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The purpose of this study is to enhance understanding labour supply dynamics of the UK workers by examining whether and to what extent there is state dependence in the labour supply at both the extensive and intensive margins.

A dynamic two-tiered Tobit model is applied to the first seven waves of Understanding Society: the UK Household Longitudinal Study. The model used accounts for observed and unobserved individual heterogeneity and serially correlated transitory shocks to labour supply to draw inferences on state dependence.

The results show that both observed and unobserved individual heterogeneity contributes to observed inter-temporal persistence of the labour supply of the UK workers, and the persistence remains after these factors are controlled for, suggesting true state dependence at both the extensive and intensive margins of the labour supply. The study also finds that at both the margins, the state dependence of labour supply is larger for females than for males and that for both genders the state dependence is larger for people with low education, mature aged workers and people with long-standing illness or impairment. The results also show that estimates from a conventional Tobit model may produce misleading inferences regarding labour supply at the extensive and intensive margins.

This study adds to the international literature on labour supply dynamics by providing empirical evidence for both the extensive and intensive margins of labour supply, while previous studies tend to focus on the extensive margin of labour force participation only. Also, unlike earlier studies that often focus on females, this study compares labour supply dynamics between males and females. The study also compares the estimates from the more flexible two-tiered Tobit model with that from the conventional Tobit model.

Firms are working in a complex environment in which the updated information increase the pace of precise decision making and reduce the risk of wrong decisions. Therefore, discovering firms’ performance is a major issue. The purpose of this paper is to evaluate the efficiency of Iranian plastic producing companies by using data envelopment analysis (DEA). It also discovers various drivers that significantly affect the efficiency of enterprises.

The authors studied a sample of 17 manufacturing firms to examine the relative efficiency of companies. They, then, evaluated the effects of efficiency drivers and used two methods for these purposes: DEA and bootstrapped Tobit regression model.

The study has shown that two manufacturing firms out of selected 17 are efficient under the Charnes, Cooper, and Rhodes model. Also, nine out of 17 plastic producing companies are productive under the Banker, Charnes, and Cooper model. The results of Tobit regression shows that only two efficiency drivers out of four have a significant positive influence on the efficiency of plastic producing firms.

Considering one industry and country limits the generalizability of the results provided. Besides, data availability has limited the analysis in some parts, particularly in bootstrapped Tobit regression.

The authors listed this section into benchmarking and strategical management; more importantly, the suggestions for improving the chemical industry and its future evolution are presented.

The paper is classified into two issues: the efficiency of plastic producing firms in Iran and evaluating the reason for inefficiency, apart from internal managerial procedures.

This paper aims to examine whether the substitution of employee stock options (ESOs) for debt occurs for firms with different tax status classifications throughout the conditional distribution of interest expense before and after the implementation of Statement of Financial Accounting Standard 123R (SFAS 123R).

This study uses Censored Quantile Regression (CQR) to assess whether the substitution effect is dependent on firms' position in the conditional distribution of interest expense. Our sample firms are categorized into two groups: one group (tax‐sensitive) that is sensitive to additional deductions due to a moderate income level and the other group (tax‐insatiable) that is not sensitive because of very high income level.

The substitution effect is not present for firms with below medium level of interest expense. Only tax‐sensitive firms substitute at medium levels of interest expense while both tax‐sensitive and tax‐insatiable firms substitute at high levels of interest expense. Tax‐insatiable firms with very high levels of interest expense also substitute; however, tax‐sensitive firms with very high levels of interest expense only substitute after SFAS 123R required firms to report ESO expense in financial statements. We attribute the substitution patterns revealed by the CQR analysis to a positive relationship between interest expense and cost of debt.

To the authors' knowledge, this is the first paper to analyze firms' tax status classification impact on the substitution of ESO expense for interest expense across different levels of interest expense. Our application of CQR should benefit researchers who are interested in examining explanatory variables' impact at various points in the conditional distribution of the dependent variable. This study also refines the conjecture that ESOs are substitutes for debt by demonstrating that such relationship is dependent on the level of interest expense and tax status. Furthermore, the finding of firms substituting ESOs for debt provides accounting standard setters a reason to begin requiring firms to re‐measure the value of ESOs after the grant date until the exercise date.

This paper sets out to explore the effects that the setting‐up of an independent director system has on the operating efficiency of information electronics companies in China.

This paper uses 87 Chinese listed electronics companies during the initial stages of the independent directors system from 1999 to 2002 as sample subjects, and employs a two‐stage procedure for empirical investigation.

The non‐parametric test results verify that there is no significant difference in the operating efficiency of Chinese electronics companies following the establishment of an independent director system. The Tobit regression results show that the establishment of an independent director system in the Chinese electronics industry does not influence overall technical efficiency (TE), pure technical efficiency (PE), or scale efficiency (SE).

Whether the related schemes of the current corporate governance structure practised in China can achieve their expected results, as well as the possible future development direction of the governance structure, is of the utmost importance, and is a research subject worth examining in greater depth.

It is of the utmost urgency for such corporate governance to improve the selection mechanism for independent directors, to establish incentives and responsibility‐taking mechanisms for independent directors, and to amend the company law and securities law to perfect the rules of an independent director system.

By using DEA and the Tobit regression model, this study attempts to investigate whether China, in addition to fraud prevention, has improved corporate operating efficiency by introducing a system of independent directors.

The paper's aim is to assess performance of firms in the UK retail sector.

Economic efficiencies of 41 retail companies working in the UK between 2000 and 2005 are examined in this study using three related methodologies: data envelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regression model. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover, profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) are employed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change over the six year period 2000‐2005. DEA efficiencies are then used to test important hypotheses on the impact of environmental variables, namely head office location, type of ownership, years of incorporation, legal form and retail characteristic, on the functioning of the UK retail sector using bootstrapped Tobit regression.

DEA analysis has shown that only ten retail companies are considered as efficient under CRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about 50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twenty out of 41 retail companies have adopted advanced and efficient retailing technologies during this period. Three environmental variables, namely the type of ownership, legal form and retail characteristic, have been found to play significant roles influencing retail efficiency using bootstrapped Tobit regression.

Data availability has limited the level of analysis in some parts of this study, especially in the bootstrapped Tobit regression.

This study seems to be the first in applying productivity analysis using DEA for the UK retail sector.

This paper uses a sample of school age children from the Nepal Demographic Health Survey (NDHS) to examine the relationship between maternal education and child schooling in Nepal. Taking advantage of the two-stage stratified sample design, we estimate a sample selection model controlling for cluster fixed effects. These results are then compared to OLS and Tobit models. Our analysis shows that being male significantly increases the likelihood of attending school and for those children attending school, it also affects the years of schooling. Parental education has a similarly positive effect on child school, but interestingly we find maternal education having a relatively greater effect on the schooling of girls. Our results also point to household wealth as having a positive effect on both the probability of schooling and the years of schooling in all our models, with the magnitude of these effects being similar for male and female children. Finally, a comparison of our results with a model ignoring cluster fixed effects produces results that are statistically different both in signs and in the levels of significance.

We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. Monte Carlo experiments are provided to compare the performance of spatial entropy estimators relative to classical estimators. Finally, the estimators are applied to an illustrative model allocating agricultural disaster payments.

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.

The purpose of this paper is to study an innovative rice value chain financing system (VCFS) established in Benin, to identify the determinants of producers and processors access to formal credit, both at intensive and extensive margins. It focuses on multi-stakeholder platforms (MSP) which connect producers and processors in need of credit to potential financial lenders.

The empirical analysis uses rich cross-sectional survey data collected in Northern Benin in 2018. The sample consists of 215 rice producers and 217 rice processors randomly selected through a multi-stage sampling and interviewed with structured questionnaires. The empirical models analyze the determinants of the likelihood to receive a credit and the amount of credit received. To account for the sample selection and censored nature of the main outcome variable, the study considers a Heckman two-stage model coupled with a Tobit model for robustness checks.

The study finds that the MSP are effective in increasing access to formal credit and the amount borrowed. Producers and processors who are members of the MSP are more likely to receive credit and, conditional on being approved for credit borrower, a larger amount. Other key factors that significantly explain access to credit include the use of soft guarantee for securing a loan, the degree of participation in the platform and demographic characteristics. These findings are consistent across the Heckman and Tobit models.

The study attempts to rigorously analyze the factors explaining producers and processors access to credit using cross-sectional survey data. But it has some limitations. The main limitation is the type of data used. Ideally, one would like to run a randomized control trial (RCT) to randomly assign participation in the MSP to causally estimate its impact of access to credit. The second-best option would be to have a panel data covering the period before and after the establishment of the platform. However, in the absence of an RCT or panel data, the study resorts to cross-sectional data and empirical models that account for sample selection bias and the censored nature of the credit received.

One of the key findings of the study is that participation in the MSP (through different value chain stages associations) increases access to formal credit. This highlights an important and effective mechanism, a well-coordinated value chains that integrated lenders, that policymakers can leverage to facilitate access to credit in the agricultural sector.

Access to credit is important to boost agricultural productivity and income. Hence, the findings of the study have social implications in terms of poverty reduction in rural areas.

The study contributes to earlier theories and empirical studies on the demand for credit. It focuses on an innovative VCFS, increasingly adopted in many developing countries, adds originality and value to the understanding of mechanisms to unlock agricultural actors’ access to credit in low-income countries.

The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.

This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.

DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India.

The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression.

This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.