presentation on WES on 29th May

1. Group for Research in APplied Economics
Trade and female entrepreneurship
Magdalena Smyk
Katarzyna Śledziewska
Joanna Tyrowicz

2. Table of contents
1. Introduction: what do we know?
2. What do we want to study?
3. Data
4. Model
5. Results
6. Conclusions
2

3. 3
Motivation
 Entrepreneurship:
 as a way to express aspirations („Jack of all trades”)
 as a way to avoid barriers on the labour market
 If „aspirations” => profitability & competitiveness => strong
competitive position of the country
 If alternative to unemployment => lack of competitiveness
What does the data say?

4. What do we know so far?
Gender equality and international trade – two strands
of the literature:
1. Workers (differences in wages and participation
rates):
– Theory: taking part in the international exchange will increase
costs of discrimination – gender wage gap and gender participation
gap should narrow (Becker).
– Empirical results: trade liberalization may influence positively
(Hazaraki and Otero, 2004) as well as negatively (Ferrufino, 2011)
the gender wage gap. In some cases the relationship was
statistically insignificant (Ghiara, 1999). Results are different for
high- and low-skilled workers.
– Impact recognition: studies were based on natural experiments:
(e. g. Mexico before and after joining NAFTA)
4

5. What do we know so far?
Gender equality and international trade– two strands
of the literature:
2. Owners
– Theory: being successful in international trade is correlated with
decisions and attributes of the owner (those attributes are
potentially connected to gender):
• why was the firm established? (unemployment vs. business opportunity)
• owner growth intention
• management experience
• firm sector and size
• innovation attitude
– Empirical evidence: only size of the firm is correlated with gender;
owner’s gender is not correlated with exporting propensity (Orser
et al. 2010)
5

6. What do we want to know?
Are firms established and managed by women are equally
important for country’s strong competitive position?
How?
1. We identify female entrepreneurship intensity in
manufacturing sectors and countries.
2. We identify correlation between female entrepreneurship and
country’s competitive position.
6

7. Data
Two types of data:
– export revenues in 15 manufacturing sectors in 67 countries;
years 2002-2010
– data about entrepreneurs: Global Entrepreneurship Monitor -
Adult Population Survey (2002-2010)
7

8. 8
Global Entrepreneurship Monitor
GEM Adult Population Survey:
 at least 2 thousand respondents from each country
 survey considers business activity, but also aspirations and future
plans
 three parts: baby business, established business, future plans
 GEM is representative of whole adult population

9. Global Entrepreneurship Monitor – our database
9
 Only those who are currently managing a firm or self-employed
 67 countries
 years: 2002-2010
 one observation corresponds to one of the 15 manufacturing sectors
(two digits ISIC Rev. 3.1. code) in each country and year
 additional three groups:
 retail products (np. food, textile, paper)
 intermediates (np. chemicals, rubber, metal, fuel)
 machines and furniture

10. Relative competitve advantage
푅퐶퐴푖,푘,푔 =
푒푥푝표푟푡푖,푘
푖=1
푛 푒푥푝표푟푡푖,푘
푒푥푝표푟푡푖,푔
푖=1
푛 푒푥푝표푟푡푖,푔
, 푤ℎ푒푟푒
푖 − 푠푒푐푡표푟
푘 − 푐표푢푛푡푟푦
푔 − 푔푟표푢푝(푒. 푔. 푂퐸퐶퐷, 퐸푈)
10

11. 11
Basic model
푅퐶퐴푖,푘,푔 = 훼 + 훽1 ∙ 푓푒푚푎푙푒푖,푘 + 훽2 ∙ 푠푒푑푢푐 푖,푘
+훽3 ∙ 표푝푝표푟푡푢푛푖푡푦푖,푘 + 훽4 ∙ 푠푢푐푐푒푠푠푓푢푙푖,푘
+훽5 ∙ 푛푒푤푡푒푐ℎ푖,푘 + 훽6 ∙ 푛푒푤푝푟표푑푢푐푡푖,푘
+훽7 ∙ 푤표푟푘푒푟푠푖,푘 + 휀푖,푘
푖 − 푠푒푐푡표푟
푘 − 푐표푢푛푡푟푦
푔 − 푔푟표푢푝 (푛푝. 푂퐸퐶퐷, 퐸푈)

12. 12
Basic model
 independent variables:
 shares of firms in each manufacturing sector:
(in each country and year):
 (푓푒푚푎푙푒푖,푘) owned by women
 (푠푒푑푢푐 푖,푘) with secondary educated owners
 (표푝푝표푟푡푢푛푖푡푦푖,푘) established as a business opportunity (not as an alternative to
unemployment)
 (푠푢푐푐푒푠푠푓푢푙푖,푘 ) which owners believe that it is a „successful business”
 (푛푒푤푡푒푐ℎ푖,푘) which are using new technologies
 (푛푒푤푝푟표푑푢푐푡푖,푘) which products are considered by consumers as new and innovative
 and average number of workers (푤표푟푘푒푟푠푖,푘)

13. 13
Descriptive statistics (2002-2010)
70%
60%
50%
40%
30%
20%
10%
0%
share of
female-owned
firms
share of female
owners with
higher
education
share of
owners with
higher
education
share of
"bussiness
opportunities"
share of frims
with innovative
product

14. 14
Female entreprenurship in three main groups
80%
70%
60%
50%
40%
30%
20%
10%
0%
retail products intermediates machines and furniture

15. 15
Motivations of choosing self-employment
60%
50%
40%
30%
20%
10%
0%
retail products intermediates machines and
furniture
women - business
opportunity
men - business
opportunity
women -
unemployment
men - unemployment
women - combination
of those two
men - combination of
those two
women - better
perspective
men - better
perspective
women - other
men - other

16. 16
Female entrepreneurship and RCA_EU
whole sample 2006

17. 17
Countries – rich and aspiring
Rich Aspiring
USA,
Netherlands,
Belgium,
France,
Spain,
Switzerland,
Austria,
Great Britain,
Denmark,
Sweden,
Norway,
Germany,
Australia,
New Zeland,
Japan,
Canada
Peru,
Colombia,
Indonesia,
Philippines,
Turkey,
Pakistan,
Uganda,
Guatemala,
Ecuador,
Iran,
Russia,
Greece,
Serbia,
Croatia,
Czech Republic,
Poland

18. 18
Results – basic model
RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
female 0,92*** 0,01 0,92*** 0,06 0,90*** 0,01
seduc -0,09 0,01 0,01 0,05 -0,11 0,00
opportunity -0,09 -0,08 -0,11 -0,09 -0,09 -0,08
successful -0,33 0,83 -0,38 0,53 -0,28 0,81
newtech -0,40 -0,27 -0,39 -0,34 -0,38 -0,27
newproduct 0,05 0,04 0,02 0,06 0,05 0,06
workers -0,05*** -0,00 -0,01*** -0,00 -0,01*** -0,00
export per capita 0,87*** 0,33*** 0,93*** 0,35*** 0,85*** 0,33***
constant 4,34*** 1,64*** 4,49*** 1,63*** 4,25*** 1,61***
* p<0,05; ** p<0,01; *** p<0.001

19. 19
Results – basic model in product groups
Detale RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
female 0,23 -0,32 0,23 -0,34 0,30 -0,29
rest – without changes
(significant)
Intermediates RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich
countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
female 0,68* 0,05 0,92* -0,39 0,68 0,04
rest – without changes
(significant)
Machines RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
female 0,79 -0,00 0,67 0,21 0,79 0,03
rest – without changes
(significant)
* p<0,05; ** p<0,01; *** p<0.001

20. 20
Results – basic model with interactions (retail products manufacturing as a
reference group)
RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
interaction: sector
machines * female
-1,56** -0,94*** -1,72*** -1,06*** -1,46** -0,87***
interaction: sector
intermediates * female
-2,64*** -0,70*** -2,47*** -0,78*** -2,54*** -0,66***
female 1,42*** 0,33** 1,42*** 0,42*** 1,38*** 0,31**
seduc -0,15 -0,04 -0,04 0,00 -0,16 -0,04
opportunity -0,03 -0,03 -0,05 -0,04 -0,04 -0,03
successful -0,30 1,08 -0,34 0,81 -0,25 1,06
newtech -0,45 -0,29 -0,44 -0,37* -0,42 -0,29
newproduct 0,16 0,08 0,12 0,11 0,15 0,10
workers -0,00*** -0,00 -0,00*** -0,00 -0,00*** -0,00
export per capita 0,89*** 0,39*** 0,95*** 0,41*** 0,87*** 0,38***
constant 4,46*** 1,75*** 4,59*** 1,75*** 4,36*** 1,71***
* p<0,05; ** p<0,01; *** p<0.001

21. Per se female entrepreneurship does not
strongly correlate with competitiveness
Maybe just some sectors or
firms?
21

22. 22
Results – aspirations
RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
share of women, who
established firm as a
business opportunity
-0,31 -0,27 -0,25 -0,29 -0,25 -0,25
rest – without changes
(significant)
RCA_OECD
Aspiring
countries
RCA_OECD
Rich
countries
RCA_EU
Aspiring
countries
RCA_EU
Rich countries
RCA_DEVELOPED
Aspiring countries
RCA_DEVELOPED
Rich countries
share of women, who
established firm
because of
unemployment
0,21 0,14 0,23 0,15 0,23 0,13
rest – without changes
(significant)

23. 23
Conclusions
Female entrepreneurship has little impact on country’s
competitive position in manufacturing.
Only in manufacturing retail products (food, textiles, paper),
sector, which women choose most frequently, we can find
positive correlation between share of female businesses and
comparative advantage.
 Motivation for which women establish firms are irrelevant to
strong competitive position.

24. Thank you for your attention!
Authors: Magdalena Smyk, Katarzyna Śledziewska, Joanna Tyrowicz
e-mail: msmyk@wne.uw.edu.pl
More about our research on
http://grape.uw.edu.pl
Twitter: @GrapeUW

25. 25
Databases
 Export revenues: World Integrated Trade Solution
http://wits.worldbank.org/
GEM
http://www.gemconsortium.org/Data