Policy Research Working Paper 11004 Extreme Weather Shocks and Firms in the Middle East and North Africa Esha Zaveri Roberta Gatti Asif M. Islam Middle East and North Africa Region & Planet Vice Presidency December 2024 Policy Research Working Paper 11004 Abstract The Middle East and North Africa is the most water scarce water and electricity outages, and lower access to finance region in the world. Although studies have explored the that occur as a result of negative precipitation shocks are effect of extreme weather events on agriculture, much less found to be key channels. Negative precipitation increases is known about the effect on businesses. Using geocoded the share of temporary workers, possibly explaining the firm-level data from the World Bank’s Enterprise Surveys drop in labor productivity. A new channel of governance is across the Middle East and North Africa region, this study also uncovered—negative precipitation shocks increase the analyzes the effects of precipitation shocks on firm perfor- time spent by senior management in dealing with regula- mance. The findings show that firms in areas that experience tions and expectations of solicitations of bribes. The results negative precipitation shocks have lower sales, labor produc- also show that firms respond to precipitation shocks by tivity, and investment. The study tests a number of channels adopting greener practices, suggesting scope for adaptation identified in the literature. Poor infrastructure, such as in the region. This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Office of the Chief Economist, Planet Vice Presidency. 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 aislam@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 Extreme Weather Shocks and Firms in the Middle East and North Africa* Esha Zaveri, Roberta Gatti, Asif M. Islam JEL Codes: Q1, Q5, H54, O14, D73 * Roberta Gatti is the chief economist of the Middle East and North Africa Region of the World Bank (email: rgatti@worldbank.org). Asif M. Islam is a senior economist in the Middle East and North Africa Chief Economist Office (aislam@worldbank.org). Esha Zaveri is a Senior Economist in the Office of the Chief Economist of the Planet Vice-Presidency at the World Bank (ezaveri@worldbank.org).The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Extreme Weather Shocks and Firms in the Middle East and North Africa* 1. Introduction The Middle East and North Africa (MENA) is the most water scarce region in the world (Borgomeo et al., 2021). Over 60 percent of the population in MENA lives in areas of high-water stress. The region also faces some of the highest levels of interannual hydrological variability in the world and is thus susceptible to extreme weather events such as droughts and floods. With climate change, extreme weather events are likely to be more frequent with droughts likely to have lasting negative impacts on economic growth in developing economies (Zaveri et al., 2023; Russ, 2020). The effects of extreme weather events on the private sector in MENA are not well known. The effects of rainfall variability on labor markets and agriculture have been studied for a handful of countries in the region (Alfani et al., 2023a; Alfani et al., 2023b). This study attempts to fill this gap in the literature by analyzing the effects of precipitation shocks on the private sector across MENA economies. The focus on the private sector is particularly important for the MENA region, where the private sector faces several challenges that set it apart from other regions. There is a lack of dynamism, with low firm entry and exit (World Bank, 2015). The private sector operates alongside a large public sector and a high prevalence of state-owned enterprises that receive preferential treatment. Studies have shown the challenge of political connections and cronyism in the Arab Republic of Egypt (Diwan et al., 2020b), Lebanon (Diwan et al., 2020a), Tunisia (Rijkers et al., 2017a; Rijkers et al., 2017b), and Morocco (Ruckteschler et al., 2019). Furthermore, partially state-owned firms are likely to have poor management practices (Islam and Gatti, 2024). 2 A small private sector in uncontestable markets may be more vulnerable climate shocks. The opportunities for creative destruction are limited, and the lack of competitive forces may restrict innovation or adaptation. Thus, analysis of the private sector is valuable given the unique context. In general, the private sector is an important engine of growth, creating jobs, providing essential products and services, and spurring innovation. Using data for around 9,500 firms across seven countries in MENA, this study finds that negative precipitation shocks are detrimental for private sector firms in terms of lower sales, lower labor productivity, and a lower probability of investing in fixed assets. The seven economies in the sample include Egypt, Jordan, Lebanon, Morocco, Tunisia, the West Bank and Gaza, and the Republic of Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), except for Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013. The study also attempts to unmask various channels identified in the literature through which precipitation shocks might affect firm performance in MENA. The literature largely focuses on global or country-specific studies that are outside the MENA region to delineate possible channels. A study in India found that hot days could lead to more absenteeism and lower the labor productivity of workers (Somanathan et al., 2021). Global studies have uncovered infrastructure as another potential channel. Using a global sample of manufacturing firms, Islam and Hyland (2019) find that negative precipitation shocks (droughts) increase the intensity of water outages. Droughts have also been found to increase the frequency of power outages in Latin American cities (Desbureaux and Rodella, 2019; Damania et al., 2017). Access to finance is another channel by which precipitation shocks could affect the private sector. Using a global sample of firms, Kling et al. (2021) find that frequent climate shocks might impair the ability of banks to predict outcomes, and thus may increase the cost of capital due to additional risk. A study on Mexican firms found that weather shocks can also generate liquidity shortages, increasing the chances of loan default, and reducing access to future credit by lowering credit scores (Aguilar-Gomez et al., 2023). 3 Benincasa et al. (2024) find that extreme weather events erode balance sheets as firms that experience monetary losses from shocks become more leveraged and are seen as less credit worthy. The study also notes that firms affected by weather shocks are more likely to invest in fixed assets as they replenish damaged capital (Benincasa et al., 2024). New investments can also lead to vintage effects where replenishment of capital means newer equipment with a lower environmental footprint. On the other hand, firms may become environmentally aware and therefore increase green investments or improve green management practices. A closely related study by Benincasa et al. (2024) covers European and MENA economies but relies on a self-reported measure of monetary damages from extreme weather events in contrast to the geocoded data on precipitation shocks used in this study. The study also tests whether some of the channels highlighted in the global literature also apply to the MENA region. Overall, we see that negative precipitation shocks decrease labor productivity. The channel could be through a change in the composition of workers -- negative precipitation shocks are found to increase the share of temporary workers, potentially explaining the drop in labor productivity. Negative precipitation shocks also increase losses from power outages with some indication of increases in water outages although the finding is not as robust. Negative precipitation shocks limit access to finance by decreasing the likelihood that firms use banks to finance working capital and have access to overdraft facilities. A new channel uncovered in this study is governance. The intensity of negative precipitation shocks increases the amount of time senior management spends dealing with regulations and expectations of bribe solicitations. For the cross-section of 2019/2020 MENA surveys, there is a green module included in the survey instrument that captures green investments and green management practices. This study finds negative precipitation shocks increase the likelihood of adopting water management, more climate friendly energy on site, and machinery and equipment upgrades, suggesting scope for adaptation. The study makes several policy recommendations. Firms in MENA economies are vulnerable to climate shocks. Building infrastructure will be crucial for the private sector in MENA given the increasing 4 frequency of climate shocks. Power and water infrastructure is crucial. As climate change makes precipitation patterns more variable and unpredictable, investments in public water and power infrastructure systems are an important way in which governments can help firms adapt. Furthermore, governance matters as extreme weather events may erode the business environment for firms with burdensome regulations and corruption. Finally, extreme weather events may lead firms in MENA to adapt through adoption of green measures, although this starts from a very low base. In summary, the study contributes to the literature by examining the effects of precipitation shocks on the private sector in the MENA region. Second, it confirms findings from other studies, showing that the performance of private sector firms in MENA is similar to that of firms in other regions in terms of being vulnerable to negative precipitation shocks. Third, the study explores the various channels of the effects, highlighting channels that are similar to and different from firms in other regions. Finally, the study shows the scope of adaption with regards to adopting green measures. The rest of the paper is structured as follows. Section 2 describes the data and the empirical approach. Section 3 provides the results with robustness checks, and section 4 concludes. 2. Empirical Approach 2.1 Data 2.1.1 Enterprise Surveys The main data source is cross-sectional firm-level surveys across the world from the World Bank’s Enterprise Surveys (ES) for the MENA region. The ES are nationally representative surveys of private formal (registered) firms with 5 or more employees and cover manufacturing and services firms largely collected via face-to-face interviews with business owners or top managers. The sample is restricted to surveys in MENA where geo-located information of firms is available. The sample consists of about 9,500 firms (depending on the specification) across seven MENA economies. These include Egypt, Jordan, 5 Lebanon, Morocco Tunisia, the West Bank and Gaza, and Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), with the exception of Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013. Summary statistics are provided in table A1. Table A2 provides the survey and country composition of the sample. The ES methodology has the advantage of deploying the same definition of the universe of inference, standard sampling methodology, standardized survey instrument, and a uniform methodology of implementation. The selection of firms in each country is done by stratified random sampling with three levels of stratification: sector, size, and location within the country. Sampling weights are used to correct for unequal probability of selection as well as for ineligibility and non-response. The data are largely collected using Computer-Assisted Personal Interviewing (CAPI) software. The CAPI software collects geo coordinates of the firm’s location that we use to match rainfall and temperature data with the firm-level data. To maintain anonymity of the respondents, the geo-codes are masked around a 2km radius. Implementation of the surveys is consistent across countries. Formal training sessions of supervisors and enumerators ensure best practices are employed. Quality control checks guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10% and 50% batches of the data during the survey. Enterprise Surveys’ global methodology, sample design, and weights computation are available on the website http://www.enterprisesurveys.org. The data have been widely used in the literature (Besley & Mueller, 2018; Chauvet & Ehrhar, 2018; Hjort & Poulsen, 2019). 2.1.2 Precipitation Shocks To measure precipitation shocks, we use re-analysis data from the European Copernicus program. This is the Earth Observation component of the European Union’s space program. Through the ERA5-Land data platform they make data on precipitation available at the hourly level on a 0.1◦×0.1◦ grid for the planet from 6 1979-2022. The daily data is available from 1990-2021. The data is processed to measure annual precipitation totals within each sub-national administrative level 2 (ADM2) unit that contains at least one firm in the ES data. Annual shocks are defined as instances when the annual precipitation within an ADM2 unit is at least one standard deviation above or below that unit’s long-run average (1990-2021). About 3.8 percent of the sample faced a negative precipitation shock while 19.1 percent of the sample faced a positive precipitation shock. For reference, Benincasa (2024) finds that 9.1 percent of firms across Europe and MENA self-reported monetary losses from extreme weather events (this measure includes storms, droughts, floods and landslides). The same measure for the sample of MENA economies in this study is about 8 percent. 2.2 Empirical Approach The empirical approach exploits cross-sectional variation in the ES data combined with precipitation shocks at the ADM2 geographical unit, while accounting for sub-national ADM1 or country fixed effects. The main analysis estimates the effect of precipitation shocks at the ADM2 level on sales at the firm level while controlling for temperature and other firm-level controls. The following regression is estimated: = 1 ℎ + + + + + (1) Where: i indexes firms, a indexes sub-national ADM1 units, g indexes ADM2 units, s indexes sector (2 digit ISIC level) and t indexes survey-years. Note that ADM2 is a more geographically disaggregated unit than ADM1. There is a still a possibility that some of the shocks could be colinear with ADM1 fixed effects. Therefore we substitute the ADM1 fixed effects for country fixed effects across the estimations to show the robustness of the findings. For the cross-sectional estimations involving the green module, we deploy country fixed effects given the lack of variation over time. is the log of sales (in USD). ℎ 7 represents measures of precipitation shocks. The main precipitation shock is a binary variable that takes the value of 1 if precipitation is 1 standard deviation below the long run average for a specific ADM2 unit. Average temperature is also accounted for in the estimations. We deploy a small set of firm-level controls (X) in the estimation - whether the firm is a multi-firm, age of firm (in logs), size of firm (in logs), exporter status, foreign ownership, and whether the firm has a checking or savings account. Sector (ISIC 2 digit level) and survey year fixed effects are also included in the specification. The identification strategy does face the challenges of omitted variable bias and selection. Simultaneity bias is less of a concern given that individual firms are unlikely to influence precipitation shocks. To address omitted variable bias concerns, we control for a variety of controls at the firm-level. We also account for time-invariant omitted variables at the ADM1 geographical level through ADM1 fixed effects. The estimations do face the issue of endogeneity due to firm location selection may be affected by precipitation shocks. The best way we account for is through controls that are determinants of firm productivity. The sample of firms in MENA are survivors, therefore our estimates may understate the true effects. Potential channels through which precipitation shocks may affect sales are explored in the study replacing the outcome variable in equation 1 with various variables identified in the literature as plausible channels. Finally, the more recent rounds of the MENA surveys (around 2019) do have specific questions on green investments and adaptation. This allows us to explore the relationship between precipitation shocks and various green measures. 8 3. Results 3.1 Main Results Table 1 provides the main results. A negative precipitation shock leads to a reduction in sales, labor productivity, and the likelihood of investing in fixed assets for firms across the MENA countries in the sample. In terms of magnitude, a negative precipitation shock leads to a 40 to 52 percent reduction in sales, depending on whether ADM1 fixed effects or country fixed effects are included, respectively. The coefficient is statistically significant at the 10% level with the inclusion of country fixed effects and at the 5% level using ADM1 fixed effects. Similar magnitudes are uncovered for labor productivity. A negative precipitation shock reduces labor productivity, defined as sales per worker, by 42 (with ADM1 fixed effects) to 53 percent (with country fixed effects). The coefficients are statistically significant at the 10% and 5% level respectively. Next, we estimate the impact of negative precipitation shocks on investment. On one hand, if weather shocks limit access to finance, firms are less likely to invest. On the other hand, firms affected by weather shocks might be more likely to invest in fixed assets as they replenish damaged capital (Benincasa et al., 2024). The results show that the former mechanism is more likely at play in the MENA region. Negative precipitation shocks reduce the likelihood of investing by 9 percentage points (including ADM1 fixed effects) to 12 percentage points (including country fixed effects). The former is statistically significant at the 10% level, while the latter is statistically significant at the 5% level. This corroborates with our results in section 3.2 that shows that access to finance deteriorates with negative precipitation shocks. Although these findings are in contrast with Benincasa et al., (2024) that find a positive effect on investment, their analyses use self-reported measures of exposures to extreme weather events. However, their sample encompasses only one wave of surveys and includes both MENA and Europe, while our sample is based on multiple waves of surveys for only MENA economies. With regards to other weather-related covariates in the estimation, the coefficient of a positive precipitation shock is not robust and largely statistically insignificant. The same is the case with temperature. For firm- 9 level variables, only three variables are found to have a statistically significant correlation with performance – size, age, and access to finance (access to bank account). Size and access to finance are positively correlated with sales, labor productivity and investment. Age is positively correlated with sales and labor productivity, but negatively correlated with investment. Exports and foreign ownership have no statistically significant correlation with performance for private sector business across MENA. 3.2 Channels We explore a number of channels highlighted in the literature through which negative precipitation shocks may affect firms. In table 1 we showed that negative precipitation shocks reduce labor productivity, as defined by sales per worker. This is similar to the analysis by Somanathan et al., (2021) that finds that hot days lead to absenteeism and therefore lower labor productivity. Unfortunately, the surveys do not have information on absenteeism and health of the workers, and therefore we cannot pinpoint exactly how labor productivity is affected. In table 2 we explore whether negative precipitation shocks have any effects on the total number of full-time employees and the composition of employees in terms of temporary versus permanent workers. There is no statistically significant relationship between precipitation shocks and the number of workers, regardless of whether country fixed effects or ADM1 fixed effects are used. However, negative precipitation shocks do tend to alter the composition of workers by increasing the share of temporary workers, regardless of whether the country fixed effects or the ADM1 fixed effects model is used. The coefficients are statistically significant at the 10% level. This may be suggestive evidence as to why labor productivity falls. Another channel we explore is the effects through infrastructure. Hyland and Islam (2019) find that negative rainfall shocks lead to more water outages for a global sample of manufacturing firms. For firms across cities in Latin America, Desbureaux and Rodella (2019) find that negative precipitation shocks increase power outages. We test both these possibilities for MENA. Table 3 presents the findings. We find positive and statistically significant effects of negative precipitation shocks on the losses due to power outages. The 10 coefficient is statistically significant at the 1 percent level, regardless of whether the specification includes ADM1 fixed effects or country fixed effects. For water outages, the findings are less robust. The coefficient is statistically significant and positive for the effect of a negative precipitation shock on water outages only when ADM1 fixed effects are used. The sign flips and the coefficient loses statistical significance when country fixed effects are used instead. Note that water outage information is only available for manufacturing firms. In table 4 we explore the relationship between negative precipitation shocks and access to finance. Negative precipitation shocks are negatively correlated with having a loan or line of credit. The finding is statistically significant at the 2 percent level with country fixed effects specification, and 1 percent for the ADM1 fixed effects specification. Similar findings are found in terms of financing working capital through banks. A negative precipitation shock reduces the likelihood that a firm uses banks to finance working capital. The coefficient is statistically significant at the 5% level whether ADM1 fixed effects or country fixed effects are deployed. Positive precipitation shocks are also found to lower the likelihood that a firm will finance working capital through banks – the coefficient is statistically significant at the 1% level, and similar in magnitude of the negative precipitation shocks. At the intensive margin – the proportion of working capital financed by banks - we see a negative relationship with negative precipitation shocks. However, the coefficient is only statistically significant with ADM1 fixed effects, and not with country fixed effects. Positive precipitation shocks are negatively correlated with proportion of working capital financed by banks, statistically significant at the 1% level regardless of whether ADM1 fixed effects or country effects are employed. These findings are consistent with the negative relationship uncovered in the literature between extreme weather events and access to finance. Frequent climate shocks enervate the prediction capability of banks therefore raising interest rates due to additional risk and therefore increasing the cost of capital (Kling et al., 2021; Javadi and Masum, 2021; Brown et al., 2021). Extreme weather events may also erode balance 11 sheets as firms become more leveraged as they deal with the shocks (Benincasa et al., 2024). Weather shocks can also create liquidity shortages, increase loan default, deteriorating credit scores and thus access to future credit (Aguilar-Gomez et al., 2023). Huang et al., (2018) show that extreme weather events are associated with lower and more volatile earnings and cash flows. Exposed firms tend to hold more cash to generate more financial slack and build resilience. An additional channel that we uncover in this study that has not yet been found in the literature is that of governance. Negative precipitation shocks increase the amount of time senior management spends dealing with the requirements of government regulations. Negative precipitation shocks also increase the likelihood that a firm expects to make bribes to get things done. Findings are presented in table 5. The negative precipitation shock coefficient with regards to government regulations is statistically significant at the 1 percent level regardless of whether ADM1 fixed effects or country fixed effects are used. For bribe expectations, the coefficient is statistically significant at the 1% level for the country fixed effects specification and 5% for the ADM1 fixed effects specification. These findings suggest that weakening economic conditions might lead to more rent seeking behavior. Governments may burden firms with more regulations or extract more rents due to the budget effects of dealing with extreme weather events. 3.3 Adaptation The 2019 MENA Enterprise Surveys wave includes a green module that contains information on whether firms adopt climate friendly measure (see table A2 indicates which surveys have this information). Benincasa (2024) provides some evidence that firms exposed to extreme weather events are more likely adapt by adopting climate-friendly measures. The replenishment of physical capital damaged due to extreme weather may result in new investments that lead to vintage effects where newer equipment have lower environmental footprint. It is also possible that firms may become environmentally aware and therefore engage in green investments or green management practices. Benincasa (2014) find that firms 12 suffering losses from extreme weather display 12 percentage points greater likelihood of adopting climate- friendly measures. Table 6 provides the findings for our sample of MENA economies. Negative precipitation shocks increase the likelihood of adopting water management practices by 19.1 percentage points, more climate-friendly energy generation on site by 15.6 percentage points, machinery and equipment upgrades by 39.2 percentage points. However, there are no statistically significant relationships with energy management and heating and cooling improvements. Do note that one limitation of the green measures is that they ask whether firms have adopted new measures over the last three years. Thus, firms that may have adopted measures 4 years ago will be counted as though they have not adopted any measures. 4. Conclusion This study documented a deterioration in the performance of firms across MENA due to negative precipitation shocks. These effects were uncovered for sales, labor productivity and likelihood of investing. Several channels were investigated in accordance with the literature. Reduction in quality of power and water infrastructure, limited access to bank finance, and great exposure to bribery increasing the burden of regulations were the main channels uncovered. Negative precipitation shocks also may have resulted in the increase in adoption of some climate friendly policies for firms across MENA. The MENA private sector faces several challenges. Poor business environment together with cronyism, privileges and a large state have limited contestability and reduce dynamism. Increasing climate shocks further may make the private sector even more vulnerable. In terms of policy recommendation, the study shows that extreme weather events are an economy-wide concern, not just for rural locations or agriculture sectors. On a positive note, exposure to shocks does show that firms in MENA may adapt, although this is from a very low base. Additional interventions may be needed for firms to internalize climate shocks. Ensuring access to finance may also be needed to adapt to increasing shocks. 13 The study has several limitations that future research may address. There are several channels that the study was unable to explore, including health and worker absenteeism. Furthermore, the effects of climate shocks may differ between the public and private sectors. State-owned firms may be able to handle shocks better. This is especially important for MENA given the prevalence of state-owned firms. Of course, if extreme weather events hurt productive private sector firms while having limited effects on protected unproductive state-owned enterprises, this would hurt the economy in general. Finally, the study was unable to untangle whether reduction in performance was due to supply disruptions or declining demand. 14 References Aguilar-Gomez, Sandra, Emilio Gutierrez, David Heres, David Jaume, Martin Tobal (2023) “Thermal stress and financial distress: Extreme temperatures and firms’ loan defaults in Mexico.” Journal of Development Economics (forthcoming) Alfani, Federica, Vasco Molini, Giacomo Pallante, and Alessandro Palma (2024a). “Job Displacement and Reallocation Failure. Evidence from Climate Shocks in Morocco.” European Review of Agricultural Economics 51(1):1-31 Alfani, Federica, Giacola Pallante, Alessandro Palma, and Abdelkader Talhaoui (2024b) “When the Rain Stops Falling: Effects of Droughts on the Tunisian Labor Market.” Policy Research Working Paper No. 10766, World Bank, Washington, DC. Benincasa, Emanuela, Frank Betz, and Luca Gattini (2024) “How do Firms Cope with Losses from Extreme Weather Events?” Journal of Corporate Finance 84 102508 Besley, Timothy and Hannes Mueller (2018) “Predation, Protection, and Productivity: A Firm-Level Perspective.” American Economic Journal: Macroeconomics 10(2): 184-221. Borgomeo, Edoardo, Anders Jägerskog, Esha Zaveri, Jason Russ, and Richard Damania (2022). Ebb and flow: Volume 2. Water in the shadow of conflict in the Middle East and North Africa. World Bank, Washington DC. Brown, James R., Matthew T. Gustafson, and Ivan T. Ivanov (2021) “Weathering Cash Flow shocks.” The Journal of Finance 76 (4): 1731-1772 Chauvet, Lisa and Helene Ehrhart (2018) “Aid and Growth: Evidence from Firm-Level Data.” Journal of Development Economics 135: 461-477. Damania, Richard, Sebastien Desbureaux, Marie Hyland, Asif Islam, Aude-Sophie Rodella, Jason Russ, and Esha Zaveri (2017), Uncharted waters: The New Economics of Water Scarcity and Variability. World Bank Publications. Desbureaux, Sebastien and Aude-Sophie Rodella (2019). “Drought in the City: The Economic Impact of Water Scarcity in Latin American Metropolitan Areas.” World Development 114:13-27 Diwan, Ishac, and Jamal Ibrahim Haidar (2020a). “Political Connections Reduce Job Creation: Firm- level Evidence from Lebanon.” The Journal of Development Studies DOI:10.1080/00220388.2020.1849622 Diwan, Ishac, Keefer, Philip, and Marc Schiffbauer (2020b). “Pyramid capitalism: Political Connections, Regulation, and Firm Productivity in Egypt.” Review of International Organizations 15(1): 211–246. Hjort, Jonas, and Jonas Poulsen (2019) “The Arrival of Fast Internet and Employment in Africa.” American Economic Review, 109 (3): 1032-79. Huppertz, Maximilian (2024) “Sacking the Sales Staff: Firm Reactions to Extreme Weather and Implications for Policy Design.” mimeo https://maxhuppertz.github.io/files/max_huppertz_jmp.pdf 15 Hyland, Marie and Jason Russ (2019), “Water as Destiny–The Long-term Impacts of Drought in sub- Saharan Africa.” World Development, 115, 30–45. Huang, Henry He, Joseph Kerstein, and Chong Wang (2018) The Impact of Climate Risk on Firm Performance and Financing Choices: An International Comparison.” Journal of International Business Studies 49:633-656. Islam, Asif M. and Roberta Gatti (2024) “Management Practices and Partial Government Ownership in the Middle East and North Africa.” Economic of Transition and Institutional Change, 1-28 https://doi.org/10.1111/ecot.12414 Islam, Asif and Marie Hyland (2019), “The drivers and impacts of water infrastructure reliability–a global analysis of manufacturing firms.” Ecological Economics, 163, 143–157. Javadi, Siamak and Abdullah-Al Masum (2021). “The Impact of Climate Change on the Cost of Bank Loans.” Journal of Corporate Finance 69 102019 Kling, Gerhard, Ulrich Volz, Victor Murinde, and Sibel Ayas (2021). “The Impact of Climate Vulnerability on Firms' Cost of Capital and Access to Finance.” World Development 137 105131 Rijkers, Bob, Leila Baghdadi and Gael and Raballand (2017a). “Political Connections and Tariff Evasion Evidence from Tunisia.” The World Bank Economic Review 31: 459–482. Rijkers, Bob, Caroline Freund and Antonio Nucifora (2017b). “All in the Family: State Capture in Tunisia.” Journal of Development Economics 124: 41–59 Ruckteschler, Christian, Adeel Malik and Ferdinand Eib (2019). “The Politics of Trade Protection: Evidence from an EU-mandated Tari Liberalization in Morocco.” CSAE Working Paper Series 2019- 12, Centre for the Study of African Economies, University of Oxford. Russ, Jason (2020), “Water runoff and economic activity: The Impact of Water Supply Shocks on Growth.” Journal of Environmental Economics and Management, 101, 102322 Somanathan, E., Rohini Somanathan, Anant Sudarshan, and Meenu Tewari (2021). “The Impact of Temperature on Productivity and Labor Supply: Evidence from Indian Manufacturing.” Journal of Political Economy 129(6): 1797-1827. World Bank. 2015. “Governance Reforms of State-Owned Enterprises (SOEs). Lessons from Four Case Studies (Egypt, Iraq, Morocco and Tunisia).” World Bank Group, Washington, DC Zaveri, Esha, Richard Damania, and Nathan Engle (2023), “Droughts and Deficits: The Global Impact of Drought on Economic Growth.” Policy Research Working Paper 10453. 16 Table 1: Precipitation Shocks and Performance Model OLS Dependent Variable Log of Sales (USD) Log of Labor Productivity (USD) Invest Y/N (1) (2) (3) (4) (5) (6) Negative Precipitation Shock - (1 -0.525** -0.395* -0.529*** -0.417* -0.116** -0.093* std dev, admin 2) (0.212) (0.223) (0.198) (0.213) (0.046) (0.051) Positive Precipitation Shock - (1 0.043 -0.197 0.015 -0.223* 0.028 -0.010 std dev, admin 2) (0.133) (0.130) (0.131) (0.126) (0.043) (0.043) Average Temperature -0.259* -0.212 -0.267* -0.228 0.026 0.069 (0.146) (0.332) (0.146) (0.326) (0.051) (0.121) Average Temperature Squared 0.005 0.006 0.005 0.006 -0.001 -0.002 (0.003) (0.009) (0.003) (0.008) (0.001) (0.003) Constant 12.142*** 10.910*** 12.304*** 11.199*** -0.050 -0.526 (1.607) (3.218) (1.605) (3.156) (0.575) (1.184) Controls YES YES YES YES YES YES Country Fixed Effects YES NO YES NO YES NO Admin 1 Fixed Effects NO YES NO YES NO YES Sector (ISIC 2 Digit) Fixed YES YES YES YES YES YES Effects Year Fixed Effects YES YES YES YES YES YES Number of observations 9,633 9,627 9,561 9,555 10,710 10,705 Adjusted R2 0.614 0.635 0.366 0.401 0.116 0.151 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Log of size, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 17 Table 2: Precipitation Shocks and Labor - Size and Composition Model OLS No. Of Total Employees (Full Time Dependent Variable Proportion of Temporary Workers Equivalent) (1) (2) (3) (4) Negative Precipitation Shock - (1 std dev, admin 2) -0.180 -0.160 3.650* 3.360* (0.134) (0.171) (2.048) (1.840) Positive Precipitation Shock - (1 std dev, admin 2) -0.118 -0.067 -0.593 0.252 (0.072) (0.091) (0.886) (1.057) Average Temperature 0.010 0.114 -0.020 -1.694 (0.089) (0.242) (1.138) (2.913) Average Temperature Squared -0.0003 -0.004 -0.004 0.029 (0.002) (0.006) (0.024) (0.070) Constant 1.433 0.786 6.791 26.604 (1.018) (2.369) (13.260) (30.193) Controls YES YES YES YES Country Fixed Effects YES NO YES NO Admin 1 Fixed Effects NO YES NO YES Sector (ISIC 2 Digit) Fixed Effects YES YES YES YES Year Fixed Effects YES YES YES YES Number of observations 10,768 10,763 10,374 10,369 Adjusted R2 0.279 0.300 0.095 0.156 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 18 Table 3: Precipitation Shocks and Infrastructure Model OLS Dependent Variable Incidents of Water Shortages Losses from Power Outages (% of sales) (1) (2) (3) (4) Negative Precipitation Shock - (1 std dev, admin 2) 0.005 1.073** 2.365*** 2.038*** (0.469) (0.508) (0.793) (0.700) Positive Precipitation Shock - (1 std dev, admin 2) 0.182 -0.718 -0.677 -1.663*** (0.942) (0.992) (0.646) (0.607) Average Temperature 4.159 -4.425 4.598*** -3.187 (3.480) (3.639) (1.641) (2.193) Average Temperature Squared -0.088 0.144 -0.092*** 0.069 (0.076) (0.100) (0.035) (0.056) Constant -46.011 29.279 -48.473*** 41.493** (38.778) (31.787) (18.280) (21.145) Controls YES YES YES YES Country Fixed Effects YES NO YES NO Admin 1 Fixed Effects NO YES NO YES Sector (ISIC 2 Digit) Fixed Effects YES YES YES YES Year Fixed Effects YES YES YES YES Number of observations 4,937 4,934 10,743 10,738 Adjusted R2 0.190 0.347 0.305 0.389 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Log of size, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 19 Table 4: Precipitation Shocks and Finance Model OLS Proportion of working Use Banks to Finance Working Dependent Variable Loan or Line of Credit Y/N capital financed by banks Capital Y/N (%) (1) (2) (3) (4) (5) (6) Negative Precipitation Shock - (1 std dev, admin 2) -0.112** -0.189*** -0.105** -0.135** -4.043 -6.309** (0.048) (0.058) (0.052) (0.058) (2.659) (3.089) - Positive Precipitation Shock - (1 std dev, admin 2) -0.057* -0.018 -0.095*** -0.105*** -6.476*** 6.801*** (0.034) (0.038) (0.029) (0.034) (1.827) (2.004) Average Temperature -0.001 -0.014 -0.028 -0.013 -0.482 3.677 (0.030) (0.073) (0.029) (0.074) (1.507) (4.012) Average Temperature Squared -0.0001 0.0004 0.0004 0.0005 -0.00001 -0.075 (0.001) (0.002) (0.001) (0.002) (0.031) (0.088) Constant 0.041 0.088 0.457 0.111 9.400 -43.196 (0.361) (0.829) (0.351) (0.830) (18.036) (45.982) Controls YES YES YES YES YES YES Country Fixed Effects YES NO YES NO YES NO Admin 1 Fixed Effects NO YES NO YES NO YES Sector (ISIC 2 Digit) Fixed Effects YES YES YES YES YES YES Year Fixed Effects YES YES YES YES YES YES Number of observations 10,538 10,533 10,471 10,466 10,361 10,356 Adjusted R2 0.226 0.263 0.210 0.241 0.186 0.213 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Log of size, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 20 Table 5: Precipitation Shocks and Governance Model OLS Senior Management Time Spent in Firm Expected to Make Payment to Get Dependent Variable Dealing with Requirements of Things Done Y/N Government Regulations (%) (1) (2) (3) (4) Negative Precipitation Shock - (1 std dev, admin 2) 21.142*** 23.759*** 0.130*** 0.101** (3.843) (3.655) (0.049) (0.049) Positive Precipitation Shock - (1 std dev, admin 2) -0.048 -2.646* 0.006 0.027 (1.637) (1.416) (0.033) (0.041) Average Temperature 0.680 2.528 0.008 0.093 (1.589) (2.922) (0.050) (0.141) Average Temperature Squared -0.019 -0.106 -0.0005 -0.003 (0.033) (0.072) (0.001) (0.004) Constant -3.852 -2.916 0.178 -0.604 (19.003) (29.368) (0.539) (1.305) Controls YES YES YES YES Country Fixed Effects YES NO YES NO Admin 1 Fixed Effects NO YES NO YES Sector (ISIC 2 Digit) Fixed Effects YES YES YES YES Year Fixed Effects YES YES YES YES Number of observations 9,981 9,975 10,203 10,198 Adjusted R2 0.273 0.351 0.301 0.321 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Log of size, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 21 Table 6: Precipitation Shocks and Adaptation Model OLS Heating and Water More climate- Machinery and Energy cooling management friendly energy equipment management Dependent Variable improvements Y/N (last 3 generation on site upgrades Y/N (last Y/N (last 3 Y/N (last 3 years) Y/N (last 3 years) 3 years) years) years) (1) (2) (3) (4) (5) Negative Precipitation Shock - (1 std dev, admin 2) 0.191** 0.156** 0.392*** 0.038 0.013 (0.092) (0.076) (0.071) (0.128) (0.169) Positive Precipitation Shock - (1 std dev, admin 2) -0.016 0.023 0.106* -0.020 0.089* (0.036) (0.036) (0.054) (0.057) (0.051) Average Temperature -0.051 -0.091 -0.128 -0.016 0.057 (0.099) (0.063) (0.121) (0.111) (0.103) Average Temperature Squared 0.002 0.002 0.004 0.001 -0.001 (0.002) (0.002) (0.003) (0.003) (0.002) Constant 0.373 0.751 1.101 -0.216 -0.591 (1.012) (0.636) (1.210) (1.076) (1.066) Controls YES YES YES YES YES Country Fixed Effects YES YES YES YES YES Sector (ISIC 2 Digit) Fixed Effects YES YES YES YES YES Number of observations 4,259 4,175 4,483 4,325 4,245 Adjusted R2 0.152 0.130 0.185 0.133 0.199 note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the strata level. Survey weights used. Control variables include: Firm is part of a larger firm Y/N, Log of age of firm, Log of size, Direct exports 10% or more of sales Y/N, Foreign ownership Y/N, Establishment has checking or savings account Y/N 22 Table A1: Summary Statistics Variable Obs Mean Std. dev. Min Max Negative Precipitation Shock - (1 std dev, admin 2) 9,627 0.038 0.192 0 1 Positive Precipitation Shock - (1 std dev, admin 2) 9,627 0.191 0.393 0 1 Temperature (Average) 9,627 20.839 3.142 11.201 31.593 Average Temperature Squared 9,627 444.133 146.234 125.457 998.127 Log of Sales (USD) 9,627 12.546 1.918 5.690 20.513 Log of Labor Productivity (USD) 9,555 9.956 1.502 2.025 16.059 Invest Y/N 9,591 0.270 0.444 0 1 Incidents of Water Shortages 4,496 1.929 8.163 0 240 Losses from Power Outages (% of sales) 9,606 3.877 10.579 0 87.5 Loan or Line of Credit Y/N 9,482 0.216 0.412 0 1 Use Banks to Finance Working Capital Y/N 9,448 0.225 0.418 0 1 Proportion of working capital financed by banks (%) 9,376 9.186 20.688 0 100 Firm is part of a larger firm Y/N 9,627 0.406 0.491 0 1 Log of age of firm 9,627 2.740 0.783 0 5.088 Log of size 9,627 2.652 1.049 0 9.210 Direct exports 10% or more of sales Y/N 9,627 0.172 0.377 0 1 Foreign ownership Y/N 9,627 0.050 0.218 0 1 Establishment has checking or savings account Y/N 9,627 0.756 0.429 0 1 Senior Management Time Spent in Dealing with Requirements of Government Regulations (%) 8,909 7.360 18.807 0 100 Firm Expected to Make Payment to Get Things Done Y/N 9,170 0.175 0.380 0 1 Water management Y/N (last 3 years) 3,958 0.099 0.299 0 1 More climate-friendly energy generation on site Y/N (last 3 years) 3,846 0.085 0.279 0 1 Machinery and equipment upgrades Y/N (last 3 years) 4,145 0.338 0.473 0 1 Energy management Y/N (last 3 years) 4,006 0.225 0.418 0 1 Heating and cooling improvements Y/N (last 3 years) 3,906 0.253 0.435 0 1 23 Table A2: Surveys included in the study Survey No of Firms Surveys with Green Module Egypt, Arab Rep. 2013 1,345 NO Egypt, Arab Rep. 2016 1,374 NO Egypt, Arab Rep. 2020 2,795 YES Jordan 2013 517 NO Jordan 2019 312 YES Lebanon 2013 275 NO Lebanon 2019 350 YES Morocco 2013 179 NO Morocco 2019 345 YES Tunisia 2013 484 NO Tunisia 2020 459 YES West Bank and Gaza 2013 377 NO West Bank and Gaza 2019 304 YES Yemen, Rep. 2010 267 NO Yemen, Rep. 2013 244 NO Total 9,627 24