Women at Work: An International Perspective on the Interconnected Forces of Human Development, Female Education, and Labour Force Participation

Women at Work: An International Perspective on the Interconnected Forces of Human Development, Female Education, and Labour Force Participation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Women at Work: An International Perspective on the Interconnected Forces of Human Development, Female Education, and Labour Force Participation Malsha Silva, Ruwan Jayathilaka, Suren Peter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4508525/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The purpose of this study was to investigate the effects of the Human Development Index (HDI) and Tertiary School Enrolment Rate of Female (TSEF) on Female Labour Force Participation (FLPR) globally and by continent. Secondary data on sixty countries, including nine African countries, 18 Asian countries, 27 European countries, two North American countries, and four South American countries, were examined between 2000 and 2020. The Panel regression model was used to investigate the regional impact, and the Multiple Linear regression model was used to investigate the country-specific impact. The study found that HDI and TSEF are two significant factors influencing FLPR. When country-specific results were considered, the effect of each variable on FLPR revealed mixed results, with positive and negative impacts based on the characteristics of the selected country. The findings offer an in-depth understanding of how HDI and TSEF affect FLPR, which will aid policy makers in establishing and amending strategies to accelerate women's employment and, consequently, economics growth. This study focused on the HDI and TSEF variables that were rarely used in existing literature together on FLPR. Human Development Tertiary School Enrolment Female Labour Force Participation 1. Introduction The Female Labour Force Participation (FLPR) has received increased attention among policy makers as the impact on the economies of nations by the female population are becoming significant. Countries today prioritise women's employment for several reasons, including productivity, efficiency, impact on family finances, etc. While some scholars posit the negative side of increased female labour force participation (Doğan and Akyüz 2017), policymakers, academics, and campaigners, all pay attention to female labour force participation since it is a critical component of global socio-economic development (Lechman and Kaur 2015). Beyond an individual's employment situation, the importance of female labour market participation extends to broad socio-economic ramifications for a nation. It plays a crucial role in economic expansion (Rahman 2020). Furthermore, it is crucial in reducing poverty, especially in households where women are the head of the family (Mulugeta and Finance 2021). In addition, gender equality and female labour force participation go hand in hand (Ortiz Rodríguez and Pillai 2019). These elements highlight how women can succeed in various vocations and positions while questioning traditional gender norms and roles. Therefore, women's employment is a significant component of economic development. Female labour force participation is strongly influenced by human development, a broad framework that includes education, health, income, and gender equality in various geographic contexts. Globally, there is a clear correlation between female labour force participation and the Human Development Index (HDI) (SC Naidu 2016). The impact of human development on female labour market participation is also seen across the African continent. Female involvement rates reach 40% in nations with better HDI outcomes, such as Seychelles and Mauritius (SC Naidu 2016). Japan and South Korea are two Asian countries with strong HDI that continuously report higher female labour force participation rates, frequently reaching 50% (Schaner and Das 2016). Therefore, promoting human development becomes a critical factor in increasing the proportion of women in the labour market globally. As a significant factor that shapes the behaviour of female labour force participation, rather than discussing education in general, this study focuses on the Tertiary School Enrolment Rate of Females (TSEF), which emphasises the impact and importance of education. Territorial school enrolment, which guarantees access to high-quality education within particular geographic areas, significantly affects women's participation in the labour sector (Schaner and Das 2016). From a global perspective, territorial school enrolment is crucial in determining the labour force involvement of women (Qureshi 2012) in South American nations like Uruguay and Argentina, where education is more widely available and female labour force participation rates above 50%, the impact of territorial school enrolment on female labour force involvement may be seen both in developed and developing countries (Pal and Chaudhuri 2020; Tanaka et al. 2020). Therefore, to increase the participation of women in the workforce globally, it is crucial to guarantee equal access to high-quality education for women. The impact of HDI indicators, including gender equality, income, health, and education, on women's labour force participation is significant, and their role in guaranteeing women's access to education has been emphasised. Human development covers the other societal aspects, whereas enrolment in territory schools covers entirely the educational elements. Thus, this study examines the impact of TSEF and HDI on the FLPR, emphasising the significance of each and providing information on which factors require greater focus to achieve the ideal participation rate. Consequently, the study makes four contributions which advance the body of literature. Firstly, the study examines the global perceptiveness of African, Asian, European, North American, and South American continents, considering over sixty countries and spanning almost two decades, to analyse the overall impact of HDI and TSEF on FLPR. While previous research has concentrated on certain nations or regions, a thorough worldwide analysis has yet to focus on these continents collectively. Secondly, this study adds a comparative perspective to the body of literature by performing a country-by-country analysis aimed at each country's behaviour to learn more about the influence of HDI and TSEF on FLPR. Thirdly, current research has only focused on the factors influencing FLPR in individual nations. Nevertheless, empirical studies regarding the combined impacts of the HDI and TSEF is absent in this area and on many continents. The paucity of literature on the subject also inspired this research. Therefore, the findings contribute to filling in the empirical gaps in this field. Finally, the current study also helps to adjust policies to improve existing economic circumstances while offering suggestions to the policy makers and other economic decision-makers. It also enables policymakers to evaluate the impact of their past decisions and policies on the behaviour of women who participate in the labour force. The article is organised as follows: an introduction to the topic that includes information about the essential background of the study and outline; an overview of relevant research that highlights variables related to Africa, Asia, Europe, North America, and South America regions; data and methodology; results and discussion of the empirical findings that explain how HDI and TSEF affect FLPR; and, finally, the conclusion of the study and policy implications. 2. Literature Review The literature on female labour force participation often emphasises the relationship between women's employment and human development and the enrolment rate in tertiary education. Education and human development have been highlighted as essential to enabling women to work and thus stimulating the country's economic growth. A discussion of earlier research relevant to the variables examined in this study is discussed based on continents. 2.1 Africa The majority of African nations have been caught up in civil war, plagued by poverty, underdevelopment, and gender discrimination (Bajpai 2014). In contrast to men, African women have higher unemployment rates in the educated and less educated segments of the population due to obstacles preventing them from fully participating in the economy (Bajpai 2014). The poor performance of the South African economy leads to low levels of human development and high levels of income inequality (Gumede 2021). Studies from several African countries show how closely HDI and female labour force participation interact. The average HDI across African nations is statistically and significantly lower than the global average (Kpolovie et al. 2017). Despite advancements in HDI metrics, inequalities persist in affecting female employment prospects (Olowolagba 2022). Moreover, infrastructure constraints, cultural norms, and socio-economic variables affect women's access to higher education and their participation in the workforce (Lebeau and Oanda 2020). Therefore, initiatives aimed at improving girls' education have gained momentum in recent years, which has caused a rise in the enrolment rates of females. A positive trend is apparent with female labour force participation rising as education becomes more widely available (Ganguli et al. 2014). This highlights that increased female labour force participation is frequently correlated with higher enrolment rates in territorial schools, underscoring education's critical role in giving women economic power (Gyasi et al. 2019). However, differences in educational access and development indices mean disparities exist across different African regions, affecting how much women engage in the workforce (Gyasi et al. 2019). Pursuing long-term educational reforms and socio-economic development programs to mitigate these disparities is essential for increasing female labour force participation in various African countries. These studies highlight the importance of improving human development and education for women's employment. 2.2 Asia In certain economically developed Asian countries, such as South Korea and Japan, the relationship between female labour force participation and HDI illuminates the conducive atmosphere that higher HDI scores foster (Gaddis and Klasen 2014). However, in some South Asian nations like Bangladesh and India, with lower HDI scores, the connection between HDI and female labour force participation is more nuanced. Although advances in HDI positively affect female workforce engagement, socio-cultural norms and inequalities impede women's ability to find work (Sourander et al. 2018). Most women in South Asian countries are not very focused on education but rather on being good housewives, where the enrolment in territory education is comparatively less (Chauhan et al. 2021). Especially in countries like India and Sri Lanka, most women are more focused on managing their relationships with families and relatives rather than working. Even South Asian countries have confusing trends in territory school enrolments. Countries like Japan and China are encouraging and motivating females towards higher education, and thereby, they aim to have an educated female labour force participation, which may result in a solid business world (Phan and Coxhead 2014). Therefore, having sufficient higher education avenues would result in more engagement in the labour force. It is believed that those educated female workforce would establish their own small and medium scale businesses (Phan and Coxhead 2014). Women's participation in the workforce is positively impacted by greater access to education, reflected in higher territory school enrolment (Kinoshita and Guo 2015). However, differences still exist in areas with lower development indices and access to education, which affects women's participation in the labour force (Afridi et al. 2016). However, in South Asian countries like Sri Lanka, India, and Pakistan, despite the education level, most women are employed in different sectors such as agriculture, apparel, etc. (Schaner and Das 2016). Therefore, studies have derived another conclusion that the employment sector indirectly alters the extent of the impact of territory school enrolments and HDI on female labour force participation. This implies that human development and education indicators significantly influence women's participation in the labour force. 2.3 Europe European studies show a positive correlation between higher HDI scores and female labour force participation (Dumith et al. 2011). However, specific economic and educational opportunity differences affect women's participation in the labour force (Reig-Martínez 2013). Unlike some areas where traditional roles limit female education, Europe typically shows greater educational access and inclusivity. The region's emphasis on achieving gender parity in education has resulted in a higher percentage of female students enrolled overall (Biavaschi et al. 2012). The relationship between increased female labour force participation and territory school enrolment is still changing due to ongoing education and gender equality initiatives throughout Europe (Mau and Verwiebe 2010). Policies that address inequality, advance inclusivity, and improve the quality of education are critical to developing a positive relationship between increased female workforce participation and territory school enrolment throughout a heterogeneous socio-economic spectrum (Mau and Verwiebe 2010). Compared to all other continents in the universe, Europe has the highest HDI, with a significant and overwhelming preponderance over the global average (Kpolovie et al. 2017). Higher HDI-scoring European countries, like Norway and Sweden, also have more stable economies and better access to education, which creates an environment that encourages more elevated rates of female labour force participation (Casarico et al. 2016). Altogether, the correlation between territory school enrolments and HDI highlights how significant education and socio-economic advancement is in shaping the participation of women in the labour force in Europe. 2.4 North America The human development index of North America is not substantially different (Kpolovie et al. 2017). Nevertheless, disparities exist among North American communities and regions despite the overall higher HDI scores (Nartgün et al. 2017). These differences affect how the HDI and female labour force participation are correlated, influencing how some groups can access economic resources, high-quality healthcare, and education (Nartgün et al. 2017). North America strongly focuses on gender parity in education and generally boasts higher levels of educational accessibility and inclusivity. However, subtle differences exist between nations and areas where socio-economic factors, cultural dynamics, and regional policies affect female labour force participation (Bowen and Finegan 2015). Access to high-quality education can be complex in marginalised communities, impacting women's workforce participation (Thévenon 2013). Disparities do, however, exist in marginalised communities and areas, highlighting the necessity of inclusive development and educational policies to guarantee fair opportunities for women to participate in the workforce across North America (Busso and Fonseca 2015). To ensure sustained progress in improving female labour force participation across North America's diverse landscapes and address disparities, it is imperative that inclusive educational policies and socio-economic development initiatives continue to be prioritised. 2.5 South America Differences exist in the relationships between female labour force participation and HDI in South American nations (Kpolovie et al. 2017). The South American region struggles with socio-economic gaps and restricted access to high-quality education, especially in rural areas, which serve as obstacles for women looking to enter the workforce (Camou et al. 2017). Yet, specific disparities in cultural beliefs and educational opportunities impact women's involvement in the labour force among various South American nations (Thévenon 2013). Continuous efforts to enhance gender inclusivity and educational access, support a changing relationship between higher female labour force participation and territory school enrolment in various South American landscapes (Thévenon 2013). There is a promising trend, with an increasing proportion of women entering the workforce as access to education increases (Aedo and Walker 2012). Studies repeatedly show that greater enrolment in territorial schools is correlated with higher levels of female workforce participation. Similarly, South American nations with high HDI scores, such as Chile and Uruguay, generally have higher rates of economic opportunity and gender parity in education, which benefits women's participation in the labour force (Busso and Fonseca 2015). Women are more likely to be engaged in the workforce when they have greater access to education, as shown by higher enrolment in territory schools. Moreover, areas with higher HDI scores offer better economic stability and access to education, creating an atmosphere that encourages more elevated rates of female labour force participation. 3. Data and Methodology Panel data from 2000 to 2020 covering 60 countries were used in the study. Specifically, nine African, 18 Asian, 27 European, 2 North American and four South American countries were examined. The definitions of variables and data sources are displayed in Table 1. Table 1: Data Sources and Definitions Variables Definitions Sources FLPR Female Labor Force Participation Rate (%) World Bank Report https://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS TSEF Tertiary School Enrolment of Female (%) World Bank Report https://data.worldbank.org/indicator/TE.SEC.NENR.FE HDI Human Development Index (Scaled 0 to 1) Our World in Data https://ourworldindata.org/human-development-index Source: Author’s Compilation. This study aims to examine the impact of HDI and TSEF on FLPR. Two static linear panel models are developed based on the conceptual framework and the literature review. Using HDI and TSEF as inputs, an empirical model was developed for the FLPR as follows: Eq (1) is developed to test the regional impact of HDI and TSEF on FLPR in Africa, Asia, Europe, North America, and South America. Eq (2) is designed to test the country-specific impact on FLPR. FLPR it represents the Female Labour force Participation Rate, where the country is denoted by i and the time is denoted by t. TSEF it represents the Tertiary school enrolment of Females, HDI it denotes the Human Development Index. The random error term is represented by . A particular procedure was followed to standardise the variables. To accommodate the different sizes of the variables, the coefficients corresponding to each variable were suitably modified during the standardisation process. After that, the heteroscedasticity problem was mitigated by converting the data into a reliable standard error. 3.1 Estimation Procedures Pooled Ordinary Least Square (POLS), Fixed Effect (FE), and Random Effect (RE) models were employed at the regional level to estimate the given regression model for female labour force participation based on econometrics literature (Hoechle 2007). The study used a highly balanced panel data set and the panel regression model to determine the individual effects of HDI and TSEF on FLPR at the regional level. Country-specific impact and time-specific impact are two essential features of these models. The objective is to determine the most suitable and reliable model for analysis. Estimates employing the RE and FE models are preferred over the POLS model in the event of a regional effect when the trend remains constant across regions. The accuracy of the RE model is higher than that of the FE model. To select the best model from the Ordinary Least Squared Method, Fixed Effect model hypothesis testing and Random Effect model hypothesis testing, three specification tests were used: the F-Test, the Hausman specification test and the Breusch-Pagan Lagrange Multiplier Test (LM Test). The F-test is used to select the specification tests among FE and POLS (Vuko and Čular 2014) and the LM Test is used to choose between RE and POLS (Baltagi et al. 2012). However, the choice between the FE and RE models will be made using the Hausman test (Amini et al. 2012). Furthermore, the study uses a multiple linear regression model to investigate the country-specific impact of HDI and TSEF on FLPR. The data set analysed in this study is presented in the S1 Appendix. Moreover, the linear fits of the HDI and TSEF were also generated as scatter plot graphs to examine the trends of the influence of two independent variables on FLPR in each country, as shown in the S2 Appendix. 4. Results and Discussion Several observations, mean, standard deviation, and minimum and maximum values were observed for the variables HDI, TSEF and FLPR and have been presented in the S3 Appendix, to examine the dataset further. The descriptive statistics of the continents for the factors that were taken into consideration for the main aim of the study are presented in Table 2. Table 2: Descriptive Statistics of the Impact of HDI and TSEF on FLPR Continent Variable Obs. Mean SD Min Max Global FLPR 1260 50.180 13.852 10.950 87.123 HDI 1260 .761 .142 .297 .962 TSEF 1260 45.129 25.231 .685 99.166 Africa FLPR 189 58.183 20.668 20.733 87.123 HDI 189 .575 .153 .297 .915 TSEF 189 14.938 14.731 .685 71.498 Asia FLPR 378 44.289 17.078 10.950 73.553 HDI 378 .702 .116 .425 .921 TSEF 378 33.218 19.289 2.379 92.938 Europe FLPR 567 51.825 6.049 35.687 64.383 HDI 567 .858 .063 .675 .962 TSEF 567 61.745 18.139 9.815 95.965 North America FLPR 42 45.299 4.053 38.253 55.214 HDI 42 .761 .029 .709 .817 TSEF 42 36.812 9.012 20.491 47.799 South America FLPR 84 50.022 5.135 36.470 58.082 HDI 84 .788 .049 .666 .861 TSEF 84 58.658 19.057 24.492 99.166 Source: Author's calculation based on secondary data. According to the findings of the Breusch Pagan test and the Hausman specification test, the FE model was the preferred model for investigating the regional impact in Europe and North America. In contrast, the RE model was the most appropriate technique to analyse the regional impact in South America, Asia, and Africa. A time series multiple linear regression model was used to analyse the country-specific effects of TSEF and HDI on FLPR, and these findings are shown in the S4 Appendix. 4.1 Africa According to the results, although HDI has no regional impact on FLPR in Africa, the country-specific impact of HDI on FLPR is seen in several countries in the African continent. The country-specific impact of HDI on FLPR infers that Madagascar, Morocco, and Rwanda have illustrated a significant favourable influence on FLPR at a 1% significance level. Morocco has attained the highest positive impact of HDI on FLPR, which is essential at the 1% level, where it says FLPR will increase on average by 72% for each index increase in the HDI when all other variables are held constant. Moreover, it is evident that as per the scatter plot portrayed for Morocco, given that the data are primarily linear, the relationship is relatively strong in Morocco. Specific findings also reveal a positive relationship between HDI and African FLPR variables (Olowolagba 2022). It further claims that while HDI is a composite measure that captures various aspects of development, its effects on FLPR are not uniform throughout Africa, as evidenced by the lack of a discernible regional impact of HDI on FLPR within Africa. The fact that country-specific effects have been identified, highlights the variability of these relationships even more (Idowu and Owoeye 2019). Therefore, the strong impact of HDI in countries like Madagascar, Morocco, and Rwanda is favourable for higher rates of FLPR. However, in contrast, several countries, including Benin and Malta, two African nations, have identified a significant negative impact on FLPR at a 5% significant level. The country Benin has reached the lowest adverse effect of the HDI on FLPR, which is essential at the 1% level. It states that when all other variables are held constant, FLPR will decrease on average by 198.1% for every index increase in the HDI. Additionally, the graph portrayed for Benin clarifies that Benin exhibits a negative correlation between the variables. Previous studies show that gender indicators, including the human development index, expected and mean years of education, and labour force participation rate, all favour men among all the low-human development countries examined (Dominic et al. 2017). In their conclusion, they offer suggestions for reducing gender disparities. It implies that, even with comparatively higher levels of HDI, there are factors preventing women from employment, necessitating focused policy interventions. Findings indicate that the regional impact of TSEF on FLPR in the African region has no effect between the variables, which is insignificant. Considering the country-specific impact of TSEF, the findings depict that Benin and Malta have significant coefficients at a 1% significance level with a positive effect on FLPR. The nation of Benin has experienced the most outstanding positive TSEF impact on FLPR, which is significant at the 1% level. It says that for every 1% increase in the TSEF, FLPR will rise by an average of 1.58% when all other factors are constant. This supports the study by Mantel (2017) proving that increased female labour force participation results from improvements in women's educational outcomes. Furthermore, this contradicts the study by Idowu and Owoeye by concluding that it has been determined that in some African countries, education is a positive predictor of the availability of female labour (Idowu and Owoeye 2019). An increase in female education raises the opportunity cost of not working, meaning that women with higher levels of education have greater incentive to seek employment. Likewise, the lack of appreciable regional effect of TSEF on FLPR in Africa implies that territory school enrolment has a highly variable impact on female labour force participation throughout the continent. The finding of disparate country-specific impacts supports the idea that different national contexts influence how territory school enrolments affect FLPR (Dominic et al. 2017). However, Madagascar and Morocco indicate a significant negative impact on FLPR at a 1% significant level. At the 1% significance level, Morocco has experienced the most negligible negative implications from the TSEF on FLPR. It says FLPR will drop by 0.31% on average for every 1% increase in the TSEF when all other variables are held constant. Women also tend to work informally more frequently, which makes them more susceptible to disease, childbirth, losing their jobs, and ageing (UNESCO 2022). Furthermore, it claims that the relatively weak correlation between specific educational attainment levels and labour market participation could also explain this finding using microdata for several developing nations. 4.2 Asia Although HDI has no regional impact on FLPR in Asia, the results show that HDI has a country-specific impact on FLPR in several Asian countries. According to the findings, the country-specific impact of HDI on FLPR, Bangladesh, Brunei Darussalam, and Nepal have a significant favourable influence on FLPR at the 1% significance level, and India and Israel have a positive impact on FLPR at the 5% significance level. Brunei Darussalam has achieved the highest positive impact of the HDI on FLPR, which is significant at the 1% level. This means that when all other variables remain constant, FLPR will increase by an average of 46.43% for every index increase in the HDI. The scatter plot portrayed for Brunei Darussalam exhibits a positive scatter, with most of the scatters falling very close to the trendline, corroborating these findings. In the Asian region, the statistical findings show specific significant differences among countries, as the scatters were stated. Although the current study in Asia shows how HDI affects FLPR in different countries, this is consistent with earlier research showing that the effects of human development on FLPR vary across different national contexts (SC Naidu 2016). Research has frequently shown that although more significant human development, which includes health, income, and education, tends to correlate positively with greater participation of women in the workforce, this association may not hold everywhere (Zaheer and Qaiser 2016). Conversely, multiple countries involving Lao PDR, Saudi Arabia, and Thailand have identified a 1% significant negative impact on FLPR. According to the results, Thailand depicts the lowest negative impact of HDI on FLPR in the Asian continent. One index increase in the HDI leads to an average decrease in FLPR by 20.14% when holding other variables constant. Regionally, nationally, and for particular demographic groups, youth and women, there are disparities in human development (Salehi-Isfahani 2013). According to the study, several factors, including high reservation wages and demographic pressures, that come under HDI have contributed to the case's high unemployment rates. The results demonstrate no regional impact of TSEF on FLPR in the Asian region. Regarding the effect of TSEF on a country-by-country basis, the results indicate that FLPR is positively impacted in Bangladesh, Cyprus, Georgia, Lao PDR, Saudi Arabia, and Thailand, where Georgia, Lao PDR, Saudi Arabia, and Thailand at a 1% significant level, Cyprus at a 5% significant level and Bangladesh at a 10% significant level. The two nations where TSEF has had the most significant positive impact on FLPR are Thailand and Saudi Arabia. When TSEF rises by 1% in Thailand, FLPR rises by an average of 0.59%, with all other variables remaining unchanged. This is supported by the study done by Posel and Rudwick to estimate labour force participation regressions for married women aged 15 to 49 (Posel and Rudwick 2014). It has been stated that in Asian countries like Indonesia, Thailand, Korea, Sri Lanka and the Philippines, tertiary education has increased the probability of women participating in labour. Because education is still improving, Posel and Rudwick stated that workers with post-secondary education will make up at least two-thirds of the labour force in every economy in the future (Posel and Rudwick 2014). If female labour force participation increases, follow the presumption that the percentage of women with post-secondary education may surpass 30% of the workforce by 2050 (Loichinger and Cheng 2018). So, they suggest that integrating women with higher levels of education, in particular, may be able to partially mitigate the pressing problem of labour shrinkage. However, Israel and Nepal exhibit a significant adverse effect on FLPR at the 1% significance level, and India and Uzbekistan depicts a negative influence of TSEF on FLPR at the 5% significance level. According to the findings, Israel has the most negligible negative impact on the variables at the 1% significant level, with an average FLPR decrease of 0.29% when TSEF is increased by 1%, while the other variables remain unchanged. Nepal and Uzbekistan, have also achieved the most negligible negative impact between the variables at the 1% significant level and 5% significant level, respectively, with FLPR decreasing by an average of 0.64% when TSEF is increased by 1% while maintaining the same values for the other variables. A study by Katz-Gerro and Yaish proves that women's employment chances are unaffected by their educational attainment, while men's employment chances increase with increased education (Katz-Gerro and Yaish 2003). Consequently, it is crucial to consider a range of socio-economic structures, cultural norms when considering this relationship. The conclusions drawn from the analysis of the relationship between FLPR HDI and TSEF in Asia reflect the complex and heterogeneous nature of the effects of these variables on women's participation in the labour force, both regional variability and country-specific subtleties. 4.3 Europe The results demonstrate that HDI has a country-specific impact on FLPR in several European countries despite having no regional effect on FLPR in Europe. Regarding the country-specific impact of the HDI on FLPR, results show that Belarus and Belgium have a positive effect on FLPR that is significant at the 1% significance level. In comparison, Poland has a significant favourable influence at the 5% significance level, while Albania, Italy and Sweden have a significant influence but at the 10% significance level. Belarus and Belgium exhibit a highly positive correlation, as seen in the graphs portrayed for these countries, with the scatters falling on the trendline. When increasing HDI in one index while keeping other variables constant, findings show that FLPR increases in Belgium by an average of 205.02%. This is also claimed by the study of (S Naidu 2016), that human development over the long and short term influences women's labour force participation rates. The fact that different European countries have other impacts of HDI on FLPR highlights how varied this relationship is. Higher levels of human development in nations are linked to higher emphasis of women participation in the economy (Marois et al. 2019). Furthermore, though at differing degrees of significance, Albania, Italy, and Sweden all exhibit noteworthy effects on FLPR. On the other hand, several nations have found a significant negative impact at varying significance levels. In contrast, Bulgaria, Denmark, Finland, and Ireland have a substantial 1% significance level, Croatia, Luxembourg, and Portugal are significant at a 5% significance level, and Lithuania, Netherlands, and Spain have a 10% significance level. As per the findings, Finland shows the lowest negative impact of HDI on FLPR at a 1% significant level, where FLPR decreases on average by 27.6% for each index rise in HD, while holding her variables constant. Further the scatter portrayed for Finland also confirms this. The period shows a negative impact between the variables. In the same way, this relationship's complexity is highlighted by the discovery of multiple European nations with notable adverse effects of HDI on FLPR at various significance levels. Countries such as Denmark, Ireland, Bulgaria, and others show significant negative relationships while countries like Croatia, Luxembourg, and Portugal show similar negative associations at a significance level of 5%. Furthermore, negative impacts on FLPR are observed at relatively less significance levels in Lithuania, the Netherlands, and Spain (Gaddis and Klasen 2014). This suggests that, despite higher levels of human development, certain socio-economic policy-specific factors may impede the FLPR in these countries. The findings show that FLPR in the European region is not affected regionally by TSEF. The findings show that TSEF positively impacts FLPR in Belarus, Bulgaria, Croatia, Denmark, Finland, France, Ireland, Luxembourg, Portugal, and the United Kingdom, which is significant at the 1% significance level. Moreover, the Netherlands, Romania, and Spain show a positive impact at a 5% significance level and Switzerland at a 1% significance level. As per findings, Luxembourg has attained the highest positive effect on FLPR from TSEF at a 1% significant level, which says that FLPR increases by 1.65% on average for each 1% increase in the TSEF when holding other variables constant. Furthermore, this is supported by the scatter portrayed for Spain, as it shows a positive influence between variables. This is supported by Casarico et al (2016). It says a strong and positive correlation exists between female participation in post-secondary education. Moreover, this study advances knowledge regarding the possible influence of local labour market circumstances on women's decisions to enrol in post-secondary education. This is a significant problem, particularly in nations with a dearth of human capital (Casarico et al. 2016). According to the results, actions to enhance the local labour market for women workers may positively affect women's decisions to pursue further education (Casarico et al. 2016). Czech Republic, Hungary, Italy, Lithuania, and Poland show a significant negative impact on FLPR at the 1% significance level, while Albania is substantial at 10% and negative. Findings show only the positive effect of TSEF on FLPR in countries in the European region; they do not show any country with a significant negative impact. The scatter portrayed for Poland shows that Poland has a negative effect between the variables TSEF and FLPR. The country-specific impact's multiple regression results corroborate this as well. It shows that Poland has the least detrimental effect of TSEF on FLPR; when other factors stay the same, FLPR falls by an average of 0.16% when TSEF rises by 1%. According to the study done by Feldmann (Feldmann 2004), the author has claimed that Poland, Hungary, and Czech Republic have significant labour market rigidities that still exist in these countries. This paper has shown several suggestions to each country: Hungary should lower labour taxes for disability pensions; Poland should lower the minimum wage for younger workers and relax its legal restrictions on working hours; and the Czech Republic should boost enrolment in higher education and loosen its general stance on dismissal protection. While these steps should be taken first, they are far from adequate. 4.4 North America Findings illustrate the fixed effect and multiple linear regression results of the two countries in the North American region for the impact of HDI and TSEF on FLPR. The results demonstrate that HDI in North American countries, including Mexico and Panama, does not influence FLPR. Still, the North American region is significant at a 5% level, negatively impacting FLPR. This indicates that, when holding all other variables constant, FLPR decreases by an average of 44.76% when HDI increases by one index. However, results show that in certain countries, such as Mexico and Panama, HDI and TSEF do not significantly affect FLPR. Human development issues about women and youth are fascinating because they highlight systemic problems in Middle Eastern and North African countries that prevent these groups from living fulfilling lives utilising their potential capabilities (Salehi-Isfahani 2013). This leads to unfavourable employment outcomes for women. Furthermore, TSEF in the North American region positively influences FLPR at a 5% significance level in the fixed effect model. It described that, for every 1% increase in TSEF, FLPR rises by an average of 0.21% while keeping all other factors constant. Country-specific impact demonstrates a positive effect of TSEF on FLPR in Mexico at a 5% significance level. FLPR increases by an average of 0.22% when TSEF increases by 1%, all other things being held constant. Scatters portrayed for these two countries also support this result by showing a positive influence between variables. In North America, the positive relationship between FLPR and TSEF in some areas or nations like Mexico may point to a possible relationship between school enrolments and female labour force participation (Contreras and Plaza 2010). More students enrolled in school could indicate more educational opportunities, improving women's empowerment and skill acquisition and positively affecting women's employment. Nonetheless, the lack of a discernible effect of TSEF on FLPR in other areas or nations may suggest that, although education, as measured by school enrolments, may play a role, it may not be the only factor affecting female labour force participation (Ibourk and Elouaourti 2023). Other cultural, policy-related, or socio-economic variables may also significantly impact women's participation in the workforce. The findings highlight how difficult it is to comprehend how education indicators, such as the enrolment rate in territorial schools, relate to the female labour force participation rate. Although education is widely regarded as a crucial element in facilitating women's involvement in the workforce, its effects may differ considerably depending on contextual factors unique to particular territories or regions. Therefore, designing effective policies to increase female labour force participation requires a thorough analysis considering various factors beyond education. 4.5 South America As per the results, the regional impact of HDI on FLPR is not seen in the South American region, but several countries depict a significant impact at different significance levels. Uruguay, a South American country, has a substantial and positive influence on FLPR and is effective at a 1% significance level. On the continent of South America, this is the most significant positive impact by a single country. According to this interpretation, when HDI increases by one index point, and all other variables remain constant, FLPR increases by an average of 401.41%. This outcome is further supported by the graph depicting Uruguay in appendices. This positive impact is supported by the study conducted by Kouam, Asongu, Nantchouang & Foretia (2023). His research shows that vulnerable female employment for female labour force participation can be moderated by an ample money supply and human development as assessed by the HDI. On the contrary, Chile significantly negatively impacts FLPR at a 5% significance level. This states that FLPR decreases by an average of 381.06% when HDI increases by one index and all other variables stay constant. This is HDI's most significant negative impact on FLPR that has been reached in the South American Continent. Contreras and Plaza argue that the collective presence of cultural variables statistically correlates with low female labour participation in Chile and more than offsets the beneficial impact of human capital elements. ( Contreras and Plaza 2010 ) TSEF in the South American continent does not influence FLPR. However, certain countries have a significant impact on FLPR. Chile shows a substantial and positive effect on TSEF and is effective at a 5% significance level, as shown in scatters. The result indicates that when TSEF rises by 1%, and all other variables remain constant, FLPR increases by an average of 0.68%. The lack of a regional effect of TSEF on FLPR in South America is consistent with the knowledge that school enrolment outcomes or educational indicators may not have a consistent impact on FLPR throughout the region. However, the noteworthy effects that differ by nation show that the situation is not uniform. Higher school enrolments and higher FLPR may be related in Chile, as indicated by the country's significant positive impact on TSEF at the 5% significance level ( Serrano et al. 2019 ). On the other hand, TSEF is significant at a 1% significance level in Uruguay, demonstrating a negative influence on FLPR. Conversely, the unexpectedly sizeable negative impact of Uruguay’s TSEF on FLPR begs exciting questions regarding the intricacies of the educational system or other socio-economic factors that may impede women's labour force participation even in the face of higher levels of school enrolment. 5. Conclusion and Policy Implication Numerous studies have been carried out to identify the determinants influencing FLPR in different nations across the globe. Panel data analysis has been used in relatively few publications, even though many developed empirical papers on FLPR exist. According to the literature, no research has investigated countries on all continents. On the other hand, only few studies have been undertaken to determine how each factor affects FLPR. Consequently, the paper's primary empirical contribution is to present the combined impact of TSEF and HDI, worldwide. The study examined how the HDI and TSEF affected FLPR on continents in nations worldwide. The study spanned six continents and 60 countries worldwide between 2000 and 2020. The given regression model for FLPR was assessed in a regional context using static panel data techniques like the pooled ordinary least square, RE, and FE models. Multiple linear regression was utilised to investigate the impact of FLPR on a country-specific basis. The findings of this study have led to the identification of several key insights. The findings highlight the significance of taking the FLPR into account for each of their multiple indicators, such as the HDI and TSEF. This eliminates the possibility of inconsistent results, in which specific factors may favour FLPR. In contrast, others may have a negative or insignificant impact. The study further reveals an impact on women's economic engagement, education, and human development on a global scale. It becomes clear that countries with greater TSEF more typically display elevated FLPR. At the same time, the study highlights how crucial the HDI is in determining comprehensive development because higher HDI scores are consistently associated with greater FLPR. These worldwide insights emphasise how urgent it is to create inclusive education policies and all-encompassing development plans on a global scale. The study emphasises the global applicability of its findings by focusing on geographical areas. It exemplifies how different the landscapes are in continents like Africa, Asia, Europe, North America, and South America, each of which presents opportunities and obstacles for advancing women's economic participation. HDI functions as a significant metric for assessing comprehensive development and the state of well-being. There is a positive correlation between the HDI scores of countries and the rates of female labour force participation. Collectively, these several variables create a favourable environment for women's active engagement. Education, as evidenced by school enrolment rates, significantly impacts women's employment. Increased enrolment rates indicate enhanced accessibility to education, thereby equipping women with the necessary skills and information to participate actively in the labour market. The findings highlight the universal significance of education and human development in forming FLPR and increasing gender equality. Acknowledging that the association between these variables is inconsistent throughout various regions is imperative. These countries have a wide range of diversity, encompassing nations that differ in their economic growth, cultural standards and legislative priorities. Despite advancements in education and the HDI, women’s participation in the labour sector may still encounter obstacles in certain nations due to socio-cultural reasons. Furthermore, it is commonly observed that more excellent school enrolment rates tend to be correlated to an increase in female engagement in the labour force. However, it is important to note that this association may experience temporary variations as countries progress through various phases of economic growth. The governments in these regions must utilise the findings regarding the effects of the HDI and TSEF on FLPR in order to enact novel legislation and make related policy decisions. Regardless of how stable or unstable an economy might appear, this paper can help strengthen it by addressing the policy implications for human development and tertiary education across continents with various geographical regions. Even though HDI and TSEF significantly impact FLPR in many countries, policymakers could arrive at decisions more promptly by enhancing how those variables affect women's employment. Based on empirical research, TSEF and HDI significantly improve economic development, and promote women's employment. Despite the large sample size, some countries were excluded due to the absence of published data when each continent was analysed using its HDI and TSEF parameters. To enhance research outcomes, the empirical analysis could be expanded to include all countries on each continent, considering changes in HDI, TSEF, and FLPR values over time. Furthermore, various approaches and techniques for data analysis can be used in subsequent research projects. To obtain a more comprehensive understanding of diverse viewpoints regarding women's employment, further study initiatives may concentrate on the differences amongst HDI sub-indices, including mean and expected years of education, life expectancy at birth, and gross national income per capita. Declarations Conflict of interest The authors declare no competing interests. Data availability statement All data generated or analysed during this study are included in this published article and its supplementary information files. Consent to participate Not Applicable Consent for publication Not Applicable Funding The authors did not receive support from any organization for the submitted work. Code availability Not Applicable. Author Contributions Statement RJ conceptualised the study. MS, RJ and SP contributed to the design and conduction of the study. MS curated the data. MS and RJ undertook data analysis and interpreted the data. MS and RJ drafted the first manuscript. All authors critically reviewed, edited, and approved the final manuscript. JEL Classification code O15, N3, J21, J8, J16 References Aedo C, Walker, I (2012). Skills for the 21st Century in Latin America and the Caribbean : World Bank Publications. Afridi F, Mukhopadhyay, A, Sahoo, S (2016). Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme. IZA Journal of Labor Development, 5: 1–27. https://doi.org/10.1186/s40175-016-0053-y Amini S, Delgado, MS, Henderson, DJ, Parmeter, CF (2012). Fixed vs random: The Hausman test four decades later. In Essays in honor of Jerry Hausman (Vol. 29, pp. 479–513): Emerald Group Publishing Limited. Bajpai GC (2014). African women entrepreneur: Problems, challenges and future opportunities. International Journal of Managerial Studies Research, 2(5): 17–22. Baltagi BH, Feng, Q, Kao, C (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1): 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004 Biavaschi C, Eichhorst, W, Giulietti, C, Kendzia, MJ, Muravyev, A, Pieters, J, et al. (2012). Youth unemployment and vocational training. https://dx.doi.org/10.2139/ssrn.2158300 Bowen WG, Finegan, TA (2015). The economics of labor force participation (Vol. 2054): Princeton University Press. Busso M, Fonseca, DR (2015). Female labor force participation in Latin America: Patterns and explanations. Camou MM, Maubrigades, S, Thorp, R (2017). Gender inequalities and development in Latin America during the twentieth century : Routledge. Casarico A, Profeta, P, Pronzato, CD (2016). On the regional labour market determinants of female university enrolment in Europe. Regional Studies, 50(6): 1036–1053. https://doi.org/10.1080/00343404.2014.958455 Chauhan S, Gupta, P, Palvia, S, Jaiswal, M (2021). Information technology transforming higher education: A meta-analytic review. Journal of Information Technology Case Application Research, 23(1): 3–35. https://doi.org/10.1080/15228053.2020.1846480 Contreras D, Plaza, G (2010). Cultural factors in women's labor force participation in Chile. Feminist Economics, 16(2): 27–46. https://doi.org/10.1080/13545701003731815 Doğan B, Akyüz, M (2017). Female labor force participation rate and economic growth in the framework of Kuznets Curve: Evidence from Turkey. Review of Economic Business Studies, 10(1): 33–54. Dominic A, Amodu, L, Azuh, AE, Toluwalope, O, Oluwatoyin, MA (2017). Factors of gender inequality and development among selected low human development countries in sub-Saharan Africa. IOSR Journal of Humanities Social Science, 22(2): 1–7. http://dx.doi.org/10.9790/0837-2202040107 Dumith SC, Hallal, PC, Reis, RS, Kohl III, HW (2011). Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. Preventive medicine, 53(1–2): 24–28. https://doi.org/10.1016/j.ypmed.2011.02.017 Feldmann H (2004). How flexible are labour markets in the EU accession countries Poland, Hungary and the Czech Republic? Comparative Economic Studies, 46: 272–310. https://doi.org/10.1057/palgrave.ces.8100026 Gaddis I, Klasen, S (2014). Economic development, structural change, and women’s labor force participation: A reexamination of the feminization U hypothesis. Journal of population economics, 27: 639–681. https://doi.org/10.1007/s00148-013-0488-2 Ganguli I, Hausmann, R, Viarengo, M (2014). Closing the gender gap in education: What is the state of gaps in labour force participation for women, wives and mothers? International Labour Review, 153(2): 173–207. https://doi.org/10.1111/j.1564-913X.2014.00007.x Gumede V (2021). Revisiting poverty, human development and inequality in democratic South Africa. Indian Journal of Human Development, 15(2): 183–199. https://doi.org/10.1177/09737030211032961 Gyasi PA, Chen, Y, Hyde-Quartey, L, Ampon-Wireko, S (2019). Analysis of Impact of Female Education and Labour Force Participation on Disparities in Under-Five Mortality Rate among Selected African Countries. Public Policy Administration Research, 10(10). Hoechle D (2007). Robust standard errors for panel regressions with cross-sectional dependence. The stata journal, 7(3): 281–312. https://doi.org/10.1177/1536867X0700700301 Ibourk A, Elouaourti, Z (2023). Revitalizing Women's Labor Force Participation in North Africa: An Exploration of Novel Empowerment Pathways. International Economic Journal, 37(3): 462–484. https://doi.org/10.1080/10168737.2023.2227161 Idowu OO, Owoeye, T (2019). Female labour force participation in African countries: An empirical analysis. Indian Journal of Human Development, 13(3): 278–293. https://doi.org/10.1177/0973703019895234 Katz-Gerro T, Yaish, M (2003). Higher education: is more better? Gender differences in labour market returns to tertiary education in Israel. Oxford Review of Education, 29(4): 571–592. https://doi.org/10.1080/0305498032000153089 Kinoshita Y, Guo, F (2015). What can boost female labor force participation in Asia? : International Monetary Fund. Kpolovie PJ, Ewansiha, S, Esara, M (2017). Continental comparison of human development index (HDI). International Journal of Humanities Social Sciences Education, 4(1): 9–27. https://dx.doi.org/10.20431/2349-0381.0401002 Lebeau Y, Oanda, IO (2020). Higher education expansion and social inequalities in sub-Saharan Africa: Conceptual and empirical perspectives. UNRISD Working Paper. Lechman E, Kaur, HJNef (2015). Economic growth and female labor force participation–verifying the U-feminization hypothesis. New evidence for 162 countries over the period 1990–2012. Economics and Sociology, 162: 1990–2012. https://dx.doi.org/10.2139/ssrn.2551476 Loichinger E, Cheng, Y-hA (2018). Feminising the workforce in ageing East Asia? The potential of skilled female labour in four advanced economies. Journal of Population Research, 35: 187–215. https://doi.org/10.1007/s12546-018-9203-1 Marois G, Sabourin, P, Bélanger, A (2019). How reducing differentials in education and labor force participation could lessen workforce decline in the EU-28. Demographic Research, 41: 125–158. Mau S, Verwiebe, R (2010). European societies: Mapping structure and change : Policy Press. Mulugeta G, Finance (2021). The role and determinants of women labor force participation for household poverty reduction in Debre Birhan town, North Shewa zone, Ethiopia. Cogent Economics, 9(1): 1892927. https://doi.org/10.1080/23322039.2021.1892927 Naidu S (2016). Does human development influence women’s labour force participation rate? Evidences from the Fiji Islands. Social Indicators Research, 127(3): 1067–1084. https://doi.org/10.1007/s11205-015-1000-z Naidu SC (2016). Domestic labour and female labour force participation: Adding a piece to the puzzle. Economic Political Weekly: 101–108. https://www.jstor.org/stable/44166674 Nartgün SS, Sezen-Gültekin, G, Limon, İ (2017). Examination of 2015 Human Development Index in Terms of Education: Comparison of the Continents and Turkey. Journal of Education Practice, 8(3): 37–47. Olowolagba ZT (2022). The Influence of Religion and Human Development on the Participation of Women in the Labor Force: A Case of the African Continent . Hamad Bin Khalifa University (Qatar), Ortiz Rodríguez J, Pillai, VK (2019). Advancing support for gender equality among women in Mexico: Significance of labor force participation. International Social Work, 62(1): 172–184. https://doi.org/10.1177/0020872817717323 Pal S, Chaudhuri, S (2020). Falling female labour force participation: An analysis of selected countries. Asian Journal of Economics, Finance Management: 154–164. https://globalpresshub.com/index.php/AJEFM/article/view/859 Phan D, Coxhead, I (2014). Education in Southeast Asia: Investments, achievements, and returns. In Routledge handbook of Southeast Asian economics (pp. 245–269): Routledge. Posel D, Rudwick, S (2014). Marriage and Bridewealth (ilobolo) in contemporary Zulu society. African Studies Review, 57(2): 51–72. https://doi.org/10.1017/asr.2014.47 Qureshi MG (2012). The gender differences in school enrolment and returns to education in Pakistan. The Pakistan Development Review: 219–256. https://www.jstor.org/stable/24397949 Rahman Z (2020). Female labor force participation and economic growth: Granger causality test. Reig-Martínez E (2013). Social and economic wellbeing in Europe and the Mediterranean Basin: Building an enlarged human development indicator. Social Indicators Research, 111: 527–547. https://doi.org/10.1007/s11205-012-0018-8 Salehi-Isfahani D (2013). Rethinking human development in the Middle East and North Africa: The missing dimensions. Journal of Human Development Capabilities, 14(3): 341–370. https://doi.org/10.1080/19452829.2013.764851 Schaner S, Das, S (2016). Female labor force participation in Asia: Indonesia country study. Asian Development Bank Economics Working Paper Series(474). https://dx.doi.org/10.2139/ssrn.2737842 Serrano J, Gasparini, L, Marchionni, M, Glüzmann, P (2019). Economic cycle and deceleration of female labor force participation in Latin America. Journal for Labour Market Research, 53: 1–21. https://doi.org/10.1186/s12651-019-0263-2 Sourander A, Chudal, R, Skokauskas, N, Al-Ansari, AM, Klomek, AB, Pornnoppadol, C, et al. (2018). Unmet needs of child and adolescent psychiatrists among Asian and European countries: does the Human Development Index (HDI) count? European child adolescent psychiatry, 27: 5–8. https://doi.org/10.1007/s00787-017-1095-7 Tanaka T, Takahashi, K, Ōtsuka, K (2020). Increasing female education, stagnating female labor force participation, and gains from marriage: The case of rural Bangladesh. National Graduate Institute for Policy Studies. Thévenon O (2013). Drivers of female labour force participation in the OECD. https://dx.doi.org/10.1787/5k46cvrgnms6-en UNESCO (2022). Despite some progress, women in sub-Saharan Africa still face major obstacles in getting good-quality jobs. https://dakar.iiep.unesco.org/en/news/despite-some-progress-women-sub-saharan-africa-still-face-major-obstacles-getting-good-quality. Vuko T, Čular, M (2014). Finding determinants of audit delay by pooled OLS regression analysis. Croatian Operational Research Review: 81–91. Zaheer R, Qaiser, S (2016). Factors that affect the participation of female in labor force: A macro level study of Pakistan. IOSR Journal of Economics Finance, 7(2): 20–24. Additional Declarations No competing interests reported. Supplementary Files S1Appendix.xlsx S2Appendix.docx S3Appendix.docx S4Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4508525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317010354,"identity":"20f75124-ff9d-4e09-b704-452a2e995e4b","order_by":0,"name":"Malsha Silva","email":"","orcid":"","institution":"SLIIT Business School, Sri Lanka Institute of Information Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Malsha","middleName":"","lastName":"Silva","suffix":""},{"id":317010355,"identity":"096792d9-bbb8-47ea-9654-21914512b513","order_by":1,"name":"Ruwan Jayathilaka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYPACNjkGdsaGAwwMFgZEazFmYAZqOcAgQbQWhsQGZiBJlBbzGTmGnwsY+NLnNzM3Hv7AIGFMUIvMjRxj6RkMbLkbDkMcZkZQi4RE7gZpHpAWqF9siNGy+TdQS7p8MwlatoFsSWAg3mE8779Z8xiwGYL9csaACO9LsKcl3+apOCYv397++ENFhY1hA0E9YGBwDMYgTj0I1BCvdBSMglEwCkYeAADAITOiMsBS+gAAAABJRU5ErkJggg==","orcid":"","institution":"SLIIT Business School, Sri Lanka Institute of Information Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ruwan","middleName":"","lastName":"Jayathilaka","suffix":""},{"id":317010356,"identity":"a794d73d-88f5-443a-a962-2911ce5b4b88","order_by":2,"name":"Suren Peter","email":"","orcid":"","institution":"SLIIT Business School, Sri Lanka Institute of Information Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suren","middleName":"","lastName":"Peter","suffix":""}],"badges":[],"createdAt":"2024-05-31 11:10:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4508525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4508525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84719123,"identity":"6b5429fa-8b23-4ecb-bf87-fdf92806c5e7","added_by":"auto","created_at":"2025-06-16 14:47:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":744776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4508525/v1/f91b274f-e4f6-48a0-8454-25e5d0007aa6.pdf"},{"id":58784443,"identity":"49bfcbd6-9611-4fbd-884c-a3bdb460059d","added_by":"auto","created_at":"2024-06-21 05:42:49","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":82121,"visible":true,"origin":"","legend":"","description":"","filename":"S1Appendix.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4508525/v1/039ea9c95736d5055a506dfd.xlsx"},{"id":58784692,"identity":"659f0cc2-61e1-404f-a3fb-887e30b30062","added_by":"auto","created_at":"2024-06-21 05:50:49","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":365036,"visible":true,"origin":"","legend":"","description":"","filename":"S2Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4508525/v1/76197aa85840997358e733c5.docx"},{"id":58784440,"identity":"740f6dd2-ab9c-4289-8300-b39c41ab6011","added_by":"auto","created_at":"2024-06-21 05:42:49","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":72870,"visible":true,"origin":"","legend":"","description":"","filename":"S3Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4508525/v1/463a27cdf16974ba895ce69d.docx"},{"id":58785460,"identity":"b4160312-df7d-41b5-a5fa-33f1fc152699","added_by":"auto","created_at":"2024-06-21 05:58:49","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":63862,"visible":true,"origin":"","legend":"","description":"","filename":"S4Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4508525/v1/683e6b571160324a7f633793.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Women at Work: An International Perspective on the Interconnected Forces of Human Development, Female Education, and Labour Force Participation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Female Labour Force Participation (FLPR) has received increased attention among policy makers as the impact on the economies of nations by the female population are becoming significant. Countries today prioritise women's employment for several reasons, including productivity, efficiency, impact on family finances, etc. While some scholars posit the negative side of increased female labour force participation (Doğan and Aky\u0026uuml;z 2017), policymakers, academics, and campaigners, all pay attention to female labour force participation since it is a critical component of global socio-economic development (Lechman and Kaur 2015). Beyond an individual's employment situation, the importance of female labour market participation extends to broad socio-economic ramifications for a nation. It plays a crucial role in economic expansion (Rahman 2020).\u003c/p\u003e\n\u003cp\u003eFurthermore, it is crucial in reducing poverty, especially in households where women are the head of the family (Mulugeta and Finance 2021). In addition, gender equality and female labour force participation go hand in hand (Ortiz Rodr\u0026iacute;guez and Pillai 2019). These elements highlight how women can succeed in various vocations and positions while questioning traditional gender norms and roles. Therefore, women's employment is a significant component of economic development.\u003c/p\u003e\n\u003cp\u003eFemale labour force participation is strongly influenced by human development, a broad framework that includes education, health, income, and gender equality in various geographic contexts. Globally, there is a clear correlation between female labour force participation and the Human Development Index (HDI) (SC Naidu 2016). The impact of human development on female labour market participation is also seen across the African continent. Female involvement rates reach 40% in nations with better HDI outcomes, such as Seychelles and Mauritius (SC Naidu 2016). Japan and South Korea are two Asian countries with strong HDI that continuously report higher female labour force participation rates, frequently reaching 50% (Schaner and Das 2016). Therefore, promoting human development becomes a critical factor in increasing the proportion of women in the labour market globally.\u003c/p\u003e\n\u003cp\u003eAs a significant factor that shapes the behaviour of female labour force participation, rather than discussing education in general, this study focuses on the Tertiary School Enrolment Rate of Females (TSEF), which emphasises the impact and importance of education. Territorial school enrolment, which guarantees access to high-quality education within particular geographic areas, significantly affects women's participation in the labour sector (Schaner and Das 2016). From a global perspective, territorial school enrolment is crucial in determining the labour force involvement of women (Qureshi 2012) in South American nations like Uruguay and Argentina, where education is more widely available and female labour force participation rates above 50%, the impact of territorial school enrolment on female labour force involvement may be seen both in developed and developing countries (Pal and Chaudhuri 2020; Tanaka et al. 2020). Therefore, to increase the participation of women in the workforce globally, it is crucial to guarantee equal access to high-quality education for women.\u003c/p\u003e\n\u003cp\u003eThe impact of HDI indicators, including gender equality, income, health, and education, on women's labour force participation is significant, and their role in guaranteeing women's access to education has been emphasised. Human development covers the other societal aspects, whereas enrolment in territory schools covers entirely the educational elements. Thus, this study examines the impact of TSEF and HDI on the FLPR, emphasising the significance of each and providing information on which factors require greater focus to achieve the ideal participation rate. Consequently, the study makes four contributions which advance the body of literature. Firstly, the study examines the global perceptiveness of African, Asian, European, North American, and South American continents, considering over sixty countries and spanning almost two decades, to analyse the overall impact of HDI and TSEF on FLPR. While previous research has concentrated on certain nations or regions, a thorough worldwide analysis has yet to focus on these continents collectively. Secondly, this study adds a comparative perspective to the body of literature by performing a country-by-country analysis aimed at each country's behaviour to learn more about the influence of HDI and TSEF on FLPR. Thirdly, current research has only focused on the factors influencing FLPR in individual nations. Nevertheless, empirical studies regarding the combined impacts of the HDI and TSEF is absent in this area and on many continents. The paucity of literature on the subject also inspired this research. Therefore, the findings contribute to filling in the empirical gaps in this field. Finally, the current study also helps to adjust policies to improve existing economic circumstances while offering suggestions to the policy makers and other economic decision-makers. It also enables policymakers to evaluate the impact of their past decisions and policies on the behaviour of women who participate in the labour force.\u003c/p\u003e\n\u003cp\u003eThe article is organised as follows: an introduction to the topic that includes information about the\u0026nbsp;essential background of the study\u0026nbsp;and outline; an overview of relevant research that highlights variables related to Africa, Asia, Europe, North America, and South America regions; data and methodology; results and discussion of the empirical findings that explain how HDI and TSEF affect FLPR; and, finally, the conclusion of the study\u0026nbsp;and policy\u0026nbsp;implications.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe literature on female labour force participation often emphasises the relationship between women's employment and human development and the enrolment rate in tertiary\u0026nbsp;education. Education and human development have been highlighted as essential to enabling women to work and thus stimulating the country's economic growth. A discussion of earlier research relevant to the variables examined in this study is discussed based on\u0026nbsp;continents.\u003c/p\u003e\n\u003ch2\u003e2.1 Africa\u003c/h2\u003e\n\u003cp\u003eThe majority of African nations have been caught up in civil war, plagued by poverty, underdevelopment, and gender discrimination (Bajpai 2014). In contrast to men, African women have higher unemployment rates in the educated and less educated segments of the population due to obstacles preventing them from fully participating in the economy (Bajpai 2014). The poor performance of the South African economy leads to low levels of human development and high levels of income inequality (Gumede 2021). Studies from several African countries show how closely HDI and female labour force participation interact. The average HDI across African nations is statistically and significantly lower than the global average (Kpolovie et al. 2017). Despite advancements in HDI metrics, inequalities persist in affecting female employment prospects (Olowolagba 2022).\u003c/p\u003e\n\u003cp\u003eMoreover, infrastructure constraints, cultural norms, and socio-economic variables affect women's access to higher education and their participation in the workforce (Lebeau and Oanda 2020). Therefore, initiatives aimed at improving girls' education have gained momentum in recent years, which has caused a rise in the enrolment rates of females. A positive trend is apparent with female labour force participation rising as education becomes more widely available (Ganguli et al. 2014). This highlights that increased female labour force participation is frequently correlated with higher enrolment rates in territorial schools, underscoring education's critical role in giving women economic power (Gyasi et al. 2019). However, differences in educational access and development indices mean disparities exist across different African regions, affecting how much women engage in the workforce (Gyasi et al. 2019). Pursuing long-term educational reforms and socio-economic development programs to mitigate these disparities is essential for increasing female labour force participation in various African countries. These studies highlight the importance of improving human development and education for women's employment.\u003c/p\u003e\n\u003ch2\u003e2.2 Asia\u003c/h2\u003e\n\u003cp\u003eIn certain economically developed Asian countries, such as South Korea and Japan, the relationship between female labour force participation and HDI illuminates the conducive atmosphere that higher HDI scores foster (Gaddis and Klasen 2014). However, in some South Asian nations like Bangladesh and India, with lower HDI scores, the connection between HDI and female labour force participation is more nuanced. Although advances in HDI positively affect female workforce engagement, socio-cultural norms and inequalities impede women's ability to find work (Sourander et al. 2018). Most women in South Asian countries are not very focused on education but rather on being good housewives, where the enrolment in territory education is comparatively less (Chauhan et al. 2021). Especially in countries like India and Sri Lanka, most women are more focused on managing their relationships with families and relatives rather than working. Even South Asian countries have confusing trends in territory school enrolments. Countries like Japan and China are encouraging and motivating females towards higher education, and thereby, they aim to have an educated female labour force participation, which may result in a solid business world (Phan and Coxhead 2014). Therefore, having sufficient higher education avenues would result in more engagement in the labour force.\u003c/p\u003e\n\u003cp\u003eIt is believed that those educated female workforce would establish their own small and medium scale businesses (Phan and Coxhead 2014). Women's participation in the workforce is positively impacted by greater access to education, reflected in higher territory school enrolment (Kinoshita and Guo 2015). However, differences still exist in areas with lower development indices and access to education, which affects women's participation in the labour force (Afridi et al. 2016). However, in South Asian countries like Sri Lanka, India, and Pakistan, despite the education level, most women are employed in different sectors such as agriculture, apparel, etc. (Schaner and Das 2016). Therefore, studies have derived another conclusion that the employment sector indirectly alters the extent of the impact of territory school enrolments and HDI on female labour force participation. This implies that human development and education indicators significantly influence women's participation in the labour force.\u003c/p\u003e\n\u003ch2\u003e2.3 Europe\u003c/h2\u003e\n\u003cp\u003eEuropean studies show a positive correlation between higher HDI scores and female labour force participation (Dumith et al. 2011). However, specific economic and educational opportunity differences affect women's participation in the labour force (Reig-Mart\u0026iacute;nez 2013). Unlike some areas where traditional roles limit female education, Europe typically shows greater educational access and inclusivity. The region's emphasis on achieving gender parity in education has resulted in a higher percentage of female students enrolled overall (Biavaschi et al. 2012). The relationship between increased female labour force participation and territory school enrolment is still changing due to ongoing education and gender equality initiatives throughout Europe (Mau and Verwiebe 2010). Policies that address inequality, advance inclusivity, and improve the quality of education are critical to developing a positive relationship between increased female workforce participation and territory school enrolment throughout a heterogeneous socio-economic spectrum (Mau and Verwiebe 2010). Compared to all other continents in the universe, Europe has the highest HDI, with a significant and overwhelming preponderance over the global average (Kpolovie et al. 2017). Higher HDI-scoring European countries, like Norway and Sweden, also have more stable economies and better access to education, which creates an environment that encourages more elevated rates of female labour force participation (Casarico et al. 2016). Altogether, the correlation between territory school enrolments and HDI highlights how significant education and socio-economic advancement is in shaping the participation of women in the labour force in Europe.\u003c/p\u003e\n\u003ch2\u003e2.4 North America\u003c/h2\u003e\n\u003cp\u003eThe human development index of North America is not substantially different (Kpolovie et al. 2017). Nevertheless, disparities exist among North American communities and regions despite the overall higher HDI scores (Nartg\u0026uuml;n et al. 2017). These differences affect how the HDI and female labour force participation are correlated, influencing how some groups can access economic resources, high-quality healthcare, and education (Nartg\u0026uuml;n et al. 2017). North America strongly focuses on gender parity in education and generally boasts higher levels of educational accessibility and inclusivity. However, subtle differences exist between nations and areas where socio-economic factors, cultural dynamics, and regional policies affect female labour force participation (Bowen and Finegan 2015). Access to high-quality education can be complex in marginalised communities, impacting women's workforce participation (Th\u0026eacute;venon 2013). Disparities do, however, exist in marginalised communities and areas, highlighting the necessity of inclusive development and educational policies to guarantee fair opportunities for women to participate in the workforce across North America (Busso and Fonseca 2015). To ensure sustained progress in improving female labour force participation across North America's diverse landscapes and address disparities, it is imperative that inclusive educational policies and socio-economic development initiatives continue to be prioritised.\u003c/p\u003e\n\u003ch2\u003e2.5 South America\u003c/h2\u003e\n\u003cp\u003eDifferences exist in the relationships between female labour force participation and HDI in South American nations (Kpolovie et al. 2017). The South American region struggles with socio-economic gaps and restricted access to high-quality education, especially in rural areas, which serve as obstacles for women looking to enter the workforce (Camou et al. 2017). Yet, specific disparities in cultural beliefs and educational opportunities impact women's involvement in the labour force among various South American nations (Th\u0026eacute;venon 2013). Continuous efforts to enhance gender inclusivity and educational access, support a changing relationship between higher female labour force participation and territory school enrolment in various South American landscapes (Th\u0026eacute;venon 2013). There is a promising trend, with an increasing proportion of women entering the workforce as access to education increases (Aedo and Walker 2012). Studies repeatedly show that greater enrolment in territorial schools is correlated with higher levels of female workforce participation.\u003c/p\u003e\n\u003cp\u003eSimilarly, South American nations with high HDI scores, such as Chile and Uruguay, generally have higher rates of economic opportunity and gender parity in education, which benefits women's participation in the labour force (Busso and Fonseca 2015). Women are more likely to be engaged in the workforce when they have greater access to education, as shown by higher enrolment in territory schools. Moreover, areas with higher HDI scores offer better economic stability and access to education, creating an atmosphere that encourages more elevated rates of female labour force participation.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003ePanel data from 2000 to 2020 covering 60 countries were used in the study. Specifically, nine African, 18 Asian, 27 European, 2 North American and four South American countries were examined. The definitions of variables and data sources are displayed in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Data Sources and Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.411764705882355%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.470588235294116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.411764705882355%\" valign=\"top\"\u003e\n \u003cp\u003eFemale Labor Force Participation Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.470588235294116%\"\u003e\n \u003cp\u003eWorld Bank Report\u003c/p\u003e\n \u003cp\u003ehttps://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.411764705882355%\" valign=\"top\"\u003e\n \u003cp\u003eTertiary School Enrolment of Female (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.470588235294116%\"\u003e\n \u003cp\u003eWorld Bank Report\u003c/p\u003e\n \u003cp\u003ehttps://data.worldbank.org/indicator/TE.SEC.NENR.FE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.411764705882355%\" valign=\"top\"\u003e\n \u003cp\u003eHuman Development Index (Scaled 0 to 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.470588235294116%\"\u003e\n \u003cp\u003eOur World in Data\u003c/p\u003e\n \u003cp\u003ehttps://ourworldindata.org/human-development-index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026rsquo;s Compilation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aims to examine the impact of HDI and TSEF on FLPR. Two static linear panel models are developed based on the conceptual framework and the literature review. Using HDI and TSEF as inputs, an empirical model was developed for the FLPR as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eEq (1) is developed to test the regional impact of HDI and TSEF on FLPR in Africa, Asia, Europe, North America, and South America. Eq (2) is designed to test the country-specific impact on FLPR. FLPR\u003csub\u003eit\u0026nbsp;\u003c/sub\u003erepresents the Female Labour force Participation Rate, where the country is denoted by i and the time is denoted by t. TSEF\u003csub\u003eit\u003c/sub\u003e represents the Tertiary school enrolment of Females, HDI\u003csub\u003eit\u003c/sub\u003e denotes the Human Development Index. The random error term is represented by \u0026nbsp;.\u003c/p\u003e\n\u003cp\u003eA particular procedure was followed to standardise the variables. To accommodate the different sizes of the variables, the coefficients corresponding to each variable were suitably modified during the standardisation process. After that, the heteroscedasticity problem was mitigated by converting the data into a reliable standard error.\u003c/p\u003e\n\u003ch2\u003e3.1 Estimation Procedures\u003c/h2\u003e\n\u003cp\u003ePooled Ordinary Least Square (POLS), Fixed Effect (FE), and Random Effect (RE) models were employed at the regional level to estimate the given regression model for female labour force participation based on econometrics literature (Hoechle 2007). The study used a highly balanced panel data set and the panel regression model to determine the individual effects of HDI and TSEF on FLPR at the regional level. Country-specific impact and time-specific impact are two essential features of these models. The objective is to determine the most suitable and reliable model for analysis. Estimates employing the RE and FE models are preferred over the POLS model in the event of a regional effect when the trend remains constant across regions. The accuracy of the RE model is higher than that of the FE model. To select the best model from the Ordinary Least Squared Method, Fixed Effect model hypothesis testing and Random Effect model hypothesis testing, three specification tests were used: the F-Test, the Hausman specification test and the Breusch-Pagan Lagrange Multiplier Test (LM Test). The F-test is used to select the specification tests among FE and POLS (Vuko and Čular 2014) and the LM Test is used to choose between RE and POLS (Baltagi et al. 2012). However, the choice between the FE and RE models will be made using the Hausman test (Amini et al. 2012).\u003c/p\u003e\n\u003cp\u003eFurthermore, the study uses a multiple linear regression model to investigate the country-specific impact of HDI and TSEF on FLPR. The data set analysed in this study is presented in the S1 Appendix. Moreover, the linear fits of the HDI and TSEF were also generated as scatter plot graphs to examine the trends of the influence of two independent variables on FLPR in each country, as shown in the S2 Appendix.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eSeveral observations, mean, standard deviation, and minimum and maximum values were observed for the variables HDI, TSEF and FLPR and have been presented in the S3 Appendix, to examine the dataset further. The descriptive statistics of the continents for the factors that were taken into consideration for the main aim of the study are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Descriptive Statistics of the Impact of HDI and TSEF on FLPR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObs.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e50.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e13.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e10.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e87.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e45.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e25.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e99.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfrica\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e58.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e20.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e20.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e87.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e14.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e14.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e71.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e44.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e17.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e10.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e73.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e33.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e19.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e2.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e92.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e51.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e6.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e35.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e64.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e61.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e18.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e9.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e95.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNorth America\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e45.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e4.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e38.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e55.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e36.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e9.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e20.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e47.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth America\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.59731543624161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.583892617449665%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.093959731543624%\" valign=\"top\"\u003e\n \u003cp\u003e50.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e5.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e36.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.261744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e58.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.682926829268293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.24390243902439%\" valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.073170731707318%\" valign=\"top\"\u003e\n \u003cp\u003e58.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e19.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.447154471544716%\" valign=\"top\"\u003e\n \u003cp\u003e24.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.276422764227643%\" valign=\"top\"\u003e\n \u003cp\u003e99.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Author\u0026apos;s calculation based on secondary data.\u003c/p\u003e\n\u003cp\u003eAccording to the findings of the Breusch Pagan test and the Hausman specification test, the FE model was the preferred model for investigating the regional impact in Europe and North America. In contrast, the RE model was the most appropriate technique to analyse the regional impact in South America, Asia, and Africa. A time series multiple linear regression model was used to analyse the country-specific effects of TSEF and HDI on FLPR, and these findings are shown in the S4 Appendix.\u003c/p\u003e\n\u003ch2\u003e4.1 Africa\u003c/h2\u003e\n\u003cp\u003eAccording to the results, although HDI has no regional impact on FLPR in Africa, the country-specific impact of HDI on FLPR is seen in several countries in the African continent. The country-specific impact of HDI on FLPR infers that Madagascar, Morocco, and Rwanda have illustrated a significant favourable influence on FLPR at a 1% significance level. Morocco has attained the highest positive impact of HDI on FLPR, which is essential at the 1% level, where it says FLPR will increase on average by 72% for each index increase in the HDI when all other variables are held constant. Moreover, it is evident that as per the scatter plot portrayed for Morocco, given that the data are primarily linear, the relationship is relatively strong in Morocco. Specific findings also reveal a positive relationship between HDI and African FLPR variables (Olowolagba 2022). It further claims that while HDI is a composite measure that captures various aspects of development, its effects on FLPR are not uniform throughout Africa, as evidenced by the lack of a discernible regional impact of HDI on FLPR within Africa. The fact that country-specific effects have been identified, highlights the variability of these relationships even more (Idowu and Owoeye 2019). Therefore, the strong impact of HDI in countries like Madagascar, Morocco, and Rwanda is favourable for higher rates of FLPR.\u003c/p\u003e\n\u003cp\u003eHowever, in contrast, several countries, including Benin and Malta, two African nations, have identified a significant negative impact on FLPR at a 5% significant level. The country Benin has reached the lowest adverse effect of the HDI on FLPR, which is essential at the 1% level. It states that when all other variables are held constant, FLPR will decrease on average by 198.1% for every index increase in the HDI. Additionally, the graph portrayed for Benin clarifies that Benin exhibits a negative correlation between the variables. Previous studies show that gender indicators, including the human development index, expected and mean years of education, and labour force participation rate, all favour men among all the low-human development countries examined (Dominic et al. 2017). In their conclusion, they offer suggestions for reducing gender disparities. It implies that, even with comparatively higher levels of HDI, there are factors preventing women from employment, necessitating focused policy interventions.\u003c/p\u003e\n\u003cp\u003eFindings indicate that the regional impact of TSEF on FLPR in the African region has no effect between the variables, which is insignificant. Considering the country-specific impact of TSEF, the findings depict that Benin and Malta have significant coefficients at a 1% significance level with a positive effect on FLPR. The nation of Benin has experienced the most outstanding positive TSEF impact on FLPR, which is significant at the 1% level. It says that for every 1% increase in the TSEF, FLPR will rise by an average of 1.58% when all other factors are constant. This supports the study by Mantel (2017) proving that increased female labour force participation results from improvements in women\u0026apos;s educational outcomes.\u003c/p\u003e\n\u003cp\u003eFurthermore, this contradicts the study by Idowu and Owoeye by concluding that it has been determined that in some African countries, education is a positive predictor of the availability of female labour (Idowu and Owoeye 2019). An increase in female education raises the opportunity cost of not working, meaning that women with higher levels of education have greater incentive to seek employment. Likewise, the lack of appreciable regional effect of TSEF on FLPR in Africa implies that territory school enrolment has a highly variable impact on female labour force participation throughout the continent. The finding of disparate country-specific impacts supports the idea that different national contexts influence how territory school enrolments affect FLPR (Dominic et al. 2017).\u003c/p\u003e\n\u003cp\u003eHowever, Madagascar and Morocco indicate a significant negative impact on FLPR at a 1% significant level. At the 1% significance level, Morocco has experienced the most negligible negative implications from the TSEF on FLPR. It says FLPR will drop by 0.31% on average for every 1% increase in the TSEF when all other variables are held constant. Women also tend to work informally more frequently, which makes them more susceptible to disease, childbirth, losing their jobs, and ageing (UNESCO 2022). Furthermore, it claims that the relatively weak correlation between specific educational attainment levels and labour market participation could also explain this finding using microdata for several developing nations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e4.2 Asia\u003c/h2\u003e\n\u003cp\u003eAlthough HDI has no regional impact on FLPR in Asia, the results show that HDI has a country-specific impact on FLPR in several Asian countries. According to the findings, the country-specific impact of HDI on FLPR, Bangladesh, Brunei Darussalam, and Nepal have a significant favourable influence on FLPR at the 1% significance level, and India and Israel have a positive impact on FLPR at the 5% significance level. Brunei Darussalam has achieved the highest positive impact of the HDI on FLPR, which is significant at the 1% level. This means that when all other variables remain constant, FLPR will increase by an average of 46.43% for every index increase in the HDI. The scatter plot portrayed for Brunei Darussalam exhibits a positive scatter, with most of the scatters falling very close to the trendline, corroborating these findings. In the Asian region, the statistical findings show specific significant differences among countries, as the scatters were stated. Although the current study in Asia shows how HDI affects FLPR in different countries, this is consistent with earlier research showing that the effects of human development on FLPR vary across different national contexts (SC Naidu 2016). Research has frequently shown that although more significant human development, which includes health, income, and education, tends to correlate positively with greater participation of women in the workforce, this association may not hold everywhere (Zaheer and Qaiser 2016).\u003c/p\u003e\n\u003cp\u003eConversely, multiple countries involving Lao PDR, Saudi Arabia, and Thailand have identified a 1% significant negative impact on FLPR. According to the results, Thailand depicts the lowest negative impact of HDI on FLPR in the Asian continent. One index increase in the HDI leads to an average decrease in FLPR by 20.14% when holding other variables constant. Regionally, nationally, and for particular demographic groups, youth and women, there are disparities in human development (Salehi-Isfahani 2013). According to the study, several factors, including high reservation wages and demographic pressures, that come under HDI have contributed to the case\u0026apos;s high unemployment rates.\u003c/p\u003e\n\u003cp\u003eThe results demonstrate no regional impact of TSEF on FLPR in the Asian region. Regarding the effect of TSEF on a country-by-country basis, the results indicate that FLPR is positively impacted in Bangladesh, Cyprus, Georgia, Lao PDR, Saudi Arabia, and Thailand, where Georgia, Lao PDR, Saudi Arabia, and Thailand at a 1% significant level, Cyprus at a 5% significant level and Bangladesh at a 10% significant level. The two nations where TSEF has had the most significant positive impact on FLPR are Thailand and Saudi Arabia. When TSEF rises by 1% in Thailand, FLPR rises by an average of 0.59%, with all other variables remaining unchanged. This is supported by the study done by Posel and Rudwick to estimate labour force participation regressions for married women aged 15 to 49 (Posel and Rudwick 2014). It has been stated that in Asian countries like Indonesia, Thailand, Korea, Sri Lanka and the Philippines, tertiary education has increased the probability of women participating in labour. Because education is still improving, Posel and Rudwick stated that workers with post-secondary education will make up at least two-thirds of the labour force in every economy in the future (Posel and Rudwick 2014). If female labour force participation increases, follow the presumption that the percentage of women with post-secondary education may surpass 30% of the workforce by 2050 (Loichinger and Cheng 2018). So, they suggest that integrating women with higher levels of education, in particular, may be able to partially mitigate the pressing problem of labour shrinkage.\u003c/p\u003e\n\u003cp\u003eHowever, Israel and Nepal exhibit a significant adverse effect on FLPR at the 1% significance level, and India and Uzbekistan depicts a negative influence of TSEF on FLPR at the 5% significance level. According to the findings, Israel has the most negligible negative impact on the variables at the 1% significant level, with an average FLPR decrease of 0.29% when TSEF is increased by 1%, while the other variables remain unchanged. Nepal and Uzbekistan, have also achieved the most negligible negative impact between the variables at the 1% significant level and 5% significant level, respectively, with FLPR decreasing by an average of 0.64% when TSEF is increased by 1% while maintaining the same values for the other variables. A study by Katz-Gerro and Yaish proves that women\u0026apos;s employment chances are unaffected by their educational attainment, while men\u0026apos;s employment chances increase with increased education (Katz-Gerro and Yaish 2003). Consequently, it is crucial to consider a range of socio-economic structures, cultural norms when considering this relationship.\u003c/p\u003e\n\u003cp\u003eThe conclusions drawn from the analysis of the relationship between FLPR HDI and TSEF in Asia reflect the complex and heterogeneous nature of the effects of these variables on women\u0026apos;s participation in the labour force, both regional variability and country-specific subtleties.\u003c/p\u003e\n\u003ch2\u003e4.3 Europe\u003c/h2\u003e\n\u003cp\u003eThe results demonstrate that HDI has a country-specific impact on FLPR in several European countries despite having no regional effect on FLPR in Europe. Regarding the country-specific impact of the HDI on FLPR, results show that Belarus and Belgium have a positive effect on FLPR that is significant at the 1% significance level. In comparison, Poland has a significant favourable influence at the 5% significance level, while Albania, Italy and Sweden have a significant influence but at the 10% significance level. Belarus and Belgium exhibit a highly positive correlation, as seen in the graphs portrayed for these countries, with the scatters falling on the trendline. When increasing HDI in one index while keeping other variables constant, findings show that FLPR increases in Belgium by an average of 205.02%. This is also claimed by the study of (S Naidu 2016), that human development over the long and short term influences women\u0026apos;s labour force participation rates. The fact that different European countries have other impacts of HDI on FLPR highlights how varied this relationship is. Higher levels of human development in nations are linked to higher emphasis of women participation in the economy (Marois et al. 2019). Furthermore, though at differing degrees of significance, Albania, Italy, and Sweden all exhibit noteworthy effects on FLPR.\u003c/p\u003e\n\u003cp\u003eOn the other hand, several nations have found a significant negative impact at varying significance levels. In contrast, Bulgaria, Denmark, Finland, and Ireland have a substantial 1% significance level, Croatia, Luxembourg, and Portugal are significant at a 5% significance level, and Lithuania, Netherlands, and Spain have a 10% significance level. As per the findings, Finland shows the lowest negative impact of HDI on FLPR at a 1% significant level, where FLPR decreases on average by 27.6% for each index rise in HD, while holding her variables constant. Further the scatter portrayed for Finland also confirms this. The period shows a negative impact between the variables.\u003c/p\u003e\n\u003cp\u003eIn the same way, this relationship\u0026apos;s complexity is highlighted by the discovery of multiple European nations with notable adverse effects of HDI on FLPR at various significance levels. Countries such as Denmark, Ireland, Bulgaria, and others show significant negative relationships while countries like Croatia, Luxembourg, and Portugal show similar negative associations at a significance level of 5%. Furthermore, negative impacts on FLPR are observed at relatively less significance levels in Lithuania, the Netherlands, and Spain (Gaddis and Klasen 2014). This suggests that, despite higher levels of human development, certain socio-economic policy-specific factors may impede the FLPR in these countries.\u003c/p\u003e\n\u003cp\u003eThe findings show that FLPR in the European region is not affected regionally by TSEF. The findings show that TSEF positively impacts FLPR in Belarus, Bulgaria, Croatia, Denmark, Finland, France, Ireland, Luxembourg, Portugal, and the United Kingdom, which is significant at the 1% significance level. Moreover, the Netherlands, Romania, and Spain show a positive impact at a 5% significance level and Switzerland at a 1% significance level. As per findings, Luxembourg has attained the highest positive effect on FLPR from TSEF at a 1% significant level, which says that FLPR increases by 1.65% on average for each 1% increase in the TSEF when holding other variables constant. Furthermore, this is supported by the scatter portrayed for Spain, as it shows a positive influence between variables. This is supported by Casarico et al (2016). It says a strong and positive correlation exists between female participation in post-secondary education.\u003c/p\u003e\n\u003cp\u003eMoreover, this study advances knowledge regarding the possible influence of local labour market circumstances on women\u0026apos;s decisions to enrol in post-secondary education. This is a significant problem, particularly in nations with a dearth of human capital (Casarico et al. 2016). According to the results, actions to enhance the local labour market for women workers may positively affect women\u0026apos;s decisions to pursue further education (Casarico et al. 2016).\u003c/p\u003e\n\u003cp\u003eCzech Republic, Hungary, Italy, Lithuania, and Poland show a significant negative impact on FLPR at the 1% significance level, while Albania is substantial at 10% and negative. Findings show only the positive effect of TSEF on FLPR in countries in the European region; they do not show any country with a significant negative impact. The scatter portrayed for Poland shows that Poland has a negative effect between the variables TSEF and FLPR. The country-specific impact\u0026apos;s multiple regression results corroborate this as well. It shows that Poland has the least detrimental effect of TSEF on FLPR; when other factors stay the same, FLPR falls by an average of 0.16% when TSEF rises by 1%. According to the study done by Feldmann (Feldmann 2004), the author has claimed that Poland, Hungary, and Czech Republic have significant labour market rigidities that still exist in these countries. This paper has shown several suggestions to each country: Hungary should lower labour taxes for disability pensions; Poland should lower the minimum wage for younger workers and relax its legal restrictions on working hours; and the Czech Republic should boost enrolment in higher education and loosen its general stance on dismissal protection. While these steps should be taken first, they are far from adequate.\u003c/p\u003e\n\u003ch2\u003e4.4 North America\u003c/h2\u003e\n\u003cp\u003eFindings illustrate the fixed effect and multiple linear regression results of the two countries in the North American region for the impact of HDI and TSEF on FLPR. The results demonstrate that HDI in North American countries, including Mexico and Panama, does not influence FLPR. Still, the North American region is significant at a 5% level, negatively impacting FLPR. This indicates that, when holding all other variables constant, FLPR decreases by an average of 44.76% when HDI increases by one index. However, results show that in certain countries, such as Mexico and Panama, HDI and TSEF do not significantly affect FLPR. Human development issues about women and youth are fascinating because they highlight systemic problems in Middle Eastern and North African countries that prevent these groups from living fulfilling lives utilising their potential capabilities (Salehi-Isfahani 2013). This leads to unfavourable employment outcomes for women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, TSEF in the North American region positively influences FLPR at a 5% significance level in the fixed effect model. It described that, for every 1% increase in TSEF, FLPR rises by an average of 0.21% while keeping all other factors constant. Country-specific impact demonstrates a positive effect of TSEF on FLPR in Mexico at a 5% significance level. FLPR increases by an average of 0.22% when TSEF increases by 1%, all other things being held constant. Scatters portrayed for these two countries also support this result by showing a positive influence between variables. In North America, the positive relationship between FLPR and TSEF in some areas or nations like Mexico may point to a possible relationship between school enrolments and female labour force participation (Contreras and Plaza 2010). More students enrolled in school could indicate more educational opportunities, improving women\u0026apos;s empowerment and skill acquisition and positively affecting women\u0026apos;s employment.\u003c/p\u003e\n\u003cp\u003eNonetheless, the lack of a discernible effect of TSEF on FLPR in other areas or nations may suggest that, although education, as measured by school enrolments, may play a role, it may not be the only factor affecting female labour force participation (Ibourk and Elouaourti 2023). Other cultural, policy-related, or socio-economic variables may also significantly impact women\u0026apos;s participation in the workforce.\u003c/p\u003e\n\u003cp\u003eThe findings highlight how difficult it is to comprehend how education indicators, such as the enrolment rate in territorial schools, relate to the female labour force participation rate. Although education is widely regarded as a crucial element in facilitating women\u0026apos;s involvement in the workforce, its effects may differ considerably depending on contextual factors unique to particular territories or regions. Therefore, designing effective policies to increase female labour force participation requires a thorough analysis considering various factors beyond education.\u003c/p\u003e\n\u003ch2\u003e4.5 South America\u003c/h2\u003e\n\u003cp\u003eAs per the results, the regional impact of HDI on FLPR is not seen in the South American region, but several countries depict a significant impact at different significance levels. Uruguay, a South American country, has a substantial and positive influence on FLPR and is effective at a 1% significance level. On the continent of South America, this is the most significant positive impact by a single country. According to this interpretation, when HDI increases by one index point, and all other variables remain constant, FLPR increases by an average of 401.41%. This outcome is further supported by the graph depicting Uruguay in appendices. This positive impact is supported by the study conducted by Kouam, Asongu, Nantchouang \u0026amp; Foretia (2023). His research shows that vulnerable female employment for female labour force participation can be moderated by an ample money supply and human development as assessed by the HDI.\u003c/p\u003e\n\u003cp\u003eOn the contrary, Chile significantly negatively impacts FLPR at a 5% significance level. This states that FLPR decreases by an average of 381.06% when HDI increases by one index and all other variables stay constant. This is HDI\u0026apos;s most significant negative impact on FLPR that has been reached in the South American Continent. Contreras and Plaza argue that the collective presence of cultural variables statistically correlates with low female labour participation in Chile and more than offsets the beneficial impact of human capital elements.\u0026nbsp;(\u003ca href=\"#_ENREF_12\" title=\"Contreras, 2010 #51\"\u003eContreras and Plaza 2010\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eTSEF in the South American continent does not influence FLPR. However, certain countries have a significant impact on FLPR. Chile shows a substantial and positive effect on TSEF and is effective at a 5% significance level, as shown in scatters. The result indicates that when TSEF rises by 1%, and all other variables remain constant, FLPR increases by an average of 0.68%. The lack of a regional effect of TSEF on FLPR in South America is consistent with the knowledge that school enrolment outcomes or educational indicators may not have a consistent impact on FLPR throughout the region. However, the noteworthy effects that differ by nation show that the situation is not uniform. Higher school enrolments and higher FLPR may be related in Chile, as indicated by the country\u0026apos;s significant positive impact on TSEF at the 5% significance level\u0026nbsp;(\u003ca href=\"#_ENREF_46\" title=\"Serrano, 2019 #54\"\u003eSerrano et al. 2019\u003c/a\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, TSEF is significant at a 1% significance level in Uruguay, demonstrating a negative influence on FLPR. Conversely, the unexpectedly sizeable negative impact of Uruguay\u0026rsquo;s TSEF on FLPR begs exciting questions regarding the intricacies of the educational system or other socio-economic factors that may impede women\u0026apos;s labour force participation even in the face of higher levels of school enrolment.\u003c/p\u003e"},{"header":"5. Conclusion and Policy Implication ","content":"\u003cp\u003eNumerous studies have been carried out to identify the determinants influencing FLPR in different nations across the globe. Panel data analysis has been used in relatively few publications, even though many developed empirical papers on FLPR exist. According to the literature, no research has investigated countries on all continents. On the other hand, only few studies have been undertaken to determine how each factor affects FLPR. Consequently, the paper\u0026apos;s primary empirical contribution is to present the combined impact of TSEF and HDI, worldwide.\u003c/p\u003e\n\u003cp\u003eThe study examined how the HDI and TSEF affected FLPR on continents in nations worldwide. The study spanned six continents and 60 countries worldwide between 2000 and 2020. The given regression model for FLPR was assessed in a regional context using static panel data techniques like the pooled ordinary least square, RE, and FE models. Multiple linear regression was utilised to investigate the impact of FLPR on a country-specific basis. The findings of this study have led to the identification of several key insights. The findings highlight the significance of taking the FLPR into account for each of their multiple indicators, such as the HDI and TSEF. This eliminates the possibility of inconsistent results, in which specific factors may favour FLPR. In contrast, others may have a negative or insignificant impact.\u0026nbsp;The study further reveals an impact on women\u0026apos;s economic engagement, education, and human development on a global scale. It becomes clear that countries with greater TSEF more typically display elevated FLPR. At the same time, the study highlights how crucial the HDI is in determining comprehensive development because higher HDI scores are consistently associated with greater FLPR. These worldwide insights emphasise how urgent it is to create inclusive education policies and all-encompassing development plans on a global scale. The study emphasises the global applicability of its findings by focusing on geographical areas. It exemplifies how different the landscapes are in continents like Africa, Asia, Europe, North America, and South America, each of which presents opportunities and obstacles for advancing women\u0026apos;s economic participation. HDI functions as a significant metric for assessing comprehensive development and the state of well-being. There is a positive correlation between the HDI scores of countries and the rates of female labour force participation. Collectively, these several variables create a favourable environment for women\u0026apos;s active engagement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEducation, as evidenced by school enrolment rates, significantly impacts women\u0026apos;s employment. Increased enrolment rates indicate enhanced accessibility to education, thereby equipping women with the necessary skills and information to participate actively in the labour market. The findings highlight the universal significance of education and human development in forming FLPR and increasing gender equality. Acknowledging that the association between these variables is inconsistent throughout various regions is imperative. These countries have a wide range of diversity, encompassing nations that differ in their economic growth, cultural standards and legislative priorities. Despite advancements in education and the HDI, women\u0026rsquo;s participation in the labour sector may still encounter obstacles in certain nations due to socio-cultural reasons. Furthermore, it is commonly observed that more excellent school enrolment rates tend to be correlated to an increase in female engagement in the labour force. However, it is important to note that this association may experience temporary variations as countries progress through various phases of economic growth.\u003c/p\u003e\n\u003cp\u003eThe governments in these regions must utilise the findings regarding the effects of the HDI and TSEF on FLPR in order to enact novel legislation and make related policy decisions. Regardless of how stable or unstable an economy might appear, this paper can help strengthen it by addressing the policy implications for human development and tertiary\u0026nbsp;education across continents with various geographical regions. Even though HDI and TSEF significantly impact FLPR in many countries, policymakers could arrive at decisions more promptly by enhancing how those variables affect women\u0026apos;s employment.\u003c/p\u003e\n\u003cp\u003eBased on empirical research, TSEF and HDI significantly improve economic development, and promote women\u0026apos;s employment. Despite the large sample size, some countries were excluded due to the absence of published data when each continent was analysed using its HDI and TSEF parameters. To enhance research outcomes, the empirical analysis could be expanded to include all countries on each continent, considering changes in HDI, TSEF, and FLPR values over time. Furthermore, various approaches and techniques for data analysis can be used in subsequent research projects. To obtain a more comprehensive understanding of diverse viewpoints regarding women\u0026apos;s employment, further study initiatives may concentrate on the differences amongst HDI sub-indices, including mean and expected years of education, life expectancy at birth, and gross national income per capita.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions Statement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eRJ conceptualised the study. MS, RJ and SP contributed to the design and conduction of the study. MS curated the data. MS and RJ undertook data analysis and interpreted the data. MS and RJ drafted the first manuscript. All authors critically reviewed, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification code\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO15, N3, J21, J8, J16\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAedo C, Walker, I (2012). \u003cem\u003eSkills for the 21st Century in Latin America and the Caribbean\u003c/em\u003e: World Bank Publications.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfridi F, Mukhopadhyay, A, Sahoo, S (2016). Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme. IZA Journal of Labor Development, 5: 1\u0026ndash;27. https://doi.org/10.1186/s40175-016-0053-y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmini S, Delgado, MS, Henderson, DJ, Parmeter, CF (2012). Fixed vs random: The Hausman test four decades later. In \u003cem\u003eEssays in honor of Jerry Hausman\u003c/em\u003e (Vol. 29, pp. 479\u0026ndash;513): Emerald Group Publishing Limited.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajpai GC (2014). African women entrepreneur: Problems, challenges and future opportunities. International Journal of Managerial Studies Research, 2(5): 17\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaltagi BH, Feng, Q, Kao, C (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1): 164\u0026ndash;177. https://doi.org/10.1016/j.jeconom.2012.04.004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiavaschi C, Eichhorst, W, Giulietti, C, Kendzia, MJ, Muravyev, A, Pieters, J, et al. (2012). Youth unemployment and vocational training. https://dx.doi.org/10.2139/ssrn.2158300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowen WG, Finegan, TA (2015). \u003cem\u003eThe economics of labor force participation\u003c/em\u003e (Vol. 2054): Princeton University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusso M, Fonseca, DR (2015). Female labor force participation in Latin America: Patterns and explanations.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamou MM, Maubrigades, S, Thorp, R (2017). \u003cem\u003eGender inequalities and development in Latin America during the twentieth century\u003c/em\u003e: Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasarico A, Profeta, P, Pronzato, CD (2016). On the regional labour market determinants of female university enrolment in Europe. Regional Studies, 50(6): 1036\u0026ndash;1053. https://doi.org/10.1080/00343404.2014.958455\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan S, Gupta, P, Palvia, S, Jaiswal, M (2021). Information technology transforming higher education: A meta-analytic review. Journal of Information Technology Case Application Research, 23(1): 3\u0026ndash;35. https://doi.org/10.1080/15228053.2020.1846480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eContreras D, Plaza, G (2010). Cultural factors in women's labor force participation in Chile. Feminist Economics, 16(2): 27\u0026ndash;46. https://doi.org/10.1080/13545701003731815\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoğan B, Aky\u0026uuml;z, M (2017). Female labor force participation rate and economic growth in the framework of Kuznets Curve: Evidence from Turkey. Review of Economic Business Studies, 10(1): 33\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominic A, Amodu, L, Azuh, AE, Toluwalope, O, Oluwatoyin, MA (2017). Factors of gender inequality and development among selected low human development countries in sub-Saharan Africa. IOSR Journal of Humanities Social Science, 22(2): 1\u0026ndash;7. http://dx.doi.org/10.9790/0837-2202040107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumith SC, Hallal, PC, Reis, RS, Kohl III, HW (2011). Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. Preventive medicine, 53(1\u0026ndash;2): 24\u0026ndash;28. https://doi.org/10.1016/j.ypmed.2011.02.017\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldmann H (2004). How flexible are labour markets in the EU accession countries Poland, Hungary and the Czech Republic? Comparative Economic Studies, 46: 272\u0026ndash;310. https://doi.org/10.1057/palgrave.ces.8100026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaddis I, Klasen, S (2014). Economic development, structural change, and women\u0026rsquo;s labor force participation: A reexamination of the feminization U hypothesis. Journal of population economics, 27: 639\u0026ndash;681. https://doi.org/10.1007/s00148-013-0488-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanguli I, Hausmann, R, Viarengo, M (2014). Closing the gender gap in education: What is the state of gaps in labour force participation for women, wives and mothers? International Labour Review, 153(2): 173\u0026ndash;207. https://doi.org/10.1111/j.1564-913X.2014.00007.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGumede V (2021). Revisiting poverty, human development and inequality in democratic South Africa. Indian Journal of Human Development, 15(2): 183\u0026ndash;199. https://doi.org/10.1177/09737030211032961\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGyasi PA, Chen, Y, Hyde-Quartey, L, Ampon-Wireko, S (2019). Analysis of Impact of Female Education and Labour Force Participation on Disparities in Under-Five Mortality Rate among Selected African Countries. Public Policy Administration Research, 10(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoechle D (2007). Robust standard errors for panel regressions with cross-sectional dependence. The stata journal, 7(3): 281\u0026ndash;312. https://doi.org/10.1177/1536867X0700700301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbourk A, Elouaourti, Z (2023). Revitalizing Women's Labor Force Participation in North Africa: An Exploration of Novel Empowerment Pathways. International Economic Journal, 37(3): 462\u0026ndash;484. https://doi.org/10.1080/10168737.2023.2227161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdowu OO, Owoeye, T (2019). Female labour force participation in African countries: An empirical analysis. Indian Journal of Human Development, 13(3): 278\u0026ndash;293. https://doi.org/10.1177/0973703019895234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatz-Gerro T, Yaish, M (2003). Higher education: is more better? Gender differences in labour market returns to tertiary education in Israel. Oxford Review of Education, 29(4): 571\u0026ndash;592. https://doi.org/10.1080/0305498032000153089\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinoshita Y, Guo, F (2015). \u003cem\u003eWhat can boost female labor force participation in Asia?\u003c/em\u003e : International Monetary Fund.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKpolovie PJ, Ewansiha, S, Esara, M (2017). Continental comparison of human development index (HDI). International Journal of Humanities Social Sciences Education, 4(1): 9\u0026ndash;27. https://dx.doi.org/10.20431/2349-0381.0401002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLebeau Y, Oanda, IO (2020). Higher education expansion and social inequalities in sub-Saharan Africa: Conceptual and empirical perspectives. UNRISD Working Paper.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLechman E, Kaur, HJNef (2015). Economic growth and female labor force participation\u0026ndash;verifying the U-feminization hypothesis. New evidence for 162 countries over the period 1990\u0026ndash;2012. Economics and Sociology, 162: 1990\u0026ndash;2012. https://dx.doi.org/10.2139/ssrn.2551476\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoichinger E, Cheng, Y-hA (2018). Feminising the workforce in ageing East Asia? The potential of skilled female labour in four advanced economies. Journal of Population Research, 35: 187\u0026ndash;215. https://doi.org/10.1007/s12546-018-9203-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarois G, Sabourin, P, B\u0026eacute;langer, A (2019). How reducing differentials in education and labor force participation could lessen workforce decline in the EU-28. Demographic Research, 41: 125\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMau S, Verwiebe, R (2010). \u003cem\u003eEuropean societies: Mapping structure and change\u003c/em\u003e: Policy Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulugeta G, Finance (2021). The role and determinants of women labor force participation for household poverty reduction in Debre Birhan town, North Shewa zone, Ethiopia. Cogent Economics, 9(1): 1892927. https://doi.org/10.1080/23322039.2021.1892927\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaidu S (2016). Does human development influence women\u0026rsquo;s labour force participation rate? Evidences from the Fiji Islands. Social Indicators Research, 127(3): 1067\u0026ndash;1084. https://doi.org/10.1007/s11205-015-1000-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaidu SC (2016). Domestic labour and female labour force participation: Adding a piece to the puzzle. Economic Political Weekly: 101\u0026ndash;108. https://www.jstor.org/stable/44166674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNartg\u0026uuml;n SS, Sezen-G\u0026uuml;ltekin, G, Limon, İ (2017). Examination of 2015 Human Development Index in Terms of Education: Comparison of the Continents and Turkey. Journal of Education Practice, 8(3): 37\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlowolagba ZT (2022). \u003cem\u003eThe Influence of Religion and Human Development on the Participation of Women in the Labor Force: A Case of the African Continent\u003c/em\u003e. Hamad Bin Khalifa University (Qatar),\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz Rodr\u0026iacute;guez J, Pillai, VK (2019). Advancing support for gender equality among women in Mexico: Significance of labor force participation. International Social Work, 62(1): 172\u0026ndash;184. https://doi.org/10.1177/0020872817717323\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal S, Chaudhuri, S (2020). Falling female labour force participation: An analysis of selected countries. Asian Journal of Economics, Finance Management: 154\u0026ndash;164. https://globalpresshub.com/index.php/AJEFM/article/view/859\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhan D, Coxhead, I (2014). Education in Southeast Asia: Investments, achievements, and returns. In \u003cem\u003eRoutledge handbook of Southeast Asian economics\u003c/em\u003e (pp. 245\u0026ndash;269): Routledge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosel D, Rudwick, S (2014). Marriage and Bridewealth (ilobolo) in contemporary Zulu society. African Studies Review, 57(2): 51\u0026ndash;72. https://doi.org/10.1017/asr.2014.47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQureshi MG (2012). The gender differences in school enrolment and returns to education in Pakistan. The Pakistan Development Review: 219\u0026ndash;256. https://www.jstor.org/stable/24397949\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman Z (2020). Female labor force participation and economic growth: Granger causality test.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReig-Mart\u0026iacute;nez E (2013). Social and economic wellbeing in Europe and the Mediterranean Basin: Building an enlarged human development indicator. Social Indicators Research, 111: 527\u0026ndash;547. https://doi.org/10.1007/s11205-012-0018-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalehi-Isfahani D (2013). Rethinking human development in the Middle East and North Africa: The missing dimensions. Journal of Human Development Capabilities, 14(3): 341\u0026ndash;370. https://doi.org/10.1080/19452829.2013.764851\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaner S, Das, S (2016). Female labor force participation in Asia: Indonesia country study. Asian Development Bank Economics Working Paper Series(474). https://dx.doi.org/10.2139/ssrn.2737842\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerrano J, Gasparini, L, Marchionni, M, Gl\u0026uuml;zmann, P (2019). Economic cycle and deceleration of female labor force participation in Latin America. Journal for Labour Market Research, 53: 1\u0026ndash;21. https://doi.org/10.1186/s12651-019-0263-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSourander A, Chudal, R, Skokauskas, N, Al-Ansari, AM, Klomek, AB, Pornnoppadol, C, et al. (2018). Unmet needs of child and adolescent psychiatrists among Asian and European countries: does the Human Development Index (HDI) count? European child adolescent psychiatry, 27: 5\u0026ndash;8. https://doi.org/10.1007/s00787-017-1095-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka T, Takahashi, K, Ōtsuka, K (2020). Increasing female education, stagnating female labor force participation, and gains from marriage: The case of rural Bangladesh. National Graduate Institute for Policy Studies.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026eacute;venon O (2013). Drivers of female labour force participation in the OECD. https://dx.doi.org/10.1787/5k46cvrgnms6-en\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNESCO (2022). Despite some progress, women in sub-Saharan Africa still face major obstacles in getting good-quality jobs. https://dakar.iiep.unesco.org/en/news/despite-some-progress-women-sub-saharan-africa-still-face-major-obstacles-getting-good-quality.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuko T, Čular, M (2014). Finding determinants of audit delay by pooled OLS regression analysis. Croatian Operational Research Review: 81\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaheer R, Qaiser, S (2016). Factors that affect the participation of female in labor force: A macro level study of Pakistan. IOSR Journal of Economics Finance, 7(2): 20\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human Development, Tertiary School Enrolment, Female Labour Force Participation","lastPublishedDoi":"10.21203/rs.3.rs-4508525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4508525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe purpose of this study was to investigate the effects of the Human Development Index (HDI) and Tertiary School Enrolment Rate of Female (TSEF) on Female Labour Force Participation (FLPR) globally and by continent. Secondary data on sixty countries, including nine African countries, 18 Asian countries, 27 European countries, two North American countries, and four South American countries, were examined between 2000 and 2020. The Panel regression model was used to investigate the regional impact, and the Multiple Linear regression model was used to investigate the country-specific impact. The study found that HDI and TSEF are two significant factors influencing FLPR. When country-specific results were considered, the effect of each variable on FLPR revealed mixed results, with positive and negative impacts based on the characteristics of the selected country. The findings offer an in-depth understanding of how HDI and TSEF affect FLPR, which will aid policy makers in establishing and amending strategies to accelerate women's employment and, consequently, economics growth. This study focused on the HDI and TSEF variables that were rarely used in existing literature together on FLPR.\u003c/p\u003e","manuscriptTitle":"Women at Work: An International Perspective on the Interconnected Forces of Human Development, Female Education, and Labour Force Participation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-21 05:42:45","doi":"10.21203/rs.3.rs-4508525/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa6821ca-f8b6-42e7-9391-ea4624431666","owner":[],"postedDate":"June 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T14:39:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-21 05:42:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4508525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4508525","identity":"rs-4508525","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}