The document explores female occupational crowding and its impact on entrepreneurial outcomes, highlighting the need for better measurement tools to assess labor force differences between genders. It presents a pilot study involving 23 countries to analyze occupational sectors and gender balance, revealing significant disparities that contribute to the gender pay gap. The findings suggest that occupational crowding affects entrepreneurship and calls for further in-depth research to understand these dynamics better.

1. Female Occupational Crowding and Entrepreneurial
Outcomes:
Measurement and Public Policy Implications
Ruta Aidis, PhD
Senior Fellow, George Mason University
Gender-GEDI Project Director, The GEDI Institute
Ainsley Lloyd
Research Consultant
The GEDI Institute
R. Aidis ICSB Washington DC -Oct 16 - 18, 2014 1

2. Main topics
•Women
•Work
•Entrepreneurship
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3. Outline
• Introduction
• The Why
• Measuring occupational crowding & results
• Some interesting pie charts
• Consequences & implications
• Limitations & future research
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4. Introduction…..consider…
Your job is your first business incubator…
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5. Starting point: The need for better
measurement tools to capture the labor
force differences between men and
women for the 2014 Gender - GEDI
• www.dell.com/women
• www.thegedi.org
R. Aidis ICSB Washington DC -Oct 16 - 18, 2014
The Why…
5

6. Occupational Crowding
• Refers to the existence of male or female jobs in a country’s economy
• Crowding benefits some groups by reducing competition for the most
desirable occupations (Bergman 1974)
• Globally women are working in more limited sectors than men
• OECD countries – 50% of women work in 11 or fewer occupational
groups vs. 50% of men work in 20+ occupational groups.
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7. How to measure gender parity in the labor
force?
• Problem: Basic male/female labor force participation rates hide sector
by sector differences
• Solution: use ILO employment by sector
• Standardized sector divisions across countries
• Sex-disaggregated data
• Selected a 30 country pilot sample: but data only available for 23 of 30
selected countries with data between 2009 and 2013
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8. Sectors
1. Accommodation and food service activities
2. Activities of households as employers;
undifferentiated goods- and services-producing
activities of households for own
use
3. Administrative and support service
activities
4. Agriculture, forestry and fishing
5. Arts, entertainment and recreation
6. Community, Social and Personal Services
7. Construction
8. Education
9. Electricity, gas, steam and air conditioning
supply
10. Extraterritorial organizations and bodies
11. Financial and insurance activities
12. Fishing
13. Human health and social work activities
14. Information and communication
15. Hotels and restaurants
16. Manufacturing
17. Mining and quarrying
18. Not elsewhere classified
19. Other service activities
20. Professional, scientific and technical
activities
21. Public administration and defense;
compulsory social security
22. Real estate activities
23. Transportation and storage
24. Water supply; sewerage, waste
management and remediation activities
25. Wholesale and retail trade; repair of motor
vehicles and motorcycles
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9. Creating a comparable scoring system
An exact 1:1 male : female ratio is undesirably hard to achieve
• Allow for a range: a ratio between 4:6 or 6:4 is “balanced”
Not all countries have all sectors
• Use a percent of relevant sectors: what percentage of sectors have gender
balance?
Some sectors account for very little
• Use a materiality threshold: Count only sectors that employ >1% of total labor
force
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10. Pilot study
• Based on 23 developing, emerging and developed economies
worldwide;
• Including six main regions: Latin America, North America, Europe,
East Asia, South Asia, Africa and MENA countries.
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11. Results 1
20
18
16
14
12
10
8
6
4
2
0
Brazil
Chile
Denmark
Egypt
France
Germany
India
Japan
Korea
Malaysia
Mexico
Morocco
Pakistan
Panama
Peru
Poland
Russia
South Africa
Spain
Sweden
Thailand
Turkey
United Kingdom
United States
Sectors
total sectors sectors with balance
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12. Results 2
• 7 countries 0.40 or greater; in 2 countries 0 sectors with labor force
parity
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0.6
0.59
0.5
0.47
0.45
0.44
0.4
0.38
0.38
0.38
0.36
0.35
0.32
0.31
0.27
0.27
0.23
0.21
0.2
0.13
0.13
0
0
CHART TITLE

13. US Comparison: Women and men-owned
businesses according to select sectors
Healthcare and social assistance
48 % 52%
women men
Mining Quarrying Oil
15%
85%
women men
9%
Construction
91%
women men
Data source: National Women’s Business Council – based on 2007 Census data
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14. US Comparison: Women and men labor force
participation in select sectors
Healthcare
78%
22%
women men
Mining, Quarrying, Oil
13%
87%
Women Men
Construction
9%
91%
Data source: Catalyst (2013) R. Aidis ICSB Washington DC -Oct 16 - 18, 2014 14

15. Consequences & implications
Occupational crowding = Entrepreneurship crowding
• Occupational crowding contributes to gender pay gap because women are
more likely to work in lower-paid occupations than men (OECD Gender
Brief 2010) (ILO 2010);
• Once sector, size and age of the firm are controlled for firm performance
and profitability gendered differences diminishes significantly (OECD
2012);
• Female entrepreneur ‘crossovers’ make similar income as male
entrepreneurs in that sector and make more than other non-crossover
female entrepreneurs;
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16. Limitations
• ILO sector specifications are too broad
• Differences exist within a sector
Further research
• Adding more countries
• In depth sector analysis at the country level
• Occupational crowding occurs before the ‘job’
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