- Email: [email protected]
Does women’s participation in politics increase female labor participation? Evidence from panel data analysis
Economics Letters 170 (2018) 35–38
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Does women’s participation in politics increase female labor participation? Evidence from panel data analysis Zhike Lv a, *, Rudai Yang b a b
School of Business, Xiangtan University, Xiangtan 411105, China School of Economics, Peking University, Beijing 100871, China
highlights • • • • •
We examine the effects of women’s political participation (WPP) on FLPR. We measures the women’s political participation using the V-Dem WPE index. Both parametric and semi-parametric panel model are applied. WPP has a positive and statistically significant effect on FLPR. Our results also support the feminization U hypothesis.
article
info
Article history: Received 29 November 2017 Received in revised form 27 March 2018 Accepted 11 May 2018 Available online 22 May 2018
a b s t r a c t Using country panel data from 1991 to 2012, we make an attempt to explore whether women’s participation in politics affect female labor participation rates (FLPR). Our analysis suggests that countries characterized by more female’s participation in politics are associated with higher levels of FLPR. Moreover, we also find a U-shaped link between economic development and FLPR. © 2018 Elsevier B.V. All rights reserved.
JEL classification: C5 J7 O1 Keywords: Female labor participation rate Women’s political participation Economic development
1. Introduction Nowadays, there is a large amount of literature concerning the determinants of female labor participation rates (Bussmann, 2009; Mishra and Smyth, 2010; He and Zhu, 2016), with lots of empirical studies exploring the relationship between GDP and FLPR. In a pioneering work, Sinha (1967) pointed out that the relationship between economic development and FLPR is a U-shaped. Almost simultaneously Boserup (1970) also came up with the same result. If correct, this idea can be helpful for policymakers to anticipate the levels of FLPR for future years based on countries’ predicted GDP. Accordingly, substantial empirical studies have examined the relationship between GDP and FLPR in different regions or countries around the world. Typical examples include Pampel and
* Corresponding author.
E-mail addresses: [email protected] (Z. Lv), [email protected] (R. Yang).
https://doi.org/10.1016/j.econlet.2018.05.013 0165-1765/© 2018 Elsevier B.V. All rights reserved.
Tanaka (1986), Tam (2011), Tsani et al. (2013) and Lahoti and Swaminathan (2016). Surprisingly, however, there are no quantitative studies examine the role of women’s political participation in influencing FLPR in the extant literature. Participation has long been considered by researchers as a key element that allows the poor, vulnerable and marginalized groups to exert an influence over institutions and decisions that critically affect their lives (Mayoux, 1995), and women’s political participation is increasingly considered as crucial to development and progress. Earlier studies have shown that having a significant number of women in positions of power will result in policy output that is more attuned to the interests of human social concern. For instance, Swamy et al. (2001) found that countries with a higher level of women’s political participation enjoy less corruption. Cross-national analyses by Ergas and York (2012) showed that air pollutions are lower in nations where women have higher political status. While York and Bell (2014) provided evidence on the
36
Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
panel data model. However, estimating a regression model in which the dependent variable is a proportion (or fraction) bounded by 0 and 1 requires econometric methods that take account of the bounded nature of the response. Considering our dependent variable (FLPR) fall between 0.12 and 0.92 (see Table 1), one way to handle this for response variables’ values strictly within the unit interval is the logit transformation (Baum, 2008; Raymond et al., 2015). More specifically, we obtain the transformed dependent variable by ∗
FLPR = log
(
FLPR 1 − FLPR
)
,
(1)
and thus the base-line econometric model has the following form FLPR∗it = α + β WPPit + γ CVit + µi + εit ,
Fig. 1. Partial fits of the relationship between economic development and FLPR*. Notes: The blue curve represents the semi-parametric estimation of m(·). Shaded areas correspond to 95% confidence intervals.
(2)
where CVit is a set of control variables, µi means the country-fixed effect which is able to capture general time-invariant countryspecific characteristics, such as religious, culture, or history. εit represents the error term with the usual properties. 3. Results
importance of women’s political status in influencing people’s life satisfaction. Furthermore, a substantial body of research suggests that women are more concerned about women’s issues like health (Kawachi et al., 1999; Wallace et al., 2017). Theoretically, we argue that women’s participation in politics will increase FLPR. Because when women are politically empowered to exert their voice and influence, they may tend to take some measures or regulations, such as anti-sex discrimination in employment field, to guarantee the rights of women. This may contribute to an increase in FLPR. Therefore, in this paper, we make an attempt to explore whether women’s participation in politics increase female labor participation rates. 2. Data and estimation strategy We use a sample of 99 countries with data covering the years from 1991 and 2012. The choice of sample selected for this analysis is primarily dictated by the availability of reliable data. The dependent variable in this analysis is FLPR, which is obtained from the International Labor Office (ILO). FLPR is defined as the number of female labor participants of age 15–64 divided by the total female population of the same age group (15–64), and labor force participation is defined as employed (paid and unpaid family workers) plus unemployed (actively seeking work). As our measure of women’s political status, we use the VDem women’s political participation (WPP) index developed by Sundström et al. (2017). This new index combines the legislative presence of women and political power distribution by gender, and it allows more precise measurement and is superior in temporal scope and coverage of countries. As a robustness check, we also use the proportion of seats held by women in national parliaments as the independent variable. In addition, we also gathered data for the following variables that have been identified by the literature as having a role in determining FLPR: GDP per capita (logged), fertility, urbanization, unemployment rates and education,1 and the data of all these control variables are obtained from World Development Indicators. Table 1 presents the descriptive statistics of the variables used in this study. In this paper, consistent with previous studies (Tsani et al., 2013; Lahoti and Swaminathan, 2016), we apply the fixed effect 1 Here we use the data of school enrollment, primary, female (% net) to measure education.
The main regression results are reported in Table 2. Column 1 shows the results from simple static fixed-effects specifications. WPP has a positive and statistically significant effect on FLPR at the 1% level. The control variables display the expected signs and are statistically significant in several cases. Both the coefficients of linear and quadratic term of economic development are statistically significant at the 1% level and have the expected signs, which verify the curvilinear relationship between the level of development and FLPR. As expected, high fertility rates reduce FLPR, and high level of urbanization increase FLPR, whereas variables unemployment and education are statistically not significant. Next, we explore whether our main finding is driven by the choice of WPP measures, so we use the proportion of seats held by women in national parliaments for robustness check.2 The sign and significance of the estimated coefficients for WPP remains virtually unchanged (column 2. Additionally, to avoid functional form misspecification of GDP in Eq. (1), we apply a more flexible semi-parametric panel data regression approach (Baltagi and Li, 2002) to explore their relationship.3 The results of the linear part of the model are presented in column (3). Although the magnitude of β is relatively smaller than the results obtained from parametric model, the sign and statistically significant of WPP is unchanged, and as expected, the U-shaped relationship between WPP and FLPR is also confirmed (see Fig. 1). Finally, an increasing number of researchers adopt the dynamic panel data model to investigate the determinants of FLPR (e.g., Tam, 2011; Gaddis and Klasen, 2014), as they argue that if FLPR is persistent or/and there exist the reverse causality between FLPR and WPP, then the results obtained from the static model could be biased. To take into account the dynamic effects and the endogeneity issue, we further apply the dynamic panel data to study the impact of WPP on FLPR. Specifically, we adopt the system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998), and we deal with potential reverse causality and omitted variable bias by treating WPP and fertility as endogenous 2 The data is obtained from World Development Indicators. 3 The semi-parametric model is given by: FLPR∗it = α + m (ln GDPit ) + β WPPit + γ CVit + µi + εit where the functional form m(·) is unspecified. For more detail about the estimation procedure, please see Baltagi and Li (2002). In our empirical analysis, we use a Bspline regression approach (Desbordes and Verardi, 2012).
Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
37
Table 1 Summary statistics. Variable
Obs.
Mean
Std. Dev.
Min
Max
FLPR WPP GDP per capita (logged) Fertility Urbanization Unemployment rates Education
2,178 2,178 2,178 2,178 2,178 2,178 2,178
0.574 0.670 8.856 3.241 52.834 8.596 96.629
0.162 0.235 1.241 1.740 23.952 5.906 21.355
0.116 0.078 5.870 1.090 5.491 0.300 17.825
0.915 0.999 11.083 7.761 97.732 39.300 151.308
Notes: All the data are annually over 1991–2012. Table 2 Main results. Variable FLPR*(lagged) WPP WNP GDP per capita GDP per capita Sq. Fertility Urbanization Unemployment rates Education Constant Number of cross-sections Number of instruments AR(1) test p-value AR(2) test p-value Hansen J test p-value
(1)
(2)
0.1682*** (4.59)
−1.8387*** (−13.48) 0.1112*** (14.25) −0.0261** (−2.42) 0.0054*** (3.86) −0.0017(−1.15) 0.0003(0.81) 7.3957*** (12.25) 99
0.3594*** (4.50) −2.3232*** (−10.36) 0.1379*** (10.95) −0.0521*** (−3.06) 0.0147*** (7.89) 0.0032** (1.99) −0.0002(−0.39) 9.0237*** (8.79) 60
(3)
(4)
0.0357* (1.77)
0.9243*** (32.74) 0.0747** (2.30)
−0.0113(−0.59) 0.0124** (2.83)
−0.0003(−0.37) −0.0000(−0.11) 99
−0.2995* (−1.69) 0.0171* (1.79) 0.0095(0.77) 0.0000(0.15) −0.0017* (−1.83) 0.0005(1.58) 1.2060(1.42) 99 90 0.000 0.483 0.460
Notes: Here WPP means the V-Dem women’s political participation index developed by Sundström et al. (2017), while WNP represents the proportion of seats held by women in national parliaments. Figures in parentheses are t-values. Instruments are collapsed as suggested by Roodman (2009). Hansen test is a test for overidentifying restrictions, the null hypothesis is that the instruments are valid. AB test (1), (2) are Arellano–Bond test for AR(1), AR(2) in first differences, respectively, the null hypothesis for (1) is that the first-differenced regression errors show no first-order serial correlation, the null hypothesis for (2) is that the first-differenced regression errors show no second-order serial correlation. * Significant at 10%. **
Significant at 5%.
***
Significant at 1%.
in our estimation method.4 Column (4) presents the results from the system GMM estimator. Note first that the specification tests show that the models are well-specified, the null hypothesis of the Hansen J test of the joint validity of the instruments cannot be rejected. The null hypothesis of no first-order serial correlation is statistically rejected at the 1% significance level, which further substantiates the necessity of using the system GMM model. The null hypothesis of no second-order autocorrelation cannot be rejected at conventional significance levels.5 The lagged dependent variable is positive and highly significant. It shows that FLPR in a certain year is strongly affected by its previous value. Although its coefficient is quite high, indicating considerable persistence, it is statistically different from unity.6 Meanwhile, WPP presents a significant positive impact on FLPR, which further provides evidence that countries characterized by more female’s participation in politics are associated with higher levels of FLPR. Moreover, the magnitude of the long-run estimated coefficient is much greater than the short-run one,7 suggesting that the impact of WPP on FLPR would usually also take some time to materialize fully. Again,
4 We sincerely thank an anonymous referee for pointing out this issue. 5 Second-order autocorrelation must be absent in order for the estimators to be consistent, while first-order autocorrelation exists by design. 6 We sincerely thank the editor for pointing out this issue. The results of panel unit root tests show that the null hypothesis of unit-root can be rejected, and the results are available upon request. 7 The coefficient of the lagged dependent variable is 0.9243, implying a long-run effect that is 13 times the short-run effect.
a U-shaped relationship between FLPR and economic development is further confirmed, which is consistent with Tam (2011) study. 4. Conclusions and policy implications This paper sets out to address the gap in the literature by seeking to answer the research question: whether women’s participation in politics affects female labor participation rates? Our study offers strong evidence that female political representation benefits FLPR. After controlling for a variety of other factors, our results show that WPP has a positive impact on FLPR. From a policy point of view, our estimation results suggest that improving women’s political status worldwide can promote female employment opportunities. Acknowledgments This paper benefited immensely from the contributions of an anonymous referee and the Editor Prof. Badi H. Baltagi of Economic Letters. They made some pertinent comments on the previous version of this article, and also gave us some valuable suggestions and hints. In addition, the author Zhike Lv also thanks to his boy (Yicheng Lv), who make his life meaningful and make him much happier, and wish he grow up healthily and happily. This research is partially supported by National Natural Science Foundation of China (No. 71703140), and also supported by Natural Science Foundation of Hunan Province of China (No. 2017JJ3293). Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.
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Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. J. Econ. 68 (1), 29–51. Baltagi, B.H., Li, D., 2002. Series estimation of partially linear panel data models with fixed effects. Ann. Econ. Fin. 3 (1), 103–116. Baum, C.F., 2008. Stata tip 63: Modeling proportions. Stata J. 8 (2), 299–303. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87 (1), 115–143. Boserup, E., 1970. Women’S Role in Economic Development. St. Martin’s Press, New York. Bussmann, M., 2009. The effect of trade openness on women’s welfare and work life. World Dev. 37 (6), 1027–1038. Desbordes, R., Verardi, V., 2012. Refitting the Kuznets curve. Econom. Lett. 116 (2), 258–261. Ergas, C., York, R., 2012. Women’s status and carbon dioxide emissions: A quantitative cross-national analysis. Soc. Sci. Res. 41 (4), 965–976. Gaddis, I., Klasen, S., 2014. Economic development, structural change, and women’s labor force participation. J. Pop. Econ. 27 (3), 639–681. He, X., Zhu, R., 2016. Fertility and female labour force participation: Causal evidence from urban China. The Man. Sch. 84 (5), 664–674. Kawachi, I., Kennedy, B.P., Gupta, V., Prothrow-Stith, D., 1999. Women’s status and the health of women and men: a view from the states. Soc. Sci. Med. 48 (1), 21–32. Lahoti, R., Swaminathan, H., 2016. Economic development and women’s labor force participation in India. Femi. Econ. 22 (2), 168–195. Mayoux, L., 1995. Beyond naivety: women, gender inequality and participatory development. Dev. Cha. 26 (2), 235–258.
Mishra, V., Smyth, R., 2010. Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing. J. Econ. Bus. 62 (1), 48–64. Pampel, F.C., Tanaka, K., 1986. Economic development and female labor force participation: A reconsideration. Soc. Forces 64 (3), 599–619. Raymond, W., Mairesse, J., Mohnen, P., Palm, F., 2015. Dynamic models of R & D, innovation and productivity: Panel data evidence for Dutch and French manufacturing. Eur. Econ. Rev. 78, 285–306. Roodman, D., 2009. How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J. 9, 86–136. Sinha, J.N., 1967. Dynamics of female participation in economic activity in a developing economy. In: United Nations Department of Economic and Social Affairs. Proceedings of the World Population Conference, Belgrade 1965, vol. IV. UN Publications, New York, pp. 336–337. Sundström, A., Paxton, P., Wang, Y.T., Lindberg, S.I., 2017. Women’s political empowerment: A new global index, 1900–2012. World Dev. 94, 321–335. Swamy, A., Knack, S., Lee, Y., Azfar, O., 2001. Gender and corruption. J. Dev. Econ. 64 (1), 25–55. Tam, H., 2011. U-shaped female labor participation with economic development: Some panel data evidence. Econom. Lett. 110 (2), 140–142. Tsani, S., Paroussos, L., Fragiadakis, C., Charalambidis, I., Capros, P., 2013. Female labour force participation and economic growth in the South Mediterranean countries. Econom. Lett. 120 (2), 323–328. Wallace, M.E., Evans, M.G., Theall, K., 2017. The status of women’s reproductive rights and adverse birth outcomes. Wom. Health Iss. 27 (2), 121–128. York, R., Bell, S.E., 2014. Life satisfaction across nations: The effects of women’s political status and public priorities. Soc. Sci. Res. 48, 48–61.
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Does women’s participation in politics increase female labor participation? Evidence from panel data analysis Zhike Lv a, *, Rudai Yang b a b
School of Business, Xiangtan University, Xiangtan 411105, China School of Economics, Peking University, Beijing 100871, China
highlights • • • • •
We examine the effects of women’s political participation (WPP) on FLPR. We measures the women’s political participation using the V-Dem WPE index. Both parametric and semi-parametric panel model are applied. WPP has a positive and statistically significant effect on FLPR. Our results also support the feminization U hypothesis.
article
info
Article history: Received 29 November 2017 Received in revised form 27 March 2018 Accepted 11 May 2018 Available online 22 May 2018
a b s t r a c t Using country panel data from 1991 to 2012, we make an attempt to explore whether women’s participation in politics affect female labor participation rates (FLPR). Our analysis suggests that countries characterized by more female’s participation in politics are associated with higher levels of FLPR. Moreover, we also find a U-shaped link between economic development and FLPR. © 2018 Elsevier B.V. All rights reserved.
JEL classification: C5 J7 O1 Keywords: Female labor participation rate Women’s political participation Economic development
1. Introduction Nowadays, there is a large amount of literature concerning the determinants of female labor participation rates (Bussmann, 2009; Mishra and Smyth, 2010; He and Zhu, 2016), with lots of empirical studies exploring the relationship between GDP and FLPR. In a pioneering work, Sinha (1967) pointed out that the relationship between economic development and FLPR is a U-shaped. Almost simultaneously Boserup (1970) also came up with the same result. If correct, this idea can be helpful for policymakers to anticipate the levels of FLPR for future years based on countries’ predicted GDP. Accordingly, substantial empirical studies have examined the relationship between GDP and FLPR in different regions or countries around the world. Typical examples include Pampel and
* Corresponding author.
E-mail addresses: [email protected] (Z. Lv), [email protected] (R. Yang).
https://doi.org/10.1016/j.econlet.2018.05.013 0165-1765/© 2018 Elsevier B.V. All rights reserved.
Tanaka (1986), Tam (2011), Tsani et al. (2013) and Lahoti and Swaminathan (2016). Surprisingly, however, there are no quantitative studies examine the role of women’s political participation in influencing FLPR in the extant literature. Participation has long been considered by researchers as a key element that allows the poor, vulnerable and marginalized groups to exert an influence over institutions and decisions that critically affect their lives (Mayoux, 1995), and women’s political participation is increasingly considered as crucial to development and progress. Earlier studies have shown that having a significant number of women in positions of power will result in policy output that is more attuned to the interests of human social concern. For instance, Swamy et al. (2001) found that countries with a higher level of women’s political participation enjoy less corruption. Cross-national analyses by Ergas and York (2012) showed that air pollutions are lower in nations where women have higher political status. While York and Bell (2014) provided evidence on the
36
Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
panel data model. However, estimating a regression model in which the dependent variable is a proportion (or fraction) bounded by 0 and 1 requires econometric methods that take account of the bounded nature of the response. Considering our dependent variable (FLPR) fall between 0.12 and 0.92 (see Table 1), one way to handle this for response variables’ values strictly within the unit interval is the logit transformation (Baum, 2008; Raymond et al., 2015). More specifically, we obtain the transformed dependent variable by ∗
FLPR = log
(
FLPR 1 − FLPR
)
,
(1)
and thus the base-line econometric model has the following form FLPR∗it = α + β WPPit + γ CVit + µi + εit ,
Fig. 1. Partial fits of the relationship between economic development and FLPR*. Notes: The blue curve represents the semi-parametric estimation of m(·). Shaded areas correspond to 95% confidence intervals.
(2)
where CVit is a set of control variables, µi means the country-fixed effect which is able to capture general time-invariant countryspecific characteristics, such as religious, culture, or history. εit represents the error term with the usual properties. 3. Results
importance of women’s political status in influencing people’s life satisfaction. Furthermore, a substantial body of research suggests that women are more concerned about women’s issues like health (Kawachi et al., 1999; Wallace et al., 2017). Theoretically, we argue that women’s participation in politics will increase FLPR. Because when women are politically empowered to exert their voice and influence, they may tend to take some measures or regulations, such as anti-sex discrimination in employment field, to guarantee the rights of women. This may contribute to an increase in FLPR. Therefore, in this paper, we make an attempt to explore whether women’s participation in politics increase female labor participation rates. 2. Data and estimation strategy We use a sample of 99 countries with data covering the years from 1991 and 2012. The choice of sample selected for this analysis is primarily dictated by the availability of reliable data. The dependent variable in this analysis is FLPR, which is obtained from the International Labor Office (ILO). FLPR is defined as the number of female labor participants of age 15–64 divided by the total female population of the same age group (15–64), and labor force participation is defined as employed (paid and unpaid family workers) plus unemployed (actively seeking work). As our measure of women’s political status, we use the VDem women’s political participation (WPP) index developed by Sundström et al. (2017). This new index combines the legislative presence of women and political power distribution by gender, and it allows more precise measurement and is superior in temporal scope and coverage of countries. As a robustness check, we also use the proportion of seats held by women in national parliaments as the independent variable. In addition, we also gathered data for the following variables that have been identified by the literature as having a role in determining FLPR: GDP per capita (logged), fertility, urbanization, unemployment rates and education,1 and the data of all these control variables are obtained from World Development Indicators. Table 1 presents the descriptive statistics of the variables used in this study. In this paper, consistent with previous studies (Tsani et al., 2013; Lahoti and Swaminathan, 2016), we apply the fixed effect 1 Here we use the data of school enrollment, primary, female (% net) to measure education.
The main regression results are reported in Table 2. Column 1 shows the results from simple static fixed-effects specifications. WPP has a positive and statistically significant effect on FLPR at the 1% level. The control variables display the expected signs and are statistically significant in several cases. Both the coefficients of linear and quadratic term of economic development are statistically significant at the 1% level and have the expected signs, which verify the curvilinear relationship between the level of development and FLPR. As expected, high fertility rates reduce FLPR, and high level of urbanization increase FLPR, whereas variables unemployment and education are statistically not significant. Next, we explore whether our main finding is driven by the choice of WPP measures, so we use the proportion of seats held by women in national parliaments for robustness check.2 The sign and significance of the estimated coefficients for WPP remains virtually unchanged (column 2. Additionally, to avoid functional form misspecification of GDP in Eq. (1), we apply a more flexible semi-parametric panel data regression approach (Baltagi and Li, 2002) to explore their relationship.3 The results of the linear part of the model are presented in column (3). Although the magnitude of β is relatively smaller than the results obtained from parametric model, the sign and statistically significant of WPP is unchanged, and as expected, the U-shaped relationship between WPP and FLPR is also confirmed (see Fig. 1). Finally, an increasing number of researchers adopt the dynamic panel data model to investigate the determinants of FLPR (e.g., Tam, 2011; Gaddis and Klasen, 2014), as they argue that if FLPR is persistent or/and there exist the reverse causality between FLPR and WPP, then the results obtained from the static model could be biased. To take into account the dynamic effects and the endogeneity issue, we further apply the dynamic panel data to study the impact of WPP on FLPR. Specifically, we adopt the system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998), and we deal with potential reverse causality and omitted variable bias by treating WPP and fertility as endogenous 2 The data is obtained from World Development Indicators. 3 The semi-parametric model is given by: FLPR∗it = α + m (ln GDPit ) + β WPPit + γ CVit + µi + εit where the functional form m(·) is unspecified. For more detail about the estimation procedure, please see Baltagi and Li (2002). In our empirical analysis, we use a Bspline regression approach (Desbordes and Verardi, 2012).
Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
37
Table 1 Summary statistics. Variable
Obs.
Mean
Std. Dev.
Min
Max
FLPR WPP GDP per capita (logged) Fertility Urbanization Unemployment rates Education
2,178 2,178 2,178 2,178 2,178 2,178 2,178
0.574 0.670 8.856 3.241 52.834 8.596 96.629
0.162 0.235 1.241 1.740 23.952 5.906 21.355
0.116 0.078 5.870 1.090 5.491 0.300 17.825
0.915 0.999 11.083 7.761 97.732 39.300 151.308
Notes: All the data are annually over 1991–2012. Table 2 Main results. Variable FLPR*(lagged) WPP WNP GDP per capita GDP per capita Sq. Fertility Urbanization Unemployment rates Education Constant Number of cross-sections Number of instruments AR(1) test p-value AR(2) test p-value Hansen J test p-value
(1)
(2)
0.1682*** (4.59)
−1.8387*** (−13.48) 0.1112*** (14.25) −0.0261** (−2.42) 0.0054*** (3.86) −0.0017(−1.15) 0.0003(0.81) 7.3957*** (12.25) 99
0.3594*** (4.50) −2.3232*** (−10.36) 0.1379*** (10.95) −0.0521*** (−3.06) 0.0147*** (7.89) 0.0032** (1.99) −0.0002(−0.39) 9.0237*** (8.79) 60
(3)
(4)
0.0357* (1.77)
0.9243*** (32.74) 0.0747** (2.30)
−0.0113(−0.59) 0.0124** (2.83)
−0.0003(−0.37) −0.0000(−0.11) 99
−0.2995* (−1.69) 0.0171* (1.79) 0.0095(0.77) 0.0000(0.15) −0.0017* (−1.83) 0.0005(1.58) 1.2060(1.42) 99 90 0.000 0.483 0.460
Notes: Here WPP means the V-Dem women’s political participation index developed by Sundström et al. (2017), while WNP represents the proportion of seats held by women in national parliaments. Figures in parentheses are t-values. Instruments are collapsed as suggested by Roodman (2009). Hansen test is a test for overidentifying restrictions, the null hypothesis is that the instruments are valid. AB test (1), (2) are Arellano–Bond test for AR(1), AR(2) in first differences, respectively, the null hypothesis for (1) is that the first-differenced regression errors show no first-order serial correlation, the null hypothesis for (2) is that the first-differenced regression errors show no second-order serial correlation. * Significant at 10%. **
Significant at 5%.
***
Significant at 1%.
in our estimation method.4 Column (4) presents the results from the system GMM estimator. Note first that the specification tests show that the models are well-specified, the null hypothesis of the Hansen J test of the joint validity of the instruments cannot be rejected. The null hypothesis of no first-order serial correlation is statistically rejected at the 1% significance level, which further substantiates the necessity of using the system GMM model. The null hypothesis of no second-order autocorrelation cannot be rejected at conventional significance levels.5 The lagged dependent variable is positive and highly significant. It shows that FLPR in a certain year is strongly affected by its previous value. Although its coefficient is quite high, indicating considerable persistence, it is statistically different from unity.6 Meanwhile, WPP presents a significant positive impact on FLPR, which further provides evidence that countries characterized by more female’s participation in politics are associated with higher levels of FLPR. Moreover, the magnitude of the long-run estimated coefficient is much greater than the short-run one,7 suggesting that the impact of WPP on FLPR would usually also take some time to materialize fully. Again,
4 We sincerely thank an anonymous referee for pointing out this issue. 5 Second-order autocorrelation must be absent in order for the estimators to be consistent, while first-order autocorrelation exists by design. 6 We sincerely thank the editor for pointing out this issue. The results of panel unit root tests show that the null hypothesis of unit-root can be rejected, and the results are available upon request. 7 The coefficient of the lagged dependent variable is 0.9243, implying a long-run effect that is 13 times the short-run effect.
a U-shaped relationship between FLPR and economic development is further confirmed, which is consistent with Tam (2011) study. 4. Conclusions and policy implications This paper sets out to address the gap in the literature by seeking to answer the research question: whether women’s participation in politics affects female labor participation rates? Our study offers strong evidence that female political representation benefits FLPR. After controlling for a variety of other factors, our results show that WPP has a positive impact on FLPR. From a policy point of view, our estimation results suggest that improving women’s political status worldwide can promote female employment opportunities. Acknowledgments This paper benefited immensely from the contributions of an anonymous referee and the Editor Prof. Badi H. Baltagi of Economic Letters. They made some pertinent comments on the previous version of this article, and also gave us some valuable suggestions and hints. In addition, the author Zhike Lv also thanks to his boy (Yicheng Lv), who make his life meaningful and make him much happier, and wish he grow up healthily and happily. This research is partially supported by National Natural Science Foundation of China (No. 71703140), and also supported by Natural Science Foundation of Hunan Province of China (No. 2017JJ3293). Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.
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Z. Lv, R. Yang / Economics Letters 170 (2018) 35–38
References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. J. Econ. 68 (1), 29–51. Baltagi, B.H., Li, D., 2002. Series estimation of partially linear panel data models with fixed effects. Ann. Econ. Fin. 3 (1), 103–116. Baum, C.F., 2008. Stata tip 63: Modeling proportions. Stata J. 8 (2), 299–303. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87 (1), 115–143. Boserup, E., 1970. Women’S Role in Economic Development. St. Martin’s Press, New York. Bussmann, M., 2009. The effect of trade openness on women’s welfare and work life. World Dev. 37 (6), 1027–1038. Desbordes, R., Verardi, V., 2012. Refitting the Kuznets curve. Econom. Lett. 116 (2), 258–261. Ergas, C., York, R., 2012. Women’s status and carbon dioxide emissions: A quantitative cross-national analysis. Soc. Sci. Res. 41 (4), 965–976. Gaddis, I., Klasen, S., 2014. Economic development, structural change, and women’s labor force participation. J. Pop. Econ. 27 (3), 639–681. He, X., Zhu, R., 2016. Fertility and female labour force participation: Causal evidence from urban China. The Man. Sch. 84 (5), 664–674. Kawachi, I., Kennedy, B.P., Gupta, V., Prothrow-Stith, D., 1999. Women’s status and the health of women and men: a view from the states. Soc. Sci. Med. 48 (1), 21–32. Lahoti, R., Swaminathan, H., 2016. Economic development and women’s labor force participation in India. Femi. Econ. 22 (2), 168–195. Mayoux, L., 1995. Beyond naivety: women, gender inequality and participatory development. Dev. Cha. 26 (2), 235–258.
Mishra, V., Smyth, R., 2010. Female labor force participation and total fertility rates in the OECD: New evidence from panel cointegration and Granger causality testing. J. Econ. Bus. 62 (1), 48–64. Pampel, F.C., Tanaka, K., 1986. Economic development and female labor force participation: A reconsideration. Soc. Forces 64 (3), 599–619. Raymond, W., Mairesse, J., Mohnen, P., Palm, F., 2015. Dynamic models of R & D, innovation and productivity: Panel data evidence for Dutch and French manufacturing. Eur. Econ. Rev. 78, 285–306. Roodman, D., 2009. How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J. 9, 86–136. Sinha, J.N., 1967. Dynamics of female participation in economic activity in a developing economy. In: United Nations Department of Economic and Social Affairs. Proceedings of the World Population Conference, Belgrade 1965, vol. IV. UN Publications, New York, pp. 336–337. Sundström, A., Paxton, P., Wang, Y.T., Lindberg, S.I., 2017. Women’s political empowerment: A new global index, 1900–2012. World Dev. 94, 321–335. Swamy, A., Knack, S., Lee, Y., Azfar, O., 2001. Gender and corruption. J. Dev. Econ. 64 (1), 25–55. Tam, H., 2011. U-shaped female labor participation with economic development: Some panel data evidence. Econom. Lett. 110 (2), 140–142. Tsani, S., Paroussos, L., Fragiadakis, C., Charalambidis, I., Capros, P., 2013. Female labour force participation and economic growth in the South Mediterranean countries. Econom. Lett. 120 (2), 323–328. Wallace, M.E., Evans, M.G., Theall, K., 2017. The status of women’s reproductive rights and adverse birth outcomes. Wom. Health Iss. 27 (2), 121–128. York, R., Bell, S.E., 2014. Life satisfaction across nations: The effects of women’s political status and public priorities. Soc. Sci. Res. 48, 48–61.