foreign direct investment and entrepreneurship: gender

Foreign Direct Investment and Entrepreneurship: Gender Differences Across the World

Rajeev K. Goel

Department of Economics

Illinois State University

Normal, IL 61790-4200

USA

[email protected]

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.

mailto:[email protected]

Rajeev K Goel, Illinois State University

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.


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


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.


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.


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.


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.


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.


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.


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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)

http://www.freedomhouse.org/


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.


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.


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.

foreign direct investment and entrepreneurship: gender

Documents