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A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

The paper aims to provide a structured framework for comparing different productivity estimation methodologies and evaluate their sensitivity to operational coefficients variation for excavation operations.

Two process‐oriented methodologies were analysed in a deterministic fashion in terms of their input requirements and their respective outputs. A phase‐oriented framework was presented to enable their comparison. The research methodology allows the estimation of excavation productivity in relation to the selected operational coefficients.

The system productivity is significantly influenced by operational conditions, such as the digging depth and the swing angle from the excavation front to the dumping position. Each methodology presents a differing sensitivity to every operational factor. Since the excavator is considered as the system's leading resource, the variation on productivity has direct implications for the truck fleet size and the unit cost of operations.

The proposed approach is useful in analyzing process‐oriented productivity estimation methodologies under a given set of operational coefficients when no historical data is available. Thus, it provides an alternative to intuitive estimates based solely on personal judgment. The concept of “baseline reference” conditions is introduced, so as to enable the transformation of any operational scenario into equivalent mathematical models that allow comparisons between different estimation methodologies and computational approaches.

This paper aims to study how firms’ longitudinal and dynamic growth will be affected by their bribing decisions to address the controversies existing in the extant literature on the impacts of briberies.

The authors acquired information from Enterprise Survey by the World Bank and compiled a unique panel data set including firms from five South American countries between 2006 and 2017. The authors used multiple methods to estimate firms’ productivity. A comprehensive inspection of firms’ longitudinal development using a two-step estimation method that addressed the endogeneity issue was then conducted.

Bribery could significantly shorten the waiting time for resources to become available. However, bribery also substantially and robustly slows down firms’ productivity growth over time. Meanwhile, a bribing firm is very likely to bribe again in the future.

This paper contributes to the extant literature by pioneering the empirical study of firms’ bribing decisions and their longitudinal growth. First, the authors constructed unique panel data and established a longitudinal investigation upon firms’ dynamic growth after bribing, filling the literature gap by studying the time-lagging effect of bribery on firms’ growth. Second, the authors performed a comprehensive overview of South American firms’ growth by looking into the dynamics of their production, employment, resource delay and productivity across years. Third, the authors found that bribing exerted contingent impacts upon firms’ growth, reconciling the mixed evidence in the literature.

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.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

With an increasingly competitive global market, the UK construction industry finally realised that in order to survive, a marked increase in efficiency and effectiveness have to be achieved in all areas. This paper will describe the UK's approach to planning and designing the concrete operations that form a major part of many civil engineering construction projects. A productivity study has been carried out on three different construction projects and over 200 concrete pours have been observed. The data and knowledge collected on site have been subjected to lean construction philosophies, producing a “lean” measure of productivity, and it has been shown that major productivity increases could be achieved by implementing several relatively simple principles.

Raise in temperatures due to climate change is likely to increase the heat stress in occupations that are physically exerting and performed outdoors which might potentially have adverse health and productivity consequences. The purpose of this paper is to estimate the productivities in construction work under the influence of heat stress using the predicted mean vote (PMV) index.

Field studies were conducted during May 2014 which is summer time in Chennai. Continuous heart rate of workers and wet bulb globe temperature measurements are conducted for workers engaged in different jobs in construction. Metabolic rates and the workload of the workers from heart rate were calculated using the ISO method 8996 and the PMV values are calculated using the tool developed by Malchaire based on the method ISO 7730. Direct observations and personal interviews were conducted to substantiate the productivity estimations.

The results showed that workers working outdoors with moderate and heavy workload exceeded the threshold limit value of 28°C and had adverse productivity impacts (18-35 per cent productivity loss), whereas the workers engaged in light indoor work was not affected by heat stress and consequent productivity losses. The productivity estimations using the PMV index is found to be statistically significant for three types of construction works (Pearson correlation coefficient value of −0.78) and also correlated well with the observations and self-reported productivities of the workers.

The method used in this paper provides a scientific and reliable estimation of the productivities which may benefit the industry to set realistic project completion goals in hot weather and also implement interventions and policies to protect workers’ health. Developing adaptive strategies and implementing control measures are the need of the hour to protect worker’s health and economic losses in the face of climate change.

The authors investigated cocoa farmers' access to subsidized fertilizer in Ghana and implications on productivity.

Primary data were sourced from 435 cocoa farmers. Cragg hurdle and two-step Tobit model with continuous endogenous regressors/covariates were applied for the drivers of cocoa farmers' participation in fertilizer subsidy programme and productivity. Propensity score matching (PSM), inverse-probability weights (IPW) and augmented inverse-probability weights (AIPW) were applied for productivity impact assessment of fertilizer subsidy.

All the farmers were aware of fertilizer subsidy for cocoa production in Ghana. Farmers became aware of fertilizer subsidy through extension officers, media and other farmers. Half of cocoa farmers benefitted from fertilizer subsidy. Averagely, cocoa farmers purchased 292 kg of subsidized fertilizer. Many socio-economic, farm-level characteristics and institutional factors determine cocoa farmers' participation in fertilizer subsidy programme, quantity of subsidized fertilizer obtained and productivity. Beneficiaries of fertilizer subsidy recorded higher cocoa productivity than non-beneficiaries. Hence, fertilizer subsidy for cocoa production in Ghana leads to a gain in productivity.

There should be more investments in fertilizer subsidy so that all cocoa farmers benefit and obtain the required quantities.

The authors provide new evidence on cocoa productivity gain or loss emanating from fertilizer subsidy by combining different impact assessment techniques for deeper analysis: PSM, IPW and AIPW.

The purpose of this study is to investigate labor congestion in the automotive industry in Mexico.

By using the cluster and subcluster definitions by Delgado et al. (2016) and relying on an efficiency and production function perspective, this study estimates a standard production function and measures marginal returns of labor at the regional cluster and subclusters levels. To assess whether wages affect the finding of congestion and productivity, the model also measures the individual impact of wages on both total productivity and marginal returns of labor.

Among other results, this paper finds evidence of labor congestion in the automotive cluster in Mexico. This congestion deepens with wages and it is specific to some regions and some subclusters.

The methods used are based on panel data techniques but are fundamentally cross-section in nature. The time period available may condition these findings.

To the authors’ knowledge, this is the first study reporting congestion in the automotive cluster in Mexico.

The purpose of this paper is to study the impact of infrastructure and governance quality on the state-level productivity of Indian manufacturing for the period 2008–2011.

The authors first rank Indian states on their quality of governance using benefit-of-the-doubt approach. Next, to explain state-level differences in total factor productivity (TFP), the authors assess the impact of a composite index of governance on industrial TFP of Indian states using alternate techniques and controlling for endogeneity. The authors also decompose the composite effect of governance in terms of economic, social and financial infrastructure and other key governance dimensions, which serves as another robustness check for the findings.

The authors find that TFP varies significantly across states, so does governance quality. Further, results suggest that TFP of Indian industries is sensitive toward public service deliveries of economic, social and financial infrastructure. However, the authors fail to find any impact of law and order indicators, for instance, rate of violent crimes, police strength and judicial service quality on the manufacturing productivity. The estimated coefficient of governance index is robust across alternate methodologies.

To the authors’ knowledge, this is the first study to assess the impact of regional governance factors on the manufacturing sector of India. The study has identified governance factors that impact manufacturing productivity in the Indian states. Findings suggest that an effective way to eliminate regional growth inequality in India is to ensure that the lagging states initiate reforms to improve the quality of institutions, regulation and governance. Findings of the study contribute to the limited literature on governance at the regional/sub-national level.