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India depicts the picture of severe social stringencies keeping girls away from attending school education due to the harsh reality of early child marriage and denial of aspirations of girl students in Indian society. The gender disparity in school educational attainment is evident as the figures of girls' enrollment in comparison to boys' enrollment at higher secondary stage of education in India always turn lower. In this context, measurement of technical efficiency (TE) is important because existence of technical inefficiency implies that one cannot produce maximum amount of output, given the resources, which can be interpreted as the penalty that the system is paying, and there is also the need to find out the relation between TE and gender inequality. The chapter contributes to the literature by (i) in the first stage estimating output-oriented TE of Indian higher secondary education for the period 2010–2011 to 2015–2016, using nonparametric Data Envelopment Analysis, for general category states and (ii) in the second stage, using the estimated TE scores from the first stage, and the regression analysis establishing the positive impact of the girls' enrollment relative to boys' on the resulting TE and hence the positive role of gender equality in enrollment on enhancing TE. The favorable role of (1) “government expenditures on education (as a ratio to aggregate expenditure for the state),” “proportion of para teachers” and the adverse role of (2) “percentage of schools without girl's toilet” and “percentage of schools without building,” in determining TE of Indian higher secondary education are evident.

Using 13 major Indian state-level data of the rural sector, covering the period 2004–2005 to 2011–2012 and by estimating a simultaneous-panel model employing Baltagi's Instrumental-Variable EC2SLS estimation method, this chapter contributes to the literature by establishing: (i) the simultaneous dependence between female labor force participation rate (FLFPR) and female health status as measured by female life expectancy (FLE), (ii) the negative impact of outdoor air pollution as measured by prevalence of SPM, SO₂, and NO₂ on FLE, and (iii) the interaction among different demographic factors in determining both FLFPR and FLE. The interaction effect of air pollution with (i) economic growth and (ii) poverty (POV) on FLE is negative implying that the partial effect of a change in growth (POV) depends on air pollution level. Thus reduction in air pollution will increase FLE and hence FLFPR, as the simultaneous positive dependence between FLFPR and FLE is supported. The interaction effect of women's political power and education on rural FLFPR is significant and nonlinear with positive marginal effect. Thus the partial effect of a change in women's political power on FLFPR will in turn depend on level of education and vice versa. The positive impact of other demographic factors like (i) education, (ii) female leader, (iii) POV, and (iv) urbanization on FLFPR and (a) education, (b) female household head, (c) female leader, (d) sex ratio, and (e) growth on FLE are apparent. However, the household size significantly and negatively affects FLFPR.

In contrast to the available literature which uses common frontier for efficiency measurement of Indian secondary education (ISE) with state level data, output-oriented technical efficiency (OUTTE) of Indian states and union territories (UT) is estimated to find whether maximum possible output of ISE given the resources are being generated, by creating two separate frontiers for (i) General Category States (GCS) and (ii) Special Category States (SCS) and UT, as two groups operate under different economic and fiscal conditions, for 2010–2011 to 2015–2016 under variable returns to scale and using non-parametric Data Envelopment Approach. Not all GCS and SCS&UT are perfectly technically efficient, implying GCS/SCS can increase output of ISE given the existing inputs. OUTTE of ISE varies within and between GCS/SCS. A second step determinant analysis suggests both for GCS and SCS, OUTTE is related positively to (i) percentage of girls to boy’s enrollment in ISE, supporting the positive role of gender equality in education regarding enrollment; and (ii) ratio of government education expenditure to aggregate government expenditure and negatively to poor infrastructural variables. OUTTE is also positively related to per capita net state domestic product at factor cost (constant prices), proportion of para-teachers for GCS and percentage of ST enrollment for SCS.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

The present study aims to determine the existence of simultaneous relationship between economic growth, income inequality, fiscal policy, and total trade of the 13 emerging market economies as a group for the period 1980–2010. After establishing the existence of simultaneity between the above relationships, a simultaneous panel model has been formulated and estimated incorporating the nonlinearity among the variables as suggested by the existing literature. An inverted U-shape relationship is evident between (1) economic growth, income inequality, and total trade in economic growth equation, (2) income inequality, economic growth, and per capita income in income inequality equation, and (3) total trade and economic growth in total trade equation. Thus, the existence of a two-way nonlinear relationship is highlighted between economic growth, income inequality, and total trade. Apart from these nonlinear relationships, positive and significant effect of (1) gross capital formation, inflation, population growth, human capital, fiscal policy, monetary policy, and domestic credit to private sector on economic growth; (2) civil liabilities on income inequality; (3) gross capital formation and inflation on total trade; (4) total trade, population growth of those aged 65 years and above, political system on fiscal policy is highlighted. Also, negative and significant effect of (1) fiscal policy on income inequality and (2) income inequality on fiscal policy is revealed.

The contributions of this chapter are to establish (a) simultaneous dependence between female labor force participation rate (FLFPR) and their health status measured by the life expectancy; (b) the roles of (i) air pollutants in explaining female life expectancy (FLE); and (ii) joint interactions of different explanatory variables in determining both FLFPR and FLE, by estimating a simultaneous panel model comprising equations of FLFPR and FLE, using Baltagi’s Instrumental-Variable EC2SLS method and 13 major Indian state-level data for urban sector, over 2004–2005 to 2011–2012. The air pollutants (measured by prevalence of SO₂ and NO₂) have significant negative impacts on FLE. The interaction effect of air pollutants with economic growth on FLE is negative implying that the partial effect of a change in growth depends on air pollution level. FLFPR can be improved by reducing air pollution through health, as FLE significantly affects FLFPR positively. The roles of other socioeconomic variables affecting FLFPR and FLE are also evident.