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Foreign Direct Investment and Entrepreneurship: Gender Differences Across the World
Rajeev K. Goel
Department of Economics
Illinois State University
Normal, IL 61790-4200
USA
Keywords: Entrepreneurship; Gender; Foreign Direct Investment; Institutions; Government
JEL classification: M16; F6
Draft for Asian Development Bank
2016 Conference on Foreign Direct Investment, June 2016
Abstract
Using recent cross-national data this paper examines the impact of foreign direct investment
(FDI) on entrepreneurship activity. The impact of FDI on entrepreneurship is not clear a priori
with the possibilities of both a negative effect (crowding out) and a positive effect (synergy or
complementarity). Results find support for the crowding out effect; however, this effect varies
across nations with different prevalence of entrepreneurship. Another focus of this work is on
gender differences. The crowding out effect is stronger for the full sample, rather than the
subsample of female entrepreneurship and this finding stands up to a battery of robustness
checks. Policy implications of the findings are discussed.
Rajeev K Goel, Illinois State University Page 2
1. Introduction
Research on the causes of entrepreneurship has gained momentum in recent years as
policymakers and researchers have recognized the need for fostering entrepreneurial activity (for
a recent compilation of papers, see Cebula et al. (2015)). Within this context, the influences of
various factors on entrepreneurial activity have been examined, although the challenges with
underlying measurement issues limit the applicability of some of the findings (see, for example,
Acs et al. (2008), Acs et al. (2014), OECD (2012)). However, definitive answers for fostering
entrepreneurship, especially among various demographic groups, remain elusive.
Another significant related aspect is the differences across gender in the effectiveness of various
factors driving entrepreneurial activity. Do similar factors significantly affect entrepreneurship
by males and females across nations? Attention to gender differences in entrepreneurial research
is relatively new, with many dimensions still not formally examined (Acs et al. (2011), Elam
(2008), Jennings and Brush (2013), Shinnar et al. (2012), Stephan and El-Ganainy (2007) and
Sullivan and Meek (2012)). The issue of gender and entrepreneurship has been well-recognized,
e.g., “The lack of integrative frameworks for understanding the nature and implications of issues
related to sex, gender, and entrepreneurship has been a major obstacle”, Fischer et al. (1993);
also see Minniti and Naudé (2010).
This paper examines the effect of foreign direct investment (FDI) on entrepreneurial activity
across a large sample of nations, with a focus on related gender differences. Research on this
aspect is important because it is not clear a priori whether FDI necessarily fosters
entrepreneurship. On the one hand, FDI could have positive effects on entrepreneurship via
dissemination of technology and know-how; on the other hand, there could be negative
consequences when FDI crowds out domestic entrepreneurship. Only a handful of articles have
considered the FDI-entrepreneurship nexus and none have studied gender differences (Albulescu
and Tămăşilă (2014), Danakol et al. (2013) and Jennings and Brush (2013)). It could be the case
that FDI fosters entrepreneurship among males but crowds out female entrepreneurship (or vice
versa). We will bring empirical evidence to bear on this aspect.
Along another dimension of this research, the effect of FDI along nations with varying
prevalence of entrepreneurship will be studied. Is FDI more effective at fostering
entrepreneurship in nations with a relatively high degree of entrepreneurship? For instance, some
Asian nations receive large FDI inflows but are not at the top of entrepreneurial activity. It would
be informative to learn about the factors inhibiting entrepreneurship in such cases. Besides
gender aspects, the study of effectiveness of FDI across nations with varying prevalence of
entrepreneurship is a key contribution of this work.
We turn next to a discussion of the formal model and the data employed.
Rajeev K Goel, Illinois State University Page 3
2. The Model and Data
This section outlines the empirical model employed and the data used in the estimation.
2.1 The Model
Although several channels of influence impact entrepreneurship, the linkage between FDI and
entrepreneurship can be nested in knowledge spillover theory of entrepreneurship (see Acs et al.
(2007)). The basic underlying idea is that spillovers of knowledge present entrepreneurship
opportunities for individuals. New knowledge (about products and processes) generate ideas for
new products or complementary components that lead to new businesses. However, the context
in which entrepreneurship evolves (specifically by the setting up of a new firm or a new venture)
is shaped by the institutional setup in a nation. For example, nations with a stronger property
rights protection and a tradition of due legal process provide good conditions for fostering
entrepreneurship.
With regard to FDI, often the technologies associated with foreign investments are new or
cutting-edge that bring new knowledge. Such knowledge can bring new opportunities for
domestic entrepreneurs and in this case FDI will be complementary to domestic entrepreneurship
(by generating knowledge spillovers that lead to entrepreneurial startups). On the other hand,
more “routine” FDI by foreign firms would substitute for domestic entrepreneurship, especially
if the foreign investors are large conglomerates so that budding domestic entrepreneurs feel
threatened. The extant empirical evidence on the impact of FDI on entrepreneurship is mixed
(see, for example, Da Backer and Sleuwaegen (2003) and Liu et al. (2014)).
We employ a simple model to explain factors driving entrepreneurial activity. The main
contributions of this work lie in (i) examining the impact of foreign direct investment on
entrepreneurial activity; (ii) studying differences in factors driving entrepreneurial activity for
females as compared to overall entrepreneurship; (iii) investigating whether the effect of FDI is
different among nations with varying prevalence of entrepreneurship; and (iv) using recent data
on a large sample of nations.
Formally, entrepreneurship in country i is taken to be driven by FDI, economic prosperity
(GDP), institutions, country size (population (POP)), unemployment (UNEMP) and the degree of
urbanization (URBAN). The estimated equation takes the following general form
Rajeev K Goel, Illinois State University Page 4
Entrepreneurshipij = f(FDIi, Economic properityi, Institutionsik, Country sizei, Unemploymenti,
Urbanizationi) (1)1
i = 1, 2,…,128
j = GeneralENT, FemaleENT
k = DEM, EconFree, GovtCons, EconFree-GSP, EconFree-Corr, EconFree-Prop
The dependent variable is alternately taken as an index of overall entrepreneurship climate
(GeneralENT) and entrepreneurship climate for females (FemaleENT). Gender differences are
present in the context of entrepreneurship due to a different representation of women in certain
professions (which, among other things, affects networking inhibiting new entrepreneurs), fewer
resources/opportunities available to women and women being naturally better inclined to certain
professions (see Shinnar et al. (2012)). An understanding of these differences can aid in the
design of appropriate policies to foster entrepreneurship.
The main determinant of interest in this study is whether foreign direct investment is a
complement or substitute for entrepreneurship and further if such substitution/complementarity
varies across gender. FDI takes many forms dictated by relative business/institutional climates
and comparative advantages across nations. Thus, in some cases foreign investments might
crowd out domestic enterprise (due to superior ability, networks, etc.), while in other cases
foreign investments might be complementary (e.g., where the domestic entrepreneurs are able to
add expertise to alter the product/service to local conditions). The extant evidence in this regard
is inconclusive (see De Backer and Sleuwaegen (2003) and Liu et al. (2014)). Besides shedding
light on the FDI-entrepreneurship nexus for a large sample of nations for a recent time period,
another important focus of this work is on related gender differences. It could be the case that the
substitution/complementarity between FDI and entrepreneurship does not uniformly hold across
gender. This potential gender difference does not seem to have been previously studied.
Turning to other factors affecting entrepreneurship, greater economic prosperity signifies greater
opportunity and we would expect a positive relationship between prosperity and
entrepreneurship. On the other hand, greater unemployment might encourage entrepreneurial
activity, although the unemployed might have heightened desire but might not be able to raise
resources. Larger nations, ceteris paribus, might have more opportunities on one hand, but there
might be greater regional disparities. Further, greater urbanization might foster easier
transmission of information via formal and informal networks and this could aid entrepreneurial
activity. Again, the gender influences of these factors might vary and the current work will prove
informative in this regard.
1 There could potentially be a bi-directional causality between FDI and entrepreneurship. However, our focus on a
single year (2015) somewhat alleviates concerns about reverse feedbacks. Given appropriate time series data, this
issue merits additional attention in the future.
Rajeev K Goel, Illinois State University Page 5
The institutional setup is very important to entrepreneurial activity and we consider several
dimensions (see Knack and Keefer (1995) for a general study in this context and Acs et al.
(2007) and Estrin and Mickiewicz (2011) for specifics). Institutions dictate the rules of the game
and a “fair” set of rules instills confidence in new entrepreneurs. The degree of democracy
(DEM) involves freedom of the press and civil liberties which have bearing upon transparency of
the government and due political process. So we would expect more democratic nations to have
greater entrepreneurship.
Greater economic freedom would also promote entrepreneurship. However, the degree of
economic freedom involves freedom of trade, protection of property rights, freedom from
overbearing regulations, freedom from corruption, etc. Given the multi-faceted nature, we
consider several dimensions. First, an overall composite index of economic freedom (EconFree)
is employed. Second, we consider government’s involvement in the economy, alternately via an
index (EconFree-GSP) and a more direct measure of government consumption (GovtCons).2
Government involvement can be a bottleneck when it adds red tape and bureaucratic delays, but
it can be beneficial when it promotes checks and balances. Third, the (lack of) corrupt activity is
captured by EconFree-Corr.3 Corruption undermines due process and creates inequities as
individuals and firms with resources are able to buy favors. This dissuades new entrepreneurs,
especially since they are resource constrained. Finally, the property rights protection aspect of
economic freedom is accounted for by including the index EconFree-Prop. Stronger property
rights instill confidence regarding protection of investments and appropriation of profits and thus
encourage entrepreneurship.
2.2 Data
We restrict our analysis to the year 2015, with the main constraint being the availability of
comparable time series data on female entrepreneurship (and due to the corruption index (see
Footnote #3)). Most of the other data are also for that year or the closest year available (see
Table 1). Although the sample size with FemaleENT is smaller, the correlation between
GeneralENT and FemaleENT is 0.93 (see Table A1 in the Appendix).
There was considerable variation in the FemaleENT scores across nations for 2015. The United
States was at the top of the list with an index score of 82.9, while Pakistan was at the bottom
with a score of 15.2. The average score for FemaleENT was 44.94 (Table 1). In contrast, the
2 As noted by Gicheva and Link (2015), there might be gender differences in government support for
entrepreneurship. Also see Rosa and Dawson (2006). 3 The corruption measure used is based off the well-known measure of corruption perceptions from the
Transparency International (www.trasparency.org). This measure has good cross-national comparability of
corruption; however, its time series comparability is limited.
Rajeev K Goel, Illinois State University Page 6
overall index, GeneralENT, was also led by the United States with a score of 85 with Bangladesh
ranked at the bottom with a score of 14.4. The sample mean for GeneralENT in 2015 was 39.07.
Turning to FDI, the mean value (as a percent of GDP) was 34.22; with the range being 76.37 and
-32.93, respectively (with the negative value signifying FDI outflows). The correlation between
FDI and GeneralENT was 0.17 and that was also the case for the correlation between FDI and
FemaleENT. The formal analysis below will test the validity of these relations when other
relevant factors have been accounted for.
The data employed for this study are in the public domain (e.g., Global Entrepreneurship and
Development Index, Freedom House, Heritage Foundation, etc.) and appropriate estimation
techniques to address the above questions are employed (e.g., OLS and quantile regression). The
use of indices for many variables addresses cross-national measurement/comparability issues.
Details about the variables are provided in Table 1 and a correlation matrix of key variables is in
Table 1A.
3. Results
All the models in the baseline results are estimated using Ordinary Least Squares with t-statistics
based on robust standard errors reported. The overall fit of all the models is decent as shown by
the R2 that is at least 0.75. As a test of the overall specification, we ran the RESET test. This test
showed that, relatively speaking, the fit of female entrepreneurship models was better than that
of the overall entrepreneurship. To address specification issues, we conducted a set of
robustness checks (see Sections 3.2 and 3.3).4
3.1 Baseline models
The baseline models in Table 2 show that the crowding out effect of FDI holds for general
entrepreneurship in all the models – the coefficient on FDI is statistically significant in Models
2A.1-2A.7 in Panel A. This is consistent with the idea that foreign investment dissuades or
replaces some domestic entrepreneurial activity. However, with the sample of female
entrepreneurship, the negative impact of FDI is statistically insignificant (Panel B). It could
either be the case that female entrepreneurs are not easily deterred or the focus of foreign
investments is generally in areas in which female entrepreneurs tend to not specialize. The
crowding out effect supports earlier findings of Da Backer and Sleuwaegen (2003) for Belgium
and those of Danakol et al. (2013). In terms of magnitude, the elasticity of GeneralENT with
respect to FDI is -0.025 (based on models 2A.1-2A.5). The differing effects of FDI on
4 While it is possible that there is a bi-directional causality between FDI and entrepreneurship whereby the level of
entrepreneurship in a nation reverse-causes (or invites) foreign investment, this issue is somewhat mitigated given
the cross-sectional estimation employed.
Rajeev K Goel, Illinois State University Page 7
GeneralENT and FemaleENT are noteworthy given the similar pairwise correlations of FDI with
the two entrepreneurship measures (Table 1A).
Greater economic prosperity, greater economic freedom (EconFree), freedom from corruption
(EconFree-Corr), stronger property rights protection (EconFree-Prop) and greater democracy
(note that higher values of DEM imply lower democracy – Table 1) increase entrepreneurial
activity in all cases and this is true for both overall entrepreneurship and for female
entrepreneurship. These findings are in accord with intuition. On the other hand, neither type of
entrepreneurship is significantly affected by the country size (denoted by population) and the
unemployment rate.
The effect of urbanization is positive and mostly statistically significant, with the relative
magnitude of the effect being greater on overall entrepreneurship than on female
entrepreneurship. Government size, whether denoted by GovtCons or EconFree-GSP, has a
significant (positive) impact on overall entrepreneurship but not on female entrepreneurship.5
The positive effect of government size is consistent with the checks and balances story, rather
than with the bottlenecks story.
3.2 Robustness check1: Effect of FDI on entrepreneurship across varying prevalence of
entrepreneurship
To examine another aspect and to answer one of the questions posed in the introduction, we
examine the effect of FDI on entrepreneurship across nations with different prevalence of
entrepreneurship. For this purpose, we employ the quantile regression (see Koenker and Hallock
(2001) for background on the quantile regression), and report regression results for both overall
and female entrepreneurship across q25, q50 and q75 in Table 3. Here q50 can be seen as
focusing on the median regression, while q25 and q75, respectively, capture low and high
entrepreneurship climates.
Results, across two model setups, show that the overall pattern of findings is similar to that in
Table 2. However, while FDI has a negative impact on entrepreneurship across all quantiles, the
negative or crowding out effect has statistical significance in the median regression for overall
entrepreneurship. Thus, crowding out is not significant in the tails (or in the case of female
entrepreneurship). This suggests that nations with mature (or nascent) entrepreneurship activity
have to be less concerned with the negative effects of FDI.6
5 Note that higher values of GovtCons and low values of EconFree-GSP both imply large government spending (see
Table 1). 6 An important caveat, however, that has to be kept in mind is that there are finer disaggregations of data that might
yield some differences in results. For instance, entrepreneurship may be classified as need-driven, opportunity-
driven, academic or underground (informal) entrepreneurship, and FDI may be inward or outward (see Danakol et
al. (2013), Goel et al. (2015a, 2015c)). We leave these extensions for future work, pending comparable data.
Rajeev K Goel, Illinois State University Page 8
In other findings, similar to Table 2, greater democracy and greater economic prosperity promote
entrepreneurship in all cases and both for the overall and the female sample. Also, like Table 2,
the effect of unemployment is statistically insignificant. Interesting differences, however,
emerge with regard to the effect of urbanization. Whereas greater urbanization promotes overall
entrepreneurship in all except high entrepreneurship prevalence nations (q75), the reverse is the
case for female entrepreneurship – urbanization promotes female entrepreneurship in nations
with high entrepreneurship climates. While this issue merits further research, these findings are
suggestive of gender differences in information exchange (see Goel et al. (2015b)) and perhaps
some minimum threshold level of entrepreneurship climate for females to benefit from
urbanization.
3.3 Robustness check2: Employing similar samples for general and female
entrepreneurship
There is substantial difference coverage in the number of countries represented in the overall
sample and the female entrepreneurship sample. Specifically, the overall sample has 127
countries, while the female entrepreneurship subsample has 76 countries. To check the validity
of our findings, we restricted the full sample so that the same number of countries were covered
in both the samples.
Rerunning the set of regressions from Table 2 for the full sample showed the pattern of results to
be similar. In particular, with regard to the main variable of interest, the coefficient on FDI was
again negative in all cases, confirming the crowding out effect of foreign direct investment.
However, given the reduced sample size, the statistical significance was more modest. The
results with regard to the other regressors were similar to what is presented in Table 2. Further,
the RESET test showed that the specification of the general entrepreneurship models in the
restricted sample was better.7
4. Concluding remarks
This paper examines the effect of foreign direct investment (FDI) on entrepreneurial activity
across a large sample of nations, with a focus on related gender differences. Research on this
aspect is important because it is not clear a priori whether FDI necessarily fosters
entrepreneurship. Results find support for the crowding out effect. Another focus of this work is
on gender differences. The crowding out effect is stronger for the full sample, rather than the
subsample of female entrepreneurship and this finding stands up to a battery of robustness
checks. Numerically, a ten percent increase in FDI (as a percentage of GDP) lowers overall
7 Details are available upon request.
Rajeev K Goel, Illinois State University Page 9
entrepreneurship by about 0.2 percent. We also find some differences in the efficacy of the
crowding out effect across nations with different prevalence of overall entrepreneurship.
Specifically, nations in the middle prevalence of entrepreneurship face the negative effects of
FDI and such consequences are not borne in the context of female entrepreneurship.
Several implications for public policy emerge from our findings. First, the obvious and
important policy implication is that gender differences warrant special efforts to foster
entrepreneurship among women. Second, the negative spillovers on entrepreneurship from FDI
should go into the cost-benefit calculations of efforts to encourage foreign investments. Third,
the importance of good institutions, including economic freedom, corruption control, and
property rights protection in fostering entrepreneurship is recommended. Fourth, a large
government size is not necessarily an impediment to entrepreneurship but could rather be helpful
by strengthening checks and balances. Fifth, the existing prevalence of entrepreneurship activity
should be kept in mind in framing policies. Sixth, the differing effects of urbanization across
gender have some implications for knowledge spillovers. Finally, given the positive effect of
economic prosperity, rich and poor nations perhaps need to approach entrepreneurship-
enhancement somewhat differently.
Rajeev K Goel, Illinois State University Page 10
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Rajeev K Goel, Illinois State University Page 12
Table 1. Variable definitions, summary statistics and sources
Variable Definition (Mean; SD) Source
GeneralENT General Entrepreneurship Index – “… index
that measures the health of the
entrepreneurship ecosystems ... It then ranks
the performance of these against each other.
The GEDI methodology collects data on the
entrepreneurial attitudes, abilities and
aspirations of the local population and then
weights these against the prevailing social and
economic ‘infrastructure’” (39.07; 16.78)
The Global Entrepreneurship
and Development Institute
thegedi.org/global-
entrepreneurship-and-
development-index/
FemaleENT Female Entrepreneurship Index – “… Index is
a barometer of a country's current situation
relative to a group of other countries with
respect to the conditions present that will fuel
high potential female entrepreneurship
development”, (44.94; 15.98)
The Global Entrepreneurship
and Development Institute
thegedi.org/female-
entrepreneurship-index-
2015-report/
FDI FDI(mill.$)/GDP(bill.$) ratio (34.22; 76.37) Heritage Foundation
EconFree Index of economic freedom; (62.95; 9.44) Heritage Foundation
www.heritage.org/index/dow
nload
DEM Index of democracy (sum of political rights
and press freedom), higher value, lesser
democracy (6.33; 3.70), 2014
Freedom House
www.freedomhouse.org
EconFree-Prop Index of freedom of property rights (46.52;
24.95)
Heritage Foundation
EconFree-Corr Index of freedom from corruption (based on
Transparency International’s corruption
perceptions index), (46.07; 19.13)
Heritage Foundation
EconFree-GSP Index of economic freedom related to
government spending (nonlinear weighting
with nations with very large government
spending given a zero score), (61.92; 23.40)
Heritage Foundation
GovtCons Government expenditure (% of GDP), (33.94;
10.99)
Heritage Foundation
POP Population in millions (51.18; 166.16) Heritage Foundation
GDPpc GDP per capita (PPP$), (18259.45; 17535.16) Heritage Foundation
UNEMP Unemployment rate (%), (8.80; 6.33) Heritage Foundation
URBAN Urbanization rate (%), (61.80; 22.62), 2014 World Development
Indicators
Note: All data are by country for 2015 (unless otherwise noted)
Rajeev K Goel, Illinois State University Page 13
Table 1A: Correlation matrix of key variables
GeneralENT FemaleENT EconFree DEM GDPpc FDI
GeneralENT 1.00
FemaleENT 0.93 1.00
EconFree 0.75 0.75 1.00
DEM -0.57 -0.65 -0.58 1.00
GDPpc 0.90 0.84 0.69 -0.51 1.00
FDI 0.17 0.17 0.36 -0.08 0.26 1.00
N = 77; see Table 1 for variable definitions.
Rajeev K Goel, Illinois State University Page 14
Table 2: Panel A- Determinants of general entrepreneurship
Dependent variable: GeneralENT
2A.1 2A.2 2A.3 2A.4 2A.5 2A.6 2A.7
GDPpc 0.001**
(5.9)
0.0005**
(4.7)
0.0004**
(3.5)
0.0004**
(3.1)
0.001**
(6.2)
0.0003**
(2.5)
0.0005**
(4.4)
DEM -1.18**
(3.8)
-0.98**
(3.5)
-0.89**
(3.4)
-0.49**
(2.1)
-1.37**
(4.0)
-0.39*
(1.7)
-1.07**
(3.9)
FDI -0.03**
(2.2)
-0.03**
(2.9)
-0.03**
(2.8)
-0.03**
(3.5)
-0.03**
(2.6)
-0.02**
(2.7)
-0.02**
(2.0)
EconFree 0.54**
(3.9)
0.51**
(4.2)
GovtCons 0.15* (1.7) EconFree-GSP -0.07* (1.9)
EconFree-Corr 0.45**
(5.0)
EconFree-Prop 0.31** (5.6) URBAN 0.14**
(3.2)
0.15**
(3.4)
0.13**
(2.7)
POP 0.003
(0.6)
0.004
(1.0)
0.001
(0.4)
UNEMP -0.06 (0.8)
N 128 127 126 128 128 127 127
R2 0.75 0.78 0.79 0.81 0.74 0.81 0.76
F-Value 62.38** 72.25** 72.38** 87.71** 68.74** 92.36** 73.78**
RESET 9.44** 10.87** 11.81** 4.50** 11.38** 5.41** 9.72**
Panel B- Determinants of female entrepreneurship
Dependent variable: FemaleENT
Independent
Variables
2B.1 2B.2 2B.3 2B.4 2B.5 2B.6 2B.7
GDPpc 0.001**
(8.7)
0.001**
(7.1)
0.0005**
(5.5)
0.0005**
(4.4)
0.001**
(9.8)
0.0005**
(4.0)
0.001**
(6.5)
DEM -1.24**
(4.0)
-1.02**
(3.6)
-0.94**
(3.2)
-1.03**
(3.8)
-1.28**
(4.0)
-0.91**
(2.8)
-1.25**
(3.9)
FDI 0.0002
(0.01)
-0.04
(1.3)
-0.05
(1.3)
-0.02
(0.7)
-0.02
(0.6)
-0.02
(0.8)
-0.01
(0.2)
EconFree 0.44**
(3.8)
0.46**
(4.3)
GovtCons 0.14 (1.6)
EconFree-GSP -0.04 (1.0)
EconFree-Corr 0.23**
(2.8)
EconFree-Prop 0.15** (2.4)
URBAN 0.09**
(2.1)
0.09*
(1.8)
0.08
(1.5)
POP 0.003
(0.5)
0.003
(0.6)
0.002
(0.3)
UNEMP 0.18 (1.4) N 77 77 76 77 77 76 76
R2 0.77 0.80 0.81 0.79 0.77 0.79 0.78
F-Value 56.00** 53.56** 53.85** 54.49** 61.79** 54.86** 53.47**
RESET 0.32 0.96 0.48 0.58 0.28 0.27 0.17
Notes: See Table 1 for variable details. Constant included but not reported. The numbers in parentheses are absolute values of
t-statistics based on robust standard errors from OLS regressions. * and **, respectively, denote statistical significance at the
10% and 5% (or better) levels.
Rajeev K Goel, Illinois State University Page 15
Table 3
Determinants of entrepreneurship across varying prevalence
Dependent variable: GeneralENT
Model 1 Model 2
q25 q50 q75 q25 q50 q75
GDPpc 0.0005**
(3.4)
0.001**
(5.7)
0.001**
(6.1)
0.0005**
(2.9)
0.001**
(6.4)
0.001**
(5.4)
DEM -0.82**
(2.6)
-0.76**
(2.9)
-0.71
(1.6)
-0.80**
(2.6)
-0.74**
(2.8)
-0.94**
(2.0)
FDI -0.02
(1.0)
-0.04*
(1.7)
-0.005
(0.2)
-0.02
(0.8)
-0.05*
(1.9)
-0.01
(0.4)
URBAN 0.12**
(2.4)
0.14**
(2.4)
0.11
(1.5)
0.10*
(1.9)
0.13**
(2.3)
0.09
(1.3)
UNEMP 0.10
(0.9)
-0.03
(0.3)
-0.19
(1.3)
N 127 127 127 127 127 127
Pseudo R2 0.43 0.54 0.60 0.44 0.54 0.61
Dependent variable: FemaleENT
Model 1 Model 2
q25 q50 q75 q25 q50 q75
GDPpc 0.001**
(6.3)
0.001**
(4.5)
0.001**
(4.3)
0.001**
(6.8)
0.001**
(4.9)
0.001**
(3.7)
DEM -1.19**
(2.7)
-1.00*
(1.8)
-1.09**
(2.2)
-1.14**
(2.5)
-0.98*
(1.7)
-1.00*
(1.7)
FDI -0.05
(1.1)
-0.004
(0.1)
-0.02
(0.3)
-0.05
(1.0)
-0.01
(0.1)
-0.02
(0.5)
URBAN 0.02
(0.3)
0.01
(0.2)
0.16*
(1.8)
0.04
(0.6)
0.07
(0.9)
0.16*
(1.5)
UNEMP 0.20
(1.0)
0.32
(1.5)
0.09
(0.4)
N 76 76 76 76 76 76
Pseudo R2 0.52 0.54 0.58 0.53 0.55 0.58
Notes: See Table 1 for variable details. Constant included but not reported. The results
are quantile regression results based on 200 bootstrap iterations. q25, q50, and q75,
respectively, denote 25th
quantile, 50th
quantile and 75th
quantile. * and **, respectively,
denote statistical significance at the 10% and 5% (or better) levels.
16
Appendix
List of Countries
Albania Costa Rica* Iran* Morocco Slovenia*
Algeria* Cote d'Ivoire Ireland* Mozambique South Africa*
Angola* Croatia* Israel* Myanmar Spain*
Argentina* Cyprus Italy* Namibia Sri Lanka
Australia* Czech Republic* Jamaica* Netherlands* Suriname
Austria* Denmark* Japan * Nicaragua Swaziland
Bahrain Dominican
Republic
Jordan Nigeria* Sweden*
Bangladesh* Ecuador* Kazakhstan Norway* Switzerland*
Barbados* Egypt* Kenya Oman Taiwan*
Belgium* El Salvador* Korea* Pakistan* Tanzania
Benin Estonia* Kuwait Panama* Thailand*
Bolivia* Ethiopia* Lao PDR Paraguay Trinidad &
Tobago*
Bosnia and
Herzegovina*
Finland* Latvia* Peru* Tunisia*
Botswana* France* Lebanon Philippines Turkey*
Brazil* Gabon Liberia Poland* Uganda*
Brunei
Darussalam
Gambia Libya Portugal* Ukraine
Bulgaria Germany* Lithuania* Puerto Rico United Arab
Emirates*
Burkina Faso Ghana* Luxembourg Qatar United
Kingdom*
Burundi Greece* Macedonia* Romania* Uruguay*
Cambodia Guatemala* Madagascar Russia* USA*
Cameroon Guyana Malawi* Rwanda Venezuela*
Canada Honduras Malaysia* Saudi Arabia* Vietnam
Chad Hong Kong Mali Senegal Zambia*
Chile* Hungary* Mauritania Serbia
China* Iceland* Mexico* Sierra Leone
Colombia* India* Moldova Singapore*
Indonesia Montenegro* Slovakia* * Indicates countries used in FemaleENT dataset. The number of countries in the analysis
varies due to missing data for other variables.