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The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of total factor productivity (TFP) over the period 2005–2016.

This study utilises Färe‒Primont index (FPI) to access the change in TFP and its components: technical change, technical efficiency and mix and scale efficiency over the observation period. Moreover, it employs the Mann–Whitney U-test to scrutinise the difference between the public and the private insurers in terms of growth in productivity.

The results reveal that the insurance sector possesses a very low level of TFP. Also, the results divulge an improvement of 11.98 per cent in TFP of the insurance sector at an annual average rate of 12.41 per cent over the observation period. The growth in productivity is mainly attributable to the improvement of 10.81 per cent in the scale‒mix efficiency. The progress in scale‒mix efficiency is mainly the result of improvements in residual scale and residual mix efficiency. The results also show that the privately owned insurers have experienced a high productivity growth rate than the state-owned insurers.

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

This study is the first of its kind to use FPI, which satisfies all economically relevant axioms and tests defined by the index number theory to comprehensively access the change in TFP of the Indian non-life insurance sector.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

Total factor productivity (TFP) change is an important driver of long-run economic growth in the construction sector. However, examining TFP alone is insufficient to identify the cause of TFP changes. Therefore, this paper employs the infrequently used Geometric Young Index (GYI) and stochastic frontier analysis (SFA) to measure and decompose the TFP Index (TFPI) at the firm-level from 2009 to 2018 based on Malaysian construction firms' data.

To improve the TFPI estimation, normally unobserved environmental variables were included in the GYI-TFPI model. These are the physical operation of the firm (inland versus marine operation) and regional locality (West Malaysia versus East Malaysia). Consequently, the complete components of TFPI (i.e. technological, environmental, managerial, and statistical noise) can be accurately decomposed.

The results reveal that TFP change is affected by technological stagnation and improvements in technical efficiency but a decline in scale-mix efficiency. Moreover, the effect of environmental efficiency on TFP is most profound. In this case, being a marine construction firm and operating in East Malaysia can reduce TFPI by up to 38%. The result, therefore, indicates the need for progressive policies to improve long-term productivity.

Monitoring and evaluating productivity change allows an informed decision to be made by managers/policy makers to improve firms' competitiveness. Incentives and policies to improve innovation, competition, training, removing unnecessary taxes and regulation on outputs (inputs) could enhance the technological, technical and scale-mix of resources. Furthermore, improving public infrastructure, particularly in East Malaysia could improve regionality locality in relation to the environmental index.

This study contributes to knowledge by demonstrating how TFP components can be completely modelled using an aggregator index with good axiomatic properties and SFA. In addition, this paper is the first to apply and include the GYI and environmental variables in modelling construction productivity, which is of crucial importance in formulating appropriate policies.

This chapter tries to hedge extreme financial risk of entrepreneurs who work with wheat by combining wheat with four stock indices of developed and emerging European markets in a portfolio. Extreme risk of the portfolios is measured by the parametric and historical value-at-risk (VaR) metrics. Portfolios that target maximum return-to-VaR ratio are also constructed because different market participants prefer different goals. Preliminary equicorrelation results indicate that integration between wheat and emerging markets is lower (0.218) vis-á-vis the combination of wheat and developed markets (0.307), which gives preliminary advantage to emerging markets in diversification efforts. The results show that portfolios with emerging stock indices have significantly lower parametric (–0.816) and historical (–0.831) VaR than portfolios with developed indices, –1.080 and –1.295, respectively. As for optimal portfolios, the portfolios with developed indices have a slight upper hand. This chapter shows that parametric VaR is not a good measure of extreme risk, because it neglects the third and fourth moments.

This paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.

The study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.

The empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.

The practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.

This study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

This paper introduces the concept of policy efficiency of banks as their efficiency in implementing the government's policies. It further compares the Indian public sector banks (PSBs) and private sector banks (PVBs) on two efficiency paradigms, operational efficiency and policy efficiency.

A three-stage analysis is carried out on data collected for 19 PSBs and 16 PVBs for ten years. Non-radial DEA with slack-based measure (SBM) is used to obtain efficiency scores of the banks for the two efficiency paradigms. The efficiency scores and the changes in efficiency and Malmquist index are further analysed by Tobit regression and seemingly unrelated regression (SUR) models.

PVBs are found to be more operationally efficient than PSBs. On the contrary, PSBs are found to be more policy efficient. Among the PSBs, the older and larger banks performed better in both the paradigms. Though Indian banks have become more operational and policy efficient over the years, the rate of improvement is slowing down.

Results imply that evaluating banks, especially PSBs, only on their operational efficiency is myopic. Their efficacies must also be measured by the roles they play on social and policy front. The loss of efficiency of Indian PSBs in a competitive environment should provoke thoughts of reforms. The study suggests that the proposed merger of PSBs to form large banks might be fruitful.

The study contributes to the literature by introducing the measure of policy efficiency. It shows that the Indian PSBs are indispensable as vehicles of government policy implementation.

The purpose of this paper is to empirically investigate the effect of lean manufacturing on productivity changes and to identify the root sources of productivity changes. Furthermore, the authors explore the moderating effects of research and development (R&D) to examine the relationship between lean manufacturing and productivity changes.

This paper employs the propensity score matching (PSM) model combined with the difference-in-difference (DID) estimation to overcome the selectivity bias. The Malmquist productivity index is used to capture productivity changes. By analyzing 671 Chinese manufacturing listed firms from 2009 to 2014, the moderating effects of R&D on the relationship between lean manufacturing and productivity changes are measured.

The results reveal that lean manufacturing implementation has non-significant effects on productivity changes in principle, while a detailed analysis indicates that lean manufacturing could improve scale efficiency significantly. While engaged in R&D could significantly improve the efficiency of technological changes for lean manufacturing implementation firms, there exist negative effects on pure technical efficiency.

This research only covers manufacturing listed firms in China. Further studies should extend the generalizability of the findings.

This study helps managers to identify the important role of R&D on the relationship between lean manufacturing and productivity changes and provides insights into how to improve the lean manufacturing performance.

This paper appears to be one of the earliest studies on the relationship between lean manufacturing and productivity changes by applying the PSM combined with DID estimation in Chinese manufacturing environment.

Many large construction firms (LCFs) adopt product diversification (PD) to counter downturns and spread risks. However, no detailed information is available concerning the type of PD that improves their performance. In addition, it is still uncertain how much changes in institutional dimensions influence the effectiveness of PD. Therefore, the aim is to resolve this issue by establishing a model that shows the extent of this influence.

The generalised method of moments (GMM) estimator is used to model the PD strategies of 86 LCFs in Malaysia over 14 years (2003–2016) and its impact on productivity and profitability performance.

Unrelated diversification (UD) decreased firm performance in 2003–2016, while related diversification (RD) had a positive impact during the more liberal 2010–2016 phase. The models show that the impact of PD is highly dependent on changes in institutional dimensions.

Firstly, managers may adjust the type of PD and its level of diversification to improve firm performance. Secondly, they may devise PD strategies based on changes in institutional dimensions to maximise their effectiveness.

The study contributes to the literature by determining the optimal amount of PD (including RD and UD) and its impact on performance. Secondly, the study is the first to investigate the moderating relationship of the institutional dimensions of economic and regulatory institutions on PD-firm performance. Thirdly, the study is the first to explore the components of technical-scale-scope economies (movement towards and around the production frontier), this being crucial to the strategy that was only conjectured in previous studies.

There is ample empirical evidence to show that larger banks are more efficient than smaller banks in developed countries. However, there is very little empirical evidence to show that in small developing economies, such as Vietnam, bank size is associated with increased risk, especially credit risk. This paper aim to provide empirical evidence to fill in this gap. This paper employs a slack-based directional distance function using the intermediation approach in measuring the inefficiency of banks in Vietnam during the period 2006–2015. Non-performing loans are used as an undesirable output to capture credit risk. The results show that small banks are more efficient than large banks at the mean level and across the entire distributions of inefficiency of the two groups. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies. The study’s empirical results contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to enhance the performance of Vietnam's banking system as a whole.

This paper uses the non-radial slack-based directional technology distance function developed by Färe and Grosskopf (2010) to estimate the efficiency of banks using the data envelopment analysis technique. Data for 44 commercial banks are used.

The empirical results of the paper contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to improve the performance of Vietnam's banking system as a whole.

This paper extends the extant literature by examining whether efficiency is associated with size in a typical transitional developing economy. The classic Cournot model, the structure-conduct-performance and the efficiency structure hypotheses state that larger banks are more efficient than smaller banks (Bikker and Bos, 2008). Empirical studies of Berger (2003), Mester (2005), Wheelock and Wilson (2012) lend support to the statement in developed countries. However, not much empirical literature focuses on small developing economies such as Vietnam to show that bank size is associated with increased risk, especially credit risk. The study’s empirical results show that size enlargement is not positively associated with risk-adjusted efficiency. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies.

This paper aims to measure the change and the sources of change in total factor productivity (TFP) of the Indian non-life insurance sector over the period 2005–2016.

This study employs the bootstrapped Malmquist index (MI) to assess the changes in the TFP and adopts a decomposition approach proposed by Balk and Zofío (2018). Moreover, it utilises truncated regression to identify the determinants of the TFP. In addition, it employs Wilcoxon-W test and t-test to scrutinise the difference between the state-owned and the private insurers in terms of variations in TFP and its various components.

The results divulge a miniature improvement in TFP of the insurance sector, which is primarily attributable to the improvement in scale efficiency (economies of scale). The results also reveal that there are no significant TFP differences across the ownership. However, private insurers have better scale efficiency and lower input-mix efficiency than state-owned insurers. In addition, the results unveil that size, diversification and reinsurance have a negative impact on the TFP, while age has a positive impact on it.

The results may help the policymakers to frame new consolidation policies. Moreover, the findings may guide the decision-makers of the Indian non-life insurance companies to abate inefficiency and improve TFP.

This study estimates bias-corrected changes in TFP and efficiency in the non-life insurance sector. Moreover, it adopts an elaborated decomposition of the MI to identify the true sources of change in the TFP.