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The paper aims to investigate factors that drive the total factor productivity (TFP) and its growth in the Czech food industry over 2003–2017. The authors’ analysis focuses on firm-level characteristics such as location choice, sub-sector affiliation, use of debt, liquidity, asset turnover, firm size and firm age.

The determinants of productivity were tested econometrically by estimation of multivariate regression models. The firm-level panel data set consisted of 14,488 observations (data of 980 firms spanning 15 years). TFP was estimated by three regression-based techniques – ordinary least squares (OLS) regression, instrumental variables (IV) approach and two-way generalized method of moments (GMM) regression. All three measures of TFP were used as outcome variables to estimate the impact of firm-level determinants on both TFP level and growth.

The results have shown statistically significant and reversed U-shaped relationship between the firm age and the TFP level (with a turning point in the age of 12.5 years). However, the dynamic models investigating the TFP growth have found that younger firms achieve higher productivity growth in comparison with older ones. Higher market share and assets turnover were positively associated with both TFP level and its growth.

This study brings several relevant propositions for future research. First, the authors recommend future researchers to study not only differences in the levels of productivity but also determinants of its growth. Second, the authors believe that adding a non-linear component to age as a factor explaining changes in the levels of productivity might be a very relevant contribution to the literature.

Although it is generally accepted that successful and sustainable growth of firms, regions and economies can be achieved particularly through viable companies with high productivity, there is still a limited number of firm-level studies explaining the determinants of productivity levels and growth in agribusiness sectors in transition economies. Therefore, this study is expected to contribute to a better understanding of this important topic.

The purpose of this paper is to present a refined framework providing clarity in terms of the components of profitability and productivity change from the perspective of the firm level.

The literature is analysed with a scoping study and a systematic literature review. Productivity measurement approaches are compared using data at the product level.

The definition of total factor productivity (TFP) in the literature negatively affects the accuracy of profitability and productivity measurement. In the usual case of a dynamic output mix, TFP change encompasses biasing output mix effects relating to profitability, but not to productivity change. Therefore, this paper defines changes of a ratio of output quantities to input quantities not as TFP change, but as quantitative profitability (QP) change. A framework is proposed decomposing profitability change into price recovery and QP change, whereas the latter comprises of valid productivity change (encompassing technological, technical efficiency and productivity-related scale effects) and output mix change (encompassing proportion, quality, output switching and profitability-related scale effects).

Future research should include literature from the industrial organisation field of economics. The presented framework should be transferred to the standard production function framework used in economics.

The paper can help preventing faulty decision making or distrust due to the use of biased profitability or productivity indicators. TFP-based productivity indicators are unsuitable for most firms. To measure productivity meaningfully, firms should use adequate approaches (e.g. standard input- or adjusted total factor productivity-based ones).

The paper contributes to a more accurate performance measurement approach, as researchers and practitioners better understand the components of profitability and productivity change.

The objective of the study is to identify and explore factors affecting the productivity of companies in the Czech Republic with a focus on the role of firm size, firm age, indebtedness and long-term negative equity, efficiency of assets usage, liquidity, legal form, location and sector affiliation.

The study utilizes a large unbalanced panel dataset of 91,257 firms (548,998 observations in total) covering the period 2000–2019. The dependent variable, i.e. total factor productivity (TFP), reflecting the overall firm productivity, was estimated by ordinary least squares (OLS) regression. The main findings were obtained through the estimation of two econometric models explaining the effects of factors on firm-level TFP. First, the OLS regressions together with Nomenclature of Territorial Units for Statistics (NUTS) 3 regions, year dummies and robust standard errors were estimated. Second, as a robustness check, the very same model was estimated with the random effects (RE) generalized least squares (GLS) method.

The analysis has shown a statistically significant U-shaped relationship (with the turning point of 38, resp. 36 years) between firm age and the overall TFP among the Czech enterprises. The authors provide two key findings in terms of a firm size-productivity relationship. Firms with fewer employees, often officially registered as self-employed individuals/freelancers, report higher levels of productivity. Nevertheless, when it comes to firm property (assets), the authors find a positive relationship between firm size and TFP. A high proportion of debts in the capital structure of analysed companies, or even negative equity, has been negatively associated with TFP levels.

More research is needed in the deeper exploration of sectoral and regional determinants of firm TFP, as both regional and sectoral heterogeneity were observed in the study. The authors propose the employment of a multi-level modelling approach, including a range of continuous variables and investigation of their role in shaping firm-level productivity.

Concerning the results, managers should be mindful of optimal capital structure principles due to the negative impact of a high level of debts on the productivity level. High indebtedness means high-interest payments drawing earnings off, which may be, especially in the long term, a hindrance to investments. The entrepreneurship and small- and medium-sized enterprise policies may be targeted at the soft policy actions, including advisory services and counselling on business development or risk and on the provision of financial capital allowing firms to strive for growth-oriented projects.

To the best of the authors' knowledge, this is the first attempt to provide insight into the firm-level productivity determinants, based on the large dataset covering enterprises across the whole economy over the long term, representing the structure of the country's entrepreneurial activity.

The Indian garment industry has witnessed a significant change since the inception of the New Textile Policy 2000 that suggests removing the industry from the list of small‐scale industries with a view to improving its competitiveness in the global market. As productivity is the driving factor in enhancing the competitiveness of any decision‐making entity (firm), a study of total factor productivity (TFP) and its sources can provide vital inputs to a firm for improving its competitiveness. Keeping this as a backdrop, the paper attempts to measure the TFP in the Indian garment‐manufacturing firms; identify sources of the TFP; and suggest measures for the firms to enhance their productivity.

The study is based on the firm‐level panel data collected from the Centre for Monitoring Indian Economy for the years 2002‐2007. One output variable, namely, gross sale and four input variables, namely, net fixed assets, wages & salaries, raw material, and energy & fuel, have been selected. The DEA‐based Malmquist Productivity Index (MPI) approach has been applied to measure the TFP.

The Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent per annum during the study period. The small‐scale firms are found to be more productive than the medium‐ and large‐scale firms. The decomposition of TFP growth into technical efficiency change (catch‐up effect) and technological change (frontier shift) reveals that the productivity growth is contributed largely by technical efficiency change rather than by technological change.

Earlier studies on the Indian garment industry have applied the partial factor productivity approach, which has several limitations. This paper measures the TFP and identifies its sources through applying a non‐parametric DEA‐based MPI approach. Through this approach, the productivity growth is decomposed into technical efficiency change and technological change. Further, an attempt has also been made to study the variation in the productivity growth rates across location, scale‐size and type of garments.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The purpose of this paper is to compare and analyze the modern productivity estimation techniques, namely, Levinsohn and Petrin (LP, 2003), Ackerberg Caves and Frazer (ACF, 2006), Wooldridge (2009) and Mollisi and Rovigatti (MR, 2017) on unit-level data of 32 Indian industries for the period 2009-2015.

The paper first analyzes different issues encountered in total factor productivity (TFP) measurement. It then categorizes the productivity estimation techniques into three logical generations, namely, traditional, new and advanced. Next, it selects four contemporary estimation techniques, computes the industrial TFP for Indian states by using them and investigates their empirical outcomes. The paper also performs the robustness check to ascertain, which estimation technique is more robust.

The result indicates that the TFP growth of Indian industries have differed greatly over this seven-years of period, but the estimates are sensitive to the techniques used. Further results suggest that ACF and Wooldridge yield the consistent outcomes as compared to LP and MR. The robustness test confirms Wooldridge to be the most robust contemporary technique for productivity estimation followed by ACF and LP.

To the authors’ knowledge, this is the first study that compares the contemporary productivity estimation techniques. In this backdrop, this paper offers two novelties. First, it uses advanced production estimation techniques to compute TFP of 32 diverse industries of an emerging economy: India. Second, it addresses the fitment of estimation techniques by drawing a comparison and by conducting a robustness test, hence, contributing to the limited literature on comparing contemporary productivity estimation techniques.

This paper aims to measure and compare agricultural total factor productivity (TFP) growth in China and India and relates TFP growth in each country to policy milestones and investment in agricultural research.

TFP is measured using a non‐parametric Malmquist index which allows the decomposition of TFP growth into its components: efficiency and technical change.

Comparing TFP growth in China and India it is found that efficiency improvement played a dominant role in promoting TFP growth in China, while technical change has also contributed positively. In India, the major source of productivity improvement came from technical change, as efficiency barely changed over the last three decades, which explains lower TFP growth than in China. Agricultural research has significantly contributed to improve agricultural productivity in both China and India. Even today, returns to agricultural R&D investments are very high, with benefit/cost ratios ranging from 20.7 to 9.6 in China and from 29.6 to 14.8 in India.

The applied methodology and the comparison between TFP growth patterns contribute to a better understanding of the consequences that the different approaches to agricultural reform followed by China and India had on the performance of agriculture in both countries.

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.

In this study, an investigation into the asymmetric impacts of crime rate on total factor productivity (TFP) in Nigeria is conducted.

The study employs linear and non-linear autoregressive distributed lag (ARDL) modelling techniques to analyse Nigerian data spanning over a period of 1986–2017. In addition, Granger causality tests are conducted under error correction technique.

The study establishes that crime rate has a significant impact on TFP in the short and long run. In addition, the positive component of crime rate has positive impacts on TFP in the short run while the negative shocks have negative impacts on TFP. However, in the long run, both positive and negative components have negative impacts on TFP in Nigeria.

This study is the first to analyse the asymmetric impact of crime rate on TFP. The study also advances the literature by examining the symmetric impact of crime rate on TFP in an African country (Nigeria) where crime-related activities are rampant. The study is one of the few studies that shed light on nonlinearities in criminal behaviour.

This paper aims to estimate the values of environmental efficiency of each province in China. Subsequently, it analyzes the changes of total factor productivity (TFP) before and after taking the environmental factors into account. Finally, the paper measures the effect of the components of environmental TFP on the convergence of economic growth.

The environmental control variables are taken as output variables and combined with a traditional data envelopment analysis model to build an environmental TFP Index. The growth of environmental TFP is compared among regions and the differences are analyzed. Furthermore, the decomposed components (advances in environmental technology and environmental technical efficiency) are used for regression analysis with labor productivity.

The environmental efficiency of most provinces in China has improved although some regions' efficiency remains low in Northeastern China. The value of environmental TFP is higher and more fluctuant during the period of 1997-2009 than that of traditional TFP. Technical efficiency has a convergence effect on economic growth of regions/provinces, but, after adding environmental factors, it turns into a divergence effect.

Because of data limitations, this paper does not consider the impacts of human capital and other factors on in the convergence analysis on economic. Neither does it consider the use of panel data to analyze the convergence of economic growth and validate the conclusions. These are potential further research directions.

The study confirms that improvements in environmental technologies play a dominant role in enhancing China's environmental TFP. Furthermore, it demonstrates that China's economic growth largely depends on technological progress. This finding, that China's economic growth depends on advances in environmental technology, implies that China should strive to improve its capacity for technological innovation.

The paper measures the value of environmental efficiency in the China's provinces and analyzes the relationship between pollution and economic development in each province. Panel data are used to compare the difference among environmental TFP, environmental factors and traditional TFP. The convergence of economic growth is analyzed in respect of environmental control variables.