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The purpose of this paper is to examine the trends of cost efficiency (CE) of Indian banks in response to financial deregulation programme launched in early 1990s. More specifically, the findings of this paper offer empirical testing of the basic underlined hypothesis that the CE of banks will rise in the more liberal and competitive environment.

The study employs input-oriented data envelopment analysis (DEA) models that incorporate the quasi-fixed inputs to compute the cost, technical, and allocative efficiency scores for individual banks. The unbalanced panel data spanning from the financial year 1992-1993 to 2007-2008 are used for obtaining efficiency measures. In addition, the panel data Tobit model has been applied to investigate the bank-specific factors explaining variations in the CE.

The empirical findings pertaining to the trends of efficiency measures suggest that: first, deregulation programme has had a positive impact on the CE of Indian banks, and the observed increase in CE is entirely due to improvements in technical efficiency (TE); second, the ranking of ownership groups provides that public sector banks are more cost efficient along with the foreign than private banks; and third, there is a strong presence of global advantage hypothesis in the Indian banking industry. The results of post-DEA analysis reveal that size and exposure to off-balance sheet activities are the key determinants of CE. The results also support the existence of bad luck or bad management hypothesis in Indian banking industry.

The practical implication of the research findings is that the financial deregulation programme seems to be successful in achieving the CE gains in the Indian banking industry. This explicitly signals that the cautious approach of banking reforms adopted by Indian policy makers has started bearing fruit in terms of the creation of an efficient banking system, which is immune to any sort of financial crisis, and resilient to both internal and external shocks.

The present study offers new evidence on the time-series properties of cost, allocative, and TEs of Indian banks. The DEA models used in this study explicitly incorporate the equity as a quasi-fixed input, which accounts for “risk” in the bank efficiency measurement.

The aim of this paper is to examine whether accredited police agencies display higher clearance rates than their non‐accredited counterparts.

The study group consists of all municipal police departments operating continuously in the State of Florida from 1997 through 2006. Independent variables capture organizational characteristics for nearly 260 agencies to determine whether becoming accredited improves clearance rates.

Random‐effects Tobit analysis suggests that accreditation status does not affect violent and property crime clearance rates. Clearance rates are more influenced by the number of sworn personnel and law enforcement expenditures per capita.

Much of what is currently known about the impact of accreditation stems from anecdotal and testimonial evidence. Still, the industry manages to expand and flourish. A glaring need for sound empirical research is evident.

Instead of advancing the protection of local communities and bringing about meaningful organizational reform, accreditation appears to be a useful tool for bureaucrats who wish to further their own careers.

Advocates link accreditation status to a number of benefits, including better investigatory practices that culminate in more solved cases. Recent academic work suggests that accreditation has dubious benefits, despite claims to the contrary. This study adds to that literature by showing that accreditation also fails to elevate clearance rates.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

Several popular and academic pieces of late have expressed concerns regarding the sustainability of public defined benefit pension funds. Since the onset of the Great Recession, concern has increased. In this paper recent arguments are analyzed in the context of three related data sets: panel data on public sector pensions spanning 2001-2009, historic asset return data, and business cycle data. Findings generally indicate that while public sector plans have suffered a difficult decade, current anxieties may be somewhat overwrought. Several remedial policies are investigated. Remedial policies, such as improving plan administration, altering portfolio allocations, and increasing both employee and employer contributions, are observed to be more promising than either freezing or closing the funds.

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.

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.

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.

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.

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.

The purpose of this paper is to identify the main determinants of foreign direct real estate investments (foreign direct investment (FDI)) in selected Middle Eastern and North African (MENA) countries.

The empirical work of this study is an econometric analysis of FDI in the commercial real estate sector for eight MENA markets, namely Algeria, Egypt, Morocco, Qatar, Saudi Arabia, Turkey, Tunisia and the UAE during the period 2003-2009. The econometric analysis is carried out using the pooled Tobit model technique for panel data.

The paper finds that both country-specific factors and real estate sector-specific variables consistently support hypotheses explaining commercial real estate-related FDI, and find evidence that political stability explains why some selected MENA countries attract more real estate investments than other MENA countries.

The findings should be seriously considered in any policy making effort on the part of governments in the region.

The authors contribute to the existing literature in many ways. First, the study aims to develop econometric models, using both conventional and unique variables, to be generalised and applied to any developed or emerging market. The study applies relevant techniques in estimating the models, including the pooled Tobit model. Second, the research studies eight selected MENA real estate markets from 2003 to 2009, a timeframe and geography not examined in previous published empirical work on commercial real estate investments. Lastly, and for the first time in real estate literature, the study applies the location dimension of Dunning’s OLI paradigm as a theoretical explanation for the behaviour of foreign investors in commercial real estate towards the selected MENA markets.

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.

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.

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.

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.

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 examine the influence of the innovation ability of universities (IAU) on the efficiency of University–Industry knowledge flow and investigate whether the level of provincial innovative agglomeration (PIA) moderates the relationship between IAU and the efficiency of the University–Industry knowledge flow.

This study uses the super-efficiency data envelopment analysis model to measure knowledge research efficiency (KRE) and knowledge transformation efficiency (KTE) and then studies the influencing mechanism of the two kinds of efficiency using the spatial Tobit model with panel data from 2008 to 2017.

The results show that the overall KRE in Chinese universities is higher than the KTE. IAU has a significantly positive impact on KRE and KTE. PIA has a significantly inverted U-shaped influence on KRE and KTE and positively moderates the promoting effect of IAU on KRE and KTE.

Due to the limitations of the data, this paper only selects several secondary indicators to measure KRE and KTE with reference to previous studies.

This study enriches the future research of University–Industry cooperation and knowledge flow and it is conducive to promoting the efficiency of University–Industry knowledge research and transformation from the perspective of universities, enterprises and local governments.

This study proposes the concept of University–Industry knowledge flow and divides the knowledge flow into the knowledge research stage and the knowledge transformation stage based on the knowledge supply chain theory. Moreover, the paper expands the theoretical framework of the impact of IAU on the efficiency of University–Industry knowledge flow and provides findings on the moderating effect of PIA.

The efficient use of organizational resources is integral to the existence of prime firms. This study, using Malmquist data envelopment analysis (DEA), aims to assess in the level of operational and financial efficiency and its determinants for ensuring and sustaining excellent performance in 33 Egyptian private hospitals.

This study adopted a Malmquist DEA approach to assess the changes in operational and financial efficiency in Egyptian hospitals. Tobit regression was also used to identify the significant variables affecting their efficiency. In addition, a sensitivity analysis is carried out for model validations.

Out of 33 hospitals, 17 were found inefficient due to the decline in their technical efficiency. Moreover, the total value of the software programs and operational expenses and the total number of employees are common factors affecting both operational and financial efficiency. In addition, the number of physicians significantly affects the hospital's financial efficiency.

The study sheds light on the value of using DEA to assess efficiency. DEA in the context of emerging economy such as Egypt's can be a useful tool for decision-makers and practitioners in identifying and addressing performance weaknesses and thus supports continuous improvement in performance.

Several studies have adopted the DEA approach to assess the overall efficiency of hospitals in Europe and the United States. However, in the MENA region, these studies are uncommon. This study is thought to be one of the earliest attempts to assess hospitals' efficiency in Egypt.