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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

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.

This study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.

The method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.

Pumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.

Because the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This chapter explores the impact of equality, diversity, and inclusivity (EDI) on internal change agents’ (ICAs) personal and professional development. We have surveyed 117 ICAs that undergo a four-year digital development programme at Edinburgh Business School (EBS). Our survey design draws from expectancy, surprise, sensemaking, and attribution theories to test four hypotheses using Spearman’s rank. We found that diversity features such as gender, age, sector affiliation, work experience, management responsibility, and programme stage do not strongly impact ICAs’ experience of personal and professional development. Surprisingly, some diversity features had a modest or moderate impact on ICAs’ experience of personal and professional development. This disconfirmed our basic assumption about the effectiveness of inclusivity practices in the digital development programme at EBS. We conclude that future research should further investigate the impact of evaluation on ICAs’ personal and professional development and how we can secure it in a digital Business School context.

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

The rapid global economic development in the last century, led by industrialization, brings environmental issues to the forefront as a serious concern. While some country-specific studies are undertaken to find the effectiveness of different mechanisms for funding environment-friendly projects, to the authors' knowledge, no study has been conducted to examine the impact of green bonds (GBs) on CO2 emissions for a global sample. Against this backdrop, this study examines the general impact of GBs on CO2 emissions and its differential impact for developed and developing countries and country categorizations based on sustainable development.

The study selects a sample of 44 countries from 2016–2020. The authors use trend analysis and box plots to analyze the present GBs and CO2 emissions scenarios. Further, the panel data regression model is used to examine the overall impact of GBs on CO2 emissions and uncover the variation in such relationships regarding country-level economic and sustainable development. Generalized methods of moments (GMM) and instrumental variables (IV) models are used for robustness.

The yearly trend of GBs is upward at the global level, while CO2 emissions exhibit a marginal decline during the study period. However, significant variations are observed in such trends between developed and developing countries and country-level sustainable development. The authors' regression results show that GBs significantly negatively impact CO2 emissions globally. In addition, the effect of GBs on CO2 emissions is strongly negative for developing countries, while the same influence becomes weak for developed nations. Similar variations exist between countries based on sustainable development.

This is the first study in extant literature to examine such a relationship for a global sample of 44 countries. Further, this study makes a novel contribution by analyzing the variations in the GBs-CO2 emissions nexus for developed and developing countries and country-level sustainable development.

This paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.

The authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.

The results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.

This paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

Exploiting a novel measure of hostile takeover exposure primarily based on the staggered adoption of state legislations, we explore a crucial, albeit largely overlooked, aspect of corporate social responsibility (CSR). In particular, we investigate CSR inequality, which is the inequality across different CSR categories. Higher inequality suggests a less balanced, more lopsided, CSR policy.

In addition to the standard regression analysis, we perform several robustness checks including propensity score matching, entropy balancing and an instrumental-variable analysis.

Our results show that more takeover exposure exacerbates CSR inequality. Specifically, a rise in takeover vulnerability by one standard deviation results in an increase in CSR inequality by 4.53–5.40%. The findings support the managerial myopia hypothesis, where myopic managers promote some CSR activities that are useful to them in the short run more than others, leading to higher CSR inequality.

Our study is the first to exploit a unique measure of takeover vulnerability to investigate the impact of takeover threats on CSR inequality, which is an important aspect of CSR that is largely overlooked in the literature. We aptly fill this void in the literature.