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This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

The productivity of a port is a measure that is important to different stakeholders: port administrators (port authority), third-party logistics providers, manufacturers and consumers, among others. This study analyses productivity in terms of vessel movement efficiencies (loading/unloading of cargo) and container release from port facilities. Both factors add to the overall productivity in any port.

A comparative analysis of the productivity of three ports is measured using a Quality Function Deployment (QFD) and benchmarking analysis to help establish strategies that will help improve productivity. Considering the information confidentially the authors will call the ports according to their geographic location. The ports under study are the USA Southeast Port (Port of America), Central Asian Port (Port of Asia) and Central Europe Port (Port of Europe).

This study has established an analysis strategy that allows seeing points of sale in the ports. This study will compare three different continents, only to demonstrate the applicability of QFD and benchmarking. Still, the strategy can be used in ports that compete due to their proximity and location. Identifying the variables to be analyzed made it possible to establish a strategy to increase productivity.

There are many studies that analyze port productivity, but none try to standardize the variables to be compared in different scenarios. This study has compared three ports from three different geographical areas, using the same variables in all three cases. The study critically analyses the performance of three ports and proposes a strategy based on QFD and benchmarking research.

This paper aims to uncover the significance of capacity, capacity utilization (CU) and its role in the quality and productivity improvement in an industrial environment. Besides, the current study is also aiming to explore the various ways to estimate CU and its status across the world.

In the present study, a comprehensive literature review on capacity and CU is carried out to expose the research direction in the field of CU. This work is primarily focused on capacity, CU and their estimation methods based on the research in various industries of different countries and current status in present scenario across the world.

The literature reveals that CU estimation is carried out by some government/central agencies at the national or sector level rather than the industry level in most of the productive nations. As far as industrial growth is concerned, capacity management should be carried out at a particular industry level so that engineering managers can be able to find out loopholes for huge capacity waste within the plant. It is observed that CU in the industrial sectors mainly computed by time series method, survey method, economic approach and engineering approach worldwide.

This paper tries to cover almost all research work in the field of CU in various industrial sectors. However, the organizations which are producing the product with limited demand may get benefit inadequately.

This paper provides a vision to management toward productivity improvement through optimal utilization of available resources. As in most organizations, CU issues are much neglected areas.

This paper provides valuable insights on capacity and CU in the industrial sector across the world. Besides, it focused on comprehensive literature of capacity and various methods to estimate CU in industrial sectors.

This paper aims to investigate firm‐level interactions between productivity and exporting in Uganda's manufacturing sector.

The paper empirically tested two hypotheses that relate to the dynamic gains from trade and also have tended to dominate the literature; self‐selection and learning‐by‐exporting hypotheses. It employs proxies of self‐selection and learning‐by‐exporting obtained from indices of path dependence to fit maximum likelihood estimates of export behavior.

The results provide support for both hypotheses and it is also found that more experienced exporters reap more productivity gains from learning effects which is in line with the view that knowledge spillovers to exporting firms increase with the level of interaction in the global market place. Thus, learning‐by‐exporting is not a “short term” occurrence which takes place only in the first few years of entry in export markets after which it would fizzle out as a firm's exporting experience increases but rather, it is a cumulative process.

This paper generates a number of insights that can guide policy makers in designing policies to promote firm productivity growth that is an engine of growth in the private sector and by extension, would fuel up overall economic growth and poverty reduction.

Previous studies on exports and growth in Uganda have been basically focused on macro‐data analysis; yet, promoting rapid expansion of manufactured exports may require more than just a good macroeconomic policy environment. This study fills the research gap by relating firm‐level productivity performance to the microeconomic environment in which manufacturing firms operate.

The main purpose of this study is to examine the impact of imported inputs on firms' productivity in selected South Asian economies, namely Pakistan, India and Bangladesh. Furthermore, this study explores the complementarity between firms' capabilities and imported inputs in an augmented productivity framework.

A dataset comprising 7117 manufacturing firms of selected South Asian economies was taken from the World Bank for 2013 and 2014. The empirical analysis was based on stochastic frontier models, the ordinary least square method and instrumental variable estimation techniques.

The empirical results show that imported inputs have positive and significant effects on the firms' productivity in the selected countries. Moreover, the study findings demonstrate that firms' capabilities play a complementary role in expanding the firms' production frontier.

The study outcomes suggest that reducing tariffs on imported inputs will enhance the firms' productivity in the selected emerging economies. However, the study further finds that the potential gain of imported inputs is conditional on the firm's capabilities. It implies that firms operating in these countries can improve their performance by allocating more resources to capabilities, such as workers’ training, management and internal R&D effort.

The existing literature on the subject is sceptical about the positive impact of imported inputs on firms' productivity in the case of developing countries. In this regard, the shortage of skilled labour and firms' capabilities are compelling rationales that need to be explored. Thus, the potential contribution of the study lies in explaining the moderating role of firm's capabilities operating in the selected emerging economies in the nexus of imported inputs and productivity.

Total factor productivity growth (TFPG) is not only the technological progress. It is a boarder concept. It may be the increase in the productivity of inputs, economies of scale, capacity utilization, technological progress, etc. In this study, we have tried to estimate TFPG and its components for the manufacturing industries of West Bengal and overall India, for the period 1980–1981 to 2016–2017, using stochastic frontier approach. The main data source of this study is the Annual Survey of Industries (ASI), which is published by the Central Statistical Organization, Government of India. The entire period has been sub-divided into two phases; pre-World Trade Organization (WTO) regime (1980–1981 to 1995–1996) and post-WTO regime (1995–1996 to 2016–2017). This study also tries to make a comparative analysis between the TFPG of manufacturing sector of West Bengal and all India level for the time period mentioned above. For West Bengal, TFPG has decreased for the post-WTO regime and it has increased in post-WTO regime for overall.

Understanding and simulating construction activities is a vital issue from a macro-perspective, since construction is an important contributor in economic development. Although the construction labor productivity frontier has attracted much research effort, the temporal and regional characteristics have not yet been explored. The purpose of this paper is to investigate the long-run equilibrium and dynamics within construction development under a conditional frontier context.

Analogous to the simplified production function, this research adopts the conditional frontier theory to investigate the convergence of construction labor productivity across regions and over time. Error correction models are implemented to identify the long-run equilibrium and dynamics of construction labor productivity against three types of convergence hypotheses, while a panel regression method is used to capture the regional heterogeneity. The developed models are applied to investigate and simulate the construction labor productivity in the Australian states and territories.

The results suggest that construction labor productivity in Australia should converge to stable frontiers in a long-run perspective. The dynamics of the productivity are mainly caused by the technology utilization efficiency levels of the local construction industry, while the influences of changes in technology level and capital depending appear limited. Five regional clusters of the Australian construction labor productivity are suggested by the simulation results, including New South Wales; Australian Capital Territory; Northern Territory, Queensland, and Western Australia; South Australia; and Tasmania and Victoria.

Three types of frontier of construction labor productivity is proposed. An econometric approach is developed to identify the convergence frontier of construction labor productivity across regions over time. The specified model can provides accurate predictions of the construction labor productivity.

One of the distinctive features in Korea and the U.S. trucking industries is the huge difference in the share of owner-operators. While it is around 10~15 percent in the U.S., 80~90% of drivers operate their own truck in Korea. Different from historical explanations of previous researches, this paper deals with this feature theoretically. We examine what brings the difference in asset ownership structures between the Korean and the U.S. trucking industries. Using an analytic framework, we investigate the determinants of truck ownership and the changes in ownership patterns. The model introduces several parameters related to productivities of drivers’ efforts and contractibility to affect drivers’ decision, and values of these parameters in both countries are discussed qualitatively and found to be consistent with the aforementioned characteristics.

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

Economic inequality in contemporary advanced societies is strongly tied to the variation in wages across occupations. We examine the extent to which this variation is captured by social class and occupational prestige and ask how the associations between class, prestige, and wages can be explained. On the basis of data from 11 countries in the European Social Survey (ESS) 2004, we find (a) that class and prestige account for a very large proportion of the occupational variation in wages; (b) that the tight links between class, prestige, and wages are strongly associated with the skill requirements of jobs but only weakly tied to other positional traits, including authority, autonomy, and scarcity; and (c) that these findings are highly similar in all countries examined. We conclude that the rank order of positions in the labor market is a social constant driven by efficiency requirements of work organizations rather than by the exercise of power. This iron law of labor market inequality clearly contradicts major class theoretical models, including Wright's and Goldthorpe's. In addition to empirically refuting contemporary class theory, we offer a number of more conceptual arguments to the same effect. At a macro level, however, power relations arguably affect the rate of economic inequality by determining the reward distance between positions in the constant rank order, as indicated by the large cross-national variation in wage dispersion.