Women at Work: Unveiling the Impact of FLFPR and Adolescent Fertility on India's Economic Landscape

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Sharma, Mahima Chandauriya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4878773/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 This paper explores the relationship between female labour force participation (FLFPR), adolescent fertility rates, and economic development in India from 2012 to 2022. Drawing on regression analysis and secondary data sources, including the World Bank and the International Labour Organisation, the study investigates the impact of FLFPR, normalised weight (FGERSE), and adolescent fertility rates on GDP per capita. Results indicate a strong positive correlation between FLFPR and GDP per capita, highlighting the significant role of women in driving economic growth. Conversely, adolescent fertility rates exhibit a negative correlation with GDP per capita, emphasising the need for policies to address reproductive health outcomes. The findings underscore the importance of promoting gender inclusivity education to foster sustainable economic development and gender equality in India. Gender Studies Development Economics Female Labour Force Participation Adolescent Fertility Economic Development India Gender Inclusivity Regression Analysis GDP per Capita Figures Figure 1 Figure 2 Introduction The economy of a country, encompassing the production, distribution, and consumption of goods and services across various sectors, is intricately linked with labour dynamics. Labour serves as a fundamental component in each sector, from agricultural production and industrial manufacturing to service delivery and financial transactions. The interactions between labour, businesses, and government entities not only drive economic productivity but also determine employment opportunities, wage levels, and overall economic stability (Forgha & Mbella, 2016 ) . For example, in agriculture, labor is crucial for planting, harvesting, and processing crops, directly impacting food production and rural employment. In the industrial sector, skilled labour drives innovation and manufacturing efficiency, contributing to technological advancement and export competitiveness. Meanwhile, the services sector heavily relies on labour to deliver essential services like education, healthcare, and banking, which are vital for societal well-being and economic growth(Cai, 2010 ). Adequate labour supply, along with efficient utilization of labour, is essential for enhancing productivity and fostering economic prosperity. Moreover, labour markets influence wage levels, income distribution and social mobility within a society (Role_of_Labour_in_India_Development, 2022). Gender inclusion in the labour force is crucial for several reasons. The global population stands at approximately 7.9 billion, with women comprising slightly over 49 percent of this figure ( Facts and Figures: Economic Empowerment | UN Women – Headquarters , n.d.). India is one of the world's most populous countries where women represent approximately 48.5 percent of the population, which exceeds 1.3 billion (Pal & Shekhar, 2024 ). The participation of women in socio-economic sectors of India represents an important aspect of the global discourse on gender equality and economic development, transcending geographical boundaries and societal contexts. Across the world, engagement of women in the workforce is not just matter as individual empowerment but it is a crucial force for economic growth, social progress, and sustainable development (World Economic Forum, 2021). The engagement of women in the workforce is critical for the economic development of any region as they contribute an almost half of its population but despite significant efforts in various fields, Indian women face considerable challenges that hinder their full participation in the economy. Factors such as social norms, safety concerns, lack of access to education, and inadequate infrastructure contribute to low labour force participation rates among women (Das et al., 2020). The labour force participation rate for women in India was around 20.3% in 2019, significantly lower than the global average of about 47% (World Bank, 2021). Efforts to address these challenges have been undertaken at multiple levels. The Indian government has launched several initiatives aimed at promoting gender equality and women's economic empowerment. For example, the Beti Bachao Beti Padhao (Save the Girl Child, Educate the Girl Child) scheme focuses on improving the welfare of girls and ensuring their education and participation in social sector. (Ministry of Women and Child Development, 2020). Other than this, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) ensures that at least one-third of beneficiaries are women, providing them with opportunities for paid work, financial independence and social security. (Ministry of Rural Development, 2019). The introduction of policies like the Maternity Benefit (Amendment) Act, 2017, which ensures paid maternity leave to 26 weeks and the Pradhan Mantri Mudra Yojana, which offers financial support to women entrepreneurs, has enhanced the participation of women in workforce (Ministry of Labour and Employment, 2017; Ministry of Finance, 2021). These measures, while progressive, need to be supported by broader societal changes and effective implementation then only a more enabling environment for women can be created. Corporate India has also started to recognise the value of gender inclusivity to drive business to success. Companies are rapidly adopting gender diversity policies and creating more inclusive workplaces. The Securities and Exchange Board of India (SEBI) has mandated the inclusion of at least one woman on the boards of various listed companies, aiming to improve gender diversity in corporate leadership (SEBI, 2018). Despite these efforts, significant work is still remains to be done. Minimizing the gender gap in India requires a multifaceted approach that includes policy reforms, societal changes, and the active participation of all stakeholders, including government, private sector, and both urban rural societies. Learning from global practices, while tailoring suitable solutions to the unique Indian context, is needed to accelerate progress toward gender equality and women's empowerment (UN Women, 2023). Nations such as Sweden, Norway, and Iceland have led the charge by adopting comprehensive strategies that prioritize the needs of working women, while Western countries have made notable progress in this regard, challenges persist, particularly in developing nations like India. Here, entrenched gender norms, socio-cultural barriers, and structural inequalities continue to pose significant obstacles to women's full integration into the workforce. The Indian context presents a complex tapestry of socio-economic dynamics, characterized by vast disparities in women's labor force participation rates across different states and regions. Recognizing the significance of women's participation in economic development is paramount, both on a global scale and within the context of India. Neglecting the participation of women in the economy means overlooking a significant portion of the workforce and talent pool (Verick et al., 2014). By excluding women from economic activities, countries limit their potential for growth and innovation. Furthermore, promoting gender equality in the workforce is essential for achieving sustainable development goals. Research consistently demonstrates that empowering women economically leads to broader socioeconomic benefits, including poverty reduction, improved health outcomes, and greater social inclusion (Angala Eswari, 2019a ). Women's participation in the labour force contributes to higher household incomes, increased consumer spending, and enhanced overall productivity (Klasen et al., 2012 ). Women's economic participation contributes significantly to GDP both globally and in India, encompassing various sectors and roles. Globally, women contribute an estimated $ 18 trillion to GDP (Women and Growth March 2019 | Volume 56), representing a substantial portion of the world's economic output. In India, women's economic participation is similarly vital, with a diverse range of contributions across industries. In the formal sector, women participate in various roles, including manufacturing, services, finance and technology. In many countries, including India, women are increasingly joining the workforce, driving productivity and innovation. However, gender disparities persist, with women often facing lower wages, limited career advancement opportunities, and underrepresentation in leadership positions (Akhtar et al., 2023 ). In the context of a country like India, where gender disparities in the labour force are prevalent, addressing barriers to women's economic participation is imperative for realizing its full developmental potential. Policies and initiatives aimed at promoting women's education, skills development, and access to employment opportunities are essential for fostering inclusive growth and advancing towards becoming a developed nation. This paper underscores the indispensable nature of women's involvement in economic development initiatives. It contends that involving women in economic processes not only empowers their rights but also facilitates the potential for rapid and inclusive GDP development. According to the World Bank, closing the gender gap in workforce, participation could increase India's GDP by 27%. Gender Overview: Development News, Research, Data | World Bank, 2023. Moreover, research by A.D.B. (Asian Development Bank, 2016) suggests that advancing gender equality could add $ 12 trillion to global GDP by 2025. During the 1980s, the Gender and Development (GAD) approach emerged from the examination of Women in Development (WID) initiatives. Gender and Development acknowledged the significance of gender dynamics in improving women's lives, emphasising the need to focus on both women and men. This approach highlights that merely including women and girls in existing development processes is insufficient; addressing the root causes of their exclusion and power imbalances is essential. In India, women fulfil dual roles as producers of goods and services alongside their domestic responsibilities as wives and mothers (Angala Eswari, 2019b ). Despite this multifaceted contribution, their role in economic development has often been overlooked. Commonly cited challenges include issues related to health, malnutrition, frequent childbearing, and access to education. To enhance women's participation in economic development, it is imperative to provide them with essential services such as training in income-generating activities, easy access to low-interest loans, and family planning services to enable them to control childbearing. Globally, it has been evident that educating and empowering women serve as catalysts for rapid socioeconomic growth. Societies with greater gender equality not only provide enhanced socio-economic opportunities for women but also experience accelerated and more equitable growth (Agarwala & Hazarika, 2004 ). This is evidenced by gains in poverty reduction, environmental sustainability, consumer choice, innovation, and broader participation in decision-making processes. Economic development and gender equality are intrinsically linked, as lowering barriers faced by women facilitates their integration into economic activities in various sectors, fostering inclusive growth. Notably, a significant proportion of women operate micro, small, and medium-sized enterprises MSMEs, with women owning more than 30% of such enterprises. However, there remains considerable untapped potential, as only one in five women participates. (Angala Eswari, 2019b ). Over time, economic growth models have evolved to emphasise the human factor in economic progress leading to the development of endogenous growth theories in scholarly literature. These theories predominantly focus on growth catalyzed by input advancements rooted in investments in human capital. The endogenous growth theory aims to elucidate economic growth, particularly per capita GDP, through the accumulation process itself, without relying on external, exogenous components (Herrera, 2006 ). Scholars such as Pal, Shekhar, Sajid and Omran (Pal & Shekhar, 2024 ),(Sajid et al., 2021 ),(OMRAN et al., 2022) underscore the significance of female labour force participation, female education and fertility rate in driving economic growth. Within the framework of the endogenous growth model, labour force participation emerges as a key determinant, explored by scholars across various dimensions such as age, race, sex and income groups (Willis et al., 2020 ). Gender studies concerning labour participation remain pertinent, reflecting ongoing concerns in development discourse for several countries. Examining Lucas's endogenous growth model, it becomes evident that the relationship between economic growth and female labour force participation holds dual significance. An increase in the female labour force participation rate not only fosters economic growth but also contributes to women's empowerment, indicating a mutually reinforcing relationship between economic development and gender equality (Ernesto et al., 2013 ) Scholars widely acknowledge that empowering women can enhance efficiency, provided they have access to education, healthcare, and earning opportunities, underscoring the importance of addressing gender disparities for sustained economic growth (Duflo, 2012 );(Klasen, 2020 ). Moreover, the long-term benefits of increasing women's education positively impact growth, as educated women are positioned for better careers, higher incomes, and increased opportunities for economic advancement. Recent research has shed further light on the intricate relationship between female labour force participation and economic growth. For instance, a study (Balafoutas & Sutter, 2012 ) found that increasing women's participation in the labour force in India led to significant improvements in household welfare, including better child health and education outcomes. Similarly (Klasen et al., 2009) and (Yıldırım et al., 2020) demonstrated that reducing gender disparities in access to education and employment opportunities could substantially boost economic growth and reduce poverty in developing countries. Researchers such as Stella Tsani, Chinasa Urama and Nivedita Jha, etc. (Hwa et al., 2020 ; Jha et al., 2019; OMRAN et al., 2022; Tsani et al., 2015 ; Urama et al., 2022 ) continue to devote considerable attention to exploring the correlation between female labour force participation and economic growth, delving into the intricacies of this relationship. Objective: To analyze the contribution of women to India's workforce from 2012 to 2022 and evaluate its impact on the country's economic development. Data The dependent variable of the study is gross domestic production (GDP) per capita, and data are in (constant 2015 US $ ). The source of the variable is World Bank national accounts data ( World Bank- 2023 , n.d.) and OECD ( India - OECD , n.d.) national accounts data files. The explanatory variable used to demonstrate and independent variable of the study is the female labour force participation rate (FLFPR). FLFPR is the ratio of the economically active female population to all economically active people. The source of the variable is the International Labour Organization ‘ILOSTAT’ ( Data Catalogue - ILOSTAT , n.d.) and World Bank database (World Bank, Labour force participation rate, female (% of female population ages 15+). The control variables in this study are Normalized Weight (FGERSE): This variable serves as a control variable in the regression model. The conversion of FGERSE percentages to weights and subsequent normalization followed a two-step process. Initially, percentages were converted to weights by dividing each percentage by 100. Then, these weights were normalized by dividing each by the sum of all weights, ensuring a proportional contribution. This methodology, drawn from standard statistical practices, facilitated the establishment of normalized weights representing the relative significance of FGERSE values. This approach, commonly utilized in research analyses, enhances data interpretation and analysis accuracy (Izonin et al., 2022 ) and (Gangopadhyay, n.d.). Adolescent Fertility Rate: The fertility rate is another control variable that generally has a trend of decrease in female labour force participation since women are generally excluded from work for a while or permanently after giving birth. The fertility rate indicates the average number of children born per 1,000 women if a woman manages to live to the end of her childbearing period (World Bank, Fertility rate, total births per 1,000 women). These variables are included as control variables in the regression model to account for their potential influence on GDP per capita, independent of the main variables of interest (FLFPR and GDP per capita). The variables of the study were chosen based on the endogenous growth model and the literature on the relationship between economic growth and the female labour force. Thus, female labour force participation, fertility rate, and education enrolment are used in the model considering their proposed relationship with GDP. For instance, most studies indicate that a reduced fertility rate through education contributes to the empowerment of women (Forgha & Mbella, 2016 ). The relationship between fertility and female labour force level is inverse or weakly inverse or does not exist at all. Data are collected for 11 consecutive years. The list of years is available in Table 1 . The study covers the period of 2012–2022. The time period is selected based on availability. Table 2 displays descriptive statistics of the variables. Table 1 Temporal Trends and Key Indicators of Economic Growth, Female Labour Force Participation, and Adolescent Fertility Rates in India (2012–2022) YEAR FLFPR GDP per capita ( $ ) Normalized weight (FGERSE) Female gross enrollment ratio in secondary education (FGERSE) in % Adolescent fertility rate (births per 1,000 women ages 15–19) 2012 27.06 1,434 0.092 66 35 2013 27.17 1,438 0.093 67 34 2014 27.26 1,560 0.100 72 35 2015 27.38 1,590 0.101 72 22 2016 27.47 1,714 0.102 73 20 2017 27.55 1,958 0.103 72 20 2018 27.6 1,974 0.101 72 19 2019 27.76 2,050 0.102 73 18 2020 27.89 1,913 0.103 74 17 2021 28.00 2,238 0.106 76 17 2022 28.09 2,389 0.109 78 17 Source: World Bank, United Nations, and OECD. Calculated values based on regression analysis conducted by the authors Methodology This study employs secondary data analysis, drawing from sources such as the National Sample Survey Office NSSO, OECD STATS, the World Bank website, etc. The focus lies on examining the relationship between female labour force participation and economic development over the period from 2012 to 2022. Utilizing regression analysis (Na-Chiengmai, n.d.; Omair et al., 2020 ; Thaddeus et al., 2022 ) as the primary methodology and Cochran's C test performed to assess the presence of first-order autocorrelation, also known as serial correlation, in the residuals of a regression model. It examines the differences between adjacent residuals to determine if there is a pattern of correlation over time (Cai, 2010 ). The test produces a p-value, which indicates the significance of the autocorrelation (Table 2 ), the study seeks to uncover the nuanced dynamics between women's engagement in the workforce and the broader economic landscape. Through this approach, it aims to provide empirical evidence to inform policies aimed at promoting gender equality and fostering inclusive economic growth. The regression model used in the analysis can be represented by the following formula: GDP per capita = β 0 + β 1 × FLFPR + β 2 × FGERSE + β 3 × Adolescent fertility rate + ε Where: Explanation of Variables and Coefficients GDP per capita: This is the dependent variable in the model, representing the Gross Domestic Product per person in a given population. It is a measure of economic output that is used to gauge the economic health and prosperity of a country or region. FLFPR (Female Labour Force Participation Rate): This independent variable measures the percentage of women in the labour force out of the total female population. It indicates the level of female engagement in economic activities and is crucial for understanding gender dynamics in the labour market. FGERSE (Female Gross Enrollment Ratio in Secondary Education): This independent variable reflects the proportion of females enrolled in secondary education relative to the total female population. It indicates educational attainment among women, which is closely linked to their future economic opportunities and contributions. Adolescent Fertility Rate: This independent variable measures the number of births per 1,000 women aged 15–19. It indicates the rate of adolescent pregnancies, which can have significant implications for the economic prospects of young women, affecting their education and labour market participation. β0 (Intercept): The intercept is the constant term in the regression equation. It represents the expected value of GDP per capita when all independent variables (FLFPR, FGERSE, and Adolescent fertility rate) are zero. In practical terms, it provides a baseline level of GDP per capita. β1, β2, and β3 (Coefficients): These are the coefficients associated with the respective independent variables. They indicate the change in GDP per capita for a one-unit increase in each of the independent variables, holding other factors constant. β1 (Coefficient for FLFPR): Represents the change in GDP per capita for a one-unit increase in the Female Labour Force Participation Rate. A positive β1 suggests that higher female labor force participation is associated with higher GDP per capita. β2 (Coefficient for FGERSE): Represents the change in GDP per capita for a one-unit increase in the Female Gross Enrollment Ratio in Secondary Education. A positive β2 indicates that higher enrollment rates in secondary education among females contribute to economic growth. β3 (Coefficient for Adolescent Fertility Rate): Represents the change in GDP per capita for a one-unit increase in the Adolescent Fertility Rate. A negative β3 suggests that higher adolescent fertility rates are associated with lower GDP per capita, likely due to the economic disadvantages faced by young mothers. ε (Error Term): The error term captures the discrepancies between the observed values of GDP per capita and the values predicted by the regression model. It accounts for the variance in GDP per capita that is not explained by the independent variables included in the model ( See Table 3 for detailed calculation). Table 2 Regression Coefficients and Statistical Significance Variable Coefficient (B) Std. Error t-value p-value 95% Confidence Interval Constant -25513.761 7201.051 -3.543 0.009 (-42541.541, -8485.980) FLFPR 986.308 293.318 3.363 0.012 (292.721, 1679.895) Normalized Weight (FGERSE) 576.288 15652.112 0.037 0.726 (-36435.075, 37587.651) Adolescent fertility rate 3.261 8.946 0.365 0.365 (-17.894, 24.416) Source: Calculated values based on regression analysis conducted by the author using SPSS Table 3 Regression Analysis Summary and Diagnostic Statistics for Factors Impacting GDP per Capita Variable Value Cochrane-Orcutt Test Results Autocorrelation Coefficient (AR1) -0.826 p-value 0.001 Descriptive Statistics Mean GDP per capita 1841.64 Std. Deviation GDP per capita 320.047 Mean FLFPR 27.600 Std. Deviation FLFPR 0.3317 Mean Normalized Weight (FGERSE) 0.10064 Std. Deviation Normalized Weight (FGERSE) 0.004925 Mean Adolescent fertility rate 23.09 Std. Deviation in Adolescent fertility rate 7.595 Correlations Pearson Correlation (GDP per capita, FLFPR) 0.962 Pearson Correlation (GDP per capita, Normalized Weight (FGERSE)) 0.873 Pearson Correlation (GDP per capita, Adolescent fertility rate) -0.826 Model Summary R 0.963 R Square 0.927 Adjusted R Square 0.896 Std. Error of the Estimate 103.135 R Square Change 0.927 F Change 29.766 df1 3 df2 7 Sig. F Change 0.000 ANOVA Sum of Squares (Regression) 949842.921 df (Regression) 3 Mean Square (Regression) 316614.307 F (Regression) 29.766 Sig. (Regression) 0.000 Coefficients (Constant) -25513.761 FLFPR 986.308 Normalized Weight (FGERSE) 576.288 Adolescent fertility rate 3.261 95.0% Confidence Interval for B (Constant) Lower Bound: -42541.541, Upper Bound: -8485.980 FLFPR Lower Bound: 292.721, Upper Bound: 1679.895 Normalized Weight (FGERSE) Lower Bound: -36435.075, Upper Bound: 37587.651 Adolescent fertility rate Lower Bound: -17.894, Upper Bound: 24.416 Residuals Statistics Minimum Predicted Value 1382.34 Maximum Predicted Value 2319.75 Mean Predicted Value 1841.64 Std. Deviation Predicted Value 308.195 Mean Residual -206.029 Std. Deviation Residual 126.810 Std. Predicted Value -1.490 Std. Residual -1.998 (Calculated values based on regression analysis conducted by the author using SPSS) Interpretation of the Model This regression model aims to quantify the impact of female labor force participation, female education, and adolescent fertility on economic growth in India. By analyzing the coefficients: A positive β1 implies that policies and initiatives aimed at increasing female labor force participation could significantly boost economic growth. A positive β2 highlights the importance of investing in female education, as higher secondary education enrollment rates among females are associated with higher GDP per capita. A negative β3 suggests that reducing adolescent fertility rates could positively impact economic growth, as early pregnancies can limit the economic potential of young women. Displayed in Fig. 1. The histogram of GDP per capita exhibits a slight negative skewness, indicating that higher values of GDP per capita tend to be associated with lower Adolescent fertility rates, higher Female Labour Force Participation Rates (FLFPR), and higher Normalized Weight values (FGERSE). This distribution suggests that as economic output per person increases, there is a tendency for lower rates of adolescent pregnancies and greater participation of women in the labor force, coupled with better educational attainment among females. The scatter plot reveals a clear positive correlation between GDP per capita and FLFPR. Points on the graph show a consistent upward trend from left to right, indicating that higher levels of female labor force participation are generally linked with higher GDP per capita. This correlation underscores the economic benefits of integrating more women into the workforce, contributing to overall economic growth and productivity. In contrast, the correlation between GDP per capita and Normalized Weight (FGERSE) appears weaker compared to FLFPR. While there is some variability in GDP per capita across different levels of FGERSE, the trend line is less steep, suggesting that improvements in female secondary education enrollment have a less pronounced impact on economic output per capita compared to female labor force participation. Findings The regression analysis conducted on the dataset spanning the period from 2012 to 2022 in India revealed several significant insights into the relationship between economic growth, measured by GDP per capita, and various contributing factors such as female labor force participation rate (FLFPR), normalized weight (FGERSE), and adolescent fertility rate. Correlation Analysis The correlation analysis highlighted strong positive correlations between GDP per capita and FLFPR (r = 0.962, p < 0.001), indicating that higher female labor force participation is strongly associated with higher GDP per capita. This shows that as more women participate in the labor market, the overall economic output increases, reflecting the critical role of gender inclusion in economic development. A strong positive correlation was found between FLFPR and normalized weight (FGERSE) (r = 0.906, p < 0.001). The normalized weight, representing the female gross enrollment ratio in secondary education, underscores the interdependence between education and labor force participation. This implies that higher educational attainment among women leads to greater participation in the labor force, reinforcing the importance of education in empowering women to contribute economically. A moderate positive correlation was observed between GDP per capita and normalized weight (FGERSE) (r = 0.873, p < 0.001). This relationship indicates that improvements in female education are moderately associated with increases in economic growth, emphasizing the role of education in enhancing economic productivity. A strong negative correlation emerged between GDP per capita and adolescent fertility rate (r = -0.826, p < 0.001). This implies that higher rates of adolescent fertility are associated with lower economic growth, highlighting the economic drawbacks of early pregnancies, which can limit educational and employment opportunities for young women, thereby hindering economic progress. Regression Model The regression model exhibited a high degree of explanatory power, with an R-squared value of 0.927, indicating that approximately 92.7% of the variance in GDP per capita could be explained by FLFPR, normalized weight (FGERSE), and adolescent fertility rate. This high R-squared value signifies that these factors are highly predictive of economic growth in India, showcasing their importance in economic analysis and policy formulation. Moreover, the ANOVA test results were significant (F(3,7) = 29.766, p < 0.001), indicating that the regression model adequately fits the data. The significant ANOVA results confirm that the overall model is statistically robust and that the relationships identified are not due to random chance. Coefficients Regarding the coefficients, FLFPR demonstrated a significant positive effect on economic growth, with a coefficient of 986.308 (p = 0.012). This suggests that for every unit increase in the female labor force participation rate, there is an associated increase of approximately 986.308 units in GDP per capita, holding other factors constant. This finding underscores the substantial impact of female labor participation on economic performance, indicating that policies aimed at increasing FLFPR could significantly boost economic growth. Conclusion This comprehensive study resonates deeply with the economic landscape of India, emphasizing the pivotal role of female labour force participation (FLFPR) in propelling economic growth. The consistent positive correlation observed over the examined decade between FLFPR and GDP per capita underscores the pivotal role of women in propelling economic development. As participants in the labor force, women contribute essential skills, knowledge, and perspectives that are critical for innovation and productivity enhancement across various sectors of the economy. In context of India's economic landscape, where gender disparities in labour force participation persist, enhancing FLFPR emerges as a strategic imperative. Increasing women's participation in economic activities can lead to broader economic benefits, including higher household incomes, improved living standards, and overall economic stability. This aligns with global research highlighting that economies, where women are more actively engaged in the workforce, tend to experience higher levels of economic growth and resilience. Moreover, the study highlights the detrimental impact of high adolescent fertility rates on economic progress. The inverse relationship between GDP per capita and adolescent fertility rates underscores how early motherhood can disrupt educational attainment, limit career opportunities for young women, and perpetuate cycles of poverty. Addressing these challenges requires comprehensive policies that promote reproductive health education, provide access to affordable and quality healthcare services, and empower young women with the knowledge and resources to make informed decisions about their reproductive choices. Looking ahead, effective policymaking in India must prioritize gender-inclusive strategies that dismantle barriers to women's participation in the labor market. This includes implementing supportive policies such as maternity leave benefits, childcare facilities, flexible work arrangements, and initiatives to combat gender-based discrimination in hiring and promotions. By creating an enabling environment for women to fully utilize their potential in economic activities, India can harness demographic dividends and accelerate its path towards sustainable development goals. In this way, advocating for gender equality in the labour market is not just a matter of social justice but also an economic imperative. Investing in women's education, skills development, and entrepreneurship can unleash significant economic potential and contribute to building a more inclusive and resilient economy. By fostering an environment where women can thrive professionally and personally, India can achieve sustainable economic growth that benefits all segments of society. Hence, the study underscores the interconnectedness between female labour force participation, adolescent fertility rates, and economic growth in India. 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World World Dev 128:104862. https://doi.org/10.1016/J.WORLDDEV.2019.104862 Klasen S, Lamanna F, Pieters J (2012) Push or Pull? Drivers of Female Labour Force Participation during India’s Economic Boom. https://www.iza.org/publications/dp/6395/push-or-pull-drivers-of-female-labour-force-participation-during-indiaseconomic-boom Na-Chiengmai D n.d. Female Labour Force Contribution to Economic Growth. In Chiang Mai University Journal of Economics 22 (3). https://so01.tcithaijo.org/index.php/CMJE/article/view/150804 Omair M, Alharbi A, Alshangiti A, Tashkandy Y, Alzaid S, Almahmud R, Almousa M, Alenazi E, Aldooh FH, Binhazzaa SH (2020) The Saudi Women Participation in Development Index. J King Saud Univ - Sci 32(1):1233–1245. https://doi.org/10.1016/j.jksus.2019.10.007 OMRAN EAM, Y. BILAN (2022) Female Labour Force Participation and the Economic Development in Egypt. Eur J Interdisciplinary Stud. https://doi.org/10.24818/ejis.2022.01 Pal SK, Shekhar C (2024) Association between High-Risk Fertility Behaviour and Anaemia among Urban Indian Women (15–49 Years). BMC Public Health 24(1). https://doi.org/10.1186/s12889-024-18254-x Sajid S, Abdullah N, Chik AR (2021) Economic Growth, Female Labour Force Participation and Interacting Role of Education in Developing-8 Countries: A Sustainable. https://doi.org/10.17762/pae.v58i1.2082 . Development Policy Perspective. Thaddeus KJ, Bih D, Nebong NM, Ngong CA, Mongo EA, Akume AD, Onwumere JUJ (2022) Female Labour Force Participation Rate and Economic Growth in Sub-Saharan Africa: A Liability or an Asset. J Bus Socio-Economic Dev 2(1):34–48. https://doi.org/10.1108/JBSED-09-2021-0118 McKinsey G, Institute L, Woetzel, Report et al (2015) URL. Accessed.4 January 2024. https://www.mckinsey.com/featured-insights/employment-and-growth/how-advancing-womens-equality-can-add-12-trillion-to-global-growth Tsani S, Paroussos L, Fragiadakis C, Charalambidis I, Capros P (2015) Female Labour Force Participation and Economic Development. In Economic and Social Development of the Southern and Eastern Mediterranean Countries, 303–318. Springer International Publishing. https://doi.org/10.1007/978-3-319-11122-3_19 Urama CE, Ukwueze ER, Obodoechi D, Ogbonna OE, Eze AA, Alade OB, Ugwu P (2022) Women’s Labour Force Participation: Economic Growth Nexus in Sub-Saharan African Countries. https://www.semanticscholar.org/paper/db937356c213993276c29747f05e3c90a4ef3ac9 Verick S (2014) Female Labour Force Participation in Developing Countries. The IZA World of Labour. https://doi.org/10.15185/izawol.87 Willis DB, Hughes DW, Boys KA, Swindall DC (2020) Economic Growth through Entrepreneurship: Determinants of Self-Employed Income across Regional Economies. Papers Reg Sci 99(1):73–95. https://doi.org/10.1111/pirs.12482 Women and Growth - FINANCE & DEVELOPMENT - March 2019 | Volume 56 | Number 1 -International Monetary Fund. n.d. World Bank Data Bank World Development Indicators 2023. https://data.worldbank.org/ . Accessed 12 March 2024 Yıldırım DÇ, Akinci H (2020) The Dynamic Relationships between the Female Labour Force and the Economic Growth. J Economic Stud. https://doi.org/10.1108/jes-05-2020-0227 Additional Declarations The authors declare no competing interests. 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-4878773","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337542906,"identity":"a6f88a17-08bb-408d-a44d-4ddc24e1fe47","order_by":0,"name":"P.K. Sharma","email":"","orcid":"https://orcid.org/0000-0003-2817-9703","institution":"University of Allahabad, Prayagraj, India","correspondingAuthor":false,"prefix":"","firstName":"P.K.","middleName":"","lastName":"Sharma","suffix":""},{"id":337542907,"identity":"584d2cb7-5111-4db8-9b1d-beae10f01a7a","order_by":1,"name":"Mahima Chandauriya","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5603-5307","institution":"University of Allahabad, Prayagraj, India","correspondingAuthor":true,"prefix":"","firstName":"Mahima","middleName":"","lastName":"Chandauriya","suffix":""}],"badges":[],"createdAt":"2024-08-08 07:00:51","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4878773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4878773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62068289,"identity":"806e286b-7bba-4427-b31e-32be8cc2522d","added_by":"auto","created_at":"2024-08-09 01:59:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29320,"visible":true,"origin":"","legend":"\u003cp\u003eRegression Analysis: Impact of Adolescent Fertility Rate, Normalized Weight (FGERSE), and FLFPR on GDP per Capita: India (2012-2022)\u003c/p\u003e\n\u003cp\u003eSource: Made by author using SPSS\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4878773/v1/b8ce4ab736c7a4c7f6eed5b9.png"},{"id":62068162,"identity":"138d2d6e-f214-4c48-b8af-865a753d38ff","added_by":"auto","created_at":"2024-08-09 01:51:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40603,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot Analysis: GDP per Capita vs. FLFPR, Normalized Weight, and Adolescent Fertility Rate (2012-2022)\u003c/p\u003e\n\u003cp\u003eSource: Made by author using SPSS\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4878773/v1/cdbfb694d223163a05b6f374.png"},{"id":62068425,"identity":"b3ffef20-5774-4606-841a-aab80947f58e","added_by":"auto","created_at":"2024-08-09 02:07:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":629497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4878773/v1/4c88e5f2-851f-43e4-88d9-d5bc41c6bf5d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eWomen at Work: Unveiling the Impact of FLFPR and Adolescent Fertility on India's Economic Landscape\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe economy of a country, encompassing the production, distribution, and consumption of goods and services across various sectors, is intricately linked with labour dynamics. Labour serves as a fundamental component in each sector, from agricultural production and industrial manufacturing to service delivery and financial transactions. The interactions between labour, businesses, and government entities not only drive economic productivity but also determine employment opportunities, wage levels, and overall economic stability (Forgha \u0026amp; Mbella, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eFor example, in agriculture, labor is crucial for planting, harvesting, and processing crops, directly impacting food production and rural employment. In the industrial sector, skilled labour drives innovation and manufacturing efficiency, contributing to technological advancement and export competitiveness. Meanwhile, the services sector heavily relies on labour to deliver essential services like education, healthcare, and banking, which are vital for societal well-being and economic growth(Cai, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Adequate labour supply, along with efficient utilization of labour, is essential for enhancing productivity and fostering economic prosperity. Moreover, labour markets influence wage levels, income distribution and social mobility within a society (Role_of_Labour_in_India_Development, 2022). Gender inclusion in the labour force is crucial for several reasons. The global population stands at approximately 7.9\u0026nbsp;billion, with women comprising slightly over 49 percent of this figure (\u003cem\u003eFacts and Figures: Economic Empowerment | UN Women \u0026ndash; Headquarters\u003c/em\u003e, n.d.). India is one of the world's most populous countries where women represent approximately 48.5 percent of the population, which exceeds 1.3\u0026nbsp;billion (Pal \u0026amp; Shekhar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The participation of women in socio-economic sectors of India represents an important aspect of the global discourse on gender equality and economic development, transcending geographical boundaries and societal contexts. Across the world, engagement of women in the workforce is not just matter as individual empowerment but it is a crucial force for economic growth, social progress, and sustainable development (World Economic Forum, 2021). The engagement of women in the workforce is critical for the economic development of any region as they contribute an almost half of its population but despite significant efforts in various fields, Indian women face considerable challenges that hinder their full participation in the economy. Factors such as social norms, safety concerns, lack of access to education, and inadequate infrastructure contribute to low labour force participation rates among women (Das et al., 2020). The labour force participation rate for women in India was around 20.3% in 2019, significantly lower than the global average of about 47% (World Bank, 2021).\u003c/p\u003e \u003cp\u003eEfforts to address these challenges have been undertaken at multiple levels. The Indian government has launched several initiatives aimed at promoting gender equality and women's economic empowerment. For example, the Beti Bachao Beti Padhao (Save the Girl Child, Educate the Girl Child) scheme focuses on improving the welfare of girls and ensuring their education and participation in social sector. (Ministry of Women and Child Development, 2020). Other than this, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) ensures that at least one-third of beneficiaries are women, providing them with opportunities for paid work, financial independence and social security. (Ministry of Rural Development, 2019).\u003c/p\u003e \u003cp\u003eThe introduction of policies like the Maternity Benefit (Amendment) Act, 2017, which ensures paid maternity leave to 26 weeks and the Pradhan Mantri Mudra Yojana, which offers financial support to women entrepreneurs, has enhanced the participation of women in workforce (Ministry of Labour and Employment, 2017; Ministry of Finance, 2021). These measures, while progressive, need to be supported by broader societal changes and effective implementation then only a more enabling environment for women can be created.\u003c/p\u003e \u003cp\u003eCorporate India has also started to recognise the value of gender inclusivity to drive business to success. Companies are rapidly adopting gender diversity policies and creating more inclusive workplaces. The Securities and Exchange Board of India (SEBI) has mandated the inclusion of at least one woman on the boards of various listed companies, aiming to improve gender diversity in corporate leadership (SEBI, 2018).\u003c/p\u003e \u003cp\u003eDespite these efforts, significant work is still remains to be done. Minimizing the gender gap in India requires a multifaceted approach that includes policy reforms, societal changes, and the active participation of all stakeholders, including government, private sector, and both urban rural societies. Learning from global practices, while tailoring suitable solutions to the unique Indian context, is needed to accelerate progress toward gender equality and women's empowerment (UN Women, 2023).\u003c/p\u003e \u003cp\u003eNations such as Sweden, Norway, and Iceland have led the charge by adopting comprehensive strategies that prioritize the needs of working women, while Western countries have made notable progress in this regard, challenges persist, particularly in developing nations like India. Here, entrenched gender norms, socio-cultural barriers, and structural inequalities continue to pose significant obstacles to women's full integration into the workforce. The Indian context presents a complex tapestry of socio-economic dynamics, characterized by vast disparities in women's labor force participation rates across different states and regions.\u003c/p\u003e \u003cp\u003eRecognizing the significance of women's participation in economic development is paramount, both on a global scale and within the context of India.\u003c/p\u003e \u003cp\u003eNeglecting the participation of women in the economy means overlooking a significant portion of the workforce and talent pool (Verick et al., 2014). By excluding women from economic activities, countries limit their potential for growth and innovation. Furthermore, promoting gender equality in the workforce is essential for achieving sustainable development goals. Research consistently demonstrates that empowering women economically leads to broader socioeconomic benefits, including poverty reduction, improved health outcomes, and greater social inclusion (Angala Eswari, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Women's participation in the labour force contributes to higher household incomes, increased consumer spending, and enhanced overall productivity (Klasen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWomen's economic participation contributes significantly to GDP both globally and in India, encompassing various sectors and roles. Globally, women contribute an estimated \u003cspan\u003e$\u003c/span\u003e18 trillion to GDP (Women and Growth March 2019 | Volume 56), representing a substantial portion of the world's economic output. In India, women's economic participation is similarly vital, with a diverse range of contributions across industries. In the formal sector, women participate in various roles, including manufacturing, services, finance and technology. In many countries, including India, women are increasingly joining the workforce, driving productivity and innovation. However, gender disparities persist, with women often facing lower wages, limited career advancement opportunities, and underrepresentation in leadership positions (Akhtar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of a country like India, where gender disparities in the labour force are prevalent, addressing barriers to women's economic participation is imperative for realizing its full developmental potential. Policies and initiatives aimed at promoting women's education, skills development, and access to employment opportunities are essential for fostering inclusive growth and advancing towards becoming a developed nation.\u003c/p\u003e \u003cp\u003eThis paper underscores the indispensable nature of women's involvement in economic development initiatives. It contends that involving women in economic processes not only empowers their rights but also facilitates the potential for rapid and inclusive GDP development. According to the World Bank, closing the gender gap in workforce, participation could increase India's GDP by 27%. Gender Overview: Development News, Research, Data | World Bank, 2023. Moreover, research by A.D.B. (Asian Development Bank, 2016) suggests that advancing gender equality could add \u003cspan\u003e$\u003c/span\u003e12 trillion to global GDP by 2025.\u003c/p\u003e \u003cp\u003eDuring the 1980s, the Gender and Development (GAD) approach emerged from the examination of Women in Development (WID) initiatives. Gender and Development acknowledged the significance of gender dynamics in improving women's lives, emphasising the need to focus on both women and men. This approach highlights that merely including women and girls in existing development processes is insufficient; addressing the root causes of their exclusion and power imbalances is essential.\u003c/p\u003e \u003cp\u003eIn India, women fulfil dual roles as producers of goods and services alongside their domestic responsibilities as wives and mothers (Angala Eswari, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Despite this multifaceted contribution, their role in economic development has often been overlooked. Commonly cited challenges include issues related to health, malnutrition, frequent childbearing, and access to education. To enhance women's participation in economic development, it is imperative to provide them with essential services such as training in income-generating activities, easy access to low-interest loans, and family planning services to enable them to control childbearing.\u003c/p\u003e \u003cp\u003eGlobally, it has been evident that educating and empowering women serve as catalysts for rapid socioeconomic growth. Societies with greater gender equality not only provide enhanced socio-economic opportunities for women but also experience accelerated and more equitable growth (Agarwala \u0026amp; Hazarika, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This is evidenced by gains in poverty reduction, environmental sustainability, consumer choice, innovation, and broader participation in decision-making processes. Economic development and gender equality are intrinsically linked, as lowering barriers faced by women facilitates their integration into economic activities in various sectors, fostering inclusive growth. Notably, a significant proportion of women operate micro, small, and medium-sized enterprises MSMEs, with women owning more than 30% of such enterprises. However, there remains considerable untapped potential, as only one in five women participates. (Angala Eswari, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver time, economic growth models have evolved to emphasise the human factor in economic progress leading to the development of endogenous growth theories in scholarly literature. These theories predominantly focus on growth catalyzed by input advancements rooted in investments in human capital. The endogenous growth theory aims to elucidate economic growth, particularly per capita GDP, through the accumulation process itself, without relying on external, exogenous components (Herrera, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Scholars such as Pal, Shekhar, Sajid and Omran (Pal \u0026amp; Shekhar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e),(Sajid et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e),(OMRAN et al., 2022) underscore the significance of female labour force participation, female education and fertility rate in driving economic growth.\u003c/p\u003e \u003cp\u003eWithin the framework of the endogenous growth model, labour force participation emerges as a key determinant, explored by scholars across various dimensions such as age, race, sex and income groups (Willis et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGender studies concerning labour participation remain pertinent, reflecting ongoing concerns in development discourse for several countries. Examining Lucas's endogenous growth model, it becomes evident that the relationship between economic growth and female labour force participation holds dual significance. An increase in the female labour force participation rate not only fosters economic growth but also contributes to women's empowerment, indicating a mutually reinforcing relationship between economic development and gender equality (Ernesto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eScholars widely acknowledge that empowering women can enhance efficiency, provided they have access to education, healthcare, and earning opportunities, underscoring the importance of addressing gender disparities for sustained economic growth (Duflo, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e);(Klasen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the long-term benefits of increasing women's education positively impact growth, as educated women are positioned for better careers, higher incomes, and increased opportunities for economic advancement.\u003c/p\u003e \u003cp\u003eRecent research has shed further light on the intricate relationship between female labour force participation and economic growth. For instance, a study (Balafoutas \u0026amp; Sutter, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that increasing women's participation in the labour force in India led to significant improvements in household welfare, including better child health and education outcomes. Similarly (Klasen et al., 2009) and (Yıldırım et al., 2020) demonstrated that reducing gender disparities in access to education and employment opportunities could substantially boost economic growth and reduce poverty in developing countries.\u003c/p\u003e \u003cp\u003eResearchers such as Stella Tsani, Chinasa Urama and Nivedita Jha, etc. (Hwa et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jha et al., 2019; OMRAN et al., 2022; Tsani et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Urama et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) continue to devote considerable attention to exploring the correlation between female labour force participation and economic growth, delving into the intricacies of this relationship.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eTo analyze the contribution of women to India's workforce from 2012 to 2022 and evaluate its impact on the country's economic development.\u003c/p\u003e \u003c/div\u003e"},{"header":"Data","content":"\u003cp\u003eThe dependent variable of the study is gross domestic production (GDP) per capita, and data are in (constant 2015 US \u003cspan\u003e$\u003c/span\u003e). The source of the variable is World Bank national accounts data (\u003cem\u003eWorld Bank- 2023\u003c/em\u003e, n.d.) and OECD (\u003cem\u003eIndia - OECD\u003c/em\u003e, n.d.) national accounts data files. The explanatory variable used to demonstrate and independent variable of the study is the female labour force participation rate (FLFPR).\u003c/p\u003e \u003cp\u003eFLFPR is the ratio of the economically active female population to all economically active people. The source of the variable is the International Labour Organization \u0026lsquo;ILOSTAT\u0026rsquo; (\u003cem\u003eData Catalogue - ILOSTAT\u003c/em\u003e, n.d.) and World Bank database (World Bank, Labour force participation rate, female (% of female population ages 15+). The control variables in this study are Normalized Weight (FGERSE): This variable serves as a control variable in the regression model.\u003c/p\u003e \u003cp\u003eThe conversion of FGERSE percentages to weights and subsequent normalization followed a two-step process. Initially, percentages were converted to weights by dividing each percentage by 100. Then, these weights were normalized by dividing each by the sum of all weights, ensuring a proportional contribution. This methodology, drawn from standard statistical practices, facilitated the establishment of normalized weights representing the relative significance of FGERSE values. This approach, commonly utilized in research analyses, enhances data interpretation and analysis accuracy (Izonin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and (Gangopadhyay, n.d.). Adolescent Fertility Rate: The fertility rate is another control variable that generally has a trend of decrease in female labour force participation since women are generally excluded from work for a while or permanently after giving birth. The fertility rate indicates the average number of children born per 1,000 women if a woman manages to live to the end of her childbearing period (World Bank, Fertility rate, total births per 1,000 women). These variables are included as control variables in the regression model to account for their potential influence on GDP per capita, independent of the main variables of interest (FLFPR and GDP per capita).\u003c/p\u003e \u003cp\u003eThe variables of the study were chosen based on the endogenous growth model and the literature on the relationship between economic growth and the female labour force. Thus, female labour force participation, fertility rate, and education enrolment are used in the model considering their proposed relationship with GDP. For instance, most studies indicate that a reduced fertility rate through education contributes to the empowerment of women (Forgha \u0026amp; Mbella, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The relationship between fertility and female labour force level is inverse or weakly inverse or does not exist at all. Data are collected for 11 consecutive years. The list of years is available in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study covers the period of 2012\u0026ndash;2022. The time period is selected based on availability. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays descriptive statistics of the variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal Trends and Key Indicators of Economic Growth, Female Labour Force Participation, and Adolescent Fertility Rates in India (2012\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYEAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFLFPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormalized weight (FGERSE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale gross enrollment ratio in secondary education (FGERSE) in %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdolescent fertility rate (births per 1,000 women ages 15\u0026ndash;19)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: World Bank, United Nations, and OECD. Calculated values based on regression analysis conducted by the authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs secondary data analysis, drawing from sources such as the National Sample Survey Office NSSO, OECD STATS, the World Bank website, etc. The focus lies on examining the relationship between female labour force participation and economic development over the period from 2012 to 2022. Utilizing regression analysis (Na-Chiengmai, n.d.; Omair et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thaddeus et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as the primary methodology and Cochran's C test performed to assess the presence of first-order autocorrelation, also known as serial correlation, in the residuals of a regression model.\u003c/p\u003e \u003cp\u003eIt examines the differences between adjacent residuals to determine if there is a pattern of correlation over time (Cai, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The test produces a p-value, which indicates the significance of the autocorrelation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the study seeks to uncover the nuanced dynamics between women's engagement in the workforce and the broader economic landscape. Through this approach, it aims to provide empirical evidence to inform policies aimed at promoting gender equality and fostering inclusive economic growth.\u003c/p\u003e \u003cp\u003eThe regression model used in the analysis can be represented by the following formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003eGDP per capita\u0026thinsp;=\u0026thinsp;β\u003c/b\u003e \u003csub\u003e \u003cb\u003e0\u003c/b\u003e \u003c/sub\u003e\u0026thinsp;\u003cb\u003e+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e\u0026times;\u0026thinsp;FLFPR\u0026thinsp;+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e\u0026times;\u0026thinsp;FGERSE\u0026thinsp;+\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e\u0026times;\u0026thinsp;Adolescent fertility rate\u0026thinsp;+\u0026thinsp;ε\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eExplanation of Variables and Coefficients\u003c/p\u003e \u003cp\u003eGDP per capita: This is the dependent variable in the model, representing the Gross Domestic Product per person in a given population. It is a measure of economic output that is used to gauge the economic health and prosperity of a country or region.\u003c/p\u003e \u003cp\u003eFLFPR (Female Labour Force Participation Rate): This independent variable measures the percentage of women in the labour force out of the total female population. It indicates the level of female engagement in economic activities and is crucial for understanding gender dynamics in the labour market.\u003c/p\u003e \u003cp\u003eFGERSE (Female Gross Enrollment Ratio in Secondary Education): This independent variable reflects the proportion of females enrolled in secondary education relative to the total female population. It indicates educational attainment among women, which is closely linked to their future economic opportunities and contributions.\u003c/p\u003e \u003cp\u003eAdolescent Fertility Rate: This independent variable measures the number of births per 1,000 women aged 15\u0026ndash;19. It indicates the rate of adolescent pregnancies, which can have significant implications for the economic prospects of young women, affecting their education and labour market participation.\u003c/p\u003e \u003cp\u003eβ0 (Intercept): The intercept is the constant term in the regression equation. It represents the expected value of GDP per capita when all independent variables (FLFPR, FGERSE, and Adolescent fertility rate) are zero. In practical terms, it provides a baseline level of GDP per capita.\u003c/p\u003e \u003cp\u003eβ1, β2, and β3 (Coefficients): These are the coefficients associated with the respective independent variables. They indicate the change in GDP per capita for a one-unit increase in each of the independent variables, holding other factors constant.\u003c/p\u003e \u003cp\u003eβ1 (Coefficient for FLFPR): Represents the change in GDP per capita for a one-unit increase in the Female Labour Force Participation Rate. A positive β1 suggests that higher female labor force participation is associated with higher GDP per capita.\u003c/p\u003e \u003cp\u003eβ2 (Coefficient for FGERSE): Represents the change in GDP per capita for a one-unit increase in the Female Gross Enrollment Ratio in Secondary Education. A positive β2 indicates that higher enrollment rates in secondary education among females contribute to economic growth.\u003c/p\u003e \u003cp\u003eβ3 (Coefficient for Adolescent Fertility Rate): Represents the change in GDP per capita for a one-unit increase in the Adolescent Fertility Rate. A negative β3 suggests that higher adolescent fertility rates are associated with lower GDP per capita, likely due to the economic disadvantages faced by young mothers.\u003c/p\u003e \u003cp\u003eε (Error Term): The error term captures the discrepancies between the observed values of GDP per capita and the values predicted by the regression model. It accounts for the variance in GDP per capita that is not explained by the independent variables included in the model ( See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for detailed calculation).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Coefficients and Statistical Significance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-25513.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7201.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-42541.541, -8485.980)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e986.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(292.721, 1679.895)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized Weight (FGERSE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e576.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15652.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-36435.075, 37587.651)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdolescent fertility rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-17.894, 24.416)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Calculated values based on regression analysis conducted by the author using SPSS\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Analysis Summary and Diagnostic Statistics for Factors Impacting GDP per Capita\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCochrane-Orcutt Test Results\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutocorrelation Coefficient (AR1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDescriptive Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean GDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1841.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation GDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean FLFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation FLFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Normalized Weight (FGERSE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation Normalized Weight (FGERSE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Adolescent fertility rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation in Adolescent fertility rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrelations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson Correlation (GDP per capita, FLFPR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson Correlation (GDP per capita, Normalized Weight (FGERSE))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson Correlation (GDP per capita, Adolescent fertility rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Summary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Error of the Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR Square Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSig. F Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum of Squares (Regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e949842.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf (Regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Square (Regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e316614.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF (Regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSig. (Regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25513.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e986.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized Weight (FGERSE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdolescent fertility rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e95.0% Confidence Interval for B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound: -42541.541, Upper Bound: -8485.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound: 292.721, Upper Bound: 1679.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized Weight (FGERSE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound: -36435.075, Upper Bound: 37587.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdolescent fertility rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound: -17.894, Upper Bound: 24.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResiduals Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum Predicted Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1382.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum Predicted Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2319.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Predicted Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1841.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation Predicted Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Residual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-206.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Deviation Residual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Predicted Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Residual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Calculated values based on regression analysis conducted by the author using SPSS)\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of the Model\u003c/h2\u003e \u003cp\u003eThis regression model aims to quantify the impact of female labor force participation, female education, and adolescent fertility on economic growth in India. By analyzing the coefficients:\u003c/p\u003e \u003cp\u003eA positive β1 implies that policies and initiatives aimed at increasing female labor force participation could significantly boost economic growth.\u003c/p\u003e \u003cp\u003eA positive β2 highlights the importance of investing in female education, as higher secondary education enrollment rates among females are associated with higher GDP per capita.\u003c/p\u003e \u003cp\u003eA negative β3 suggests that reducing adolescent fertility rates could positively impact economic growth, as early pregnancies can limit the economic potential of young women.\u003c/p\u003e \u003cp\u003eDisplayed in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe histogram of GDP per capita exhibits a slight negative skewness, indicating that higher values of GDP per capita tend to be associated with lower Adolescent fertility rates, higher Female Labour Force Participation Rates (FLFPR), and higher Normalized Weight values (FGERSE). This distribution suggests that as economic output per person increases, there is a tendency for lower rates of adolescent pregnancies and greater participation of women in the labor force, coupled with better educational attainment among females.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe scatter plot reveals a clear positive correlation between GDP per capita and FLFPR. Points on the graph show a consistent upward trend from left to right, indicating that higher levels of female labor force participation are generally linked with higher GDP per capita. This correlation underscores the economic benefits of integrating more women into the workforce, contributing to overall economic growth and productivity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn contrast, the correlation between GDP per capita and Normalized Weight (FGERSE) appears weaker compared to FLFPR. While there is some variability in GDP per capita across different levels of FGERSE, the trend line is less steep, suggesting that improvements in female secondary education enrollment have a less pronounced impact on economic output per capita compared to female labor force participation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Findings","content":"\u003cp\u003eThe regression analysis conducted on the dataset spanning the period from 2012 to 2022 in India revealed several significant insights into the relationship between economic growth, measured by GDP per capita, and various contributing factors such as female labor force participation rate (FLFPR), normalized weight (FGERSE), and adolescent fertility rate.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eThe correlation analysis highlighted strong positive correlations between GDP per capita and FLFPR (r\u0026thinsp;=\u0026thinsp;0.962, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher female labor force participation is strongly associated with higher GDP per capita. This shows that as more women participate in the labor market, the overall economic output increases, reflecting the critical role of gender inclusion in economic development.\u003c/p\u003e \u003cp\u003eA strong positive correlation was found between FLFPR and normalized weight (FGERSE) (r\u0026thinsp;=\u0026thinsp;0.906, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The normalized weight, representing the female gross enrollment ratio in secondary education, underscores the interdependence between education and labor force participation. This implies that higher educational attainment among women leads to greater participation in the labor force, reinforcing the importance of education in empowering women to contribute economically.\u003c/p\u003e \u003cp\u003eA moderate positive correlation was observed between GDP per capita and normalized weight (FGERSE) (r\u0026thinsp;=\u0026thinsp;0.873, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This relationship indicates that improvements in female education are moderately associated with increases in economic growth, emphasizing the role of education in enhancing economic productivity.\u003c/p\u003e \u003cp\u003eA strong negative correlation emerged between GDP per capita and adolescent fertility rate (r = -0.826, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This implies that higher rates of adolescent fertility are associated with lower economic growth, highlighting the economic drawbacks of early pregnancies, which can limit educational and employment opportunities for young women, thereby hindering economic progress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRegression Model\u003c/h2\u003e \u003cp\u003eThe regression model exhibited a high degree of explanatory power, with an R-squared value of 0.927, indicating that approximately 92.7% of the variance in GDP per capita could be explained by FLFPR, normalized weight (FGERSE), and adolescent fertility rate. This high R-squared value signifies that these factors are highly predictive of economic growth in India, showcasing their importance in economic analysis and policy formulation.\u003c/p\u003e \u003cp\u003eMoreover, the ANOVA test results were significant (F(3,7)\u0026thinsp;=\u0026thinsp;29.766, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the regression model adequately fits the data. The significant ANOVA results confirm that the overall model is statistically robust and that the relationships identified are not due to random chance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCoefficients\u003c/h2\u003e \u003cp\u003eRegarding the coefficients, FLFPR demonstrated a significant positive effect on economic growth, with a coefficient of 986.308 (p\u0026thinsp;=\u0026thinsp;0.012). This suggests that for every unit increase in the female labor force participation rate, there is an associated increase of approximately 986.308 units in GDP per capita, holding other factors constant. This finding underscores the substantial impact of female labor participation on economic performance, indicating that policies aimed at increasing FLFPR could significantly boost economic growth.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comprehensive study resonates deeply with the economic landscape of India, emphasizing the pivotal role of female labour force participation (FLFPR) in propelling economic growth. The consistent positive correlation observed over the examined decade between FLFPR and GDP per capita underscores the pivotal role of women in propelling economic development. As participants in the labor force, women contribute essential skills, knowledge, and perspectives that are critical for innovation and productivity enhancement across various sectors of the economy.\u003c/p\u003e \u003cp\u003eIn context of India's economic landscape, where gender disparities in labour force participation persist, enhancing FLFPR emerges as a strategic imperative. Increasing women's participation in economic activities can lead to broader economic benefits, including higher household incomes, improved living standards, and overall economic stability. This aligns with global research highlighting that economies, where women are more actively engaged in the workforce, tend to experience higher levels of economic growth and resilience.\u003c/p\u003e \u003cp\u003eMoreover, the study highlights the detrimental impact of high adolescent fertility rates on economic progress. The inverse relationship between GDP per capita and adolescent fertility rates underscores how early motherhood can disrupt educational attainment, limit career opportunities for young women, and perpetuate cycles of poverty. Addressing these challenges requires comprehensive policies that promote reproductive health education, provide access to affordable and quality healthcare services, and empower young women with the knowledge and resources to make informed decisions about their reproductive choices.\u003c/p\u003e \u003cp\u003eLooking ahead, effective policymaking in India must prioritize gender-inclusive strategies that dismantle barriers to women's participation in the labor market. This includes implementing supportive policies such as maternity leave benefits, childcare facilities, flexible work arrangements, and initiatives to combat gender-based discrimination in hiring and promotions. By creating an enabling environment for women to fully utilize their potential in economic activities, India can harness demographic dividends and accelerate its path towards sustainable development goals.\u003c/p\u003e \u003cp\u003eIn this way, advocating for gender equality in the labour market is not just a matter of social justice but also an economic imperative. Investing in women's education, skills development, and entrepreneurship can unleash significant economic potential and contribute to building a more inclusive and resilient economy. By fostering an environment where women can thrive professionally and personally, India can achieve sustainable economic growth that benefits all segments of society.\u003c/p\u003e \u003cp\u003eHence, the study underscores the interconnectedness between female labour force participation, adolescent fertility rates, and economic growth in India. By advancing gender equality, promoting women's economic empowerment, and investing in reproductive health and education, policymakers can lay the foundation for a prosperous future where every individual, regardless of gender, has the opportunity to contribute to and benefit from India's economic advancement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwala AK, Hazarika P (2004) Developmental Disparities: A Quantitative Insight. Akansha Pub. House\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar R, Masud MM, Jafrin N, Shahabudin SM (2023) Economic Growth, Gender Inequality, Openness of Trade, and Female Labour Force Participation: A Nonlinear ARDL Approach. 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J Economic Stud. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/jes-05-2020-0227\u003c/span\u003e\u003cspan address=\"10.1108/jes-05-2020-0227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Allahabad","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":"Female Labour Force Participation, Adolescent Fertility, Economic Development, India, Gender Inclusivity, Regression Analysis, GDP per Capita","lastPublishedDoi":"10.21203/rs.3.rs-4878773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4878773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the relationship between female labour force participation (FLFPR), adolescent fertility rates, and economic development in India from 2012 to 2022. Drawing on regression analysis and secondary data sources, including the World Bank and the International Labour Organisation, the study investigates the impact of FLFPR, normalised weight (FGERSE), and adolescent fertility rates on GDP per capita. Results indicate a strong positive correlation between FLFPR and GDP per capita, highlighting the significant role of women in driving economic growth. Conversely, adolescent fertility rates exhibit a negative correlation with GDP per capita, emphasising the need for policies to address reproductive health outcomes. The findings underscore the importance of promoting gender inclusivity education to foster sustainable economic development and gender equality in India.\u003c/p\u003e","manuscriptTitle":"Women at Work: Unveiling the Impact of FLFPR and Adolescent Fertility on India's Economic Landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 01:51:50","doi":"10.21203/rs.3.rs-4878773/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":"172d1765-31ed-4a0b-9a23-4868197e5fe8","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35751152,"name":"Gender Studies"},{"id":35751153,"name":"Development Economics"}],"tags":[],"updatedAt":"2024-08-09T01:51:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 01:51:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4878773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4878773","identity":"rs-4878773","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}