## 1. Prevalence of Women in Senior Positions

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### Overview and context
- With millions of women joining the labor force in Europe over the past three decades, women remain underrepresented in senior positions in top corporate firms.
- Data for large listed firms in Europe (largest publicly listed companies in each country, up to 50 companies per country):
  - 19 percent of corporate board seats were occupied by women.
  - 14 percent of senior executive positions were occupied by women.
  - 4 percent of chief executive officers were female.
- Policy responses and targets:
  - Norway passed a law in 2003 mandating 40 percent representation of both men and women on the board of publicly listed companies.
  - Sixteen European countries have legislated similar reforms.
  - Germany passed a law requiring publicly listed companies to have 30 percent of supervisory seats occupied by women as of 2016.
  - The European Commission called on publicly listed companies to voluntarily commit to increasing the presence of women on boards to 30 percent by 2015 and 40 percent by 2020 among non-executive directors, excluding listed small- and medium-sized firms and allowing an exclusion for firms with a strong gender imbalance in the workforce (measured at less than 10 percent of the under-represented gender).

### Potential channels linking gender diversity to firm performance
- Possible positive channels:
  - Heterogeneity in values, beliefs, and attitudes broadens perspectives in decision making.
  - Increased critical thinking and creativity.
  - Complementarities between managerial styles of men and women.
  - Mitigation of gender differences between managers and subordinates could enhance workers’ productivity.
  - Female managers potentially better positioned to serve consumer markets dominated by women.
- Possible negative channels:
  - Increased diversity could undermine performance if associated with greater misunderstandings, communication problems, personal conflicts, or negative reactions from stakeholders.

### Empirical evidence and data limitations
- Existing evidence on the impact of gender diversity in the boardroom on firm performance is inconclusive:
  - Some studies documented a strong positive association between representation of women on boards and corporate performance.
  - Other studies, including those that more plausibly identify causal impacts, challenged positive associations.
- Common data limitations:
  - Analyses typically constrained to publicly listed companies in individual countries, resulting in small sample sizes.
  - Little is known about women in senior management in the broader corporate sector (including non-listed firms), important in Europe where SMEs often comprise a large share of output and employment.

### New evidence and sample description
- Sample coverage:
  - Raw dataset extracted in July 2015 contains the unconsolidated financial statements of 4.4 million firms across 34 European countries.
  - Final analysis sample after cleaning and selection (firms reporting basic financial data for 2013 and with at least two senior managers/board members) is about 2 million firms.
  - Roughly 99 percent of companies in the Orbis database are private.
- Prevalence in the broader corporate sector:
  - Compared to Europe’s largest listed firms, women in the broader corporate sector have made somewhat greater strides: on average, almost a quarter of senior management and board positions in the authors’ sample were held by women.
  - Women accounted for 46 percent of the labor force in Europe in 2013, but less than a quarter of senior positions were held by women in the broader corporate sector.
  - Country examples: Austrian firms have less than 10 percent of managerial positions held by women; Ukrainian firms have 40–50 percent.

### Key empirical findings (associations with firm performance)
- Main association:
  - Replacing one man by a woman in senior management or on the corporate board is associated with 8–13 basis points higher ROAs.
  - Exchanging one male member for a female member implies about a 3–8 percent increase in profitability (based on sample averages).
  - Bringing gender balance in the senior team (without increasing size) is associated with 7-11 basis points higher ROA.
- Sectoral heterogeneity:
  - Services: an additional woman in senior positions is associated with a 21 basis points increase in ROA (based on profits).
  - Manufacturing: an additional woman in senior positions is associated with a 12 basis points increase in ROA.
  - Trade and construction: no statistically significant difference detected.
  - For a firm in a sector at the 75th percentile of female intensity (women comprise about 52 percent of the workforce), replacing a man with a woman in the senior team is estimated to boost ROA by about 14-18 basis points.
  - For a sector at the 25th percentile of female intensity (women comprise just under a quarter of the labor force), the boost to ROA is estimated at 0-4 basis points.
  - In high-tech manufacturing or knowledge-intensive services, an additional woman in a senior position is associated with a 34–40 higher ROA; in remaining sectors the boost is only 0–4 basis points and not statistically distinguishable from zero.
- Nonlinearities:
  - The squared term of the share of women in senior positions is negative and statistically significant.
  - The estimated peak optimal share of women in senior positions is about 60 percent.

### Regression evidence and selected numerical estimates
- Main coefficients (ROA based on Net income / Profit BT / EBIT) from large-sample regressions:
  - Share of women in senior positions: 0.0041*** / 0.0044*** / 0.0028*** (standard errors (0.0009)(0.0011)(0.0010)); Observations = 2,003,279; Mean dep. variable = 0.016 / 0.027 / 0.032; Mean share of women = 0.26; Mean N senior positions = 3.29.
  - Alternative subsample: Share of women = 0.0070*** / 0.0074*** / 0.0062*** (standard errors (0.0017)(0.0020)(0.0018)); Observations = 777,462 / 775,053 / 771,695; Mean ROA = 0.020 / 0.033 / 0.034.
- Reported increases in ROA (basis points and percent reported in tables): 
  - Increase in ROA (basis points): 12 13 18 17 19 20 16
  - Increase in ROA (percent): 7.9 5.0 2.6 11.3 7.2 4.1 2.2

### Interaction and nonlinear specification estimates (selected)
- Interaction with Female intensity (Share of women * Female intensity): 0.0163*** / 0.0174*** / 0.0192*** (standard errors (0.0057)(0.0065)(0.0055)); Observations = 2,003,279 / 2,000,422 / 1,992,658.
- Interaction with High tech/knowledge intensity (Share of women * High tech/knowledge intensity): 0.0102*** / 0.0119*** / 0.0115*** (standard errors (0.0019)(0.0023)(0.0021)); Observations = 2,003,279 / 2,000,422 / 1,992,658.
- Nonlinear terms (example coefficients, Net income / Profit BT / EBIT):
  - Share of Women: 0.0081*** / 0.0094*** / 0.0066*** (standard errors (0.0019)(0.0022)(0.0022)).
  - Share of Women ^ 2: -0.0056*** / -0.0070*** / -0.0054*** (standard errors (0.0018)(0.0021)(0.0021)).
  - Share of Women * Female Intensity: 0.0308*** / 0.0365*** / 0.0329*** (standard errors (0.0106)(0.0122)(0.0110)).
  - Share of Women ^ 2 * Female Intensity: -0.0183 / -0.0244** / -0.0171 (standard errors (0.0106)(0.0124)(0.0118)).
  - Share of Women * Knowledge Intensity: 0.0157*** / 0.0189*** / 0.0169*** (standard errors (0.0037)(0.0044)(0.0042)).
  - Share of Women ^ 2 * Knowledge Intensity: -0.0075** / -0.0095** / -0.0072* (standard errors (0.0038)(0.0043)(0.0042)).
- Observations in nonlinear specifications: 2,003,279 / 2,000,422 / 1,992,658.

### Mechanisms and identification strategy
- Core regression framework:
  - Primary estimating equation: ROA_i,n,c = f(share of women in senior positions, firm controls, ~16,000 country-industry fixed effects).
  - Firm controls include indicators for firm size, firm age, number of directors/senior managers, and the log of tangible assets.
  - Standard errors clustered at the industry level.
  - Country-industry fixed effects absorb time-invariant differences across industry-country pairs.
- Difference-in-differences-style heterogeneity approach:
  - Identification relies on stronger impacts in industries with relatively more female labor (female-intensity) and in high-technology or knowledge-intensive sectors.
- Proposed causal channels empirically explored:
  - Role-model effects raising productivity of female workers, family-friendly corporate policies, better task-worker matching.
  - Diversity increasing the set of ideas/solutions, especially valuable in technology- or knowledge-intensive sectors.
  - Authors note equilibrium correlations across sector characteristics complicate causal disentangling.

### Data sources, measurement, and sample statistics
- Primary data: European subset of the Orbis database (Bureau van Dijk Electronic Publishing).
- Measures of female representation: share of total members of senior management or company board who are women.
- Firm performance measures (three ROA variants): net income over total assets; profits before taxes over total assets; earnings before interest and taxes (EBIT) over total assets.
- Sectoral female intensity: Do and others (2016) for manufacturing (UNIDO Industrial Statistics Database) and OECD annual labor force employment statistics for non-manufacturing sectors.
- Technology/knowledge classification: Eurostat taxonomy of high- and medium-technology manufacturing sectors and knowledge intensive services at the NACE 3-digit level.
- Sample summary statistics (selected):
  - All firms 2/: mean share of firms with at least one woman in senior positions = 0.370; StDev = 0.215; N firms = 4,676,366.
  - All firms 2/: share of senior positions held by women = 0.528; StDev = 0.241; N firms = 2,003,279.
  - Country examples (Share of firms with at least one woman in senior positions / Share of senior positions held by women / N firms):
    - Austria: 0.208 / 0.223 / 7,074
    - Belgium: 0.409 / 0.349 / 14,593
    - Finland: 0.681 / 0.261 / 108,340
    - Ireland: 0.729 / 0.272 / 10,997
    - United Kingdom: 0.621 / 0.256 / 132,966

### Robustness checks and sensitivity analyses
- Outlier treatments:
  - Baseline excludes top and bottom five percent of firm performance values.
  - Alternatives: winsorize top/bottom five percent; exclude top/bottom two percent; winsorize at 2nd and 98th percentiles. Interaction coefficients remain similar in magnitude and statistically significant.
- Country exclusion:
  - Estimated equation (2) repeatedly dropping one country at a time (34 iterations); minimum and maximum coefficients on key interactions fall in a narrow range.
- Alternative time periods:
  - Using 2012 firm ROA with 2013 measured gender share produces very similar main findings.
- Alternative performance measure:
  - Labor productivity (total output per employee or per total labor cost) used to maximize sample; interaction between gender diversity and female-/knowledge-intensity remains positive and statistically significant, though the main effect of gender diversity on this productivity measure is negative.
- Horse-race between channels:
  - When both female intensity and knowledge-/high-tech indicators are included simultaneously, female intensity becomes statistically insignificant while knowledge-/high-tech remains significant, suggesting technological characteristics of sectors may be the dominant channel; authors caution this result is suggestive.

### Policy implications and recommendations
- Boosting gender diversity in senior positions could have a sizable impact on the financial performance of firms in Europe, especially in high female-intensity and knowledge-/technology-intensive sectors.
- Policy measures to build the pipeline of women for senior corporate positions:
  - Strengthen policies to facilitate women’s full-time attachment to the labor force.
  - Remove fiscal disincentives to women’s full-time work.
  - Provide services complementary to women’s market work.
- If higher involvement by women in senior positions improves firm profitability, it may support corporate investment and productivity, mitigating the slowdown in potential growth.

### Limitations highlighted by the authors
- Cross-sectional nature of the data limits precise identification of causal effects; board/management changes over time are not observed in Orbis.
- Endogeneity concerns remain because board composition is jointly determined with firm performance.
- Sectoral correlations between female intensity and technology intensity make disentangling channels empirically challenging.

*Source: _wp1650 — IMF staff calculations and empirical results as presented in the content unit.*

### 1. Prevalence of Women in Senior Positions ...........................................................................19

### 1. Prevalence of Women in Senior Positions ...........................................................................19

### Overview and context
- With millions of women joining the labor force in Europe over the past three decades, women remain underrepresented in senior positions in top corporate firms.
- Data referenced for large listed firms in Europe (largest publicly listed companies in each country, up to 50 companies per country) indicate:
  - 19 percent of corporate board seats were occupied by women.
  - 14 percent of senior executive positions were occupied by women.
  - 4 percent of chief executive officers were female.
- Policy responses and targets:
  - Norway passed a law in 2003 mandating 40 percent representation of both men and women on the board of publicly listed companies.
  - Sixteen European countries have legislated similar reforms.
  - Germany passed a law requiring publicly listed companies to have 30 percent of supervisory seats occupied by women as of 2016.
  - The European Commission (EC) called on publicly listed companies to voluntarily commit to increasing the presence of women on boards to 30 percent by 2015 and 40 percent by 2020 among non-executive directors, with the EC proposal excluding listed small- and medium-sized firms and allowing an exclusion for firms with a strong gender imbalance in the workforce (measured at less than 10 percent of the under-represented gender).

### Potential channels linking gender diversity to firm performance
- Possible positive channels:
  - Heterogeneity in values, beliefs, and attitudes broadens perspectives in decision making (OECD, 2012).
  - Increased critical thinking and creativity (Lee and Farh, 2004).
  - Complementarities between managerial styles of men and women given documented differences in preferences and behavior (Croson and Gneezy, 2009).
  - Mitigation of gender differences between managers and subordinates could enhance workers’ productivity (Giuliano and others, 2006).
  - Female managers potentially better positioned to serve consumer markets dominated by women (CED, 2012; CAHRS, 2011).
- Possible negative channels:
  - Increased diversity could undermine performance if associated with greater misunderstandings, communication problems, personal conflicts, or negative reactions from stakeholders (Akerlof and Kranton, 2000; Becker, 1957; Choi, 2007; Kremer, 1993; Lazear, 1999).

### Empirical evidence and data limitations
- Existing evidence on the impact of gender diversity in the boardroom on firm performance is inconclusive:
  - Some studies (McKinsey, 2007; Catalyst, 2007) documented a strong positive association between representation of women on boards and corporate performance.
  - Other studies, including those that more plausibly identify causal impacts (for example, Ahern and Dittmar, 2012), challenged positive associations.
- Common limitation across studies:
  - Data availability typically constrains analysis to publicly listed companies in individual countries, resulting in small sample sizes that make it hard to detect statistically significant effects, especially if effects are small.
  - Little is known about women in senior management in the broader corporate sector (including non-listed firms), which matters in Europe where small- and medium-sized enterprises often comprise a large share of output and employment.

### New evidence and sample description
- This paper analyzes more than 2 million listed and non-listed firms with at least two people in the senior management team or in the corporate board across 34 European countries.
- Compared to evidence from Europe’s largest listed firms, women in the broader corporate sector have made somewhat greater strides in senior positions:
  - On average, almost a quarter of senior management and board positions in the authors’ sample were held by women.

*Source: _wp1650 - 1. Prevalence of Women in Senior Positions ...........................................................................19*

### 2013. That said, whereas cross-country variation is large, there is still a sizable gap between

### _wp1650 - 2013. That said, whereas cross-country variation is large, there is still a sizable gap between

### Key empirical findings
- Sample and coverage
  - Raw dataset extracted in July 2015 contains the unconsolidated financial statements of 4.4 million firms across 34 European countries.
  - Final analysis sample after cleaning and selection (firms reporting basic financial data for 2013 and with at least two senior managers/board members) is about 2 million firms.
  - Roughly 99 percent of companies in the Orbis database are private.
- Prevalence of women
  - Women accounted for 46 percent of the labor force in Europe in 2013, but less than a quarter of senior positions were held by women in the broader corporate sector.
  - Eurostat (2015) for the 620 largest listed companies: share of female executives 14 percent and female board members 19 percent.
  - Country examples from the sample: Austrian firms have less than 10 percent of managerial positions held by women; Ukrainian firms have 40–50 percent.
- Correlation with firm performance (ROA measures)
  - Replacing one man by a woman in senior management or on the corporate board is associated with 8–13 basis points higher ROAs.
  - Exchanging one male member for a female member implies about a 3–8 percent increase in profitability (based on sample averages).
  - Bringing gender balance in the senior team (without increasing size) is associated with 7-11 basis points higher ROA.
  - Effects vary by sector:
    - Services: an additional woman in senior positions is associated with a 21 basis points increase in ROA (based on profits).
    - Manufacturing: an additional woman in senior positions is associated with a 12 basis points increase in ROA.
    - Trade and construction: no statistically significant difference detected.
  - Sectoral heterogeneity (interaction results)
    - For a firm in a sector at the 75th percentile of female intensity (women comprise about 52 percent of the workforce), replacing a man with a woman in the senior team is estimated to boost ROA by about 14-18 basis points.
    - For a sector at the 25th percentile of female intensity (women comprise just under a quarter of the labor force), the boost to ROA is estimated at 0-4 basis points.
    - In high-tech manufacturing or knowledge-intensive services, an additional woman in a senior position is associated with a 34–40 higher ROA; in remaining sectors the boost is only 0–4 basis points and not statistically distinguishable from zero.
  - Nonlinearities
    - The squared term of the share of women in senior positions is negative and statistically significant.
    - The estimated peak optimal share of women in senior positions is about 60 percent.

### Mechanisms and identification strategy
- Core regression framework
  - Primary estimating equation: ROA_i,n,c = f(share of women in senior positions, firm controls, ~16,000 country-industry fixed effects) (referenced as equation (1)).
  - Firm-specific controls include indicators for firm size, firm age, number of directors/senior managers, and the log of tangible assets.
  - Standard errors clustered at the industry level.
  - Country-industry fixed effects absorb time-invariant differences across industry-country pairs.
- Difference-in-differences-style heterogeneity approach
  - Identifying assumption: if women in senior positions improve firm performance, the impact should be stronger in:
    - Industries with relatively more female labor (female-intensity).
    - Industries with greater demand for creativity and critical thinking (high-technology and knowledge-intensive sectors).
  - Interaction specification estimated (referenced as equation (2)) includes SEC_n defined as female intensity of the sector and/or an indicator for high-technology or knowledge-intensive sectors.
- Proposed causal channels (empirically explored)
  - Female leaders may raise productivity of female workers via role-model effects, family-friendly corporate policies, or better task-worker matching.
  - Diversity may increase the set of ideas/solutions, especially valuable in technology- or knowledge-intensive sectors.

### Robustness checks and sensitivity analyses
- Outlier treatments
  - Baseline excludes top and bottom five percent of firm performance values.
  - Alternatives: winsorize top/bottom five percent; exclude top/bottom two percent; winsorize at 2nd and 98th percentiles. Interaction coefficients remain similar in magnitude and statistically significant.
- Country exclusion
  - Estimated equation (2) repeatedly dropping one country at a time (34 iterations); minimum and maximum coefficients on key interactions fall in a narrow range.
- Alternative time periods
  - Using 2012 firm ROA with 2013 measured gender share produces very similar main findings (column 7 of robustness tables).
- Alternative performance measure
  - Labor productivity (total output per employee or per total labor cost) used to maximize sample; interaction between gender diversity and female-/knowledge-intensity remains positive and statistically significant, though the main effect of gender diversity on this productivity measure is negative.
- Horse-race between channels
  - When both female intensity and knowledge-/high-tech indicators are included simultaneously, female intensity becomes statistically insignificant while knowledge-/high-tech remains significant, suggesting the technological characteristics of sectors may be the dominant channel; authors caution this result is suggestive given equilibrium correlations between sector characteristics and workforce composition.

### Policy implications and recommendations (as discussed)
- Boosting gender diversity in senior positions could have a sizable impact on the financial performance of firms in Europe, especially in high female-intensity and knowledge-/technology-intensive sectors.
- Policy measures to build the pipeline of women for senior corporate positions:
  - Strengthen policies to facilitate women’s full-time attachment to the labor force.
  - Remove fiscal disincentives to women’s full-time work.
  - Provide services complementary to women’s market work.

### Limitations highlighted by the authors
- Cross-sectional nature of the data limits precise identification of causal effects; board/management changes over time are not observed in Orbis.
- Endogeneity concerns remain because board composition is jointly determined with firm performance.
- Sectoral correlations between female intensity and technology intensity make disentangling channels empirically challenging.

*Source: _wp1650 - 2013. That said, whereas cross-country variation is large, there is still a sizable gap between*

### introduction of gender quotas in Norway, Matsa and Miller (2013) find that firms affected by

### _wp1650 - introduction of gender quotas in Norway, Matsa and Miller (2013) find that firms affected by

### Main findings on gender diversity and firm performance
- Sample: more than 2 million companies across 34 European countries in 2013.
- Strong positive association between the share of women in senior positions and firms’ ROAs:
  - Substituting one male for one female person in senior management or on the corporate board is associated with between 8 and 13 basis points higher ROAs.
- Firms affected by quotas undertake fewer workforce reductions than comparison firms, increasing relative labor costs and employment levels (evidence from Norway).
- During the Great Recession, female-led private firms in the United States were significantly less likely to downsize their workforce (Matsa and Miller, 2014).
- The management style associated with increased labor hoarding does not come at the expense of lower profitability.

### Quantitative sample and summary statistics (Table 1)
- All firms 2/: mean share of firms with at least one woman in senior positions = 0.370; StDev = 0.215; N firms = 4,676,366.
- All firms 2/: share of senior positions held by women = 0.528; StDev = 0.241; N firms = 2,003,279.
- Country-level examples (Share of firms with at least one woman in senior positions / Share of senior positions held by women / N firms):
  - Austria: 0.208 / 0.223 / 7,074
  - Belgium: 0.409 / 0.349 / 14,593
  - Finland: 0.681 / 0.261 / 108,340
  - Ireland: 0.729 / 0.272 / 10,997
  - United Kingdom: 0.621 / 0.256 / 132,966
  - All (summary row repeated): 0.370 / 0.528 / 4,676,366 (first set) and 0.215 / 0.241 / 2,003,279 (second set)

### Regression results linking share of women in senior positions to ROA (selected estimates from Table 2 and robustness)
- Main coefficients (ROA based on Net income / Profit BT / EBIT):
  - Share of women in senior positions: 0.0041*** / 0.0044*** / 0.0028*** (standard errors in parentheses (0.0009)(0.0011)(0.0010)); Observations = 2,003,279; Mean dep. variable = 0.016 / 0.027 / 0.032; Mean share of women = 0.26; Mean N senior positions = 3.29.
  - Alternative specification (subsample): Share of women = 0.0070*** / 0.0074*** / 0.0062*** (standard errors (0.0017)(0.0020)(0.0018)); Observations = 777,462 / 775,053 / 771,695; Mean ROA = 0.020 / 0.033 / 0.034.
- Reported increases in ROA (Table 2, reported rows):
  - Increase in ROA (basis points): 12 13 18 17 19 20 16
  - Increase in ROA (percent): 7.9 5.0 2.6 11.3 7.2 4.1 2.2
- Notes: All regressions include country-industry fixed effects, indicators for firm size, firm age, and control for the log of firm's fixed assets and number of senior positions. Robust standard errors are clustered at the industry level.

### Sectoral heterogeneity (Tables 3, 4, 5)
- Positive association between gender diversity in senior positions and financial performance is stronger in:
  - Sectors that employ significantly more women in the labor force (female-intensive sectors).
  - Industries with greater demand for higher creativity and critical thinking (high-tech and knowledge intensive sectors).
- Interaction estimates (Table 5, ROA based on Net income / Profit BT / EBIT):
  - Share of women * Female intensity: 0.0163*** / 0.0174*** / 0.0192*** (standard errors (0.0057)(0.0065)(0.0055)); Observations = 2,003,279 / 2,000,422 / 1,992,658.
  - Share of women * High tech/knowledge intensity: 0.0102*** / 0.0119*** / 0.0115*** (standard errors (0.0019)(0.0023)(0.0021)); Observations = 2,003,279 / 2,000,422 / 1,992,658.

### Nonlinearities and thresholds (Table 6)
- Evidence of nonlinear effects of the share of women in senior positions on ROA:
  - Share of Women in Senior Positions: 0.0081*** / 0.0094*** / 0.0066*** (Net income / Profit BT / EBIT) in some specifications (standard errors (0.0019)(0.0022)(0.0022)).
  - Share of Women in Senior Positions ^ 2: -0.0056*** / -0.0070*** / -0.0054*** (standard errors (0.0018)(0.0021)(0.0021)).
  - Interactions with Female Intensity and Knowledge Intensity show positive linear terms and negative squared terms:
    - Share of Women * Female Intensity: 0.0308*** / 0.0365*** / 0.0329*** (standard errors (0.0106)(0.0122)(0.0110)).
    - Share of Women ^ 2 * Female Intensity: -0.0183 / -0.0244** / -0.0171 (standard errors (0.0106)(0.0124)(0.0118)).
    - Share of Women * Knowledge Intensity: 0.0157*** / 0.0189*** / 0.0169*** (standard errors (0.0037)(0.0044)(0.0042)).
    - Share of Women ^ 2 * Knowledge Intensity: -0.0075** / -0.0095** / -0.0072* (standard errors (0.0038)(0.0043)(0.0042)).
- Observations in nonlinear specifications: 2,003,279 / 2,000,422 / 1,992,658 across columns.

### Robustness checks (Tables 7 and 8 excerpts)
- Robustness across alternative labor productivity measures, country selections, and winsorization:
  - Share of women (baseline and alternative specs): coefficients range from negative small magnitudes to positive (examples: -0.0026, -0.0025, 0.0013, 0.0027** , 0.0012, 0.0040** , 0.0023***, 0.0014).
  - Share of women * Female Intensity and Share of women * Knowledge Intensity remain positive and often statistically significant across many robustness specifications (examples: 0.0163***, 0.0261***, 0.0202***, 0.0196***, 0.0297***).
  - Observations across robustness columns: up to 2,110,620 in some specifications; other reported counts include 2,003,279; 2,078,719; 1,893,589; 1,279,949.
- 2012 financial data and labor productivity alternative specifications reported; specific coefficients preserved in the tables.

### Interpretation and policy implications (Conclusion)
- Increased female representation in senior positions could play an important role in boosting Europe’s potential output.
- If higher involvement by women in senior positions improves firm profitability, it may support corporate investment and productivity, mitigating the slowdown in potential growth.
- Policy recommendation: leveling the playing field through policies to facilitate women’s full-time attachment to the labor force could help build the pipeline of women for senior corporate positions (reference to Christiansen and others, 2016).

*Sources: Orbis and IMF Staff calculations; empirical results and tables as presented in the content unit.*

### References

### _wp1650 - References

### References (selected)
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- Adler, R. 2001, “Women and Profits,” Harvard Business Review, Vol. 79, No. 10, p. 30, November.
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- Christiansen, L., H. Lin, J. Pereira, P. Topalova, and R. Turk, [2016], “Individual Choice or Policies? Drivers of Female Employment in Europe,” IMF Working Paper, forthcoming.
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### Table A1 — Overview of the Literature on the Impact of Women on Boards (impact and findings)
- Adams and Ferreira (2009) — Impact: Negative. Finding: Whereas female directors have a significant impact on board inputs and firm outcomes, the average effect of gender diversity on firm performance is negative.
- Ahern and Dittmar (2012) — Impact: Negative. Finding: The law quota announcement caused a significant drop in the stock price, less experienced boards, increase in leverage and acquisitions, and a deterioration in firm performance.
- Matsa and Miller (2009) — Impact: Negative. Finding: Firms affected by the quota in Norway undertook fewer workforce reductions than other firms, increasing their relative costs and lowering profitability, especially for firms without female board members beforehand. Other corporate decisions were unchanged.
- Du Reitz and Henrekson (2000) — Impact: Insignificant. Finding: Systematic differences between female- and male-headed firms, but no evidence of female underperformance, especially at firms with only one employee.
- Lam and others (2013) — Impact: Limited evident of a link. Finding: Female CEOs are more likely to emerge in firms where at least one female director is present but their copmensation has less favourable terms compared to male CEOs.
- Smith and others (2005) — Impact: Varies from none to positive. Finding: Gender diversity in top management positively affects firm performance.
- Campbell and Vera (2010) — Impact: Positive. Finding: Positive stock market reaction to appointment of female director and also over the long run.
- Castiglione, Infante, and Smirnova (2014) — Impact: Positive. Finding: The presence of female managers increases productivity differential due to geographical localization. Also, management-diverse firms are more productive than female- or male-only managed firms.
- Dezso and Ross (2012) — Impact: Positive. Finding: Gender diversity in top management brings in informational and performance benefits to the extent that the firm's strategy is focused on innovation.
- Farrell and Hersch (2005) — Impact: Positive. Finding: Female additions on boards are not a result of better qualified female labor but a gender call, and they do not generate significant market reaction.
- Huang and Kisgen (2013) — Impact: Positive. Finding: Male executives exhibit overconfidence in decision making relative to female executives: they undertake more acquisitions and issue more debt, but their decisions yield lower announcement returns.
- Kang, Ding, and Charoenwong (2010) — Impact: Positive. Finding: Investors are most receptive to announcement of female appointment when the new director is independent and least receptive when the director assumes the role of CEO.
- Khan and Vieito (2013) — Impact: Positive. Finding: Firms managed by a female CEO associate with better performance compared to male CEO firms; equity compensation packages can act as an incentive for female CEOs to take risks.

### Table A2 — Sectoral Female Intensity (NACE Rev. 2, Share of women in employment)
- A Agriculture, forestry, and fishing: 0.25
- B Mining and quarrying: 0.17
- C Manufacturing: 0.33
- D Electricity, gas, steam, and air conditioning supply: 0.24
- E Water supply; sewerage, waste management, and remediation activities: 0.24
- F Construction: 0.09
- G Wholesale and retail trade; repair of motor vehicles and motorcycles: 0.46
- H Transportation and storage: 0.22
- I Accommodation and food service activities: 0.56
- J Information and communication: 0.52
- K Financial and insurance activities: 0.52
- L Real estate activities: 0.41
- M Professional, scientific, and technical activities: 0.52
- N Administrative and support service activities: 0.56
- O Public administration and defense; compulsory social security: 0.43
- P Education: 0.70
- Q Human health and social work activities: 0.64
- R Arts, entertainment, and recreation: 0.70
- S Other service activities: 0.38
- T Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use: 0.92
- U Activities of extraterritorial organizations and bodies: 0.41

- Source note in table: Source: OECD Annual Labor Force Statistics and IMF staff calculaitons. Employment data are reported by the OECD at the ISIC Rev. 3 and are converted to the NACE Rev. 2 industrial code level. Female intensity for the individual industries within the manufacturing sector is from Do and others (2016).

### Table A3 — High-Tech and Knowledge-Intensive Sectors (NACE Rev. 2)

- Manufacturing — High-technology:
  - Manufacture of basic pharmaceutical products and pharmaceutical preparations (21)
  - Manufacture of computer, electronic and optical products (26)
  - Manufacture of air and spacecraft and related machinery (30.3)
- Manufacturing — Medium-high-technology:
  - Manufacture of chemicals and chemical products (20)
  - Manufacture of weapons and ammunition (25.4)
  - Manufacture of electrical equipment (27)
  - Manufacture of machinery and equipment n.e.c. (28)
  - Manufacture of motor vehicles, trailers and semi-trailers (29)
  - Manufacture of other transport equipment (30) excluding Building of ships and boats (30.1) and excluding Manufacture of air and spacecraft and related machinery (30.3)
  - Manufacture of medical and dental instruments and supplies (32.5)

- Services — High-tech knowledge-intensive services:
  - Motion picture, video and television programme production, sound recording and music publishing activities (59)
  - Programming and broadcasting activities (60)
  - Telecommunications (61)
  - Computer programming, consultancy and related activities (62)
  - Information service activities (63)
  - Scientific research and development (72)

- Services — Knowledge-intensive market services (excluding financial intermediation and high-tech services):
  - Water transport (50)
  - Air transport (51)
  - Legal and accounting activities (69)
  - Activities of head offices; management consultancy activities (70)
  - Architectural and engineering activities; technical testing and analysis (71)
  - Advertising and market research (73)
  - Other professional, scientific and technical activities (74)
  - Employment activities (78)
  - Security and investigation activities (80)

- Services — Knowledge-intensive financial services:
  - Financial service activities, except insurance and pension funding (64)
  - Insurance, reinsurance and pension funding, except compulsory social security (65)
  - Activities auxiliary to financial services and insurance activities (66)

- Services — Other knowledge-intensive services:
  - Publishing activities (58)
  - Veterinary activities (75)
  - Public administration and defence; compulsory social security (84)
  - Education (85)
  - Human health activities (86)
  - Residential care activities (87)
  - Social work activities without accommodation (88)
  - Creative, arts and entertainment activities (90)
  - Libraries, archives, museums and other cultural activities (91)
  - Gambling and betting activities (92)
  - Sports activities and amusement and recreation activities (93)

- Source note in table: Source: Eurostat, European Commission websites: http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech_classification_of_manufacturing_industries http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Knowledge-intensive_services_(KIS).

*References and tables reproduced from _wp1650 - References.*

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