## sdnea2024003

## Source details

**Canonical URL:** [sdnea2024003](https://www.imf.org/-/media/files/publications/sdn/2024/english/sdnea2024003.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/sdn/2024/english/sdnea2024003.pdf.md)
- [Structured JSON version](/-/media/files/publications/sdn/2024/english/sdnea2024003.pdf.json)

---

### Executive Summary
- Scope:
  - Focuses on labor-market impacts of the green transition across advanced economies (AEs) and emerging market and developing economies (EMDEs), emphasizing gender and STEM skills.
  - Primary policy focus: short- to medium-term impacts under current technologies and climate policies, with particular attention to energy taxation.

### Key findings: distribution, trends, and drivers
- Green and polluting job shares:
  - Green jobs account for about 10 to 15 percent of employment.
  - 14 percent of workers in AEs are employed in green jobs.
  - Approximately 11 percent of workers in EMDEs are employed in green jobs.
  - Since 2018: green jobs rose from 13.4 to 13.8 percent in AEs and from 10.2 to 10.8 percent in EMDEs.
  - Polluting jobs in AEs: 9.2 percent of employment versus 13.8 percent in green jobs (contextual comparison).
  - Polluting jobs in EMDEs: 12.5 percent of employment while green jobs are 10.8 percent of employment.
  - Over the last five years: polluting jobs decreased by more than 0.6 percentage point in AEs and increased by almost 0.4 percentage point in EMDEs.
  - Neutral jobs account for about 77 percent of employment on average.
- Gender distribution:
  - Women hold only one-third of green jobs in both AEs and EMDEs.
  - More than two-thirds of green jobs are occupied by men and only one-third by women.
  - Green jobs account for 20.3 percent of men’s employment and 6 percent for women in AEs.
  - Green jobs account for 16 percent of men’s employment and 4.6 percent for women in EMs.
- Drivers:
  - Supply of STEM-educated workers and more equal gender treatment are associated with faster and lower-cost transitions.
  - Energy taxes are associated with larger reductions in GHG emissions intensity—by 2 to 4 percentage points—in countries with a larger supply of STEM-educated workers and more equal treatment of women (result robust to controls).

### Labor returns and wages
- Green wage premium (after controlling for worker and sectoral characteristics):
  - About 7 percent on average for men.
  - About 12 percent on average for women.
- Green wage premium is larger for women, producing a smaller gender pay gap in green jobs than in other jobs.
- Country-level Mincerian regression coefficients (log wage-per-hour):
  - Brazil: Female (β_wf) = –0.22 ∗∗∗; Green (β_wg) = 0.04 ∗∗∗; Female×Green (β_wfg) = 0.02 ∗∗.
  - Colombia: Female (β_wf) = –0.14 ∗∗∗; Green (β_wg) = 0.09 ∗∗∗; Female×Green (β_wfg) = 0.07 ∗∗∗.
  - South Africa: Female (β_wf) = –0.09 ∗∗∗; Green (β_wg) = 0.24 ∗∗∗; Female×Green (β_wfg) = 0.20 ∗∗∗.
  - United Kingdom: Female (β_wf) = –0.11 ∗∗∗; Green (β_wg) = 0.04 ∗∗∗; Female×Green (β_wfg) = 0.07 ∗∗∗.
  - United States: Female (β_wf) = –0.15 ∗∗∗; Green (β_wg) = 0.11 ∗∗∗; Female×Green (β_wfg) = 0.05 ∗∗∗.
- Notes:
  - Analyses adjust for education, marital status, main occupation group, informality, experience, age, and broad sectors.
  - Results may be affected by selection and omitted variable biases.

### Gender patterns and skill composition
- Occupational composition:
  - Over 60 percent of occupations classified as green are in STEM fields.
  - Among college-educated workers, green employment is concentrated in STEM fields.
  - The green gender employment gap among college-educated workers largely reflects the gender gap in STEM degrees and managerial gaps.
  - Among non-college workers, the green gender employment gap mirrors a gender gap in manual jobs.
- Decomposition (five-country analysis: Brazil, Colombia, South Africa, United Kingdom, United States):
  - College-educated workers:
    - More than half of the employment gender gap linked to STEM occupational gaps in Brazil, Colombia, United Kingdom, and United States.
    - In South Africa, STEM occupations account for about a quarter of the gap.
    - Managerial gaps contribute substantially: South Africa managerial gap = 56 percent of green jobs gap; United Kingdom managerial gap = 39 percent; Brazil, Colombia, United States managerial gap ≈ 20 to 25 percent.
  - Non-college workers:
    - Manual occupations account for nearly 80 percent of the gender green employment gap in Brazil, Colombia, South Africa.
    - In United States and United Kingdom, manual occupations explain about 70 percent and 50 percent of the gap, respectively.

### Distributional and inequality concerns
- Concentration of polluting jobs among middle-income men:
  - Brazil and South Africa: polluting jobs account for 15 to 20 percent of jobs held by middle-class men.
  - Colombia, United Kingdom, United States: polluting jobs account for about 10 percent of middle-class men’s jobs.
- Country examples where transition may increase inequality:
  - Brazil, Colombia, South Africa: declines in polluting jobs (middle-class) and rising demand for green jobs (typically higher-earning) may increase income inequality and hollow out the middle class.
- Global polluting job gender share:
  - About 70 percent of polluting jobs are held by men globally.

### Empirical evidence: energy taxes, STEM supply, and gender equality
- Data and sample:
  - Panel of 108 countries (35 AEs and 73 EMDEs) from 2003 to 2020.
  - Energy tax shock measured as one standard deviation = 1 percentage point of GDP change in cyclically adjusted energy tax revenues relative to GDP.
- Key local projection results:
  - STEM graduates:
    - A 1 percentage point increase in cyclically adjusted energy tax revenues relative to GDP is associated with approximately a 3 percent reduction in GHG emissions intensity of GDP in countries with a high proportion of STEM graduates.
    - In countries with fewer STEM graduates, the same tax increase has negligible and statistically insignificant effect.
    - STEM graduates stimulate green innovation and, to a lesser extent, green employment.
  - Gender equality measures:
    - Countries scoring well on “women, business, and the law” or on the broader “gender equality” indicator experience a statistically significant similar 6 percent reduction in GHG emission intensity three years post-reform when energy taxes are increased.
    - Countries with poor gender equality performance see smaller and statistically insignificant effects.
    - High “gender equality” scores correlate with stronger increases in green employment in the medium term.
    - High “women, business, and the law” scores associate with stronger green innovation responses but not with green employment.
  - Output effects:
    - The impact of energy taxes on output is less negative or statistically insignificant in countries performing well across these labor market indicators.

### General equilibrium model and simulated scenarios
- Model features:
  - Multisector GE model: final goods use green (M_G), brown/polluting (M_B), and neutral (M_N) intermediates with CES and Cobb-Douglas aggregations.
  - Intermediate production uses male and female, high- and low-skilled labor; green intermediary production is more STEM-intensive.
  - Men and women are imperfect substitutes; share of STEM workers is exogenous; model calibrated to the United States.
- Simulated carbon tax shock:
  - Carbon tax imposed to reduce emissions from the energy sector by 50 percent.
- Four scenarios (only gender distribution of STEM varies; share of STEM workers kept constant):
  1. Current share of population in STEM.
  2. Share of male STEM workers is 10 percentage points higher.
  3. Share of female STEM workers is 10 percentage points higher.
  4. Share of male and female STEM workers is each 5 percentage points higher.
- Main simulation results:
  - A larger share of workers in STEM and greater involvement of women in STEM correlate with lower economic costs of emission reductions by more than 20 percent.
  - Economic impact on output is lowest—just 0.3 percent—when there is a larger share of women in STEM.
  - Mixed increase (each gender +5 percentage points) is the second least costly scenario; the least effective is no increase in STEM education.
  - Welfare declines from the green transition are smaller when women are more actively engaged—simulations suggest welfare effects are less negative by more than 0.5 percentage point with higher female presence in the green sector.
- Distributional welfare effects:
  - Men in non-STEM jobs: concentrated welfare losses.
  - Women in non-STEM jobs: second most negative impact.
  - Men and women in STEM occupations: experience welfare gains.

### Calibration, parameters, and target moments
- Model parameterization:
  - Total parameters: 26 (11 exogenously calibrated; 15 calibrated to match US moments).
  - Selected exogenous values:
    - η = –0.65
    - γ_H = 2.85
    - γ_L = 1.00
    - η_L = 0.00
    - η_H = 0.00
    - ψ_f = 0.10
    - ψ_m = 0.02
    - L̄_m = 1.00
    - L̄_f = 0.77
    - ζ = 0.50
    - p_H = 1.19
  - Selected calibrated values:
    - ω = 0.83
    - α = 0.77
    - α_G = 0.49
    - α_N = 0.35
    - α_B = 0.37
    - z_G = 1.72
    - z_N = 1.29
    - z_B = 1.83
    - θ_G,H = 0.15
    - θ_G,L = 0.20
    - θ_B,H = 0.03
    - θ_B,L = 0.19
    - θ_N,H = 0.48
    - θ_N,L = 0.94
    - τ = 0.22
- Data vs model target moments (selected):
  - Women employment share, STEM green: 0.10 vs 0.10.
  - Men employment share, STEM green: 0.23 vs 0.24.
  - Gender wage gap, green sector: 9.6% vs 9.6%.
  - Gender wage gap, non-green sector: 14.5% vs 14.5%.
  - Green share in energy: 20% vs 20%.
  - Polluting production share: 10% vs 7%.
  - Average labor tax rate: 0.24 (Data) vs 0.22 (Model).

### AI exposure and complementary implications
- AI and green jobs:
  - Nearly 80 percent of AI-exposed green jobs are likely to benefit from AI, compared to less than 50 percent in non-green jobs.
  - Across all countries, over 70 percent of college-educated workers in green jobs could benefit from high AI complementarity.
  - Middle school–educated workers in green jobs have a notably smaller proportion that could benefit from high AI complementarity.
- Policy implication:
  - Ensure a sufficient supply of STEM workers, as AI development could compete for STEM talent needed for the green transition.

### Policy prescriptions and implementation priorities
- Priority labor-market and gender policies:
  - Expand STEM education supply, mentoring programs, exposure to female role models in STEM, and public–private collaborations.
  - Strengthen policies for women’s economic empowerment: reduce labor market barriers; improve access to finance; remove legal barriers; use gender budgeting; reform legal frameworks; increase women’s board representation.
  - Improve access to childcare services, parental leave, and remote work to support working mothers and workforce attachment.
  - Enforce anti-discrimination laws and reform tax policies that penalize secondary earners.
  - Support workers in declining polluting industries: retraining programs, place-based investments, pension enhancements, economic diversification, stakeholder-led transition plans, and stronger social safety nets.
  - Prioritize investments with high net benefits when fiscal space is limited—such as education and childcare—even if benefits materialize over time.
- Complementarities:
  - Policies that expand STEM-educated workers and improve gender equality can make the transition faster, increase the effectiveness and lower the cost of climate policies (e.g., energy taxes), and stimulate green innovation and employment.

### Country examples and empirical notes
- Ireland case study:
  - Ireland saw a threefold increase in STEM female bachelor graduates and a doubling of male bachelor graduates.
  - Between 2014 and 2018, number of teachers increased by 13.6 percent; over 7,000 additional teachers since 2016.
  - Pupil-to-teacher ratios improved from 15.7 to 14.5 in primary and from 13.4 to 12.3 in postprimary.
  - Specific goals include increasing female participation in STEM by 4 percent and boosting extracurricular STEM activities by 20 percent.
- Telework statistics (United States, first half of 2023):
  - Women: 29.3 percent of paid workdays worked from home.
  - Men: 27 percent of paid workdays worked from home.

### Conclusion
- A sustainable future requires significantly expanding the workforce in green jobs globally.
- Economies with a robust supply of STEM-educated workers and more equal gender treatment are better positioned to transition faster and at lower cost.
- Revenue from carbon taxes can be more than enough to compensate low-skilled workers negatively impacted by the policy.
- Coordinated action by policymakers, the private sector, and civil society is essential to achieve a sustainable and inclusive green transition.

*Staff Discussion Note: Green Jobs and the Future of Work for Women and Men (SDN/2024/003).*

### Executive Summary ......................................................................................................

### sdnea2024003 - Executive Summary

### Introduction and scope
- The green transition requires shifting the workforce away from carbon-intensive and environmentally harmful jobs toward employment that reduces greenhouse gas (GHG) emissions and maintains a low carbon footprint.
- The note focuses on labor-market impacts across advanced economies (AEs) and emerging market and developing economies (EMDEs), emphasizing gender and STEM (science, technology, engineering, and mathematics) skills as key determinants of an inclusive and effective transition.
- Primary policy focus: short- to medium-term impacts under current technologies and climate policies, with particular attention to energy taxation.

### Key findings: distribution, trends, and drivers
- Green jobs are expanding globally; polluting jobs are declining in importance in AEs but continue to rise in EMDEs.
- Women are underrepresented in green jobs: only one-third of green jobs are held by women in both AEs and EMDEs.
- The supply of STEM-educated workers and more equal gender treatment are associated with faster and lower-cost transitions to a green economy.
- Energy taxes are associated with larger reductions in GHG emissions intensity—by 2 to 4 percentage points—in countries with a larger supply of STEM-educated workers and more equal treatment of women. This result holds after controlling for tertiary education rates, institutional quality, income level, and past growth.
- STEM-educated workers and gender equality policies appear to stimulate green innovation and green employment, helping reduce bottlenecks to the transition.

### Labor returns and wages
- Green jobs are associated with a wage premium:
  - About 7 percent on average for men after controlling for workers’ and sectoral characteristics.
  - About 12 percent on average for women after controlling for workers’ and sectoral characteristics.
- The green wage premium is larger for women, producing a smaller gender pay gap in green jobs than in other jobs in the economy.
- Green jobs are well-suited to benefit from artificial intelligence, which could boost productivity and wage potential over time.

### Gender patterns and skill composition
- Among college-educated workers, green employment is concentrated in STEM fields. The green gender employment gap among college-educated workers largely reflects the gender gap in STEM degrees and, to a lesser extent, the managerial gender gap.
- Among workers without a college degree, the green gender employment gap mirrors a gender gap in manual jobs.
- Gender diversity in green jobs supports the transition through three channels:
  1. Increasing environmental concern (women tend to be more climate conscious).
  2. Enhancing allocation of talent by reducing barriers and biases.
  3. Stimulating innovation through diverse perspectives.

### Distributional and inequality concerns
- In countries such as Brazil, Colombia, and South Africa, declines in polluting jobs—typically held by middle-class workers—coupled with rising demand for green jobs—typically higher-earning jobs—may increase income inequality and exacerbate hollowing out of the middle class.
- EMDEs have a higher proportion of polluting jobs than AEs, increasing the risk of unequal impacts from the transition.

### Policy prescriptions to make the transition inclusive and effective
- Expand STEM education and related interventions:
  - Enhance STEM education supply.
  - Strengthen mentoring programs.
  - Provide exposure to female role models in STEM.
- Strengthen policies for women’s economic empowerment:
  - Reduce labor market barriers.
  - Improve access to finance.
  - Remove legal barriers.
  - Use gender budgeting.
  - Reform legal frameworks.
  - Increase women’s board representation.
- Support workers in declining polluting industries:
  - Offer retraining programs.
  - Implement place-based investments.
  - Strengthen social safety nets.
- Note on costs and benefits:
  - Some measures (for example, investing in STEM education and childcare) may require short-term government costs, but research indicates long-term benefits exceed these costs.

### Implementation and complementarities
- Labor market conditions, workforce composition, and worker preferences (STEM vs non-STEM) influence the pace and cost of the transition.
- Policies that expand the pool of STEM-educated workers and improve gender equality can both make the transition faster and increase the effectiveness of climate policies such as energy taxes.
- Coordinated action by policymakers, the private sector, and civil society is essential to seize the opportunity for a sustainable and inclusive future of work for women and men.

*Staff Discussion Note: Green Jobs and the Future of Work for Women and Men (Executive Summary).*

### 4. How can the supply of STEM workers and gender parity influence the effectiveness of green policies?

### 4. How can the supply of STEM workers and gender parity influence the effectiveness of green policies?

### Definitions and methodology
- Green jobs: an occupation is classified as green if more than 5 percent of its tasks weighted by task importance are green (a green task directly improves environmental sustainability or reduces GHG emissions).  
- Polluting jobs: a sector is identified as polluting if the sector’s emissions per worker for at least three polluting substances fall within the top 5 percent of polluters; an occupation is classified as polluting if the share of the occupation employed in that sector is at least seven times larger than in other sectors.  
- Four job categories used: (1) green (green tasks, non-polluting sector), (2) green and polluting (green tasks within a polluting sector), (3) polluting (non-green tasks in a polluting sector), (4) neutral (non-green tasks in a non-polluting sector).  
- Data sources: aggregated ILO three-digit ISCO data for cross-country patterns; four-digit ISCO labor force surveys for Brazil, Colombia, South Africa, the United Kingdom, and the United States for detailed worker characteristics and returns.  
- Key methodological limitations noted:
  - Does not model ease of worker reallocation across occupations.
  - Relies on current definition of green tasks and assumes occupations perform the same tasks across countries.
  - May overestimate polluting jobs at risk as sectors could decarbonize via low-carbon technologies over time.

### Prevalence and cross-country patterns
- Green jobs account for about 10 to 15 percent of employment and are slightly more prevalent in AEs than in EMDEs.  
- Exact averages reported:
  - 14 percent of workers in AEs are employed in green jobs.
  - Approximately 11 percent of workers in EMDEs are employed in green jobs.
- Recent changes:
  - Since 2018, green jobs rose from 13.4 to 13.8 percent in AEs.
  - Since 2018, green jobs rose from 10.2 to 10.8 percent in EMDEs.
- Polluting jobs:
  - In AEs, polluting jobs account for 9.2 percent of employment versus 13.8 percent in green jobs.
  - In EMDEs, polluting jobs account for 12.5 percent while green jobs make up 10.8 percent of employment.
  - Over the last five years, share of workers in polluting jobs decreased by more than 0.6 percentage point in AEs and increased by almost 0.4 percentage point in EMDEs.
- Most workers are in neutral jobs: neutral jobs account for about 77 percent of employment on average.
- Occupational distribution differences:
  - In AEs, green jobs are concentrated among managerial and professional occupations.
  - In EMDEs, green jobs are concentrated among craft and trades, plant and machine operators, and elementary occupations (example: in Brazil these latter categories represent 66 percent of green jobs).

### Gender imbalances and drivers of the green employment gap
- Gender gaps in green employment:
  - In AEs: green jobs account for 20.3 percent of men’s employment and 6 percent for women.
  - In EMs: green jobs account for 16 percent of men’s employment and 4.6 percent for women.
  - More than two-thirds of green jobs are occupied by men and only one-third by women across country groups.
- Employment gender gap (same sample):
  - AEs: 6 percentage points.
  - EMs: 30 percentage points.
  - LICs: 17 percentage points.
- Decomposition findings (five-country analysis: Brazil, Colombia, South Africa, United Kingdom, United States):
  - For college-educated workers:
    - More than half of the employment gender gap is associated with a gender gap in STEM occupations in countries such as Brazil, Colombia, the United Kingdom, and the United States.
    - In South Africa, STEM occupations account for about a quarter of the gap among college-educated workers.
  - Gender gaps in managerial occupations contribute significantly:
    - South Africa: managerial gap accounts for 56 percent of the green jobs gap.
    - United Kingdom: managerial gap contributes 39 percent of the discrepancy.
    - Brazil, Colombia, United States: managerial gap accounts for about 20 to 25 percent.
  - For workers without a college education:
    - The gender gap predominantly reflects disparities in manual occupations.
    - Brazil, Colombia, South Africa: manual occupations account for nearly 80 percent of the gender green employment gap among those without higher education.
    - United States and United Kingdom: manual occupations explain about 70 percent and 50 percent of the employment gap, respectively.

### Economic returns, AI exposure, and policy relevance
- Green jobs tend to be occupied by individuals with higher education and often enjoy a wage premium associated with their jobs.  
- Gender pay gap is currently smaller in green occupations than in the rest of the economy; incorporating more women into green jobs can reduce gender disparities as green jobs expand.  
- Women in green jobs are in occupations likely to benefit from artificial intelligence (AI), which could further boost their productivity.  
- The note finds that a greater supply of STEM graduates and greater gender parity are associated with:
  - Greater effectiveness of environmental policies.
  - Reduced cost of environmental policies.

### Policy implications and priorities
- Increasing STEM education supply and improving gender parity are highlighted as important complementary policies to:
  - Ensure inclusiveness of the green transition.
  - Enhance the effectiveness and lower the cost of climate and environmental policies.
- Removing obstacles to women’s participation in STEM and managerial occupations is crucial to increase female representation in green jobs and to amplify the benefits of green policies.

*Source: IMF staff discussion note chapter "4. How can the supply of STEM workers and gender parity influence the effectiveness of green policies?"*

### 1.     College educated (percent) 2.     Non-college educated (percent)

### sdnea2024003 - 1.     College educated (percent) 2.     Non-college educated (percent)

### Data and notes
- Sources: American Community Survey (United States, 2019); Gran Encuesta Integrada de Hogares (Colombia, 2022); Labor Force Survey (United Kingdom, 2022); Labor Market Dynamics in South Africa Survey (South Africa, 2019); Pesquisa Nacional por Amostra de Domicilios Continua (Brazil, 2022); and IMF staff calculations.
- Note: The figure decomposes the employment gap between men and women in green jobs by employment in STEM, STEM and managerial, managerial, other green manual, and other green non-manual occupations. Panel 1 examines the gap among college-educated workers, while panel 2 looks at those without a college degree.
- ISO country codes used in figure labels. STEM = science, technology, engineering, and mathematics.

### Gender distribution in green and polluting jobs
- In advanced economies (AEs):
  - Polluting jobs account for 13.7 percent of men’s employment and 3 percent of women’s employment.
- In emerging markets (EMs):
  - Polluting jobs account for 10.2 percent of men’s employment and 7.6 percent of women’s employment.
- In low-income countries (LICs):
  - Polluting jobs account for 8.6 percent of men’s employment and 10.7 percent of women’s employment.
- Global share:
  - About 70 percent of polluting jobs are held by men globally.
- Occupational composition:
  - The majority of polluting jobs are in manual occupations such as craft and trades, plant and machine operators, and elementary occupations.

### Green and polluting jobs by earnings and education
- Green jobs are predominantly found among higher-earning segments in the countries considered.
  - In the United Kingdom and the United States:
    - More than 25 percent of male workers within the top earnings decile are employed in green jobs.
    - 20 percent of female workers within the top earnings decile are employed in green jobs.
    - Green jobs make up only 12 percent of total employment in these countries.
  - Colombia and South Africa:
    - Green jobs are concentrated at the higher end of the earnings distribution and are more prevalent among men.
- Polluting jobs tend to be concentrated among middle-income male workers with basic or intermediary education.
  - Brazil and South Africa:
    - Polluting jobs account for 15 to 20 percent of jobs held by middle-class men.
  - Colombia, the United Kingdom, and the United States:
    - Polluting jobs account for about 10 percent of middle-class men’s jobs.
  - Female presence in polluting jobs is small and concentrated in light manufacturing, garment industries, and food preparation.

### Wage premiums and gender pay gap
- Polluting jobs:
  - Exhibit a positive wage premium for men in Brazil, Colombia, and the United States:
    - Polluting wage premium for men ranges from 4 percent in Brazil to 8 percent in the United States.
  - The United Kingdom experiences a wage penalty for polluting jobs.
  - Polluting jobs carry a larger gender pay gap than other jobs in Brazil and the United States; in Colombia and the United Kingdom they do not significantly impact the gender pay gap.
- Green jobs:
  - Associated with a positive wage premium over non-green jobs.
  - Green wage premium ranges from 4 percent in Colombia to nearly 16 percent in the United Kingdom.
  - The green wage premium is higher for women in all sampled countries; the differential reaches nearly 8 percentage points in Colombia and the United Kingdom.
  - The size of green wage premiums outweighs polluting wage premiums across genders and countries.
- Gender pay gap overall:
  - Varies from nearly 20 percent in Brazil to less than 5 percent in the United Kingdom.
  - On average, the gender pay gap is 30 percent narrower within green jobs compared to the broader economy.
- Notes on interpretation:
  - Analyses use Mincerian wage regressions adjusting for education, marital status, main occupation group, informality, experience, age, and broad sectors of employment for Brazil, Colombia, the United Kingdom, and the United States.
  - Results may be affected by selection and omitted variable biases since analyses are restricted to labor force participants.

### STEM and gender equality implications
- STEM human capital and green jobs:
  - Over 60 percent of occupations classified as green are in STEM fields.
  - Female STEM graduate shares (percentage of population aged 15 to 34 graduating in STEM):
    - Greece, Ireland, and New Zealand: about 0.3 percent.
    - Japan and Luxembourg: less than 0.1 percent.
  - Male STEM graduate shares:
    - Ireland, Korea, and Switzerland: 0.6 to 0.7 percent.
    - Luxembourg and Sweden: below 0.15 percent.
  - Notable country changes in female STEM graduation shares:
    - Ireland: from 0.1 percent to nearly 0.3 percent.
    - Korea: from 0.2 percent to almost 0.3 percent.
- Labor supply constraints and policy relevance:
  - The larger green wage premium suggests labor supply may not be meeting green labor demand, potentially due to an undersupply of workers with STEM education.
  - The imbalance appears larger for women than men, implying a need to boost women’s participation in STEM to facilitate an inclusive green transition.
  - Boxed country examples (Ireland, Korea) indicate targeted policies can raise female STEM graduation rates by integrating STEM across educational levels, focusing on early education for girls, gender-focused curricula, educator training, public–private collaborations, and educator compensation measures.
- Gender equality and labor force participation:
  - Despite progress, women’s participation remains below men’s and has declined over the last decade in EMDEs.
  - Contributing factors include unpaid care burdens, lower employment rates, fewer working hours, labor market segregation, and underrepresentation in senior leadership (women hold less than 30 percent of middle and senior management positions).
  - In EMDEs, four out of five new jobs for women are in the informal economy, compared to two out of three for men.

*International Monetary Fund — STAFF DISCUSSION NOTES Green Jobs and the Future of Work for Women and Men (excerpts and figures as provided)*

### 1. Gender Inequality Index

### 1. Gender Inequality Index

### Data and definitions
- Indicators used:
  - Gender Inequality Index (0 = Equality)
  - Women, Business, and the Law Index (100 = Equality)
  - Female Labor Force Participation (Percent)
- Sources: ILO (2022); IMF, Gender Data Hub; UNDP (2022); World Bank (2022); and IMF staff calculations.
- Notes:
  - The Gender Inequality Index incorporates three dimensions: reproductive health, empowerment, and the labor market.
  - Female labor force participation rate uses the modeled ILO estimate for those aged 15 and older.
  - Women, Business, and the Law compares laws on gender equality across mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pension across 190 countries.
  - AEs = advanced economies; EMDEs = emerging market and developing economies; ILO = International Labour Organization.

### Role of women and gender equality in the green transition
- Empirical and literature evidence:
  - Women tend to be more climate-conscious (McCright 2010; Franzen and Vogl 2013; Clayton and others 2023).
  - A higher share of female managers is associated with larger declines in carbon dioxide emissions (Altunbas and others 2022).
  - Female-led firms achieve significantly higher environmental, social, and governmental scores (European Investment Fund 2022).
  - Banks with gender-diverse boards are more likely to lend to green companies and reduce loans to high-pollution firms (Gambacorta and others 2022).
  - Removing barriers to women’s economic participation improves talent allocation and productivity (Hsieh and others 2019; Ostry and others 2018).
- Policy levers to enhance women’s participation:
  - Enhance access to childcare services, parental leave, and remote work to support working mothers and workforce attachment (IMF 2022b).
  - Robust implementation and enforcement of anti-discrimination laws (OECD 2023b).
  - Revise tax policies that penalize secondary earners (Bick and Fuchs-Schündeln 2018; Fabrizio et al. 2020).
  - Implement gender budgeting to integrate gender perspectives into fiscal planning (Alonso-Albarran and others 2021).
  - Expand opportunities for formal employment and access to finance for women (UN Women and African Development Bank 2021; World Bank 2021; IMF 2022a).
  - Reform legal frameworks to tackle discrimination.

### Labor market interactions with green policies — empirical findings
- Methodology highlights:
  - Impulse response analysis using Jordà (2005) local projections.
  - Three labor market dimensions considered: (1) share of graduates with a STEM degree, (2) “women, business, and the law” indicator, (3) broader gender equality indicator.
  - Controls include institutional quality, share of tertiary-educated workers, interactions, past growth, GDP per capita, year and country fixed effects.
  - Sample: 108 countries (35 AEs and 73 EMDEs) from 2003 to 2020.
  - Energy tax shock measured as one standard deviation (1 percentage point of GDP) change in cyclically adjusted energy tax revenues relative to GDP.
- Key empirical results:
  - Energy taxes are more effective at reducing GHG emission intensity of GDP in countries with favorable labor market conditions (countries at the top quartile).
  - STEM graduates:
    - A 1 percentage point increase in the cyclically adjusted energy tax revenues relative to GDP is associated with approximately a 3 percent reduction in GHG emissions intensity of GDP in countries with a high proportion of STEM graduates.
    - In countries with fewer STEM graduates, the same tax increase has negligible and statistically insignificant effect.
    - STEM graduates stimulate green innovation and, to a lesser extent, green employment.
  - Gender equality measures:
    - Countries scoring well on “women, business, and the law” or on the broader “gender equality” indicator experience a statistically significant similar 6 percent reduction in GHG emission intensity three years post-reform when energy taxes are increased.
    - Countries with poor gender equality performance see smaller and statistically insignificant effects of energy taxes on emission intensity.
    - High “gender equality” scores correlate with stronger increases in green employment in the medium term.
    - High “women, business, and the law” scores associate with stronger green innovation responses but not with green employment.
  - Output effects:
    - The impact of energy taxes on output is less negative or statistically insignificant in countries performing well across these labor market indicators.
    - A greater STEM workforce and more gender equality can reduce the economic cost of the transition by supporting green innovation and creating jobs in green occupations.

### General equilibrium model and simulated scenarios
- Model structure and calibration:
  - Multisector general equilibrium model with final goods produced using green, polluting, and neutral intermediary sectors.
  - Green and polluting goods highly substitutable; neutral goods complementary to both.
  - Intermediary production uses STEM and non-STEM labor from men and women; polluting intermediary goods predominantly rely on men’s labor.
  - Green intermediary production is more STEM-intensive.
  - Men and women are imperfect substitutes; share of STEM workers is exogenous. Model calibrated to the United States.
- Simulated policy shock:
  - A carbon tax is imposed to reduce emissions from the energy sector by 50 percent.
- Four scenarios (share of STEM workers kept constant across scenarios; only gender distribution of STEM varies):
  1. Current share of population in STEM.
  2. Share of male STEM workers is 10 percentage points higher.
  3. Share of female STEM workers is 10 percentage points higher.
  4. Share of male and female STEM workers is each 5 percentage points higher.
- Main simulation results:
  - A larger share of workers in STEM and greater involvement of women in STEM correlate with lower economic costs of emission reductions by more than 20 percent.
  - The economic impact on output is lowest—just 0.3 percent—when there is a larger share of women in STEM.
  - The second least costly scenario is the mixed increase (each gender +5 percentage points); the least effective is no increase in STEM education.
  - Mechanism: STEM labor is a key input for green goods; economies with more STEM workers require a smaller carbon tax to achieve the same emission reduction.

### Distributional and welfare implications
- Welfare impacts:
  - Welfare declines from the green transition are smaller when women are more actively engaged in the green economy—simulations suggest welfare effects are less negative by more than 0.5 percentage point when there is higher presence of women in the green sector.
  - Welfare changes by group:
    - Men in non-STEM jobs: concentrated welfare losses due to carbon tax effects in polluting sector.
    - Women in non-STEM jobs: second most negative impact due to increased competition for non-STEM jobs.
    - Men and women in STEM occupations: experience welfare gains from increased demand in the green sector.
  - Policy implication: targeted support is needed for workers in polluting jobs to mitigate concentrated welfare losses and reduce resistance to climate policies.

### Policy recommendations and implementation priorities
- Labor market and gender policies to facilitate the green transition:
  - Improve access to childcare, parental leave, and remote work to sustain women’s workforce attachment (IMF 2022b).
  - Enforce anti-discrimination laws and legal reforms to remove barriers to women’s economic participation (OECD 2023b).
  - Reform tax policies that penalize secondary earners to increase female labor force participation (Bick and Fuchs-Schündeln 2018; Fabrizio et al. 2020).
  - Use gender budgeting to integrate gender perspectives into fiscal planning (Alonso-Albarran and others 2021).
  - Expand formal employment opportunities and access to finance for women (UN Women and African Development Bank 2021; World Bank 2021; IMF 2022a).
  - Design comprehensive labor-market support for lower-skilled workers and coal-dependent communities, including retraining, pension enhancements, economic diversification, and stakeholder-led transition plans (World Bank 2020; World Bank 2022; OECD 2021; OECD 2023a).
- Complementary actions:
  - Strengthen STEM education and increase female participation in STEM to reduce economic costs of decarbonization and boost green innovation.
  - Target transition support to workers in polluting sectors and their communities to address concentrated job losses and spillovers (example: stakeholder-designed plans for displaced workers).
  - Further research recommended to explore additional channels and refine identification strategies in empirical analyses.

*International Monetary Fund — STAFF DISCUSSION NOTES Green Jobs and the Future of Work for Women and Men*

### Conclusion

### Conclusion

### Key findings
- A sustainable future hinges on significantly expanding the workforce in green jobs globally.
- Economies with a robust supply of STEM-educated workers and more equal gender treatment appear better positioned to transition to a green economy faster and at a lower cost.
- Incorporating the environmental benefits of the transition will change the welfare impact but will not affect the order since all experiments generate the same reduction in emissions, which ultimately generates the same environmental benefit.
- Revenue from carbon taxes can be more than enough to compensate low-skilled workers negatively impacted by the policy.

### STEM education: roles and priorities
- STEM degrees are crucial for the green transition, technological progress, and economic growth.
- STEM education requires a comprehensive approach that goes beyond improving math scores to include:
  - Increasing exposure to STEM activities from an early age.
  - Fostering mentorship programs.
  - Encouraging public-private partnerships, where companies contribute resources, expertise, and innovation.
- Targeted education policies can be effective despite short-term costs; Ireland’s experience demonstrates substantial short-run increases in STEM graduations.

### Gender gaps and labor market dynamics
- Despite a significant green wage premium, women remain underrepresented in green occupations.
- Gender disparity drivers:
  - Gender gaps in STEM and managerial positions, especially in AEs and to a certain extent in emerging markets.
  - Gender inequality in manual jobs in some emerging markets.
- Reducing gender gaps in STEM education is key to closing participation gaps in green jobs.
- The inherent job flexibility and remote work opportunities in STEM fields are attracting more women to these sectors.
- Without deliberate policy action, the green transition risks perpetuating existing barriers faced by women (limited access to finance, fewer managerial opportunities, systemic discrimination).

### Artificial intelligence (Box 1): opportunities and risks
- AI can support the green transition but may pose a risk of displacing workers.
- Workers in green jobs, particularly the highly educated and women, are poised to benefit more from AI advancements.
- Classification and complementarity:
  - Jobs are categorized by AI “exposure” and by a complementarity index into groups such as “high exposure, high complementarity” (more likely to benefit) and “high exposure, low complementarity” (at risk of automation).
- Key statistics:
  - Nearly 80 percent of AI-exposed green jobs are likely to benefit from AI, compared to less than 50 percent in non-green jobs.
  - Across all countries, over 70 percent of college-educated workers in green jobs could benefit from the high AI complementarity with their occupations.
  - The proportion of workers with middle school education in green jobs that could benefit from high AI complementarity is notably smaller.
- Policy implication: ensure a sufficient supply of STEM workers since AI development could compete for STEM talent needed for the green transition.

### Simulation of carbon tax and labor-market scenarios (Figure 13)
- Simulation target: reduction in emissions from the energy sector by 50 percent by implementing a carbon tax.
- Four labor-market scenarios simulated:
  1. Business as usual.
  2. A 10 percentage point higher share of men in STEM.
  3. A 10 percentage point higher share of women in STEM.
  4. A 5 percentage point higher share of men and women in STEM.
- Outcomes assessed:
  - Panel 1: effects on GDP and its components (brown, neutral, green).
  - Panel 2: aggregate welfare gains constructed as the consumption equivalence percentage change of the environment with the carbon tax policy versus its respective baseline.
- Note: In panel 1, net GDP percent change is depicted by the red dot and percentage number; bars describe percent changes of GDP components. In panel 2, aggregate welfare gain is depicted by the red dot and percentage number; component colors describe welfare gains by gender and skill.

### Policies for an inclusive and just green transition
- Priority policy actions to facilitate women’s participation and an equitable transition:
  - Enhance access to childcare services and parental leave to support working mothers.
  - Prioritize investments with high net benefits when fiscal space is limited—such as education and childcare—even if benefits materialize over time.
  - Use gender budgeting to increase the efficiency of resource allocation and use.
  - Robust implementation and enforcement of anti-discrimination laws.
  - Revise direct and indirect tax biases that penalize secondary earners, predominantly women.
  - Expand opportunities for women to secure formal employment and increase women’s access to finance.
  - Support flexible work arrangements, mentorship, and active promotion of gender diversity in hiring and promotions within the green sector—especially important for EMDEs facing brain drain.
- Measures to support workers in polluting jobs:
  - Offer robust retraining programs tailored to emerging industries.
  - Ensure access to lifelong learning opportunities.
  - Implement place-based economic diversification investments to create new local industries.
  - Provide adequate social safety nets to support workers and families during transitions.

### Ireland case study (Box 2): selected outcomes and measures
- Policy framework: STEM Education Policy Statement (2017), “Innovation 2020,” and a STEM Education Implementation Plan with ambitions to position Ireland as a STEM education frontrunner by 2026.
- Outcomes and targets:
  - Ireland has seen a threefold increase in STEM female bachelor graduates and a doubling of male bachelor graduates.
  - Between 2014 and 2021, Ireland produced either the highest or second highest proportion of graduates (per 1,000 graduates) in STEM in the European Union.
  - Specific goals include increasing female participation in STEM by 4 percent and boosting extracurricular STEM activities by 20 percent.
- Teacher and system investments:
  - Teachers are recognized and well-compensated (ranking seventh highest in the OECD).
  - Between 2014 and 2018, the number of teachers increased by 13.6 percent across primary and postprimary levels, with over 7,000 additional teachers since 2016.
  - Pupil-to-teacher ratios improved from 15.7 to 14.5 in primary education and from 13.4 to 12.3 in postprimary.
- Programmatic approaches:
  - Embedding STEM across all educational levels, focusing on skills and learning.
  - Industry partnerships (for example, events and summits with major technology firms) and programs linking preservice teachers with STEM industries (STInt) to deepen teachers’ STEM understanding.

*Source: STAFF DISCUSSION NOTES — Green Jobs and the Future of Work for Women and Men*

### Annex Figure 5.2. Female Sectoral Employment

### Annex Figure 5.2. Female Sectoral Employment

### Country Coverage
- Country names use ISO country codes. ACS = American Community Survey (United States); AEs = advanced economies; EMs = emerging markets; EMDEs = emerging market and developing economies; GEIH = Gran Encuesta Integrada de Hogares (Colombia); ILO = International Labour Organization; LICs = low-income countries; LFS = Labor Force Survey (United Kingdom); LMD = Labor Market Dynamics in South Africa Survey (South Africa); OECD = Organisation for Economic Co-operation and Development; PNADC = Pesquisa Nacional por Amostra de Domicilios Continua (Brazil); UNDP = UN Development Programme.
- The country sample coverage list (ISO3, Country Name, Income Group) includes, among others:
  - AFG Afghanistan LIC
  - AGO Angola EM
  - ALB Albania EM
  - ARE United Arab Emirates EM
  - AUT Austria AE
  - BDI Burundi LIC
  - BEL Belgium AE
  - BGD Bangladesh LIC
  - BIH Bosnia and Herzegovina EM
  - BLR Belarus EM
  - BLZ Belize EM
  - BOL Bolivia EM
  - BRA Brazil EM
  - BRB Barbados EM
  - BRN Brunei Darussalam EM
  - BTN Bhutan LIC
  - BWA Botswana EM
  - CHE Switzerland AE
  - CHL Chile EM
  - CIV Côte d’Ivoire LIC
  - COD Congo, Democratic Republic of the LIC
  - COL Colombia EM
  - CRI Costa Rica EM
  - CYP Cyprus AE
  - CZE Czechia AE
  - DEU Germany AE
  - DNK Denmark AE
  - DOM Dominican Republic EM
  - ECU Ecuador EM
  - EGY Egypt EM
  - ESP Spain AE
  - EST Estonia AE
  - FIN Finland AE
  - FRA France AE
  - FSM Micronesia, Federated States of EM
  - GBR United Kingdom AE
  - GIN Guinea LIC
  - GMB Gambia LIC
  - GNB Guinea-Bissau LIC
  - GRC Greece AE
  - GRD Grenada EM
  - GUY Guyana EM
  - HND Honduras LIC
  - HRV Croatia AE
  - HUN Hungary EM
  - IND India EM
  - IRL Ireland AE
  - IRQ Iraq EM
  - ISL Iceland AE
  - ITA Italy AE
  - JOR Jordan EM
  - KHM Cambodia LIC
  - KIR Kiribati LIC
  - KOS Kosovo EM
  - LKA Sri Lanka EM
  - LTU Lithuania AE
  - LUX Luxembourg AE
  - LVA Latvia AE
  - MDV Maldives EM
  - MEX Mexico EM
  - MHL Marshall Islands EM
  - MKD North Macedonia EM
  - MMR Myanmar LIC
  - MNG Mongolia EM
  - MUS Mauritius EM
  - NGA Nigeria LIC
  - NLD Netherlands AE
  - NOR Norway AE
  - NRU Nauru EM
  - PAK Pakistan EM
  - PAN Panama EM
  - PHL Philippines EM
  - PLW Palau EM
  - PNG Papua New Guinea LIC
  - POL Poland EM
  - PRT Portugal AE
  - ROU Romania EM
  - RWA Rwanda LIC
  - SEN Senegal LIC
  - SLB Solomon Islands LIC
  - SLE Sierra Leone LIC
  - SLV El Salvador EM
  - SRB Serbia EM
  - SUR Suriname EM
  - SVK Slovakia AE
  - SWE Sweden AE
  - SWZ Eswatini EM
  - SYC Seychelles EM
  - THA Thailand EM
  - TLS Timor-Leste LIC
  - TON Tonga EM
  - TUN Tunisia EM
  - TUV Tuvalu EM
  - UGA Uganda LIC
  - URY Uruguay EM
  - USA United States AE
  - VNM Vietnam LIC
  - VUT Vanuatu EM
  - ZAF South Africa EM
  - ZMB Zambia LIC
- Source: World Economic Outlook.
- Note: AE = advanced economy; EM = emerging market; LIC = low-income country.

### Mincerian Regression: Labor Market Returns by Gender and Job Greenness
- Regression specification: log(wage-per-hour) regressed on indicators for female, green job, female×green, and control vector X (age cohorts, education, experience, marriage status, sectors, and informality for emerging markets). Reported coefficients capture percentage change in wage-per-hour remuneration from being a woman, from working in a green job, and from the cross-effect of being a woman working in a green job. Green is measured by having a green job index at least of 5 percent.
- Key finding: Statistically significant positive interaction between green jobs and women across countries; evidence that green jobs disproportionately increase the wages of women, suggesting a smaller gender pay gap after controlling for other variables.

- Annex Table 2.1. Mincerian Regression results (coefficients; standard errors in parentheses)
  - Brazil
    - Female (β_wf): –0.22 ∗∗∗ (0.004)
    - Green (β_wg): 0.04 ∗∗∗ (0.003)
    - Female×Green (β_wfg): 0.02 ∗∗ (0.008)
    - Informality: Yes
    - Observations: 565,452
    - R-squared: 0.393
  - Colombia
    - Female (β_wf): –0.14 ∗∗∗ (0.006)
    - Green (β_wg): 0.09 ∗∗∗ (0.006)
    - Female×Green (β_wfg): 0.07 ∗∗∗ (0.011)
    - Informality: Yes
    - Observations: 119,801
    - R-squared: 0.539
  - South Africa
    - Female (β_wf): –0.09 ∗∗∗ (0.013)
    - Green (β_wg): 0.24 ∗∗∗ (0.018)
    - Female×Green (β_wfg): 0.20 ∗∗∗ (0.046)
    - Informality: Yes
    - Observations: 55,437
    - R-squared: 0.189
  - United Kingdom
    - Female (β_wf): –0.11 ∗∗∗ (0.007)
    - Green (β_wg): 0.04 ∗∗∗ (0.010)
    - Female×Green (β_wfg): 0.07 ∗∗∗ (0.018)
    - Informality: No
    - Observations: 30,028
    - R-squared: 0.391
  - United States
    - Female (β_wf): –0.15 ∗∗∗ (0.001)
    - Green (β_wg): 0.11 ∗∗∗ (0.001)
    - Female×Green (β_wfg): 0.05 ∗∗∗ (0.002)
    - Informality: No
    - Observations: 5,563,305
    - R-squared: 0.290

- Controls included in regressions:
  - Age: Yes (cohorts of five years from 15 to 64 years old)
  - Education: Yes (less than elementary finished, elementary finished, high school finished, college finished or more)
  - Married: Yes
  - Married-Female: Yes
  - Sector: Yes (main ISIC aggregates)
- Note on statistical significance: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

### Local Projection Model: Energy Tax Shocks and GHG Emissions
- Regression specification: Panel local projection (Jordà 2005) estimating log of GHG emissions intensity of GDP responses to energy tax shocks conditional on labor market reforms (LM). LM is categorical with countries in bottom 25 percentile (bottom reformers), middle (25–75 percentile), and top 25 percentile (top reformers). Model includes country and year fixed effects, control vector X (including two lags of dependent variable, energy tax shock, economic growth, other reform shocks, log GDP per capita), interactions with first-generation reforms (FG) and share of workers with tertiary education (TE). Estimation via OLS with Driscoll and Kraay (1998) robust standard errors.
- Energy tax definition (three components):
  1. Taxes on energy products for transport purposes (for example, petrol, diesel, natural gas, and kerosene).
  2. Taxes on energy products for stationary purposes (for example, biofuels, fuel oil, and district heat and electricity consumption and production).
  3. GHG emissions taxes.
- Energy tax shock identification:
  - Step 1: Regress energy-tax-revenue-to-GDP ratio on current and past growth (up to two lags).
  - Step 2: Use residual ε_t as proxy for non-growth-related component of the ratio (energy tax shock).
  - Data source: IMF’s Climate Change Indicators Dashboard.
- Limitations and robustness:
  - Limitations: endogeneity and fiscal foresight issues in shock identification.
  - Mitigations: control for interactions with FG and TE; include public perception about climate change in robustness checks; alternative checks using GHG emissions instead of GHG emissions per GDP; replacing energy tax revenue with pollution tax revenue; use of carbon tax rates noted but limited by country-year coverage.
- Sample and temporal coverage:
  - Analysis based on a sample of 35 AEs and 73 EMDEs from 2003 to 2020.

### Impacts on GDP, Green Innovation, and Green Employment (Transmission and Conditional Effects)
- Findings summarized in annex figures:
  - Annex Figure 3.1: Policies promoting pro-market regulations (more flexible wage determination), gender equality, and higher STEM share of graduates could reduce more GHG emissions at lower expense of output when energy taxes are hiked.
    - The depicted shock corresponds to a major historical change in the energy-tax-revenues-to-GDP ratio: one standard deviation, or 1 percentage point of GDP.
    - t = 0 is the year of the shock. Dashed lines and shaded bands indicate the 90 percent confidence interval.
  - Annex Figure 3.2: Transmission channels examined include:
    1. Green Innovation: Cumulative percentage change in the number of green patent filings.
    2. Green Employment: Cumulative percentage point change in the share of green jobs, conditional on:
       - STEM Graduates
       - Gender Equality
       - Women, Business, and the Law
    - Caveat: green job data coverage in EMDEs is limited.
  - Annex Figure 3.3: Response of GHG emissions intensity to change in climate policy count (additional results).
  - Annex Figure 3.4: Tests show the difference in response of energy tax shock between top and bottom reformers is statistically significant.

### Overview of Labor Market Reform Indicators
- Gender Equality Index:
  - Composite measure reflecting equality in achievement between women and men in three dimensions: (1) reproductive health, (2) empowerment, (3) labor market.
  - Range: 0 (one gender fares as poorly as possible in all measured dimensions) to 1 (women and men fare equally).
- Women, Business, and the Law Index:
  - Measures how laws and regulations affect women’s economic opportunity.
  - Overall scores are the average of subindices (mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets and pension).
  - Scale: 0 to 100, with 100 representing the highest possible score.
- Graduates in STEM:
  - Measures the share (in percentage) of all tertiary graduates who completed STEM programs.

*Source: Annex Figure 5.2 and surrounding annex material from the IMF staff discussion notes "Green Jobs and the Future of Work for Women and Men."*

### Annex Figure 3.3. Impact of Climate Policy Count on GHG Emissions Intensity Conditional on Labor

### Annex Figure 3.3. Impact of Climate Policy Count on GHG Emissions Intensity Conditional on Labor Market Reforms

### Key empirical setup and findings
- Analysis based on a sample of 35 AEs and 73 EMDEs from 2003 to 2020.
- t = 0 is the year of the shock.
- Lines denote the response to a 10 percentage point increase in the growth rate of climate policy count.
- Dashed lines and shaded bands indicate the 90 percent confidence interval.
- The figure shows the impact of energy tax conditional on gaps in labor market reforms.
- Labor market condition indicators used:
  - Share of graduates from STEM (science, technology, engineering, and mathematics).
  - Gender equality in three dimensions: (1) reproductive health, (2) empowerment, and (3) the labor market.
  - Index of “women, business, and the law” (analyzes the pace of legal reforms to create equal economic opportunities for women and countries’ efforts to implement those laws).

### Comparative figure (Annex Figure 3.4)
- Shows the difference in response of GHG emissions intensity between top and bottom reformers.
- Dashed whiskers denote the 90 percent CIs.
- Panels conditional on:
  1. STEM Graduates
  2. Gender Equality
  3. Women, Business, and the Law

---

### Annex 4. Model — Structure and Equilibrium Conditions

### Final good producer
- Final good uses green (M_G), brown/polluting (M_B), and neutral (M_N) intermediate inputs.
- Brown and green aggregated with a CES function; neutral compounded with Cobb-Douglas.
- Functional form expressed as F(M_N, M_G, M_B) = M_N^α [ ω M_G^{(η-1)/η} + (1-ω) M_B^{(η-1)/η} ]^{(1/(1/η)) (1-α) } with:
  - ω ∈ (0,1) weight of green intermediate inputs.
  - η ≠ 1 elasticity of substitution between green and brown intermediate inputs.
  - α ∈ (0,1) weight of neutral intermediate inputs.

### Intermediate producers (neutral, green, polluting)
- Each intermediate representative producer uses:
  - Male and female high-skilled workers (L_{·,H,m}, L_{·,H,f}).
  - Male and female low-skilled workers (L_{·,L,m}, L_{·,L,f}).
- Aggregation within each intermediate sector:
  - High-skilled and low-skilled labor aggregated using a CES function.
  - High-skilled and low-skilled composites aggregated using a Cobb-Douglas function.
- Productivity and gender-productivity parameters per sector:
  - z_N, z_G, z_B represent sector technologies.
  - θ_{·,H}, θ_{·,L} ∈ (0,1) represent female labor productivity contractors in each sector for high- and low-skilled workers.
  - η_H, η_L denote elasticities of substitution between high- and low-skilled labor between men and women.
  - α_{·} ∈ (0,1) share of high-skilled labor in each sector.

### Representative households and labor supply
- Households encompass high- and low-skilled gendered workers.
- Male and female representative households have share ψ_f, ψ_m ∈ (0,1) of high-skilled workers.
- Utility derived solely from consumption of final goods.
- Men supply a unit of labor hours to the market and women supply a fixed fraction of a unit.
- Representative worker maximization:
  - Max ln(c_{j,g}) subject to c_{j,g} = (1-τ) (w_N^{j,g} l_N^{j,g} + w_G^{j,g} l_G^{j,g} + w_B^{j,g} l_B^{j,g})
  - Aggregate labor constraint L̄_g = l_N^{j,g} + l_G^{j,g} + l_B^{j,g}
  - τ ≥ 0 is labor tax rate; L̄_m = 1 and L̄_f ≤ 1 represent total hours labor supply for men and women.

### Government budget constraint
- Government expenditures include environmental costs contingent on brown intermediate output Y_B: K(Y_B) = κ Y_B^{1+ζ} with κ, ζ ≥ 0 and ζ = 0.50 in calibration.
- Government finances high-skilled workers education for men and women at price p_H.
- Revenues from taxes on household earnings and intermediate purchases of brown inputs.
- Balanced budget condition expressed as:
  K(M_B) + p_H (ψ_f L̄_f + ψ_m L̄_m) = τ_B p_B M_B + Σ_{g,j,·} τ w_{·}^{j,g} l_{·}^{j,g}

### Market clearing and feasibility
- Female labor market clearing (examples):
  - L_{G,H,f} = γ_H ψ_f l_{G,H,f}, etc.
- Male labor market clearing analogous with ψ_m.
- Intermediate goods markets clear:
  - M_B = F_B(L_{B,H,m}, L_{B,H,f}, L_{B,L,m}, L_{B,L,f}), etc.
- Feasibility condition:
  ψ_f c_{H,f} + (1-ψ_f) c_{L,f} + ψ_m c_{H,m} + (1-ψ_m) c_{L,m} + K(M_B) + p_H (ψ_f L̄_f + ψ_m L̄_m) = F(M_N, M_G, M_B)

---

### Calibration Strategy and Parameter Values

### Overview
- Model has 26 parameters in total.
- Parameters divided into two blocks:
  - First block: 11 parameters calibrated directly from data or literature.
  - Second block: 15 parameters calibrated to match key moments from US data.
- Calibration aims to capture US experience using the American Community Survey of 2019.

### Exogenous calibration (selected parameters and values)
- η = –0.65 (Papageorgiou and others (2017))
- γ_H (Wage return to no STEM worker on average) = 2.85 (Data ACS, 2019)
- γ_L (Wage return to STEM worker on average) = 1.00 (Normalization)
- η_L (Elasticity of substitution between men and women not in STEM) = 0.00 (Ostry and others (2018))
- η_H (Elasticity of substitution between men and women in STEM) = 0.00 (Ostry and others (2018))
- ψ_f (Share of employed STEM women) = 0.10 (Data ACS, 2019)
- ψ_m (Share of employed STEM men) = 0.02 (Data ACS, 2019)
- L̄_m (Male labor force participation) = 1.00 (Normalization)
- L̄_f (Female labor force participation) = 0.77 (Data value)
- ζ (Polluting output externality convex curvature) = 0.50 (Semi-quadratic)
- p_H (Price of STEM education) = 1.19 (College cost to GDP)

### Target moments calibration (selected calibrated parameter values)
- ω (Green energy weight) = 0.83
- α (Share of neutral goods in production) = 0.77
- α_G (Share of STEM workers in green sector) = 0.49
- α_N (Share of STEM workers in neutral sector) = 0.35
- α_B (Share of STEM workers in polluting sector) = 0.37
- z_G (Green sector productivity) = 1.72
- z_N (Neutral sector productivity) = 1.29
- z_B (Polluting sector productivity) = 1.83
- θ_G,H (Female labor productivity contractor in green sector for STEM workers) = 0.15
- θ_G,L (Female labor productivity contractor in green sector for no STEM workers) = 0.20
- θ_B,H (Female labor productivity contractor in polluting sector for STEM workers) = 0.03
- θ_B,L (Female labor productivity contractor in polluting sector for no STEM workers) = 0.19
- θ_N,H (Female labor productivity contractor in neutral sector for STEM workers) = 0.48
- θ_N,L (Female labor productivity contractor in neutral sector for no STEM workers) = 0.94
- τ (Average labor tax rate) = 0.22

---

### Data and Model Target Moments (selected comparisons)
- Women employment share (Data vs Model):
  - STEM green: 0.10 vs 0.10
  - STEM polluting: 0.01 vs 0.01
  - STEM neutral: 0.89 vs 0.89
  - No STEM green: 0.05 vs 0.05
  - No STEM polluting: 0.03 vs 0.03
  - No STEM neutral: 0.92 vs 0.92
- Men employment share (Data vs Model):
  - STEM green: 0.23 vs 0.24
  - STEM polluting: 0.06 vs 0.10
  - STEM neutral: 0.71 vs 0.66
  - No STEM green: 0.18 vs 0.18
  - No STEM polluting: 0.11 vs 0.11
  - No STEM neutral: 0.71 vs 0.71
- Gender wage gap:
  - Green sector: 9.6% vs 9.6%
  - Non-green sector: 14.5% vs 14.5%
- Green and polluting shares:
  - Green share in energy: 20% vs 20%
  - Polluting production share: 10% vs 7%
  - Green production share: 5% vs 5%
- Polluting output externality:
  - Average labor tax rate: 0.24 (Data) vs 0.22 (Model)

---

### Annex 5. Women Employment in STEM — Empirical Findings (United States)

### Labor force participation and trends
- Among women with a college degree, more than 90 percent participate in the labor force; this figure has increased by more than 1 percentage point recently (Annex Figure 5.1).
- Female labor participation for women with a college education but without a STEM degree is close to 87 percent and has risen by almost 3 percentage points in recent years.
- Among women without a college degree engaged in STEM occupations, participation exceeds 80 percent and is on an upward trend.
- Women in non-STEM occupations without a college degree have a labor participation rate closer to 65 percent.

### Occupational concentration and sectoral shifts
- Top occupations for college-educated women in STEM: engineering professionals, civil engineers, and information and communications technology sales representatives.
- Women without a college degree in STEM are predominantly employed as information and communications technology sales representatives, medical sales professionals, and engineering technicians.
- Sectoral distribution shifts (2012 to 2022, Annex Figure 5.2):
  - Women with a college degree in STEM: increasing employment in professional services (research, engineering and computer systems) and a notable shift toward manufacturing jobs; less employment in trade sectors.
  - Women without a college degree in STEM: most significant increases in health and education sectors.

### Telework and implications for female participation
- Professional jobs are more amenable to telework; women show higher preference for such jobs due to better work-life balance.
- In the first half of 2023, American women worked from home slightly more than men:
  - Women: 29.3 percent of paid workdays
  - Men: 27 percent of paid workdays
  - Source cited: Barrero, Bloom, and Davis 2023 (as reported in the text).
- Evidence suggests countries with a higher proportion of workers engaged in remote work experienced a faster rebound in female labor force participation (Jaumotte and others (2023) referenced).

### Policy-relevant implications highlighted
- Growing demand for STEM qualifications combined with increased teleworkability could attract more women to STEM fields.
- Teleworkable sectors and flexible arrangements can help balance gender disparities in STEM and enhance job satisfaction and participation rates among women.

*Source: IMF staff calculations; Fraser Institute; IMF, Climate Change Indicators Dashboard; UN Development Programme; World Bank; American Community Survey (United States, 2012–22 and 2019).*

### References

### sdnea2024003 - References

### Gender, Labor Market, and Inclusion
- Alexander, Naomi-Rose, Longji Li, Jorge Mondragon, Sahar Priano, and Marina Mendes Tavares. 2024. “The Green Future: Labor Market Implications for Men and Women.” IMF Working Paper 2024/156, International Monetary Fund, Washington, DC.
- Alonso-Albarran, Virginia, Teresa R. Curristine, Gemma Preston, Alberto Soler, Nino Tchelishvili, and Sureni Weerathunga. 2021. “Gender Budgeting in G20 Countries.” IMF Working Paper 2021/269, International Monetary Fund, Washington, DC.
- Asai, Miyoko, Qiaoe Chen, Jiro Honda, Xingwei Hu, and Qianqian Zhang. 2023. “The Role of Structural Fiscal Policy on Female Labor Force Participation in OECD Countries.” IMF Working Paper 2023/186, International Monetary Fund, Washington, DC.
- Fabrizio, Stefania, Ana Fruttero, Daniel Gurara, Lisa L. Kolovich, Vivian Malta, Marina Mendes Tavares, Nino Tchelishvili, and Stefania Fabrizio. 2020. “Women in the Labor Force: The Role of Fiscal Policies.” IMF Staff Discussion Note 2020/003, International Monetary Fund, Washington, DC.
- Jayachandran, Seema. 2021. “Social Norms as a Barrier to Women’s Employment in Developing Countries.” IMF Economic Review 69(3): 576–95.
- Ostry, Jonathan D., Jorge Alvarez, Rephael Espinoza, and Chris Papageorgiou. 2018. “Economic Gains from Gender Inclusion: New Mechanisms, New Evidence.” IMF Staff Discussion Note 2018/06, International Monetary Fund, Washington, DC.
- UN Women and African Development Bank. 2021. “Green Jobs for Women in Africa.” New York.
- World Economic Forum. 2023. “Global Gender Gap Report 2023.” Geneva.

### Green Transition, Green Jobs, and Climate-related Labor Studies
- Adão, Rodrigo, Martin Beraja, and Nitya Pandalai-Nayar. 2022. “Fast and Slow Technological Transitions.” Unpublished. https://economics.mit.edu/sites/default/files/2022-09/transitions_website.pdf.
- Bergant, Katherina, Rui Mano, and Ippei Shibata. 2022. “From Polluting to Green Jobs: A Seamless Transition in the U.S.?” IMF Working Paper 2022/129, International Monetary Fund, Washington, DC.
- Bluedorn, John, Niels-Jakob Hansen, Diaa Noureldin, Ippei Shibata, and Marina M. Tavares. 2023. “Transitioning to a Greener Labor Market: Cross-Country Evidence from Microdata.” Energy Economics 126: 106836.
- Hasna, Zeina, Florence Jaumotte, Jaden Kim, Samuel Pienknagura, and Gregor Schwerhoff. 2023. “Green Innovation and Diffusion: Policies to Accelerate Them and Expected Impact on Macroeconomic and Firm-Level Performance.” IMF Staff Discussion Note 2023/008, International Monetary Fund, Washington, DC.
- International Monetary Fund (IMF). 2022a. “A Greener Labor Market: Employment, Policies, and Economic Transformation.” In World Economic Outlook: War Sets Back the Global Recovery. Washington, DC: April.
- International Monetary Fund (IMF). 2023. Fiscal Monitor—Climate Crossroads: Fiscal Policies in a Warming World. Washington, DC, October.
- Papageorgiou, Chris, Marianne Saam, and Patrick Schulte. 2017. ”Elasticity of Substitution between Clean and Dirty Inputs: A Macroeconomic Perspective.” Review of Economics and Statistics. 99(2), 281-290.
- Vona, Francesco, Giovanni Marin, Davide Consoli, and David Popp. 2018. Environmental Regulation and Green Skills: An Empirical Exploration. Journal of the Association of Environmental and Resource Economists 5 (4): 713–53.
- Wiese, Katy, and Marguerite Culot. 2022. “Reimagining Work for a Just Transition.” European Environmental Bureau, Brussels.
- World Bank. 2020. “A Road Map for a Managed Transition of Coal-Dependent Regions in Western Macedonia.” Washington, DC.
- World Bank. 2022. “Global Perspective on Coal Jobs and Managing Labor Transition out of Coal: Key Issues and Policy Responses.” Washington, DC.

### STEM Education, Skills, and Workforce Development
- Department of Education. 2017. “STEM Education: Policy Statement 2017-2026.” Government of Ireland.
- Department of Education. 2020. “Statement of Strategy 2021-2023.” Government of Ireland.
- Department of Education. 2022. “Recommendations on Gender Balance in STEM Education 2022. “ Government of Ireland.
- Department of Education. 2024. “STEM Education Policy.” Government of Ireland.
- Department of Enterprise, Trade and Employment. 2015. “Action Pan for Jobs.” Government of Ireland.
- Devereux, Michael, Elizabeth Heffernan, Susan McKeever, Julie Dunne, Leslie Shoemaker, and Ciarán O’Leary. 2022. “Reflections on Selected Gender Equality in STEM Initiatives in an Irish University.” In Women in STEM in Higher Education: Good Practices of Attraction, Access and Retainment in Higher Education, edited by Francisco José García-Peñalvo, Alicia García-Holgado, Angeles Dominguez, and Jimena Pascual (pp. 69–83). Singapore: Springer Nature Singapore.
- Hurley, Amanda, Marc G. Chevrette, Deepa D. Acharya, Gabriel L. Lozano, Manuel Garavito, Jen Heinritz, Luis Balderrama, and others. 2021. “Tiny Earth: A Big Idea for STEM Education and Antibiotic Discovery.” ASM Journals 12 (1).
- Lee, Yi-Fang, Lung-Sheng Lee, and Hoang Bao Ngoc Nguyen. 2022. “A Comparison of STEM Education Status and Trends in Ten Highly Competitive Countries.” In Status and Trends of STEM Education in Highly Competitive Countries: Country Reports and International Comparison (pp. 497–526). Taipei, Taiwan: Technological and Vocational Education Research Center, National Taiwan Normal University.
- Seery, Niall, Ronan Dunbar, and Clodagh Reid. 2022. “Status and Trends of STEM Education in Ireland.” In Status and Trends of STEM Education in Highly Competitive Countries: Country Reports and International Comparison (pp. 207–59). Taipei, Taiwan: Technological and Vocational Education Research Center, National Taiwan Normal University.
- The Teaching Council. 2020. “Céim: Standards for Initial Teacher Education”. Kildare.
- Microsoft. 2024. “Dream Space in Ireland.” https://dreamspace.microsoft.com/en-us/dreamspace/ireland.
- Women in STEM. 2024. “Women in STEM Summit 2024.” https://womeninstem.ie/.

### AI, Digitalization, and Occupational Exposure
- Brollo, Fernanda, Era Dabla-Norris, Ruud de Mooij, Daniel Garcia-Macia, Tibor Hanappi, Li Liu, and Anh D. M. Nguyen. 2024. “Broadening the Gains from Generative AI: The Role of Fiscal Policies.” IMF Staff Discussion Note 2024/002, International Monetary Fund, Washington, DC.
- Felten, Edward, Manav Raj, and Robert Seamans. 2021. “Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and its Potential Uses.” Strategic Management Journal 42 (12): 2195–217.
- Felten, Edward, Manav Raj, and Robert Seamans. 2023. “How will Language Modelers like ChatGPT Affect Occupations and Industries?” Wharton University of Pennsylvania, Philadelphia, PA.
- Jaumotte, Florence, Longji Li, Andrea Medici, Myrto Oikonomou, Carlo Pizzinelli, Ippei Shibata, Jiaming Soh, and others. 2023. “Digitalization During the COVID-19 Crisis: Implications for Productivity and Labor Markets in Advanced Economies.” IMF Staff Discussion Note 2023/003, International Monetary Fund, Washington, DC.
- Pizzinelli, Carlo, Augustus J. Panton, Marina Mendes Tavares, Mauro Cazzaniga, and Longji Li. 2023. “Labor Market Exposure to AI: Cross-Country Differences and Distributional Implications.” IMF Working Paper 2023/216, International Monetary Fund, Washington, DC.

### Empirical Methods, Data Sources, and Occupational Classification
- Dierdorff, Erich C., Jennifer J. Norton, Donald W. Drewes, and Christina M. Kroustalis. 2009. “Greening of the World of Work: Implications for O*NET-SOC and New and Emerging Occupations.” National Center for O*NET Development, Raleigh, NC.
- Driscoll, John C., and Aart C. Kraay.1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data.” The Review of Economics and Statistics 80 (4): 549–60.
- Felten, Edward, Manav Raj, and Robert Seamans. 2021. “Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and its Potential Uses.” Strategic Management Journal 42 (12): 2195–217.
- O*NET Center. 2021. “Green Occupations.” https://www.onetonline.org/.
- Bescond, D., S. Kapsos, and D. Limani. 2024a. “Share of Selected ISIC (or ISCO) Categories at the Global/Regional Level.” International Labour Organization, Geneva.
- Bescond, D., S. Kapsos, and D. Limani. 2024b. “Global and Regional Estimates of Employment in Selected Economic Activities and/or Occupations.” International Labour Organization, Geneva.
- Felten, Edward, Manav Raj, and Robert Seamans. 2023. “How will Language Modelers like ChatGPT Affect Occupations and Industries?” Wharton University of Pennsylvania, Philadelphia, PA.

### Regional, Behavioral, and Attitudinal Studies
- Charmes, Jacques. 2019. “The Unpaid Care Work and the Labour Market: An Analysis of Time Use Data Based on the Latest World Compilation of Time-use Surveys.” International Labour Organization, Geneva.
- Clayton, Susan D., Panu Pihkala, Britt Wray, and Elizabeth Marks. 2023. “Psychological and Emotional Responses to Climate Change among Young People Worldwide: Differences Associated with Gender, Age, and Country.” Sustainability 15 (4): 3540.
- Franzen, Axel, and Dominikus Vogl. 2013. “Two Decades of Measuring Environmental Attitudes: A Comparative Analysis of 33 Countries.” Global Environmental Change 23(5): 1001–08.
- McCright, Aaron M. 2010. “The Effects of Gender on Climate Change Knowledge and Concern in the American Public.” Population and Environment 32: 66–87.
- Harrington, Emma, and Matthew E. Kahn. 2023. “Has the Rise of Work-from-Home Reduced the Motherhood Penalty in the Labor Market?” https://www.lpsk.lt/lpsk-web/wp-content/uploads/2024/06/HastheRiseofWork-from-HomeReducedtheMotherhoodPenaltyintheLaborMarket__UniversityofVirginiaandUniversityofSouthernCalifornia.pdf.
- Köhler, Timothy, and Haroon Bhorat. 2023. “Wages and Wage Inequality During the COVID-19 Pandemic in South Africa.” DPRU Working Paper 202308, Development Policy Research Unit, University of Cape Town.

### Policy, Institutional, and Multilateral Reports
- International Labour Organization (ILO). 2022.
- International Labour Organization (ILO). 2023.
- International Monetary Fund (IMF). 2022b. “IMF Strategy Toward Mainstreaming Gender.” IMF Policy Paper 22/278. Washington, DC.
- International Monetary Fund (IMF). 2023. Fiscal Monitor—Climate Crossroads: Fiscal Policies in a Warming World. Washington, DC, October.
- Organisation for Economic Co-operation and Development (OECD). 2021. “OECD Regional Outlook 2021 Adsressing COVID-19 and Moving to Net Zero.” Paris.
- Organisation for Economic Co-operation and Development (OECD). 2022.
- Organisation for Economic Co-operation and Development (OECD). 2023a. “Job Creation and Local Economic Development 2023: Bridging the Great Green Divide.” Paris.
- Organisation for Economic Co-operation and Development (OECD). 2023b. “Joining Forces for Gender Equality: What is Holding Us Back?” Paris.
- Organisation for Economic Co-operation and Development (OECD). 2023c. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. Paris: OECD Publishing.
- UN Development Programme (UNDP). 2022.
- World Bank. 2020. “A Road Map for a Managed Transition of Coal-Dependent Regions in Western Macedonia.” Washington, DC.
- World Bank. 2022. “Global Perspective on Coal Jobs and Managing Labor Transition out of Coal: Key Issues and Policy Responses.” Washington, DC.

*Green Jobs and the Future of Work for Women and Men — Staff Discussion Note No. SDN/2024/003*

---


_Source: https://www.imf.org/-/media/files/publications/sdn/2024/english/sdnea2024003.pdf_