FDI inflow, ICT and economic performance of India: an empirical investigation

PurposeThe aim of this study is to examine the impact of foreign direct investment (FDI) inflow and information and communication technology (ICT) on the economic performance of India by analysing annual data from 1991 to 2019.Design/methodology/approachThis study has used data collected from secondary sources. The variables considered for the analysis are based on the review of theoretical and empirical literature. Moreover, apart from the quantitative variables, two qualitative variables have also been considered through the use of dummy variables. The Cobb–Douglas, Transcendental logarithmic and Simultaneous equations models have been used for the study.FindingsThe result reveals that the partial elasticities of the per-capita gross domestic product (PCGDP) of India with respect to FDI, mobile density (MD) and internet density (ID) are 0.074, 0.024 and 0.036, respectively. The positive and significant coefficient of the interaction among FDI, MD and ID in the estimation of the transcendental logarithmic function indicates the importance of ICT infrastructure in extracting the best out of FDI (the coefficient is 0.011 for the model without any control variables and it is 0.005 with control variables).Originality/valueThe findings of this study are more reliable as the latest available data have been analysed through the appropriate econometric models. This study will be useful for the policymakers in the formulation of policies with regard to foreign capital and digitalisation.

A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

Quantile Treatment Effects in the Presence of Covariates

This paper proposes a method to estimate unconditional quantile treatment effects (QTEs) given one or more treatment variables, which may be discrete or continuous, even when it is necessary to condition on covariates. The estimator, generalized quantile regression (GQR), is developed in an instrumental variable framework for generality to permit estimation of unconditional QTEs for endogenous policy variables, but it is also applicable in the conditionally exogenous case. The framework includes simultaneous equations models with nonadditive disturbances, which are functions of both unobserved and observed factors. Quantile regression and instrumental variable quantile regression are special cases of GQR and available in this framework.

The Gender-specific Role of Body Weight for Health, Earnings and Life Satisfaction in Piecewise and Simultaneous Equations Models

Based on the German Socio-Economic Panel, the correlation between the body mass index, health, earnings and life satisfaction is analysed by gender. The previous literature has found no consistent results. This might have several reasons. The purpose of this paper is to analyse the gender-specific role of weight in single equation, piecewise and simultaneous equations models. We ask whether this distinction is important for the degree of association between health, earnings, satisfaction and body weight. In our context, piecewise modelling means a separate inspection of weight coefficients for under- and overweight people, allowing the detection of non-linear influences. As a benchmark, we begin our estimations under the assumption that the association between health, earnings, satisfaction, and weight is the same for under- and overweight people, and that there are no jointly dependent influences between our three outcome variables. The basic results are: health worsens, income declines and satisfaction is poorer with higher body mass index. If the association with weight is separately determined for over- and underweight people, the estimates show striking differences between overweight men and women. Underweight women earn more and overweight less than others. For normal-weight men the income is on average higher than for over- and underweight men but this difference is insignificant. When matching and instrumental variables procedures are applied, the health outcome for overweight people matches that of independent and unmatched estimates. Stronger positive effects on health are found for underweight women. No clear-cut advantages in income of overweight women can be found. Underweight women and especially underweight men tend to be less happy. For overweight men this influence is ambiguous but more speaks in favour of a lesser level of satisfaction. Overweight women seem to be happier.

Nonparametric estimation of triangular simultaneous equations models under weak identification

This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced‐form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized as concurvity, which motivates the introduction of a regularization scheme. The paper proposes a penalized series estimation method to alleviate the effects of weak instruments and shows that it achieves desirable asymptotic properties. A data‐driven procedure is proposed for the choice of the penalization parameter. The findings of this paper provide useful implications for empirical work. To illustrate them, Monte Carlo results are presented and an empirical example is given in which the effect of class size on test scores is estimated nonparametrically.

A Comparison of Some Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments

We compare the finite sample performance of a number of Bayesian and classical procedures for limited information simultaneous equations models with weak instruments by a Monte Carlo study. We consider Bayesian approaches developed by Chao and Phillips, Geweke, Kleibergen and van Dijk, and Zellner. Amongst the sampling theory methods, OLS, 2SLS, LIML, Fuller’s modified LIML, and the jackknife instrumental variable estimator (JIVE) due to Angrist et al. and Blomquist and Dahlberg are also considered. Since the posterior densities and their conditionals in Chao and Phillips and Kleibergen and van Dijk are nonstandard, we use a novel “Gibbs within Metropolis–Hastings” algorithm, which only requires the availability of the conditional densities from the candidate generating density. Our results show that with very weak instruments, there is no single estimator that is superior to others in all cases. When endogeneity is weak, Zellner’s MELO does the best. When the endogeneity is not weak and ρω12>0, where ρ is the correlation coefficient between the structural and reduced form errors, and ω12 is the covariance between the unrestricted reduced form errors, the Bayesian method of moments (BMOM) outperforms all other estimators by a wide margin. When the endogeneity is not weak and βρ < 0 (β being the structural parameter), the Kleibergen and van Dijk approach seems to work very well. Surprisingly, the performance of JIVE was disappointing in all our experiments.

Share classes, families and asset purchases: Canadian evidence

Purpose The purpose of this paper is to test if relative asset purchase values (RAPVs) differ between single- and dual-class purchasers (not) differentiated by family ownership for Canadian firms. Design/methodology/approach The paper uses multivariate regressions and 2SLS estimations of simultaneous equations models with both continuous and dichotomous endogenous variables. Data on share structures and family involvements are hand collected. Findings RAPVs for dual-class purchasers are significantly different (larger) than their single-class counterparts only for family-controlled samples. Larger RAPVs for dual-class purchases are associated with higher degrees of dual-class structures, higher family ownerships and with boards with no more than one family member. Research limitations/implications RAPV is important because of its common use as a primary determinant of the wealth effects of M&As, its use as an exchange-rate proxy in two-stage regressions used to determine the amount of abnormal returns attributable to short selling activity around M&A announcements, and its use as a channel for conveying information about deal complexity, seller’s bargaining power, additional monitoring benefits from purchase and/or greater challenges in incorporating a purchase into existing assets. Larger sample size would facilitate more differentiated examinations. Practical implications Findings imply that dual-class share structures assist family shareholders in elevating their control over corporate decisions involving asset purchases. Social implications This paper furthers the authors’ knowledge about the effects of agency issues on corporate decisions. Originality/value It provides an extension and robustness test of the US evidence for asset purchases by providing evidence for Canada given its greater preponderance of families as the ultimate controlling shareholders, restricted or subordinated voting shares issued and pyramidal structures.