metadata: authors: 0: author: Kochhar,Nishtha
1: author: Knippenberg,Erwin Willem Yvonnick Leon
count: Afghanistan
docna: 0: docna: Droughts and Welfare in Afghanistan
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies the effect of the
            2018 drought on household consumption and poverty in
            Afghanistan, a semi-arid and conflict-affected country. The
            paper combines geolocated household data with remote-sensing
            weather data on precipitation, vegetation, and temperature.
            The findings show that drought-like conditions decreased
            monthly per capita consumption expenditures and hence
            increased poverty, with a highly nonlinear relationship
            between consumption and weather shocks. When forced to cut
            back, households reduced nonfood consumption to maintain
            their food consumption; only under severe stress did they
            reduce food consumption. Households that owned agricultural
            land were more resilient to the 2018 drought. Based on the
            historical distribution of weather shocks, estimates of
            vulnerability to poverty suggest that 62.5 percent of people
            have a one in four probability of falling into poverty due
            to weather shocks. Given that climate change will exacerbate
            the frequency and severity of future droughts, these
            findings highlight the importance of investments in
            resilience and shock-responsive social protection to
            supplement urgent humanitarian assistance.
display_title: Droughts and Welfare in Afghanistan
listing_relative_url: /research/2023/01/33974204/droughts-welfare-afghanistan
topicv3: Financial Vulnerability and Risk
            Assessments m1326070 327,Poverty m1326687 695,Climate Change m1326829 567,Drought m1503313 3162
publishtoextweb_dt: 2023-01-09T00:00:00Z
dois: 10.1596/1813-9450-10272
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1204793
1: author_key: 1484194
model_extractions: Policy Research Working Paper 10272 Droughts and Welfare in Afghanistan _Nishtha Kochhar Erwin Knippenberg_ Poverty and Equity Global Practice January 2023
Policy Research Working Paper 10272

# **Abstract**

This paper studies the effect of the 2018 drought on household consumption and poverty in Afghanistan, a semi-arid and conflict-affected country. The paper combines geolocated household data with remote-sensing weather data on precipitation, vegetation, and temperature. The findings show that drought-like conditions decreased monthly per capita consumption expenditures and hence increased poverty, with a highly nonlinear relationship between consumption and weather shocks. When forced to cut back, households reduced nonfood consumption to maintain their food consumption; only under severe stress did they reduce food consumption. Households that owned agricultural land were more resilient to the 2018 drought. Based on the historical distribution of weather shocks, estimates of vulnerability to poverty suggest that 62.5 percent of people have a one in four probability of falling into poverty due to weather shocks. Given that climate change will exacerbate the frequency and severity of future droughts, these findings highlight the importance of investments in resilience and shock-responsive social protection to supplement urgent humanitarian assistance.

This paper is a product of the Poverty and Equity Global Practice. 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 nkochhar@worldbank.org and eknippenberg@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
# Droughts and Welfare in Afghanistan

Nishtha Kochhar<sup>∗</sup> and Erwin Knippenberg

**Keywords:** Drought, Afghanistan, Natural Disasters, Poverty

**JEL classi ication:** J12, O12, Q54

- ∗ Corresponding author, e-mail: `nkochhar@worldbank.org` .

Affiliations: Kochhar, Consultant, Poverty and Equity GP, The World Bank, H street, Washington DC USA, e-mail: nkochhar09@worldbank.org; Knippenberg, Economist, Poverty and Equity GP, The World Bank, H street, Washington DC USA, email: eknippenberg@worldbank.org. This analytical piece is a contribution towards the Afghanistan Poverty work program. We are indebted to Erich Battistin, Andrew Dabalen, Garance Genicot, and Martin Ravallion for constant advice and encouragement. We are extremely grateful to Johanna Lee Belanger and Walker Kosmidou-Bradley for help with extracting the geospatial data. We also thank Sandra Baquie, Cesar Cancho, Ruth Hill, Ghazala Mansuri, Ximena Del Carpio, Nandini Krishnan and participants at the World Bank seminar for their very helpful comments and suggestions on earlier versions of this paper. All errors remain our own.
# **1 Introduction**

Climate change is causing widespread impacts across the globe, and the latest UN Climate Change Report notes that risks will escalate exponentially, often causing irreversible impacts IPCC (2022). Afghanistan, which experiences an arid to semi-arid climate, has been facing extreme weather conditions in the recent decades.<sup>1</sup> Although less than 12 percent area is cultivable in the mountainous country, over two-thirds of the population depends on agriculture for their livelihood. The country has also been torn by incessant conflict for over 40 years, rendering its institutions fragile and its people highly dependent on humanitarian assistance. In 2019-20, 47 percent of the population was classified as poor, which represents a doubling of the poverty rate since 2006. Extreme weather events are likely to push people further into poverty, especially when most people can barely make ends meet and have little access to safety nets.

In this context, we study the effects of weather shocks in Afghanistan, mainly focusing on the effects of the 2018 drought on measures of consumption and poverty. We focus on the 2018 drought for two reasons. First, the 2018 drought was dubbed the worst drought in two decades and affected 22 of the 34 provinces in the country. About 13.5 million Afghan people faced heightened food insecurity in 2018, with lost livelihoods, livestock deaths, and forced displacement into urban areas (FAO, 2019).<sup>2</sup> Second, a nationally representative geo-coded household survey, the Integrated Expenditure and Labor Force Survey (IE-LFS), was administered right after the drought year, in 2019-20, enabling us to study the effects of the drought in the last growing season on household consumption.

There are multiple mechanisms whereby drought can affect household consumption in Afghanistan, where 76% of households live in rural areas and 46% of workers are in agriculture (NSIA, 2020). Grains and bread, primarily wheat, constitute 73% of calories consumed. Drought adversely affects farmers’ agricultural yields, reducing the availability of food for own consumption and surplus that can be sold. The risk associated with a bad harvest can also disincentivize productive investments, leaving farmers stuck in a low productivity poverty trap (Barrett and Carter, 2013). For non-farmers, reduced local

> 1The last two decades have seen two droughts a decade. `https://borgenproject.org/ the-drought-in-afghanistan/` While the precipitation rates have not changed dramatically over the whole of Afghanistan in the past decades, the drier conditions have become more extreme over the years: annual mean temperatures have increased since 1950 by 1.8 degrees Celsius warming across the country (NEPA and UN Environment, 2016). At the same time, climate predictions indicate a rise in flooding events in the country (rather than a decrease in precipitation), which are likely to be restricted to some northeast parts that receive rains. The mean annual temperatures are also expected to rise by 2 degrees Celsius by 2050, thereby intensifying warming (NEPA and UN Environment, 2016), which is expected to dry up drinking water and irrigation sources at an accelerated pace.

> 2https://www.thenewhumanitarian.org/news-feature/2021/6/2/as-drought-looms-afghanistanfaces-another-migration-crisis

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availability tends to drive up food prices, cutting into their limited budgets and forcing painful trade-offs.

For our analysis, we combine the remote sensing satellite-based monthly weather data for 15 years (2006-2020) with the geo-coded 2019-2020 IE-LFS, a nationally representative cross-section of household data that collects detailed information on socioeconomic indicators and household consumption. Our sample comprises 14000 households, both rural and urban, from 1495 clusters<sup>3</sup> covering all 34 provinces of the country. We extract data on precipitation, Normalized Difference Vegetation Index (NDVI),<sup>4</sup> and land surface temperatures.

To identify the effects of the 2018 drought, we construct a standardized measure (zscore) of weather shocks for a cluster in the 2018 growing season based on long-run weather distribution in the country. The z-score for a weather indicator differences the 15-year national growing season average (over 2006-2020) from the cluster’s 2018 growing season realization and normalizes this difference by the country’s long-run growing season standard deviation. This allows us to identify anomalies in cluster weather realizations in 2018 from the _usual_ national weather distribution.

We exploit the spatial variation in the intensity of weather anomalies in the household cross-section to identify the effects of weather shocks. To allow for flexibility in the relationship between weather shock and consumption, we fit a partially linear model where weather anomalies enter non-linearly. To mitigate concerns around potential omitted variables, we include several household and cluster characteristics as controls that may explain consumption and district fixed effects that control for any district characteristics that may confound the effect of weather anomalies. However, there remain potential historical factors that may have affected welfare measures and weather, which we are unable to control for in our cross-sectional specification. In light of such omitted historical factors, our results can be interpreted as the contemporaneous impact of the 2018 drought on consumption and poverty in a conflict-affected state, rather than a measure of the longterm equilibrium effect of weather on welfare arising from such historical processes (Dell et al., 2014).<sup>5</sup> Our results show that consumption decreases with lower NDVI and higher temperature

> 3Clusters are the lowest administrative units in Afghanistan, from which ten households were randomly sampled in the survey.

> 4NDVI is the Normalized Difference Vegetation Index that is an index of vegetation that relies on light reflected by plants and captures many vegetation-related metrics. See Section 3 for details.

> 5This paper is not an attempt to capture the reasons behind the effects we observe, but only to quantify the best estimates of the effects of weathe shocks in Afghanistan, which is highly vulnerable to extreme weather shocks.

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anomalies. Although these relationships are monotonic, they are significantly nonlinear across the range of weather anomalies. The relationship between temperature anomalies and the log monthly per capita consumption expenditure (MPCE) is close to a quadratic fit. There is a sharp increase in consumption with more negative temperature anomalies (z-score<-2). However, consumption does not decrease much as positive anomalies become more extreme (z-score>0). On the other hand, the decrease in consumption is steep with adverse vegetation anomalies (z-score<-1). Moreover, we find some evidence of households decreasing non-food consumption to save themselves from food insecurity. While both food and non-food consumption decrease with adverse vegetation shocks (zscore,-1), households were able to maintain their food consumption by reducing non-food consumption when temperature anomalies were positive in 2018 (z-scores).

We also show that the distribution of rainfall anomalies has little variation across clusters in 2018. Moreover, we observe less variation in the distributions of rainfall shocks over time: rainfall distributions are tight, with a significant overlap between 2018 (a declared drought year) and 2019 (a normal weather year). This finding is not surprising given the scarcity of rainfall in most parts of the country due to its arid climate. Hence, we use NDVI and temperature anomalies as measures of drought in our analysis.

We explore several potential sources of heterogeneity. Given the complex security situation at the time in Afghanistan, we find suggestive evidence that adverse vegetation shocks disproportionally affected the consumption of households in districts under Taliban control. This may be because these districts had limited access to humanitarian assistance. We find that households who owned agricultural land could mitigate the adverse effects of these weather shocks. This aligns well with extensive evidence that these assets allow households to ‘self-insure’ and smooth their consumption (see, for instance, Dercon 2002). We find no evidence that livestock owners are better insulated from shocks, in line with evidence that livestock ownership is an imperfect coping mechanisms as their sale under duress cannot fully compensate for income shortfalls (Fafchamps et al., 1998). Taliban-managed districts were more remote, with restricted movements of people and goods, including limited access to food and humanitarian aid, which may explain these differential effects. When faced with extremely hot temperatures, we also see a divergence in consumption expenditures between rural and urban households.

We also estimate the vulnerability to poverty of Afghan people due to future droughts. We follow Hill and Porter (2017), who use the historical distribution of covariate weather shocks in Ethiopia to estimate vulnerability to poverty by simulating log monthly per capita consumption with varying weather conditions. We find that 62.5 percent of households have a greater than one in four chance of falling into poverty in a given year, 3
compared to the poverty rate of 47 percent in 2018. Coupled with a high degree of insecurity, loss of livelihoods, and displacement, these results on households’ vulnerability to poverty illustrate the importance of urgent humanitarian action and investments in shock-responsive social safety nets.

Our contributions are as follows: First, our paper contributes to the literature on the impacts of climate change and weather shocks on welfare outcomes in a fragile and conflict-prone context (see section 2 for details). We show that droughts, measured by temperature and NDVI anomalies as deviations from long-run weather distribution in the country, adversely affect consumption and poverty rates in a conflict-affected region.

Second, from a methodological perspective we show that rainfall deficits are not ideal for measuring drought in arid and semi-arid regions that receive little to no rainfall. In Afghanistan, we show the distribution of rainfall anomalies (and absolute levels of precipitation) are tight, with little variation across clusters (less spatial variation) or between years. That is, rainfall-based measures do not adequately capture the full intensity of droughts. This finding has two implications: (i) past literature that uses rainfall deficits in arid contexts (Sub-Saharan African nations, for instance) may be underestimating the impact of droughts, and therefore, (ii) in such regions, other measures, such as temperatures and vegetation, seem to capture the drought-like conditions more appropriately, as illustrated in this paper.

Finally, our paper contributes to the growing literature that uses a combination of remote sensing satellite and household data to quantify households’ vulnerability to extreme weather events, events exacerbated by climate change.<sup>6</sup> Remote sensing methods are particularly valuable in a context with little on-the-ground data, such as conflict-affected Afghanistan. These data allow us to capture the spatial and temporal variation in weather shocks with higher precision to quantify households’ vulnerability to such shocks to inform future policy decisions.

The rest of the paper is organized as follows. Section 2 briefly presents related literature. Section 3 describes the socioeconomic and weather data, followed by a description of how we construct weather anomalies in 2018 in Section 3.5. Section 4 describes the empirical strategy. Section 5 presents key results and discusses the heterogeneity in impact estimates. Finally, Section 7 concludes.

> 6See Fisker et al. (2021) for details on how to use the data in economic applications and a detailed list of literature using remote sensing data.

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# **2 Related Literature**

Previous literature has documented the effects of many weather shocks, including temperature, precipitation, NDVI, windstorms, and soil quality, on economic activity (Dell et al., 2014). These papers study short-run effects, such as on labor (Graff Zivin and Neidell, 2014), on agriculture (Schlenker and Lobell, 2010; Deschênes and Greenstone, 2007), and long-run effects, such as on education and health (Groppo and Kraehnert, 2017; Graff Zivin et al., 2018; Maccini and Yang, 2009), on economic growth (Dell et al., 2012a).

We contribute to the growing literature that studies the effect of weather shocks on consumption, poverty, and vulnerability to poverty. Dell et al. (2012b) use a world sample from 2000 to show that countries are, on average, 8.5 percent poorer per 1 degree warmer weather. Other within-country evidence shows that rainfall deficits affect rice farming households negatively in Ethiopia, who lower non-food expenditures to meet their food expenditures (Essama-Nssah et al., 2011). Baquie and Fuje (2020) find that only nonassistance consumption reduces, and households decrease food consumption in response to the growing season rainfall deficit. Vulnerability to poverty increased by 40 percent in Kenya (Christiaensen and Subbarao, 2004), and 42 and 21 percent of households in rural and urban areas, respectively, were vulnerable to poverty in Ethiopia in response to rainfall shocks (Hill and Porter, 2017). Vulnerability to poverty was also estimated to be higher in drought-prone areas vis-à-vis other ecological zones in Ethiopia (Skoufias et al., 2021). Pape and Wollburg (2019) show an increase in rural poverty and hunger due to vegetation (NDVI) deficits in Somalia (also a conflict-affected nation).

Interestingly, Letta et al. (2018) shows a reduction in rural household income and a slowdown in income convergence due to temperature shocks in Tanzania. In a forthcoming paper, Calcutt et al. (forthcoming) use multiple satellite-based measures of weather to find that food consumption is more strongly impacted by weather shocks in the growing season than aggregate consumption, and poorer households with less education in remote areas in dry agro-ecological zones are most affected in four Sub-Saharan Africa countries. Contrary to their findings, we see a decrease in households’ per capita total consumption expenditure due to bad NDVI and temperature shocks. While both food and non-food consumption were negatively affected due to adverse NDVI anomalies in 2018, households reduced non-food consumption to save themselves from food insecurity when faced with extremely hot temperatures.

While many different measures of drought have been used in the recent decade (Calcutt et al. forthcoming, for instance, use multiple measures), a consensus is yet to emerge 5
in the literature on the best indicator to measure droughts (Trenberth et al., 2014) and weather shocks, in general.<sup>7</sup> Our paper also contributes to this literature by showing that temperature and NDVI are likely more appropriate drought measures in arid and semi-arid climatic conditions over rainfall-based measures, such as in Afghanistan.<sup>8</sup>

# **3 Data**

## **3.1 Household Data**

We use the 2019-2020 Integrated Expenditure and Labor Force Survey (IE-LFS) collected by the National Statistics Information Authority (NSIA) of Afghanistan, with the assistance of international organizations. This nationally representative household survey is the fifth in the series, a repeated cross-section collected as a follow-up to the Afghanistan Livelihoods Conditions Survey (ALCS) conducted in 2007-08 2011-12, 2013-14, and 201617, supported by the World Bank and other international organizations.<sup>9</sup> There are various advantages of using this survey. First, this survey contains geo-tagged locations of surveyed households, which we leverage to combine with the satellite-based remote sensing data on weather and vegetation for the given region. Second, this survey collects detailed information on (i) household demographics, (ii) household’s asset holdings and access to basic facilities, (iii) labor market participation of every individual in the household, and (iv) consumption expenditure. This survey is nationally and provincially representative and is used to compute welfare measures, including poverty estimates. The results are intended to inform the government’s and development partners’ policy making.

Unit non-response in IE-LFS arises from security concerns, and road access led to undersampling in some provinces. The final sample was below planned coverage (<70 percent) in three provinces (Faryab, Badghis, and Kapisa). This under-sampling can be a potential source of bias. Various checks, including comparisons with the 2016-17 survey data and sensitivity analysis to see if poverty trends are dramatically different in under-sampled and other provinces, find little cause for concern.

A second issue arises from poorly identified geographical locations for 19 percent of the

> 7For a comprehensive review of data sources and weather indicators (satellite and non-satellite) used in economic development literature, please refer to Fisker et al. (2021); Dell et al. (2014).

> 8While land surface temperatures are shown to have a negative relationship with NDVI, Rousta et al. (2020) shows that lagged precipitation deficits (or surpluses), soil moisture, and other factors also affect NDVI.

> 9The 2007-08 survey was called the National Risk and Vulnerability Assessment (NRVA).

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total sample of 18344 households<sup>10</sup> . We drop these households from the current analysis since we are unable to map them with the remote sensing satellite data, and this exclusion may result in a selected sample. In table A.1, we present results from a series of weighted regressions of a dummy variable for bad geocodes (households dropped out of analysis) on demographic and location covariates. We find that households with bad geocodes were highly likely to be interviewed using paper surveys. While only 5 percent of households interviewed with tablets had bad geographical locations, 88 percent of households interviewed with paper questionnaires had incorrectly mapped locations. Households in rural areas, and Taliban-controlled and contested-regions were also more likely to have incorrect location data. Moreover, households with bad geocodes were also slightly more likely to be poor. With our sample skewed along the mentioned characteristics, our results should be interpreted as a lower bound on the impact of drought on welfare, as the analysis may be excluding poor households in the most remote areas.

With these caveats, Table 1 describes the household sample. Expectedly, households in our sample are predominantly rural, have male heads with less than 50 percent of the heads being literate. Half of the sample was collected after the impositions of COVID-19 restrictions. Over 50 percent of the sampled households resided in government-controlled districts, whereas 8 percent were in Taliban-controlled districts.

In Appendix B, we detail the methodology used to construct consumption aggregates and poverty rates based on cost of basic needs methods. The consumption expenditures per person per adult equivalent and poverty lines are estimated for each of the 14 regions.<sup>11</sup> Table 2 show the description of consumption aggregates and poverty line constructed with this socioeconomic data. Forty-seven percent of the Afghan people are poor, with urban poverty only slightly lower than rural poverty.

# **3.2 Weather Data**

As discussed earlier, we expect temperature and NDVI deviations to be better measures of weather shock in the country. In addition, we also use variations in precipitation to confirm if this hypothesis is valid in Afghanistan. Below we detail the data sources for each of these measures.

_Precipitation (CHIRPS):_ The daily rainfall data is available at a 5-kilometer resolution

> 10Incorrect locations are characterized by missing household latitude or longitude, point outside the specified district, etc.

> 11Afghanistan has 14 poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

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from the Climate Hazards Group InfraRed Precipitation Station (CHIRPS) in association with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. Several other satellite-based rainfall products lack geographical and temporal coverage. The CHIRPS data, however, has a relatively high spatial and temporal coverage and quasi-global coverage. We extract the 15-year monthly average precipitation for Afghanistan from September 2006 to September 2020.

_Vegetation Index (NDVI):_ The Normalized Difference Vegetation Index (NDVI) data is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the NASA Earth Orbiting System Terra satellites. MODIS provides an NDVI estimate every eight days at an approximately 250-meter ground surface (pixel) resolution.<sup>12</sup> NDVI values range between -1 and 1. For this paper, we retrieved the average monthly NDVI between September 2006 to September 2020. NDVI relies on the light reflected by plants, in particular the different ways that near-infrared light and visible red light are absorbed and reflected (Huete et al., 2002; Fensholt and Sandholt, 2005), and is used as a tool to measure a wide variety of vegetation related metrics: land cover, crop type, and cover, famine, and drought warning, and indicators of biodiversity (see Morikawa (2014) for details). Higher NDVI values are associated with greener, healthier vegetation, while lower values are indicative of drier vegetation and bare soils (Hulley et al., 2019).

_Temperature (LST):_ The Land Surface Temperature (LST) was extracted from the NASA MODIS Terra satellite data. Temperatures are measured in Kelvin (which we converted to Celsius) at a 1-kilometer spatial resolution. We extract the 15-year monthly average temperature between September 2006 and September 2020.

# **3.3 Mapping Household Data with Remote Sensing Data**

We create a 2-km buffer around each household location to extract data for weather indicators around the buffer area. For the weather indicators, we combine descriptive statistics (mean, minimum, maximum, standard deviation) for every month between September 2006 and September 2020 in one image from the data sources described above. Then, these descriptive statistics were extracted into a single month-year image for each household location (and its buffer). Since a cluster is the smallest administrative unit (from which 10 households were sampled in the IE-LFS 2019-20), each household within a cluster is expected to experience similar weather conditions. Thus, weather data is extracted at the cluster-level. For each cluster, weather data (averaged for the month) is

> 12A higher resolution can be obtained through the use of Landsat data, although this product is more raw, and the researcher needs to calculate the relevant vegetation indexes manually.

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extracted as a 15-year monthly panel between 2006 and 2020.

# **3.4 Taliban Control**

Afghanistan’s war between the government and the Taliban for two decades led to the country’s changing profile of territorial control. Areas under the control of the Taliban increased since the reduced presence of U.S. forces in 2011, with the entire country under the Taliban government since August 2021. We extract data on district control in 2018 from FDD’s Long War Journal that maintains real-time data on district control in Afghanistan: contested, government-controlled, Taliban-controlled. A district is marked as contested if the district center is under the government, but the Taliban control most areas outside the district center. A district is marked under Taliban control if the district center is under the Taliban.<sup>13</sup> In our study sample, 134 districts (39 percent sampled households) in Afghanistan were contested in 2018, 124 districts (52.5 percent sampled households) were under the government, and 30 districts were under the Taliban control (8.5 percent households). Figure 1 maps data on district control in 2018.

# **3.5 Measure of Weather Shock**

We construct the measures of weather shock at the cluster level, the lowest administrative unit, from which ten households were randomly sampled. Although we combine the data for coordinates of every household in IE-LFS 2019-20, each household in a given cluster is almost surely going to experience the same weather conditions.<sup>14</sup> Further, weather variables in the last growing season are more likely to affect incomes and consumption in the current period. With this in mind, we construct measures of weather shock for the 2018 growing season to analyze its effects on 2019-20 consumption.<sup>15</sup> There are several ways to define the functional form of the weather variable. Most studies either focus on levels or level changes in weather indicators. Another common approach is to use frequencies at which weather realizations fall into different categories (e.g., days with temperature between 0<sup>_◦_</sup> _−_ 10<sup>_◦_</sup> , etc.). This approach is advantageous as it avoids

> 13More information available at `https://www.longwarjournal.org/mapping-taliban-control-in-afghanistan`

> 14The Survey contains information on 1495 clusters in 275 districts of 34 provinces; roughly about 60 households were interviewed per district. The cluster is the lowest administrative unit, the primary sampling unit in IE-LFS 2019. And the district is the administrative unit above the cluster.

> 15To identify the growing months in the year, we rely on the agricultural module from the 201112 round of the Afghanistan IE-LFS (known as Afghanistan Livelihoods Conditions Survey, 2011-12) conducted at the cluster level. This survey collected detailed information about the most grown crops in the cluster and the annual cropping cycle for the crop. We first identify the most grown crop for each region, which helps determine the relevant growing months.

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using any functional forms on the weather indicator (Dell et al., 2014). Some studies use measures of variability changes or anomalies in weather realizations from the geographyspecific long-run distributions, where the latter help detect irregular patterns in weather conditions (Hoffmann et al., 2021).<sup>16</sup> We create weather anomalies based on the 15-year national distributions of the growing season weather, characterized by their mean and variance, for our cross-sectional analysis. We difference the 2018 growing season realization of the weather indicator at the cluster level from the national 15-year growing season mean and scale it by the country’s 15-year growing season standard deviation: where weather value is observed for a cluster, _c_ in district _d_ of province _p_ in 2018 growing months ( _g_ ). The 15-year national mean and standard deviation is constructed for the growing months _g_ .<sup>17</sup> Note that such construction allows a measure of irregularities from the long-run weather conditions in the country: level changes in a cluster matter in proportion to the national variation in weather. In so far as 2018 was dubbed one of the worst in two decades, this measure should also capture the drought-like conditions in 2018. Moreover, by eliminating scale effects, this measure allows us to rank clusters on a common scale and retain the spatial variation in the intensity of deviations from the ‘usual’ weather, which is the source of variation we exploit in our analysis.

# **3.6 Weather Anomalies in 2018**

In Figures 2, 3 and 4, we map the rainfall, NDVI and temperature anomalies from 2018, respectively, at the district-level. The maps highlight the spatial differences across districts, with some districts drier, hotter, or with less vegetation than others.

Figures 5, we plot the distributions of growing season weather anomalies in 2018 (a declared drought year) and 2019 (a typical weather year). In Figure 6, we also plot the

> 16In this approach, the weather shock is calculated as the level difference from the within-spatial area long-run mean and divided by the within-spatial area long-run standard deviation. This construction implies that the level changes matter not in absolute terms but in proportion to an area’s usual variation (Dell et al., 2014; Barrios et al., 2010).

> 17An alternative would be to use cluster-specific or district-specific long-run weather conditions to construct this measure of weather shock. The issue is that creating a standardized measure constructed from district distribution makes this measure a function of the district fixed effect and plausibly endogenous. In this specification, the district fixed effects account for spatial heterogeneity, while deviations from a national distribution provide an nation-wide ranking of cluster based on weather conditions within and across districts.

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empirical CDFs of weather anomalies in 2018 and 2019 and test for the equality in these distributions.<sup>18</sup> The horizontal black line at the bottom of the graphs shows the range where the CDF equality is rejected at a familywise error rate (FWER) of 5 percent.

First, note that the distribution of rainfall shocks is extremely tight in both years (Figure 5a). Rainfall z-scores exceed 1 in 10 percent of the clusters and 4 in 3 percent of the clusters, respectively, implying that a very small sample of clusters received more than usual rainfall variation in the country in 2018. The remaining clusters show almost no rainfall deviations from the national long-run distribution in both years. This finding is not surprising since most of the country receives very little rainfall even in an average year, given the arid conditions.<sup>19</sup> With little variations in the measure over time, rainfall shocks do not capture the full intensity of droughts experienced in the country in 2018 and, thus, are not appropriate to measure drought-like conditions.

Although the correlation between 2018 and 2019 NDVI and temperature anomalies is high, the distribution of NDVI and temperature z-scores is left- and right-skewed, respectively, in 2018 compared to 2019 (Figures 5b and 5c, respectively). In Figure 6b, we also reject the test of equality of distributions between 2018 and 2019 for most ranges. The growing season NDVI z-score in 2018 was lower than the 2019 z-score in 93 percent clusters. NDVI anomalies in 2018 are reflective of drought-like conditions in the country. Similarly, in Figure 6c, we reject equality in distributions of temperature z-scores when z-scores is between 0 and 2. In this range, temperatures were higher in 2018 than in 2019.<sup>20</sup> In sum, NDVI and temperature z-scores exhibit spatial and temporal variation in the country. Extreme drought conditions in 2018 are most apparent in the more negative NDVI anomalies and, to an extent, are also captured by positive temperature anomalies. On the other hand, the distribution of rainfall z-scores is highly concentrated at zero and does not capture the intensity of the drought experienced in 2018. Therefore, in this paper, we focus on NDVI and temperature anomalies as measures of the 2018 drought in Afghanistan.

> 18We use the method proposed in Goldman and Kaplan (2018) which assesses equality of the distributions point by point and performs slightly better in the tails over the Kolgomorov-Smirnov test.

> 19We also observe little variation in absolute precipitation levels in 2018 and 2019 similar to this relative z-score measure presented here.

> 20Temperatures z-scores were higher in 64 percent of the clusters in 2018 compared to 2019.

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# **4 Empirical Strategy**

Our primary outcomes of interest are three measures of consumption: log of monthly per capita consumption expenditure: total, food, and non-food. We also show results on an indicator variable for whether the household is poor (headcount ratio). We focus on two measures of weather anomalies in 2018 growing season defined at the cluster level: NDVI, and land surface temperature z-scores, as measures of drought conditions. We also present the main result with rainfall z-scores with the caveat that it is not an ideal measure of drought in Afghanistan (details in section 5).

The relationship between consumption and weather may be nonlinear: while high temperatures may result in crop loss, low-temperature realizations may also adversely affect the harvest. To allow for flexibility in the relationship between consumption and weather anomalies, we use a semiparametric partially linear model, in which we assume a flexible functional form on weather anomalies, whereas the other regressors (controls) enter linearly: _Yicdp_ is the outcome variable for household _i_ in cluster _c_ in-district _d_ of province _p_ in 2019-20 based on past 12 month consumption data (see Appendix B for details). We add district dummies and include several explanatory variables (explained below). _zcdpg,_ 2018 is the weather anomaly constructed as described in Section 3.5. We do not assume a functional form on _f_ ( _._ ). We use Yatchew (1998)’s method to fit a partially linear model. The data is sorted on _z_ ( _weather_ ) _cdpg,_ 2018, and a first difference model from equation 1 is estimated.<sup>21</sup> Under the assumptions that _f_ ( _._ ) is smooth, single-valued with bounded first derivative and _z_ ( _weather_ ) _cdpg,_ 2018 has a compact support, _plim_ ∆ _f_ ( _._ ) = 0 as the sample size increases. In this case, parameters _γ_ 1 and _γ_ 2 can be consistently estimated with OLS, withˆ out explicitly modeling _f_ ( _._ ). Finally, _f_ ( _._ ) is estimated from regressing ( _Yicdp − γ_ 1 _X_ 1 _icdp − γ_ ˆ2 _X_ 2 _cdp_ ) on _z_ ( _weather_ ) _cdpg,_ 2018 nonparametrically.<sup>22</sup>

> 21In a cross section, the first difference here eliminates the observation with the lowest value of z-score from the estimation.

> 22We implement this partial linear model using the plreg command in STATA developed in Lokshin (2006).

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We control for several household- and cluster-level variables that may confound the effect of weather anomalies that enter linearly in equation 1. _X_ 1 _icdp_ is the set of household-level observable characteristics, including (i) household head’s gender, age, age-squared, and indicator for whether the head is literate, and (ii) indicator variables for rural household location, land ownership, and livelihood ownership, access to piped water, electricity and use of gas/electricity as cooking fuel.<sup>23</sup> _X_ 2 _cdp_ community-level characteristics: dummy variables for the presence of a primary school in the community, distance to the nearest primary health care facility, and distance to the nearest hospital, and the quarter in which households in a cluster were interviewed.<sup>24</sup> We also include district fixed effects, _μd_ , to control time-invariant district characteristics, observed or unobserved, such as access to institutions or agricultural and labor markets. The district dummies also enable us to disentangle the effect of any possible omitted factors at the district level that may confound the effects of the weather anomalies.

In addition, we will present the main results assuming _f_ ( _._ ) in equation 3 to be linear. This is mainly presented for comparability with semi-parametric estimates and for benchmarking effect sizes with the previous literature. In addition to including controls and district fixed effects, all standard errors are clustered at the cluster level in this specification. _β_ 1 is the main parameter of interest that measures the effect of the weather anomaly on outcome measure _Yicdp_ .

Our specification in equation 1 leverages the spatial variation in weather anomalies using a household cross-section. We include many explanatory variables, district fixed effects, and weather shocks as deviations from usual weather conditions in the growing season.<sup>25</sup> However, there may still be other long-term historical factors that may affect both weather anomalies and consumption, which are omitted from our cross-sectional specification.<sup>26</sup> In this light, our results can be interpreted as the contemporaneous effect of the drought

> 23While we do not include household size in 2019 in the primary specification, our results are robust to including retrospective household size in 2019 as a control variable.

> 24Poverty is seasonal in Afghanistan, with welfare deteriorating considerably in the winter. We, therefore, include quarter dummies to control for the season. The COVID-19 pandemic led to restrictions in several major cities of many provinces starting on March 22, 2020. The quarter dummies control for this effect since two quarters (3 and 4) coincide with the imposition of local lockdowns.

> 25To the extent the weather indicators are exogenous, our measures of weather anomalies are based on the past growing season, which makes reverse causality less of a concern in our setting.

> 26Dell et al. (2014) discuss that panel analysis allows for a strong causal interpretation with few identifying assumptions. However, we are limited by a single cross-section.

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(weather anomalies) on consumption and poverty in 2018, rather than measure the longterm equilibrium effect arising as a result of such historical processes (Dell et al., 2014).

Lastly, we also explore heterogeneity in impacts between different groups in the population. We will fit the semi-parametric partial linear regression model separately for each sub-group in the population.

We may worry that our measures of weather shocks for NDVI are endogenous. We seek to attenuate any concerns about simultaneity in households’ consumption patterns and vegetation levels concerns by studying the 2018 growing season weather shock’s lagged effect on 2019 welfare aggregates. The use of a Z score should mitigate concerns about settlements internalizing the communities’ long term weather conditions and adapting accordingly, leading to improved vegetation. As deviations from the long term mean, Z scores represent unpredictable shock for households residing in the clusters. Our results are also robust in sign and significant to using temperature z-scores, suggesting we are not introducing a differential bias with the use of NDVI.

_Within-district variability of z-scores:_ Since we include district dummies, we formally check for within-district variation of weather anomalies. In Figure 7, we plot residuals from the regression of weather anomalies at the cluster level on district dummies. These residuals capture the within-district variation in rainfall, NDVI, and temperature weather anomalies. Consistent with our discussion in section 3.5, there is very little variation in rainfall z-scores within districts, whereas there is sufficient within-district variation in NDVI and temperature anomalies.

_District Compositions:_ Since we include district dummies, ideally, we want the district’s demographic composition to not vary with the intensity of weather anomalies to identify the effects correctly. We test for this in Section C. Overall, NDVI z-scores pass this test well, and we find no significant variation in district demography with district NDVI anomalies. Broadly, the demographic composition of districts also does not vary significantly with temperature z-scores. Except higher temperature anomalies are correlated with slightly lower access to private water and private electricity connection with the sampled households in the district.

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# **5 Results**

## **5.1 Main Results**

Table 3 presents the first set of results, where we impose a linear relationship between the measures of welfare and weather anomalies. Monthly per capita consumption decreased by 5 percent with a standardized deviation decrease in NDVI anomaly, and poverty increased by seven percentage points (3 percent). We observe similar effects of the higher temperature deviations. However, the linear assumption masks the potential heterogeneity in the relationship across the range of z-scores.

Figure 8 plots the results for the non-parametric estimates on weather anomalies from the partial linear model.<sup>27</sup> As noted in Figure 8a and 8b, the relationships exhibit nonlinearities, which may be hidden by the average effects presented in Table 3 and justify our proposed non-parametric approach. The significance test on the weather anomalies that enter the specification non-linearly indicates that the weather anomalies are highly significant ( _p − value_ = 0 _._ 000).

The relationship between NDVI anomalies and log consumption is positive (figure 8a), whereas between temperature and log MPCE it is negative (figure 8b). Consumption decreases sharply with decreased vegetative cover ( _z_ ( _NDV I_ ) _< −_ 1) highlighting negative effects of abnormally dry conditions on household consumption. On the other hand, higher NDVI anomalies consistently increase household consumption. The relationship between log consumption expenditure and temperature is negative and closer to a quadratic parametric fit. Consumption is high at very low temperatures, and decreases sharply as temperatures rise until _z_ ( _temp_ ) _< −_ 2 (corresponding to a temperature less than 22<sup>_◦_</sup> ), beyond which point consumption decreases with increasing temperatures but at a decreasing rate. Consistent with the above findings, we see the incidence of poverty is higher with negative vegetation anomalies and increasing temperatures (figure 9).<sup>28</sup> The relationship between log consumption and rainfall anomalies is weak for _z_ ( _rainfall_ ) _∈_ [0 _,_ 4], which holds for 97 percent of our sample (figure 8c). In the 3 percent of clusters that received rainfall above usual in 2018, consumption is positively related to rainfall anomalies: higher rainfall z-scores increase consumption, possibly due to better agricultural yields.<sup>29</sup> However, results are based on a small subset of observations and are not

> 27The linear estimates from the semi-parametric estimation with other explanatory variables are not presented in this draft.

> 28Our sample includes 2.8 percent Kuchi population, a group of pastoral nomads. Since Kuchi people tend to migrate more frequently, we may worry that our results are biased by including this population. In Appendix Figure A.2, we show our main findings are robust to exclusion of this group.

> 29Heavy rainfall does not necessarily translate to flooding in this context, as floods are generally

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intended for extrapolations.<sup>30</sup> Next, we separately estimate the effects of vegetation anomalies on food and non-food consumption in Figures 10 and 11. Interestingly, the curve on non-food consumption is smooth, suggesting a gradual scale back as conditions get worse. Conversely, households try to retain their level of food consumption until a certain point ( _z_ ( _NDV I_ ) _< −_ 1), beyond which it drops off significantly. This suggests that cutting back on non-food consumption is used as a coping mechanism, and only under severe stress will households resort to cutting back on food consumption. The relationship of non-food and food consumption with temperature anomalies are nonlinear and non-monotonic (Figures 11a and 11b).Afghan households faced with hotter than usual temperature anomalies seem to decrease their non-food expenditures to continue to meet their basic needs for food.

# **5.2 Heterogeneity in Effects**

Of particular interest in Afghanistan is whether territorial control and conflict worsen the impacts of the weather shocks. In Figure 12, we present the non-parametric estimates on weather anomalies from three separate partial linear models for districts that were contested in 2018, districts under the government and districts under the Taliban.

There is suggestive evidence of a disproportionately negative effect of adverse vegetation shocks on consumption levels in Taliban-controlled districts compared to governmentcontrolled districts, where consumption levels remain relatively stable. Across district types, consumption is stable when temperature anomalies are hotter than usual.<sup>31</sup> During the conflict, Taliban-controlled districts tended to be more remote, with restrictions on the movement of people and goods. Humanitarian agencies that sought to provide assistance were often asked to pay a tax on any assistance provided. Limited access to food and humanitarian aid during the 2018 drought in Taliban-managed districts relative to other districts may partly explain the differential effects.<sup>32</sup> Next, we look at heterogeneity in impacts due to households’ ownership of agricultural land and livestock. As noted by Fafchamps et al. (1998), Reardon et al. (1988) among others, households facing substantial risks with little access to insurance or credit may driven by rapid snowmelt and landslides.

> 30Given little aggregate rainfall in the country, the z-score may be less informative and may not capture drought conditions well due to the extremely skewed distribution of precipitation data. We show that our results are robust to using number of months with positive rainfall in 2018 as an alternative measure of rainfall deficit in the clusters (Figure A.1).

> 31The smoothness of the curves for Taliban-controlled districts is affected by the small sample size. While the relationships are significant in the partial linear models, we treat this only as suggestive evidence owing to a small sample from Taliban regions.

> 32https://www.reuters.com/article/us-afghanistan-taliban-idUSKBN1O91SI

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rely on liquid assets such as livestock to mitigate negative effects. In our sample, just over half the households own agricultural land, and 60 percent own some type of livestock.<sup>33</sup> In Figures 13 and 14, we do find evidence that landed households are able to protect their consumption in a time of adverse vegetation anomalies, whereas the consumption of the landless decreases. However, the trends in consumption are no different between landed and landless households when temperature anomalies are positive. Livestock ownership does not seem to protect a household’s consumption in times of adverse weather shocks. Our findings are in line with Christiaensen and Subbarao (2004), and Kazianga and Udry (2006), who show livestock ownership as ineffective in mitigating consumption losses due to drought in Kenya and Burkina Faso, respectively.

Afghanistan’s population is primarily rural (78 percent), who are either directly or indirectly dependent on agriculture or rearing livestock. Among households in urban areas, we may expect better resilience to adverse effects of weather due to their access to diversified livelihoods outside agriculture. In Figure A.3, we find that the adverse effects of low NVDI and high temperatures are concentrated in rural areas, while in urban areas the effect is noisier, suggesting that local weather extremes do not impact livelihoods in the same way.

# **6 Vulnerability to Poverty**

Beyond the observed impacts on household welfare, we want to understand long-term household vulnerability to poverty in the context of increasingly extreme climate in Afghanistan. Even for households not affected by the 2018 drought, similar or more severe shocks could drive them into poverty in the future. The identified households may live close to the edge of poverty and are vulnerable to backsliding.

Drawing from Hill and Porter (2017), we estimate the vulnerability to absolute poverty for Afghan households due to drought. We combine the historical weather data with the estimated impacts of droughts on consumption to predict future consumption based on a posited threshold for the probability of falling into poverty and the estimated likelihood of future shocks based on the past weather shocks.

For simplicity, we rely on the linear estimates of NDVI anomalies on log consumption. As is clear from Figure 8a, imposing a linear relationship through the range of z-scores may underestimate the effect of severe droughts (NDVI z-score <-1), so these estimates should be considered a lower bound of household vulnerability to weather shocks.

> 33Over four-fifths of cultivating households in IE-LFS 2019-20 possess livestock, and livestock rearing is the second-largest income-generating activity for over two-thirds of the farming households.

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In order to create a counterfactual for household consumption, we first subtract the observed linear effect of the NDVI z-score on welfare. This removes the estimated effect of drought, leaving us with a demeaned welfare estimate _Y_<sup> ̄</sup> _icdp_ . Next, we draw a counterfactual NDVI z-score ( _z_ ( _NDV I_ ) _cdp,Y r_ ) based on observed NDVI z-scores in years _Y r ∼_ (2007 _,_ 2020), adding a random shock _ε ∼ N_ (0 _,_ 2) to allow for deviations from historical means and thereby multiply the set of plausible climate scenarios. This simulated NDVI z-score is added back to the demeaned welfare estimate in order to calculate the counterfactual welfare outcome. The simulation is repeated over 100 iterations The distribution of observed consumption _Y_<sup> ̄</sup> _icdp_<sup>_CF_is used to calculate the probability that a</sup> = household will be below the poverty line, defining it as vulnerable, where _V ulnerability P_ ( _Y_<sup>ˆ</sup> _icdp_<sup>_CF≤Pline_).Byconstruct,vulnerabilityisthereforeafunctionoftheprobability</sup> threshold for households. We posit a threshold of 25%, the welfare effect of a one in four year drought. We further explore a 20%, 10% and 5% threshold for robustness.<sup>34</sup> We present results using NDVI shocks in the paper. We get similar estimates with temperature shocks.

From the simulation, an estimated 61.8 percent of individuals would be pushed under the poverty line by a one in four year drought, compared to the 47.1 percent classified as poor in 2018 (see figure 15). Absent the effect of the 2018 drought, poverty would be 37.4 percent. Vulnerability climbs to 63.1 percent for a one in five year drought, 65.6 percent for a one in 10 year drought, and jumps to 98.6 percent for a one in 20 year drought. This last figure highlights how above a certain threshold of shock almost all households exhaust their coping capacities. Given that household must often resort to adverse coping strategies, such as selling assets or pulling children out of school, once a household falls into poverty it can be difficult for them to emerge from it. This highlights Afghans’ sensitivity to changing climatic conditions, which risk becoming more frequent and more severe over time.

> 34 By construct, a metric of vulnerability requires a probability threshold. While 50% illustrates the point where the expected consumption coincides with the poverty line (see i.e. Skoufias et al. (2021)), a lower threshold captures shock specific tail-end risks. For example, a 25% threshold illustrates the welfare effect of a one-in-four year moderate drought, a 10% threshold the welfare effects of a one in 10 year severe drought, and so forth.

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# **7 Policy Implications**

Our paper studies the effect of weather shocks in 2018 on consumption in Afghanistan, a country affected by conflict that is also witnessing extreme weather events driven by climate change. We combine a geo-coded cross-sectional household survey (IE-LFS 201920) with satellite-based precipitation, temperature, and vegetation measures. The 2018 drought measured by positive temperature anomalies and negative vegetation anomalies decreased consumption and increased poverty rates.

Working in a data scarce environment, we are able to deliver important insights on the relationship between weather shocks and welfare in a conflict affected country, showcasing techniques that can be used in similar context where only cross-sectional data is available. We find evidence that households decreased non-food consumption to save themselves from food insecurity. While both food and non-food consumption expenditures decreased with adverse vegetation anomalies, households were able to maintain their food consumption by reducing non-food monthly per capita consumption when faced with bad temperature anomalies.

In terms of resilience, households owning land could mitigate the adverse effects of droughts, whereas livestock ownership could not protect households against welfare losses due to severe droughts. This confirms that asset ownership may offer a means of insuring against weather shocks by allowing households to either sell or borrow against the asset; however, this coping mechanism is often imperfect (Dercon, 2002; Fafchamps et al., 1998). Given the ongoing conflict at the time in Afghanistan, we also find suggestive evidence that consumption was disproportionately lower in Taliban districts in 2018 that experienced bad vegetation anomalies.

In terms of vulnerability, while 47 percent of households were classified as poor in the survey, a simulation exercise based on historical weather data suggests that 62.5 percent of individuals have a greater than 1 in 4 chance of falling into poverty due to weather shocks. This is likely a lower bound, given that the methodology does not account for displaced households or adaptive strategies. It highlights the country’s heightened vulnerability to drought, which climate change will only exacerbate.

These results highlight the importance of policies to mitigate the impact of climate change on the most vulnerable in Afghanistan. A warming climate means more droughts, higher temperature, and increased risk of flash floods. These shocks risk exacerbating an already dire humanitarian situation. In 2021, the country faced one of the worst droughts in 19
decades.<sup>35</sup> With depleted government capacity and restrictions on access to humanitarian assistance, the risk is that a substantial proportion of households have fallen into poverty. In the short-term, humanitarian aid provided by the U.N. can help address severe food insecurity. It is crucial that this assistance be delivered in a timely manner to mitigate the impact.

However, such aid cannot be a permanent solution. The strong link between higher NDVI values and consumption suggests that expanding irrigation networks could help by allowing for more intensive agriculture. Further, while household assets, such as land, can help buffer against weather shocks, social safety nets play a key role at a community or country level. These have been shown to mitigate both the immediate and long-term effects of drought on household consumption (Knippenberg and Hoddinott, 2019).

Prior to the Taliban take-over, a number of efforts by development partners have sought to provide shock-responsive assistance through a combination of cash transfers and investments in public works. These efforts include the community-driven Citizen’s Charter program as well as investments in local irrigation systems. As the international community re-engages in Afghanistan, these efforts should be revived to build resilience against drought.

> 35https://www.csis.org/analysis/reshaping-us-aid-afghanistan-challenge-lasting-progress

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**Table 1:** Household sample descriptive statistics

||(1)<br>Mean|(2)<br>sd|
|---|---|---|
|Average age of household head (yrs)|42.11|14.16|
|Proportion of households with literate head|0.38|0.49|
|Proportion of households residing in rural areas|0.75|0.43|
|Household size|7.27|3.37|
|Proportion of households with female head|0.02|0.12|
|Distance to PHC (hrs)|1.69|0.95|
|Distance to hospital (hrs)|2.53|1.59|
|Proportion of households with school in community|0.35|0.48|
|Proportion of households using clean fuel|0.32|0.47|
|Proportion of households with electric connection|0.90|0.30|
|Proportion of interviews after COVID restrictions|0.51|0.50|
|Proportion of households with private water connection|0.19|0.39|
|Proportion of households owning land|0.44|0.50|
|Proportion of households owning livestock|0.54|0.50|
|Proportion of households in Taliban district|0.09|0.28|
|Proportion of households in Contested district|0.39|0.49|
|Proportion of households in govt controlled district|0.53|0.50|

_Note_ : This table reports summary statistics for household demographic characteristics from IE-LFS 2019-20.

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**Table 2:** Consumption Aggregates and Poverty Rate

||(1)<br>Mean|(2)<br>sd)|
|---|---|---|
|Per capita monthly consumption expenditure (in Afghani)|2614.22|1728.48|
|Components of per capita expenditures (in Afghani)|||
|Food|1473.40|982.47|
|Durables|111.81|196.34|
|Rent|315.11|438.00|
|Non-food|713.90|691.18|
|National poverty line (in Afghani)|2239.62|530.72|
|Urban food poverty rate|.48|.50|
|Rural food poverty rate|.52|.50|
|Food poverty rate|.52|.50|
|Urban poverty rate|.45|.50|
|Rural poverty rate|.48|.50|
|Poverty rate|.47|.50|

_Note_ : This table reports summary statistics for consumption aggregates and poverty rates from IE-LFS 2019-20.

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<!-- Start of picture text --> 3<br>2<br>2<br>Qo<br>Cc<br>oa<br>[a]<br>1<br>[2 2018<br>3 2019<br>0<br>0 2 4 6 8 10<br>Rainfall z-scores<br><!-- End of picture text --> <!-- Start of picture text --> 38<br>6<br>2<br>2<br>5 4<br>[a]<br>2<br>ME 2018<br>3 2019<br>0<br>-2 0 2 4<br>NDVI z-scores<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>4<br>2<br>Qo<br>Cc<br>oa<br>[a]<br>2<br>[Mm 2018<br>3 2019<br>0<br>4 -2 0 2 4<br>Temperature z-scores<br><!-- End of picture text -->
<!-- Start of picture text --> 1=<br>8<br>no<br>2<br>=<br>2<br>w 6<br>5<br>G<br>|<br>=<br>N<br>5 4<br>Ww<br>a<br>oO<br>Ww<br>2 | — year=2019<br>year=2018<br>0 - | Se === rejected ranges (5% FWER)<br>0 2 4 6 8 10<br>Z_nat_nat_chirps<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br>8<br>Ss<br>me]<br>5<br>a 6<br>is}<br>a<br>N<br>Oo 4<br>ww<br>a<br>oO<br>Ww<br>2<br>— year=2019<br>year=2018<br>0 ee === rejected ranges (5% FWER)<br>-2 0 2 4<br>Z_nat_nat_ndvi<br><!-- End of picture text --> <!-- Start of picture text --> 1—<br>8<br>&<br>a!<br>o<br>|= 6<br>3<br>NIcS<br>Ke)<br>u4<br>a<br>oO i<br>uw /<br>/<br>2 /<br>— year=2019<br>year=2018<br>0 _ ——~ : a === rejected ranges (5% FWER)<br>4 -2 0 2 4<br>z_nat_nat_Ist<br><!-- End of picture text -->
<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>4 3 -2 A 0 1 2 3 4<br>(a) Rainfall<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>1<br>5<br>0<br>4 3 2 A 0 1 2 3 4<br>(b) NDVI<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>1<br>5<br>0<br>4 3 2 A 0 1 2 3 4<br>(c) Temperature<br><!-- End of picture text -->
**Table 3:** Effect on consumption and poverty

||(1)<br>lg(cons)|(2)<br>lg(non-food)|(3)<br>lg(food)|(4)<br>Poor|(5)<br>Food Poor|
|---|---|---|---|---|---|
|NDVI Z-score (2018)|0.052<sup>_∗∗∗_</sup>|0.140<sup>_∗∗∗_</sup>|0.014|-0.058<sup>_∗∗∗_</sup>|-0.019|
||(0.014)|(0.020)|(0.018)|(0.013)|(0.014)|
|R-squared|0.438|0.586|0.294|0.208|0.183|
|Temperature Z-score (2018)|-0.050<sup>_∗∗_</sup>|-0.143<sup>_∗∗∗_</sup>|-0.001|0.051<sup>_∗∗∗_</sup>|-0.001|
||(0.020)|(0.030)|(0.026)|(0.019)|(0.018)|
|R-squared|0.423|0.450|0.284|0.198|0.179|
|Mean Dep var (transformed)|7.751|6.186|7.229|0.47|0.52|
|Mean Dep var|2571|694|1495|0.47|0.52|
|Observations|13930|13930|13930|13930|13930|
|HH and Shura Controls|✓|✓|✓|✓|✓|
|District FE|✓|✓|✓|✓|✓|

_Note_ : This table reports the coefficient on the z-score of the weather indicators in equation 3). For each weather indicator (NDVI and temperature), we run three separate regressions. Controls include (i) household head’s gender, age, age-squared, and indicator for whether the head is literate, (ii) indicator variables for rural household location, land ownership, and livelihood ownership, access to piped water, electricity and use of gas/electricity as cooking fuel, and (iii) indicators for the presence of a primary school in the community, distance to the nearest primary health care facility, and distance to the nearest hospital in the community. Dummies for quarter in which the interview was conducted are also included. District fixed effects are included. Robust standard errors reported in parenthesis are clustered at the cluster level. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

31
<!-- Start of picture text --> 8<br>7.9<br>Lo}ol 78<br>2<br>7.7<br>7.6<br>2 0 2 4<br>Z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.1<br>8<br>Lo}a 79<br>2<br>7.8<br>7.7<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.4<br>8.2<br>Lo}<br>a8<br>2<br>7.8<br>7.6<br>0 2 4 6 8 10<br>z_rainfall2018<br><!-- End of picture text -->
<!-- Start of picture text --> 5<br>4<br>3<br>S<br>fo}<br>a<br>2<br>A<br>0<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>4<br>S<br>fo}<br>a<br>2<br>0<br>-4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text -->
<!-- Start of picture text --> 7<br>68<br>rac<br>ol 66<br>2<br>6.4<br>6.2<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 74<br>73<br>7.2<br>“|<br>°<br>D<br>Ke)<br>7.4<br>7<br>69<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text -->
<!-- Start of picture text --> 68<br>67<br>rac<br>aD<br>2<br>66<br>65<br>4 2 0 2 4<br>z_temperature2018<br><!-- End of picture text --> <!-- Start of picture text --> 75<br>74<br>.<br>D 7.3<br>Ke)<br>7.2<br>74<br>4 2 0 2 4<br>z_temperature2018<br><!-- End of picture text -->
<!-- Start of picture text --> 8<br>78<br>|<br>8 76<br>7.4<br>— Contested districts<br>Govt districts<br>—— Taliban districts<br>7.2<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text -->

<!-- Start of picture text --> 8.5<br>8<br>/<br>|<br>D<br>2<br>/] 7 ~<br>— Contested districts<br>Govt districts<br>—— Taliban districts<br>7<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text -->
<!-- Start of picture text --> 8.1<br>8<br>7.9<br>d<br>a<br>D<br>2<br>78<br>77<br>— Landed<br>76 Landless<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.4<br>8.2<br>Lo}<br>oa 8<br>2<br>° LF — Landed<br>76 Landless<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text -->
<!-- Start of picture text --> 8 Lo<br>78<br>d<br>a<br>D<br>Ke)<br>76<br>— Own livestock<br>74 No livestock<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.4<br>8.2<br>d<br>a<br>D<br>2 8<br>78<br>NO — Own livestock<br>76 No livestock<br>4 -2 ty) 2 4<br>z_temperature2018<br><!-- End of picture text -->
<!-- Start of picture text --> 800 ae<br>600 - 4<br>> 7<br>re) hot<br>5 it<br>louSs 400 rthot<br>2 tt<br>ie ,<br>200 j<br>0 f - - 1 a a<br>4 6 8 10 12<br>predicted log per capita consumption<br>GE chronic poverty GH vulnerable to poverty [-)_ non-poor, non-vulnerable<br>Note: red vertical line is the poverty line(PL), orange line is 1.25*PL, and green line is 1.50*PL<br><!-- End of picture text -->
<!-- Start of picture text --> 78<br>7.75<br>77<br>ba<br>D<br>Ke}<br>7.65<br>76<br>7.55<br>6 8 10 12<br>months_rainfall2018<br><!-- End of picture text -->
**Table A.1:** Regressions to determine correlates of bad geocodes

||(1)<br>Coefcient (diference)|(2)<br>Constant|
|---|---|---|
|Rural|0.231<sup>_∗∗∗_</sup>|0.0314|
||(0.00577)|(0.00349)|
|Interviewed with paper survey|0.830<sup>_∗∗∗_</sup>|0.0476|
||(0.00925)|(0.00221)|
||0.38|0.49|
|Taliban areas|0.344<sup>_∗∗∗_</sup>|0.176<sup>_∗∗∗_</sup>|
||(0.0182)|(0.00356)|
|Contested areas|0.206<sup>_∗∗∗_</sup>|0.125<sup>_∗∗∗_</sup>|
||(0.00771)|(0.00397)|
|Household size|0.00245<sup>_∗∗_</sup>|0.187<sup>_∗∗∗_</sup>|
||(0.00104)|(0.00839)|
|Proportion of households with female head|-0.116<sup>_∗∗∗_</sup>|0.206<sup>_∗∗_</sup>|
||(0.0223)|(0.00373)|
|Proportion of households with literate head|-0.0317<sup>_∗∗∗_</sup>|0.213<sup>_∗∗∗_</sup>|
||(0.00746)|(0.00485)|
|Average age of household head|-0.00292<sup>_∗∗∗_</sup>|0.327<sup>_∗∗∗_</sup>|
||(0.000239)|(0.0113)|
|Distance to PHC (hrs)|-0.0144<sup>_∗∗∗_</sup>|0.229<sup>_∗∗∗_</sup>|
||(0.00325)|(0.00689)|
|Proportion of households with school in community|-0.116<sup>_∗∗∗_</sup>|0.245<sup>_∗∗∗_</sup>|
||(0.00713)|(0.00485)|
|Poverty rate|-0.0213<sup>_∗∗∗_</sup>|0.218<sup>_∗∗∗_</sup>|
||(0.00806)|(0.00551)|

_Note_ : Each row is a regression of a dummy for whether the household has a badly geo-coded location on the covariate in the row. We include survey weights and report robust standard errors.

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**Table A.2:** Effect of drought on welfare outcomes, Piecewise regression with 5 knots

||(1)<br>Total C|(2)<br>Non-Food C|(3)<br>Food C|(4)<br>Poor|(5)<br>Food Poor|
|---|---|---|---|---|---|
|NDVI Z-score 2018 (.,-2)|0.283|0.813<sup>_∗∗_</sup>|0.028|-0.141|-0.094|
||(0.197)|(0.370)|(0.176)|(0.172)|(0.185)|
|NDVI Z-score 2018 (-2,-1)|0.124<sup>_∗_</sup>|0.337<sup>_∗∗∗_</sup>|0.057|-0.029|0.03 7|
||(0.073)|(0.126)|(0.073)|(0.066)|(0.057)|
|NDVI Z-score 2018 (-1,0)|0.030|0.051|0.024|-0.061<sup>_∗∗_</sup>|-0.047|
||(0.035)|(0.053)|(0.041)|(0.030)|(0.029)|
|NDVI Z-score 2018 (0,1)|0.017|0.081|-0.042|-0.019|0.017|
||(0.038)|(0.060)|(0.040)|(0.032)|(0.032)|
|NDVI Z-score 2018 (1,2)|0.091|0.008|0.187<sup>_∗∗_</sup>|-0.097|-0.127<sup>_∗∗_</sup>|
||(0.074)|(0.107)|(0.073)|(0.063)|(0.063)|
|NDVI Z-score 2018 (2,.)|-0.065|-0.058|-0.074|0.053|0.162<sup>_∗_</sup>|
||(0.106)|(0.149)|(0.080)|(0.065)|(0.085)|
|Observations|14183|14183|14183|14183|14183|
|R-squared|0.340|0.463|0.240|0.146|0.149|
|Mean Dep var|7.751|6.648|7.229|0.414|0.455|
|Household Controls|✓|✓|✓|✓|✓>|
|Province FE|✓|✓|✓|✓|✓>|

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<!-- Start of picture text --> 8<br>7.9<br>|<br>B78<br>7.7<br>7.6<br>2 0 2 4<br>Z_ndvi2018<br><!-- End of picture text -->

<!-- Start of picture text --> 8.4<br>8.2<br>Lo}<br>a8<br>2<br>7.8<br>7.6<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.6<br>8.4<br>8.2<br>|<br>D<br>2<br>8<br>7.8<br>7.6<br>0 2 4 6 8 10<br>z_rainfall2018<br><!-- End of picture text -->
<!-- Start of picture text --> 8.2<br>8<br>|<br>oD<br>7.6<br>— Rural<br>74 Urban<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text -->

<!-- Start of picture text --> 8.4<br>8.2<br>Lo}<br>ao 8<br>2<br>7.8<br>— Rural<br>76 Urban<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text -->
# **B Constructing consumption aggregates and poverty rates**

To construct the consumption aggregates, we use the detailed module on household food consumption that collects information on (i) quantities and expenditures on a 7-day recall for most food items; (ii) expenditure on non-food items (electricity, water bills, etc.) on a monthly recall; (iii) imputed monthly rent and (iv) expenditure on miscellaneous items (such as school fee, clothes, etc.) on annual recall. The total consumption expenditure includes food and non-food items purchased from the market, produced at home (important component for farming households), food received as gifts, and the imputed value of rent and durables.

To capture the effect of seasonal variations, the data were collected over a 12-month span (October 2019 to September 2020) by randomly allocating sampled clusters to different months.<sup>a</sup> Temporal and spatial price deflators are used to adjust for price variations across regions and space to create comparable consumption measures that account for the different cost of living.<sup>b</sup> Additionally, to adjust for variations in household size and composition, consumption expenditure is divided by the officially used adult-equivalent scales, which are based on calorie requirements and vary by age and sex.

The poverty line is based on a cost of basic needs method to achieve the minimum daily calorie requirement of 2,100 kilocalories per adult in Afghanistan and adjusted upwards to include non-food consumption expenditures using the upper bound on the poverty line proposed in Ravallion (1998). The consumption expenditures per person per adult equivalent and poverty lines are estimated for each of the fourteen regions (urban and rural separately). The national poverty line, which is 2240 Afghani per adult equivalent, per month is used for construction of poverty rates. Afghanistan has fourteen poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

> aThe idea here was to assign clusters in the same month as was done in the previous rounds of the survey. A total of ten households were sampled from every cluster. bThe price data is used for four quarters and 14 geographical regions, rural and urban areas separately in South (all rural), Southwest, North, Northeast, West, West-Central (all rural), Central, and East regions.

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# **C District-level weather shocks and demographics**

Since we are using district fixed effects, we would ideally want demographic composition of the district to not vary significantly with weather anomalies. To check this, we standardize the district-level demographic variables: average age, literacy, and gender of household head, proportion of rural residents, presence of primary school, distance to hospital and primary health care unit, the proportion of households in the district with access to clean fuel, electricity, and water, and those interviewed after COVID-19 restrictions were enforced. Note that these are also out controls in equation 1. We also generate z-scores of weather anomalies at the district level as deviations from the growing season national long-run distribution. We estimate the following specification in a seemingly unrelated regression (SURE) model: where _z_ ( _characteristic_ ) is the standardized value of the district characteristic, _z_ ( _weather_ ) _dpg_ is the z-score capturing weather shock at the district level in growing season, _g_ (rainfall, NDVI, and temperature, separately). We also include province-fixed effects to account for any province-level differences in these variables. In Columns 1 and 2 of Tables C.1, C.2 and C.3 report coefficients and p-values from the SURE model.

There is no significant correlation between district demography and NDVI anomalies. Moreover, temperature z-scores and rainfall z-scores are not significantly related with a majority demographic characteristics, except higher temperature anomalies are correlated with less private water and electricity connection in the district, and younger household heads, and higher rainfall z-scores are in more rural areas in the sample, and increased distance to primary health clinics (however, coefficients are small). Bonferroni-adjusted p-values that correct for multiple hypothesis testing align with the results from SURE model presented here.

46
**Table C.1:** SURE Model: District-level characteristics and Rainfall z-scores

|Standardized outcome|(1)<br>Coefcient|(2)<br>SURE|
|---|---|---|
|variable at district level|_βdp_|p-value|
|HH head Age|.08|.46|
|HH head literate|-.23|.04|
|Rural|-.20|.07|
|HH head female|-.36|.00|
|HH size|.99|.00|
|Covid restrictions|.01|.95|
|Private water connection|-.04|.71|
|Electricity connection|.12|.27|
|Use clean fuel|-.12|.28|
|Distance to hospital|-.07|.51|
|Distance to PHC|.26|.02|
|School in community|.01|.89|

_Note_ : This table reports the coefficient from equation 5) from district-level regression of standardized district demographic variables on rainfall anomalies at the district-level. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

47
**Table C.2:** SURE Model: District-level characteristics and NDVI z-scores

|Standardized outcome|(1)<br>Coefcient|(2)<br>SURE|
|---|---|---|
|variable at district level|_βdp_|p-value|
|HH head Age|.01|.46|
|HH head literate|.18|.24|
|Rural|.13|.30|
|HH head female|-.16|.27|
|HH size|.21|.18|
|Covid restrictions|.07|.59|
|Private water connection|.14|.29|
|Electricity connection|.22|.08|
|Use clean fuel|-.02|.90|
|Distance to hospital|-.00|.99|
|Distance to PHC|-.17|.24|
|School in community|.02|.89|

_Note_ : This table reports the coefficient from equation 5) from district-level regression of standardized district-level demographic variables on district-level NDVI z-scores.

48
**Table C.3:** SURE Model: District-level characteristics and Temperature z-scores

|Standardized outcome|(1)<br>Coefcient|(2)<br>SURE|
|---|---|---|
|variable at district level|_βdp_|p-value|
|HH head Age|-.61|0|
|HH head literate|-.10|.39|
|Rural|-.25|.04|
|HH head female|.00|.98|
|HH size|-.19|.87|
|Covid restrictions|-.01|.46|
|Private water connection|-.49|0|
|Electricity connection|.49|0|
|Use clean fuel|-.06|.61|
|Distance to hospital|-.07|.51|
|Distance to PHC|-.04|.73|
|School in community|-.16|.19|

_Note_ : This table reports the coefficient from equation 5) from district level regression of standardized district demographic variables on temperature anomalies at the district level.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

49