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The purpose of this paper is to provide a total factor productivity index for the African banking industry. It also investigates the impact of some internal and external determinants affecting bank productivity.

The biennial Malmquist productivity index and various regression models (ordinary least squares, Tobit and truncated bootstrapped regression) are employed in analyzing data from 120 banks in 24 African countries from 2007 to 2012.

The results indicate a general decline in productivity of banks in Africa, largely due to inadequate technological progress. State banks are found to be more productive than foreign and private banks. The regression analyses showed that non-executive directors, leverage, management quality, credit risk, competition and exchange rate have significant impact on bank productivity, but ownership and CEO-duality do not.

The results have implications for management of banks, governments and regulators. It shows the need for policy and investments that improve state-of-the art technology. The findings also seem to suggest poor management practices in input usage, especially in operational management, as well as costs emanating from non-interest sources. Bank managers need to address these deficiencies to improve productivity in African banking markets.

A major contribution of this paper is the productivity index provided for the African banking industry. This study is also the first to apply the biennial Malmquist to analyze productivity in the African banking industry.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

The purpose of this paper is to empirically study the impact of product patent regime on the productivity of different categories such as ownership, R&D, size and product-wise of Indian pharmaceutical firms using non-parametric data envelopment analysis.

The present study has applied Ray and Desli’s Malmquist productivity index and its decomposition to measure total factor productivity (TFP) change, pure technical efficiency change, scale efficiency change and technical change under variable returns to scale (VRS) technology assumption for 141 Indian pharmaceutical firms during 2000-2001 to 2014-2015.

The study found the negligible impact of product patent regime on productivity. The technological change has played a positive role in the growth of productivity, whereas technical efficiency change depicts the judicious utilization of resources for improving performance. From the results, it is found that R&D intensive firms depict better stability in the TFP than the non-R&D firms. However, Granger causality between R&D and productivity found no relationship. Productivity is more directly affected by investment in fixed assets rather than in R&D, which focusses on incremental value additions in a largely branded/plain generic product market. In case of ownership, private foreign firms found to have registered progress in TFP while others have recorded marginal regress, which probably could be attributed to the superior marketing and management skills of the foreign firms, besides possessing proprietary technology. Both small and large firms have shown positive growth in the new regime as compared to the pre-patent regime. These small firms are able to compete with large firms because of their up-gradation of the technological base by improving access to better foreign technology. TFP growth for all the firms can be attributed to improvement in technology, and innovation in terms of high capital-output ratio. Further, the paper tried to identify the determinants of productivity from panel random effect regression, and it is found that export intensity, age and the new patent regime have negative and significant relationship with productivity, whereas other variables such as R&D, ownership, size and capital imports are insignificant. In the end, the results of sensitivity analysis have confirmed the validity of the selected variables.

The results suggest that Indian pharmaceutical firms need substantive improvement in TFP by improving managerial and scale efficiency. Indian pharmaceutical industry (IPI) needs to improve productivity across the network and drive cost excellence initiatives across the spend base through operational excellence and digital initiatives. The results of this paper can be applied in framing policies for future growth and improvement in the productivity of IPI.

The paper aims to make several new contributions to the existing literature. Most of the research papers only analysed TFP of the industry as a whole and detailed firm-wise analysis is needed to capture the true impact at a unit level. This study has analysed the impact of different categories such as ownership, R&D, size and product-wise, and determinants of productivity. The study has used a broader time period and larger panel data to predict the better picture.

Pharmaceutical industry is one of the sunrise industries in the Indian manufacturing sector. It has flourished in the recent past. This chapter makes a comparative analysis of the productivity growth of Indian pharmaceutical industry using production function approach and adopting two distinct measures of labour input and also explains whether the growth and productivity is eco-friendly or not. Annual Survey of Industries (ASI) data is considered as data base and the time period 1980–1981 to 2016–2017 is considered which is sub-divided into four periods (1980–1981 to 1989–1990; 1990–1991 to 1999–2000; 2000–2001 to 2009–2010; and rest of the period). The pattern of result for both the measures are more or less in the same direction. A remarkable growth in total factor productivity (TFP) is observed after the initiation of new economic policy for both the method used. So far as the environmental issues are concerned, this industry seems to have been polluting the environment, as per unit use of energy is increasing over time.

The purpose of this study is twofold. First, this research estimates banks' efficiencies from the perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in the investment-efficiency stage. Second, the current study examines the relationship between intellectual capital (IC) and banks' efficiencies.

First, this study uses a dynamic network data envelopment analysis approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second, this research utilizes various regression techniques, namely, ordinary least squares (OLS), robust least squares and truncated regression, to gauge the impact of IC on banks' efficiencies. Typically, IC is determined based on a monetary value-based measure and value-added intellectual coefficient (VAICTM).

Resource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over the sample period, thereby suggesting that the average banks' efficiencies hardly increase. Regression analyses show a significantly positive relationship between IC and banks' overall resource-utilization and investment efficiencies.

Overall, this study suggests that researchers should consider risks when estimating banks' efficiencies owing to their connection to banks' investment performance. From banks' dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future economic benefits.

Different from prior studies, this study improves banks' efficiency evaluation models by incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis. Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used VAICTM does.

The purpose of this study is to examine efficiency differences among petroleum firms based on their ownership status, with the aim of helping these firms understand how specific levels of state-ownership affects efficiency and to bring new perspective to the ownership-performance literature.

The study uses ten-year data (2001-2010) of 32 global petroleum firms categorized into four groups based on ownership types. The metafrontier analysis is used with the dynamic slack-based measure to estimate dynamic efficiency differences among the groups while respectively, accounting for carryover variables such as oil and gas reserves.

Fully state-owned firms outperformed private, majority and minority state-owned firms, indicating that not all types of state-owned petroleum firms are outperformed by private firms. Additionally, firms with shared ownership between state and private are seen to have a lesser comparative advantage in the industry than those with full private or state ownership.

Jointly owned petroleum firms should consider converting ownership to either full private or full state control. Conflict management measures should be used to handle possible conflicts between different shareholding groups.

This is among the first studies to sub-group state ownership into various levels to comprehensively examine specific levels of state ownership that is detrimental to the performance of petroleum firms. It is also the premier oil efficiency study to use the metafrontier framework to cater for group heterogeneity. The study treats oil and gas reserves as interconnecting variables that are not consumed only in the period for which they are discovered to ensure fair assessment.

Increasing carbon productivity is an effective way to reduce carbon emissions, while boosting economic prosperity. For appropriate formulating and enforcement of energy saving and carbon emissions reduction policies in various sectors, it is of great significance to investigate the evolution characteristics and convergence modes of carbon productivity across the manufacturing sectors.

Using slack-based measure directional distance function (SBM-DDF) and global Malmquist–Luenberger (GML) productivity index, this paper measures the carbon productivities of 29 manufacturing subsectors in Shanghai, China, from 2001 to 2016 under the total factor framework. Furthermore, based on the convergence theories, it empirically examines the convergence of carbon productivity across these manufacturing sectors.

The measurement results suggest that the carbon productivities of the manufacturing sectors in Shanghai show an increasing tendency on the whole, and technical efficiency instead of technological change makes a main contribution to the increase. It is found that there is no obvious σ convergence across the manufacturing sectors in Shanghai, but there exist both absolute ß convergence and conditional ß convergence. Moreover, there is heterogeneity in convergence characteristics between the clean sectors and polluting sectors. The findings also show that firm size and industry structure have significant positive impacts on the growth of carbon productivities of the manufacturing sectors, whereas the impacts of capital deepening and energy consumption structure are significantly negative.

This paper measures the carbon productivities of the manufacturing subsectors by applying SBM-DDF and GML index, so as to improve the accuracy. It provides an insight into the convergence of carbon productivity across the manufacturing sectors.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

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 develop a decision-making protocol to meet the new requirements in an atypical panorama, such as the economic instability, in the textile industry.

The methodology consists of analyzing technical criteria, costing parameters and efficiency scores of knitted fabrics using the data envelopment analysis (DEA) and classification and regression (C&R) trees models, together with statistical techniques. From these tools, it is possible to guide the portfolio management of these products in a textile company, identifying those that are inefficient and require immediate management measures. The results are expected to be replicated in other companies because the DEA and C&R trees analytical procedures are applicable to different portfolios, whether in the same industry or not.

The results allowed identifying inefficient textile products regarding the input-output relationship and the main technical reasons related to the most significant inefficiencies, such as fiber composition and knitted fabrics rapports used in manufacturing.

DEA and C&R trees, in combination with the study of textile technical parameters, can be fundamental to investigating the efficiency and profitability of industries in periods of economic instability or other adverse situations. In addition, it is noteworthy that there are practically no studies in the literature on DEA applied in the textile industry, indicating excellent development potential.