The Effect of Global Gender Inequality and Female Employment on The Economic Growth: Panel ARDL

International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 6, issue. 1, 2025, pp. 1-18 https://doi.org/10.47259/ijrebs.2025.611 © Kiliç 1 The Effect of Global Gender Inequality and Female Employment on The Economic Growth: Panel ARDL Jiyan KILIÇ1 1 Department of Foreign Trade, Sindirgi Vocational School, Balikesir University, Turkey Email: [email protected] Citation: Kiliç, J. (2025). The Effect of Global Gender Inequality and Female Employment on the Economic Growth: Panel ARDL. International Journal of Research in Entrepreneurship & Business Studies, 6(1), 118. https://doi.org/10.47259/ijrebs.2025.611 Received on 24th Oct. 2024 Revised on 17th Dec. 2024 Published on 11th Jan. 2025 Copyright: © 2025 by the authors. Licensee: Global Scientific Publications, Oman. Publishers Note: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This is an openaccess journal and the articles published in this journal are distributed under the terms of CC-BY-SA. Abstract Purpose: This study aims to analyse whether global gender inequality and female employment have any short-term and long-term effects on economic growth in Organization for Economic Cooperation Development (OECD) countries and to explore the existence and direction of causality between global gender inequality, women’s employment, and economic growth in OECD countries during the study period. Design/methodology/approach: In the study, the dependent variable is the person Per Capita Growth, and the dependent variables are the Global Gender Gap Index (GGGI) and Female Labour Force Participation Rate (KIKO). The data set of the variables consists of the period 2006-2023. The relationship between the variables was tested with the Pooled Mean Group (PMG) Panel ARDL test. In addition, the Pairwise Dumitrescu Hurlin (PDH) Panel Causality test was used to measure homogeneous or heterogeneous causality between the variables. Findings: According to the results of the PMG Panel ARDL test; while it was determined that only the KIKO data affected Economic Growth in the short term, it was concluded that both independent variables affected the dependent variable in the long term. According to the PDH Panel Causality Test, it was determined that there is a bidirectional heterogeneous causality relationship between gender inequality and economic growth. While women’s employment is the heterogeneous cause of economic growth, economic growth is the homogeneous cause of women’s employment. Finally, there is also a bidirectional homogeneous causality between women’s employment and gender inequality. Research limitations/implications: According to the results of the Pooled Mean Group Panel ARDL test; a 1% increase in the GGGI independent variable causes a decrease of approximately 2.83% in the dependent variable PERGDP in the long term. On the other hand, a 1% increase in the KIKO independent variable causes an increase of approximately 0.05% in PERGDP in long term. the short-term coefficients are examined as a group average, the KIKO independent variable affects PERGDP in the short term, while the GGGI independent variable does not affect PERGDP in the short term. Social Implications: When the analysis results obtained in this report are evaluated, they provide extremely strategic results for policymakers in the decisions they will make. The effect of gender inequality on economic growth allows the evaluation of gender differences in economic, educational, health, and political outcomes between women and men based on existing data. On the other hand, this study can contribute to both the contribution to national income and social and gender equality, considering the contribution that the increase in women’s employment rate will provide to the country’s economy in both the short and long term. Originality / Value: In line with its purpose, this study is thought to fill the gap in the literature in terms of the period it covers, the countries, the variables used and the method chosen. Keywords: Gender Inequality, Women’s Employment, Economic Growth, Panel ARDL, Global Gender Gap Index. Introduction Human capital is one of the most important production factors of a country. It plays a highly strategic role in sustainable economic growth. Providing competitive advantage, optimal use of human resources, and utilizing the International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 5, issue. 4, 2024, pp. 1-14 https://doi.org/10.47259/ijrebs.541 2 Effect of Global Gender Equality on Economic Growth efficiency of human capital will be possible by ensuring gender equality with social responsibility awareness. Gender equality is vital for economic growth and sustainable development. Gender equality is included in both the International Labour Organization’s decent work agenda and the UNDP’s Sustainable Development Goals (SDGs) (ILO, 2024). This goal is specifically dedicated to achieving gender equality and the empowerment of women and girls, which is set as Goal 5 of the SDGs. In addition to economic injustice and inequality, there are also gender-based inequalities. Despite the rapid increase in women’s employment, significant gender differences have continued in the labour market. The employment rates of women have remained lower than men. Women and men are seriously discriminated against by being employed in different occupations in different sectors and different occupational groups. It has also been observed that although women and men are often at the same level in terms of working hours, their annual earnings are at different rates. This situation shows that individuals doing the same job receive different levels of wages. In addition to gender discrimination in paid jobs, gender discrimination against women continues in terms of daily unpaid labour performed at home (Gornick, 1999). The participation of women, who make up almost half of the population, in the workforce will lead to a more optimal use of the country’s economic resources. On the other hand, it is possible to say that the added value that women will create will also make very positive contributions to economic growth. Research Questions This study sought answers to the following questions in OECD countries between 2006-2023: 1. Is there any relationship between global gender inequality and economic growth in both the short and long term? 2. Is there any relationship between women’s employment and economic growth in both the short and long term? 3. Is there any causal relationship between global gender inequality, women's employment, and economic growth? If so, what is the direction of this causality? Research Objectives 1. To Examine the relationship between Global Gender Inequality and Economic Growth o To analyse whether global gender inequality impacts economic growth in OECD countries in both the short and long term 2. To investigate the relationship between Women’s Employment and Economic Growth o To assess the effect of women’s employment on the economic growth of OECD countries in both the short and long term 3. To determine the causal relationships between variables o To explore the existence and direction of causality between global gender inequality, women’s employment, and economic growth in OECD countries during the study period. These objectives aim to provide a comprehensive understanding of how gender dynamics and employment patterns influence economic outcomes, thereby guiding policies aimed at promoting inclusive and sustainable economic growth. It is thought that this study will contribute to the literature as a result of the answers it will give to the above research questions in terms of the period covered, countries, variables, and methods used. Therefore, in this study, the theoretical background of the study was first given. Then, a literature review on the relevant field was included. The data set, the methodology used, the method, and the findings were discussed. Gender Inequality, Women’s Employment and Economic Growth Obtaining optimal efficiency from labour, which is one of the most important elements of production factors, is one of the most important building blocks of economic growth. Therefore, although men are in the majority in terms of participation in the labour force, the high rate of female participation in the labour force is equally important. Minimizing gender discrimination in the labour force and the efficient use of production factors with the participation of women, the other large majority of the population, in the labour force will contribute to economic growth. In addition to fulfilling their traditional duties (house cleaning, cooking, giving birth to children, raising them, etc.) for women, who constitute a significant portion of the country’s population, earning money by working in the market became widespread with the industrial revolution. The labour that was wasted in agriculture International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 6, issue. 1, 2025, pp. 1-18 https://doi.org/10.47259/ijrebs.2025.611 © Kiliç 3 with the transition to mass production during the industrial revolution later shifted to service sectors such as accounting, cleaning, and secretarial work. Thus, women’s participation in the labour market as labour was also ensured. Subsequently, they began to take part in various other sectors (manufacturing, industry, etc.). A significant increase in the role of women in the labour market was observed after World War II. During the years of war, since the majority of men were at war, the wages of the male workforce were higher than in previous periods and their numbers decreased, therefore, women’s labour force was needed. In addition, since there were no men at home, there was not much housework and women had difficulty making ends meet during the war period and due to need of income women entered business life (Özer and Biçerli, 2003). Although sex distinction is common in many societies, for women to take care of housework and men to earn money by working outside, situation could not be eliminated. Today, a sexist attitude is displayed towards many occupational groups and it is continued to be accepted as ‘women’s work – men’s work’. Equality of opportunities and treatment in the labour market plays a critical role in a decent life. Despite this, when evaluated globally, women still face many obstacles in finding jobs, reaching decision-making positions in certain sectors as they prefer. This horizontal and vertical gender segregation of employment leads to a wage gap between women and men. One of the indicators expressing gender inequality is the Global Gender Gap Index. The Global Gender Gap Index was first introduced by the World Economic Forum in 2006 to benchmark progress toward gender parity across four dimensions: economic opportunities, education, health, and political leadership (Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment). The index focuses on measuring equality between women and men. Full equality means that the index value is equal to 1. The widening of the gap between women and men means that the index value is moving away from 1. As a result, the lower the index value, the higher the gap between women and men. It is the longeststanding index tracking the progress of numerous countries’ efforts towards closing these gaps over time since its inception in 2006 (Pal et al., 2024). An increase in gender inequality in a country also affects the level of development of that country. While gender inequality is seen as an important development indicator, it also reflects the economic profile of that country. It is generally possible to say that countries with high gender inequality are developing countries that are lagging in terms of economic growth. Therefore, it can be said that countries with high gender inequality have lower economic growth figures. Review of Literature Table 1 below shows the studies conducted on the relevant subject. As examined, studies on economic growth, gender inequality, and women’s employment have been handled with quite different analyses and different relationships in different periods. It is thought that this study will contribute to the literature in terms of the effect of women’s labour force participation rate and gender inequality on economic growth. Table 1. Research conducted in the relevant field Research Imprint Country/Region Method Results Whitehouse (1992) Legislation and Labour Market Gender Inequality: An Analysis of OECD Countries OECD (1974-1986) Time series regression analysis and statistical analysis were performed While gender equality measures are likely to make female participation in the labour market highly favourable, the relative and public employment pattern of total employment suggests that women are the most likely to achieve relatively high earnings. Gornick (1999) Gender Equality in the Labour Market: Women’s Employment and Earnings Luxembourg, Denmark, Finland, Norway, Sweden, Spain, Belgium, France, Germany, Italy, Luxembourg, Netherlands, Australia, Canada, United Kingdom and USA Variation analysis As a result of the three different variation analyses applied, gender is a strong source of differentiation. International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 5, issue. 4, 2024, pp. 1-14 https://doi.org/10.47259/ijrebs.541 4 Effect of Global Gender Equality on Economic Growth (1989-1992) Özer & Bicerli (2003) Türkiye’de kadın işgücünün panel veri analizi Panel Data Analysis of Female Labour Force in Turkey Türkiye (1988 – 2001) Fixed effects and random effects are modeled with unit-specific fixed terms using restricted OLS regression. In the models, it was concluded that female labour force participation is not directly sensitive to macro variables, but rather to micro variables in sociological dimensions. Fortin (2005) Gender Role Attitudes and the Labour-market Outcomes of Women across OECD Countries 25 OECD (1990-1995-1999) Survey Non-egalitarian views were found to have the strongest negative association with female employment rates and the gender wage gap. The inevitable conflict between family values and egalitarian views is another obstacle on the path to gender equality in the labour market, taking the form of an internal conflict for many women. Jütting et al. (2008) Measuring Gender (In) Equality: The OECD Gender, Institutions and Development Data Base OECD (1990-2006) Comparative Regression Analysis The OECD Development Centre shows that there is a negative correlation between gender inequality and female labour force participation. The most important factor here is the low literacy rate among women. Engelhardti et al. (2008) Fertility and women’s employment reconsidered: A macro-level time-series analysis for developed countries, 1960–2000 France, West Germany, Italy, Switzerland, England and the USA (1960-2000) Vector error correction model, time series, and Grangercausality tests This is consistent with simultaneous movements of both variables brought about by common exogenous factors such as social norms, institutions, financial incentives, and contraceptive availability and acceptability. There is a negative and significant correlation until the mid-1970s and a nonsignificant or weaker negative correlation thereafter. Dedeoglu (2012) Equality or Discrimination? Social State, Gender Equality Policies and Women’s Employment in Turkey Türkiye and AB countries Situation analysis Equality policies encourage women's employment and fail to provide equality to women in employment, and some norms have the effect of discouraging employed women from working. Ince (2010) Women's Employment and Demand for Women’s Labour The Example of Türkiye Türkiye (1990-2007) Regression Analysis using Generalized Method of Moments It has been concluded that the determined variables have an impact on the demand for female labour. Kabeer (2005) Gender equality and women’s empowerment: A critical analysis of the third millennium development goal 192 member countries of the UN (1990-2015) Situation analysis It is explained that the policies determined in the Millennium Development Goals Plan are inadequate for Gender Equality and women's empowerment, contrary to the achievement of the goals. International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 6, issue. 1, 2025, pp. 1-18 https://doi.org/10.47259/ijrebs.2025.611 © Kiliç 5 Hegewisch & Gornick (2012) The impact of workfamily policies on women’s employment: a review of research from OECD countries Developed countries such as EU countries, the USA, Japan and Transition countries Comparative Current Situation Analysis It discusses the growing literature on the negative and unintended consequences of work-family policies for gender equality and highlights existing knowledge gaps by reviewing the research literature on leave policies, flexible and/or alternative work arrangements, and childcare supports. Nıeuwenhuıs et al. (2012) Institutional and Demographic Explanations of Women’s Employment in 18 OECD Countries, 1975 – 1999 OECD (1975-1999) Analysis with Multilevel Logistic Regression It shows that over time, women are increasingly likely to combine motherhood and employment in many but not all countries. Both mothers and childless women are more likely to be employed in societies with large service sectors and low unemployment. Kizilgol (2012) Determinants of Female Labour Force Participation: An Econometric Analysis Türkiye (2002-2008) Logit Model Among the factors affecting women's labour force participation decisions, the variables determined have the most important role. Kılıc & Ozturk (2012) Obstacles to Women’s Labour Force Participation in Turkey and Solutions: An Empirical Application Türkiye (2002-2008) Probit Model It has been determined that women's education, economic and marital status, place of residence, and gender perception factors have an impact on employment. Heintz (2013) The Absence of Women: G20, Gender Equality and Global Economic Governance G20 countries (2012) Situation analysis When determining the policies to be created in G20 countries, the consequences for women and men are not seriously taken into consideration. Ozguler (2013) Comparative Analysis of the European Union and Turkish Labour Markets UN Countries and Türkiye (1993-2010) Current Comparative Situation Analysis Especially since 2008, the contraction in production, population changes, and technological transformations directly affect employment and unemployment. Kuçuksen (2013) Female Labour Force Participation and Economic Development A Study of the U-shaped Female LabourForce Participation Hypothesis for Heterogeneous Country Groups 130 countries (1980-2009) EKK, SystemGMM, and AMG methods It has been determined that all methods used are following the relevant theories. Cornwall & Rivas (2015) From ‘gender equality and ‘women’s empowerment’ to global justice: reclaiming a transformative agenda for gender and development 192 member countries of the United Nations Situation analysis It is emphasized that women's full and equal participation in political, civil, economic, social, and cultural life at national, regional, and international levels and the elimination of all forms of discrimination on the grounds of gender should be a priority in the international arena. International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 5, issue. 4, 2024, pp. 1-14 https://doi.org/10.47259/ijrebs.541 6 Effect of Global Gender Equality on Economic Growth Thévenon & Pero (2015) Gender Equality for Economic Growth? Effects of Reducing the Gender Gap in Education on Economic Growth in OECD Countries OECD countries (1960-2008) Comparative Regression Analysis The increase in women’s level of education has a higher positive and significant effect on GDP per capita than the increase in men’s level of education in recent periods. Yavuz (2016) Women’s Employment and Economic Violence in the Axis of Gender Inequality OECD and Türkiye (20132014) Situation analysis Women are subject to discrimination in all economic fields. The biggest factor in gender equality and discrimination in women’s employment is the lack of equal educational opportunities. Taşseven et al. (2016) The Determinants of Female Labour Force Participation for OECD Countries OECD (1990-2013) Panel Binary Qualitative Preference Method It was found that unemployment rate, gross domestic product per capita and fertility rate positively and significantly affect the female labour force participation rate. Durmaz (2016) Women in the Labour Market and the Obstacles They Face World (2014) Statistical Analysis Techniques It has been suggested that the gender approach is a significant obstacle to women’s employment in the world and that this situation can be prevented by implementing studies to increase the level of education. Trapp et al. (2017) Gender budgeting in OECD countries OECD countries (2016) Survey As the introduction of gender budgeting is relatively new in many countries, it is likely to have a wider range of impacts in the future. Useful areas for further work and policy action include: embedding genderspecific approaches into normal annual budgeting routines, with routine availability of sexdisaggregated data, and executive-led approaches complemented by external quality assurance. Ducan & Polat (2017) The Effect of Female Employment on Economic Growth: Panel Data Analysis for OECD Countries OECD (2004-2014) Panel Unit Root Test, Panel Data Regression Analysis F Test, and Hausman Test In OECD countries, the increase in the female/male labour force participation rate has a negative effect on GDP growth, and this effect is greater for G7 countries than for other OECD countries. Ilalan (2017) Female Labour Force Participation in OECD Countries: Analysis in Terms of Economic Development and Tax Burden OECD (2000-2012) Panel Unit Root and Cointegration Methods with Heterogeneous Panel Data Estimations and OLS As a result of the estimations applied using the existence of the U-shaped relationship and the tax burden and fiscal freedom variables, it was concluded that there is a positive relationship between the female labour force participation rate and fiscal freedom and a negative International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 6, issue. 1, 2025, pp. 1-18 https://doi.org/10.47259/ijrebs.2025.611 © Kiliç 7 relationship with the tax burden on labour costs. Guris et al. (2019) Examining the Factors Affecting Female Labour Force Participation in OECD Countries Using Panel Qualitative Choice Models OECD (2004-2016) Random Effects and Fixed Effect Unbalanced Panel Logit Estimation, Walt Test, Hausman Test The determined variables have an increasing effect on women’s employment. Akdemir et al. (2019) The Place of Women in the Labour Market in Turkey and Selected Countries Norway, Sweden, Denmark, Mexico, Türkey, Yemen, Afghanistan and Egypt (2017) Comparative Current Situation Analysis In developed countries, the female and male labour force participation rates are approximately equal, while in countries with lower levels of development, the female labour force participation rate is lower than the male participation rate. Colak (2021) Econometric Analysis on Gender Inequality and Economic Growth Relationship in Turkey Türkiye (2005-2019) LS-Least Squares (NLS ve ARMA) Economic growth had the greatest impact on the gender wage gap mostly among employees and then graduates of high or equivalent schools. It has been revealed that economic growth is a factor that decreases the gender wage gap in favor of women, both among employees and high school graduates. Research Methodology In this study, the effects of the gender equality index and female labour force participation rate on economic growth between 2016-2023 in 38 OECD countries were investigated with the help of Panel Data Analysis. These countries are; Turkey, United States, Germany, Australia, Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, the Republic of Korea, Netherlands, England, Ireland, Spain, Israel, Sweden, Switzerland, Italy, Japan, Iceland, Canada, Colombia, Costa Rica, Latvia, Lithuania, Luxembourg, Hungary, Mexico, Norway, Poland, Portugal, Slovakia, Slovenia, Chile, New Zealand, Greece. In the established model, while the Global Gender Gap Index (GGGI) and Female Labour Force Participation Rate (KIKO) data were used as independent variables, the data set of GDP per capita growth (annual %) variable was used as the dependent variable. The data sets used in the analysis were the gender inequality index data obtained from the Global Gender Gap Report prepared by The World Economic Forum and the female labour force participation rate data obtained by The International Labour Organization. In addition, all tests and analyses were made using the E-Views 10 econometric program. Table 2. Variables, their symbols in the Study, and Data Set Sources Variables Symbol Year Source Per Capita Growth PERGDP 2006-2023 World Bank Group, 2024 Global Gender Gap Index GGGI 2006-2023 World Economic Forum, 2024 Female Labour Force Participation Rate KIKO 2006-2023 International Labour Organization, 2024 Panel Data Analysis In econometric analyses, data are divided into three classes time, cross-section, and mixed data consisting of a combination of both types of data. Cross-section data provides information about only one period for many units, while time series data includes information about only one unit according to periods. When information according to periods and units is investigated together, panel data is used. Panel data allows for more complex behavioural models to be made and tested by bringing together cross-section data belonging to units such as International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 5, issue. 4, 2024, pp. 1-14 https://doi.org/10.47259/ijrebs.541 8 Effect of Global Gender Equality on Economic Growth individuals, countries, firms, and households in a certain time interval (Tatoğlu, 2018). Information on the dynamic responses of units is very important in understanding economic events. Panel data can meet the need for a very long time series by using existing information on the dynamic responses of different units. Some of the features of panel data are as follows: 1. In any cross-sectional data, countless unmeasured explanatory variables affect the behaviour of the units under study (households, firms, countries, etc.). Excluding these variables leads to biased estimates. Panel data allows this problem to be overcome. 2. Panel data reduces multicollinearity by combining the change between periods with the change between micro units, creating variability. 3. Panel data can be used to examine issues that cannot be evaluated with cross-sectional or time series data alone. The panel data regression model can be shown in its most general form as follows: yit = β1it +β2it X2it +… βkitXkit +eit i = 1, 2, …,N ;t=1,2,…,T In the model, k is the number of variables, i = 1, 2, …, N is the cross-sectional unit, and t = 1, 2, …, T is the time. The train coefficients from Β2it to βkit may differ for different units and different periods as unknown response coefficients. Panel ARDL Model is an analysis applied on the condition that variables are stationary at different levels (stationary at level value or first difference) as a result of unit root tests, but second-order stationarity is not accepted. It allows us to test the short-term and long-term relationship between variables. Model Selection The Hausman test was used to choose between fixed effects and random effects models in econometric analysis. The preference of these two models depends on the characteristics of the analysed panel data and what the regression model was. The Hausman test confirms whether there is a relationship between the independent variables and the error terms in a regression model (endogeneity). The relationship is critical in terms of the distribution of some conditions. Fixed effect model (FE): It is preferred to run it as it is between the independent variables and the error term. The model controls individual characteristics and prevents the estimates of these characteristics from being distorted. Random effect model (RE): Non-expansive changes are preferred between the independent variables and the error condition. It contains fewer parameters and provides more efficient estimates. H0: Random Effect Model is suitable H1: Fixed Effect Model is suitable Table 3. Hausmann Test Test Summary Chi-Sq. value df Prob Cross-section random 11.990303 2 0.0025* * Statistically significant at a 1% significance level According to the Hausmann Test result, the probability value (0.0025) was found to be less than a 5% significance level. Therefore, the Null hypothesis was rejected and the Alternative hypothesis was accepted. In that case, our Panel model established was determined as a fixed effect. Below in Table 4, the Fixed Effect Model, which was decided after the Hausmann Test and determined by the Panel Least Squares method, is given. Table 4. Fixed Effect Model with Panel Least Squares Method Variable Coefficient Std. Error t-Statistic Prob C 3.9199 4.1931 0.9349 0.3502 GGGI -17.5980 6.2651 -2.8089 0.0051* KIKO 0.2076 0.0498 4.1722 0.0000* Effects Specification International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 6, issue. 1, 2025, pp. 1-18 https://doi.org/10.47259/ijrebs.2025.611 © Kiliç 9 Cross-section fixed (dummy variables) F-statistic 2.2376 Durbin-Watson stat 1.8641 Prob(Fstatistic) 0.000037* Schwarz criterion 5.6984 Akaike info criterion 5.4336 Hannan-Quinn criteria 5.5361 * Statistically significant at a 1% significance level When the Fixed Effect Model estimation was examined, the probability values of GGGI and KIKO were found to be statistically significant at the 1% significance level. Therefore, first of all, a 1% increase in GGGI reduced PERGDP by 17.59%. Finally, a 1% increase in KIKO increased PERGDP by 0.20%. Since the probability value of the F statistic was also found to be significant at the 1% significance level, the established Fixed Effect Model was also statistically significant. Panel Unit Root Tests Panel unit root tests are tests used to determine whether variables in panel data sets are stationary. Stationarity is an important feature in time series analysis and plays a critical role in linear regression models in economic analysis. Panel Unit Root Tests are used to determine whether a variable has a constant mean and variance over time. If a variable is not stationary, it can lead to spurious regression problems in the analysis. This can lead to misinterpretation of model estimation. The following tests, which are among the Panel Unit Root Tests and are frequently used in the literature, were preferred: Im, Pesaran, and Shin W-stat, ADF - Fisher Chi-square, and PP - Fisher Chi-square Unit Root Tests. H0: The series contains a unit root and is not stationary H1: The series does not contain a unit root and is stationary Table 5. Panel Unit Root Tests (LevelI(0)) Individual Effects TESTS Statistic Prob. Crosssections Obs PERGDP Im, Pesaran and Shin Wstat -15.8026 0.0000* 38 608 ADF - Fisher Chi-square 370.944 0.0000* 38 608 PP - Fisher Chi-square 590.686 0.0000* 38 646 GGGI Im, Pesaran and Shin Wstat 1.20608 0.8861 38 608 ADF - Fisher Chi-square 68.0050 0.7318 38 608 PP - Fisher Chi-square 82.0448 0.2975 38 646 KIKO Im, Pesaran and Shin Wstat 7.12245 1.0000 38 608 ADF - Fisher Chi-square 27.2852 1.0000 38 608 PP - Fisher Chi-square 53.5959 0.9761 38 646 Individual Effects and Trend PERGDP Im, Pesaran and Shin Wstat -13.5483 0.0000* 38 608 ADF - Fisher Chi-square 303.901 0.0000* 38 608 PP - Fisher Chi-square 566.174 0.0000* 38 646 GGGI Im, Pesaran and Shin Wstat -1.20467 0.1142 38 608 ADF - Fisher Chi-square 91.2344 0.1122 38 608 PP - Fisher Chi-square 69.0025 0.7024 38 646 KIKO Im, Pesaran and Shin Wstat 3.45092 0.9997 38 608 ADF - Fisher Chi-square 39.6535 0.9998 38 608 PP - Fisher Chi-square 42.5441 0.9993 38 646 * Statistically significant at a 1% significance level International Journal of Research in Entrepreneurship & Business Studies eISSN-2708-8006, Vol. 5, issue. 4, 2024, pp. 1-14 https://doi.org/10.47259/ijrebs.541 16 Effect of Global Gender Equality on Economic Growth 3. Supportive Work Environments Employers should create supportive work environments that facilitate women’s inclusion and productivity. Measures could include: • Introducing family-friendly workplace policies, such as flexible working hours and remote work options. • Establishing on-site childcare facilities and providing maternity and paternity leave to support work-life balance. • Preventing workplace discrimination and harassment by enforcing strict regulations and encouraging reporting mechanisms. 4. Leadership and Decision-Making Roles Increasing the representation of women in leadership and senior decision-making roles is crucial. Companies and governments should implement affirmative action programs and mentorship initiatives to prepare women for such roles and ensure diversity at the top levels of organizations. 5. Investing in Education and Skill Development Equal access to quality education for girls and women should be prioritized. Policies should aim to close gender gaps in education, particularly in STEM (Science, Technology, Engineering, and Mathematics) fields, where female representation remains low. 6. Addressing Social and Cultural Barriers Programs to challenge deep-seated societal norms and attitudes that perpetuate gender inequality should be introduced. Advocacy campaigns and educational initiatives can promote the idea that gender equality benefits everyone in society, not just women. 7. Legal Reforms Legal barriers that hinder women’s participation in economic and social life should be removed. This includes reforming inheritance laws, property rights, and labour laws that disproportionately affect women. 8. Monitoring and Accountability Regular monitoring and evaluation of gender equality policies and initiatives are essential to track progress and make necessary adjustments. OECD countries should establish standardized metrics to assess their performance in reducing gender inequality and promoting female employment. 9. International Collaboration OECD countries should collaborate to share best practices and successful strategies for reducing gender inequality. Initiatives like international conferences, partnerships, and joint research projects can provide insights and solutions applicable across different contexts. 10. Targeted Programs for Vulnerable Groups Special programs should be designed for marginalized women, such as those in rural areas, minority groups, and low-income households. Tailored interventions can help bridge the gaps in access to resources and opportunities for these groups. By implementing these recommendations, OECD countries can address gender inequality more effectively, increase female participation in the labour force, and ultimately enhance their economic growth. 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