# Heat and Hurdles: Unpacking the Impact of Climate Risks on Women's Empowerment

City Eldeep, Fida Karam, Chahir Zaki

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City Eldeep, Fida Karam, Chahir Zaki. Heat and Hurdles: Unpacking the Impact of Climate Risks on Women's Empowerment. 2026. hal-05505905

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Document de Recherche du Laboratoire d'Économie d'Orléans

Working Paper Series, Economic Research Department of the University of Orléans, University Of Tours
and University Clermont-Auvergne, France

DR LEO 2026-06

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# Heat and Hurdles: Unpacking the Impact of Climate Risks on Women's Empowerment

City Eldeep, Fida Karam et Chahir Zaki

MISE EN LIGNE / ONLINE | 11/02/2026

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Laboratoire d'Économie d'Orléans
Collegium DEG
Rue de Blois - BP 26739
45067 ORLÉANS Cedex 2

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MAIL | leo@univ-orleans.fr

www.leo-univ-orleans.fr
Heat and Hurdles: Unpacking the Impact of Climate Risks
on Women's Empowerment

City Eldeep<fnref n="1" />

Fida Karam<fnref n="2" />

Chahir Zaki<fnref n="3" />

Abstract

Climate change poses complex risks that extend beyond environmental impacts, shaping social and economic inequalities. This paper examines how physical risks, such as extreme weather, and transition risks, linked to decarbonization and green policies, affect women's empowerment in firms across developing and developed contexts. Using firm-level data from the World Bank Enterprise Surveys and World Development Indicators, we measure empowerment through female employment, management, and ownership. Our results reveal contrasting effects: physical risks reduce female ownership, while transition risks boost female management, particularly in smaller, younger, non-exporting, and non-privately owned firms. The negative impact of physical risks is largely uniform, except in foreign-owned firms, and operates mainly through constraints on finance and land. These findings highlight the need for integrated climate and gender policies that recognize women not only as vulnerable to climate change but also as key agents of sustainable economic transformation.

**Keywords:** Gender, physical risk, climate risk, female owners, female managers, climate change.

**JEL classification:**

<footnote n="1">¹ Postdoctoral Research Fellow, Economic and Social Research Institute (ESRI), Ireland. Email: City.Ahmed@esri.ie</footnote>

<footnote n="2">² Associate Professor of Economics, Department of Economics and Finance and GUST Center for Sustainable Development, Gulf University for Science and Technology. Email: karam.f@gust.edu.kw</footnote>

<footnote n="3">³ Professor of Economics, University of Orleans and Laboratoire d'Economie d'Orleans. Email: chahir.zaki@univ-orleans.fr</footnote>
# 1. Introduction

Climate change is increasingly recognized not only as an environmental challenge but also as a powerful driver of socioeconomic inequality. A growing literature shows that both physical climate risks—such as extreme weather events, droughts, and floods—and transition risks associated with decarbonization policies and technological change have uneven distributional effects across countries, sectors, and social groups (Diffenbaugh & Burke, 2019; Hallegatte et al., 2016; IPCC, 2022). Gender has emerged as a central dimension of this inequality, as women tend to be disproportionately exposed to climate shocks due to sectoral segregation, unequal access to assets and finance, and persistent social norms that constrain economic agency (Alston, 2014; Dankelman, 2010; Goh, 2012). While global policy frameworks increasingly emphasize gender-responsive climate action (UNFCCC, 2019; OECD, 2021), empirical evidence on how climate risks shape women's economic empowerment remains limited.

This paper examines how climate risks affect women's empowerment within firms, focusing on three dimensions: female employment, female management, and female ownership. Using firm-level data from the World Bank Enterprise Surveys (WBES) matched with country-level climate indicators from the World Development Indicators, we distinguish between physical climate risks and transition risks. This topic is critical because firms are key transmission channels through which climate risks translate into labor market and ownership outcomes, yet gender-differentiated effects at the firm level remain largely unexplored. Understanding these mechanisms is essential for designing climate and industrial policies that avoid reinforcing existing gender gaps and instead promote inclusive, resilient growth (IPCC, 2022; UN Women, 2022).

Existing research on climate change and gender has largely focused on household-level outcomes, agricultural livelihoods, and community-based adaptation, highlighting women's increased vulnerability as well as their role in resilience and adaptation (Goh, 2012; Djoudi et al., 2016; Jerneck, 2018). Parallel firm-level literatures study the impact of climate shocks and environmental regulation on productivity, employment, and investment but typically treat firms as gender-neutral entities (Cole et al., 2013; Battiston et al., 2017; Dechezleprêtre et al., 2019). Studies on women's entrepreneurship and leadership, in turn, document persistent barriers in access to finance, management, and ownership without explicitly accounting for climate risks as a source of constraint (Aterido et al., 2013; Jayachandran, 2021; Klapper & Parker, 2011). As a result, there is little systematic evidence linking climate risks to women's empowerment within firms across countries.

Against this background, the current paper makes three main contributions. First, it provides the first cross-country firm-level evidence on the differential effects of physical and transition climate risks on women's empowerment within firms. Second, it uncovers substantial heterogeneity in these effects across firm characteristics such as export status, size, age, and
ownership structure. Third, it sheds light on the mechanisms through which climate risks affect women by adopting a mediation approach that examines key firm-level channels, including access to finance, assets, land, productivity, and innovation. Employing linear probability models with high-dimensional fixed effects, complemented by mixed multilevel models to account for the nested structure of firms within countries and years, we analyze how firm characteristics shape the gendered impact of climate risks. Specifically, we examine how exposure to international markets, firm size, age, and ownership structures moderate these effects and use a mediation approach to explore key channels such as access to finance, asset ownership, land, productivity, and innovation capacity. This methodological approach allows us to account for heterogeneity across countries, years, and institutional contexts, providing robust evidence on the gendered consequences of climate shocks.

Our results reveal a sharp contrast between physical and transition climate risks. Physical climate risks significantly reduce female ownership within firms, whereas transition risks are positively associated with female management. The effects of transition risks are not uniform; they are strongest in smaller, younger, non-exporting, and non-privately owned firms, suggesting that certain firm characteristics enhance women's opportunities in green transitions. By contrast, the negative impact of physical risks on female ownership is broadly consistent across firms, with the exception of foreign-owned firms, where the effect is more pronounced. Mediation analysis suggests that physical risks operate mainly through constraints on finance and land, while transition risks show limited influence through these channels, indicating that different types of climate risks affect women's empowerment through distinct mechanisms.

The remainder of the paper is structured as follows. Section 2 reviews the related literature. Section 3 describes the data and presents stylized facts. Section 4 outlines the empirical methodology. Section 5 presents the empirical results, including heterogeneity and mediation analyses, and robustness checks. Section 6 concludes and discusses policy implications.

## 2. Literature Review

Physical and transition climate risks are conceptually distinct: physical risks stem from direct environmental shocks and long run climatic changes, whereas transition risks arise from the policy, regulatory, and technological adjustments associated with the shift toward a low carbon economy. In the literature, physical climate risks are primarily measured through observable environmental shocks and long run climatic stressors. Several studies rely on extreme weather events—including droughts, floods, and storms—as key indicators of physical risk, often using historical disaster exposure to quantify their economic and social impacts (Diffenbaugh & Burke, 2019; Hallegatte et al., 2016; Felbermayr & Gröschl, 2014). Other work measures physical risks through temperature and precipitation related shocks, such as heat stress or rainfall variability, which directly influence productivity, agricultural output, and labor markets (Hsiang & Jina, 2014). The
literature also incorporates chronic environmental degradation, including declining natural resource productivity and water scarcity, as indicators of long term physical vulnerability (Islam & Winkel, 2017; Chitiga Mabugu et al., 2023). At the firm level, physical risks are captured through climate related disruptions to production, such as weather induced interruptions or damage to infrastructure (Cole et al., 2013). Collectively, these measures reflect the multidimensional nature of physical climate risks, spanning both acute shocks and slow onset environmental pressures.

In contrast, transition risks are measured through indicators linked to regulatory change, technological requirements, and market pressures associated with decarbonization. Several studies capture transition risks through policy driven changes, such as carbon taxes, energy subsidy reforms, and broader regulatory shifts that alter firms’ cost structures and investment incentives (Battiston et al., 2017; Dechezleprêtre et al., 2019; Eldeep et al., 2025). Transition risks also arise from compliance with environmental standards, particularly for export oriented firms facing increasingly stringent sustainability requirements in global markets (IFC, 2023; UN Women, 2022). Another set of measures reflects technological and innovation pressures, where firms face heightened risk if they lack the capital, networks, or capabilities needed to adopt greener technologies or adjust to low carbon production models (Huyer, 2016; Khan et al., 2021; Nwosu & Orji, 2022). The literature further highlights sectoral exposure to the green transition, noting that firms operating in carbon intensive industries encounter greater regulatory and market uncertainty. Together, these measures underscore that transition risks are multifaceted and shaped by policy environments, technological readiness, and the degree of integration into global value chains. Climate change is widely recognized as a multidimensional phenomenon that generates environmental degradation while deepening existing socioeconomic inequalities. A substantial body of research shows that both physical climate risks—such as extreme weather events, droughts, and floods—and transition risks associated with decarbonization disproportionately affect vulnerable populations, particularly in lower income countries with limited adaptive capacity (Diffenbaugh & Burke, 2019; Hallegatte et al., 2016). These risks undermine productivity, disrupt labor markets, and constrain long term development trajectories, with marginalized groups facing the greatest exposure and the fewest resources to respond (Felbermayr & Gröschl, 2014; Hsiang & Jina, 2014; Islam & Winkel, 2017).

Within this broader inequality landscape, gender has emerged as a critical dimension shaping vulnerability and resilience to climate change. Extensive evidence demonstrates that women and men experience climate impacts differently due to gendered divisions of labor, unequal access to assets, and social norms that restrict mobility and decision making (Alston, 2014; Dankelman, 2010). Goh’s (2012) influential review synthesizes these dynamics across multiple regions, identifying consistent gender differentiated impacts in agricultural production, food security, health, access to water and energy, migration, and exposure to natural disasters. Women often face more severe consequences because they are concentrated in climate sensitive sectors and have
fewer adaptive resources. At the same time, recent studies emphasize women's agency in climate adaptation, highlighting their roles in community resilience, natural resource management, and sustainable development (Djoudi et al., 2016; Resurrección, 2013). This shift aligns with global policy frameworks, such as the UNFCCC Gender Action Plan, which calls for integrating gender considerations into climate governance (UNFCCC, 2019).

Despite growing recognition of gendered climate impacts, the intersection between climate risks and women's economic empowerment within firms remains underexplored. Firm level studies show that climate shocks can disrupt production, reduce employment, and alter investment patterns (Cole et al., 2013; Dechezleprêtre et al., 2019), while transition risks introduce new uncertainties related to regulatory change and technological shifts (Battiston et al., 2017). Yet, little is known about how these risks shape gendered outcomes such as women's employment, managerial representation, and ownership. This gap is notable given extensive evidence that women face persistent barriers in labor markets and entrepreneurship—stemming from social norms, credit constraints, and discriminatory practices (Aterido et al., 2013; Jayachandran, 2021; Klapper & Parker, 2011)—and that gender diversity in leadership can enhance firm performance and innovation (Bloom et al., 2019; Fikru et al., 2025; Noland et al., 2016).

Broader policy-oriented literature underscores the need for integrated climate-gender strategies that move beyond vulnerability framing and position women as central actors in sustainability transitions (OECD, 2021; UN Women; UNEP, 2015). Ajani et al. (2013) highlight that empowering women in climate related economic activities strengthens both mitigation and adaptation outcomes, yet structural and institutional barriers often prevent women from benefiting from these opportunities. Similarly, Chitiga Mabugu et al. (2023) show that climate impact transmitted through declining natural resource productivity—disproportionately affect women's livelihoods, but that gender responsive adaptation strategies can generate broader economic gains. A growing strand of research deepens the understanding of how gendered structures mediate climate vulnerability and adaptive capacity. Call and Sellers (2019) demonstrate that gender blind interventions can unintentionally reinforce inequalities, while Jerneck (2018) argues that adaptation research must interrogate the gendered power relations that shape who benefit from climate initiatives. Rietveld et al. (2023) further show that restrictive gender norms within agrifood systems limit women's access to climate smart technologies and decision-making spaces, making norm transformation essential for climate resilience. Complementary studies document women's adaptive strategies—such as income diversification and community-based resource management—highlighting both vulnerability and agency (Md et al., 2022; Adhikari & Ghimire, 2025).

Regarding the mechanisms behind climate and gender, the literature consistently shows that physical climate risks disproportionately constrain women-led and women-owned firms, primarily through channels such as access to finance, land, and productivity. These constraints
reflect deeper structural inequalities in credit markets, asset ownership, and institutional support systems (De Mel et al., 2009; Doss et al., 2018). Smaller firms—where women are overrepresented—face heightened vulnerability because they possess fewer financial buffers, weaker bargaining power, and limited access to adaptive technologies (UNIDO, 2022). Trade performance further shapes this vulnerability: export-oriented women-owned firms may struggle to meet increasingly stringent environmental standards, especially when lacking capital or technological capacity (IFC, 2023). Yet integration into global markets can also expose women-led firms to greener technologies and sustainability-oriented value chains, potentially enhancing resilience to physical and regulatory shocks (UN Women, 2022).

In contrast, the literature is more divided on the implications of transition risks for women’s economic empowerment. Some studies highlight that the green transition can create new opportunities for women, particularly in emerging sustainability-oriented sectors and in firms with stronger export capabilities (OECD, 2021; UN Women, 2022). Women leaders are often associated with long-term, risk-aware decision-making and a greater propensity to adopt sustainability-aligned business models, which can translate into proactive innovation under regulatory or market pressure (Huyer, 2016; Khan et al., 2021). Larger women-led firms, in particular, may be better positioned to leverage green financing instruments and comply with environmental standards. However, other scholars caution that transition risks may deepen gender gaps when regulatory burdens increase compliance costs or when women-led firms lack the networks, capital, or scale needed to pivot toward greener technologies (Nwosu & Orji, 2022). These findings underscore that the climate-gender nexus is highly heterogeneous, shaped by firm size, leadership structure, and integration into global markets.

While national strategies emphasize gender inclusion, implementation gaps persist. Studies show that women's limited access to land, finance, and formal employment constrains their adaptive capacity (World Bank, 2023; Daoud, 2021). At the same time, women's roles in agriculture, household resource management, and community-based adaptation highlight their importance as agents of resilience. Research on women's entrepreneurship underscores how regulatory and financial constraints shape women's participation in firms, with implications for how climate risks may influence employment, leadership, and ownership patterns (El Fiky, 2020). Recent work also highlights how multiple crises compound gendered vulnerabilities. Salah et al. (2024) show that climate injustice, gender inequality, and pandemic related shocks interact to constrain women's economic agency, reinforcing the need for climate policies that explicitly integrate gender justice. These insights provide a conceptual bridge to firm level analyses by illustrating why women's economic empowerment, particularly in employment, management, and ownership—may be especially sensitive to climate related disruptions.

A wide range of methodological approaches has been used to study gendered climate impacts, spanning qualitative, quantitative, and economy-wide modeling techniques. Qualitative
studies—such as Goh (2012), Jerneck (2018), and Call and Sellers (2019)—rely on case studies, ethnography, and participatory rural appraisal to capture women's lived experiences and the social norms shaping vulnerability and adaptation. Quantitative household- and sector-level analyses, including Ajani et al. (2013), Chitiga Mabugu et al. (2023), and Adhikari and Ghimire (2025), typically employ partial-equilibrium econometric models using survey data and climate indicators to estimate how environmental shocks affect labor allocation, agricultural productivity, and welfare. These partial-equilibrium studies focus on specific markets or sectors and therefore capture direct effects but not broader economy-wide feedbacks. In contrast, a smaller but growing body of research uses general-equilibrium approaches, most notably the dynamic Computable General Equilibrium (CGE) model developed by Eldeep et al. (2025), which evaluates how carbon taxes and energy subsidy reforms affect gendered economic outcomes in Egypt. Their CGE analysis reveals that policy shocks generate indirect and economy-wide effects—such as changes in wages, sectoral output, and household income—that partial-equilibrium models cannot capture, leading to different conclusions about the distributional and gendered impacts of climate policies. While partial-equilibrium studies often find localized or sector-specific gender vulnerabilities, general-equilibrium results show that climate and fiscal policies can amplify or mitigate gender inequality through broader structural channels. This methodological diversity highlights the richness of the literature but also underscores the absence of firm-level, gender-disaggregated empirical analyses, a gap the present study addresses.

While extensive research documents the unequal distribution of climate risks and the gendered nature of vulnerability, these insights remain largely disconnected from firm-level analyses of women's economic empowerment. Climate studies focus predominantly on macro-level or household impacts, and gender research highlights persistent disparities in employment, management, and ownership without considering climate risks as a contributing factor. At the same time, firm-level analyses of climate shocks typically treat firms as gender-neutral entities and overlook how physical and transition risks may differentially affect women's roles within them. As a result, there is no systematic empirical evidence linking climate risks to women's empowerment inside firms across countries. By examining how physical and transition climate risks influence women's employment, managerial representation, and ownership using firm-level data from the World Bank Enterprise Surveys, this study addresses this critical gap and contributes to an emerging research agenda that connects macro-level climate vulnerabilities with micro-level indicators of women's economic agency. In doing so, it aligns with broader calls for integrated climate-gender policies that recognize women not only as disproportionately affected populations but as pivotal actors in driving resilient and inclusive economic transformation.
### 3. Data and Stylized Facts

Before turning to the econometric analysis, this section presents a set of stylized facts that describe gender differences in firm characteristics and their association with climate risks. These descriptive patterns, based on the World Bank Enterprise Surveys and climate indicators from the World Bank Development Indicators database, provide an initial, non-causal overview of how women's participation in firms (as managers and owners) differs systematically from men's, and how these differences correlate with physical and transition climate risks.

The World Bank Enterprise Surveys<fnref n="4" /> offers an expansive array of economic data on 195,000 firms in 155 countries operating in 56 manufacturing and services sectors. Formal (registered) companies with 5 or more employees are targeted for interview. Firms with 100% government/state ownership are not eligible to participate in an Enterprise Survey. The surveys capture important characteristics of firms such as age, legal status, domestic vs. foreign ownership, internationally recognized quality certification, and cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, trade, technology and innovation, gender participation and performance measures. The manufacturing and services sectors are the primary business sectors of interest. This corresponds to firms classified with ISIC codes 15-37, 45, 50-52, 55, 60-64, and 72 (ISIC Rev.3.1). Services firms include construction, retail, wholesale, hotels, restaurants, transport, storage, communications, and IT.

Figure 1 compares firm characteristics according to the gender of the manager. Several notable patterns emerge. Firms managed by women tend to be smaller than those managed by men (77% of firms managed by females are small vs. 69% managed by males). This pattern is consistent with the idea that women face stronger barriers to accessing managerial positions as organizational hierarchies become more complex and formalized (Aterido et al., 2013; Jayachandran, 2021; Klapper & Parker, 2011). Figure 1 also highlights a clear gender gap in export participation. About 87% of firms managed by women are non-exporters, compared to a lower share among male-managed firms. This suggests that female managers are disproportionately concentrated in domestically oriented firms, which may face different competitive pressures and adjustment capacities in the presence of climate risks.

<footnote n="4">⁴ https://www姓enterprisesurveys.org/en/enterprisesurveys</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 1: Firms Characteristics- by the gender of the manager</figcaption></figure>

Source: Authors own elaboration using the WBES.

Figure 2 presents analogous comparisons based on the gender of firm owners. In contrast to the managerial dimension, women-owned firms tend to be older than men-owned firms (26% of firms owned by women are medium and 6% are large vs. respectively 23% and 5.7% of firms owned by men). This finding suggests that female ownership may emerge later in the firm life cycle, possibly reflecting the time required for women entrepreneurs to overcome financial, institutional, and social constraints and consolidate ownership positions. Additionally, Figure 2 shows that 17% of female-owned firms are exporters, compared to 14% of male-owned firms. This may reflect a selection effect whereby women who overcome the substantial barriers to firm ownership are more likely to operate more productive, competitive firms capable of accessing international markets, or strategically use exporting to scale up and diversify risk beyond domestic markets.

<figure><img src="image_6.png" /><figcaption>Figure 2: Firms Characteristics- by the gender of the owner</figcaption></figure>

Source: Authors own elaboration using the WBES.
Table 3 complements the graphical evidence by summarizing key differences in firm characteristics by the gender of managers and owners. Female-managed firms are slightly younger than male-managed ones, consistent with the idea that women's access to managerial positions is more common in newer firms with less entrenched organizational hierarchies and more literacy about gender equality. In contrast, female-owned firms are older on average, suggesting that women's ownership tends to materialize later in the firm life cycle, possibly reflecting the time needed to accumulate capital, networks, and credibility. Across both management and ownership dimensions, firms with women in leadership display a higher share of private ownership, indicating that private governance structures may be more conducive to women's economic agency. By comparison, the share of foreign ownership is slightly lower for female managers/owners, suggesting that foreign participation may be associated with sectoral composition or adjustment dynamics that do not necessarily translate into more favorable outcomes for women.

<table><caption>Table 3: Firms Characteristics- by the gender of the owner and the manager</caption><thead><tr><th rowspan="2"></th><th colspan="2">Managers</th><th colspan="2">Owners</th></tr><tr><th>Male</th><th>Female</th><th>Male</th><th>Female</th></tr></thead><tbody><tr><th>Share of private own.</th><td>69.1</td><td>74.3</td><td>67.3</td><td>75.7</td></tr><tr><th>Share of foreign own.</th><td>1.5</td><td>1.4</td><td>1.6</td><td>1.5</td></tr><tr><th>Age</th><td>15.9</td><td>15.0</td><td>15.3</td><td>16.8</td></tr></tbody></table>

Source: Authors own elaboration using the WBES.

Figures 4 and 5 illustrate the correlations between climate risks and women's empowerment indicators. Figure 4 shows a negative correlation between physical climate risks (measured by climate-related deaths) and female ownership. Countries with higher exposure to physical risks tend to exhibit a lower share of firms owned by women. This descriptive pattern is consistent with the idea that climate shocks disproportionately undermine women's asset accumulation and entrepreneurial capacity, even before controlling for firm- and country-level characteristics. The fitted line for female managers is almost flat, indicating little to no systematic correlation between physical climate risks and the likelihood that a firm is managed by a woman. This suggests that physical risks do not appear to directly shape women's access to managerial positions, in contrast to their clearer association with female ownership.

In contrast, Figure 5 reveals a positive correlation between transition risks (proxied by CO₂ emissions per capita) and female management and ownership. Countries facing stronger decarbonization pressures appear to have a higher prevalence of female managers and owners. This pattern suggests that structural transformation associated with climate transition may coincide with organizational changes that are more favorable to women in leadership roles. Importantly, these correlations differ markedly across empowerment dimensions, underscoring the need to distinguish between ownership and management when analyzing gendered climate impacts. They also highlight the importance of separating physical and transition risks, as their associations with women's outcomes move in opposite directions.
<figure><img src="image_7.png" /><figcaption>Figure 4: Correlation between physical risks and gender</figcaption></figure>

Source: Authors’ own elaboration.

<figure><img src="image_8.png" /><figcaption>Figure 5: Correlation between transition risks and gender</figcaption></figure>

Source: Authors’ own elaboration.
In sum, the stylized facts reveal systematic gender differences in firm characteristics and suggest contrasting correlations between different climate risks and different dimensions of women's empowerment. These descriptive patterns motivate the econometric analysis that follows, which seeks to identify whether these relationships persist once firm heterogeneity and institutional contexts are taken into account.

## 4. Methodology

To estimate the impact of climate risks on women's empowerment within firms, we employ a linear probability model (LPM) with high-dimensional fixed effects using firm-level data from the WBES combined with country-level climate indicators. Women's empowerment is captured along three complementary dimensions: female ownership, female management as well as the share of female workers with a distinction between production and non-production workers. Formally, we estimate the following baseline specification:

$$ WomenEmpower_{ijst} = a1 X_{ijst} + a2 Climate\ Risks_{jt} + cd + sd + yd + v_{ijst} \quad (1) $$

where $X_{ijst}$ is a vector of controls that characterize firm i in country j in sector s at year t, cd, sd, yd are respectively country, sector and year dummies to control for unobserved heterogeneity across institutional environments, industries, and time, and $v_{ijst}$ the discrepancy term.

The dependent variable in equation (1) is alternatively one of the two dimensions of women empowerment: FemaleOwner$_{ijst}$ is a binary variable that takes the value of 1 if firm i in country j in sector s at time t is owned by a female, and 0 otherwise; FemaleManager$_{ijst}$ is a binary variable that takes the value of 1 if firm i in country j in sector s at time t is managed by a female, and 0 otherwise; Ln(Emp. Female)$_{ijst}$ is the logarithm of the share of female workers in firm i in country j in sector s at time t.

Inspired from Karam and Zaki (2021 and 2024) and following the literature on gender inequality, the control variables include firms characteristics such as the firm's age, size, the share of foreign and private ownership, as well as the export and import status of the firm. Indeed, younger firms are expected to display lower gender bias due to rising female literacy, changing social norms, and weaker entrenchment of traditional employment practices (Karam and Zaki, 2024). Therefore, younger firms are hypothesized to employ more females and to have a higher probability of being managed or owned by women. Firm size is captured using dummy variables for medium and large firms. Larger firms tend to employ more skilled workers and adopt more formal organizational structures (Juhn et al., 2014). Large size firms are expected to be leaders in innovation with sufficient financial resources to employ more skilled workers. Therefore, the effect of firm size could negatively affect women workers embodying relatively lower skills. While larger size may reduce women's participation in low-skill jobs, managerial positions require
skills, suggesting a potentially positive association with female management. However, since
women face stronger barriers to accessing managerial positions as organizational hierarchies
become more complex and formalized (Aterido et al., 2013; Jayachandran, 2021; Klapper &
Parker, 2011), the association may be negative. The effect on female ownership is also ambiguous
and depends on access to capital and entrepreneurial constraints. Foreign-owned firms are
expected to be more supportive of women's empowerment due to stronger exposure to gender
equality norms prevalent in developed countries (Juhn et al., 2014). A higher share of private
ownership may be associated with greater managerial autonomy and flexibility, reducing
institutional or bureaucratic constraints that can disadvantage women. Private owners may also be
more open to merit-based leadership and internal promotion, increasing the likelihood of women
serving as managers or owners. International engagement often exposes exporting and importing
firms to stronger competitive pressures, global standards, and diverse business practices that
encourage more inclusive employment and governance structures. Exporting and importing firms
are more likely to interact with multinational buyers, suppliers, and regulators that value gender
equality and diversity, pushing firms to adopt better managerial practices, including the inclusion
of women in ownership and management roles. Moreover, participation in international markets
typically requires higher productivity and skills upgrading, which can expand formal employment
opportunities and reduce discrimination, leading to a higher share of female workers.

Our variable of interest *Climate Risks*<sub>j,t</sub> captures two distinct dimensions of climate risks in country j at time t: transition risks as well as physical risks. Transition risks, proxied by the logarithm of CO<sub>2</sub> emissions per capita (Di Febo, 2025; Chang et al., 2025), reflect the extent to which countries are exposed to policy-driven decarbonization pressures, regulatory tightening, and structural economic transformation. Higher emissions are associated with stronger expected mitigation efforts, carbon pricing, and technological shifts, which may reshape labor demand and managerial structures. These changes can either create opportunities for women - through skill-biased technological change and organizational restructuring - or reinforce existing inequalities if adjustment costs disproportionately affect them. Physical risks, proxied by the logarithm of climate-related deaths (Maes et al., 2022), captures exposure to extreme weather events, natural disasters, and climate shocks. This measure reflects the severity of physical climate impacts that disrupt production, labor supply, and firm operations. Physical risks may reduce women's economic participation by increasing care burdens, health risks, and job insecurity, particularly in contexts with weak social protection. By jointly considering transition and physical risks, the analysis distinguishes between forward-looking policy and technological pressures as well as immediate climate shocks, allowing for a more nuanced understanding of how climate change affects women's empowerment within firms.

We employ a linear probability model (LPM) for several reasons. First, the LPM allows
for the inclusion of multiple high-dimensional fixed effects. Second, it provides coefficients that
can be directly interpreted as elasticities, which is especially useful when analyzing interaction
terms and heterogeneous effects. Third, given the large sample size and the focus on average partial
effects, the LPM yields consistent and transparent estimates. Robust standard errors are used
throughout to account for heteroskedasticity inherent in binary dependent variables.

The regressions are extended in different ways: Since firms vary in their exposure to
international markets, size, age, and ownership structures, and these characteristics can condition
how climate risks translate into gendered outcomes, we analyze a set of moderating factors that
shape the relationship between climate risks and women's empowerment. Indeed, we interact the
climate risk variables, alternatively, with the export status, age, size, and ownership types of the
firm. Second, we extent the analysis by examining the impact on employment Ln(Fem.
Production)_{ijst} is the logarithm of the share of female production workers in firm i in country j in
sector s at time t; Ln(Fem. Non-Production)_{ijst} is the logarithm of the share of female non-
production workers in firm i in country j in sector s at time t. Third, we employ a mediation
approach to explore the mechanisms through which climate risks influence women's
empowerment by focusing on a set of key firm-level channels, including asset ownership, access
to finance, productivity, land access, and innovation capacity (Gannon et al., 2022). These
channels capture critical dimensions through which climate shocks may affect firms' economic
resilience and internal resource allocation, with potential implications for gender outcomes.
Finally, we assess the robustness of our results by employing a mixed multilevel model that
explicitly accounts for the nested structure of firms within countries and years. Unlike the fixed-
effects LPM, this approach allows for random intercepts at higher aggregation levels, capturing
unobserved cross-country and temporal heterogeneity in institutional quality, climate exposure,
and gender norms.

**5. Empirical Results**

**5.1. Baseline Results**

Table 1 presents the baseline estimates of the impact of climate risks on women’s empowerment, measured by the probability that a firm is managed by a female or owned by a female. As expected, the export status of the firm is positively and significantly associated with the probability of the firm to be owned by a female, while the coefficient for female manager is not significant. The age of the firm is negatively linked to the probability of the manager of the firm to be a female, with the age coefficient being significant. This finding is in line with Karam and Zaki (2024) who argue that younger firms might have relatively less gender differences in employment due to rising literacy in recent years. However, age is found to be positively associated with the probability of the owner of the firm to be female. This unexpected result could be explained by the fact that older firms may provide greater stability, accumulated resources, and established networks, making female ownership more sustainable and observable in later stages of
the firm life cycle. Women entrepreneurs may require time to overcome financial and institutional constraints, gradually consolidating ownership positions as firms mature.

Medium and large firms are significantly less likely to be managed by women, suggesting that women face stronger barriers to accessing managerial positions as organizational hierarchies become more complex and formalized (Aterido et al., 2013; Jayachandran, 2021; Klapper & Parker, 2011). In contrast, firm size does not significantly affect female ownership, highlighting the conceptual distinction between managerial authority and entrepreneurial control emphasized by Klapper & Parker (2011).

Contrary to expectations, foreign ownership is negatively associated with female management and ownership in the baseline specification. This finding suggests that foreign participation may interact with sectoral composition or adjustment pressures in ways that do not automatically favor women.

Private ownership is positively linked to the probability of the firm to be owned or managed by a female, as leadership selection is often less constrained by formal hierarchies or standardized career ladders that tend to disadvantage women. It may also reflect self-selection, as women entrepreneurs are more likely to establish or retain ownership in privately held firms where decision-making power is not diluted.

Turning to climate risks, the results highlight sharply differentiated effects. Transition risks, proxied by CO2 emissions per capita, is positively and significantly associated with the likelihood that a firm is managed by a woman. This finding aligns with the argument that decarbonization and technological change can induce skill-biased organizational restructuring that benefits women in leadership roles (Djoudi et al., 2016; Resurrección, 2013). By contrast, transition risks do not significantly affect female ownership, suggesting that while climate transitions may open managerial opportunities, they do not necessarily ease structural barriers to women's entrepreneurship. Physical risks, measured by climate-related deaths, exerts a negative and statistically significant effect on female ownership. This result is consistent with a large body of literature showing that climate shocks disproportionately undermine women's asset accumulation, entrepreneurial capacity, and economic security (Goh, 2012; Islam & Winkel, 2017; World Bank, 2023). Physical risks appear to constrain women's ownership through heightened vulnerability, asset losses, and increased unpaid care responsibilities, rather than through managerial channels.

Table A1 in the Appendix complements the baseline analysis by focusing on female employment outcomes. The coefficients on the control variables are largely consistent with theoretical expectations. Export status is positive and highly significant across all specifications and employment categories. Exporting firms employ a higher share of women overall, aligned with the trade-gender literature, which shows that exposure to international markets is associated
with reduced discrimination due to competitive pressures and compliance with global standards (Juhn et al., 2014; Karam & Zaki, 2024). Export-oriented firms are also more likely to formalize employment relationships, which disproportionately benefits women, especially in clerical, administrative, and professional roles.

The coefficients on medium and large firms are positive and highly significant across all employment outcomes, indicating that medium and large firms employ a higher share of women than small firms. This effect is strongest for total female employment and remains robust for both production and non-production workers. These results reflect the greater capacity of larger firms to generate formal employment opportunities, comply with labor regulations, and adopt standardized human resource practices that reduce arbitrary exclusion (Bloom et al., 2019; Juhn et al., 2014). Medium and large firms are also more likely to operate in sectors with higher skill requirements and administrative intensity, which increases demand for non-production labor where women are relatively better represented. Moreover, larger firms typically offer greater job stability, social protection, and wage regularity that disproportionately attract and retain female workers in contexts where women face higher employment risk and care-related constraints (Aterido et al., 2013; Jayachandran, 2021). At the same time, these positive employment effects contrast sharply with the negative association between firm size and female management observed in the baseline results. This divergence highlights an important distinction between employment inclusion and access to decision-making positions. While larger firms appear more capable of absorbing female labor, their more complex hierarchies and formal promotion structures may reinforce glass-ceiling effects that limit women's progression into leadership roles (Klapper & Parker, 2011). Thus, firm size expands women's participation in employment without necessarily translating into greater managerial authority.

Private ownership has a mixed effect across occupational categories. While it does not significantly affect total female employment and non-production workers, it is negatively associated with female production workers. This suggests that private ownership may be associated with efficiency-driven labor allocation that reduces women's participation in manual or routine production jobs. This interpretation is consistent with evidence that women are more vulnerable to exclusion in competitive production environments, particularly in the absence of protective labor institutions (Jayachandran, 2021).

Firm age is positively and significantly associated with total female employment and female non-production employment but not with female production employment. Older firms may benefit from accumulated organizational capital, formalized human resource practices, and greater stability, which support women's participation in clerical, administrative, and professional roles (Bloom et al., 2019). This pattern is consistent with the previous ownership results in Table 1, where older firms are more likely to be owned by women, reflecting gradual accumulation of resources and networks over the firm life cycle.
Foreign ownership is positively and significantly associated with female employment, particularly in non-production roles. This finding supports the argument that multinational firms tend to adopt global corporate norms related to diversity and inclusion, leading to greater female participation in white-collar occupations (Juhn et al., 2014). However, as shown in Table 1, this does not necessarily translate into greater female leadership, highlighting a distinction between employment inclusion and decision-making power.

Turning to climate risk variables, physical risks are negatively associated with overall female employment and female employment in non-production jobs. Indeed, non-production jobs tend to be more formal and stable, making them more sensitive to firm-level contraction following climate shocks. This finding is consistent with evidence that climate-induced disruptions disproportionately reduce women's labor supply due to increased care burdens, health risks, and job insecurity (Goh, 2012; Islam & Winkel, 2017; World Bank, 2023). By contrast, transition risks display a reallocation effect. While they are negatively linked to overall female employment, they are positively associated with female non-production employment. This suggests that decarbonization pressures induce occupational restructuring toward higher-skilled and administrative roles, where women are relatively better represented. This pattern is consistent with the literature on skill-biased organizational change under environmental regulation and green transitions (Dechezleprêtre et al., 2019; OECD, 2021), and complements the positive association between transition risks and female management found in Table 1.

Overall, the baseline results suggest that transition and physical climate risks affect distinct dimensions of women's empowerment through fundamentally different mechanisms, reinforcing the importance of distinguishing between these two types of climate risks.
<table><caption>Table 1: Baseline Results</caption><thead><tr><th></th><th colspan="3">Female manager</th><th colspan="3">Female Owner</th></tr></thead><tbody><tr><td>Export status</td><td>-0.00197<br>(0.00370)</td><td>-0.000629<br>(0.00476)</td><td>0.00545<br>(0.00542)</td><td>0.0296***<br>(0.00487)</td><td>0.0319***<br>(0.00664)</td><td>0.0420***<br>(0.00790)</td></tr><tr><td>Import status</td><td>0.00595<br>(0.0106)</td><td>-0.0162<br>(0.0102)</td><td>0.00490<br>(0.0109)</td><td>-0.000761<br>(0.0189)</td><td>0.0123<br>(0.0285)</td><td>0.00373<br>(0.0317)</td></tr><tr><td>Medium</td><td>-0.0329***<br>(0.00317)</td><td>-0.0350***<br>(0.00409)</td><td>-0.0305***<br>(0.00506)</td><td>0.00453<br>(0.00342)</td><td>0.000395<br>(0.00451)</td><td>-0.000194<br>(0.00560)</td></tr><tr><td>Large</td><td>-0.0527***<br>(0.00843)</td><td>-0.0529***<br>(0.0101)</td><td>-0.0425***<br>(0.0121)</td><td>0.0104<br>(0.00637)</td><td>0.00681<br>(0.00897)</td><td>0.00634<br>(0.0110)</td></tr><tr><td>Ln(Priv. Own)</td><td>-0.00125<br>(0.00177)</td><td>0.000550<br>(0.00164)</td><td>0.00360**<br>(0.00153)</td><td>0.0232***<br>(0.00312)</td><td>0.0238***<br>(0.00303)</td><td>0.0235***<br>(0.00396)</td></tr><tr><td>Ln(Age)</td><td>-0.00565***<br>(0.00101)</td><td>-0.00426***<br>(0.00107)</td><td>-0.00442***<br>(0.00128)</td><td>0.0137***<br>(0.00218)</td><td>0.0143***<br>(0.00214)</td><td>0.00834***<br>(0.00237)</td></tr><tr><td>Ln(For)</td><td>-0.00627***<br>(0.00163)</td><td>-0.00405***<br>(0.00153)</td><td>-0.00303*<br>(0.00156)</td><td>-0.00306<br>(0.00272)</td><td>-0.00668**<br>(0.00284)</td><td>-0.00630*<br>(0.00363)</td></tr><tr><td>Ln(CO2/cap)</td><td></td><td>0.0609***<br>(0.0200)</td><td></td><td></td><td>0.00620<br>(0.0208)</td><td></td></tr><tr><td>Ln(Death)</td><td></td><td></td><td>0.00386<br>(0.00269)</td><td></td><td></td><td>-0.0155***<br>(0.00519)</td></tr><tr><td>Observations</td><td>254,445</td><td>157,038</td><td>116,979</td><td>254,445</td><td>157,038</td><td>116,979</td></tr><tr><td>R-squared</td><td>0.093</td><td>0.110</td><td>0.122</td><td>0.108</td><td>0.103</td><td>0.123</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.

## 5.2. What are the moderating factors?

While the baseline estimates provide evidence of average effects, they may conceal substantial heterogeneity across firms. Firms differ in their exposure to international markets, organizational scale, maturity, and ownership structure, all of which can shape how climate risks translate into gendered outcomes. To uncover these conditional effects, we next examine a series of moderating factors that influence the relationship between climate risks and women's empowerment.

Table 2 explores whether export orientation moderates the relationship between climate risks and women's empowerment. The positive effect of transition risks on female management persists, but the interaction term between transition risks and export status is negative and significant, suggesting that the effect of transition risks on women management is lower for an exporter relative to a non-exporter. This indicates that while transition pressures generally favor female managers, exporting firms may experience adjustment costs that dampen these gains, possibly due to heightened competitive pressures. This finding resonates with firm-level trade literature showing that exposure to international competition can intensify adjustment costs and organizational restructuring (Cole et al., 2013), potentially reinforcing gender disparities rather than alleviating them. For physical risks, the negative effect on female ownership persists, but the
interaction term with export status is not significant, suggesting that integration into global markets does not shield women entrepreneurs from climate-induced vulnerabilities.

<table><caption>Table 2: Exporting status</caption><thead><tr><th rowspan="2"></th><th colspan="2">Transition Risk</th><th colspan="2">Physical Risk</th></tr><tr><th>Female manager</th><th>Female Owner</th><th>Female manager</th><th>Female Owner</th></tr></thead><tbody><tr><td>Export status</td><td>0.00532<br>(0.00499)</td><td>0.0369***<br>(0.00750)</td><td>-0.00667<br>(0.0146)</td><td>0.0273<br>(0.0201)</td></tr><tr><td>Import status</td><td>-0.0168<br>(0.0102)</td><td>0.0117<br>(0.0282)</td><td>0.00486<br>(0.0109)</td><td>0.00369<br>(0.0316)</td></tr><tr><td>Medium</td><td>-0.0350***<br>(0.00409)</td><td>0.000315<br>(0.00451)</td><td>-0.0304***<br>(0.00516)</td><td>-7.48e-05<br>(0.00567)</td></tr><tr><td>Large</td><td>-0.0531***<br>(0.0101)</td><td>0.00662<br>(0.00890)</td><td>-0.0427***<br>(0.0118)</td><td>0.00609<br>(0.0108)</td></tr><tr><td>Ln(Priv. Own)</td><td>0.000593<br>(0.00164)</td><td>0.0239***<br>(0.00304)</td><td>0.00360**<br>(0.00153)</td><td>0.0235***<br>(0.00396)</td></tr><tr><td>Ln(Age)</td><td>-0.00428***<br>(0.00107)</td><td>0.0143***<br>(0.00213)</td><td>-0.00440***<br>(0.00128)</td><td>0.00836***<br>(0.00237)</td></tr><tr><td>Ln(For)</td><td>-0.00411***<br>(0.00154)</td><td>-0.00672**<br>(0.00285)</td><td>-0.00303*<br>(0.00155)</td><td>-0.00630*<br>(0.00363)</td></tr><tr><td>Risk</td><td>0.0622***<br>(0.0201)</td><td>0.00727<br>(0.0208)</td><td>0.00307<br>(0.00290)</td><td>-0.0165***<br>(0.00529)</td></tr><tr><td>Risk*Export status</td><td>-0.00864***<br>(0.00332)</td><td>-0.00731<br>(0.00532)</td><td>0.00304<br>(0.00418)</td><td>0.00369<br>(0.00540)</td></tr><tr><td>Observations</td><td>157,038</td><td>157,038</td><td>116,979</td><td>116,979</td></tr><tr><td>R-squared</td><td>0.110</td><td>0.103</td><td>0.122</td><td>0.123</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.

Table 3 examines heterogeneity by firm size. The positive association between transition risks and female management weakens significantly for medium and large firms, as indicated by the negative interaction terms. This suggests that climate-induced restructuring benefits women managers primarily in smaller firms, where organizational flexibility is greater.

In contrast, the negative effect of physical risks on female owners do not vary significantly by firm size, reinforcing the idea that climate shocks impose broad constraints on women's ownership regardless of firm scale.
<table><caption>Table 3: Firm size</caption><thead><tr><th rowspan="2"></th><th colspan="2">Transition Risk</th><th colspan="2">Physical Risk</th></tr><tr><th>Female manager</th><th>Female Owner</th><th>Female manager</th><th>Female Owner</th></tr></thead><tbody><tr><td>Export status</td><td>-0.000617<br>(0.00475)</td><td>0.0320***<br>(0.00660)</td><td>0.00545<br>(0.00550)</td><td>0.0420***<br>(0.00795)</td></tr><tr><td>Import status</td><td>-0.0174*<br>(0.0102)</td><td>0.0102<br>(0.0278)</td><td>0.00467<br>(0.0109)</td><td>0.00364<br>(0.0317)</td></tr><tr><td>Medium</td><td>-0.0325***<br>(0.00417)</td><td>0.00527<br>(0.00482)</td><td>-0.0344***<br>(0.0111)</td><td>-0.00271<br>(0.0138)</td></tr><tr><td>Large</td><td>-0.0458***<br>(0.0108)</td><td>0.0180<br>(0.0111)</td><td>-0.0607***<br>(0.0204)</td><td>-0.00184<br>(0.0270)</td></tr><tr><td>Ln(Priv. Own)</td><td>0.000511<br>(0.00166)</td><td>0.0238***<br>(0.00304)</td><td>0.00358**<br>(0.00154)</td><td>0.0235***<br>(0.00396)</td></tr><tr><td>Ln(Age)</td><td>-0.00427***<br>(0.00107)</td><td>0.0143***<br>(0.00213)</td><td>-0.00436***<br>(0.00128)</td><td>0.00836***<br>(0.00238)</td></tr><tr><td>Ln(For)</td><td>-0.00429***<br>(0.00155)</td><td>-0.00708**<br>(0.00286)</td><td>-0.00294**<br>(0.00149)</td><td>-0.00626*<br>(0.00363)</td></tr><tr><td>Risk</td><td>0.0645***<br>(0.0201)</td><td>0.0126<br>(0.0209)</td><td>0.00250<br>(0.00381)</td><td>-0.0162***<br>(0.00580)</td></tr><tr><td>Risk*Medium</td><td>-0.00474**<br>(0.00233)</td><td>-0.00931***<br>(0.00320)</td><td>0.000979<br>(0.00310)</td><td>0.000621<br>(0.00334)</td></tr><tr><td>Risk*Large</td><td>-0.0103**<br>(0.00453)</td><td>-0.0165**<br>(0.00704)</td><td>0.00432<br>(0.00673)</td><td>0.00194<br>(0.00688)</td></tr><tr><td>Observations</td><td>157,038</td><td>157,038</td><td>116,979</td><td>116,979</td></tr><tr><td>R-squared</td><td>0.110</td><td>0.103</td><td>0.122</td><td>0.123</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.
Table 4 introduces interactions between climate risks and firm age. The interaction between transition risks and firm age is positive and significant for female ownership, indicating that older firms may provide more stable platforms for women entrepreneurs to navigate decarbonization pressures. However, no significant moderating effect is observed for female management. Physical risks remain negatively associated with female ownership, with no meaningful variation across firm age, consistent with evidence that climate shocks erode women's economic security across the firm life cycle (Dankelman, 2010; Daoud, 2021).

<table><caption>Table 4: Age of the firm</caption><thead><tr><th rowspan="2"></th><th colspan="2">Transition Risk</th><th colspan="2">Physical Risk</th></tr><tr><th>Female manager</th><th>Female Owner</th><th>Female manager</th><th>Female Owner</th></tr></thead><tbody><tr><td>Export status</td><td>-0.000614<br>(0.00476)</td><td>0.0318***<br>(0.00664)</td><td>0.00546<br>(0.00544)</td><td>0.0420***<br>(0.00794)</td></tr><tr><td>Import status</td><td>-0.0162<br>(0.0102)</td><td>0.0123<br>(0.0287)</td><td>0.00490<br>(0.0109)</td><td>0.00369<br>(0.0317)</td></tr><tr><td>Medium</td><td>-0.0350***<br>(0.00408)</td><td>0.000426<br>(0.00452)</td><td>-0.0305***<br>(0.00508)</td><td>-0.000230<br>(0.00561)</td></tr><tr><td>Large</td><td>-0.0528***<br>(0.0101)</td><td>0.00623<br>(0.00899)</td><td>-0.0425***<br>(0.0121)</td><td>0.00624<br>(0.0111)</td></tr><tr><td>Ln(Priv. Own)</td><td>0.000549<br>(0.00164)</td><td>0.0238***<br>(0.00303)</td><td>0.00360**<br>(0.00153)</td><td>0.0235***<br>(0.00396)</td></tr><tr><td>Ln(Age)</td><td>-0.00401***<br>(0.00104)</td><td>0.0129***<br>(0.00206)</td><td>-0.00469<br>(0.00380)</td><td>0.0113*<br>(0.00683)</td></tr><tr><td>Ln(For)</td><td>-0.00407***<br>(0.00153)</td><td>-0.00658**<br>(0.00283)</td><td>-0.00303*<br>(0.00156)</td><td>-0.00630*<br>(0.00363)</td></tr><tr><td>Risk</td><td>0.0622***<br>(0.0201)</td><td>-0.000974<br>(0.0209)</td><td>0.00368<br>(0.00342)</td><td>-0.0136*<br>(0.00701)</td></tr><tr><td>Risk*Age</td><td>-0.000659<br>(0.000851)</td><td>0.00368***<br>(0.00138)</td><td>6.38e-05<br>(0.000793)</td><td>-0.000706<br>(0.00156)</td></tr><tr><td>Observations</td><td>157,038</td><td>157,038</td><td>116,979</td><td>116,979</td></tr><tr><td>R-squared</td><td>0.110</td><td>0.103</td><td>0.122</td><td>0.123</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.
Table 5 focuses on foreign and private ownership as moderating factors. The results reveal that foreign ownership significantly conditions the impact of climate risks on women ownership only, with no mediating effect on women management. The effect of the transition risks on female ownership decreases with higher foreign ownership, while the negative effect of physical risks on female ownership exacerbates for foreign owned firms. These findings suggest that foreign-owned firms may prioritize rapid adjustment to climate risks in ways that unintentionally disadvantage women. This interpretation aligns with Call and Sellers (2019), who caution that gender-blind climate responses may unintentionally reinforce existing inequalities.

The effect of transition risks on women's management is weaker in privately owned firms than in non-private ones. While transition risks may be associated with higher female participation in management position, privately owned firms appear less likely to translate climate-related pressures into gender-inclusive adjustments. By contrast, transition risks appear to reduce the likelihood of female ownership. The positive and significant interaction with private ownership suggests that this adverse effect is attenuated in privately owned firms, implying that private ownership partially buffers women owners against the negative ownership impacts of transition risks. Physical risks remain negatively associated with female ownership, with no meaningful variation across firm with private ownership.

Overall, the heterogeneity analysis shows that the impact of climate risks on women's empowerment is highly conditional on firm characteristics. Transition risks are most conducive to female managerial representation in smaller, younger, non-privately owned and non-exporting firms, where organizational flexibility and lower adjustment costs facilitate women's access to leadership roles. In contrast, physical climate risks consistently undermine female ownership across firm types, with this negative effect being more pronounced for foreign owned firms. Together, these findings indicate that climate risks do not operate uniformly across firms, underscoring the importance of firm-level context in shaping whether climate change exacerbates or alleviates gender disparities.
<table><caption>Table 5: Ownership types</caption><thead><tr><th rowspan="2"></th><th colspan="4">Transition Risk</th><th colspan="4">Physical Risk</th></tr><tr><th colspan="2">Female manager</th><th colspan="2">Female Owner</th><th colspan="2">Female manager</th><th colspan="2">Female Owner</th></tr></thead><tbody><tr><td>Export status</td><td>-0.000787<br>(0.00476)</td><td>-0.000744<br>(0.00477)</td><td>0.0324***<br>(0.00659)</td><td>0.0321***<br>(0.00662)</td><td>0.00551<br>(0.00541)</td><td>0.00553<br>(0.00541)</td><td>0.0424***<br>(0.00786)</td><td>0.0422***<br>(0.00787)</td></tr><tr><td>Import status</td><td>-0.0157<br>(0.0103)</td><td>-0.0158<br>(0.0102)</td><td>0.0108<br>(0.0280)</td><td>0.0113<br>(0.0281)</td><td>0.00486<br>(0.0108)</td><td>0.00481<br>(0.0108)</td><td>0.00343<br>(0.0317)</td><td>0.00350<br>(0.0317)</td></tr><tr><td>Medium</td><td>-0.0348***<br>(0.00407)</td><td>-0.0349***<br>(0.00407)</td><td>-1.05e-05<br>(0.00449)</td><td>0.000174<br>(0.00450)</td><td>-0.0305***<br>(0.00507)</td><td>-0.0305***<br>(0.00506)</td><td>-0.000298<br>(0.00559)</td><td>-0.000254<br>(0.00560)</td></tr><tr><td>Large</td><td>-0.0530***<br>(0.0101)</td><td>-0.0530***<br>(0.0101)</td><td>0.00703<br>(0.00896)</td><td>0.00711<br>(0.00896)</td><td>-0.0425***<br>(0.0121)</td><td>-0.0425***<br>(0.0121)</td><td>0.00623<br>(0.0110)</td><td>0.00626<br>(0.0110)</td></tr><tr><td>Ln(Priv. Own)</td><td>0.000512<br>(0.00163)</td><td>0.000753<br>(0.00162)</td><td>0.0239***<br>(0.00305)</td><td>0.0233***<br>(0.00301)</td><td>0.00364**<br>(0.00155)</td><td>0.00125<br>(0.00303)</td><td>0.0239***<br>(0.00398)</td><td>0.0175***<br>(0.00657)</td></tr><tr><td>Ln(Age)</td><td>-0.00423***<br>(0.00107)</td><td>-0.00423***<br>(0.00107)</td><td>0.0142***<br>(0.00214)</td><td>0.0143***<br>(0.00214)</td><td>-0.00443***<br>(0.00128)</td><td>-0.00443***<br>(0.00128)</td><td>0.00829***<br>(0.00236)</td><td>0.00832***<br>(0.00237)</td></tr><tr><td>Ln(For)</td><td>-0.00436***<br>(0.00149)</td><td>-0.00415***<br>(0.00153)</td><td>-0.00566**<br>(0.00279)</td><td>-0.00643**<br>(0.00283)</td><td>-0.00160<br>(0.00311)</td><td>-0.00310**<br>(0.00156)</td><td>0.00397<br>(0.00710)</td><td>-0.00648*<br>(0.00364)</td></tr><tr><td>Risk</td><td>0.0601***<br>(0.0200)</td><td>0.0664***<br>(0.0203)</td><td>0.00888<br>(0.0209)</td><td>-0.00683<br>(0.0219)</td><td>0.00400<br>(0.00270)</td><td>0.00128<br>(0.00428)</td><td>-0.0145***<br>(0.00522)</td><td>-0.0222**<br>(0.00943)</td></tr><tr><td>Risk*Ln(For)</td><td>0.00104<br>(0.000653)</td><td></td><td>-0.00341***<br>(0.00121)</td><td></td><td>-0.000368<br>(0.000647)</td><td></td><td>-0.00264*<br>(0.00152)</td><td></td></tr><tr><td>Risk*Ln(Priv. Own)</td><td></td><td>-0.00138**<br>(0.000638)</td><td></td><td>0.00329***<br>(0.00117)</td><td></td><td>0.000602<br>(0.000773)</td><td></td><td>0.00155<br>(0.00180)</td></tr><tr><td>Observations</td><td>157,038</td><td>157,038</td><td>157,038</td><td>157,038</td><td>116,979</td><td>116,979</td><td>116,979</td><td>116,979</td></tr><tr><td>R-squared</td><td>0.110</td><td>0.110</td><td>0.103</td><td>0.103</td><td>0.122</td><td>0.122</td><td>0.123</td><td>0.123</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.

## 5.3. What are the mechanisms behind climate and gender?

Building on the heterogeneous effects identified in the previous section, this section explores the underlying mechanisms through which physical and transition climate risks shape women's economic empowerment at the firm level, by examining key channels highlighted in the literature: assets, access to finance, productivity, land access, and innovation (Quisumbing et al., 2019; Delavallade & Godlonton, 2020; Agarwal, 2018; Khan, 2022).

The results in Table 6 reveal stark differences between physical and transition risks. Physical climate risks primarily operate through constraint-based channels, confirming structural explanations of women's vulnerability. Reduced access to finance, productivity and land, emerge as key transmission mechanisms, consistent with extensive evidence on women's disproportionate exposure to climate-induced credit and asset constraints (Aterido et al., 2013; World Bank, 2023), while access to finance and innovation appear as key channels for women participation in
management position. Transition risks, by contrast, show limited mediation through these channels, suggesting that their positive association with female management reflects organizational and institutional changes rather than resource accumulation. This reinforces the argument that physical and transition risks require differentiated policy responses.

<table><caption>Table 6: Mechanisms for firms managed or owned by females</caption><thead><tr><th rowspan="2">Channel</th><th colspan="2">Female Manager</th><th colspan="2">Female Owner</th></tr><tr><th>Physical</th><th>Transition</th><th>Physical</th><th>Transition</th></tr></thead><tbody><tr><td>Assets</td><td>-0.0102<br>(0.0100)</td><td>0.0277<br>(0.0486)</td><td>0.00396<br>(0.00666)</td><td>0.0502<br>(0.0367)</td></tr><tr><td>Access to finance</td><td>-0.0235***<br>(0.00604)</td><td>0.00753<br>(0.0343)</td><td>-0.0175*<br>(0.00925)</td><td>0.0430<br>(0.0268)</td></tr><tr><td>Productivity</td><td>-0.0989<br>(0.0776)</td><td>0.141<br>(0.758)</td><td>-0.198**<br>(0.0873)</td><td>-1.118<br>(0.705)</td></tr><tr><td>Access to land</td><td>-0.0475<br>(0.0586)</td><td>0.00705<br>(0.125)</td><td>-0.0576**<br>(0.0270)</td><td>-0.283***<br>(0.103)</td></tr><tr><td>Innovation</td><td>0.0501***<br>(0.0130)</td><td>0.0846<br>(0.0607)</td><td>0.0103<br>(0.00878)</td><td>0.0663<br>(0.0545)</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Each cell represents an individual where the dependent variable is the channel studied and the independent variable is either the transition risk or the physical risk. Country, year and sectors fixed effects and all controls are included in the model.

## 5.4. Robustness Checks

To assess the robustness of our results, Table 7 shows the outcomes of the mixed multilevel model that explicitly accounts for the nested structure of firms within countries and years.

The results closely mirror the baseline estimates. Transition risks remain positively associated with female management, while physical risks continue to exert a negative and statistically significant effect on female ownership. The consistency across estimation strategies strengthens confidence in the main findings and suggests that the observed relationships are not driven by model specification or unobserved clustering effects.
<table><caption>Table 7: Mixed Multilevel model</caption><thead><tr><th></th><th>Female manager</th><th>Fem. Owner</th></tr></thead><tbody><tr><td>Ln(CO2/cap)</td><td>0.0609***<br>(0.0200)</td><td>0.00620<br>(0.0208)</td></tr><tr><td>Ln(Death)</td><td>0.00386<br>(0.00268)</td><td>-0.0155***<br>(0.00518)</td></tr><tr><td>Observations</td><td>116,979</td><td>116,979</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects and all controls are included in the model.

In sum, our results can be summarized in the following points: First, physical climate risks significantly reduce female ownership (but not management) within firms, while transition risks are positively associated with female management (but not ownership). Second, the effects of transition risks are heterogeneous across firm size, export status, age, and ownership structure. Indeed, transition risks are most conducive to female managerial representation in smaller, younger, non-privately owned and non-exporting firms. By contrast, the effects of physical risks are not heterogeneous across firm's characteristics, with the only exception of foreign owned firms for which the negative effect of physical risks on women ownership is more pronounced. Third, physical risks appear to operate mainly through finance and land constraints, while transition risks show limited mediation through constrained-based channels.

## 6. Conclusion

This paper investigates how physical and transition climate risks affect women's empowerment within firms, using cross-country firm-level data from the World Bank Enterprise Surveys and climate risk indicators from the World Bank Development Indicators. Women's empowerment is captured through three dimensions: female overall employment with a distinction between female employment in production and non-production roles, female management, and female ownership. Employing linear probability models with high-dimensional fixed effects, complemented by mixed multilevel models to account for the nested structure of firms within countries and years, we analyze how firm characteristics shape the gendered impact of climate risks. Specifically, we examine how exposure to international markets, firm size, age, and ownership structures moderate these effects and use a mediation approach to explore key channels such as access to finance, asset ownership, land, productivity, and innovation capacity. This methodological approach allows us to account for heterogeneity across countries, years, and institutional contexts, providing robust evidence on the gendered consequences of climate shocks.

Our findings reveal several key patterns. Physical climate risks significantly reduce female ownership within firms, whereas transition risks are positively associated with female management. The effects of transition risks are not uniform; they are strongest in smaller, younger, non-exporting, and non-privately owned firms, suggesting that certain firm characteristics enhance
women's opportunities in green transitions. By contrast, the negative impact of physical risks on female ownership is broadly consistent across firms, with the exception of foreign-owned firms, where the effect is more pronounced. Mediation analysis suggests that physical risks operate mainly through constraints on finance and land, while transition risks show limited influence through these channels, indicating that different types of climate risks affect women's empowerment through distinct mechanisms.

These results have important policy implications. Climate adaptation strategies should explicitly address the barriers that constrain women's economic participation. Expanding access to affordable finance and insurance for women-led firms can help mitigate the negative impacts of climate shocks, while strengthening social protection mechanisms can prevent the erosion of female entrepreneurship during extreme events. Policies should also facilitate women's access to land and property rights, ensuring that ownership and resource constraints do not limit their ability to withstand and recover from climate-related shocks.

Climate transition policies offer opportunities to enhance female managerial participation, particularly in emerging green sectors. Programs supporting skill development in low-carbon technologies, sustainable business practices, and innovation can help women take advantage of these opportunities. Encouraging the inclusion of women in managerial and leadership positions through mentorship, capacity-building, and incentives can promote more equitable outcomes in industries undergoing transition. Integrating gender considerations into trade, industrial, and innovation strategies ensures that women benefit from structural shifts in the economy while helping firms leverage diverse leadership for more sustainable growth.
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Appendix 1: List of surveys

<table><thead><tr><th>Country</th><th>Year</th><th>Country</th><th>Year</th><th>Country</th><th>Year</th><th>Country</th><th>Year</th></tr></thead><tbody><tr><td>Afghanistan</td><td>2008</td><td>Belize</td><td>2010</td><td>Ecuador</td><td>2010</td><td>Croatia</td><td>2019</td></tr><tr><td>Afghanistan</td><td>2014</td><td>Bolivia</td><td>2006</td><td>Ecuador</td><td>2017</td><td>Hungary</td><td>2009</td></tr><tr><td>Angola</td><td>2006</td><td>Bolivia</td><td>2010</td><td>Egypt</td><td>2013</td><td>Hungary</td><td>2013</td></tr><tr><td>Angola</td><td>2010</td><td>Bolivia</td><td>2017</td><td>Egypt</td><td>2016</td><td>Hungary</td><td>2019</td></tr><tr><td>Albania</td><td>2007</td><td>Brazil</td><td>2009</td><td>Egypt</td><td>2020</td><td>Indonesia</td><td>2009</td></tr><tr><td>Albania</td><td>2013</td><td>Barbados</td><td>2010</td><td>Eritrea</td><td>2009</td><td>Indonesia</td><td>2015</td></tr><tr><td>Albania</td><td>2019</td><td>Bhutan</td><td>2009</td><td>Spain</td><td>2021</td><td>India</td><td>2014</td></tr><tr><td>Argentina</td><td>2006</td><td>Bhutan</td><td>2015</td><td>Estonia</td><td>2009</td><td>India</td><td>2022</td></tr><tr><td>Argentina</td><td>2010</td><td>Botswana</td><td>2006</td><td>Estonia</td><td>2013</td><td>Ireland</td><td>2020</td></tr><tr><td>Argentina</td><td>2017</td><td>Botswana</td><td>2010</td><td>Estonia</td><td>2019</td><td>Iraq</td><td>2011</td></tr><tr><td>Armenia</td><td>2009</td><td>Central Africa</td><td>2011</td><td>Ethiopia</td><td>2011</td><td>Iraq</td><td>2022</td></tr><tr><td>Armenia</td><td>2013</td><td>Chile</td><td>2006</td><td>Ethiopia</td><td>2015</td><td>Israel</td><td>2013</td></tr><tr><td>Armenia</td><td>2020</td><td>Chile</td><td>2010</td><td>Finland</td><td>2020</td><td>Italy</td><td>2019</td></tr><tr><td>Antiguaandbarbuda</td><td>2010</td><td>China</td><td>2012</td><td>Fiji</td><td>2009</td><td>Jamaica</td><td>2010</td></tr><tr><td>Austria</td><td>2021</td><td>Côte d'Ivoire</td><td>2009</td><td>France</td><td>2021</td><td>Jordan</td><td>2013</td></tr><tr><td>Azerbaijan</td><td>2009</td><td>Côte d'Ivoire</td><td>2016</td><td>Micronesia</td><td>2009</td><td>Jordan</td><td>2019</td></tr><tr><td>Azerbaijan</td><td>2013</td><td>Cameroon</td><td>2009</td><td>Gabon</td><td>2009</td><td>Kazakhstan</td><td>2009</td></tr><tr><td>Azerbaijan</td><td>2019</td><td>Cameroon</td><td>2016</td><td>Georgia</td><td>2008</td><td>Kazakhstan</td><td>2013</td></tr><tr><td>Burundi</td><td>2006</td><td>DRC</td><td>2006</td><td>Georgia</td><td>2013</td><td>Kazakhstan</td><td>2019</td></tr><tr><td>Burundi</td><td>2014</td><td>DRC</td><td>2010</td><td>Georgia</td><td>2019</td><td>Kenya</td><td>2007</td></tr><tr><td>Belgium</td><td>2020</td><td>DRC</td><td>2013</td><td>Ghana</td><td>2007</td><td>Kenya</td><td>2013</td></tr><tr><td>Benin</td><td>2009</td><td>Congo</td><td>2009</td><td>Ghana</td><td>2013</td><td>Kenya</td><td>2018</td></tr><tr><td>Benin</td><td>2016</td><td>Colombia</td><td>2006</td><td>Guinea</td><td>2006</td><td>Kyrgyz Republic</td><td>2009</td></tr><tr><td>BurkinaFaso</td><td>2009</td><td>Colombia</td><td>2010</td><td>Guinea</td><td>2016</td><td>Kyrgyz Republic</td><td>2013</td></tr><tr><td>Bangladesh</td><td>2007</td><td>Colombia</td><td>2017</td><td>Gambia</td><td>2006</td><td>Kyrgyz Republic</td><td>2019</td></tr><tr><td>Bangladesh</td><td>2013</td><td>Cabo Verde</td><td>2009</td><td>Gambia</td><td>2018</td><td>Cambodia</td><td>2013</td></tr><tr><td>Bangladesh</td><td>2022</td><td>Costa Rica</td><td>2010</td><td>Bissau</td><td>2006</td><td>Cambodia</td><td>2016</td></tr><tr><td>Bulgaria</td><td>2007</td><td>Cyprus</td><td>2019</td><td>Greece</td><td>2018</td><td>StKittsandNevis</td><td>2010</td></tr><tr><td>Bulgaria</td><td>2009</td><td>Czechia</td><td>2009</td><td>Grenada</td><td>2010</td><td>Lao PDR</td><td>2009</td></tr><tr><td>Bulgaria</td><td>2013</td><td>Czechia</td><td>2013</td><td>Guatemala</td><td>2006</td><td>Lao PDR</td><td>2012</td></tr><tr><td>Bulgaria</td><td>2019</td><td>Czechia</td><td>2019</td><td>Guatemala</td><td>2010</td><td>Lao PDR</td><td>2016</td></tr><tr><td>Bahamas</td><td>2010</td><td>Germany</td><td>2021</td><td>Guatemala</td><td>2017</td><td>Lao PDR</td><td>2018</td></tr><tr><td>Bosnia and Herzegovina</td><td>2009</td><td>Djibouti</td><td>2013</td><td>Guyana</td><td>2010</td><td>Lebanon</td><td>2013</td></tr><tr><td>Bosnia and Herzegovina</td><td>2013</td><td>Dominica</td><td>2010</td><td>Honduras</td><td>2006</td><td>Lebanon</td><td>2019</td></tr><tr><td>Bosnia and Herzegovina</td><td>2019</td><td>Denmark</td><td>2020</td><td>Honduras</td><td>2010</td><td>Liberia</td><td>2009</td></tr><tr><td>Belarus</td><td>2008</td><td>DominicanRepublic</td><td>2010</td><td>Honduras</td><td>2016</td><td>Liberia</td><td>2017</td></tr><tr><td>Belarus</td><td>2013</td><td>DominicanRepublic</td><td>2016</td><td>Croatia</td><td>2007</td><td>StLucia</td><td>2010</td></tr><tr><td>Belarus</td><td>2018</td><td>Ecuador</td><td>2006</td><td>Croatia</td><td>2013</td><td>SriLanka</td><td>2011</td></tr></tbody></table>
<table><thead><tr><th>Country</th><th>Year</th><th>Country</th><th>Year</th><th>Country</th><th>Year</th><th>Country</th><th>Year</th></tr></thead><tbody><tr><td>Lesotho</td><td>2009</td><td>Mauritius</td><td>2009</td><td>Romania</td><td>2013</td><td>Thailand</td><td>2016</td></tr><tr><td>Lesotho</td><td>2016</td><td>Malawi</td><td>2009</td><td>Romania</td><td>2019</td><td>Tajikistan</td><td>2008</td></tr><tr><td>Lithuania</td><td>2009</td><td>Malawi</td><td>2014</td><td>Russia</td><td>2009</td><td>Tajikistan</td><td>2013</td></tr><tr><td>Lithuania</td><td>2013</td><td>Malaysia</td><td>2015</td><td>Russia</td><td>2012</td><td>Tajikistan</td><td>2019</td></tr><tr><td>Lithuania</td><td>2019</td><td>Malaysia</td><td>2019</td><td>Russia</td><td>2019</td><td>Timor-Leste</td><td>2009</td></tr><tr><td>Luxembourg</td><td>2020</td><td>Namibia</td><td>2006</td><td>Rwanda</td><td>2006</td><td>Timor-Leste</td><td>2015</td></tr><tr><td>Latvia</td><td>2009</td><td>Namibia</td><td>2014</td><td>Rwanda</td><td>2011</td><td>Timor-Leste</td><td>2021</td></tr><tr><td>Latvia</td><td>2013</td><td>Niger</td><td>2009</td><td>Rwanda</td><td>2019</td><td>Tonga</td><td>2009</td></tr><tr><td>Latvia</td><td>2019</td><td>Niger</td><td>2017</td><td>Saudi Arabia</td><td>2022</td><td>Trinidad and Tobago</td><td>2010</td></tr><tr><td>Morocco</td><td>2013</td><td>Nigeria</td><td>2007</td><td>Sudan</td><td>2014</td><td>Tunisia</td><td>2013</td></tr><tr><td>Morocco</td><td>2019</td><td>Nigeria</td><td>2014</td><td>Senegal</td><td>2007</td><td>Tunisia</td><td>2020</td></tr><tr><td>Moldova</td><td>2009</td><td>Nicaragua</td><td>2006</td><td>Senegal</td><td>2014</td><td>Turkiye</td><td>2008</td></tr><tr><td>Moldova</td><td>2013</td><td>Nicaragua</td><td>2010</td><td>Solomon Islands</td><td>2015</td><td>Turkiye</td><td>2013</td></tr><tr><td>Moldova</td><td>2019</td><td>Nicaragua</td><td>2016</td><td>Sierra Leone</td><td>2009</td><td>Turkiye</td><td>2019</td></tr><tr><td>Madagascar</td><td>2009</td><td>Netherlands</td><td>2020</td><td>Sierra Leone</td><td>2017</td><td>Tanzania</td><td>2006</td></tr><tr><td>Madagascar</td><td>2013</td><td>Nepal</td><td>2009</td><td>ElSalvador</td><td>2006</td><td>Tanzania</td><td>2013</td></tr><tr><td>Madagascar</td><td>2022</td><td>Nepal</td><td>2013</td><td>ElSalvador</td><td>2010</td><td>Uganda</td><td>2006</td></tr><tr><td>Mexico</td><td>2006</td><td>Pakistan</td><td>2007</td><td>ElSalvador</td><td>2016</td><td>Uganda</td><td>2013</td></tr><tr><td>Mexico</td><td>2010</td><td>Pakistan</td><td>2013</td><td>Serbia</td><td>2009</td><td>Ukraine</td><td>2008</td></tr><tr><td>North Macedonia</td><td>2009</td><td>Pakistan</td><td>2022</td><td>Serbia</td><td>2013</td><td>Ukraine</td><td>2013</td></tr><tr><td>North Macedonia</td><td>2013</td><td>Panama</td><td>2006</td><td>Serbia</td><td>2019</td><td>Ukraine</td><td>2019</td></tr><tr><td>North Macedonia</td><td>2019</td><td>Panama</td><td>2010</td><td>South Sudan</td><td>2014</td><td>Uruguay</td><td>2006</td></tr><tr><td>Mali</td><td>2007</td><td>Peru</td><td>2006</td><td>Suriname</td><td>2010</td><td>Uruguay</td><td>2010</td></tr><tr><td>Mali</td><td>2010</td><td>Peru</td><td>2010</td><td>Suriname</td><td>2018</td><td>Uruguay</td><td>2017</td></tr><tr><td>Mali</td><td>2016</td><td>Peru</td><td>2017</td><td>Slovak Republic</td><td>2009</td><td>Uzbekistan</td><td>2008</td></tr><tr><td>Malta</td><td>2019</td><td>Philippines</td><td>2009</td><td>Slovak Republic</td><td>2013</td><td>Uzbekistan</td><td>2013</td></tr><tr><td>Myanmar</td><td>2014</td><td>Philippines</td><td>2015</td><td>Slovak Republic</td><td>2019</td><td>Uzbekistan</td><td>2019</td></tr><tr><td>Myanmar</td><td>2016</td><td>Papua New Guinea</td><td>2015</td><td>Slovenia</td><td>2009</td><td>StVincentandGrenadines</td><td>2010</td></tr><tr><td>Montenegro</td><td>2009</td><td>Poland</td><td>2009</td><td>Slovenia</td><td>2013</td><td>Venezuela</td><td>2006</td></tr><tr><td>Montenegro</td><td>2013</td><td>Poland</td><td>2013</td><td>Slovenia</td><td>2019</td><td>Venezuela</td><td>2010</td></tr><tr><td>Montenegro</td><td>2019</td><td>Poland</td><td>2019</td><td>Sweden</td><td>2014</td><td>Viet Nam</td><td>2009</td></tr><tr><td>Mongolia</td><td>2009</td><td>Portugal</td><td>2019</td><td>Sweden</td><td>2020</td><td>Viet Nam</td><td>2015</td></tr><tr><td>Mongolia</td><td>2013</td><td>Paraguay</td><td>2006</td><td>Eswatini</td><td>2006</td><td>Vanuatu</td><td>2009</td></tr><tr><td>Mongolia</td><td>2019</td><td>Paraguay</td><td>2010</td><td>Eswatini</td><td>2016</td><td>Samoa</td><td>2009</td></tr><tr><td>Mozambique</td><td>2007</td><td>Paraguay</td><td>2017</td><td>Chad</td><td>2009</td><td>Kosovo</td><td>2009</td></tr><tr><td>Mozambique</td><td>2018</td><td>West Bank And Gaza</td><td>2013</td><td>Chad</td><td>2018</td><td>Kosovo</td><td>2013</td></tr><tr><td>Mauritania</td><td>2006</td><td>West Bank And Gaza</td><td>2019</td><td>Togo</td><td>2009</td><td>Kosovo</td><td>2019</td></tr><tr><td>Mauritania</td><td>2014</td><td>Romania</td><td>2009</td><td>Togo</td><td>2016</td><td>Yemen</td><td>2010</td></tr></tbody></table>
<table><tr><td>Yemen</td><td>2013</td></tr><tr><td>SouthAfrica</td><td>2007</td></tr><tr><td>SouthAfrica</td><td>2020</td></tr><tr><td>Zambia</td><td>2007</td></tr><tr><td>Zambia</td><td>2013</td></tr><tr><td>Zambia</td><td>2019</td></tr><tr><td>Zimbabwe</td><td>2011</td></tr><tr><td>Zimbabwe</td><td>2016</td></tr></table>

## Appendix 2: Further results

<table><caption>Table A1: Impact on Employment</caption><thead><tr><th></th><th colspan="2">Ln(Emp. Fem.)</th><th colspan="2">Ln(Fem. Production)</th><th colspan="2">Ln(Fem. Non-Production)</th></tr></thead><tbody><tr><td>Export status</td><td>0.122***<br>(0.0240)</td><td>0.143***<br>(0.0325)</td><td>0.330***<br>(0.0284)</td><td>0.331***<br>(0.0360)</td><td>0.179***<br>(0.0189)</td><td>0.187***<br>(0.0215)</td></tr><tr><td>Import status</td><td>-0.0670<br>(0.0710)</td><td>-0.134<br>(0.0961)</td><td>-0.0403<br>(0.0603)</td><td>-0.0477<br>(0.0471)</td><td>0.00501<br>(0.0445)</td><td>-0.0376<br>(0.0581)</td></tr><tr><td>Medium</td><td>0.999***<br>(0.0207)</td><td>0.996***<br>(0.0271)</td><td>0.707***<br>(0.0272)</td><td>0.676***<br>(0.0316)</td><td>0.608***<br>(0.0318)</td><td>0.597***<br>(0.0370)</td></tr><tr><td>Large</td><td>2.549***<br>(0.0355)</td><td>2.536***<br>(0.0479)</td><td>2.190***<br>(0.0993)</td><td>2.094***<br>(0.111)</td><td>1.749***<br>(0.0843)</td><td>1.728***<br>(0.100)</td></tr><tr><td>Ln(Priv. Own)</td><td>-0.00509<br>(0.00681)</td><td>0.0116<br>(0.00866)</td><td>-0.0276***<br>(0.00924)</td><td>-0.0222**<br>(0.00953)</td><td>0.00139<br>(0.00658)</td><td>0.00989<br>(0.00787)</td></tr><tr><td>Ln(Age)</td><td>0.0360***<br>(0.00653)</td><td>0.0236***<br>(0.00778)</td><td>0.0133<br>(0.00965)</td><td>0.00336<br>(0.0111)</td><td>0.0734***<br>(0.00761)</td><td>0.0701***<br>(0.00845)</td></tr><tr><td>Ln(For)</td><td>0.0512***<br>(0.00583)</td><td>0.0619***<br>(0.00762)</td><td>0.0227**<br>(0.0112)</td><td>0.0295**<br>(0.0135)</td><td>0.0559***<br>(0.00849)</td><td>0.0624***<br>(0.0101)</td></tr><tr><td>Ln(CO2/cap)</td><td>-0.130**<br>(0.0550)</td><td></td><td>0.122<br>(0.0909)</td><td></td><td>0.261***<br>(0.0613)</td><td></td></tr><tr><td>Ln(Death)</td><td></td><td>-0.0242**<br>(0.00971)</td><td></td><td>-0.0253<br>(0.0156)</td><td></td><td>-0.0384***<br>(0.0124)</td></tr><tr><td>Observations</td><td>61,616</td><td>40,783</td><td>78,028</td><td>62,446</td><td>76,449</td><td>61,013</td></tr><tr><td>R-squared</td><td>0.584</td><td>0.584</td><td>0.506</td><td>0.496</td><td>0.549</td><td>0.549</td></tr></tbody></table>

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note: Country, year and sectors fixed effects are included in the model.