Technical Efficiency Change Research Articles

Does firm size matter? Decomposing Korean firms’ productivity growth based on a stochastic frontier approach and its policy implications

ABSTRACT Despite the significance of transition in the growth stages, there is a lack of evidence on the different characteristics of firm size in terms of the sources of TFP growth. This study applies a stochastic frontier analysis to Korean firms to decompose the sources of total factor productivity growth into technical progress, technical efficiency changes, scale changes and allocative efficiency changes. Specifically, this study analyses the determinants of TFP growth by firm size (i.e. small-, mid- and large-sized firms) and explores firm-size-specific factors that influence their productivity growth rates. Our results suggest that contributions of four components to TFP growth by firm-size classes show somewhat different patterns. These results imply that each class has different sets of challenges in terms of improving productivity growth potentials. The main findings are expected to lay foundations for designing elaborate firm-size-dependent policies to spur the evolutionary process of a firm’s capacity building.

Technical efficiency and productivity change analysis: A case study of the regional and local airports in Thailand

This study investigates airport performance in terms of efficiency and productivity change using regional and local airports in Thailand as a case study. The study employs data envelopment analysis and the Malmquist productivity index to analyze the technical efficiency scores and capture the total productivity change during 2009–2018 for 31 airports in Thailand. Using the BCC–data envelopment analysis–Malmquist model with an output-oriented framework, we found that most regional and local airports in Thailand operated technically inefficiently during the study period, whereas the total productivity change of most airports showed progress (around 8.4%). Decompositions of the Malmquist productivity index reported a positive change in technical efficiency and airport technology. This study also presents policy recommendations arising from the case study for airport practitioners.

Decomposition of Green Agricultural Productivity Gain Under a Multiple-Frontier Framework

This paper investigates structural variation of agricultural productivity growth in China using a multiple-frontier approach. Agricultural productivity gain in Sichuan province is decomposed into technological progress, technical, mix and scale efficiency changes in terms of economic and environmental performance. The results show that agricultural Luenberger productivity indicator in Sichuan presents a significant increase trend with annual growth rate of 2.5% over the period of 1997-2017. The agricultural green growth in Sichuan is most driven by technological progress. Of the decline in the OI changes, over 90% results from the decline of environmental inefficiency. The technological progress is the main driven force of the economic growth while the TEC (technical inefficiency) incumbers its growth. The structural effects (mix and scale components) incumber environmental growth. The results imply that policymakers should pay more attention to new agricultural technological development and resource misallocation to improve agricultural green growth in Sichuan.

A study on the interaction between logistics industry and manufacturing industry from the perspective of integration field.

Studying the linkage between manufacturing industry and logistics industry is conducive to explore and improve the efficiency of the common development of them. In order to study the interaction of logistics industry on the development of manufacturing industry and the development of two-industry-linkage, it first calculates the high-quality development level of logistics industry and manufacturing industry, then uses the coupling coordination model to theoretically analyze and empirically test the coupling and coordinated development level of high-quality development of logistics industry and manufacturing industry from three aspects: coupling degree, coordination degree and coupling coordination degree, and based on the perspective of integration field theory, it takes the three basic synthetic fields of logistics integrator, logistics base-nuclear and logistics connection-key as the analysis dimension, PVAR model was introduced for in-depth analysis the impact of logistics industry on manufacturing industry and the level of the two-industry-linkage. It was found that the high-quality development of China’s logistics industry and manufacturing industry is close on the whole, and the development trend is consistent, the high-quality development of them is mainly caused by the change of scale, but there is no obvious change in technical efficiency, which also provides a way for the high-quality development of the two-industry-linkage in the future. The two-industry-linkage mostly belongs to the situation of low-level mutual restriction, which has not yet reached a high level of mutual promotion, resulting in the overall coupling coordination degree basically in a state of barely coordination. The development of logistics industry and manufacturing industry need to go through certain practice and running in, when there is an error matching between the two, the logistics industry will inhibit the two-industry-linkage. When the economy develops to a certain extent, the expansion of the logistics system scale to the level of the two-industry-linkage is not necessarily beneficial, blindly exceeding the demand for logistics investment will cause a waste of resources, which is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries. In the long run, the change of the logistics basic-nuclear capacity, the logistics integrator scale and logistics connection-key level will have a positive impact on the change of green total factor productivity in manufacturing industry.

Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata

In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal with undesirable outputs have been developed and applied in empirical studies for assessing the sustainability of decision-making units. These models are getting more and more attention from researchers and managers. The teddf command discussed in the present article allows users to measure technical efficiency, both radial and nonradial, when some outputs are undesirable. Technical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring productivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and productivity change measurement accounting for undesirable outputs, and we describe the syntax and options of the new commands. We also illustrate with examples how to perform the technical efficiency and productivity analysis with the newly introduced commands.

Assessment of research efficiency of critical care medicine in hospitals affiliated S university based on data envelopment method: taking the 13th Five-Year Plan period as an example

To investigate the static and dynamic trends of scientific research efficiency of the critical care medicine in hospitals affiliated S university during the 13th Five-Year Plan period. Based on the scientific research data of 16 hospitals affiliated to Beijing S University from 2014 to 2020, the scientific research investment funds and the number of physicians involved in scientific research were selected as input evaluation indexes, and the number of science citation index (SCI) papers, Chinese science citation database (CSCD) papers, and the number of masters and doctors trained were selected as output evaluation indexes, and the evaluation index system of scientific research efficiency of critical care medicine was constructed. SPSS version 23.0 software was used for descriptive data statistics, and data envelopment analysis (DEA)-BCC model and DEA-Malmquist index model of DEAP 2.1 software were used for static and dynamic evaluation of its scientific research efficiency from 2016 to 2020, respectively. (1) The scientific research technical efficiency (TE) of critical care medicine in 16 hospitals affiliated with S universities varied greatly from 2016 to 2020, but pure technical efficiency (PTE) and scale efficiency (SE) were at a good level, and 6-11 affiliated hospitals in critical care medicine kept DEA effective for 5 consecutive years. (2) Dynamic analysis of their total factor productivity (TFP) of scientific research from 2016 to 2020 showed a trend of rising and then falling and then rising again. The mean value was 0.985. The technical efficiency change (TEC) showed a decreasing and then increasing trend, and the technical progress change (TC) showed a slow increasing and then decreasing trend, with a mean value of 0.953. While the mean values of TEC, pure technical efficiency change (PTEC) and scale efficiency change (SEC) were above 1, which showed that the growth of total factor productivity index of research and innovation depended more on the technical efficiency index. The "gain effect" and "catch-up effect" of scientific research efficiency in the specialty of critical care medicine in hospitals affiliated S universities are obvious, but the "growth effect" is not obvious. "Although the research efficiency of the 13th Five-Year Plan period has been significantly improved, there is still much room for improvement in scientific and technological innovation and international academic influence.

The growth effects of knowledge-based technological change on Taiwan’s industry: A comparison of R&D and education level

This paper employs data envelopment analysis (DEA) and the Malmquist index (MI) to investigate the growth effects of knowledge-based technological change and technological efficiency change on the output of the industrial sector in Taiwan. The results reveal two main findings: First, due to the lack of knowledge-based scale efficiency, there is no improvement in knowledge-based technological efficiency during the period from 1997 to 2019. Although both the direction of the change in R&D expenditure per researcher and that in education spending time per worker can explain the knowledge-based technological efficiency change, the long-term average of the growth rate of technical efficiency change is negative; thus, the positive coefficient of the technical efficiency change to output growth still makes a negative contribution to the output growth. In other words, if the scale efficiency can be improved, the contribution of knowledge-based technological efficiency change to output growth can be reversed. Second, an increase in R&D expenditure per researcher is more important than an increase in education spending time per worker, especially in knowledge-based technological change. In addition, the results indicate that Taiwan’s economic growth driver mainly comes from technological change. The contribution of knowledge-based technological change to manufacturing output growth is close to 65%, especially in the last 20 years. Furthermore, both knowledge-based technological efficiency change and technological change have a positive and significant coefficient of output growth in the Taiwanese industry. Therefore, in addition to continuing to increase R&D to enhance the growth of technological change and facilitate the growth of output, if the contribution of technological efficiency change to output growth can be improved, Taiwan’s output growth will be more stable in the future. Overall, knowledge plays a key role in industrial growth and economic growth, and R&D promotes more direct growth than education in Taiwanese industry.

Productivity changes in the automotive industry of three European countries. An application of the Malmquist index decomposition analysis

This paper analyzes the changes in productivity of small and large firms operating in the automotive industry in France, Italy and Spain, occurred during the pre-crisis (2001-2008) and post-crisis (2009-2014) periods. To this aim, the Malmquist index as a measure of total factor productivity (TFP) variation is computed, by using the non-parametric technique DEA, integrated by a bootstrap technique proposed by Simar and Wilson (1999). The analysis allows distinguishing among different possible sources of TFP changes, i.e. pure technical efficiency change, scale efficiency change and technological change. The latter comes up to be the main channel through which the global crisis negatively impacted on productivity, presumably by hampering the primary engines of technological change, i.e. research, innovation and investment through reduced availability of financial resources for SMEs, worsened perspectives for firm growth and greater concentration in selected geographical areas of investments by parent companies.

Decomposition of climate-induced productivity growth in Indian agriculture

This paper adopts a stochastic frontier approach to investigate the trend and determinants of total factor productivity (TFP) growth in the agriculture sector of India, using extensive district-level data. The assessment of the production frontier highlights the efficiency aspect of Indian agriculture and contributes to an analysis of those factors that might be directly engaged in the production process. After controlling for the district-specific climatic effect in the production of eighteen major crops, TFP growth is deconstructed into technical progress, technical efficiency change and scale effects. Four weather parameters, average temperature, rainfall, evapotranspiration and windspeed, are defined as exogenous determinants of the technical inefficiency term to analyze the influence of changing climate. Based on the true fixed effect model and maximum likelihood method, the estimated TFP growth averaged 0.688% per year between 1990 and 2015. The relative performance of Indian states apparently differs according to estimated productivity scores. The findings show that changes in technical efficiency account for most TFP growth, whereas differences in scale components account for annual and cross-state productivity growth disparities. The study suggests that region-specific policies are required to enhance agricultural productivity and add to the understanding of the arguments over TFP growth in Indian agriculture.

Research on carbon productivity and its spatial convergence of steel industry in China.

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

Cloud-based big data analytics integration with ERP platforms

PurposeThe importance of integrating cloud-based big data analytics software with enterprise resource planning (ERP) platforms is not clearly understood. Specifically, this study aims to look into firms that implemented SAP during the boom of ERP implementations. Further, this study aims to look into the type of cloud-based big data analytics software that those firms installed when cloud-based packages started to be available.Design/methodology/approachThis study specifically looks at productivity and the sources of productivity, such as technical progress and efficiency change, using a non-parametric approach that does not constrain the analysis to any production function.FindingsThis study found that by the time cloud-based big data analytics software started to be available, SAP-adopters already had a competitive advantage over the non-SAP adopters manifested through productivity and specifically through technology and not efficiency. Later, when the same firms decided to integrate their ERP platforms with cloud-based big data analytics software, the firms that had installed SAP already had an initial advantage over the non-SAP-adopters.Research limitations/implicationsIn support of the theory of technology organization environment (Tornatzky and Fleisher, 1990) and Posner's theoretical framework (Posner, 1961), a cloud-based big data analytics software will not change the relative position that firms have in the industry, so a cloud-based big data analytics software by itself will not provide a competitive advantage over competitors. Still, it will ensure that the preliminary technological gap that SAP-adopters already had is not magnified.Practical implicationsKnowing the sources of productivity improvement and technological improvements will give managers greater leverage when negotiating budgets, negotiating long-term contracts in better terms and in the decision process.Originality/valueThis study fills a research gap by looking into the implementation of a cloud-based big data analytics software with ERP.

Research on China Cities' Total Factor Productivity of Carbon Emission: Based on Decoupling Effect.

Economic development depends on energy consumption, which is a major source of carbon emission. How to achieve economic decarbonization has become one of the key questions urgently needing to be solved on the road of carbon peak and carbon neutral development in China. Advancing total factor productivity (TFP) of carbon emission is an important way to promote economic decarbonization. For the carbon emission TFP, current research is mainly conducted from province level or an industry perspective, and studies its deference with various geographical locations, economic development levels, urbanization levels, etc., lacking the research that combines the decoupling effect to carbon emission TFP. The carbon emission TFP of Chinese cities and how to improve it remain unclear. Therefore, based on Tapio decoupling theory, this paper firstly analyzed the decoupling effect of China’s 284 cities from 2005 to 2019, and aggregated the cities into four groups according to the decoupling effect. Then, using the DEA–Malmquist index, this paper researched the carbon emission TFP and its driving factors based on the aggregation. The result shows that weak decoupling is the main decoupling status in China. As a whole, carbon emission TFP of Chinese cities does not perform well, but it shows a growth trend over time. Strong decoupling cities outperform expansive negative decoupling cities on carbon emission TFP. Technical change and pure technical efficiency change have inhibiting effect and promoting effect on carbon emission TFP, respectively, which are the main factors for the difference of carbon emission TFP between strong decoupling cities and expansive negative decoupling cities. Based on these findings, some common but differentiated recommendations are provided for improving Chinese cities’ carbon emission TFP.

Investigating the role of international industrial transfer and technology spillovers on industrial land production efficiency: Fresh evidence based on Directional Distance Functions for Chinese provinces

Participation in international industrial transfer (IIT) provides a significant opportunity and a motivation to improve China's industrial land production efficiency (ILPE). This study aims to investigate the overall role, heterogeneous impact and indirect mechanism of IIT in improving Chinese ILPE. This paper uses the Directional Distance Functions - Data Envelope Analysis (DDF-DEA) model to measure ILPE, economic output efficiency, and pollution reduction efficiency, the Global Malmquist Luenberger (GML) index to decompose the growth of ILPE into “technical efficiency change (EC)” and “technological change (TC)”. The panel data of Chinese provinces from 2003 to 2018 and the Fixed-Effect model is used to examine the impact of IIT on ILPE. The primary outcomes suggest that participation in IIT has generally improved the ILPE in China. This promotion effect is evident after 2010, and IIT has a significant impact on the improvement of ILPE in extensive and inefficient areas. Similarly, IIT promotes the economic output efficiency of industrial land from the perspective of expected output and reduces unexpected output. This impact helps in improving pollution reduction efficiency of industrial land. The technology spillover brought by IIT improves the Chinese ILPE from two channels, including promoting technological progress and improving technical efficiency. However, the technology spillover mainly promotes the ILPE in land use high-efficient areas by promoting technological progress. Likewise, the ILPE is promoted by improving technical efficiency from a regional perspective in extensive and inefficient areas. This study provides valuable insights for the improvement of ILPE to optimize land resources.

Dynamic Measurement of Water Use Level Based on SBM-DEA Model and Its Matching Characteristics with Economic and Social Development: A Case Study of the Yellow River Basin, China

Enhancing the level of water use and alleviating the constraints of water shortage on economic and social development are powerful supports to realize the harmonious balance of water and economic society. In this study, the data envelopment analysis (DEA) window analysis method is applied to the study of water use level, and the SBM-DEA model (slack based measure, SBM) is combined to explore the spatial and temporal evolution characteristics of composite water use index (CWUI) in nine provinces from 2012 to 2018. The Malmquist index model is used to decompose the intrinsic causes of total factor productivity (TFP) changes, and the spatial matching degree calculation method is applied to study the matching degree between CWUI and economic and social development levels (E-SDL). The results showed that: (1) the overall trend of CWUI in the nine provinces from 2012 to 2018 was increasing, with significant spatial variability in water use levels; (2) the improvement of TFP of water in the nine provinces was mainly driven by technological change (TC), and the main factor limiting the improvement of TFP of water was technical efficiency change (EC); (3) E-SDL of the nine provinces showed an increasing trend, with the spatial distribution characteristics of decreasing E-SDL of the downstream, midstream and upstream provinces in sequence; (4) the degree of matching between CWUI and E-SDL shows strong regional differences, with different types of matching.

Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

Changes in productivity in healthcare services in the Kingdom of Saudi Arabia

BackgroundAmid the rising demand for healthcare services in Saudi Arabia, there is a need to monitor, evaluate, and improve performance in the delivery of these services. In this regard, the aim of this study was to estimate changes in total factor productivity (TFP) in healthcare services across the health system administrative regions in Saudi Arabia from 2006 to 2018. The contributions of changes in efficiency and technology to the observed changes in TFP were further evaluated.MethodsThe data used for this study were extracted from annual Ministry of Health Statistical Yearbooks for the period of 2006–2018. TFP changes were estimated using the Malmquist Productivity Index, in which technology frontiers were constructed through data envelopment analysis. The changes in TFP were decomposed into changes in technology, changes in pure technical efficiency, and changes in scale efficiency following the Färe-Grosskopf-Norris-Zhang method. As robustness checks, we used bootstrapping to construct intervals and applied alternative decomposition methods. The changes in TFP and its sources were also compared between public and private hospitals.ResultsOver the period from 2006 to 2018, TFP for healthcare services has decreased on average by 5.6% per year solely on account of a technical regress. Public hospitals registered a higher deterioration in productivity (6.0% per year) than private hospitals (4.8% per year).ConclusionUsing the available resources, there is potential for realising gains in productivity of healthcare services by addressing existing technological challenges. Since the decline in TFP is entirely a problem of technical regress, the primary solution should focus on strategies for achieving technical progress (innovation) through investment in more or better machinery, equipment, and structures for healthcare service provision.

Productivity and efficiency in Czech agriculture: Does farm size matter?

This paper deals with the sources of total factor productivity (TFP), namely technical efficiency, scale efficiency, and technological change, considering the size of agricultural producers and using balanced panel data in the period 2014–2018 drawn from the Farm Accountancy Data Network (FADN) database for three sectors of Czech agriculture – cereals, milk, and beef. The investigation is based on the stochastic frontier (SF) modelling of an input distance function (IDF) with four error components (heterogeneity, statistical noise, persistent and transient inefficiency). The sector-specific models are estimated by a four-step estimating procedure with a system generalised method of moments (GMM) estimator to address the endogeneity problem. The results reveal inter- and intra-sectoral differences in productivity drivers. In particular, the smallest producers lag considerably behind the largest ones due to the scale effect (SEC). While large farms should focus on technological change, improvements in scale and technical efficiency have been identified as the main sources of coping with productivity losses for small farmers.

Training, technology upgrading, and total factor productivity improvement of farms: A case of cassava (Manihot esculenta Crantz) production in Dak Lak province, Vietnam

This study focused on analyzing the determinants of technology upgrading and productivity growth in cassava production of farms in Dak Lak province from 2015 to 2018. Using the Data Envelopment Analysis method (DEA) to obtain the Malmquist indices of the total factor productivity (TFPCH) and decomposing it into technical efficiency change (EFCH) and technological change (TECHCH), we found a general improvement in productivity of the farms. Meanwhile, general technology upgrading was observed, but the technical efficiency of the farms was reduced. From logistic regression analyses, it is shown that institutional factors such as technical training and accessibility to banks and economic factors such as capital stock of farms increase the probability of productivity growth in the farms. However, technical training is the only factor that significantly contributed to the likelihood of the technology upgrading of the farms. In particular, we did not find household characteristics such as gender, ethnicity, schooling, and experience in cassava production significantly impact the farms’ technology upgrading and productivity growth.

Evaluation of the Efficiency of Financial Investment in Science and Technology in Xi’an Metropolitan Area Research with Enhancement Strategies

This paper uses the super-efficient SBM-Malmquist-Tobit integrated model to measure the efficiency of financial science and technology inputs in the Xi’an metropolitan area from 2016 to 2020, explore the sources of inefficiency in each city in the circle, decompose the total factor productivity of financial science and technology inputs, and further explore the influencing factors of financial science and technology input efficiency. It is found that: 1) the efficiency of financial science and technology inputs in the Xi’an metropolitan area between 2016 and 2020 did not reach DEA, and there are evident and increasing gaps in efficiency among cities; 2) Xi’an has the highest efficiency level in the Xi’an metropolitan area and can play the leading role as a core city to be classified as a high-efficiency city. Xianyang, Tongchuan, and Yangling are mainly due to insufficient or redundant inputs, which can be classified as input inefficiency. In contrast, the inefficiency of Weinan is mainly influenced by insufficient outputs, which can be classified as output inefficiency; 3) The change in technical efficiency is the main reason for the change in total factor productivity in Xi’an Metropolitan Area. The direction of change between the two is the same, showing the characteristics of the mirror "N"-type change, first subtracting and then increasing; The Technological Progress Change Index maintained steady growth; 4) The ratio of personnel in scientific research institutions and the number of scientific research institutions, the proportion of total exports of high-tech products in GDP, and the proportion of patents granted all positively affect the efficiency of financial investment in science and technology. The summary is that as each indicator increases, efficiency grows. The R&D investment intensity and the fiscal share of science and technology will, to a certain extent, constrain the development of the efficiency of budgetary science and technology investment. The more the acquisition, the higher the efficiency. Based on the above research findings, recommendations are made.

Decomposing total factor productivity of SMEs in agricultural sector in Aceh, Indonesia

This study measures and decomposes the Total Factor Productivity (TFP) of the SMEs in the agricultural sector across 23 regencies/cities in Aceh province, Indonesia during the 2015-2019 period. Using Data Envelopment Analysis (DEA), the study found a low level of SMEs’ productivity during the study period. The SMEs in Aceh province recorded different levels of TFP. Overall, the SMEs’ TFP has slightly declined, contributed mainly by an increase in efficiency level and a decline in the technical efficiency change. These findings showed the importance of adopting advanced agricultural-related technologies and implementing good SMEs’ governance principles to further improve SMEs’ TFP. Finally, the government must prioritize promoting non-productive SMEs across the 23 regencies/districts in the province by offering sufficient financial aids and conducting professional managerial training programs.