Narrow Results

This unique study examines the moderation effect of institutional quality (IQ) on the relationship between financial inclusion (FI) and financial development (FD) of 45 Organization of Islamic Cooperation (OIC) countries. For empirical analysis, panel data are used for the period 2000–2016. We use the Arellano–Bond generalized method of moments (GMM) and two-stage least-squares (2SLS) method in our estimations to draw multidimensional results. The empirical results confirm the significant positive relationship between FI, IQ and FD. Interestingly, we find that IQ moderates FI and has a significant positive impact on FD. Our findings are robust to alternative econometric specifications of FI, IQ and FD. Therefore, policymakers must sensibly understand the pivotal role of FI and IQ in establishing sustainable future development of OIC countries.

Due to the geographic location of Australia and New Zealand, air transport is the dominant mode of travel between the two nations and to and from the rest of the world. While the trans- Tasman air passenger market between the two countries has grown over the last 20 years, direct air routes to Australian destinations from New Zealand’s regional cities of Dunedin, Hamilton and Palmerston North have seen a major decline and, in most cases, the complete closure of those routes. This study uses the two-stage least squares (2SLS) gravity model to investigate the determinants of air passenger numbers on eight sampled city-pair routes. Empirical results show that for these trans-Tasman markets, expanded seat capacity has a strong positive impact on air passenger numbers. A longer driving time to travel to the nearest alternative international airport, the 2008/09 GFC and the winter season in New Zealand are also associated with an increase in air passenger numbers. In contrast, the presence of full-service network carriers has a negative impact on air passenger numbers.

India gradually started liberalizing its industrial, trade and foreign direct investment (FDI) related policies since the launch of economic reforms in 1991. The empirical evidence on the impact of FDI spillovers on industrial wages in India has generated mixed results so far. The current study analysed the impact of FDI inflows on the real wage rates of the workers using data from seven major industrial sectors between 2001–2002 to 2019–2020. The two-stage least squares (2SLS) estimation results suggest that FDI inflows positively influenced both the skilled and unskilled wage rates. Moreover, FDI inflows in productive sectors led to a greater rise in wages vis-à-vis the unproductive sectors. In addition, key government policy initiatives like the Make-in-India (MII) scheme and FDI reforms were found to be effectively enhancing wage rates, though the impact on skilled wage rates was greater. The paper stressed the need to create a holistic environment that can attract FDI in both upstream and downstream segments, facilitating India’s deeper participation in global value chains (GVCs) and IPNs on the one hand and realisation of associated dynamic benefits on the other.

In this chapter we first discuss possibilities that the orthogonality condition E(ε_t|X_t) = 0 may fail, which will generally render inconsistent the OLS estimator for the true model parameter. We then introduce a consistent Two-Stage Least Squares (2SLS) estimator, investigating its statistical properties and providing intuitions for the nature of the 2SLS estimator. Hypothesis tests are constructed. We consider various test procedures corresponding to the cases for which the regression disturbance is an MDS with conditional homoskedasticity, an MDS with conditional heteroskedasticity, and a non-MDS process, respectively. The latter case will require consistent estimation of a long-run variance-covariance matrix. It is important to emphasize that the t-test and F-test statistics obtained from the second stage regression estimation cannot be used even for large samples. Finally, we conclude this chapter by presenting a summary of econometric theory for linear regression models developed in Chapters 2 to 7.