metadata: authors: 0: author: Zaveri,Esha Dilip
1: author: Damania,Richard
2: author: Engle,Nathan Lee
count: World
docna: 0: docna: Droughts and Deficits: The Global Impact of
            Droughts on Economic Growth
docty: Policy Research Working Paper
abstracts: cdata!: As climate change intensifies, dry
            rainfall shocks and droughts are a growing concern. At the
            same time, scientific evidence suggests that the world has
            surpassed the safe planetary boundary for green water, which
            is water stored in biomass and soil that is crucial for
            maintaining climate resilience. Yet, evidence at the global
            scale of these combined forces on economic growth is poorly
            understood. This paper attempts to fill this gap by using
            data on annual subnational gross domestic product for 82
            countries from 1990&#x2013;2014. Using rainfall shocks
            as plausibly exogenous variations in a spatially specific
            panel at the grid level, the analysis finds that the global
            effects of droughts on economic activity are substantial.
            Moderate to extreme droughts reduce gross domestic product
            per capita growth between 0.39 and 0.85 percentage point, on
            average, depending on the level of development and baseline
            climatic conditions, with low- and middle-income countries
            in arid areas sustaining the highest relative losses. In
            high-income countries, moderate droughts have no impact, and
            only extreme droughts have adverse effects, reducing growth
            by about 0.3 percentage point, a little less than half the
            impact felt in the low- and middle-income country sample for
            the same intensity of drought. Crucially, the impact of a
            dry shock of a given magnitude also depends on antecedent
            green water availability. The results show that increases in
            soil moisture in previous years can neutralize the harmful
            impacts from a dry shock, with suggestive evidence that
            local and upstream forest cover are key channels through
            which these impacts manifest. These findings have important
            implications for measuring the economic impact of droughts
            and can inform adaptation investments.
display_title: Droughts and Deficits: The Global
            Impact of Droughts on Economic Growth
listing_relative_url: /research/2023/05/34067050/droughts-deficits-global-impact-droughts-economic-growth
topicv3: Water Security and Integrated Resource
            Management m1326089 141,Economic Growth m1326241 471,Water m1326522 137,Climate Change m1326829 567,Climate Resilient Development m1327078 572
publishtoextweb_dt: 2023-05-23T00:00:00Z
dois: 10.1596/1813-9450-10453
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1498461
1: author_key: 762027
2: author_key: 995605
model_extractions: Policy Research Working Paper 10453 Droughts and Deficits The Global Impact of Droughts on Economic Growth _Esha D. Zaveri Richard Damania Nathan Engle_ Water Global Practice May 2023
Policy Research Working Paper 10453

# **Abstract**

As climate change intensifies, dry rainfall shocks and - droughts are a growing concern. At the same time, scien tific evidence suggests that the world has surpassed the safe planetary boundary for green water, which is water stored in biomass and soil that is crucial for maintaining climate resilience. Yet, evidence at the global scale of these combined forces on economic growth is poorly understood. This paper attempts to fill this gap by using data on annual subnational gross domestic product for 82 countries from 1990–2014. Using rainfall shocks as plausibly exogenous variations in a spatially specific panel at the grid level, the analysis finds that the global effects of droughts on economic activity are substantial. Moderate to extreme droughts reduce gross domestic product per capita growth between 0.39 and 0.85 percentage point, on average, depending on the level of development and baseline climatic conditions, with low- and middle-income countries in arid areas sustaining the highest relative losses. In high-income countries, moderate droughts have no impact, and only extreme droughts have adverse effects, reducing growth by about 0.3 percentage point, a little less than half the impact felt in the low- and middle-income country sample for the same intensity of drought. Crucially, the impact of a dry shock of a given magnitude also depends on antecedent green water availability. The results show that increases in soil moisture in previous years can neutralize the harmful impacts from a dry shock, with suggestive evidence that local and upstream forest cover are key channels through which these impacts manifest. These findings have important implications for measuring the economic impact of droughts and can inform adaptation investments.

This paper is a product of the Water 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 ezaveri@worldbank.org; rdamania@worldbank.org; and nengle@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 Deficits: The Global Impact of Droughts on Economic Growth Esha D. Zaveri<sup>1</sup> , Richard Damania<sup>2</sup> , Nathan Engle<sup>1</sup>

**Keywords:** rainfall, droughts, GDP growth, soil moisture, land use

**JEL Codes:** O13, O44, Q5, Q25, Q24

> 1 Water Global Practice, The World Bank, Washington, D.C., 2Office of the Chief Economist for Sustainable Development, The World Bank, Washington, D.C.
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the stability and resilience of the Earth's biosphere (Rockstrom et al 2009). Recently, a new boundary – termed green water - has been introduced (Wang-Eraldson et al 2022). Green water represents the moisture that is stored in the root zone of soil and used by plants. It is essential for maintaining the productivity and health of terrestrial ecosystems, and it also plays a crucial role in regulating the Earth's climate. Climate change, deforestation, and water management practices can all impact the availability of green water. This paper contributes to the literature by empirically demonstrating the role of green water in promoting resilience to droughts and quantifying the resulting net economic benefits. The results are relevant to the large literature on adaptation to rainfall shocks and climate change. Most of the literature highlights the need for climate resilient infrastructure to reduce the adverse effects of floods and droughts (Adger et al., 2005, Kirschke et al, 2021). Others emphasize the importance of economic diversification, drought resistant crop management techniques, early warning systems and insurance schemes to address negative impacts on affected communities (Dasgupta et al., 2014, Lobell et al., 2015). We add further insights to this literature by empirically demonstrating how resilience to dry shocks is impacted by natural conditions such as green water which is affected by upstream forest cover and antecedent conditions.

Confirming previous work, this paper demonstrates that rainfall deficits have material impacts on GDP per capita growth rates. However, it provides new results to show that there is considerable heterogeneity across countries and over time. The paper also demonstrates that green water – a term used to describe the portion of precipitation that infiltrates into the root-zone of soil – plays a crucial role in determining the economic impact of a dry shock of a given magnitude. It is known that green water – soil moisture levels – can be influenced by the amount of precipitation that has occurred over an extended period, rather than just the most recent rainfall event (Niu et al., 2011, Pen Arabica et al., 2016). Accordingly, the results show that recent and past climate history are key factors in determining the magnitude of impacts (all else equal). When recent years are drier than normal, the impacts of dry shocks on economic growth are found to be considerably stronger. Conversely, if the recent past has been exceptionally wet, a one-off dry rainfall shock has a more muted impact on economic growth.

Forests also play an important role in regulating the water cycle and when managed appropriately can preserve green water and maintain soil moisture levels in watersheds (Piao et al. 2015, Sivaplan et al 2003, Grosset, Papp and Taylor 2023). Impacts of dry shocks are also found to be systematically more severe in regions where there has been local and upstream deforestation, highlighting the importance of halting forest loss to support green water functioning and climate resilience. These findings, perhaps the first empirical estimates, highlight the importance of “green water” (soil moisture) in shielding economic growth from adverse rainfall shocks. Negative growth effects are also found to be heavily concentrated in developing countries in arid and semi-arid regions. The disproportionate impact on poorer countries is a consequence of the often harsher climatic conditions and the limited resources to manage the consequences of dry shocks and droughts.

The rest of the paper is organized as follows. Section 2 describes the datasets used in this analysis. Section 3 presents the empirical strategy. Section 4 presents the main results, an analysis of heterogeneity of impacts and potential mechanisms, and robustness checks. Finally, Section 5 concludes with a discussion of the results and policy implications.
# 2. Data

We use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by the University of Delaware (Willmott and Matsuura 2001) that has widely been used in the economics literature (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015, among others). This data set provides monthly total precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each month between 1901 and 2014. Annual precipitation at the cell level between 1990 and 2014 ranged between 3mL in Sudan to 10,187mL in India.

To define when a grid cell experienced a shock, the z-score of rainfall is calculated in each grid cell, and in rainit−raini each time period as per zit = , where zit is the z-score of rainfall in grid cell _i_ at time _t_ , rainit is σi rainfall in grid cell _i_ at time _t_ , raini is mean rainfall in grid cell _i_ over the full time period of the climate dataset, and σi is the standard deviation of rainfall in grid cell _i_ over the same time period. The z-score for rainfall is interpreted as the number of SDs away from the long-run mean in time _t_ . Once z-scores are calculated, binary variables are generated to indicate whether the z-score passes certain thresholds. A grid cell is considered to have a _dry shock_ if rainfall in a given year is lower than the long-run annual mean for the grid cell by at least X SD ( z≤X), where X can be any range of integer values. Similarly, a grid cell is considered to have a _wet shock_ if rainfall in a given year is higher than the long-run annual mean for the grid cell by at least X SD ( z≥X). Doing this allows for a semiparametric test of the impacts of deviations from the long-run mean. It also allows for impacts to be both nonlinear and nonsymmetric around zero. These properties are important for distinguishing the possible heterogeneous impacts of deluges versus droughts.

Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of global total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).

To give an indication of how wealth and economic composition impact the relationship between droughts and economic growth, we use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries. Classifications are based on mean per-capita GNI in 2015 where low-income countries have GNI per capita below $1,025, middle-income countries are between $4,036 and $12,475, and high-income countries are above $12,475. We also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity. Additionally, local and upstream shares of forest cover are measured using satellite data from the European Space Agency.

# 3. Empirical Strategy

Our econometric specification uses a panel fixed-effects model to link data on droughts to data on economic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the equator) between 1991 and 2014, the period for which economic data is available at a granular scale.

In order to estimate the impact of droughts on economic growth, we follow much of the empirical climate change literature and estimate a reduced-form production function-style equation (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015; Deryugina and Hsiang, 2017; Newell et al., 2018; Kahn et al., 2019;
Tol, 2019,; Diffenbaugh and Burke, 2019; Henseler and Schumacher, 2019; Burke and Tanutama, 2019; Damania, Desbureaux and Zaveri, 2020; Kotz, Levermann, and Wenz, 2022; Callahan and Mankin, 2022; Felbermayr et al., 2022). For each 0.5-degree grid cell and year, we calculate the annual growth rate of the cell’s GDP per capita adjusted for inflation ( _g_ ) and estimate equation (1) to determine if _g_ is impacted by different levels of drought severity.

Cells are indexed by _i_ and years by _t_ . Thus, the unit of observation is the grid cell-year. The relationship between growth and droughts, f(Dit) , could take many forms.

Following best practices, the analysis models the effect of shocks on growth in GDP per capita, instead of levels of GDP per capita, because GDP levels exhibit high serial correlation that are indistinguishable from a random walk and have a unit root (Burke, Hsiang and Miguel, 2015). Unit root processes are non-stationary and as a result regressions using unit root outcomes often generate spurious results and traditional test statistics fail. Accounting for country-specific trends in levels does not alleviate this concern. To avoid the statistical pitfalls, the exercise is performed in terms of first differences, i.e., using growth as an outcome. This also has important dynamic implications for the persistence of impacts. If rainfall shocks were to affect the level of GDP, it suggests a temporary impact that disappears the following year. For example, this may occur if agricultural yields collapse one year and recover the next year. But if there were an impact on the growth of GDP, this implies persistence because growth effects compound over time. Regressions in appendix B show that growth impacts persist for three years after a dry shock, after which there is a reversion to the previous growth rate.

To measure droughts, we use rainfall variability measured in terms of local deviations from the long-run mean, which is plausibly exogenous since it reflects random draws from a climate distribution (Dell, Jones, and Olken 2014). Much like the standard precipitation index, we define rainfall variability by using z-scores, and classify observations into bins based on the value of the z-score. In robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation index. It should be noted that this index is very sensitive to the chosen definition of evapotranspiration (Vicente-Serrano and National Center for Atmospheric Research Staff 2015), hence we do not rely exclusively on this index. The main conclusions are found to be robust to the use of this alternative measure. We estimate the following model: where binit indicates the distribution of drought severity where k denotes the threshold of the z-score and b denotes the number of bins. In the first specification we use k=1 and b=3. In this instance, a grid cell is considered to have a _dry shock,_ if rainfall in a given year is lower than the long-run annual mean for the grid cell by at least 1 standard deviation ( z≤1) . Similarly, a grid cell is considered to have a _wet shock_ if rainfall in a given year is higher than the long-run annual mean for the grid cell by at least 1 standard deviation ( z≥1) . Accounting for such shocks allows for a semiparametric test of the impacts of deviations from the long-run mean. It also allows for impacts to be both nonlinear, and nonsymmetric around zero. These properties are important for distinguishing the possible heterogeneous impacts of deluges versus droughts.
With 3 bins, coefficients α2 and α3 measure the impact of these dry and wet conditions relative to the near normal condition (the excluded category is −1 < z< 1 ).

git = α1 + α2Zit−1 + α3Zit+1 + βXit + γi + φt + μc×t + εi,t (3) We estimate several such 3-bin models, using different thresholds for k= 1.6, 1.3, 0.8 and 0.5 to check for sensitivity of the results to different thresholds. Next, we estimate models with different number of bins and corresponding thresholds together such that the estimated coefficients demonstrate how drought responses may change across the entire distribution of drought severity.

Following standard practices in the literature that use statistical analysis, we employ a fixed effects panel regression because of the wide variety of latent and non-latent factors that can impact economic growth. A very large and long-standing economics literature exists on the determinants of economic growth and development (see, for instance, Ciccone and Jarocinsky 2010; Acemoglu, Johnson, and Robinson 2005; Barro 2003). In this paper, we are not attempting to explore these factors. Rather, we wish to isolate the impact of one such potential impediment to growth—drought. In order to do so, we control for factors that may simultaneously impact both economic growth and droughts.

This is done through controlling for a strong set of fixed effects and time trends, including: grid cell fixed effects ( γi ), which control for time invariant, local characteristics that may impact economic growth; year fixed effects ( φt ), which controls for changes in global patterns of economic growth; country-specific time trends ( μc×t ) which accounts for country specific trends in both droughts and economic growth. Since weather variables tend to be correlated over time in the same location, we also include a vector Xit that accounts for a quadratic in temperature to account for the well-established response of the overall economy to temperature increases. The rich set of fixed effects and controls in this specification isolates localized and unexpected fluctuations in shocks, facilitating causal inference, and is necessary to isolate the impact of exogenous changes in droughts on economic growth. Similar approaches have been used in previous climate and weather impact studies (Burke and Tanutama, 2019; Callahan and Mankin, 2022; Damania, Desbureaux, and Zaveri, 2020).

Standard errors are clustered at the Administrative 1 level (the largest subnational level in a country, e.g., a state in the US) in order to account for spatial and serial correlation. We test the robustness of the results through additional tests for spatial autocorrelation to account for spatial and temporal dependence. Following other literature, we weight observations based on grid cell population. Because observations corresponding to grids with higher population have smaller variance, population weighting in the grid cell regressions accounts for heteroscedasticity and yields more efficient estimates (Damania, Desbureaux, and Zaveri 2020; Solon et al. 2015).

Unlike studies which estimate economy-wide costs for specific drought events ex-post, our econometric approach yields estimates of economic impact for marginal increases in the occurrence and intensity of droughts. This information can be particularly useful for policy makers who need to allocate scarce financial resources for drought assistance and emergency programs.

Droughts covary with other variables that may drive heterogeneity in the effect of drought such as recent and past climate history, as well as income. Therefore, we also consider models that allow heterogeneity in the response to droughts, by soil moisture conditions, aridity and income characteristics of the grid cell. Equation (1) is modified to allow interaction between the drought variables and different measures of
heterogeneity ( Hit ). This will allow us to ascertain the sensitivity of drought impacts across recent wet and dry periods, arid versus humid climatic zones and income classes.

gi,t = f(Dit) × Hit + βXit + γi + φt + μc×t + εi,t (4) To estimate how antecedent wet or dry conditions affect the relationship, the analysis calculates how much rainfall has deviated from long-run averages in the three years prior to the shock by summing z-scores over the three-year period. If there were significant rainfall deficits, these deficits would stack, and the cumulative z-score calculation would record a very deep three-year water deficit. This cumulative z-score is interacted with the main dry shock variables in the regression to assess whether wetter or drier conditions prior to a dry shock determines its impact on growth. For example, a higher and positive cumulative z-score captures rainfall increases in previous years that can raise green water or soil moisture in the root zone of crops.

To investigate the role of forests and landscapes in influencing conditions through which green water is maintained, past local and upstream share of forest cover is measured using satellite data from the European Space Agency. High forested areas denote places where the forest area is more than the 50th percentile of the forest distribution among all grid cells in each country. The interaction of high forested areas with the main dry shock variables in the regression allows us to assess whether higher local and upstream forest cover can help buffer the growth impacts of dry shocks.

Geography can also matter and different regions with different climatological baseline climates may respond differently to droughts. This also allows us to test for heterogenous responses to droughts. On the one hand, if regions where dry shocks are common are able to adapt to drier conditions, droughts should be less harmful to economic growth. In other words, economic growth would be less sensitive to dry shocks in arid areas. Conversely, recurring dry shocks may have cumulative impacts that weaken adaptive capacity. We test for such effects using a binary indicator for arid and semi- arid areas as the interaction term. Likewise, poorer countries may also face higher marginal damages due to the limited ability of exposed populations to manage the consequences of droughts. We test this hypothesis by splitting the sample by country income level to give an indication of how wealth and economic composition impact this relationship. Our analytical strategy therefore incorporates both an absolute metric of baseline climate, as well as the relative ability of different populations in different regions to manage the risks of droughts.

# 4. Results

This section is split into 4 subsections. Section 4.1 presents the main results of 1 SD dry shocks from estimating equation (3) and the various heterogeneities from estimating equation (4). Section 4.2 presents the impacts of dry shocks along the distribution of drought severity. To provide a sense of magnitudes of loss over the entire time-period, Section 4.3 uses the estimates to calculate the cumulative mean annual losses in GDP per capita growth in each grid cell. Section 4.4. presents robustness checks.

## 4.1 Impact of 1 SD rainfall shocks

Table 1 shows the main results. Columns (1) and (2) provide estimates of equation (1) for different specifications for rainfall. Column 1 includes a quadratic in precipitation and column 2 models rainfall as a z- score. In both models the coefficient is positive and significant showing that generally higher precipitation is associated with increases in growth.
Column (3) estimates equation (3) and uses three categories of bins comparing 1 SD dry and wet shocks relative to the excluded category ( −1 < z< 1) . Results in column (3) show that a 1 SD dry shock reduces GDP growth by 0.47 percentage points, with effects concentrated in the developing world where the impacts are slightly larger than in the full sample at 0.54 percentage points. In the high-income country sample, the impact of a 1 SD dry shock is small and statistically insignificant, indicating a precisely estimated null impact.

Positive 1 SD shocks show a less consistent pattern across income classes. They improve growth in the developing country sample but can be harmful in high-income countries. This may be because aboveaverage rainfall could both be a blessing, in the form of increased water resources, or a curse, in the form of flooding. Because most developing countries are in drier parts of the world and are more dependent on agriculture, above-average rainfall is typically associated with increases in crop yields and may act as a buffer for future dry shocks by storing soil moisture in the root zone. These results are consistent with the hydrological literature which emphasizes the importance of green water for supporting biomass growth (Li et al, 2019). In high-income countries, the negative effects of wet shocks reflect the effects of floods on infrastructure and urban assets. Recent research has found that in high-income countries extreme wet events, such as extreme daily rainfall or the number of wet days, are particularly adverse for economic growth via the manufacturing and services sectors (Kotz, Levermann, and Wenz, 2022). Improvements in disaster risk management and relief also mean that recovery from floods is rapid in developed economies, with limited observable effect on economic activity (Kocornik-Mina et al. 2020).

**Table 1 Impact of rainfall on GDP per capita growth**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. Var.<br>Δlog(GDPp.c.)|Global|High Income|Developing|Global|High Income|Developing|
|Precip|1.5734***|-0.2635|1.9399***||||
||(0.334)|(0.494)|(0.381)||||
|Precip sq|-0.2506***|-0.0869|-0.3000***||||
||(0.069)|(0.118)|(0.081)||||
|Dry shock (z<-1)||||-0.4761***|-0.1103|-0.5429***|
|||||(0.107)|(0.100)|(0.125)|
|Wet shock (z>1)||||0.0981|-0.4115+|0.2721*|
|||||(0.121)|(0.226)|(0.137)|
|Avg Temp(C)|0.6928**|0.2420*|0.7683*|0.6992**|0.2458*|0.7802*|
||(0.225)|(0.107)|(0.338)|(0.225)|(0.108)|(0.340)|
|Avg Temp Sq.|-0.0211**|-0.0129***|-0.0238**|-0.0220***|-0.0130***|-0.0250**|
||(0.006)|(0.004)|(0.008)|(0.006)|(0.004)|(0.008)|
|N|869447|201466|667981|869447|201466|667981|
|Cell FE|y|y|y|y|Y|Y|
|Year FE|y|y|y|y|Y|Y|
|Country Trends|y|y|y|y|Y|Y|
|R-sq|0.319|0.392|0.305|0.319|0.393|0.305|

_Note: Dependent variable is change in gridcell log(GDP p.c.). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._ Is the relationship between dry shocks and GDP in the developing world explained by agriculture, the sector that is more directly affected by rainfall variability? We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent. Having
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coefficients of dry shocks monotonically increase in magnitude as the value of the threshold rises. Depending on the threshold used, GDP growth decreases between 0.38 to 0.72 percentage points, on average, indicating that more severe dry shocks have more damaging consequences. Wet shocks remain beneficial to growth, but the impacts are more muted, and are not always significant.

## **Table 2 Impact of drought severity on GDP per capita growth, 2 bins with various thresholds, Low- and Middle-Income Countries Sample**

|Dep. Var.<br>Δlog(GDPp.c.)|(1)|(2)|(3)|(7)|(8)|
|---|---|---|---|---|---|
|Threshold X:|1.6|1.3|1|0.8|0.5|
|||Impacts are|relative to categ|ory (-X<z<X)||
|Extreme(z<-1.6)|-0.7204***<br>(0.188)|||||
|Extreme(z>1.6)|0.1554<br>(0.197)|||||
|Dry (z<-1.3)||-0.6196***<br>(0.151)||||
|Wet(z>1.3)||0.2194<br>(0.159)||||
|Dry(z<-1)|||-0.5429***<br>(0.125)|||
|Wet(z>1)|||0.2721*<br>(0.137)|||
|Dry(z<-0.8)||||-0.5060***<br>(0.109)||
|Wet(z>0.8)||||0.2531*<br>(0.128)||
|Dry(z<-0.5)|||||-0.3864***<br>(0.099)|
|Wet(z>0.5)|||||0.1295<br>(0.115)|
|Avg Temp(C)|0.7697*|0.7742*|0.7802*|0.7818*|0.7770*|
||(0.340)|(0.339)|(0.340)|(0.340)|(0.340)|
|Avg Temp Sq.|-0.0263**<br>(0.009)|-0.0257**<br>(0.008)|-0.0250**<br>(0.008)|-0.0247**<br>(0.008)|-0.0250**<br>(0.008)|
|N|667981|667981|667981|667981|667981|
|Cell FE|y|Y|y|y|Y|
|Year FE|y|y|y|y|Y|
|Country Trends|y|y|y|y|Y|
|R-sq|0.304|0.304|0.305|0.305|0.304|

_Note: Dependent variable is change in gridcell log(GDP p.c.). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._

# 4.2 Impacts along the distribution of drought severity

What is clear from the previous section is that rainfall deficits that are 1 standard deviation below normal levels have adverse impacts on GDP per capita growth rates, especially in the developing country sample. To investigate how the impacts can vary based on different sizes of shocks, this section unpacks the baseline results to assess impacts across the full distribution of shocks that range from mild to extreme. To assess how the impacts differ across the entire distribution of drought severity, Table 3 assesses equation
(2) with different number of bins for both the developing and high-income sample. Columns (1) and (2) estimate models with _b_ =11, using all of the bins defined in Mu et al. (2013).

**Table 3 Impact of drought severity on GDP growth, various bins**

|Dep. Var. Δlog(GDP p.c.)|(1)|(2)|(3)<br>(4)|
|---|---|---|---|
|# bins|10|bins|5bins|
||impacts relative|to (-0.5<z<0.5)|impacts relative to (-0.5<z<0.5) and all wet<br>categories|
||High-income|Developing|High-income<br>Developing|
|**Dry shocks**||||
|Extreme(z<-1.6)|-0.3946*|-0.8595***|-0.3576*<br>-0.9069***|
||(0.194)|(0.207)|(0.169)<br>(0.202)|
|Severe(-1.6<z<-1.3)|0.1911|-0.5760**|0.2279<br>-0.6253***|
||(0.159)|(0.176)|(0.139)<br>(0.170)|
|High(-1.3<z<-1)|0.0037|-0.4691**|0.0419<br>-0.5187***|
||(0.141)|(0.153)|(0.128)<br>(0.147)|
|Moderate(-1<z<-0.8)|-0.0161|-0.3920**|0.0240<br>-0.4413***|
||(0.107)|(0.137)|(0.127)<br>(0.130)|
|Mild(-0.8<z<-0.5)|0.0465|-0.1106|0.0811<br>-0.1600|
||(0.140)|(0.128)|(0.096)<br>(0.140)|
|**Wet shocks**||||
|Mild(0.5<z<0.8)|0.4168+|-0.0731||
||(0.215)|(0.175)||
|Moderate(0.8<z<1)|0.0399|0.1972||
||(0.178)|(0.127)||
|High(1<z<1.3)|-0.1549|0.3282*||
||(0.147)|(0.158)||
|Severe(1.3<z<1.6)|-0.4733|0.2870||
||(0.391)|(0.194)||
|Extreme(z>1.6)|-0.4320|0.1184||
||(0.276)|(0.215)||
|Avg Temp(C)|0.2476*|0.7826*|0.2419*<br>0.7729*|
||(0.111)|(0.339)|(0.106)<br>(0.339)|
|Avg Temp Sq.|-0.0130***|-0.0245**|-0.0124***<br>-0.0246**|
||(0.004)|(0.008)|(0.004)<br>(0.008)|
|N|201466|667981|201466<br>667981|
|Cell FE|y|y|y<br>Y|
|Year FE|y|y|y<br>Y|
|Country Trends|y|y|y<br>Y|
|R-sq|0.395|0.305|0.392<br>0.305|

_Note: Dependent variable is change in gridcell log(GDP p.c). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._ Figure 5 demonstrates the coefficients in columns (1) and (2) of Table 3 graphically. In the developing sample, the estimated coefficients on the dry side of z-score are negative and statistically significant for conditions of moderate (−1 < z< −0.8 ) to extreme drought ( z< −1.6 ) compared to normal conditions. They are negative, but insignificant, for all of the other “non-normal” dry categories.

The magnitude of impacts appears to grow with drought severity. This is true for all of the coefficients moving away from normal on the dry side of z-score, though only the moderate, high, severe and extreme
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**Table 4 Robustness checks, alternative specifications**

|Dep. Var. Δlog(GDP<br>p.c)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||base|No oil<br>countries|No<br>US/China|Continent<br>Yr FE|Continent<br>Yr FE + no<br>Trend|Country<br>Trend - No<br>Yr FE|LDV 1 Lag|LDV 3 Lags|
|Dry(z<-1)|-0.5429***|-0.5092***|-0.5258***|-0.5293***|-0.5643***|-0.7250***|-0.4943***|-0.3112*|
||(0.125)|(0.128)|(0.146)|(0.114)|(0.120)|(0.130)|(0.129)|(0.138)|
|Wet(z>1)|0.2721*|0.2420+|0.4093*|0.2492+|0.1873|0.2446+|0.3214*|0.2903*|
||(0.137)|(0.139)|(0.169)|(0.134)|(0.132)|(0.148)|(0.137)|(0.136)|
|N|667981|532121|589933|667981|667981|667981|640152|584494|
|R-sq|0.305|0.319|0.207|0.368|0.342|0.269|0.306|0.274|

Notes: This table presents robustness checks for our main result presented in Table 1, column 6. Robustness checks are: Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Columns 7 and 8: as in (1) but including one lag and three lags of growth as in a Arellano Bond approach. Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1

**Table 5 Robustness checks, alternative specifications using GDP growth rate**

|Dep. Var. Δlog(GDP)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||base|No oil<br>countries|No<br>US/China|Continent<br>Yr FE|Continent<br>Yr FE + no<br>Trend|Country<br>Trend - No<br>Yr FE|LDV 1 Lag|LDV 3 Lags|
|Dry(z<-1)|-0.5895***|-0.5590***|-0.5717***|-0.5759***|-0.6043***|-0.7692***|-0.5500***|-0.3512*|
||(0.127)|(0.131)|(0.150)|(0.118)|(0.125)|(0.131)|(0.132)|(0.137)|
|Wet(z>1)|0.2876*|0.2512+|0.4312*|0.2660*|0.2051|0.2548+|0.3395*|0.3045*|
||(0.137)|(0.138)|(0.168)|(0.133)|(0.132)|(0.147)|(0.138)|(0.135)|
|N|667879|532039|589831|667879|667879|667879|640052|584394|
|R-sq|0.290|0.296|0.224|0.355|0.327|0.254|0.291|0.256|

Notes: This table presents robustness checks for our main result presented in Table 1, column 6 but replaces the GDP pc growth rate with the GDP growth rate. Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Columns 7 and 8: as in (1) but including one lag and three lags of growth as in a Arellano Bond approach. Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1

**Table 6 Robustness checks, alternative specifications using SPEI**

|Dep. Var. Δlog(GDP<br>p.c)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||base|No oil<br>countries|No<br>US/China|Continent<br>Yr FE|Continent<br>Yr FE + no<br>Trend|Country<br>Trend - No<br>Yr FE|LDV 1 Lag|LDV 3 Lags|
|Dry(spei<-1)|-0.3279*|-0.3251*|-0.3848*|-0.3253*|-0.3751**|-0.4268*|-0.3480*|-0.3818**|
||(0.150)|(0.157)|(0.179)|(0.136)|(0.124)|(0.171)|(0.152)|(0.135)|
|Wet(spei>1)|0.0888|-0.0575|0.2805+|0.1174|0.1315|0.1182|0.1268|0.2078|
||(0.125)|(0.125)|(0.147)|(0.127)|(0.125)|(0.144)|(0.122)|(0.134)|
|N|666383|530645|588383|666383|666383|666383|638619|583091|
|R-sq|0.302|0.316|0.203|0.365|0.341|0.266|0.302|0.271|

Notes: This table presents robustness checks for our main result presented in Table 1, column 6 but replaces rainfall shocks with SPEI shocks. Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Columns 7 and 8: as in (1) but including one lag and three lags of growth as in a Arellano Bond approach. Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
The main specification allows for arbitrary serial correlation of errors across observations from the same Level 1 administrative level or the same country. This approach assumes no correlation in the error structure for grid cells in different areas, even though they may be proximate in space. In Appendix Table 2, we relax this assumption and account for spatial autocorrelation by using spatially corrected Conley standard errors (Conley, 1999). Due to computation limits, we focus attention on the grid cells in the South Asia region (~1,700 cells) among the ~60,000 available for cell-level regression. We implement the procedure using the acreg package developed by Colella et al. (2019). The results continue to remain significant at the 5 percent level.

# 5. Policy Implications and Way Forward

While the evidence points to water scarcity as a growing problem, it is floods that have captured the attention of policy makers and researchers. This may be a consequence of the greater visibility of floods, which are rapid, high-impact events that destroy infrastructure, damage homes, and disrupt livelihoods. They are difficult to ignore. Consequently, a flood typically triggers a rapid policy response through the array of relief systems available within countries, with the backstop support of international relief agencies (Botzen et al., 2019). Perhaps as a result, recent research suggests that even when the short-term impacts of a flood are severe and alarming, economic recovery can come rapidly too (Kocornik-Mina et al., 2020; Felbermayr et al., 2022). Droughts, on the other hand, are spatially diffuse, slow onset natural disasters that are harder to identify. Their impacts can emerge gradually and less visibly and perhaps as a consequence the trigger for a policy response is less evident (Damania et al., 2017). At the same time, these long, silent crises can have much greater economic impact globally than other disasters.

The findings of this paper have several implications for development practitioners. First, they are a starting point for understanding and appreciating the vulnerability to drought within and between countries. The spatial differences in economic impacts need unpacking to gain a more nuanced understanding toward the drivers of these vulnerabilities and how droughts manifest in a particular economy.

Second, they send a clear message that the developing world has been ill-prepared for managing the risks and impacts of droughts over the past few decades. Climate change is expected to lead to greater drought severity in many regions. Without significant improvements to how policy makers manage droughts, the world is on a path to even greater losses in economic growth and development gains due to these prolonged dry shocks.

And third, the results highlight the significance of green water (soil moisture and forests) in mitigating drought impacts, which has been overlooked in economic deliberations. It highlights the need for stewardship of forests and other natural capital that affect the hydrological cycle but are seldom associated with the growth impacts of droughts. As the climate heats up, many regions will face less predictable local rain. There is already evidence that there is a drying trend across much of the developing world. This makes the dependable waters of soils, groundwater and rivers, and the well-managed forests that nurture them frontline climate warriors.

Countries need to proactively invest to address the vulnerabilities through upgrades in information systems, institutions, and infrastructure that build drought resilience. There are several specific ways in which the World Bank and other development partners can support countries in addressing these challenges and needs.
Foremost is to support the development, implementation, and integration of early warning systems in countries and regions where they are lacking or insufficient. Governments often monitor drought by a single indicator, such as the SPI or soil moisture levels, and the indicator often changes depending on the sector or ministry. This creates competing definitions and understanding of the country’s drought situation. Thus, the lack of triangulation across indicators for monitoring droughts and inconsistent definitions in a given country context result in inaccuracies and, ultimately, inaction. Integrated and consistent monitoring programs that more comprehensively and objectively define drought can help countries get ahead of a drought. These programs can also link drought severity designations with predetermined actions that are triggered as a drought unfolds. The World Bank can develop similar internal protocols for monitoring drought situations worldwide, so the institution is better positioned to offer timelier and less reactive support to clients as drought situations worsen in a given location.

There is a need for more regular and proactive evaluation of the vulnerabilities and impacts, as well as determining countries’ drought preparedness capabilities. With floods, tropical cyclones, earthquakes, and other types of natural disasters, the vulnerabilities, impacts, capabilities, and needs are often evaluated in the immediate wake of the event through the application of a post-disaster needs assessment (PDNA). Unfortunately, such exercises inadequately capture drought events. This is because of the slow onset nature of the event and the highly diffuse and cascading impacts that cross multiple sectors across space and time, among other reasons. Therefore, policy makers need to develop new diagnostic approaches that not only accommodate the unique nature of the hazard, but also address the historical shortcomings of how countries have previously prepared or not for the impacts. The approaches need to encompass evaluation and inclusion of immediate response and recovery activities and build a pipeline of longer-term investments to minimize risks in future drought events. Unlike a traditional PDNA, drought needs assessments (DNAs) would be done iteratively, not only during a drought crisis, and consider potential impacts from shifting drought hazard profiles due to climate change.

Conducting DNAs would help countries prioritize amid potential investments for bolstering drought resilience. Investment packages might include (a) upgrades to information systems, such as early warning systems; (b) infrastructure, such as storage, nature-based solutions, groundwater and managed aquifer recharge, desalination, water reuse and recycling, nonrevenue water reduction, irrigation schemes, or rainwater harvesting; (c) institutions, such as drought policy and legislation to codify roles and responsibilities and standing drought committees; and (d) risk financing mechanisms tailored to vulnerable groups in each country.

Managing through a drought efficiently, effectively, and equitably requires considerable investments in coordinated planning before a drought hits. These include short-term emergency planning and drought contingency planning at scales that define who does what and when as a drought is unfolding. These actions would be triggered with the early warning system and associated drought protocols. Similarly, countries need strategic long-term planning efforts, including scenario planning, to identify the most robust set of measures for improving long-run drought resilience. The scales for these two sets of planning activities are manifold, and include utility, city or town, river basin, province, and national levels.

Appendix C provides two examples of recent or ongoing World Bank engagements that illustrate how Brazil and Eswatini have addressed these challenges and needs. However, there is considerably more work to do on this agenda. The World Bank is well-placed to help structure a more effective program around drought
resilience-building that can be offered to clients in all regions around the world. This assistance can help countries avoid drought-induced poverty traps in the future.