## 2.   Drivers of Women’s Exposure to Natural Disasters

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---

### Overview and context
- Publication date: November 14, 2025.
- Focus: Diagnostic analysis of how climate shocks interact with gender gaps in Rwanda and implications for macroeconomic resilience and policy.
- Key framing: "Climate shocks represent a recurrent macroeconomic risk for Rwanda, affecting growth, fiscal stability, and household welfare. Gender inclusion is macro-critical to Rwanda’s growth and resilience strategy."
- Central finding: "Women are highly exposed to these shocks due to their concentration in agriculture and informal employment, as well as more limited access to finance, assets, and coping mechanisms." Closing gender gaps enhances effectiveness of adaptation policies and accelerates post-shock recovery.

### Institutional framework and policy coordination
- Institutions and instruments integrating gender and climate:
  - Ministry of Gender and Family Promotion (MIGEPROF): policy lead on gender equality.
  - Gender Monitoring Office (GMO): oversight and accountability for gender mainstreaming.
  - Ministry of Finance and Economic Planning (MINECOFIN): integration of gender objectives into budgets via Gender Budget Tagging (GBT) and Climate Budget Tagging (CBT).
  - Development Bank of Rwanda (BRD), FONERWA, and Ireme Invest: green-finance facilities.
  - Rwanda TVET Board (RTB), Dynamic Social Registry, VUP, Ejo Heza, National Bank of Rwanda (BNR).
- Select recent reforms and documents (verbatim terms preserved):
  - GMO five-year plan (2024–29).
  - Revised National Gender Policy (2021).
  - Financial Sector Development Strategy (FSDS) and WE Finance Code (2025).
  - Financial Sector Development Strategy 2024–29.
  - Financial Sector Development Strategic Plan 2018–24 (foundational framework still in effect pending rollout of FSDS 2025–30).
  - RTB Strategic Plan 2021–24.
  - National FinTech Strategy 2024–29.
- Role of instruments: CBT, GBT, and the dynamic social registry underpin the National Strategy for Transformation (NST2) and Rwanda’s Green Growth and Climate Resilience Strategy by improving targeting and efficiency of public spending and strengthening fiscal resilience.

### Drivers of women’s exposure and channels of vulnerability
- Structural drivers highlighted in the chapter:
  - Concentration of women in agriculture and informal employment (lower-productivity, climate-exposed sectors).
  - Heavier care responsibilities/time poverty that constrain adaptation and rebuilding.
  - Limited access to credit, productive assets, and disaster communications/early warnings (especially in female-headed households).
  - Housing material vulnerability leading to greater damage in disasters.
- Empirical characterization: "Women accounted for about a half of labor force growth over the past two decades" (verbatim).

### Micro-level evidence and household responses
- Primary microdata:
  - Integrated Household Living Conditions Survey (EICV7), conducted between October 2023 and October 2024.
  - Labor Force Survey (LFS), conducted annually since 2017; wage data available from 2021 onward.
  - District-level disaster reports from the Ministry in Charge of Emergency Management (MINEMA).
- Key EICV7 variables:
  - Individual characteristics: gender, education level, housing materials.
  - Socioeconomic indicators: hourly wages, sector of employment, receipt of government support.
  - Climate-disaster exposure: self-reported household experience of heavy rainfall, floods, droughts, landslides or mudslides in preceding 12 months (household classified as having experienced a climate-related shock if at least one event reported).
- VUP program components captured in data:
  - Direct Support (DS).
  - Classic Public Works (cPW).
  - Expanded Public Works (ePW).
  - Nutrition-Sensitive Direct Support (NSDS).
- Observed household responses to climate shocks (microdata findings):
  - Individuals hit by shocks report allocating more time to domestic responsibilities, reducing time available for productive work.
  - Households respond by working longer hours, reducing consumption, and drawing down savings.
  - The majority of households had not fully recovered at the time of the survey.
  - Recovery is slower for female-headed households.

### Aggregated damage index construction (LFS + MINEMA)
- LFS district-year aggregation: average wages for men and women across employment sectors in each of Rwanda’s 30 districts over time.
- MINEMA disaster reports: number of houses and hectares of crops damaged by natural disasters, scaled by district population and farmland area respectively.
- Composite "disaster damage" index: normalized scaled indicators combined as an equally weighted average.

### Econometric approach and identification
- Individual-level EICV7 cross-sectional regression (as presented in source):
  - log(WWWW_e_e_i) = α + β_CCCCCC_WWC_e_i + β_W_WWW_CWn_i + β_CW_CCCCC_WWC_e_i × WWW_CWn_i + Σ β_VVVVPP_iV M_V=1 + Σ β_CVVC_CCCCC_CC_e_i × VVVVPP_iV M_V=1 + Σ β_WVVV_WWWC_WWn_i × VVVVPP_iV M_V=1 + Σ β_CWVV_CCCCC_CC_e_i × WWW_CW_WWn_i × M_V=1 VVVVPP_iV + Σ X_iiii_K_i=1 + ε_i.
  - In words: dependent variable is the natural logarithm of the hourly wage of individual i; key dummies include whether the individual suffered a climate-related disaster and whether the individual is a woman; VUP support dummies (among M options) are included; a set of control variables {X} accounts for various individual and household characteristics; standard errors clustered at the district level.
- Main coefficient of interest:
  - Denoted 휷휷_CAC (presented as 휷휷_CCC in the source), the interaction between the climate shock and gender indicators. It captures the differential impact of climate-related shocks on women’s wages relative to men’s, holding other factors constant.
- Control variables (explicitly listed in source):
  - Area of residence (urban/rural), household position (household head or not), level of educational attainment, employment sector, housing materials, and province of residence (Rwanda comprises five provinces).
  - Rationale: control for omitted variable bias where, for example, low-quality housing both raises disaster exposure and correlates with lower wages.
- Aggregate (LFS) district-year regression specification:
  - log(WageGap_{j,t}) = α + β_D · DisasterDamage_{j,t} + ε_{j,t}
  - log(WageGap_{j,t}) is the natural logarithm of the gender wage gap in district j in year t (ratio of the average hourly wage of men to that of women).
  - DisasterDamage_{j,t} is the “disaster damage” index.
  - β_D captures the relationship between natural disaster damages and the gender wage gap.
  - Regression includes year and district fixed effects and is estimated for subsamples by employment sector and education; individual-level EICV7 regressions include interaction terms with program support (NSDS VUP).

### Empirical patterns and results
- Sectoral vulnerability:
  - Microdata indicate women in private agriculture and domestic work are among the most affected by climate shocks.
- Individual-level results (EICV7):
  - Climate disasters are generally associated with lower hourly wages across most population subsets (Table 2).
  - No additional adverse effect of disasters on women’s wages relative to men’s is observed in agriculture and industry.
  - Services sector: estimated effect is positive—women’s wages increase relative to men’s following natural disasters (counterintuitive finding prompting further analysis).
  - NSDS VUP interaction: a positive association emerges between wages and the triple interaction of climate shocks × gender × NSDS VUP support.
    - Triple interaction is statistically significant for the full sample and for the agriculture and industry subsamples, but not for services.
    - Interpretation: NSDS support—targeting pregnant women and households with young children in poverty—may cushion vulnerable women from income losses associated with climate shocks.
    - Caveat: the positive and unexplained relationship between the disaster×gender×NSDS interaction and wages in the services sector warrants further investigation.
- Aggregate-level results (LFS):
  - Main finding: positive relationship between disaster damages and the gender wage gap at the district level.
    - Relationship is statistically significant for the full sample and the services subsample.
    - β_D is not significant in agriculture and industry (Table 3).
  - Interpretation: infrastructure damage critical to services (e.g., roads, business premises) may reduce earnings indirectly; aggregated LFS district-level damage index captures indirect, infrastructure-mediated income effects that EICV7 self-reports may miss.
  - Women's greater exposure to vulnerable employment and lower seniority may make disasters affecting workplaces disproportionately adverse for women, helping explain wider gender wage gaps in services where disaster damages are larger.

### Macro-level model: gender equality and climate adaptation
- Model context and purpose: small open-economy model (inspired by DIGNAD) with a gender dimension to analyze complementarities between disaster-resilience policies and female labor force participation, and to compare recovery paths from a major flood under alternative resilience and wage-gap settings.
- Key model features:
  - Government invests in standard infrastructure and adaptation capital (flood defenses, resilient roads, drought-ready irrigation). Adaptation capital: lower depreciation and reduces disaster-induced productivity losses (damages manifest as temporary losses of productivity).
  - Gender empowerment modeled as a reduction of the wage gap between men and women. In the model, the wage gap is reduced mechanically by recalibrating the steady state to achieve a 50 percent reduction while holding other parameters fixed.
  - Two sectors: (i) exportable agricultural sector (women over-represented), and (ii) non-tradable sector (services, construction, manufacturing). Both sectors employ men and women and are subject to natural disaster shocks partially shielded by public adaptation capital.
- Calibration targets and data moments:
  - Average wage gap is 30 percent: for every RWF 1,000 a man earns, women earn RWF 700.
  - Roughly half of working men work in agriculture; 72 percent of female workers work in agriculture.
- Table 4 matched moments (Data vs Model):
  - Value-added in non-tradable sector: 48.0% (Data) vs 41.6% (Model)
  - External private debt to GDP ratio: 20.4% vs 20.5%
  - Domestic public debt to GDP ratio: 15.7% vs 11.5%
  - Imports to GDP ratio: 35.5% vs 35.5%
  - Public infrastructure investment to GDP ratio: 13.1% vs 16.9%
  - Average wage gap between men and women: 30.5% vs 29.8%
- Disaster shock calibration: extreme flood event associated with a 7.5 percent reduction in total factor productivity.

### Simulation 1 — Long-term steady-state comparison
- Experiment design: compare two economies differing only in gender wage gaps: current-gap scenario vs low-gap scenario (observed gap cut by half). Both economies start with very low adaptation infrastructure; public investment raises adaptation capital in 5-percentage-point steps up to a 50 percent increase; for each step the balanced growth steady state is recomputed.
- Main quantitative findings:
  - A 1 percent increase in the adaptation capital stock yields GDP that is 0.13 percentage point larger in the low-gap economy.
  - For a 30 percent increase in adaptation capital stock, the low-gap economy grows 1.15 percentage point higher.
  - For a 50% increase it reaches 1.73 percentage point larger GDP.
- Channels for larger payoffs when wage gaps are lower:
  - Misallocation channel: narrower gaps improve allocation of men’s and women’s skills across sectors, raising effective labor input and amplifying output response to productivity boosts.
  - External constraint channel: more equal wages facilitate attracting labor into tradables (agriculture), expanding exports and relaxing balance-of-payments constraints needed to finance imported inputs for adaptation investment.
  - Fiscal base channel: higher female earnings broaden tax base, making public investment less fiscally expensive in general equilibrium.
    - At a given 1 percent increase in adaptation capital, the tax-revenue-to-GDP ratio in the low-gap economy is about 4.25 percentage points higher than in the current-gap economy.
    - Even with no additional adaptation, the low-gap economy already raises roughly 4.1 percentage points more tax revenue, indicating most fiscal gain stems from narrowing the wage gap.
  - Adaptation alone slightly narrows the wage gap through a composition effect by protecting sectors where women are over-represented.

### Simulation 2 — Short-run recovery to shocks
- Experiment design: two economies hit with the same natural-disaster shock (temporary TFP drop reverting in AR(1) fashion). Compare pre-shock settings: low vs higher adaptation capital and current vs reduced gender wage gap.
- Main findings:
  - Pre-positioned resilience and narrower wage gaps reduce peak-to-trough output loss and speed up recovery.
  - Mechanisms:
    - Smaller effective supply shock: resilience lifts underlying productivity and attenuates the TFP hit.
    - Faster reallocation: tighter wage gap improves labor supply incentives—especially for women—toward higher-return margins when flexibility is needed.
    - Stronger automatic stabilizers: broader tax base from higher female earnings cushions revenue shortfalls and lowers required fiscal adjustment.
  - Net outcome: milder output contraction and steeper recovery of output and employment relative to current-gap, low-resilience baseline.

### Policy instruments for inclusive climate resilience
- Data-driven tools:
  - CBT identifies and classifies expenditures contributing to climate mitigation and adaptation across sectors (agriculture, energy, infrastructure).
  - GBT provides information on how public spending supports women’s economic participation and welfare outcomes, strengthening gender-responsive budgeting.
  - Combined, CBT and GBT enable cross-analysis linking adaptation spending with gender impacts to evaluate inclusiveness of climate investment.
- Dynamic social registry and adaptive social protection:
  - Integrates household-level data for rapid identification of vulnerable households during shocks and links to programs such as VUP to scale up assistance to those most affected, including women and informal-sector workers—supporting timely, targeted, and fiscally sustainable fiscal responses.

### Rwanda’s green transition: opportunities, risks, and policy priorities
- Opportunities and risks:
  - Investments in renewable energy, climate-smart agriculture, and the circular economy can generate employment and productivity gains.
  - Women remain under-represented in many emerging green sectors; without targeted interventions, the transition could widen existing labor-market gaps.
  - Digitalization risks: women remain less likely to own smartphones, access the internet, or possess digital literacy skills needed to benefit from e-finance, e-commerce, and online training.
- Education, training, and finance measures:
  - Expand access to TVET and strengthen MSME finance initiatives to help women participate more fully in green industries.
  - RTB Strategic Plan 2021-24 and recent investments in TVET centers of excellence aim to modernize curricula, integrate ICT skills, and build stronger links with employers; gaps remain in digital infrastructure and female enrolment in technical fields.
  - 2025-29 Financial Sector Development Strategy and the FinScope 2024 Gender Report highlight efforts to close credit gaps for women-led MSMEs, including commitments to dedicate at least half of microfinance and SACCO lending to women.
  - Integrate gender criteria into green-investment facilities such as Ireme Invest.
- Data systems and social registry:
  - Rwanda’s Dynamic Social Registry (Imibereho System)
    - Launched February 29, 2024
    - 2.7 out of 3 million households already included in the system
    - Collaboration with Land Registry, Rwanda Information Society Authority, National Identification Agency, and Rwanda Revenue Authority
    - Innovative features allowing for constant data updates including through exchanging data with other government systems
    - Collects vital socioeconomic data to support poverty alleviation efforts
  - Continued investment in CBT, GBT, and the social registry is critical to track outcomes, inform course corrections, and ensure Rwanda’s climate and gender agendas reinforce each other.
- Policy priorities and recommended actions:
  - Strengthen the integration of CBT and GBT:
    - Establish consistent cross-analysis of adaptation and gender-related expenditures, supported by clear institutional responsibilities and regular reporting by MINECOFIN, sector ministries, and the Rwanda Gender Monitoring Office.
  - Expand adaptive social protection through the social registry:
    - Continue improving coverage, interoperability, and real-time updating to allow programs—particularly the VUP—to scale up efficiently during shocks while maintaining fiscal discipline.
  - Promote women’s participation in the green transition:
    - Facilitate access to green finance, technical and vocational training, and digital skills to ensure women benefit from employment opportunities in renewable energy, sustainable agriculture, and the circular economy.
    - Integrate gender criteria into green-investment facilities such as Ireme Invest.
  - Enhance access to productive assets and finance:
    - Broaden financial inclusion, strengthen women’s property and land rights, and expand tailored support for women-owned MSMEs to raise productivity and resilience.
  - Invest in data and evaluation:
    - Expand the use of gender-disaggregated and climate-tagged data to assess the effectiveness of adaptation and inclusion programs and guide evidence-based policy refinement.

### Key CBT and GBT projects in FY25/26 (projects larger than USD10 million)
- Agriculture
  - GBT Improved access to farm inputs for women 53.9 (RWF Billion)
  - CBT Climate-smart agriculture and input access 71.0 (RWF Billion)
- Employment
  - GBT Job creation and training for women 84.7 (RWF Billion)
  - GBT Employment in housing/construction for women 48.1 (RWF Billion)
  - GBT Employment in road construction for women 97.4 (RWF Billion)
- Energy
  - GBT Expansion of electricity access for women 128.0 (RWF Billion)
  - CBT Renewable energy and energy efficiency 169.9 (RWF Billion)
  - CBT Resilience-focused energy infrastructure 44.6 (RWF Billion)
- Environment
  - CBT Integrated environmental protection 67.9 (RWF Billion)
- Transport
  - CBT Road rehabilitation and resilient transport 88.1 (RWF Billion)
- Social Protection
  - GBT Direct VUP support to women 36.3 (RWF Billion)
  - GBT Support to female genocide survivors 16.4 (RWF Billion)
  - CBT Support to households affected by climate shocks 39.1 (RWF Billion)
- Education
  - CBT Climate-resilient schools; awareness programs 15.3 (RWF Billion)
- Health
  - GBT HIV prevention and treatment for women 15.0 (RWF Billion)
- Water and Sanitation
  - GBT Improved access to water/sanitation for women 55.7 (RWF Billion)
  - CBT Expansion of water and sanitation infrastructure 47.1 (RWF Billion)
  - CBT Improved water treatment and sanitation systems 34.3 (RWF Billion)

*Source: sipea2026041 — 2.   Drivers of Women’s Exposure to Natural Disasters*

### 2.   Drivers of Women’s Exposure to Natural Disasters ___________________________________ 5

### 2.   Drivers of Women’s Exposure to Natural Disasters

### Overview and context
- Publication date: November 14, 2025.
- Focus: Diagnostic analysis of how climate shocks interact with gender gaps in Rwanda and implications for macroeconomic resilience and policy.
- Key framing: "Climate shocks represent a recurrent macroeconomic risk for Rwanda, affecting growth, fiscal stability, and household welfare. Gender inclusion is macro-critical to Rwanda’s growth and resilience strategy."
- Central finding: "Women are highly exposed to these shocks due to their concentration in agriculture and informal employment, as well as more limited access to finance, assets, and coping mechanisms." Closing gender gaps enhances effectiveness of adaptation policies and accelerates post-shock recovery.

### Institutional framework and policy coordination
- Institutions and instruments integrating gender and climate:
  - Ministry of Gender and Family Promotion (MIGEPROF): policy lead on gender equality.
  - Gender Monitoring Office (GMO): oversight and accountability for gender mainstreaming.
  - Ministry of Finance and Economic Planning (MINECOFIN): integration of gender objectives into budgets via Gender Budget Tagging (GBT) and Climate Budget Tagging (CBT).
  - Development Bank of Rwanda (BRD), FONERWA, and Ireme Invest: green-finance facilities.
  - Rwanda TVET Board (RTB), Dynamic Social Registry, VUP, Ejo Heza, National Bank of Rwanda (BNR).
- Select recent reforms and documents (verbatim terms preserved):
  - GMO five-year plan (2024–29).
  - Revised National Gender Policy (2021).
  - Financial Sector Development Strategy (FSDS) and WE Finance Code (2025).
  - Financial Sector Development Strategy 2024–29.
  - Financial Sector Development Strategic Plan 2018–24 (foundational framework still in effect pending rollout of FSDS 2025–30).
  - RTB Strategic Plan 2021–24.
  - National FinTech Strategy 2024–29.
- Role of instruments: CBT, GBT, and the dynamic social registry underpin the National Strategy for Transformation (NST2) and Rwanda’s Green Growth and Climate Resilience Strategy by improving targeting and efficiency of public spending and strengthening fiscal resilience.

### Drivers of women’s exposure and channels of vulnerability
- Structural drivers highlighted in the chapter:
  - Concentration of women in agriculture and informal employment (lower-productivity, climate-exposed sectors).
  - Heavier care responsibilities/time poverty that constrain adaptation and rebuilding.
  - Limited access to credit, productive assets, and disaster communications/early warnings (especially in female-headed households).
  - Housing material vulnerability leading to greater damage in disasters.
- Empirical characterization: "Women accounted for about a half of labor force growth over the past two decades" (verbatim).

### Micro-level evidence and household responses
- Primary microdata:
  - Integrated Household Living Conditions Survey (EICV7), conducted between October 2023 and October 2024.
  - Labor Force Survey (LFS), conducted annually since 2017; wage data available from 2021 onward.
  - District-level disaster reports from the Ministry in Charge of Emergency Management (MINEMA).
- Key EICV7 variables:
  - Individual characteristics: gender, education level, housing materials.
  - Socioeconomic indicators: hourly wages, sector of employment, receipt of government support.
  - Climate-disaster exposure: self-reported household experience of heavy rainfall, floods, droughts, landslides or mudslides in preceding 12 months (household classified as having experienced a climate-related shock if at least one event reported).
- VUP program components captured in data:
  - Direct Support (DS).
  - Classic Public Works (cPW).
  - Expanded Public Works (ePW).
  - Nutrition-Sensitive Direct Support (NSDS).

- Observed household responses to climate shocks (microdata findings):
  - Individuals hit by shocks report allocating more time to domestic responsibilities, reducing time available for productive work.
  - Households respond by working longer hours, reducing consumption, and drawing down savings.
  - The majority of households had not fully recovered at the time of the survey.
  - Recovery is slower for female-headed households.

### Aggregated (LFS + MINEMA) damage index construction
- LFS district-year aggregation: average wages for men and women across employment sectors in each of Rwanda’s 30 districts over time.
- MINEMA disaster reports: number of houses and hectares of crops damaged by natural disasters, scaled by district population and farmland area respectively.
- Composite "disaster damage" index: normalized scaled indicators combined as an equally weighted average.

### Econometric approach and identification
- EICV7 individual-level cross-sectional regression specification (as presented in source):
  - log(WWWW_e_e_i) = α + β_CCCCCC_WWC_e_i + β_W_WWW_CWn_i + β_CW_CCCCC_WWC_e_i × WWW_CWn_i + Σ β_VVVVPP_iV M_V=1 + Σ β_CVVC_CCCCC_CC_e_i × VVVVPP_iV M_V=1 + Σ β_WVVV_WWWC_WWn_i × VVVVPP_iV M_V=1 + Σ β_CWVV_CCCCC_CC_e_i × WWW_CW_WWn_i × M_V=1 VVVVPP_iV + Σ X_iiii_K_i=1 + ε_i.
  - In words: dependent variable is the natural logarithm of the hourly wage of individual i; key dummies include whether the individual suffered a climate-related disaster and whether the individual is a woman; VUP support dummies (among M options) are included; a set of control variables {X} accounts for various individual and household characteristics; standard errors clustered at the district level.
- Main coefficient of interest:
  - Denoted 휷휷_CAC (presented as 휷휷_CCC in the source), the interaction between the climate shock and gender indicators. It captures the differential impact of climate-related shocks on women’s wages relative to men’s, holding other factors constant.
- Control variables (explicitly listed in source):
  - Area of residence (urban/rural), household position (household head or not), level of educational attainment, employment sector, housing materials, and province of residence (Rwanda comprises five provinces).
  - Rationale: control for omitted variable bias where, for example, low-quality housing both raises disaster exposure and correlates with lower wages.

### Empirical patterns highlighted in the chapter
- Sectoral vulnerability: Microdata indicate women in private agriculture and domestic work are among the most affected by climate shocks.
- Gender gap in wages by sector (verbatim metric in source): Gender gap is expressed as male-to-female ratio of average hourly wages; sectoral series noted for All, Agriculture, Industry, Services.
- Household coping strategies (verbatim categories in source): Worked More; Reduced Food Spending; Borrowed Money; Sold Female Animals; Used Savings; Got Help from Family; Got Public Help; Other Suffered Climate Shock.

### Policy-relevance and linkages to later sections
- Data-driven policy instruments (covered in later sections but introduced here): Climate Budget Tagging (CBT), Gender Budget Tagging (GBT), the dynamic social registry—these tools strengthen integration of gender and climate in budget allocation, monitoring, and social protection and are foundational for aligning adaptation, inclusion, and fiscal management.
- The chapter positions the analysis as diagnostic, feeding into:
  - Section III: microdata quantification of climate shocks on women’s labor-market outcomes and household resilience.
  - Section IV: macroeconomic general equilibrium modeling showing how closing gender gaps affects adaptation investment effectiveness and recovery.
  - Section V: examination of data-driven policy instruments and opportunities/risks in the green transition, focusing on women’s participation.
  - Section VI: policy priorities to strengthen linkages across initiatives and integrate gender and climate considerations into policymaking.

*Source: sipea2026041 — 2.   Drivers of Women’s Exposure to Natural Disasters*

### 16.      The regression is estimated for various population subsets. These include groups defined

### sipea2026041 - 16.      The regression is estimated for various population subsets. These include groups defined

### Regression specification and estimation
- Empirical design (LFS dataset): repeated cross-section regression at the year–district level.  
- Estimated equation (notation adapted from source): log(WageGap_{j,t}) = α + β_D · DisasterDamage_{j,t} + ε_{j,t}  
  - log(WageGap_{j,t}) is the natural logarithm of the gender wage gap in district j in year t (ratio of the average hourly wage of men to that of women).  
  - DisasterDamage_{j,t} is the “disaster damage” index.  
  - β_D captures the relationship between natural disaster damages and the gender wage gap.  
- Fixed effects and subsamples: regression includes year and district fixed effects and is estimated for subsamples by employment sector—agriculture, industry, and services—and for samples defined by educational attainment (primary vs secondary) and for household heads in a final specification.  
- Individual-level analysis (EICV7 dataset): separate individual-level regressions are estimated, including interaction terms with program support (NSDS VUP).

### Individual-level results (EICV7)
- Climate disasters are generally associated with lower hourly wages across most population subsets (Table 2).  
- No additional adverse effect of disasters on women’s wages relative to men’s is observed in agriculture and industry.  
- Services sector: estimated effect is positive—women’s wages increase relative to men’s following natural disasters (counterintuitive finding prompting further analysis).  
- NSDS VUP interaction: a positive association emerges between wages and the triple interaction of climate shocks × gender × NSDS VUP support.  
  - This triple interaction is statistically significant for the full sample and for the agriculture and industry subsamples, but not for services.  
  - Interpretation: NSDS support—targeting pregnant women and households with young children in poverty—may cushion vulnerable women from income losses associated with climate shocks.  
  - Caveat: the positive and unexplained relationship between the disaster×gender×NSDS interaction and wages in the services sector warrants further investigation.

### Aggregate-level results (LFS)
- Main finding: positive relationship between disaster damages and the gender wage gap at the district level.  
  - Relationship is statistically significant for the full sample and the services subsample.  
  - β_D is not significant in agriculture and industry (Table 3).  
- Interpretation and reconciliation with individual-level results:  
  - Natural disasters may damage infrastructure critical to services (e.g., roads, business premises), reducing earnings indirectly.  
  - Individual-level self-reported disaster exposure in EICV7 may not capture indirect, infrastructure-mediated income effects; aggregated LFS district-level damage index does.  
  - Women’s greater exposure to vulnerable employment and lower seniority may make disasters affecting workplaces disproportionately adverse for women, helping explain the wider gender wage gaps in services where disaster damages are larger.

### Macro-level model: gender equality and climate adaptation
- Model context and purpose: small open-economy model (inspired by DIGNAD) with a gender dimension to analyze complementarities between disaster-resilience policies and female labor force participation, and to compare recovery paths from a major flood under alternative resilience and wage-gap settings.  
- Key model features:  
  - Government invests in standard infrastructure and adaptation capital (flood defenses, resilient roads, drought-ready irrigation). Adaptation capital: lower depreciation and reduces disaster-induced productivity losses (damages manifest as temporary losses of productivity).  
  - Gender empowerment modeled as a reduction of the wage gap between men and women. In the model, the wage gap is reduced mechanically by recalibrating the steady state to achieve a 50 percent reduction while holding other parameters fixed.  
  - Two sectors: (i) exportable agricultural sector (women over-represented), and (ii) non-tradable sector (services, construction, manufacturing). Both sectors employ men and women and are subject to natural disaster shocks partially shielded by public adaptation capital.  
- Calibration targets and data moments: model calibrated to match Rwandan observations:  
  - Average wage gap is 30 percent: for every RWF 1,000 a man earns, women earn RWF 700.  
  - Roughly half of working men work in agriculture; 72 percent of female workers work in agriculture.  
- Table 4 matched moments (Data vs Model):  
  - Value-added in non-tradable sector: 48.0% (Data) vs 41.6% (Model)  
  - External private debt to GDP ratio: 20.4% vs 20.5%  
  - Domestic public debt to GDP ratio: 15.7% vs 11.5%  
  - Imports to GDP ratio: 35.5% vs 35.5%  
  - Public infrastructure investment to GDP ratio: 13.1% vs 16.9%  
  - Average wage gap between men and women: 30.5% vs 29.8%  
- Disaster shock calibration: extreme flood event associated with a 7.5 percent reduction in total factor productivity.

### Simulation 1 — Long-term steady-state comparison
- Experiment design: compare two economies differing only in gender wage gaps: current-gap scenario vs low-gap scenario (observed gap cut by half). Both economies start with very low adaptation infrastructure; public investment raises adaptation capital in 5-percentage-point steps up to a 50 percent increase; for each step the balanced growth steady state is recomputed.  
- Main quantitative findings: adaptation capital and lower wage gaps are complementary—low-gap economy attains higher steady-state output at every resilience level. Specific results:  
  - A 1 percent increase in the adaptation capital stock yields GDP that is 0.13 percentage point larger in the low-gap economy.  
  - For a 30 percent increase in adaptation capital stock, the low-gap economy grows 1.15 percentage point higher.  
  - For a 50% increase it reaches 1.73 percentage point larger GDP.  
- Channels for larger payoffs when wage gaps are lower:  
  - Misallocation channel: narrower gaps improve allocation of men’s and women’s skills across sectors, raising effective labor input and amplifying output response to productivity boosts.  
  - External constraint channel: more equal wages facilitate attracting labor into tradables (agriculture), expanding exports and relaxing balance-of-payments constraints needed to finance imported inputs for adaptation investment.  
  - Fiscal base channel: higher female earnings broaden tax base, making public investment less fiscally expensive in general equilibrium. Example figures:  
    - At a given 1 percent increase in adaptation capital, the tax-revenue-to-GDP ratio in the low-gap economy is about 4.25 percentage points higher than in the current-gap economy.  
    - Even with no additional adaptation, the low-gap economy already raises roughly 4.1 percentage points more tax revenue, indicating most fiscal gain stems from narrowing the wage gap.  
  - Adaptation alone slightly narrows the wage gap through a composition effect by protecting sectors where women are over-represented.

### Simulation 2 — Short-run recovery to shocks
- Experiment design: two economies hit with the same natural-disaster shock (temporary TFP drop reverting in AR(1) fashion). Compare pre-shock settings: low vs higher adaptation capital and current vs reduced gender wage gap.  
- Main findings: pre-positioned resilience and narrower wage gaps reduce peak-to-trough output loss and speed up recovery. Mechanisms:  
  - Smaller effective supply shock: resilience lifts underlying productivity and attenuates the TFP hit.  
  - Faster reallocation: tighter wage gap improves labor supply incentives—especially for women—toward higher-return margins when flexibility is needed.  
  - Stronger automatic stabilizers: broader tax base from higher female earnings cushions revenue shortfalls and lowers required fiscal adjustment.  
- Net outcome: milder output contraction and steeper recovery of output and employment relative to current-gap, low-resilience baseline.

### Policy instruments for inclusive climate resilience
- Data-driven tools: Rwanda’s CBT (climate budget tagging) and GBT (gender budget tagging) frameworks improve visibility of spending on climate adaptation and programs benefiting women, aligning resources with NST2 and the Green Growth and Climate Resilience Strategy.  
  - CBT identifies and classifies expenditures contributing to climate mitigation and adaptation across sectors (agriculture, energy, infrastructure).  
  - GBT provides information on how public spending supports women’s economic participation and welfare outcomes, strengthening gender-responsive budgeting.  
  - Combined, CBT and GBT enable cross-analysis linking adaptation spending with gender impacts to evaluate inclusiveness of climate investment.  
- Dynamic social registry and adaptive social protection: integrates household-level data for rapid identification of vulnerable households during shocks and links to programs such as VUP to scale up assistance to those most affected, including women and informal-sector workers—supporting timely, targeted, and fiscally sustainable fiscal responses.

*Source: sipea2026041 PDF chapter/section content provided.*

### 34.      Rwanda’s green transition presents

### Rwanda’s green transition presents

### Opportunities and risks
- Investments in renewable energy, climate-smart agriculture, and the circular economy can generate employment and productivity gains.
- Women remain under-represented in many emerging green sectors; without targeted interventions, the transition could widen existing labor-market gaps.
- As Rwanda accelerates digitalization, risks of digital exclusion persist: women remain less likely to own smartphones, access the internet, or possess digital literacy skills needed to benefit from e-finance, e-commerce, and online training.

### Education, training, and finance measures to increase women's participation
- Expand access to TVET and strengthen MSME finance initiatives to help women participate more fully in green industries.
- RTB Strategic Plan 2021-24 and recent investments in TVET centers of excellence aim to modernize curricula, integrate ICT skills, and build stronger links with employers; gaps remain in digital infrastructure and female enrolment in technical fields.
- 2025-29 Financial Sector Development Strategy and the FinScope 2024 Gender Report highlight efforts to close credit gaps for women-led MSMEs, including commitments to dedicate at least half of microfinance and SACCO lending to women.
- Integrate gender criteria into green-investment facilities such as Ireme Invest to ensure new financing channels support women entrepreneurs and employment creation in sustainable sectors.

### Data systems, social registry, and monitoring
- Rwanda’s Dynamic Social Registry (Imibereho System)
  - Launched February 29, 2024
  - 2.7 out of 3 million households already included in the system
  - Collaboration with various government agencies to integrate information systems, including Land Registry, Rwanda Information Society Authority, National Identification Agency, and Rwanda Revenue Authority
  - Innovative features allowing for constant data updates including through exchanging data with other government systems
  - Collects vital socioeconomic data to support poverty alleviation efforts
- Continued investment in data systems like CBT, GBT, and the social registry will be critical to track outcomes, inform course corrections, and ensure Rwanda’s climate and gender agendas reinforce each other.
- Deepening coordination across CBT, GBT, and the dynamic social registry would enable systematic tracking of how adaptation spending affects women’s livelihoods and resilience outcomes.

### Macroeconomic findings and rationale for integrated policy
- Microdata analysis shows women’s concentration in agriculture and informal employment heightens their vulnerability to climate shocks and slows household recovery.
- Macroeconomic simulations demonstrate that reducing gender wage gaps amplifies the growth and fiscal benefits of adaptation investment.
- Policies promoting inclusion are described as both socially desirable and macro-critical for sustaining resilience and long-term development.

### Policy priorities and recommended actions
- Strengthen the integration of CBT and GBT:
  - Establish consistent cross-analysis of adaptation and gender-related expenditures, supported by clear institutional responsibilities and regular reporting by MINECOFIN, sector ministries, and the Rwanda Gender Monitoring Office.
- Expand adaptive social protection through the social registry:
  - Continue improving coverage, interoperability, and real-time updating to allow programs—particularly the VUP—to scale up efficiently during shocks while maintaining fiscal discipline.
- Promote women’s participation in the green transition:
  - Facilitate access to green finance, technical and vocational training, and digital skills to ensure women benefit from employment opportunities in renewable energy, sustainable agriculture, and the circular economy.
  - Integrate gender criteria into green-investment facilities such as Ireme Invest.
- Enhance access to productive assets and finance:
  - Broaden financial inclusion, strengthen women’s property and land rights, and expand tailored support for women-owned MSMEs to raise productivity and resilience.
- Invest in data and evaluation:
  - Expand the use of gender-disaggregated and climate-tagged data to assess the effectiveness of adaptation and inclusion programs and guide evidence-based policy refinement.

### Key CBT and GBT projects in FY25/26 (projects larger than USD10 million)
- Agriculture
  - GBT Improved access to farm inputs for women 53.9 (RWF Billion)
  - CBT Climate-smart agriculture and input access 71.0 (RWF Billion)
- Employment
  - GBT Job creation and training for women 84.7 (RWF Billion)
  - GBT Employment in housing/construction for women 48.1 (RWF Billion)
  - GBT Employment in road construction for women 97.4 (RWF Billion)
- Energy
  - GBT Expansion of electricity access for women 128.0 (RWF Billion)
  - CBT Renewable energy and energy efficiency 169.9 (RWF Billion)
  - CBT Resilience-focused energy infrastructure 44.6 (RWF Billion)
- Environment
  - CBT Integrated environmental protection 67.9 (RWF Billion)
- Transport
  - CBT Road rehabilitation and resilient transport 88.1 (RWF Billion)
- Social Protection
  - GBT Direct VUP support to women 36.3 (RWF Billion)
  - GBT Support to female genocide survivors 16.4 (RWF Billion)
  - CBT Support to households affected by climate shocks 39.1 (RWF Billion)
- Education
  - CBT Climate-resilient schools; awareness programs 15.3 (RWF Billion)
- Health
  - GBT HIV prevention and treatment for women 15.0 (RWF Billion)
- Water and Sanitation
  - GBT Improved access to water/sanitation for women 55.7 (RWF Billion)
  - CBT Expansion of water and sanitation infrastructure 47.1 (RWF Billion)
  - CBT Improved water treatment and sanitation systems 34.3 (RWF Billion)

*Source: IMF Selected Issues paper chapter on Rwanda’s green transition.*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2026/english/sipea2026041.pdf_