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Purpose: In this study, the relationship between female labour force participation rate and economic growth is investigated in middle-income countries. The study covers the period of 2001–2016 by employing a dynamic panel approach. Pooled Ordinary Least Square and Fixed Effects model estimations are calculated as a decision criterion to select proper GMM Method. The outcomes indicate that the proper estimation technique, which is a System-GMM model, evidences the U Feminisation Theory for the middle-income countries while controlling all other factors. Design/methodology/approach: The novelty of this study is that the research not only employs both difference and system generalised method of moments (GMM) estimators but also includes main explanatory variables such as education, fertility, and total labour force rate. The study provides an opportunity to review the U-shape nexus between the female labour force and economic growth while controlling education, fertility and total labour participation rate. Findings: The estimation implies that middle-income countries support a U-shaped relationship. The fertility rate does not impact on the female labour force, and education and total labour force level have a positive influence on women's participation in the labour market. Research limitations/implications: This study used data that include the period of 2001–2016 for middle-income countries. So, further studies can use different periods of data or different countries. Practical implications: The authors emphasise the importance of economic growth for female labour force for middle-income countries. Thus, a country intending to increase female labour force should also focus on its economic growth. As the study points out, middle-income countries staying under the minimum threshold, $4698.15 (per capita), should priorities their economic improvement policies to reach their female labour force participation goal. Those countries also should be prepared for a female labour force participation declining phase until they reach the turning point income level. Social implications: Furthermore, education is one of the critical determinants that have an impact on FLFPR. The equal opportunity for both genders to engage in education should be considered as a policy. If females do not have an equal chance to enrolment in education, it may influence the policy of increasing female labour force adversely. Fertility rate appears no more statistically significant in our study. Moreover, today, there are some countries they practise equality between genders by providing equally extended parental leave, which may be a promising policy for gender equality in the labour force and may worth a try. Originality/value: Some previous studies may suffer model mistakes due to lack of consideration the endogeneity problem and bias issue of the results as suggested by Tam (2011). Moreover, previous studies tend to choose either studying U-feminisation as excluding other variables or studying determinants of female labour force participation rate as excluding U-feminisation theory. There is not any panel data study acknowledging both concepts by using recent data to the best knowledge of the authors. Thus, the novelty of this study is that the research not only employs both difference and system generalised method of moments (GMM) estimators but also includes main explanatory variables such as education, fertility, and total labour force rate. The study provides an opportunity to review the U-shape nexus between the female labour force and economic growth while controlling education, fertility and total labour participation rate. [ABSTRACT FROM AUTHOR]

Human capital investments have a vital role in economic growth. Therefore the effects of human capital on the environment should be analyzed for sustainable economic growth. This paper contributes to the debate on the nexus between human capital and environmental degradation. Based on 21 EU countries' panel data over the period 1994–2018, this study aims to analyze the relationship between human capital and environmental pollution in different financial development levels. We employed the panel smooth transition regression model (PSTR) to assess the nexus between the variables. According to the estimation results, human capital decreases carbon emissions in the low growth regime whereas increases in the high growth regime. Besides, human capital increases carbon emissions in both low regimes of financial development and human capital, and decreases in high regimes. The analysis indicated that as human capital improves, there will be more innovation to protect the environment, and thus there is less environmental degradation. • Impacts of human capital on environment as economy grows. • 21 EU countries over the period 1994–2018. • Panel Smooth Transition Regression Model. • Human capital increases carbon emissions in both low regimes of financial development. • Human capital decreases carbon emissions in high regimes. [ABSTRACT FROM AUTHOR]