Climate Shocks and Tax Base: Firm-Level Evidence on Profitability and Corporate Income Tax Compliance in Uganda

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We construct a panel of over 404,000 firm-year observations from administrative tax records (2013/14-2022/23) matched with high-resolution satellite climate data. Using fixed-effects models, we isolate the impact of temperature and rainfall shocks on profitability, efficiency, and tax contributions. Our findings indicate that higher temperatures negatively affect firm outcomes across all measures, with the largest effect on asset efficiency, where a 1% temperature increase results in a 2% CIT decline and over 4% reduction in return on assets. Rainfall shocks have asymmetric effects, improving efficiency in some sectors but reducing tax compliance, creating fiscal trade-offs. Robustness checks confirm that firm size and age mediate exposure: medium and large firms contribute more to CIT yet record weaker efficiency ratios. These findings extend the literature by linking firm operations to fiscal outcomes, showing that climate shocks create both corporate and fiscal risks. The analysis recommends embedding climate risk in revenue forecasts, promoting sector-specific adaptation, expanding access to climate-smart technologies and finance, and aligning tax incentives with resilience goals. Proactive adaptation is essential to protect productivity, fiscal stability, and long-term growth under increasing climate variability JEL Classification: Q54, H25, D22, Q56, O55 Climate variability Firm performance Corporate Income Tax (CIT) compliance Climate shocks Fiscal risk Adaptation policy 1 Introduction Climate change poses profound and far-reaching implications for economic systems, livelihoods, and public welfare. Rising temperatures and erratic rainfall patterns pose a significant threat to productivity, infrastructure, and fiscal stability, particularly in low-income countries with limited adaptive capacity (Calvin et al., 2023 ). Global surface temperatures have already risen by approximately 1.1°C above pre-industrial levels, primarily driven by fossil fuel combustion and land-use change (Calvin et al., 2023 ). Under high-emission scenarios, global temperatures could rise by more than 4°C by 2100, a level that would severely challenge human habitability (Brown & Caldeira, 2017 ). These climatic shifts have intensified droughts and floods, disrupted production networks, and heightened macroeconomic volatility (Capelle, 2023 ). Although macro-level studies document the negative effects of climate shocks on aggregate output and welfare (Burke & Tanutama, 2019 ; Dell et al., 2012 ), much less is known about the firm-level mechanisms through which temperature and rainfall variability affect productivity and fiscal outcomes in Africa. In particular, the micro-fiscal dimension of climate vulnerability on how corporate performance interacts with domestic revenue mobilization remains largely unexplored (AfDB, 2025 ; OECD, 2024 ). This paper examines how temperature and rainfall variability influence firm performance and corporate income tax (CIT) compliance in Uganda, a small, climate-vulnerable economy with a narrow fiscal base and a high dependence on weather-sensitive sectors (Capelle, 2023 ). Uganda provides a compelling empirical context for three reasons. First, its equatorial geography exposes firms to frequent temperature extremes and rainfall fluctuations. Second, agriculture, manufacturing, and construction, the country’s main sectors that drive economic growth, remain highly climate dependent. Third, formal enterprises, though relatively few, generate most domestic tax revenue but are vulnerable to frequent climate shocks (MoFPED, 2024 ; NPA, 2022 ). This structural composition, coupled with limited diversification and adaptive capacity, increases exposure to climatic shocks. Understanding how these shocks are transmitted through firms to affect both private profitability and fiscal capacity is critical for assessing economic resilience under growing climate change uncertainty. The analysis draws on administrative firm-level tax records from the Uganda Revenue Authority (URA), merged with high-resolution satellite data on temperature and rainfall for 2013/14-2022/23. The resulting panel covers 404,000 firm-year observations across districts and sectors, providing a rare opportunity to examine how local climatic variations influence firms’ financial and fiscal outcomes. Using fixed-effects estimators, the study exploits within-district variation in climate conditions to identify causal effects on profitability, efficiency, and tax liabilities. The empirical model accounts for unobserved firm heterogeneity, time-invariant location factors, and sector-specific attributes to minimize confounding (Burke et al., 2015 ; Wooldridge, 2010 ). This longitudinal approach extends earlier perception-based work by Mawejje ( 2024 a) using verified administrative data that reflect realized rather than self-reported outcomes, offering more credible and policy-relevant evidence on how climate variability shapes firm behavior and fiscal performance in Uganda. Empirical results show that higher temperatures significantly reduce firm outcomes across multiple model specifications. A one-percent increase in temperature within a district lowers corporate income-tax payments by about 2 percent, return on assets by more than 4 percent, and profit margins by nearly 2 percent. Rainfall exhibits asymmetric effects: moderate increases in rainfall improve efficiency in rainfall-dependent sectors, while excessive or erratic rainfall reduces profitability and tax compliance. Firm-specific factors also shape these relationships; older and larger firms contribute more tax revenue but operate with lower efficiency, reflecting maturity and scale effects. Together, these findings provide consistent evidence that climatic stress reduces both firm productivity and fiscal performance in a low-income setting. The findings point to a dual climate, fiscal vulnerability. Climatic shocks simultaneously weaken firm productivity and profitability, thereby narrowing the corporate income-tax base and constraining fiscal space (IMF, 2024 ; World Bank, 2023 ). Addressing these intertwined risks requires taking adaptation policies that target both the production and revenue sides of the economy. Investments in climate-resilient infrastructure, energy-efficient technologies, and early-warning systems are critical to sustaining productivity amid rising temperatures and rainfall variability. Equally vital is the integration of climate risk analysis into fiscal frameworks, through climate-informed budgeting, debt-sustainability analysis, and risk-sensitive tax administration (AfDB, 2024 ; OECD, 2022 ). Tailored sectoral strategies, particularly in agriculture, manufacturing, and construction, can further enhance resilience by protecting output, safeguarding employment, and stabilizing revenue performance in the face of increasing climate volatility. This study advances two interrelated strands of literature; climate-firm performance and climate finance and fiscal resilience, by building directly on Mawejje ( 2024 a), who provided the first perception-based evidence of climate impacts on Ugandan firms. Using a short quarterly panel of business climate perceptions (2015Q3–2016Q4), Mawejje found that adverse weather and temperature shocks reduced perceived turnover, profits, capacity utilization, and optimism, particularly among micro and small firms in agriculture and industry. However, that analysis relied on subjective assessments and covered a narrow time frame, leaving the magnitude, persistence, and fiscal implications of these shocks unquantified. The present study extends this foundation in three key ways. First, it employs objective tax administration data from the Uganda Revenue Authority (URA) spanning a decade (2013/14-2022/23), linking realized profits, returns, and tax liabilities with geocoded temperature and rainfall data to capture actual financial and fiscal outcomes rather than perceptions. Second, it applies a causal identification strategy based on within-district variation and firm fixed effects, isolating the direct effects of temperature and rainfall shocks on firm performance and fiscal capacity (Burke & Tanutama, 2019 ; Dell et al., 2012 ). Third, it quantifies the fiscal transmission channel through which climate shocks, by depressing firm profitability, erode corporate tax revenues, situating these micro-level effects within Uganda’s broader macro-fiscal structure and climate-exposed production base (IMF, 2024 ). The findings show that climate variability constitutes both an environmental and fiscal challenge. Conceptually, the study bridges environmental economics and public finance by integrating firm-level climate exposure with fiscal outcomes, establishing a replicable empirical framework for assessing climate fiscal risks in developing economies (Dell et al., 2012 ; Pizer et al., 2014 ; Stern, 2006 ). The remainder of the paper is organized as follows. Section 2 describes the data sources, and Section 3 outlines the empirical strategy and modeling framework. Section 4 presents the main findings and robust checks. Section 5 discusses, and Section 6 concludes and Policy implications. 2 Data Sources 2.1 Corporate Income Tax Data This study integrates firm-level tax records from the Uganda Revenue Authority (URA) with climate data spanning 2013/14-2022/23 to assess the impact of climate variability on corporate outcomes. The tax dataset consists of ten annual waves of administrative records constructed primarily from Non-Individual Income Tax Return forms, systematically linked to the taxpayer registration database to ensure completeness and consistency. The panel contains over 346 variables capturing firms’ financial performance, tax liabilities, balance sheets, capital investments, and sectoral classifications. It incorporates both self-reported returns and official assessments, thereby providing a comprehensive view of Uganda’s corporate tax base. Annual observations range between 30,000 and 64,000 firms, reflecting the dynamic nature of corporate registration and compliance. For analytical reliability, the study retained only firms with consistent reporting over the observed period. 2.2 Climate Data Firm exposure to climatic variability is measured using high-resolution, satellite-based datasets. Daily minimum and maximum temperatures were obtained from the NOAA Climate Prediction Center (CPC) Global Temperature dataset, while daily precipitation was drawn from ClimateSERV. Both sources combine satellite and ground-based observations, offering high spatial precision and temporal continuity in data-scarce regions (Javadi & Masum, 2021; Zhao & Parhizgari, 2024 ). Climate exposure was assigned to 135 district centroids representing Uganda’s administrative boundaries. Daily values were aggregated into monthly and annual indicators, which were then matched to district-year firm tax records. This procedure produces a consistent panel of firm-year observations aligned with local temperature and rainfall shocks. 2.3 Panel Construction District names were standardized before merging datasets, and districts without registered firms were excluded to reduce sparsity. To preserve balance, boundary years (2013 and 2023) were dropped. The climate data were merged with corporate income tax records using anonymized firm identifiers, yielding a spatially consistent panel that links firm-year tax outcomes to district-level weather shocks. The resulting dataset provides more than 404,000 firm-year observations for longitudinal analysis of climate variability and its effects on profitability and tax compliance. 2.4 Data Innovations A key innovation of this study lies in combining administrative tax records with high-frequency climate data. Traditional studies often rely on aggregate indicators, which obscure firm-level exposure to climatic shocks. By contrast, this dataset directly links localized climate variation to firm behavior, reducing measurement error and strengthening causal inference (Burke et al., 2015 ). Unlike survey data, the administrative tax records capture the full universe of registered taxpayers, ensuring comprehensive coverage of the formal corporate sector. The use of daily satellite observations further enhances precision by capturing district-specific shocks rather than national averages. This integrated approach aligns with recent calls for micro-level climate-economy research that can inform policy in vulnerable economies (Treepongkaruna et al., 2024 ; World Bank, 2023 ). 2.5 Descriptive Statistics Table 1 presents descriptive statistics for the balanced panel of 404,527 firm-year observations. Firms in the sample operate in districts with a mean annual temperature of 22.5°C and rainfall of 1,376 mm, providing sufficient climatic variation to assess the impacts. Mean annual sales are UGX 1.84 billion, while profits after tax average UGX 60.7 million and CIT liabilities UGX 24.7 million, both with wide dispersion. Micro and small enterprises dominate the sample (95.5%), though medium and large firms account for a disproportionate share of sales and tax revenue. The sectoral distribution shows a concentration in services (78.1%), followed by construction (11.3%) and agriculture (4.7%). Kampala contributes nearly 58% of observations, reflecting an urban bias in the tax base and potential exposure to localized climate shocks. These descriptive patterns underscore both the representativeness of the dataset and the heterogeneity across firms that motivates the empirical analysis. Table 1 Descriptive Statistics Variable Obs Mean Std.Dev Min Max Total sales in Billion UGX 404,520 1.84 2.38 -1.08 2.59 Temperature in degrees Celsius 404,527 22.50 1.02 14.99 26.57 Rainfall in mm 404,527 1,376.40 269.46 525.49 2,694.44 Profit after tax in Billion UGX 397,756 0.06068 6.02 -3.76 2.23 Tax liability in Billion UGX 397,758 0.0247 9.30 0 1.85 Firm Age (years) 404,527 7.86 3.29 0 13 Other (1 = Yes) 404,527 0.955 0.206 0 1 PSO (1 = Yes) 404,527 0.025 0.155 0 1 Medium (1 = Yes) 404,527 0.001 0.037 0 1 Large (1 = Yes) 404,527 0.017 0.128 0 1 Oil and gas (1 = Yes) 404,527 0.002 0.045 0 1 Agriculture (1 = Yes) 390,340 0.047 0.211 0 1 Services (1 = Yes) 390,340 0.781 0.414 0 1 Manufacturing (1 = Yes) 390,340 0.043 0.203 0 1 Construction (1 = Yes) 390,340 0.113 0.317 0 1 Mining/Water/Electricity (1 = Yes) 390,340 0.016 0.125 0 1 District 404,527 0.577 0.494 0 1 Notes : The table reports the means, standard deviations (Std. Dev.), minimums, and maximums for all variables. Financial figures (sales, profit after tax, tax liability) are in billion Ugandan shillings (UGX). Climate variables are annual district-level averages for temperature (°C) and rainfall (mm). Firm age is in years since registration. Binary variables are coded 1 = Yes, 0 = No; the mean represents the share of firms in that category. 3 Empirical Strategy The empirical strategy outlines how the study tests the link between climate variability and corporate income tax (CIT) performance. Using firm-level panel data combined with temperature and rainfall indicators, the analysis identifies how climate shocks affect firms’ profitability and tax contributions. Controlling for firm and sector characteristics, the approach isolates the impact of climate variability and assesses whether these effects are stronger in climate-sensitive sectors such as agriculture, manufacturing, and construction and across the districts 3.1 Hypotheses This study tests two main hypotheses: Hypothesis 1 (H1) : Firms exposed to greater climate variability (temperature and rainfall shocks) report lower corporate income tax (CIT) contributions than less-exposed firms. Hypothesis 2 (H2) : The adverse effects of climate variability are more pronounced in climate-sensitive sectors (e.g., agriculture, manufacturing, construction), leading to larger declines in profitability and tax compliance relative to less-exposed sectors. 3.2 Modeling Framework To empirically test the stated hypotheses, this study employs a two-stage panel modelling framework designed to disentangle the direct and indirect effects of climate variability on corporate tax outcomes. The framework recognizes that climate shocks can influence firms both immediately, by disrupting operations and cash flows, and indirectly, by eroding profitability and production efficiency over time. The first model estimates the direct relation between climate variability captured through temperature and rainfall deviations from long term means and firms’ corporate income tax(CIT) contributions. This specification identifies whether exposure to climate shocks significantly affects firms’ tax declarations, controlling for firm specific characteristics, sectoral effects and time varying macroeconomic condition. The second model introduces an intermediate mechanism, where firm performance indicators such as profits or productivity serve as the transmission channel through which climate variability impacts tax outcomes. The model helps to clarify whether the reduction in tax payments arises primarily from lower profitability rather than from changes in compliance behavior. The two models provide a coherent analytical structure that isolates the magnitude and pathways of climate impacts on fiscal outcomes at firm level. The panel structure allows the inclusion of firm fixed effects to control for unobservable heterogeneity, and year effects to account for macroeconomic shocks, ensuring robust results. Model 1: Direct Effect on CIT $$\:{CIT}_{it}={\alpha\:}_{i}+{\gamma\:}_{t}+\beta\:Climate{Explosure}_{it}+{X}_{it}\theta\:+{ϵ}_{it}$$ Where \(\:{CIT}_{it}\) is the corporate income tax declared by the firm? \(\:i\) in year \(\:t\) ; \(\:ClimateExposur{e}_{it}\) captures district-level temperature and rainfall shocks; \(\:{X}_{it}\) is a vector of firm-specific controls (size, sector, age, ownership, location); \(\:{\alpha\:}_{i}\) are firm fixed effects; \(\:{\gamma\:}_{t}\) are year fixed effects; and \(\:{ϵ}_{it}\) is the error term. Model 2: Indirect Effect via Firm Performance $$\:{\pi\:}_{it}={\alpha\:}_{i}+{\gamma\:}_{t}+\beta\:Climate{Explosure}_{it}+{X}_{it}\theta\:+{ϵ}_{it}$$ where \(\:{\pi\:}_{it}\) denotes firm performance, measured by profit margin (PM) and return on assets (ROA) for firm \(\:i\) in year \(\:t\) . As in Model 1, \(\:{\alpha\:}_{i}\) ​ are firm fixed effects, \(\:{\alpha\:}_{i}\) are year fixed effects, and \(\:{X}_{it}\) includes firm characteristics. 3.3 Estimation Strategy Both models are estimated using fixed effects (FE) estimators, which control for unobserved, time-invariant heterogeneity across firms, such as managerial practices, corporate culture, or long-term strategies. FE is preferred over random effects because firm-specific unobservables are likely correlated with climate exposure and performance outcomes. To account for heteroskedasticity and serial correlation, standard errors are clustered at the firm level. This approach ensures valid inference given the large panel dimension and repeated climate exposure within firms. In addition, interaction terms between climate variables (temperature * rainfall) are introduced to test for compound effects of simultaneous shocks. Sectoral interaction terms are also estimated to capture heterogeneity across climate-sensitive and less-sensitive industries. Robustness checks include alternative specifications, exclusion of extreme weather outliers, and lagged climate variables to assess persistence. 4 Results and Discussion 4.1 Main Results Table 2 reports the baseline fixed-effects estimates linking climate variability to firm outcomes. Both temperature and rainfall are statistically significant predictors of corporate income tax (CIT) liabilities, return on assets (ROA), and profit margins. A one-percent increase in district-level temperature is associated with a 2 percent decline in CIT (p < 5%), a 4 percent decline in ROA (p < 1%), and a 1.9 percent decline in profit margins (p < 10%). These elasticities, CIT (–2.013), ROA (-4.314), Profit Margin (-1.938), imply that higher temperatures systematically erode firm profitability and, by extension, the tax base. These findings are consistent with global evidence showing that elevated temperatures reduce labor productivity, disrupt production efficiency, and raise operational costs, particularly in heat-exposed economies (Burke et al., 2015 ; Dell et al., 2012 ; Somanathan et al., 2021 ). In Uganda, such effects likely occur through higher energy consumption for cooling, reduced labor output in manufacturing and construction, and faster depreciation of machinery. Rainfall variability also significantly affects firm outcomes but in asymmetric ways. Moderate increases in rainfall improve ROA (+ 0.386, p < 1%), suggesting that adequate precipitation supports productivity in water-dependent sectors such as agro-processing and construction. Conversely, excessive or irregular rainfall reduces CIT (-0.166, p < 5%), likely reflecting infrastructure disruptions, transport delays, and supply-chain interruptions. The opposite signs of the rainfall coefficients indicate that deviations on either side of normal precipitation generate fiscal consequences through distinct channel productivity support versus physical disruption. Table 2 Estimation results: Climate effect on Corporate Income Tax and Firm Performance . Variables CIT ROA Profit Margin Temperature (ln) -2.013 ** -4.314 *** -1.938 * (0.977) (1.422) (1.027) Rainfall (ln) -0.166 ** 0.386 *** 0.0939 (0.0741) (0.100) (0.0761) Firm Age (ln) 0.882 *** -0.0258 * 0.173 *** (0.0113) (0.0132) (0.00985) Medium size 3.029 *** -0.390 *** -0.368 *** (0.0212) (0.0256) (0.0207) Large size 3.937 *** -0.497 *** -0.119 *** (0.0229) (0.0267) (0.0265) Constant 19.98 *** 8.821 * 1.643 (3.133) (4.530) (3.288) Year FE YES YES YES Sector FE YES YES YES District FE YES YES YES Observations 132,272 118,631 142,913 R-squared 0.303 0.028 0.033 Notes : * p < 0.1, ** p < 0.05, *** p < 0.01, Standard errors are in parentheses. Coefficients represent the effects of climate and firm characteristics on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. Temperature and rainfall are log-transformed district-level annual means. Firm age is log-transformed. 4.1.1 Sectoral, Regional, and Firm-Level Controls The inclusion of sector and district fixed effects in Table 2 controls for time-invariant structural and locational differences, such as technology intensity, infrastructure quality, and baseline exposure to climatic risk, ensuring that the estimated temperature and rainfall coefficients capture within-sector and within-district variation over time. This specification isolates the temporal influence of climate variability from persistent factors that differ across industries and regions. The firm-level controls in Table 2 , age and size are also statistically significant and economically meaningful. Older and larger firms pay more corporate income tax and sustain higher profit margins, reflecting experience, administrative capacity, and financial depth. However, their slightly lower ROA indicates diminishing efficiency at scale. In contrast, smaller and younger firms appear more vulnerable to climate shocks, underscoring the fragility of Uganda’s enterprise structure. These results highlight that controlling for heterogeneity is essential to obtain unbiased climate coefficients and reveal that fiscal exposure to climate risk is concentrated among a small number of large, formal firms 4.2 Robustness Checks Two robustness exercises were undertaken to validate the baseline results: (i) re-estimating the model without firm-specific controls and (ii) introducing an interaction between temperature and rainfall. Together, they test whether the observed relationships are driven by model specification or reflect persistent, nonlinear climate effects. Table 3 Estimation results: Climate effects on Corporate Income Tax and performance without firm characteristic controls Variables CIT ROA Profit Margin Temperature (ln) -1.600 -4.305 *** -1.834 * (1.048) (1.426) (1.030) Rainfall (ln) -0.385 *** 0.401 *** 0.0950 (0.0821) (0.100) (0.0762) Constant 22.38 *** 8.591 * 1.662 (3.370) (4.543) (3.297) Year FE YES YES YES Sector FE YES YES YES District FE YES YES YES Observations 132,272 118,631 142,913 R-squared 0.053 0.024 0.029 Notes : * p < 0.1, *** p < 0.01, Standard errors are in parentheses. Coefficients report the estimated effects of climate variables on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. Temperature and rainfall are log-transformed district-level annual means. All models control for year, sector, and district fixed effects. When firm characteristics (age, size) are excluded, the temperature effect on CIT (-1.60) becomes statistically insignificant, while the negative effects on ROA (-4.31 p < 1%) and profit margins (-1.83 p < 10%) remain close to baseline magnitudes. Rainfall continues to reduce CIT (-0.385 p < 1%) and raise ROA (+0.401 p < 1%). The explanatory power for CIT falls sharply (R² = 0.053 vs. 0.303) in Table 2 , confirming that firm heterogeneity, particularly scale and maturity, mediates fiscal responses to climate shocks. These findings echo the literature that structural characteristics shape resilience and tax compliance under environmental stress (Addoum et al., 2020 ; Ayyagari et al., 2014 ; Coad et al., 2018 ). 4.2.1 Interaction Effects Introducing the interaction term (Temperature × Rainfall) reveals compound effects. The interaction is negative and significant for CIT (-0.404, p < 1%) but positive for ROA (+ 0.385, p < 1%), while its impact on profit margins is negligible, as shown in Table 4 . These results imply that simultaneous increases in temperature and rainfall intensify fiscal losses but allow short-term adjustments that sustain asset returns. The pattern is consistent with evidence that compound climatic extremes magnify economic costs in climate-exposed economies ( Dell et al., 2012 ; India & Colmer, 2021 ; Lesk et al., 2016 ) Table 4 Estimation results: Climate effects on Corporate Income Tax and Firm Performance Variables CIT ROA Profit Margin Temperature × Rainfall (ln) -0.404*** 0.385*** 0.0946 (0.0821) (0.101) (0.0763) Constant 18.79*** -5.903*** -4.341*** (0.847) (1.037) (0.787) Year FE YES YES YES Sector FE YES YES YES District FE YES YES YES Observations 132,272 118,631 142,913 R-squared 0.053 0.024 0.029 Notes : *** p < 0.01, Standard errors are in parentheses, Coefficients capture the interactive effects of temperature and rainfall (log-transformed) on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. All models include year, sector, and district fixed effects. Across both tests, the direction and significance of coefficients remain stable, confirming that the baseline results are not artifacts of model specification. Firm heterogeneity strengthens resilience, while compound shocks amplify vulnerability, demonstrating that climate impacts are persistent, nonlinear, and economically significant. Together, these results validate the main conclusion that climatic variability undermines firm productivity and compresses the corporate tax base in Uganda. 5 Discussion The empirical findings presented in Table 2 through Table 4 provide robust evidence linking climate variability to firm performance and corporate tax outcomes in Uganda, there by extending the emerging body of firm-level climate impact literature in developing economies. Unlike previous perception-based analyses, this study uses administrative tax data that reflect realized fiscal and financial outcomes, enhancing the credibility of causal inference. The results corroborate Mawejje ( 2024 a), who finds that adverse weather weakens business confidence in Uganda. The results reveal that a one percent rise in temperature is associated with approximately 4 percent decline in return on assets and a 2 percent reduction in CIT, magnitudes comparable to the climate-induced financing costs reported by Zhao & Parhizgari ( 2024 ) and the productivity losses documented by Moyo & Wingard ( 2015 ) and Heo ( 2021 ). Rainfall variability produces asymmetric effects: while moderate precipitation enhances efficiency in water-dependent sectors such as agro-processing and construction, excessive rainfall disrupts supply chains and undermines tax compliance. These findings highlight that both heat stress and rainfall shocks transmit adverse effects through multiple firm-level channels ranging from reduced productivity to higher operational disruptions. From the results, two main transmission mechanisms emerge. At the micro level, temperature and rainfall anomalies directly reduce productivity, profitability and efficiency, weakening firms’ ability to meet tax obligations. At the macro level, the aggregation of these firm-level losses compresses the overall tax base and generates fiscal instability. In economies such as Uganda, where a small number of large, formal firms account for the majority of corporate tax revenue, climate variability becomes both a productivity shock and a fiscal shock. The dual nature of this impact demonstrates that climate stress in low-income economies can erode both their growth potential and fiscal capacity at the same time(AfDB, 2025 ; Capelle, 2023 ). Moreover, the interaction effects between temperature and rainfall underscore the compound nature of climate risks. Simultaneous climatic extremes amplify negative impacts on tax revenue, consistent with evidence fromDell et al. ( 2012 ) and Lesk et al. ( 2016 ), who demonstrate that combined temperature and precipitation shocks magnify economic costs beyond their individual effects. This pattern is particularly concerning for Uganda’s fiscal planning, as it implies that climate volatility can introduce nonlinear risks that traditional tax forecasting models fail to capture. By quantifying these linkages, the study bridges the microeconomic literature on firm performance and adaptation and the macroeconomic discourse on fiscal resilience and climate vulnerability. It provides empirical support for the argument advanced by Pizer et al. ( 2014 ) and Stern ( 2006 ) that climate risk should be treated as a systemic economic and fiscal issue rather than an environmental externality. The Ugandan evidence contributes to a growing recognition that in climate vulnerable, low -diversified economies, adaption is not only a production imperative but also a fiscal necessity. The discussion shows that climatic variability undermines both corporate and fiscal resilience in a mutually reinforcing cycle. Firms experiencing productivity shocks contribute less to the revenue base, while reduced fiscal space constrains the government’s capacity to fund adaptation measures. This self-reinforcing feedback loop highlights the importance of integrating climate risk assessment into both enterprise strategy and public financial management, positioning adaptation as a cross-cutting pillar for economic sustainability in Uganda and across East Africa. 6 Conclusion and Policy Implications 6.1 Conclusion This paper provides new micro-evidence on how climate variability affects corporate performance and fiscal outcomes in Uganda. Using administrative tax data merged with district-level climate indicators, the analysis shows that higher temperatures and erratic rainfall significantly weaken firm profitability and narrow the corporate income-tax base. A 1 percent increase in temperature reduces return on assets by about 4 percent and corporate tax liabilities by roughly 2 percent, while rainfall volatility has mixed effects, supporting productivity under moderate precipitation but reducing tax compliance when rainfall becomes excessive. Robustness checks confirm that these effects are stable, persistent, and nonlinear: the results remain significant across alternative specifications and become amplified under compound climate shocks. Firm-level heterogeneity further conditions the magnitude of these impacts; older and larger firms demonstrate fiscal resilience, whereas smaller enterprises are disproportionately affected. Taken together, the findings reveal a dual climate-fiscal vulnerability. At the micro level, climate shocks reduce productivity, profits, and investment capacity. At the macro level, these losses compress the tax base, generate revenue volatility, and weaken fiscal space for adaptation and development spending. In an economy where a few large firms account for most tax receipts, this link between environmental and fiscal fragility poses a structural constraint to sustainable revenue mobilization growth. The results, therefore, bridge two strands of literature: the micro-economics of firm adaptation and the macro-economics of fiscal resilience, extending the empirical evidence to an African context where such linkages remain under-studied (AfDB, 2025 ; Capelle, 2023 ; Pizer et al., 2014 ; Stern, 2006 ) 6.2 Policy Implications The results show that temperature and rainfall variability directly undermine both corporate profitability and fiscal stability, suggesting that fiscal adaptation must begin with enterprise resilience. The observed 4-percent decline in return on assets and 2-percent fall in corporate tax liabilities for every one-percent temperature rise indicate that climate risks should be embedded into fiscal planning and tax-revenue forecasting. Integrating climate-fiscal stress testing into Uganda’s medium-term expenditure and debt-sustainability frameworks would help anticipate revenue shortfalls arising from climatic shocks. Firm-level heterogeneity highlights the need to support smaller enterprises that are most exposed to heat stress, while ensuring continued compliance among large firms that contribute the bulk of corporate tax receipts. Targeted incentives for energy-efficient production, water-management systems, and climate-resilient infrastructure would protect profitability and stabilize the tax base. Finally, the evidence of compound shocks underscores the urgency of regional cooperation to harmonize climate-fiscal diagnostics across East Africa, aligning adaptation finance and tax risk management. These empirically grounded measures directly respond to the mechanisms identified in the analysis, temperature and rainfall shocks that simultaneously erode firm productivity and fiscal capacity, thereby strengthening both corporate and macro-fiscal resilience. Declarations Author Contribution The research titled “Climate Shocks and Tax Base: Firm-Level Evidence on Profitability and Corporate Income Tax Compliance in Uganda” was jointly conceptualized and conducted by Byamukama Bernis (B.B.) and Rukundo Johnson (J.R.). B.B. collected and curated the data, performed the literature review, and wrote the full manuscript draft. J.R. developed the econometric methodologies and conducted the data analysis. Both authors contributed to the interpretation of results, revised the manuscript critically for important intellectual content, and approved the final version for submission. Acknowledgement The authors gratefully acknowledge Franklin Amuakwa-Mensah (University of Gothenburg, [email protected] ) for his valuable supervision, guidance, and comments on this article. This study uses the Uganda Revenue Authority Firm Panel Dataset, developed in collabora-tion with the Uganda Revenue Authority and UNU-WIDER. The author thanks Agaba Gerald (Re-search Lab Assistant, [email protected] ) and Kwisanga Ernest ( [email protected] ) for their assistance in data processing and analysis, and appreciates the helpful feedback from profes-sors and students at the College of Business and Economics, University of Rwanda, during a semi-nar held on October 1, 2025. Financial support was provided by the Swedish International Devel-opment Cooperation Agency (Sida) under the Research Training Program (2019-2024), implement-ed through the University of Rwanda’s capacity-building initiative in collaboration with the Envi-ronment for Development (EfD) at the University of Gothenburg. 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Nature , 527 (7577), 235–239. https://doi.org/10.1038/NATURE15725 Burke, M., & Tanutama, V. (2019). Climatic Constraints on Aggregate Economic Output . https://doi.org/10.3386/W25779 Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P. W., Trisos, C., Romero, J., Aldunce, P., Barret, K., Blanco, G., Cheung, W. W. L., Connors, S. L., Denton, F., Diongue-Niang, A., Dodman, D., Garschagen, M., Geden, O., Hayward, B., Jones, C., … Ha, M. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland. https://doi.org/10.59327/IPCC/AR6-9789291691647.001 Capelle, D. (2023). Mitigating Climate Change at the Firm Level: Mind the Laggards. IMF Working Papers , 2023 (242), 1. https://doi.org/10.5089/9798400258541.001 Coad, A., Holm, J. R., Krafft, J., & Quatraro, F. (2018). Firm age and performance. Journal of Evolutionary Economics , 28 (1), 1–11. https://doi.org/10.1007/S00191-017-0532-6/METRICS Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature Shocks and Economic Growth: Evidence from the Last Half Century. American Economic Journal: Macroeconomics , 4 (3), 66–95. https://doi.org/10.1257/MAC.4.3.66 Heo, Y. (2021). Climate Change Exposure and Firm Cash Holdings. SSRN Electronic Journal . https://doi.org/10.2139/SSRN.3795298 IMF. (2024). Uganda. IMF Staff Country Reports , 2024 (290), 1. https://doi.org/10.5089/9798400288821.002 India, E. F., & Colmer, J. (2021). Temperature, Labor Reallocation, and Industrial Production: Evidence from India. American Economic Journal: Applied Economics , 13 (4), 101–124. https://doi.org/10.1257/APP.20190249 Javadi, S., & Masum, A. Al. (2021). The impact of climate change on the cost of bank loans. Journal of Corporate Finance , 69 , 102019. https://doi.org/10.1016/J.JCORPFIN.2021.102019 Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature , 529 (7584), 84–87. https://doi.org/10.1038/NATURE16467 Mawejje, J. (2024). How does the weather and climate change affect firm performance in low-income countries? Evidence from Uganda. Sustainable Futures , 7 , 100167. https://doi.org/10.1016/J.SFTR.2024.100167 MoFPED. (2024). Uganda Tax Expenditure Report FY22/23 . https://www.finance.go.ug/sites/default/files/reports/Tax%20Expenditure%20Report%20FY22-23.pdf Moyo, M., & Wingard, H. C. (2015). An assessment of the impact of climate change on the financial performance of South African companies. Journal of Governance and Regulation , 4 (2), 49–62. https://doi.org/10.22495/JGR_V4_I2_P5 NPA. (2022). NATIONAL PLANNING AUTHORITY ANNUAL PERFORMANCE REPORT . https://www.npa.go.ug/wp-content/uploads/2023/09/Final-NPA-Annual-Report-FY2021_22_compressed.pdf OECD. (2022). Tax Administration 2022 . OECD. https://doi.org/10.1787/1e797131-en OECD. (2024). The Climate Action Monitor 2024 . OECD. https://doi.org/10.1787/787786f6-en Pizer, W., Adler, M., Aldy, J., Anthoff, D., Cropper, M., Gillingham, K., Greenstone, M., Murray, B., Newell, R., Richels, R., Rowell, A., Waldhoff, S., & Wiener, J. (2014). Using and improving the social cost of carbon. Science , 346 (6214), 1189–1190. https://doi.org/10.1126/SCIENCE.1259774 Somanathan, E., Somanathan, R., Sudarshan, A., & Tewari, M. (2021). The impact of temperature on productivity and labor supply: Evidence from indian manufacturing. Journal of Political Economy , 129 (6), 1797–1827. https://doi.org/10.1086/713733 Stern, N. (2006). Stern review: the economics of climate change . Treepongkaruna, S., Jiraporn, P., Kyaw, K., & Padungsaksawasdi, C. (2024). Climate change exposure and corporate culture: A text-based approach. International Review of Economics & Finance , 95 , 103497. https://doi.org/10.1016/J.IREF.2024.103497 Wooldridge, J. M. . (2010). Econometric Analysis of Cross Section and Panel Data, Second Edition . World Bank. (2023). Uganda Economic Update . https://documents1.worldbank.org/curated/en/099020224131540261/pdf/P1798401a450b40361963b12634ab074169.pdf Zhao, L., & Parhizgari, A. M. (2024). Climate change, technological innovation, and firm performance. International Review of Economics & Finance , 93 , 189–203. https://doi.org/10.1016/J.IREF.2024.04.025 Additional Declarations No competing interests reported. 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Rising temperatures and erratic rainfall patterns pose a significant threat to productivity, infrastructure, and fiscal stability, particularly in low-income countries with limited adaptive capacity (Calvin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Global surface temperatures have already risen by approximately 1.1\u0026deg;C above pre-industrial levels, primarily driven by fossil fuel combustion and land-use change (Calvin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Under high-emission scenarios, global temperatures could rise by more than 4\u0026deg;C by 2100, a level that would severely challenge human habitability (Brown \u0026amp; Caldeira, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These climatic shifts have intensified droughts and floods, disrupted production networks, and heightened macroeconomic volatility (Capelle, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although macro-level studies document the negative effects of climate shocks on aggregate output and welfare (Burke \u0026amp; Tanutama, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), much less is known about the firm-level mechanisms through which temperature and rainfall variability affect productivity and fiscal outcomes in Africa. In particular, the micro-fiscal dimension of climate vulnerability on how corporate performance interacts with domestic revenue mobilization remains largely unexplored (AfDB, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; OECD, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper examines how temperature and rainfall variability influence firm performance and corporate income tax (CIT) compliance in Uganda, a small, climate-vulnerable economy with a narrow fiscal base and a high dependence on weather-sensitive sectors (Capelle, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Uganda provides a compelling empirical context for three reasons. First, its equatorial geography exposes firms to frequent temperature extremes and rainfall fluctuations. Second, agriculture, manufacturing, and construction, the country\u0026rsquo;s main sectors that drive economic growth, remain highly climate dependent. Third, formal enterprises, though relatively few, generate most domestic tax revenue but are vulnerable to frequent climate shocks (MoFPED, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; NPA, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This structural composition, coupled with limited diversification and adaptive capacity, increases exposure to climatic shocks. Understanding how these shocks are transmitted through firms to affect both private profitability and fiscal capacity is critical for assessing economic resilience under growing climate change uncertainty.\u003c/p\u003e\u003cp\u003eThe analysis draws on administrative firm-level tax records from the Uganda Revenue Authority (URA), merged with high-resolution satellite data on temperature and rainfall for 2013/14-2022/23. The resulting panel covers 404,000 firm-year observations across districts and sectors, providing a rare opportunity to examine how local climatic variations influence firms\u0026rsquo; financial and fiscal outcomes. Using fixed-effects estimators, the study exploits within-district variation in climate conditions to identify causal effects on profitability, efficiency, and tax liabilities. The empirical model accounts for unobserved firm heterogeneity, time-invariant location factors, and sector-specific attributes to minimize confounding (Burke et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wooldridge, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This longitudinal approach extends earlier perception-based work by Mawejje (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea) using verified administrative data that reflect realized rather than self-reported outcomes, offering more credible and policy-relevant evidence on how climate variability shapes firm behavior and fiscal performance in Uganda.\u003c/p\u003e\u003cp\u003eEmpirical results show that higher temperatures significantly reduce firm outcomes across multiple model specifications. A one-percent increase in temperature within a district lowers corporate income-tax payments by about 2 percent, return on assets by more than 4 percent, and profit margins by nearly 2 percent. Rainfall exhibits asymmetric effects: moderate increases in rainfall improve efficiency in rainfall-dependent sectors, while excessive or erratic rainfall reduces profitability and tax compliance. Firm-specific factors also shape these relationships; older and larger firms contribute more tax revenue but operate with lower efficiency, reflecting maturity and scale effects. Together, these findings provide consistent evidence that climatic stress reduces both firm productivity and fiscal performance in a low-income setting.\u003c/p\u003e\u003cp\u003eThe findings point to a dual climate, fiscal vulnerability. Climatic shocks simultaneously weaken firm productivity and profitability, thereby narrowing the corporate income-tax base and constraining fiscal space (IMF, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Addressing these intertwined risks requires taking adaptation policies that target both the production and revenue sides of the economy. Investments in climate-resilient infrastructure, energy-efficient technologies, and early-warning systems are critical to sustaining productivity amid rising temperatures and rainfall variability. Equally vital is the integration of climate risk analysis into fiscal frameworks, through climate-informed budgeting, debt-sustainability analysis, and risk-sensitive tax administration (AfDB, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; OECD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Tailored sectoral strategies, particularly in agriculture, manufacturing, and construction, can further enhance resilience by protecting output, safeguarding employment, and stabilizing revenue performance in the face of increasing climate volatility.\u003c/p\u003e\u003cp\u003eThis study advances two interrelated strands of literature; climate-firm performance and climate finance and fiscal resilience, by building directly on Mawejje (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea), who provided the first perception-based evidence of climate impacts on Ugandan firms. Using a short quarterly panel of business climate perceptions (2015Q3\u0026ndash;2016Q4), Mawejje found that adverse weather and temperature shocks reduced perceived turnover, profits, capacity utilization, and optimism, particularly among micro and small firms in agriculture and industry. However, that analysis relied on subjective assessments and covered a narrow time frame, leaving the magnitude, persistence, and fiscal implications of these shocks unquantified. The present study extends this foundation in three key ways. First, it employs objective tax administration data from the Uganda Revenue Authority (URA) spanning a decade (2013/14-2022/23), linking realized profits, returns, and tax liabilities with geocoded temperature and rainfall data to capture actual financial and fiscal outcomes rather than perceptions. Second, it applies a causal identification strategy based on within-district variation and firm fixed effects, isolating the direct effects of temperature and rainfall shocks on firm performance and fiscal capacity (Burke \u0026amp; Tanutama, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Third, it quantifies the fiscal transmission channel through which climate shocks, by depressing firm profitability, erode corporate tax revenues, situating these micro-level effects within Uganda\u0026rsquo;s broader macro-fiscal structure and climate-exposed production base (IMF, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The findings show that climate variability constitutes both an environmental and fiscal challenge. Conceptually, the study bridges environmental economics and public finance by integrating firm-level climate exposure with fiscal outcomes, establishing a replicable empirical framework for assessing climate fiscal risks in developing economies (Dell et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pizer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stern, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe remainder of the paper is organized as follows. Section 2 describes the data sources, and Section 3 outlines the empirical strategy and modeling framework. Section 4 presents the main findings and robust checks. Section 5 discusses, and Section 6 concludes and Policy implications.\u003c/p\u003e"},{"header":"2 Data Sources","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Corporate Income Tax Data\u003c/h2\u003e\u003cp\u003eThis study integrates firm-level tax records from the Uganda Revenue Authority (URA) with climate data spanning 2013/14-2022/23 to assess the impact of climate variability on corporate outcomes. The tax dataset consists of ten annual waves of administrative records constructed primarily from Non-Individual Income Tax Return forms, systematically linked to the taxpayer registration database to ensure completeness and consistency. The panel contains over 346 variables capturing firms\u0026rsquo; financial performance, tax liabilities, balance sheets, capital investments, and sectoral classifications. It incorporates both self-reported returns and official assessments, thereby providing a comprehensive view of Uganda\u0026rsquo;s corporate tax base. Annual observations range between 30,000 and 64,000 firms, reflecting the dynamic nature of corporate registration and compliance. For analytical reliability, the study retained only firms with consistent reporting over the observed period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Climate Data\u003c/h2\u003e\u003cp\u003eFirm exposure to climatic variability is measured using high-resolution, satellite-based datasets. Daily minimum and maximum temperatures were obtained from the NOAA Climate Prediction Center (CPC) Global Temperature dataset, while daily precipitation was drawn from ClimateSERV. Both sources combine satellite and ground-based observations, offering high spatial precision and temporal continuity in data-scarce regions (Javadi \u0026amp; Masum, 2021; Zhao \u0026amp; Parhizgari, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Climate exposure was assigned to 135 district centroids representing Uganda\u0026rsquo;s administrative boundaries. Daily values were aggregated into monthly and annual indicators, which were then matched to district-year firm tax records. This procedure produces a consistent panel of firm-year observations aligned with local temperature and rainfall shocks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Panel Construction\u003c/h2\u003e\u003cp\u003eDistrict names were standardized before merging datasets, and districts without registered firms were excluded to reduce sparsity. To preserve balance, boundary years (2013 and 2023) were dropped. The climate data were merged with corporate income tax records using anonymized firm identifiers, yielding a spatially consistent panel that links firm-year tax outcomes to district-level weather shocks. The resulting dataset provides more than 404,000 firm-year observations for longitudinal analysis of climate variability and its effects on profitability and tax compliance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data Innovations\u003c/h2\u003e\u003cp\u003eA key innovation of this study lies in combining administrative tax records with high-frequency climate data. Traditional studies often rely on aggregate indicators, which obscure firm-level exposure to climatic shocks. By contrast, this dataset directly links localized climate variation to firm behavior, reducing measurement error and strengthening causal inference (Burke et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Unlike survey data, the administrative tax records capture the full universe of registered taxpayers, ensuring comprehensive coverage of the formal corporate sector. The use of daily satellite observations further enhances precision by capturing district-specific shocks rather than national averages. This integrated approach aligns with recent calls for micro-level climate-economy research that can inform policy in vulnerable economies (Treepongkaruna et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Descriptive Statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for the balanced panel of 404,527 firm-year observations. Firms in the sample operate in districts with a mean annual temperature of 22.5\u0026deg;C and rainfall of 1,376 mm, providing sufficient climatic variation to assess the impacts. Mean annual sales are UGX 1.84\u0026nbsp;billion, while profits after tax average UGX 60.7\u0026nbsp;million and CIT liabilities UGX 24.7\u0026nbsp;million, both with wide dispersion. Micro and small enterprises dominate the sample (95.5%), though medium and large firms account for a disproportionate share of sales and tax revenue. The sectoral distribution shows a concentration in services (78.1%), followed by construction (11.3%) and agriculture (4.7%). Kampala contributes nearly 58% of observations, reflecting an urban bias in the tax base and potential exposure to localized climate shocks. These descriptive patterns underscore both the representativeness of the dataset and the heterogeneity across firms that motivates the empirical analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd.Dev\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sales in Billion UGX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature in degrees Celsius\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainfall in mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,376.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e269.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e525.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,694.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfit after tax in Billion UGX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e397,756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTax liability in Billion UGX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e397,758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirm Age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSO (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLarge (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOil and gas (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eServices (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruction (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMining/Water/Electricity (1\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e390,340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e404,527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNotes\u003c/b\u003e: \u003cem\u003eThe table reports the means, standard deviations (Std. Dev.), minimums, and maximums for all variables. Financial figures (sales, profit after tax, tax liability) are in billion Ugandan shillings (UGX). Climate variables are annual district-level averages for temperature (\u0026deg;C) and rainfall (mm). Firm age is in years since registration. Binary variables are coded 1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No; the mean represents the share of firms in that category.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Empirical Strategy","content":"\u003cp\u003eThe empirical strategy outlines how the study tests the link between climate variability and corporate income tax (CIT) performance. Using firm-level panel data combined with temperature and rainfall indicators, the analysis identifies how climate shocks affect firms\u0026rsquo; profitability and tax contributions. Controlling for firm and sector characteristics, the approach isolates the impact of climate variability and assesses whether these effects are stronger in climate-sensitive sectors such as agriculture, manufacturing, and construction and across the districts\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Hypotheses\u003c/h2\u003e\u003cp\u003eThis study tests two main hypotheses:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003e\u003cb\u003e(H1)\u003c/b\u003e: Firms exposed to greater climate variability (temperature and rainfall shocks) report lower corporate income tax (CIT) contributions than less-exposed firms.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003e\u003cb\u003e(H2)\u003c/b\u003e: The adverse effects of climate variability are more pronounced in climate-sensitive sectors (e.g., agriculture, manufacturing, construction), leading to larger declines in profitability and tax compliance relative to less-exposed sectors.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Modeling Framework\u003c/h2\u003e\u003cp\u003eTo empirically test the stated hypotheses, this study employs a two-stage panel modelling framework designed to disentangle the direct and indirect effects of climate variability on corporate tax outcomes. The framework recognizes that climate shocks can influence firms both immediately, by disrupting operations and cash flows, and indirectly, by eroding profitability and production efficiency over time.\u003c/p\u003e\u003cp\u003eThe first model estimates the direct relation between climate variability captured through temperature and rainfall deviations from long term means and firms\u0026rsquo; corporate income tax(CIT) contributions. This specification identifies whether exposure to climate shocks significantly affects firms\u0026rsquo; tax declarations, controlling for firm specific characteristics, sectoral effects and time varying macroeconomic condition.\u003c/p\u003e\u003cp\u003eThe second model introduces an intermediate mechanism, where firm performance indicators such as profits or productivity serve as the transmission channel through which climate variability impacts tax outcomes. The model helps to clarify whether the reduction in tax payments arises primarily from lower profitability rather than from changes in compliance behavior. The two models provide a coherent analytical structure that isolates the magnitude and pathways of climate impacts on fiscal outcomes at firm level. The panel structure allows the inclusion of firm fixed effects to control for unobservable heterogeneity, and year effects to account for macroeconomic shocks, ensuring robust results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 1: Direct Effect on CIT\u003c/b\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{CIT}_{it}={\\alpha\\:}_{i}+{\\gamma\\:}_{t}+\\beta\\:Climate{Explosure}_{it}+{X}_{it}\\theta\\:+{ϵ}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CIT}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the corporate income tax declared by the firm? \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ClimateExposur{e}_{it}\\)\u003c/span\u003e\u003c/span\u003e captures district-level temperature and rainfall shocks; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a vector of firm-specific controls (size, sector, age, ownership, location); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are firm fixed effects; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e are year fixed effects; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the error term.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 2: Indirect Effect via Firm Performance\u003c/b\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\pi\\:}_{it}={\\alpha\\:}_{i}+{\\gamma\\:}_{t}+\\beta\\:Climate{Explosure}_{it}+{X}_{it}\\theta\\:+{ϵ}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\pi\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e denotes firm performance, measured by profit margin (PM) and return on assets (ROA) for firm \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e. As in Model 1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ are firm fixed effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are year fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e includes firm characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Estimation Strategy\u003c/h2\u003e\u003cp\u003eBoth models are estimated using fixed effects (FE) estimators, which control for unobserved, time-invariant heterogeneity across firms, such as managerial practices, corporate culture, or long-term strategies. FE is preferred over random effects because firm-specific unobservables are likely correlated with climate exposure and performance outcomes. To account for heteroskedasticity and serial correlation, standard errors are clustered at the firm level. This approach ensures valid inference given the large panel dimension and repeated climate exposure within firms.\u003c/p\u003e\u003cp\u003eIn addition, interaction terms between climate variables (temperature * rainfall) are introduced to test for compound effects of simultaneous shocks. Sectoral interaction terms are also estimated to capture heterogeneity across climate-sensitive and less-sensitive industries. Robustness checks include alternative specifications, exclusion of extreme weather outliers, and lagged climate variables to assess persistence.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Main Results\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports the baseline fixed-effects estimates linking climate variability to firm outcomes. Both temperature and rainfall are statistically significant predictors of corporate income tax (CIT) liabilities, return on assets (ROA), and profit margins. A one-percent increase in district-level temperature is associated with a 2 percent decline in CIT (p\u0026thinsp;\u0026lt;\u0026thinsp;5%), a 4 percent decline in ROA (p\u0026thinsp;\u0026lt;\u0026thinsp;1%), and a 1.9 percent decline in profit margins (p\u0026thinsp;\u0026lt;\u0026thinsp;10%). These elasticities, CIT (\u0026ndash;2.013), ROA (-4.314), Profit Margin (-1.938), imply that higher temperatures systematically erode firm profitability and, by extension, the tax base. These findings are consistent with global evidence showing that elevated temperatures reduce labor productivity, disrupt production efficiency, and raise operational costs, particularly in heat-exposed economies (Burke et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dell et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Somanathan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Uganda, such effects likely occur through higher energy consumption for cooling, reduced labor output in manufacturing and construction, and faster depreciation of machinery.\u003c/p\u003e\n \u003cp\u003eRainfall variability also significantly affects firm outcomes but in asymmetric ways. Moderate increases in rainfall improve ROA (+\u0026thinsp;0.386, p\u0026thinsp;\u0026lt;\u0026thinsp;1%), suggesting that adequate precipitation supports productivity in water-dependent sectors such as agro-processing and construction. Conversely, excessive or irregular rainfall reduces CIT (-0.166, p\u0026thinsp;\u0026lt;\u0026thinsp;5%), likely reflecting infrastructure disruptions, transport delays, and supply-chain interruptions. The opposite signs of the rainfall coefficients indicate that deviations on either side of normal precipitation generate fiscal consequences through distinct channel productivity support versus physical disruption.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimation results: Climate effect on Corporate Income Tax and Firm Performance\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfit Margin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.314\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.938\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.422)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.166\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0761)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirm Age (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0258\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00985)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.390\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.368\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.937\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.497\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.119\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0229)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0265)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.98\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.821\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118,631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142,913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Standard errors are in parentheses. Coefficients represent the effects of climate and firm characteristics on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. Temperature and rainfall are log-transformed district-level annual means. Firm age is log-transformed.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.1 Sectoral, Regional, and Firm-Level Controls\u003c/h2\u003e\n \u003cp\u003eThe inclusion of sector and district fixed effects in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e controls for time-invariant structural and locational differences, such as technology intensity, infrastructure quality, and baseline exposure to climatic risk, ensuring that the estimated temperature and rainfall coefficients capture \u003cem\u003ewithin-sector\u003c/em\u003e and \u003cem\u003ewithin-district\u003c/em\u003e variation over time. This specification isolates the temporal influence of climate variability from persistent factors that differ across industries and regions. The firm-level controls in\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, age and size are also statistically significant and economically meaningful. Older and larger firms pay more corporate income tax and sustain higher profit margins, reflecting experience, administrative capacity, and financial depth. However, their slightly lower ROA indicates diminishing efficiency at scale. In contrast, smaller and younger firms appear more vulnerable to climate shocks, underscoring the fragility of Uganda\u0026rsquo;s enterprise structure. These results highlight that controlling for heterogeneity is essential to obtain unbiased climate coefficients and reveal that fiscal exposure to climate risk is concentrated among a small number of large, formal firms\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Robustness Checks\u003c/h2\u003e\n \u003cp\u003eTwo robustness exercises were undertaken to validate the baseline results: (i) re-estimating the model without firm-specific controls and (ii) introducing an interaction between temperature and rainfall. Together, they test whether the observed relationships are driven by model specification or reflect persistent, nonlinear climate effects.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results: Climate effects on Corporate Income Tax and performance without firm characteristic controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfit Margin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.305\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.834\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.385\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.401\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0762)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.591\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118,631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142,913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: \u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Standard errors are in parentheses. Coefficients report the estimated effects of climate variables on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. Temperature and rainfall are log-transformed district-level annual means. All models control for year, sector, and district fixed effects.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhen firm characteristics (age, size) are excluded, the temperature effect on CIT (-1.60) becomes statistically insignificant, while the negative effects on ROA (-4.31 p \u0026lt; 1%) and profit margins (-1.83 p \u0026lt; 10%) remain close to baseline magnitudes. Rainfall continues to reduce CIT (-0.385 p \u0026lt; 1%) and raise ROA (+0.401 p \u0026lt; 1%). The explanatory power for CIT falls sharply (R\u0026sup2; = 0.053 vs. 0.303) in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, confirming that firm heterogeneity, particularly scale and maturity, mediates fiscal responses to climate shocks. These findings echo the literature that structural characteristics shape resilience and tax compliance under environmental stress (Addoum et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ayyagari et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Coad et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Interaction Effects\u003c/h2\u003e\n \u003cp\u003eIntroducing the interaction term (Temperature \u0026times; Rainfall) reveals compound effects. The interaction is negative and significant for CIT (-0.404, p\u0026thinsp;\u0026lt;\u0026thinsp;1%) but positive for ROA (+\u0026thinsp;0.385, p\u0026thinsp;\u0026lt;\u0026thinsp;1%), while its impact on profit margins is negligible, as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. These results imply that simultaneous increases in temperature and rainfall intensify fiscal losses but allow short-term adjustments that sustain asset returns. The pattern is consistent with evidence that compound climatic extremes magnify economic costs in climate-exposed economies\u003cem\u003e(\u003c/em\u003eDell et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; India \u0026amp; Colmer, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lesk et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimation results: Climate effects on Corporate Income Tax and Firm Performance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfit Margin\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature \u0026times; Rainfall (ln)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.404***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0763)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.903***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.341***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132,272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118,631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142,913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: \u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Standard errors are in parentheses, Coefficients capture the interactive effects of temperature and rainfall (log-transformed) on Corporate Income Tax (CIT) liabilities, Return on Assets (ROA), and profit margins. All models include year, sector, and district fixed effects.\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAcross both tests, the direction and significance of coefficients remain stable, confirming that the baseline results are not artifacts of model specification. Firm heterogeneity strengthens resilience, while compound shocks amplify vulnerability, demonstrating that climate impacts are persistent, nonlinear, and economically significant. Together, these results validate the main conclusion that climatic variability undermines firm productivity and compresses the corporate tax base in Uganda.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe empirical findings presented in\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e through Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provide robust evidence linking climate variability to firm performance and corporate tax outcomes in Uganda, there by extending the emerging body of firm-level climate impact literature in developing economies. Unlike previous perception-based analyses, this study uses administrative tax data that reflect realized fiscal and financial outcomes, enhancing the credibility of causal inference. The results corroborate Mawejje (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea), who finds that adverse weather weakens business confidence in Uganda. The results reveal that a one percent rise in temperature is associated with approximately 4 percent decline in return on assets and a 2 percent reduction in CIT, magnitudes comparable to the climate-induced financing costs reported by Zhao \u0026amp; Parhizgari (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the productivity losses documented by Moyo \u0026amp; Wingard (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Heo (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rainfall variability produces asymmetric effects: while moderate precipitation enhances efficiency in water-dependent sectors such as agro-processing and construction, excessive rainfall disrupts supply chains and undermines tax compliance. These findings highlight that both heat stress and rainfall shocks transmit adverse effects through multiple firm-level channels ranging from reduced productivity to higher operational disruptions.\u003c/p\u003e\u003cp\u003eFrom the results, two main transmission mechanisms emerge. At the micro level, temperature and rainfall anomalies directly reduce productivity, profitability and efficiency, weakening firms\u0026rsquo; ability to meet tax obligations. At the macro level, the aggregation of these firm-level losses compresses the overall tax base and generates fiscal instability. In economies such as Uganda, where a small number of large, formal firms account for the majority of corporate tax revenue, climate variability becomes both a productivity shock and a fiscal shock. The dual nature of this impact demonstrates that climate stress in low-income economies can erode both their growth potential and fiscal capacity at the same time(AfDB, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Capelle, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the interaction effects between temperature and rainfall underscore the compound nature of climate risks. Simultaneous climatic extremes amplify negative impacts on tax revenue, consistent with evidence fromDell et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Lesk et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who demonstrate that combined temperature and precipitation shocks magnify economic costs beyond their individual effects. This pattern is particularly concerning for Uganda\u0026rsquo;s fiscal planning, as it implies that climate volatility can introduce nonlinear risks that traditional tax forecasting models fail to capture.\u003c/p\u003e\u003cp\u003eBy quantifying these linkages, the study bridges the microeconomic literature on firm performance and adaptation and the macroeconomic discourse on fiscal resilience and climate vulnerability. It provides empirical support for the argument advanced by Pizer et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Stern (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) that climate risk should be treated as a systemic economic and fiscal issue rather than an environmental externality. The Ugandan evidence contributes to a growing recognition that in climate vulnerable, low -diversified economies, adaption is not only a production imperative but also a fiscal necessity.\u003c/p\u003e\u003cp\u003eThe discussion shows that climatic variability undermines both corporate and fiscal resilience in a mutually reinforcing cycle. Firms experiencing productivity shocks contribute less to the revenue base, while reduced fiscal space constrains the government\u0026rsquo;s capacity to fund adaptation measures. This self-reinforcing feedback loop highlights the importance of integrating climate risk assessment into both enterprise strategy and public financial management, positioning adaptation as a cross-cutting pillar for economic sustainability in Uganda and across East Africa.\u003c/p\u003e"},{"header":"6 Conclusion and Policy Implications","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Conclusion\u003c/h2\u003e\u003cp\u003eThis paper provides new micro-evidence on how climate variability affects corporate performance and fiscal outcomes in Uganda. Using administrative tax data merged with district-level climate indicators, the analysis shows that higher temperatures and erratic rainfall significantly weaken firm profitability and narrow the corporate income-tax base. A 1 percent increase in temperature reduces return on assets by about 4 percent and corporate tax liabilities by roughly 2 percent, while rainfall volatility has mixed effects, supporting productivity under moderate precipitation but reducing tax compliance when rainfall becomes excessive. Robustness checks confirm that these effects are stable, persistent, and nonlinear: the results remain significant across alternative specifications and become amplified under compound climate shocks. Firm-level heterogeneity further conditions the magnitude of these impacts; older and larger firms demonstrate fiscal resilience, whereas smaller enterprises are disproportionately affected.\u003c/p\u003e\u003cp\u003eTaken together, the findings reveal a dual climate-fiscal vulnerability. At the micro level, climate shocks reduce productivity, profits, and investment capacity. At the macro level, these losses compress the tax base, generate revenue volatility, and weaken fiscal space for adaptation and development spending. In an economy where a few large firms account for most tax receipts, this link between environmental and fiscal fragility poses a structural constraint to sustainable revenue mobilization growth. The results, therefore, bridge two strands of literature: the micro-economics of firm adaptation and the macro-economics of fiscal resilience, extending the empirical evidence to an African context where such linkages remain under-studied (AfDB, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Capelle, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pizer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stern, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Policy Implications\u003c/h2\u003e\u003cp\u003eThe results show that temperature and rainfall variability directly undermine both corporate profitability and fiscal stability, suggesting that fiscal adaptation must begin with enterprise resilience. The observed 4-percent decline in return on assets and 2-percent fall in corporate tax liabilities for every one-percent temperature rise indicate that climate risks should be embedded into fiscal planning and tax-revenue forecasting. Integrating climate-fiscal stress testing into Uganda\u0026rsquo;s medium-term expenditure and debt-sustainability frameworks would help anticipate revenue shortfalls arising from climatic shocks.\u003c/p\u003e\u003cp\u003eFirm-level heterogeneity highlights the need to support smaller enterprises that are most exposed to heat stress, while ensuring continued compliance among large firms that contribute the bulk of corporate tax receipts. Targeted incentives for energy-efficient production, water-management systems, and climate-resilient infrastructure would protect profitability and stabilize the tax base. Finally, the evidence of compound shocks underscores the urgency of regional cooperation to harmonize climate-fiscal diagnostics across East Africa, aligning adaptation finance and tax risk management. These empirically grounded measures directly respond to the mechanisms identified in the analysis, temperature and rainfall shocks that simultaneously erode firm productivity and fiscal capacity, thereby strengthening both corporate and macro-fiscal resilience.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe research titled \u0026ldquo;Climate Shocks and Tax Base: Firm-Level Evidence on Profitability and Corporate Income Tax Compliance in Uganda\u0026rdquo; was jointly conceptualized and conducted by Byamukama Bernis (B.B.) and Rukundo Johnson (J.R.). B.B. collected and curated the data, performed the literature review, and wrote the full manuscript draft. J.R. developed the econometric methodologies and conducted the data analysis. Both authors contributed to the interpretation of results, revised the manuscript critically for important intellectual content, and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge Franklin Amuakwa-Mensah (University of Gothenburg, [email protected]) for his valuable supervision, guidance, and comments on this article. This study uses the Uganda Revenue Authority Firm Panel Dataset, developed in collabora-tion with the Uganda Revenue Authority and UNU-WIDER. The author thanks Agaba Gerald (Re-search Lab Assistant, [email protected]) and Kwisanga Ernest ([email protected]) for their assistance in data processing and analysis, and appreciates the helpful feedback from profes-sors and students at the College of Business and Economics, University of Rwanda, during a semi-nar held on October 1, 2025. Financial support was provided by the Swedish International Devel-opment Cooperation Agency (Sida) under the Research Training Program (2019-2024), implement-ed through the University of Rwanda\u0026rsquo;s capacity-building initiative in collaboration with the Envi-ronment for Development (EfD) at the University of Gothenburg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The author declares no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAddoum, J. M., Ng, D. T., \u0026amp; Ortiz-Bobea, A. (2020). Temperature Shocks and Establishment Sales. \u003cem\u003eThe Review of Financial Studies\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(3), 1331\u0026ndash;1366. https://doi.org/10.1093/RFS/HHZ126\u003c/li\u003e\n \u003cli\u003eAfDB. 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Climate change, technological innovation, and firm performance. \u003cem\u003eInternational Review of Economics \u0026amp; Finance\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e, 189\u0026ndash;203. https://doi.org/10.1016/J.IREF.2024.04.025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-tax-and-public-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itax","sideBox":"Learn more about [International Tax and Public Finance](http://link.springer.com/journal/10797)","snPcode":"10797","submissionUrl":"https://submission.nature.com/new-submission/10797/3","title":"International Tax and Public Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate variability, Firm performance, Corporate Income Tax (CIT) compliance, Climate shocks, Fiscal risk, Adaptation policy","lastPublishedDoi":"10.21203/rs.3.rs-8083511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8083511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the impact of climate variability on firm performance and corporate income tax (CIT) compliance in Uganda, a climate-vulnerable, low-income economy. We construct a panel of over 404,000 firm-year observations from administrative tax records (2013/14-2022/23) matched with high-resolution satellite climate data. Using fixed-effects models, we isolate the impact of temperature and rainfall shocks on profitability, efficiency, and tax contributions. Our findings indicate that higher temperatures negatively affect firm outcomes across all measures, with the largest effect on asset efficiency, where a 1% temperature increase results in a 2% CIT decline and over 4% reduction in return on assets. Rainfall shocks have asymmetric effects, improving efficiency in some sectors but reducing tax compliance, creating fiscal trade-offs. Robustness checks confirm that firm size and age mediate exposure: medium and large firms contribute more to CIT yet record weaker efficiency ratios. These findings extend the literature by linking firm operations to fiscal outcomes, showing that climate shocks create both corporate and fiscal risks. The analysis recommends embedding climate risk in revenue forecasts, promoting sector-specific adaptation, expanding access to climate-smart technologies and finance, and aligning tax incentives with resilience goals. Proactive adaptation is essential to protect productivity, fiscal stability, and long-term growth under increasing climate variability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eQ54, H25, D22, Q56, O55\u003c/p\u003e","manuscriptTitle":"Climate Shocks and Tax Base: Firm-Level Evidence on Profitability and Corporate Income Tax Compliance in Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 13:05:43","doi":"10.21203/rs.3.rs-8083511/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-22T20:36:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T10:09:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T12:27:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119112588755371286180980547665580398214","date":"2025-12-18T07:54:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273438977018484939575174607964288610862","date":"2025-12-03T09:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T16:54:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T18:20:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-11T16:14:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Tax and Public Finance","date":"2025-11-11T06:57:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-tax-and-public-finance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itax","sideBox":"Learn more about [International Tax and Public Finance](http://link.springer.com/journal/10797)","snPcode":"10797","submissionUrl":"https://submission.nature.com/new-submission/10797/3","title":"International Tax and Public Finance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8fe0ab6-4c61-486d-82d0-643078fd6b4f","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T20:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 13:05:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8083511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8083511","identity":"rs-8083511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}