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The purpose of this paper is to provide a comprehensive initial evaluation of the wealth transfer hypothesis as applied to the second‐stage events and announcements that follow carve‐outs during the period from 1983 to 2004.

Using daily security prices, such combinations are shown to have multi‐faceted wealth transfers and wealth creation.

In contrast with the wealth losses found in previous studies, wealth increases are observed for parent stockholders and bondholders in the spin‐off announcement and event phases for combination carve‐outs and spin‐offs. Also, the spin‐off is the most prevalent second divestiture choice for parents with traded debt.

This study makes several contributions to the literature. First, in contrast with recent wealth transfer studies that use monthly bond returns, daily stock and bond returns are used to examine the wealth effect for parent stockholders and bondholders during the announcement and ex‐dates of second‐stage events. Second, in contrast with previous studies that found a wealth transfer from bondholders to stockholders in the spin‐off phase, statistically significant wealth retention was observed for bondholders and for stockholders at spin‐off and other second event announcements. Third, the results reflect that increased collateral from the carve‐out phase lessens the potential for bondholder wealth loss in the spin‐off phase.

The purpose of this paper is to evaluate the efficiency of Algerian banks and examine the effects of explanatory factors on their performance.

In this paper, a methodology of two-stage network data envelopment analysis (DEA) is used to explore the efficiency of a sample of 13 Algerian banks during the 2013–2017 period. In the first stage, the network DEA is used to assess the overall and stages efficiencies. In the second stage, the partial least squares (PLS) regression is conducted to determine the potential effects of explanatory factors on stages efficiency.

The main empirical results indicate that Algerian banks need an efficiency improvement in both stages. The overall efficiency of the Algerian banking system improves over the study period. The deposit producing efficiency is positively affected by bank size and bank age. The revenue earning efficiency is negatively associated with bank size and bank age. The domestic banks are more efficient than foreign banks in the deposit producing stage and the foreign banks are more efficient than domestic banks in the revenue earning stage.

The results might be used as guidelines for both managers and policymakers in order to improve banks and banking system performance.

To the best of our knowledge, this study is the first that uses the DEA in investigating the efficiency of Algerian banks by dividing the overall efficiency into deposit producing and revenue earning efficiencies. Unlike most studies that have usually used OLS regression, Tobit regression and bootstrapped truncated regression, this study is the first in the bank efficiency literature that uses PLS regression to investigate the potential effect of explanatory variables on deposit producing and revenue earning efficiencies.

The purpose of this paper is to determine the most efficient hotels in the Indian hotel industry, the competitive positioning of these hotels, and the factors that affect their efficiency change.

This study conducts a two-stage analysis and uses data envelopment analysis (DEA) and Global Malmquist productivity index (MPI) approach in the first stage to calculate the managerial performance of a panel of 63 Indian hotels in 2019–2020 and their efficiency change from 2009–2010 to 2019–2020. Bootstrapped generalized least square (GLS) approach is applied in the second stage to evaluate the impact of contextual variables on efficiency change.

Using the results of the first stage analysis, the authors categorized the 63 Indian hotels into 7 distinct clusters. These clusters represent different levels of competitiveness and pace of growth. The GLS regression reveals a U-shaped relationship between hotel size and efficiency change and a negative relationship between pro social investments and efficiency.

This is the first study in the hotel industry that has used global MPI as a measure of efficiency change in the first stage and GLS in the second stage. In the Indian context, to the best of authors’ knowledge, no such study exists.

The purpose of this paper is to assess the efficiency of agricultural production in South Africa from 1970 to 2014, using an integrated two-stage fuzzy approach.

Fuzzy technique for order preference by similarity to ideal solution is used to assess the relative efficiency of agriculture in South Africa over the course of the years in the first stage. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of socio-economic and demographic variables on agricultural efficiency. They are compared with the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2016a).

The results show that the fuzzy efficiency estimates ranged from 0.40 to 0.68 implying inefficiency in South African agriculture. The results further reveal that research and development, land quality, health expenditure–population growth ratio have a significant, positive impact on efficiency levels, besides the GINI index. In terms of accuracy, fuzzy regressions outperformed the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2015).

Policies to increase social expenditure especially in terms of health and hence productivity should be prioritized. Also policies aimed at conserving the environment and hence the quality of land is needed.

The paper is original and has not been previously published elsewhere.

Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

Existing studies of school efficiency primarily specify teacher inputs as the number of teachers and perhaps the student-teacher ratio. As a result, there is no direct qualitative recognition of the learning environment. The purpose of this paper is to incorporate the learning environment directly into the assessment of school efficiency.

The authors employ data envelopment analysis to derive efficiency scores and the double-bootstrap truncated regression approach in Simar and Wilson’s (2007) Journal of Econometrics to quantify the sources of efficiency in 430 Queensland state primary schools. In the first stage, the outputs of student National Assessment Program-Literacy and Numeracy scores and the inputs of full-time equivalent teaching staff and cumulative capital expenditure per student are used to measure efficiency. In the second stage, the authors specify an index of community socio-educational advantage, class size, the share of teachers with postgraduate qualifications, funds spent on professional development, and surveyed opinions from parents/caregivers, students, staff and principals on the learning environment to explain these measures of efficiency.

Socio-economic background and the teaching environment affect school efficiency. Although not all variables related to teacher contribution are significant, there is evidence to suggest that teachers have a positive influence on student performance hence school efficiency. Teachers ability to clearly explain the requirements of schoolwork tasks and listening to student opinions sets an ideal student engagement environment which can have a profound impact on student learning.

From a policy perspective, policy makers should target resources at inefficient schools aimed at enhancing student learning through teacher development and, at the same time, providing financial and non-financial educational assistance to students and their families from a low socio-educational background.

This is the first large-scale primary school efficiency analysis to incorporate the Simar and Wilson (2007) approach to explaining the determinants of efficiency, including teaching environment from the perspective of students, teachers and other stakeholders.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of efficiency, productivity and returns-to-scale economies. In addition to this, it identifies the determinants of efficiency.

This study employs a two-stage data envelopment analysis (DEA) bootstrap approach to estimate the level and determinants of efficiency. In the first stage, the DEA bootstrap approach is employed to estimate bias-corrected efficiency scores. In the second stage, the truncated bootstrapped regression is used to identify the effect of firm-level characteristics on the efficiency of insurers. Moreover, the bootstrapped Malmquist index is used to examine the productivity growth over the observation period 2005–2016.

The bootstrapped DEA results show that the Indian non-life insurance sector is moderately technical, scale, cost and allocative efficient, and there is a large opportunity for improvement. Moreover, the results reveal that the public insurers are more cost efficient than the private insurers. It is also evident that all the insurers irrespective of size and ownership type are operating under increasing returns to scale. Malmquist index results divulge an improvement in productivity of insurers, which is attributable to the employment of the best available technology. Bootstrapped DEA and bootstrapped Malmquist index results also show that the global financial crisis of 2008 has not severely affected the efficiency and productivity of the Indian non-life insurance sector. The truncated regression results spell that size and reinsurance have a statistically significant negative relationship with efficiency. It also shows a statistically significant positive age–efficiency relationship.

The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies.

This study is the first of its kind that comprehensively investigates different types of robust efficiency measures, determinants of efficiency, productivity growth and returns-to-scale economies in the Indian non-life insurance market for an extended time period.

This study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.

In the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.

The DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.

The results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.

This study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

The purpose of this paper is to examine the effectiveness of hedge fund activism (HFA) in preventing corporate policy deviations.

This paper identifies HFA interventions through a hand-collected sample of Schedule 13D filings between 1994 and 2016, and uses mechanical mutual fund fire sales as the instrument variable (IV) for the likelihood of such interventions. Armed with the instrument, this paper estimates firm's distribution, managerial compensation and investment policies in response to a change in the perceived likelihood of HFA interventions.

An increase in the HFA intervention likelihood leads to increases in shareholder distribution, decreases in CEO pay and investments and increases in operating performance. Compared to the sample average, a one standard deviation increase in the intervention likelihood leads to a 9.29% increase in the firm's payout ratio, a 7.42% decrease in CEO compensation, a 2.67% decrease in capital expenditures and a 4.96% decrease in R&D expenses. These changes are consistent with the threat of intervention curbing managerial empire-building behaviors and improving firm operation. The relationships are causal, significant and robust to a variety of alternative specifications and sample divisions.

Results of this paper suggest that as a mechanism for corporate governance, the threat of HFA is effective in preventing corporate policy deviations. They also demonstrate a stronger and broader impact of HFA on corporate policy than previously documented. By showing that HFA is an effective and viable mechanism for corporate governance, this study allows policymakers to make more informed decisions to whether increase hedge fund regulations or not.

The purpose of this paper is to explore the determinants, transactional alignment and performance outcomes of franchise contract length using transaction costs theory (TCT) and resource-based theory (RBT).

The author hypothesizes that franchisors choose contract length according to TCT and RBT arguments. TCT explains the safeguarding function of contracts: the franchisors will offer longer contracts when franchisees’ specific investments are high and environmental uncertainty is low. RBT highlights the knowledge leverage function of contracts: the franchisors will offer longer contracts when the brand name and intangible knowledge assets are high. Franchise companies that design contract length aligned with transactional attributes will perform better. The author tests the misalignment hypothesis and comparative performance of franchise contracts by estimating two-stage least squares regression and Heckman two-stage procedure that control for endogeneity and self-selection.

Empirical data from the German franchise sector support the hypotheses. In addition to the safeguarding function, franchise contracts have an important knowledge leverage function. Longer contracts perform better due to the development of relational strategic assets and stronger commitment.

Franchisors must offer longer contracts when specific investments of franchisees, brand name, intangible knowledge assets are high, and environmental uncertainty is low. Franchisors should invest in the development of relational strategic assets and offer longer contracts for the benefit of superior performance.

The study addresses the significant question of transactional alignment and comparative performance of franchise contracts. It empirically confirms the importance of RBT in explaining contractual choices and performance.