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Article
Publication date: 8 July 2020

Lixin Cai

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Manpower, vol. 42 no. 3
Type: Research Article
ISSN: 0143-7720

Keywords

Article
Publication date: 9 May 2022

Narendra N. Dalei and Jignesh M. Joshi

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to…

Abstract

Purpose

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

Design/methodology/approach

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

Findings

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

Practical implications

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

Originality/value

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

Details

International Journal of Energy Sector Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6220

Keywords

Book part
Publication date: 30 December 2004

Thomas L. Marsh and Ron C. Mittelhammer

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…

Abstract

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.

Details

Spatial and Spatiotemporal Econometrics
Type: Book
ISBN: 978-0-76231-148-4

Article
Publication date: 14 December 2021

Lijun Zhou and Zongqing Zhang

China's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological…

Abstract

Purpose

China's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.

Design/methodology/approach

At first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.

Findings

Firstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.

Originality/value

Compared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 10 December 2019

Vincent Flifli, Peter Adebola Okuneye and Dare Akerele

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Social implications

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.

Originality/value

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.

Details

Agricultural Finance Review, vol. 80 no. 2
Type: Research Article
ISSN: 0002-1466

Keywords

Article
Publication date: 1 October 2018

Nassim Ghondaghsaz, Asadollah Kordnaeij and Jalil Delkhah

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Benchmarking: An International Journal, vol. 25 no. 7
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 12 December 2019

Yong Joo Lee and Seong-Jong Joo

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Benchmarking: An International Journal, vol. 27 no. 1
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 3 June 2014

Aradhana Gandhi and Ravi Shankar

– 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.

1160

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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

Details

International Journal of Retail & Distribution Management, vol. 42 no. 6
Type: Research Article
ISSN: 0959-0552

Keywords

Article
Publication date: 6 September 2011

Yenpao Chen, Chien‐Hsun Chen and Will C. Wu

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.

1533

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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).

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Economic Studies, vol. 38 no. 4
Type: Research Article
ISSN: 0144-3585

Keywords

Book part
Publication date: 21 December 2010

Hoa B. Nguyen

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To…

Abstract

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To address the endogeneity of the right-hand-side count variable, I use instrumental variables and a two-step procedure estimation approach. Two methods of estimation are employed: quasi-maximum likelihood (QML) and nonlinear least squares (NLS). Using these methods, I estimate the average partial effects, which are shown to be comparable across linear and nonlinear models. Monte Carlo simulations verify that the QML and NLS estimators perform better than other standard estimators. For illustration, these estimators are used in a model of female labor supply with an endogenous number of children. The results show that the marginal reduction in women's working hours per week is less as women have one additional kid. In addition, the effect of the number of children on the fraction of hours that a woman spends working per week is statistically significant and more significant than the estimates in all other linear and nonlinear models considered in the chapter.

Details

Maximum Simulated Likelihood Methods and Applications
Type: Book
ISBN: 978-0-85724-150-4

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