Does Globalization Encourage Female Employment? A Cross-Country Panel Study
Abstract
:1. Introduction
2. Literature Review
- (1)
- Globalization has a positive impact on FPLF, as it creates new employment opportunities.
- (2)
- Globalization has a negative impact on FPLF, as it makes the labor market more competitive for women.
3. Methodology
3.1. Empirical Model
3.2. Data Description
3.3. Estimation Strategy
- To address endogeneity bias. Ullah et al. [30] suggested that nearly 90% of papers published in premier journals may not address endogeneity bias adequately. Endogeneity arises when (a) at least one of the regressors correlates with the error term, (b) both the dependent variable and regressor simultaneously affect each other (simultaneity), and/or, (c) omitting a relevant variable from the regression model that is correlated to at least one of the included explanatory variables causes endogeneity (i.e., the included variable correlated to the error term). Endogeneity bias causes inconsistent estimates, which give misleading conclusions, wrong inference, and incorrect theoretical interpretations. Our model may suffer from potential endogeneity due to the issues described above. To deal with the potential endogeneity problem, we used the system GMM, which utilizes the lagged values of the endogenous regressor as an instrument.
- To control for the time unvarying country-specific effects. The system GMM estimation method was appropriate because the sample consists of 99 countries with specific characteristics, such as culture and geography, that do not vary over time.
- To alleviate problems stemming from a violation of traditional model assumptions. The main advantage of GMM estimation is that the model need not be homoscedastic and serially independent [28]. Another advantage is that it finds the parameter estimates by maximizing an objective function that subsumes the moment restriction such that the correlation between lagged regressor and the error term is zero.
3.4. Specification Tests
4. Empirical Results and Discussion
4.1. Result Pertaining to the Key Variable of Interest
4.2. Results Pertaining to the Control Variables
4.3. Limitations of the Study and the Role of Influential Observations
5. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Albania | Algeria | Argentina | Armenia | Azerbaijan | Bangladesh |
Belarus | Belgium | Belize | Benin | Brunei Darussalam | Bulgaria |
Burkina Faso | Burundi | Cabo Verde | Cambodia | Cameroon | Canada |
Chad | Chile | China | Colombia | Croatia | Cyprus |
Czech Republic | Denmark | Egypt, Arab Rep. | El Salvador | Estonia | Ethiopia |
Finland | France | Georgia | Ghana | Greece | Honduras |
Hong Kong SAR, China | Hungary | Iceland | India | Indonesia | Iran, Islamic Rep. |
Ireland | Israel | Italy | Jordan | Korea, Rep. | Kyrgyz Republic |
Lao PDR | Latvia | Lithuania | Luxembourg | Macao SAR, China | Madagascar |
Malaysia | Malta | Mauritania | Mauritius | Mexico | Moldova |
Mongolia | Morocco | Mozambique | Nepal | Netherlands | Niger |
North Macedonia | Norway | Oman | Pakistan | Panama | Philippines |
Poland | Portugal | Qatar | Romania | Russian Federation | Rwanda |
Saudi Arabia | Senegal | Serbia | Slovak Republic | Slovenia | Spain |
St. Lucia | Sudan | Sweden | Switzerland | Tajikistan | Tanzania |
Thailand | Tunisia | Uganda | Ukraine | United Kingdom | United States |
Uzbekistan | Vietnam | West Bank and Gaza |
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Full Sample | |||||
Variable | Mean | SD | Min | Maximum | Count |
FPLF | 41.13 | 9.61 | 11.66 | 56.03 | 1782 |
FDI inflows as % of GDP | 6.82 | 22.21 | −58.32 | 451.64 | 1776 |
Female tertiary education | 44.36 | 31.47 | 0.17 | 142.88 | 1782 |
Fixed broadband subscription | 10.56 | 12.43 | 0 | 46.32 | 1701 |
GDP per capita | 15,971.48 | 21,160.56 | 194.87 | 111,968.35 | 1782 |
Female population | 50.29 | 2.98 | 23.29 | 54.56 | 1782 |
Fertility rate | 2.597 | 1.47 | 0.86 | 7.67 | 1782 |
Low-and Middle-Income Countries | |||||
Variable | Mean | SD | Min | Maximum | Count |
FPLF | 40.1 | 9.99 | 13.46 | 56.03 | 1098 |
FDI inflows as % of GDP | 4.42 | 5.54 | −37.15 | 55.08 | 1092 |
Female tertiary education | 28.93 | 25.04 | 0.17 | 112.8 | 1098 |
Fixed broadband subscription | 3.85 | 5.96 | 0 | 33.87 | 1017 |
GDP per Capita | 3267.15 | 2860.2 | 194.87 | 12,120.08 | 1098 |
Female population | 50.5 | 1.22 | 47.97 | 54.56 | 1098 |
Fertility rate | 3.15 | 1.59 | 1.08 | 7.67 | 1098 |
High-Income Countries | |||||
Variable | Mean | SD | Min | Maximum | Count |
FPLF | 42.78 | 8.73 | 11.66 | 50.63 | 684 |
FDI inflows as % of GDP | 10.66 | 34.76 | −58.32 | 451.64 | 684 |
Female tertiary education | 69.13 | 24.05 | 10.01 | 142.88 | 684 |
Fixed broadband subscription | 20.53 | 12.84 | 0.01 | 46.32 | 684 |
GDP per capita | 36,365.28 | 21,874.29 | 7441.35 | 111,968.4 | 684 |
Female population | 49.96 | 4.54 | 23.29 | 54.21 | 684 |
Fertility rate | 1.69 | 0.49 | 0.86 | 3.83 | 684 |
FPLF | FDI | GDP per Capita | Fixed Broadband | Female Education | Female Population | Fertility Rate | |
---|---|---|---|---|---|---|---|
FPLF | 1 | ||||||
FDI | 0.02 | 1 | |||||
GDP per capita | 0.12 *** | 0.09 *** | 1 | ||||
Fixed broadband | 0.29 *** | 0.11 *** | 0.68 *** | 1 | |||
Female education | 0.21 *** | 0.01 | 0.49 *** | 0.69 *** | 1 | ||
Female population | 0.58 *** | 0.03 | −0.18 *** | 0.12 *** | 0.19 *** | 1 | |
Fertility rate | −0.09 | −0.09 *** | −0.41 *** | −0.49 *** | −0.67 *** | −0.11 *** | 1 |
Explanatory Variables | Full Sample | Low and Middle Income | High Income |
Lagged dependent variable | |||
Lagged FPLF | 0.955 *** | 0.959 *** | 0.952 *** |
(0.015) | (0.014) | (0.016) | |
Key variable of interest | |||
FDI inflows as % of GDP | 0.001 *** | 0.007 * | 0.001 *** |
(0.0004) | (0.004) | (0.0004) | |
Control Variables | |||
GDP per Capita | 0.00001 ** | 0.00002 | 0.00001 * |
(0.00000) | (0.00002) | (0.00000) | |
Fixed broadband subscription | 0.003 | 0.005 | 0.003 |
(0.003) | (0.006) | (0.004) | |
Female tertiary education | −0.003 * | −0.005 ** | 0.001 |
(0.002) | (0.003) | (0.002) | |
Female population | 0.137 ** | 0.160 * | 0.091 *** |
(0.060) | (0.094) | (0.026) | |
Fertility rate | −0.007 | 0.014 | −0.164 |
(0.037) | (0.034) | (0.129) | |
Sargan’s test | 7.11 | 4.87 | 5.05 |
(p = 0.53) | (p = 0.77) | (p = 0.75) | |
AR(1) | −5.11 | −3.87 | −3.39 |
(p = 0.00) | (p = 0.00) | (p = 0.00) | |
AR(2) | −0.54 | −0.25 | −0.61 |
(p = 0.59) | (p = 0.80) | (p = 0.54) | |
Observations (N) | 1782 | 1098 | 684 |
No. of Countries | 99 | 61 | 38 |
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Hossain, A.; Ghimire, S.; Valeva, A.; Harriger-Lin, J. Does Globalization Encourage Female Employment? A Cross-Country Panel Study. World 2022, 3, 206-218. https://doi.org/10.3390/world3020011
Hossain A, Ghimire S, Valeva A, Harriger-Lin J. Does Globalization Encourage Female Employment? A Cross-Country Panel Study. World. 2022; 3(2):206-218. https://doi.org/10.3390/world3020011
Chicago/Turabian StyleHossain, Asrifa, Shankar Ghimire, Anna Valeva, and Jessica Harriger-Lin. 2022. "Does Globalization Encourage Female Employment? A Cross-Country Panel Study" World 3, no. 2: 206-218. https://doi.org/10.3390/world3020011