeria|2019|3.19 %|
|Pooled|2011-2019|28.57 %|

_Note: Sample weights are used to calculate estimates. Current prices are used to value losses and attainable harvest_ 25
_Table 5: Share of households affected by multiple disaster shocks on their plots_

|**Country**|**Year**|**Percent of households that**<br>**report disaster losses from**<br>**multiple sources**|
|---|---|---|
|Ethiopia|2011 – 2012|8.99 %|
|Ethiopia|2013 - 2014|12.58 %|
|Ethiopia|2015 - 2016|20.92 %|
|Ethiopia|2018 - 2019|21.02 %|
|Malawi|2012 - 2013|4.50 %|
|Malawi|2015 - 2016|15.03 %|
|Malawi|2018 - 2019|15.05 %|
|Tanzania|2008|5.09 %|
|Tanzania|2010|4.25 %|
|Tanzania|2012|3.04 %|
|Tanzania|2014|1.46 %|
|Tanzania|2019|4.52%|

_Note: Sample weights are used to calculate estimates. Only waves which allow the reporting of multiple shock types on each listed crop are retained._ 26
_Table 6: Loss size by shock type_

|**Dependent variable:**Percent of harvest lost|(1)<br>Ethiopia|(2)<br>Malawi|(3)<br>Mali|(4)<br>Niger|(5)<br>Nigeria|
|---|---|---|---|---|---|
|Drought|6.570***|7.767***|2.536|3.183|-4.939|
||(1.853)|(2.668)|(3.966)|(2.235)|(4.152)|
|Pests|5.955***|-4.283|11.48*|-6.561**|-10.28**|
||(1.731)|(2.867)|(6.384)|(2.710)|(4.711)|
|Flood||15.11***||-1.935|9.263*|
|||(2.995)||(5.656)|(4.984)|
|Constant|34.08***|47.68***|45.31***|64.71***|31.80***|
||(1.145)|(1.944)|(3.875)|(2.292)|(4.936)|
|Observations|10,478|2,180|7,389|6,537|449|
|Crop FE|YES|YES|YES|YES|YES|
|Survey Wave FE<br>Note: Results from OLS regressions with the sha|YES<br>re of potential ha|YES<br>rvest lost as ou|YES<br>tcome variable.|YES<br>Base category|YES<br>for shock|

Note: Results from OLS regressions with the share of potential harvest lost as outcome variable. Base category for shock dummies is other shock. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 27
_Table 7: Heterogeneity in disaster exposure by plot characteristics_

|**Dependent variable:**Any disaster loss on plot|(1)<br>Pooled|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Mali|(5)<br>Niger|(6)<br>Nigeria|(7)<br>Tanzania|
|---|---|---|---|---|---|---|---|
|Any hired labor on plot|-0.0151|-0.00717|-0.0513***|-0.000535|-0.0291|-0.00315|-0.0130|
||(0.0111)|(0.0242)|(0.0167)|(0.0142)|(0.0205)|(0.0217)|(0.0151)|
|Any inorganic fertilizer used|-0.0445***|-0.0482**|0.0236|-0.0621***|-0.0527*|-0.0357**|-0.0885***|
||(0.0109)|(0.0187)|(0.0161)|(0.0171)|(0.0289)|(0.0154)|(0.0290)|
|Any organic fertilizer used|0.0285***|0.0666***|0.0382**|-0.00299|-0.00833|-0.0106|-0.0126|
||(0.0105)|(0.0195)|(0.0158)|(0.0160)|(0.0255)|(0.0188)|(0.0266)|
|Plot is irrigated|-0.00339|0.000150|-0.0647|-0.201***|-0.169***|0.00297|0.0370|
||(0.0211)|(0.0334)|(0.0592)|(0.0461)|(0.0535)|(0.0305)|(0.0502)|
|Plot is intercropped|0.0491***|0.0353|0.146***|-0.0335|-0.0271|-0.0221|0.0419**|
||(0.0117)|(0.0242)|(0.0181)|(0.0292)|(0.0364)|(0.0164)|(0.0207)|
|Plot is owned|-0.000302|0.0200|-0.00111|-0.0542**|-0.0529|-0.0296|-0.00966|
||(0.0121)|(0.0199)|(0.0215)|(0.0246)|(0.0329)|(0.0184)|(0.0234)|
|Log plot area (ha)|0.00875***|0.00983*|0.0219***|0.000514|-0.0272***|-0.0122*|0.0339***|
||(0.00333)|(0.00504)|(0.00568)|(0.00562)|(0.00835)|(0.00677)|(0.00803)|
|Plot topographic wetness index|0.00731***|0.00315|0.00503|0.00210|0.00349|0.00134|0.0121***|
||(0.00165)|(0.00407)|(0.00345)|(0.00244)|(0.00267)|(0.00161)|(0.00225)|
|Plot elevation (m)|-9.16e-05***|-8.41e-05***|-0.000371***|-0.000299**|-0.000172|-2.12e-05|-6.58e-05***|
||(1.60e-05)|(2.64e-05)|(3.41e-05)|(0.000131)|(0.000186)|(4.61e-05)|(2.37e-05)|
|Urban household|-0.0211|0.00330|-0.115***|-0.0356|0.0446|-0.0212|0.0273|
||(0.0206)|(0.0529)|(0.0410)|(0.0484)|(0.0472)|(0.0249)|(0.0291)|
|Household engaged in livestock farming|0.00545|0.0210|-0.00556|0.0271|-0.145***|-0.0153|0.0398**|
||(0.0101)|(0.0211)|(0.0133)|(0.0203)|(0.0466)|(0.0182)|(0.0173)|
|Household has access to electricity|0.0711***|0.120***|0.0173|-0.0240|-0.0302|0.00143|0.0195|
||(0.0161)|(0.0241)|(0.0283)|(0.0167)|(0.0493)|(0.0197)|(0.0358)|
|Observations|116,773|39,801|17,900|31,257|9,004|6,623|12,187|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Average marginal effects from multivariate log<br>parentheses. *** p<0.01, ** p<0.05, * p<0.1|YES<br>it regressions. Base c|NO<br>ategory for crop fixed|NO<br>effects is 'other cro|NO<br>p'. The estimates are|NO<br>weighted to be natio|NO<br>nally representative.|NO<br>Standard errors in|

28
_Table 8: Heterogeneity in loss size by plot characteristics_

|**Dependent variable:**Percent of harvest lost|(1)<br>Pooled|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Mali|(5)<br>Niger|(6)<br>Nigeria|
|---|---|---|---|---|---|---|
|Any hired labor on plot|-5.120***<br>|-3.632<br>|-3.179<br>|-0.707<br>|-4.162***<br>|-9.870*<br>|
||(1.762)|(2.236)|(3.237)|(1.977)|(1.454)|(5.065)|
|Any inorganic fertilizer used|-8.073***|-7.744***|-2.133|-15.47***|-4.415**|-17.28***|
||(1.512)|(1.885)|(1.885)|(2.563)|(1.955)|(4.320)|
|Any organic fertilizer used|0.707|3.476*|-3.111**|-4.282**|-6.589***|-8.291**|
||(1.315)|(1.842)|(1.522)|(2.106)|(1.183)|(4.103)|
|Plot is irrigated|3.450|1.126|9.046|-20.15***|4.865|14.80*|
||(3.063)|(3.636)|(14.07)|(6.356)|(4.255)|(8.736)|
|Plot is intercropped|-7.513***|-3.781|-1.550|-6.306*|-6.367***|-36.05***|
||(1.754)|(2.329)|(2.454)|(3.550)|(1.773)|(4.865)|
|Plot is owned|-1.945|-2.300|6.057*|-2.874|-2.586|-3.114|
||(1.556)|(2.017)|(3.122)|(3.702)|(1.645)|(4.379)|
|Log plot area (ha)|-2.565***|-2.782***|-4.610***|-0.255|-0.599|0.394|
||(0.410)|(0.497)|(1.109)|(0.686)|(0.427)|(1.383)|
|Plot topographic wetness index|0.263|0.576*|0.0928|0.993***|0.0383|0.0686|
||(0.206)|(0.301)|(0.434)|(0.344)|(0.249)|(0.451)|
|Plot elevation (m)|-0.00727***|-0.00650***|-0.0250***|-0.0179|-0.00721|0.00584|
||(0.00214)|(0.00228)|(0.00404)|(0.0187)|(0.0108)|(0.00892)|
|Urban household|4.463|12.26***|-3.747|5.014|1.584|-5.863|
||(2.772)|(2.829)|(3.387)|(6.587)|(2.659)|(5.539)|
|Household engaged in livestock farming|-2.395|1.289|0.157|3.039|-9.035***|-2.391|
||(1.733)|(2.353)|(1.820)|(3.219)|(2.203)|(4.460)|
|Household has access to electricity|2.388|1.600|3.479|-1.266|-2.625|5.226|
||(1.946)|(2.219)|(4.478)|(2.336)|(2.222)|(4.404)|
|Constant|64.21***|53.17***|61.56***|55.52***|84.30***|104.4***|
||(6.177)|(7.224)|(9.642)|(10.15)|(6.901)|(10.39)|
|Observations|30,845|14,896|2,059|7,130|6,071|689|
|Crop FE|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Results from OLS regressions with the share of pot<br>weighted to be nationally representative. Standard errors i|YES<br>ential harvest lost as outcom<br>n parentheses. *** p<0.01, *|NO<br>e variable. Base ca<br>* p<0.05, * p<0.1|NO<br>tegory for crop fi|NO<br>xed effects is 'oth|NO<br>er crop'. The esti|NO<br>mates are|

29
_Table 9: Heterogeneity in disaster exposure by plot manager characteristics_

||Poo|led|Ethi|opia|Mal|awi|M|ali|Ni|ger|Nig|eria|Tan|zania|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Dependent variable:**<br>Any disaster loss on<br>|(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|(8)<br>|(9)<br>|(10)<br>|(11)<br>|(12)<br>|(13)<br>|(14)<br>|
|plot|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|No control|Control|
|Female plot manager|0.0222**|0.0267***|0.00350|0.0126|0.0529***|0.0272**|0.0843***|0.0614***|0.0503*|0.0475*|0.0386*|0.00467|0.0138|0.0355*|
|Age of plot manager|(0.00939)|(0.00904)|(0.0171)|(0.0169)|(0.0137)|(0.0123)|(0.0237)|(0.0215)|(0.0293)|(0.0280)|(0.0207)|(0.0165)|(0.0201)|(0.0186)|
|(decades)|0.00727***|0.00708***|0.00829*|0.00750*|0.00112|0.00407|0.0124***|0.0124***|-0.0137***|-0.00896*|0.00774|0.00564|0.0119**|0.00878*|
||(0.00249)|(0.00237)|(0.00457)|(0.00418)|(0.00446)|(0.00416)|(0.00443)|(0.00429)|(0.00525)|(0.00517)|(0.00542)|(0.00533)|(0.00523)|(0.00481)|
|Plot manager has<br>primary educ|-0.0140|-0.0138|-0.0176|-0.0258|-0.0467***|-0.00906|-0.0386*|-0.0178|-0.0609|-0.0594|0.0186|-0.00118|-0.0216|-0.00847|
||(0.0125)|(0.0124)|(0.0300)|(0.0285)|(0.0145)|(0.0140)|(0.0224)|(0.0216)|(0.0480)|(0.0473)|(0.0155)|(0.0179)|(0.0314)|(0.0336)|
|Observations|115,041|115,041|39,289|39,289|17,123|17,123|31,019|31,019|8,911|8,911|6,591|6,591|12,107|12,107|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Country FE|YES|YES|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|NO|

Note: Average marginal effects from multivariate logit regressions. Controls include dummy variables for input use (any hired labor, any inorganic fertilizer, any organic fertilizer), plot characteristics (plot area, irrigration, intercropping, plot ownership, elevation, and a topographic wetness index), household characteristics (urban/rural residence, livestock farming, and electricity access) as well as main crop and country fixed effects. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 30
_Table 10: Heterogeneity in loss size by plot manager characteristics_

||Pool|ed|Ethi|opia|Mal|awi|M|ali|Nig|er|Nig|eria|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Dependent variable:**|||||||||||||
|Percent of harvest lost on plot|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|
|Female plot manager|4.405***|2.303**|3.345**|1.968|3.952|2.363|5.167|0.988|5.025***|3.789**|10.76**|-0.0518|
||(1.158)|(1.136)|(1.448)|(1.385)|(2.383)|(2.128)|(3.452)|(2.895)|(1.742)|(1.754)|(5.463)|(6.238)|
|Age of plot manager (decades)|0.231|0.285|0.373|0.407|-0.995|0.0282|-0.537|-0.385|-0.382|-0.0241|0.974|-0.303|
||(0.320)|(0.315)|(0.384)|(0.377)|(0.871)|(0.797)|(0.663)|(0.544)|(0.481)|(0.474)|(1.837)|(1.328)|
|Plot manager has primary educ|-0.0273|-1.641|-1.508|-3.215|0.497|1.915|-4.344|-1.184|-5.307*|-3.501|5.493|-1.332|
||(1.831)|(1.686)|(2.362)|(2.192)|(2.893)|(2.226)|(2.950)|(2.775)|(2.759)|(2.510)|(4.983)|(5.349)|
|Constant|49.31***|62.68***|48.94***|51.42***|55.91***|60.52***|49.98***|57.01***|65.99***|83.15***|46.19***|106.0***|
||(1.923)|(6.404)|(2.152)|(7.456)|(4.869)|(9.858)|(3.617)|(10.82)|(2.491)|(7.111)|(10.01)|(15.73)|
|Observations|30,230|30,230|14,680|14,680|1,739|1,739|7,100|7,100|6,027|6,027|684|684|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Results from OLS regression|YES<br>s with the share|YES<br>of potential h|NO<br>arvest lost as ou|NO<br>tcome variab|NO<br>le. Controls inclu|NO<br>de dummy v|NO<br>ariables for input|NO<br>use (any hire|NO<br>d labor, any inor|NO<br>ganic fertiliz|NO<br>er, any organic fe|NO<br>rtilizer), plot|

Note: Results from OLS regressions with the share of potential harvest lost as outcome variable. Controls include dummy variables for input use (any hired labor, any inorganic fertilizer, any organic fertilizer), plot characteristics (plot area, irrigration, intercropping, plot ownership, elevation, and a topographic wetness index), household characteristics (urban/rural residence, livestock farming, and electricity access) as well as main crop and country fixed effects. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 31
_Table 11: Heterogeneity in plot endowment and management by gender of the plot manager_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|**Dependent variable:**Female plot manager (dummy)|Pooled|Ethiopia|Malawi|Mali|Niger|Nigeria|Tanzania|
|Any hired labor on plot|0.0342***|0.0336**|0.000582|-0.0254**|0.0644***|0.0473**|0.0560***|
||(0.00878)|(0.0145)|(0.0148)|(0.0104)|(0.0148)|(0.0216)|(0.0201)|
|Any inorganic fertilizer used|-0.00474|0.0159|-0.0149|-0.0206|-0.00489|-0.0373**|-0.00251|
||(0.00879)|(0.0125)|(0.0154)|(0.0138)|(0.0349)|(0.0176)|(0.0275)|
|Any organic fertilizer used|-0.0128|0.0127|0.0234|-0.0381***|-0.0375**|-0.0367*|-0.0167|
||(0.00924)|(0.0114)|(0.0186)|(0.00935)|(0.0150)|(0.0211)|(0.0309)|
|Plot is irrigated|-0.0332|0.0234|-0.0671|-0.0956***|-0.244**|-0.0418|-0.0718|
||(0.0246)|(0.0339)|(0.0703)|(0.0274)|(0.100)|(0.0610)|(0.0489)|
|Plot is intercropped|0.0184**|-0.0168|0.0698***|-0.0224|-0.0544***|0.0287**|0.0430*|
||(0.00844)|(0.0149)|(0.0129)|(0.0225)|(0.0206)|(0.0144)|(0.0225)|
|Plot is owned|0.0268***|0.0907***|0.0897***|-0.00327|-0.0335**|-0.00298|-0.00916|
||(0.00988)|(0.0173)|(0.0239)|(0.0189)|(0.0148)|(0.0181)|(0.0195)|
|Log plot area (ha)|-0.0423***|-0.0245***|-0.0524***|-0.0229***|-0.0461***|-0.0498***|-0.0619***|
||(0.00258)|(0.00297)|(0.00706)|(0.00363)|(0.00667)|(0.00567)|(0.00795)|
|Plot topographic wetness index|0.00105|-0.00154|0.00110|0.00154|-0.00162|-0.000648|0.00310|
||(0.00141)|(0.00271)|(0.00386)|(0.00135)|(0.00156)|(0.00176)|(0.00323)|
|Plot elevation (m)|-2.96e-05**|2.91e-07|-9.40e-05***|-0.000297***|0.000227**|-0.000188*|-5.01e-06|
||(1.17e-05)|(1.59e-05)|(3.27e-05)|(5.32e-05)|(0.000110)|(9.64e-05)|(2.13e-05)|
|Urban household|0.00322|0.0744**|0.0166|-0.0568**|-0.0634*|-0.0347|0.0214|
||(0.0153)|(0.0356)|(0.0350)|(0.0274)|(0.0378)|(0.0281)|(0.0268)|
|Household engaged in livestock farming|-0.0888***|-0.142***|-0.103***|0.00674|-0.00526|-0.00275|-0.0921***|
||(0.00932)|(0.0152)|(0.0147)|(0.0124)|(0.0200)|(0.0202)|(0.0195)|
|Household has access to electricity|-0.00878|-0.0308**|-0.0621*|0.00794|-0.0857**|0.0115|-0.0408|
||(0.0115)|(0.0144)|(0.0345)|(0.00813)|(0.0384)|(0.0205)|(0.0333)|
|Observations|118,311|40,243|17,932|31,138|8,803|7,773|12,307|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Average marginal effects from multivariate logit regre<br>parentheses. *** p<0.01, ** p<0.05, * p<0.1|YES<br>ssions. Base categor|NO<br>y for crop fixed effec|NO<br>ts is 'other crop'. Th|NO<br>e estimates are wei|NO<br>ghted to be nationa|NO<br>lly representative.|NO<br>Standard errors in|

32
_Table 12: Heterogeneity in drought reports within enumeration areas, by crop type_

||||Drou<br>|ght<br>||
|---|---|---|---|---|---|
|||Allcrops|Maize|Sorghum|Millet|
||2011-2012|39.4|16.1|18.6|13.3|
|Ethiopia|2013-2014|43.8|26.3|24.9|12.7|
||2015-2016|55.8|34.3|29.0|23.3|
||2018-2019|48.6|27.7|26.9|10.5|
||2009-2010|62.6|58.4|76.0|30.0|
|Malawi|2012-2013|25.7|28.0|36.2|30.0|
||2015-2016|33.0|36.3|43.2|40.0|
||2018-2019|17.5|11.7|20.5|16.7|
|Mali|2014|29.7|13.9|20.6|14.4|
||2017|44.5|28.1|32.2|32.2|
|Niger|2011|78.6|41.7|68.1|82.4|
||2014|66.4|28.6|62.6|63.6|
|Nigeria|2019|20.7|8.9|7.6|6.4|
||2008|55.9|51.9|0.0|50.0|
||2010|59.3|57.6|58.1|55.6|
|Tanzania|2012|46.9|44.4|37.9|26.3|
||2014|32.1|29.7|66.7|50.0|
||2019|42.1|44.6|87.5|33.3|
|All countries|Allyears|41.1|31.1|31.5|32.1|

Note: Share of enumeration areas with heterogenous drought reports, pooled and by crop type. Heterogeneous reports are defined as enumeration areas where at least one plot recorded a drought loss and at least one plot did not. Columns 2, 3, and 4 compare only plots that grew the same crop. Enumeration areas with only a single plot with the crop are excluded.

33
_Table 13: Mean frequency of plots with drought shocks within household, conditional on at least one drought shock being recorded_

|**Country**|**Survey**|**Mean drought shock**<br>**frequency of plots with**<br>**the same crop within**<br>**household**<br>_Restricted to plots with_<br>_maize_<br>(1)|**Mean drought shock**<br>**frequency of plots with**<br>**the same crop within**<br>**household**<br>_Restricted to plots with_<br>_maize_<br>(2)|
|---|---|---|---|
|Ethiopia|2011 – 2012|68.75 %|_n≤10_|
|Ethiopia|2013 – 2014|62.52 %|70.76 %|
|Ethiopia|2015 – 2016|82.33 %|91.07%|
|Ethiopia|2018 – 2019|59.68 %|72.81 %|
|Malawi|2010|88.08 %|93.52 %|
|Malawi|2013|65.77 %|100.00 %|
|Malawi|2016|71.77 %|88.89 %|
|Malawi|2019|38.89 %|40.00 %|
|Mali|2014|65.83 %|69.91 %|
|Mali|2017|62.97 %|62.72 %|
|Niger|2011|_n≤10_|90.29 %|
|Niger|2014|_n≤10_|69.01 %|
|Nigeria|2019|24.40 %|57.14 %|
|Tanzania|2008 – 2009|43.22 %|_n≤10_|
|Tanzania|2010 – 2011|52.65 %|67.39 %|
|Tanzania|2012 – 2013|56.57 %|_n≤10_|
|Tanzania|2014 – 2015|44.25 %|_n≤10_|
|Tanzania|2019 – 2020|51.19 %|_n≤10_|
|All countries||66.56 %|79.55 %|

_Note: These frequencies are conditional on at least one plot with the same crop (maize in col (1) or sorghum in col(2)) experiencing a drought shock, the household having more than one crop plot with the same specific crop (maize in col (1) or sorghum in col(2)), and a sample size of over 10 observations to calculate shares._ 34
_Table 14: Share of the national population affected by droughts according to EM-DAT and LSMS-ISA data_

|Country|Year|Percent of the<br>population affected<br>by drought using<br>LSMS-ISA data|Percent of the<br>population affected<br>by drought using EM-<br>DAT data|
|---|---|---|---|
||2011 - 2012|7.88 %<br>[5.73 % ; 10.04 %]|6.20 %|
|Ethiopia|2013 - 2014|11.45 %<br>[ 8.83 % ; 14.07 % ]<br>|_missing_|
||2015 - 2016|32.50 %<br>[ 27.33 % ; 37.67 % ]|9.68 %|
||2018 - 2019|14.30 %<br>[ 10.88 % ; 17.72 % ]|_missing_|
||2009 - 2010|23.82 %<br>[ 20.44 % ; 27.20 % ]|_missing_|
|Malawi|2012 - 2013|8.98 %<br>[ 6.98 % ; 10.98 % ]|_missing_|
||2015 - 2016|25.51 %<br>[ 19.25 % ; 31.78 % ]|_missing_|
||2018 - 2019|7.66 %<br>[ 5.06 % ; 10.26 % ]|_missing_|
|Mali|2014|15.89 %<br>[13.35 % ; 18.43 %]|_missing_|
||2017|24.53 %<br>[ 21.54 % ; 27.53 % ]|_missing_|
|Niger|2011|56.14 %<br>[ 48.77 % ; 63.50 % ]|17.03 %|
||2014|20.78 %<br>[ 15.78 % ; 25.78 % ]|_missing_|
|Nigeria|2019|2.39 %<br>[1.49 % ; 3.29 %]|_missing_|
||2008|12.38 %<br>[10.55 % ; 14.21 %]|_missing_|
||2010|18.98 %<br>[ 16.54 % ; 21.42 % ]|_missing_|
|Tanzania|2012|15.38 %<br>[ 13.08 % ; 17.68 % ]|2.09 %|
||2014|10.27 %<br>[ 4.08 % ; 16.45 % ]|_missing_|
||2019|16.50 %<br>[ 10.07 % ; 22.93 % ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 35
_Table 15: Total estimated value of crop production lost (LSMS-ISA) and total aggregate economic losses (EM-DAT) due to droughts_

|Country|Year|Total aggregate losses<br>due to drought (in 2022<br>dollars) in LSMS-ISA|Total aggregate losses<br>due to drought (in 2022<br>dollars) in EM-DAT|
|---|---|---|---|
||2013 - 2014|589 million<br>[ 281 ; 896 ]|_missing_|
|Ethiopia|2015 - 2016|2,980 million<br>[ 2000 ; 3960 ]|2,134 million|
||2018 - 2019|1,354 million<br>[ 229 ; 2479 ]|_missing_|
|Malawi|2018 - 2019|41 million<br>[ 23 ; 60 ]|_missing_|
|Mali|2014|70 million<br>[ 55 ; 85 ]|_missing_|
||2017|165 million<br>[ 136 ; 194 ]|_missing_|
|Nir|2011|1.583 million<br>[ 1294 ; 1871 ]|_missing_|
|ge|2014|185 million<br>[ 129 ; 241 ]|_missing_|
|Nigeria|2019|80 million<br>[ 29 ; 131 ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 36
_Table 16: Share of the national population affected by floods according to EM-DAT and LSMS-ISA data_

|Country|Year|Percent of the<br>population affected<br>by floods according<br>to LSMS-ISA data|Percent of the<br>population affected<br>by floods according<br>to EM-DAT data|
|---|---|---|---|
||2012 - 2013|8.26 %<br>[5.73 %;10.78 %]|0.95 %|
|Malawi|2015 - 2016|1.18 %<br>[ 0.49 % ; 1.87 % ]|3.74 %|
||2018 - 2019|22.75 %<br>[ 16.37 % ; 29.12 % ]|5.38 %|
|Niger|2011|1.72 %<br>[0.30 %;3.13 %]|0.24 %|
||2014|3.76 %<br>[ 1.58 % ; 5.95 % ]|0.85 %|
|Nigeria|2019|4.62 %<br>[3.17 %;6.08 %]|0.03 %|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 37
_Table 17: Total estimated value of crop production lost (LSMS-ISA) and total aggregate economic losses (EM-DAT) due to floods_

|Country|Year|Total aggregate<br>flood loss (2022<br>dollars) in LSMS-<br>ISA|Total aggregate<br>flood loss (2022<br>dollars) in EM-<br>DAT|
|---|---|---|---|
|Malawi|2018 - 2019|346 million<br>[95 ; 598]|_missing_|
|Nier|2011|9 million<br>[ 3.00 ; 15 ]|_missing_|
|g|2014|81 million<br>[ 0 ; 205 ]*|3 million|
|Nigeria|2019|352 million<br>[ 208 ; 496 ]|_missing_|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata *Negative lower bounds were cut at 0._ 38
_Table 18. Estimated combined impacts of drought or flood events captured in LSMS-ISA_

|Country|Year|Number of<br>individuals<br>affected by<br>droughts<br>and/or<br>floods|Share of<br>total<br>population<br>affected by<br>droughts<br>and/or<br>floods|Value of<br>damages<br>from<br>droughts<br>and/or<br>floods|Number of<br>individuals<br>affected by<br>droughts<br>and/or<br>floods|Share of<br>total<br>population<br>affected by<br>droughts<br>and/or<br>floods|Value of<br>damages<br>from<br>droughts<br>and/or<br>floods|
|---|---|---|---|---|---|---|---|
|||LSMS-ISA|LSMS-ISA<br>|LSMS-ISA|EM-DAT|EM-DAT|EM-DAT|
||2011 -<br>2012|7.4 million<br>[ 5.4 ; 9.5 ]|7.9 %<br>[ 5.7 % ; 10.0<br>% ]||5.846 million|6.30 %||
||2013 -<br>2014|11.4 million<br>[ 8.8 ; 14.0 ]|11.4 %<br>[ 8.8 % ; 14.1<br>%]|589.6 million<br>[ 280.9 ; 898.2<br>]|52 thousand|0.10 %|3.5 million|
|Ethiopia|2015 -<br>2016|34.2 million<br>[ 28.8 ; 39.6 ]|32.5 %<br>[ 27.4 % ;<br>37.6 % ]|2,981.3<br>million<br>[ 1,999.0 ;<br>3,963.7]|10.904<br>million|10.40 %|2,134.4<br>million|
||2018 -<br>2019|16.3 million<br>[ 13.1 ; 19.5<br>]|14.3 %<br>[ 11.5 % ;<br>17.1 % ]|1,355.7<br>million<br>[ 227.9 ;<br>2,483.4]|200<br>thousand|0.20 %||
||2009 -<br>2010|3.5 million<br>[ 3.0 ; 4.0 ]|23.8 %<br>[ 20.5 % ;<br>27.1 % ]||38 thousand|0.30 %||
|Mli|2012 -<br>2013|2.6 million<br>[ 2.2 ; 3.1 ]|16.5 %<br>[ 13.5 % ;<br>19.5 % ]||2.05 million|13.00 %||
|aaw|2015 -<br>2016|4.6 million<br>[ 3.5 ; 5.7 ]|26.4 %<br>[ 20.1 % ;<br>32.8 % ]||7.341 million|42.40 %|594.6 million|
||2018 -<br>2019|5.4 million<br>[ 4.1 ; 6.7 ]|28.5 %<br>[ 21.5 % ;<br>35.4 %]|379.9 million<br>[ 123.0 ; 636.7<br>]|1.001 million|5.40 %||
||2014|2.8 million<br>[ 2.4 ; 3.2 ]|15.9 %<br>[ 13.4 % ;<br>18.4 % ]|70.0 million<br> [54.7;85.3]|No entry|||
|Mali|2017|4.7 million<br>[ 4.2 ; 5.3 ]|24.5 %<br>[ 21.7 % ;<br>27.4 %]|164.7 million<br>[ 135.4 ;<br>194.0]|No entry|||
|Niger|2011|9.8 million<br>[ 8.5 ; 11.1 ]|56.7 %<br>[ 49.1 % ;<br>64.4 % ]|1,589.9<br>million<br>[ 1,298.4 ;<br>1,881.3]|3.041 million|17.40 %||
||2014|4.7 million<br>[ 3.6 ; 5.8 ]|24.1 %<br>[ 18.4 % ;<br>29.8 %]|265.6 million<br>[ 129.5 ; 401.7<br>]|166<br>thousand|0.90 %|3.1 million|
|Nigeria|2019|13.6 million<br>[ 10.5 ; 16.7<br>]|6.8 %<br>[ 5.3 % ; 8.4<br>%]|427.5 million<br>[ 274.4 ;<br>580.6]|71 thousand|0.00 %|0.0 million|
||2008|5.3 million<br>[ 4.5 ; 6.1 ]|12.4 %<br>[ 10.6 % ;<br>14.2 % ]||10 thousand|0.00 %||
|Tanzania|2010|8.6 million<br>[ 7.5 ; 9.7 ]|19.0 %<br>[ 16.5 % ;<br>21.4 % ]||50 thousand|0.10 %||
||2012|7.3 million<br>[ 6.3 ; 8.4 ]|15.4 %<br>[ 13.1 % ;<br>17.7 % ]||1.0 million|2.10 %||

39
||2014|5.2 million<br>[ 2.1 ; 8.4 ]|10.3 %<br>[ 4.1 % ;<br>16.5 % ]||40 thousand|0.10 %|3.1 million|
|---|---|---|---|---|---|---|---|
||2019|9.9 million<br>[ 6.2 ; 13.6 ]|16.5 %<br>[ 10.4 % ;<br>22.6 % ]||5 thousand|0.00 %||
|All<br>countries|2008 -<br>2019|157.0 million<br>[ 144.6 ;<br>170.3 ]||7,824.2<br>million<br>[ 6,168.6 ;<br>9,479.8]|31.814<br>million||2,738.6<br>million|

_Note: both point estimates and 95% confidence intervals are reported_ 40
_Table 19. Overview of disaster loss information availability in the LSMS-ISA microdata_

|Country|Year|Ability to quantify<br>partial losses|List of disaster shocks<br>options provided in the<br>dataset|
|---|---|---|---|
||2011-2012|No|Dht Ri Fi|
|Ethioia|2013-2014|Yes|roug; ans: re;<br>Insects Cro disease|
|p|2015-2016|Yes|; p ;<br>Locusts Hail|
||2018-2019|Yes|;|
||2009-2010|No|Dht Il i|
|Mli|2012-2013|No|roug; rreguar rans;<br>Fld t i 1 Fi|
|aaw|2015-2016|No|oos (no n wave ); re;<br>Insects Disease|
||2018-2019|Yes|;|
|Mli|2014|Yes|Drought; Rains; Fire;|
|a|2017|Yes|Insects;Disease|
|Niger|2011|Yes|Insects and bird attacks;<br>|
||2014|Yes|Plant illness; Drought; Flood|
|Nigeria|2019|Yes|Drought; Flood; Pest;|
||2008|No||
||2010|No||
|Tanzania|2012|No|Drought; Rain; Fire; Insects|
||2014|No||
||2019|No||

41
# **Appendix A: Comparison of data from disaster inventories and microdata**

Data from disaster inventories such as EM-DAT and from microdata such as the LSMS-ISA differ in a number of meaningful ways. This includes their coverage of different shock types and sizes, the affected population, and time frame, as well as their detail, accuracy, and comparability across countries. These dimensions determine the respective strengths and weaknesses of each data source and can give rise to differences in estimates of disaster incidence and severity.

Disaster inventories collate reports from a variety of data sources on a per-event basis and are subject to the coverage and detail of information provided in these underlying sources.<sup>37</sup> Typically, this data comes aggregated, most often at the country-level. On the other hand, microdata, in our case, is based on household surveys that interview a sample from the target population and population-level estimates are formed based on this sample.

## **Shock coverage**

Coverage of shocks in disaster inventories varies depending on the underlying source data for each event. While large, salient shocks are likely reported across one or more sources that disaster inventories rely on (such as news reports or data bases from international organizations) and are therefore likely covered in disaster inventories, this is somewhat less likely for small or idiosyncratic shocks.<sup>30,37</sup> Such small shocks may therefore go unrecorded. For similar reasons, some shock types are more likely to be covered in disaster inventories than others.<sup>37,42</sup> Conversely, microdata is based on reports directly elicited from those affected by the shocks in question. They are therefore more granular and able to cover small and localized shocks. At the same time, shock recording in microdata sources is subject to the design of the survey questionnaire such as the list of shocks covered, and the number of shocks recorded.

## **Population coverage**

Disaster inventories are not limited to a specific population of interest and can, in principle, cover any event affecting any population group so long as this is reported in a source the disaster inventory can draw on. The lack of an explicit focus and reliance on reports from underlying sources, in turn, means that coverage for poor and marginalized population groups is more likely to be incomplete.

Microdata from household surveys focuses on (stratified) samples from a well-defined target population. In the case of the LSMS-ISA surveys, the samples are typically drawn to be nationally representative of the general population at the national and sub-national levels, and of urban and rural areas. This means that even poor and marginalized populations can be explicitly covered. At the same time, coverage is limited to the sample and affected by gaps in coverage wherever the sample is not fully representative of the population affected by a shock.

## **Temporal coverage**

42
A major strength of disaster inventories is their ability to record shocks at any time they occur so long as they get reported. Over the long run, this means that coverage of shocks improves as shock recording in underlying sources gets more exact and comprehensive.<sup>37</sup> In contrast, temporal coverage in microdata is intermittent: limited to the years in which the survey is implemented and the recall period of the survey.

# **Detail of available information**

The breadth of coverage in disaster inventories across time and space often comes at the cost of limited detail and missing information.<sup>30,37,48</sup> This particularly concerns the economic and welfare impact of shocks which are often difficult to quantify, especially so for uninsured damages.<sup>37,40</sup> Detail and completeness of the available information in disaster repositories is thus related to the size and salience of a shock. Further, the data provided in disaster inventories typically offers little opportunity for disaggregation, be it by affected population groups, geographies, or other key variables. As a consequence, disaster inventories often lack sufficient information to quantify disaster impacts on (asset) poor but highly vulnerable population groups.<sup>21</sup> As microdata sources elicit shock reports directly from those affected by them, they offer a high level of detail and completeness of information even for small or idiosyncratic events, for shocks that affect poor or marginalized population groups, and for their impact on uninsured damages and other, less salient domains of disaster impact. Depending on the design of the survey, shock and impact recording is often highly granular and allows for disaggregation and analysis along a broad range of ancillary information collected in the survey.

# **Accuracy of data**

Data recorded in disaster inventories typically relies on reports from one or few sources. Especially where events are small and therefore only reported in a single data source, this exposes disaster inventories to possible inaccuracies in the source from which information is drawn.<sup>37</sup> Even though reports of the number of people affected and the economic impact of any disaster are almost always estimates, disaster inventories rarely provide information that quantifies the uncertainty associated with any individual or collection of estimates.

Estimates from microdata, on the other hand, are based on a large number of data points collected from the population of interest. This also allows to explicitly quantify (part of) the uncertainty inherent in estimates of disaster impact. While the large samples underlying estimates from microdata attenuate the effect of inaccuracies in individual data points, microdata, as any data source, is still subject to possible systematic measurement error.

# **Comparability of source data**

Disaster inventories, and EM-DAT in particular, stand out for their broad coverage of events across geographies and time. To achieve this, disaster inventories need to rely on a wealth of different sources that vary according to their reliability, reporting standards, and definitions. Such idiosyncrasies are seldom 43
explicit in collated records as part of disaster inventories but limit the comparability of data across different events and contexts.<sup>37</sup> Within the same survey, information collected in microdata is typically harmonized and comparable across events covered. While microdata are subject to differences in survey and questionnaire design across contexts, such differences are usually explicit. Analysts can therefore take these differences into account when making comparisons and there is scope for harmonization across contexts as in the case of the LSMS-ISA suite of surveys.

# **Geographical differences**

Many of the previously discussed sources of inaccuracies in estimates of disaster incidence and severity are more pronounced in low- and middle-income countries (LMICs).<sup>21,30,37,42</sup> This goes in particular for data from disaster inventories that depend on the comprehensiveness of coverage in the underlying data sources they draw on. For example, the density of information on shocks and natural disasters is lower in LMICs meaning that shocks are more likely to go unreported, information is more likely to be incomplete or inaccurate, poor and marginalized population groups are more likely not to be covered, and the impact on them is more likely to be underestimated due to a higher share of uninsured damages and a lower value of affected assets. On the side of microdata, surveys in high-income countries may provide more accurate data due to the availability of more sophisticated measurement approaches and higher levels of education among respondents which may positively affect the accuracy of self-reported information.

|**Dimension**|**Disaster Inventories**|**Microdata**|
|---|---|---|
||**Coverage**||
|**Shock coverage**<br>(Threshold and hazard<br>bias)|• Subject to reporting in aggregate data<br>sources and reports<br>• Salient shocks likely covered but data<br>less sensitive to idiosyncratic or small<br>shocks<br>• Coverage varies by shock type|• Based on “grassroots” reports elicited<br>from those affected by shocks<br>• Granular and able to cover small and<br>localized shocks<br>• Shock recording subject to questionnaire<br>design (list of shocks, number of shocks<br>recorded)|
|**Population coverage**<br>(Population coverage<br>biases)|• Coverage<br>depending<br>on<br>comprehensiveness<br>of<br>coverage<br>in<br>underlying data sources (e.g. news<br>reports) but not limited to a specific<br>population of interest<br>• under-coverage<br>of<br>poor<br>and<br>marginalized population groups within<br>countries likely|• Limited to (stratified) sample of target<br>population<br>• Potential for gaps in coverage wherever<br>shock impacts not well represented by<br>sample<br>• Poor and marginalized population groups<br>explicitly covered|
|**Temporal coverage**<br>(Temporal coverage<br>biases)|• Continuous coverage but subject to<br>improvements in quality of reports in the<br>long run|• Intermittent coverage limited to years in<br>which survey was conducted and/or<br>recall period of survey questions|
||**Detail and accuracy**||
|**Detail of available**<br>**information**<br>(Missingdata biases)|• Dependent on information reported in<br>sources, limited detail and frequently<br>missing information in some dimensions|• High level of detail and completeness of<br>data collected, even for small and|

44
||of shock impacts (e.g. economic and<br>welfare impacts)<br>• Detail and completeness of information<br>related to size and salience of shock<br>• No or limited ability to disaggregate<br>information<br>• Incomplete recording of uninsured<br>damages<br>• Lack of information to quantify impact<br>on (asset-)poor but highly vulnerable<br>population groups|idiosyncratic shocks as well as poor or<br>vulnerable population groups<br>• Information<br>collected<br>at<br>highly<br>disaggregated<br>level<br>but<br>can<br>be<br>aggregated<br>• Available information subject to survey<br>design|
|---|---|---|
|**Accuracy of data**<br>(Accounting biases)|• Regularly relying on one or few sources<br>per event and exposed to measurement<br>error therein<br>• Usually<br>based<br>on<br>approximations<br>without ability to quantify uncertainty or<br>accuracy|• Estimation based on (large) sample from<br>population of interest<br>• Explicit quantification of uncertainty in<br>estimates<br>• Subject to systematic (non-classical)<br>measurement error|
|**Comparability of**<br>**source data**|• Broad coverage across geographies and<br>time<br>• Exposed to idiosyncrasies in reporting<br>protocols between different data sources<br>without them typically being explicit|• Increased scope for harmonized data<br>collection across contexts<br>• Idiosyncrasies in data collection between<br>different microdata sources are explicit|
||**Geographical differences**||
|**Geographical**<br>**differences**|• Lower information density in LMICs<br>and lower ability to draw on ancillary<br>data sources for shock recording<br>• Systematic under-coverage of events and<br>marginalized<br>population<br>groups<br>in<br>LMICs<br>• Lower accuracy of data due to lower<br>density of (independent) sources of<br>information<br>• Underestimation of impacts in LMICs<br>due to higher share of uninsured<br>damages and low value of affected goods<br>and assets|• Potentially<br>greater<br>accuracy<br>of<br>microdata in HICs due to use of more<br>sophisticated measurement approaches<br>and higher levels of education among<br>respondents|

45
# **Appendix B: Estimated impact for floods** **_or_ droughts, and for all shocks, in the LSMS-ISA**

Table B1. Estimated impact of all adverse shocks captured in the LSMS-ISA

|Country|Year|Estimate number of<br>individuals affected<br>by all shocks|Estimate share of<br>total population<br>affected by all<br>shocks|Estimate damages<br>from all shocks|
|---|---|---|---|---|
||2011-2012|14 million<br>[ 12 ; 17 ]|15.00 %<br>[ 12.40 % ; 17.60 % ]||
|Ethiopia|2013-2014|34 million<br>[ 30 ; 39 ]|34.20 %<br>[ 29.80 % ; 38.60 % ]<br>|34 million<br>[ 30 ; 39 ]|
||2015-2016|52 million<br>[ 46 ; 57 ]|49.30 %<br>[ 43.90 % ; 54.60 % ]|52 million<br>[ 46 ; 57 ]|
||2018-2019|31 million<br>[ 27 ; 36 ]|27.60 %<br>[ 23.60 % ; 31.60 % ]|31 million<br>[ 27 ; 36 ]|
||2009-2010|6 million<br>[ 5 ; 6 ]|39.00 %<br>[ 34.20 % ; 43.90 % ]||
|Malawi|2012-2013|6 million<br>[ 5 ; 7 ]<br>|36.40 %<br>[ 32.00 % ; 40.80 % ]<br>||
||2015-2016|9 million<br>[ 7 ; 11 ]|52.30 %<br>[ 41.70 % ; 62.80 % ]||
||2018-2019|9 million<br>[ 7 ; 11 ]|49.30 %<br>[ 39.00 % ; 59.50 % ]|9 million<br>[ 7 ; 11 ]|
|||4 million|21.90 %|4 million|
|Mali|2014|[ 3 ; 4 ]|[ 18.90 % ; 25.00 % ]<br>|[ 3 ; 4 ]|
||2017|5 million<br>[ 4 ; 6 ]|26.20 %<br>[ 23.10 % ; 29.30 % ]|5 million<br>[ 4 ; 6 ]|
|Nier|2011|12 million<br>[ 11 ; 13 ]|70.80 %<br>[ 63.70 % ; 77.90 % ]|12 million<br>[ 11 ; 13 ]|
|g||7 million|36.70 %|7 million|
||2014|[ 6 ; 8 ]|[ 30.00 % ; 43.40 % ]|[ 6 ; 8 ]|
|Nigeria|2019|18 million<br>[ 15 ; 22 ]|9.20 %<br>[ 7.50 % ; 11.00 % ]|18 million<br>[ 15 ; 22 ]|
||2008|11 million<br>[ 10 ; 12 ]|25.20 %<br>[ 22.50 % ; 28.00 % ]||
||2010|14 million<br>[ 13 ; 15 ]|30.80 %<br>[ 27.80 % ; 33.90 % ]||
|Tanzania|2012|11 million<br>[ 10 ; 13 ]|23.90 %<br>[ 21.00 % ; 26.90 % ]||
||2014|9 million<br>[ 5 ; 14 ]|18.30 %<br>[ 9.90 % ; 26.60 % ]||
||2019|14 million<br>[ 10 ; 19 ]|23.70 %<br>[16.20 % ; 31.20 % ]||
|All countries|2008 - 2019|268 million<br>[ 251 ; 285 ]||10,611 million<br>[ 8,892 ; 12,330 ]|

_Note: both point estimates and 95% confidence intervals are reported_ 46