Total Factor Productivity Measures Research Articles

Assessing Total Factor Productivity for Soybean Production in China Based on DEA-Malmquist Index: 2005-2017

The low and slowly increasing soybean yield restricts the development of soybean production. Accurate measures of total factor productivity (TFP) for soybean production can be helpful in identifying conditions, institutions or policies that promote soybean production development in China. In this paper, TFP growth for soybean production was estimated for a panel data of 10 major soybean producing provinces from 2005 to 2017. Results reveal that TFP grew at an average rate of 1.3% over the whole period, with technical progress contributing 2.3% and efficiency change providing the other -1.0%. The change of TFP for soybean production over that time, whether increase or decline, was mainly derived by technical change except in three years (2005-2007). Positive TFP growth in the provinces of Liaoning and Inner Mongolia, and negative TFP growth in Hebei and Anhui were mainly driven by efficiency change, specifically scale efficiency change except pure technical efficiency in Liaoning.

Services input and productivity in Indian manufacturing plants

PurposeThe purpose of this paper is to analyse econometrically determinants of total factor productivity (TFP) in Indian manufacturing plants with a focus on the influence of services input on productivity.Design/methodology/approachPlant-level data drawn from Annual Survey of Industries for the years 1998-1999 to 2012-2013 are used for the estimation of TFP at plant-level by applying the Levinsohn–Petrin methodology. Econometric models are estimated to explain variations in plant-level TFP. The explanatory variables used are services input intensity (split into manufacturing services purchased and other services), the share of information communication technology (ICT) assets in total fixed capital stock, the share of contract workers in total workers and the share of imported materials out of total materials used, with plant size taken as a control variable. Model estimation is done by applying the fixed effects model.FindingsEconometric results indicate that services input and ICT intensity have a significant positive effect on productivity of manufacturing plants in India. Use of imported materials raises productivity, whereas the use of contract workers in place of regular workers tends to lower productivity. The impact of imported materials on TFP of manufacturing plants seems to be relatively bigger for labour-intensive, low technology industries.Originality/valueCare has been taken for TFP measurement. Analysis of the impact of services input on TFP has been undertaken for Indian manufacturing using plant-level data for the first time.

Does selection of productivity estimation techniques matter?

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueTo 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.

Does FDI Promote or Inhibit the High-Quality Development of Agriculture in China? An Agricultural GTFP Perspective

This paper innovatively brings the undesirable output of agricultural carbon emission into the agricultural Total Factor Productivity (TFP) accounting framework as a measure of Green Total Factor Productivity (GTFP) and uses the Slack-based Measure and Malmquist-Luenberger (SBM-ML) index method to measure the agricultural GTFP of 24 provinces in China from 2004 to 2016. Further, the two-step system generalized moment method (GMM) is adopted to reveal the effect of agricultural (Foreign Direct Investment) FDI on the growth of agricultural GTFP and various subitems. We find that the average annual growth rate of agricultural GTFP is 3.1%, and its contribution rate to agricultural growth is 52%; the growth of agricultural GTFP shows that the progress of agricultural technology is accompanied by the deterioration of agricultural technical efficiency; the agricultural GTFP in the Eastern region, the Central region and the Western region increases in a stepped-up form, with an annual growth rate of 3.1%, 3.3% and 3.4%, respectively. Agricultural FDI has a significant promoting effect on agricultural GTFP and subitems, however, it has an inverted U-shaped feature in the long term.

Monopoly behavior of China’s manufacturing enterprises

Purpose With the increase of state capital, corporate total factor productivity (TFP) has a tendency to jump up at first and then slowly decrease. Generally, no significant “productivity paradox” can be observed in China’s manufacturing industry. With the increase of export density, corporate TFP also shows a trend of initial jump growth and subsequent slow decline. This paper aims to discuss these issues. Design/methodology/approach Using the 1996–2013 China Industrial Enterprise Database, this paper studies the monopolistic behavior of Chinese manufacturing enterprises through the measurement of TFP and corporate monopoly power. Findings Results show that China’s manufacturing monopoly enterprises are generally innovation-oriented rather than rent-seeking. However, there are certain differences between diversified types of monopoly enterprises: the ones with state capital are more inclined to innovate than those without, whereas the ones with export delivery value are more inclined to seek rent than those without. Originality/value Therefore, the government should implement differentiated policies for diversified types of monopoly enterprises, and do so in a targeted manner fully reflecting the containment of rent-seeking and the encouragement of innovation.

Agricultural productivity evolution in China: A generalized decomposition of the Luenberger-Hicks-Moorsteen productivity indicator

China has undergone a series of agricultural policy reforms since 1978. The measurement of the productivity gains and identification of the underlying drivers thereof are important facets of policy analysis. The commonly used Total Factor Productivity (TFP) measures often lack such desirable properties as completeness or independence of the direction of the optimization (orientation). In this paper, we take a top down approach by beginning with a TFP measure and then decomposing it into three mutually exclusive, exhaustive elements. In particular, we begin with the additively complete Luenberger-Hicks-Moorsteen (LHM) TFP indicator that takes into account both input and output changes when measuring productivity and then additively decompose it into measures of technological progress, technical efficiency change, and scale efficiency change. We develop a generalized decomposition of the LHM TFP indicator which encompasses both input-oriented and output-oriented changes over time. We illustrate this additively complete LHM TFP indicator using agricultural data from 31 Chinese provinces over the period 1997–2015. Our empirical results show that Chinese agricultural productivity growth (3.05% per annum) was mainly driven by technological progress (2.35% p.a.), with relatively small contributions from scale efficiency change (0.65% p.a.) and technical efficiency change (0.04% p.a.). We also found that productivity change and the relative importance of its components varied across both time and provinces.

Energy Endowments and Economic Growth

We examine the relationship between fossil fuel (FF) endowments and economic convergence. Countries with plausibly exogenous FF endowments show patterns of convergence, as indicated by standard convergence tests. By contrast, we fi nd no evidence of convergence among countries without FF endowments. These patterns of convergence are consistent across measures of physical capital, human capital, and total factor productivity. We discuss the implications of this result for economic development and comment on its implications for global climate policy.

Trade liberalization and productivity of Indian manufacturing firms

PurposeThe purpose of this paper is to assess the impact of India’s trade liberalization during the late 1990s and 2000s on productivity of manufacturing firms and verify whether the productivity-enhancing impact of reductions in input tariffs was greater than that of output tariff cuts, as found in some earlier studies.Design/methodology/approachFirm-level (company-level) data drawn from Prowess database are used for the estimation of total factor productivity (TFP) at the firm level, done by using the Levinsohn–Petrin methodology. Econometric models are estimated to explain firm-level TFP. The explanatory variables used are output and input tariff rates and quantitative restrictions on imports at the industry level and firm characteristics such as firm size, export intensity and import intensity. Firm-level panel data for 2002-2010 or for a longer period 1998-2010 are used for the estimation of econometric models. Model estimation is done by applying the fixed-effects model and IV-2SLS, 3SLS estimators and EC2SLS estimators.FindingsTrade liberalization had a significant positive effect on the productivity of Indian manufacturing firms. The lowering of output tariff had a greater beneficial impact on TFP of Indian manufacturing firms than the lowering of tariff on intermediate inputs.Originality/valueGood deal of care has been taken in the measurement of output and inputs for the purpose of TFP measurement. Two alternative frameworks, gross output and value added, are used. This helps in making a better estimate of the impact of trade liberalization on TFP.

An Efficiency-orientated Total Factor Productivity Measurement Model for Construction Projects

Modularization and mechanization are widely promoted due to their potential benefits in enhancing site efficiency. However, the outcomes of the implementation lack quantitative supports at the project level. This paper aims to develop a quantitative evidence-based methodology for measuring site efficiency and evaluating the outcomes of the two technologies. Applying the proposed methodology to a real-life building construction project, the simulation results of multiple scenarios show that site efficiency is likely to be enhanced by encouraging the utilisation of laboring-saving materials and equipment within the limited project budget. The findings provide quantitative evidence to encourage technological innovation in construction materials and equipment, thereby demonstrating the potential of substituting materials or equipment for labor in benefiting site efficiency of a construction project through adopting modularization and mechanization.

Money and the Measurement of Total Factor Productivity

Firms have greatly increased their cash holdings since the mid-1990s. These holdings have an opportunity cost; i.e., allocating firm financial capital into monetary deposits means that investment in real assets is reduced. Traditional measures of Total Factor Productivity (TFP) do not take into account these holdings of monetary assets. Given the recent large increases in these holdings in the U.S. and other advanced economies, it is expected that adding these monetary assets to the list of traditional sources of capital services will reduce the TFP of the business sector. We measure this effect for the U.S. corporate and non-corporate business sectors.

Environmental Regulation Intensity, Foreign Direct Investment, and Green Technology Spillover—An Empirical Study

Many researchers have studied the relationships among heterogeneous foreign direct investment (FDI), environmental regulation, and green total factor productivity. However, no research has been done on how different types of FDI can result in green technology spillover under different levels of environmental regulation intensity. To address this research gap, in this paper, we build a static linear panel model, a static panel threshold model, and a dynamic panel threshold model to investigate the environmental regulatory threshold effect of labor-based FDI and capital-based FDI in terms of their green technology spillover. Based on the measurement of green total factor productivity (GTFP) of 36 industry sectors in China from 2003 to 2015, we first compare the threshold effects of environmental regulation on green technology spillover between labor-based FDI and capital-based FDI with a static linear model and a static threshold model. The results show that environmental regulation is unable to significantly promote the green technology spillover of labor-based FDI. However, intensifying environmental regulation can reduce the negative impact of labor-based FDI on GTFP. The effect of environmental regulation on green technology spillover of capital-based FDI is more complex. In the static linear model, environmental regulation can significantly promote the green technology spillover of capital-based FDI. In the static threshold model, the green technology spillover of capital-based FDI exists only when the environmental regulation intensity is sufficiently low or sufficiently high. Finally, the dynamic threshold model is adopted for robustness check. The results show when the environmental regulation intensity is higher than a threshold, both types of FDI can indeed result in green technology spillover. In short, our results prove that to ensure that FDI results in green technology spillover, it is necessary to continue to strengthen environmental regulation.

Growth in Agricultural Productivity and Its Components in Bangladeshi Regions (1987–2009): An Application of Bootstrapped Data Envelopment Analysis (DEA)

The present study applies a bootstrapped data envelopment analysis (DEA) procedure to compute bias-corrected measures of agricultural total factor productivity (TFP) change and its components (technical change and technical efficiency change) using a panel data of 19 regions of Bangladesh covering a 23-year period (1987–2009), thereby overcoming the limitation of the lack of statistical inference of the conventional non-parametric DEA. Results revealed that overall productivity grew at a modest rate of 0.03%, mainly powered by technological progress at 0.03% and a negligible decline in technical efficiency at 0.004% with large disparities amongst regions. Six regions in the middle order shifted ranks with regard to TFP change following bias correction. The estimated confidence intervals demonstrated that many regions underwent either progress or regress in productivity performance over time. Investments in research and development (R&D), agricultural extension, and crop diversification are suggested to improve regional inequality and declining technical efficiency.

The Measurement of Total Factor Productivity and Its Spatial Evolution Trend in Four Regions of China

In this paper, we aim to contribute to the growth literature and provide suggestions for improving China's TFP by measuring the Total Factor Productivity (TFP) of 30 provinces (excluding Tibet) in China, and analyzing the spatial evolution trend of Total Factor Productivity (TFP) in China's four major economic regions. The study shows that the TFP in China is not high and shows a downward trend from 2000 to 2015. The scale efficiency is the main driving force for the promotion of TFP in China. In addition, the Total Factor Productivity of each province shows spatial autocorrelation in most years and is relatively backward in the central region, western region and northeastern region of China, where some provinces have relatively large negative effects on the surrounding areas and relatively strong radiation effects. The provinces with high TFP clusters are more in the eastern regions, but the radiation effects are not obvious.

DOES TOTAL FACTOR PRODUCTIVITY CONVERGE AMONG ASEAN COUNTRIES?

This paper examines the productivity convergence of the five original Association ofSoutheast Asian Nations (ASEAN) countries, namely Indonesia, Malaysia, Philippines,Singapore, and Thailand (ASEAN-5), using annual data spanning the period 1968to 2014. Results from two-break Lagrange multiplier and residual augmented leastsquares Lagrange multiplier unit root tests reveal strong evidence of productivityconvergence in case of ASEAN-5. Further, the results based on Phillips–Sul panelclub convergence also reveal productivity convergence. To check the robustness ofour finding, we use an alternative measure of total factor productivity and still findevidence of convergence. We infer that such productivity improvements may helpASEAN countries to achieve a higher pace of economic growth.

The transformation function, technical efficiency, and the CCR ratio

Charnes, Cooper, and Rhodes define the ratio of the virtual output to the virtual input as a measure of the technical efficiency of a multiple output multiple input firm. The aggregation weights used in construction the virtual output and the virtual input may be arbitrarily chosen so long as the weights are non-negative and using these weights no firm's input-output bundle shows efficiency exceeding 100%. In production economics, the ratio of aggregate output to aggregate input is a measure of total factor productivity and a direct link of the Charnes, Cooper, and Rhodes ratio to technical efficiency is not obvious. Usually the ratio measure is rationalized as efficiency by showing its equivalence to the Farrell efficiency measure. This paper offers a direct derivation of the ratio measure of efficiency from a transformation function. We also show how Banker, Charnes, and Cooper measure under variable returns to scale can be derived from the transformation function.

Efficiency and Productivity Analysis of the Saudi Manufacturing Industries

The measurement of total factor productivity for 24 manufacturing industries in Saudi Arabia between 2010 and 2016 is undertaken in the present study based on the Malmquist productivity index (MPI). Efficiency change and technical change are the two constituents of this index. According to the findings, technical efficiency enhancement was the main determinant of the productivity growth by an average of 1.063 percent across the examined industries. By contrast, regarding the rate of technological progress, the majority of the examined industries did not show a good rate of technological progress. Keywords: Malmquist Index, total factor productivity, manufacturing industries, technical efficiency, technical change DOI : 10.7176/EJBM/11-6-14

Statistical measurement of total factor productivity under resource and environmental constraints

The existing literature on the measurement of total factor productivity (TFP) only considers capital and labour input, ignoring resource and environmental factors. This paper takes resource and environmental factors into the framework of TFP measurement by constructing the environmental comprehensive indexes. Then the DEA-Malmquist index method is employed to analyze TFP from 1978 to 2016 under resource and environmental constraints. The results show that under resource and environmental constraints, China's TFP (overall) is at a slow rising stage. China's TFP is closely related to macroeconomic fluctuation, macroeconomic regulation, directional control and reform of the economic system—having impact on the changing trend and fluctuation extent of TFP.

Productivity Growth: Patterns and Determinants across the World

This is the background paper for the productivity extension of the World Bank’s Long-Term Growth Model (LTGM). Based on an extensive literature review, the paper identifies the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions. Based on underlying proxies, the paper constructs indexes representing each of the main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. This is done for every year in the three decades spanning 1985–2015 and for more than 100 countries. In parallel, the paper presents a measure of total factor productivity (TFP), largely obtained from the Penn World Table, and assesses the pattern of productivity growth across regions and income groups over the same sample. The paper then examines the relationship between the measures of TFP and its determinants. The variance of productivity growth is decomposed into the share explained by each of its main determinants, and the relationship between productivity growth and the overall determinant index is identified. The variance decomposition results show that the highest contributor among the determinants to the variance in TFP growth is market efficiency for Organisation for Economic Co-operation and Development countries and education for developing countries in the most recent decade. The regression results indicate that, controlling for country- and time-specific effects, TFP growth has a positive and significant relationship with the proposed TFP determinant index and a negative relationship with initial TFP. This relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved. The paper presents and discusses some of these simulations for groups of countries by geographic region and income level. An accompanying Excel-based toolkit, linked to the LTGM, provides a larger set of simulations and scenario analysis at the country level for the next few decades.

How does environmental regulation affect environmental performance? A case study of China's regional energy efficiency

AbstractEnvironmental regulation has been recognized as an important way to directly improve environmental performance or indirectly impact environmental performance through increasing environmental innovation. The present paper constructs an energy and carbon emission total factor productivity index using the Malmquist–Luenberger productivity index and data envelopment analysis. The proposed technique is used to measure the environmental performance of 30 Chinese provinces during the period 2010–2015. The energy and carbon emission total factor productivity measure is divided into a pure technical efficiency index and a technical progress index to provide detailed environmental performance information. Then an ordinary least squares model is adopted to analyse the impact of environmental regulation on environmental innovation operation and environmental performance by hypothesis testing. The empirical results show that environmental regulation and environmental innovation have positive direct effects on environmental performance and that market‐based environmental regulation has a positive indirect effect on environmental performance by increasing environmental innovation.

Performance of Brazilian total factor productivity from 2004 to 2014: a sectoral and regional analysis

This paper aimed to study the behavior of Brazilian productivity between 2004 and 2014 and its impact on growth in a disaggregated analysis. To this end, performance was studied by examining the measurement of total factor productivity and was also based on sectoral and regional aggregation in an attempt to verify which sectors and regions contributed most to the low aggregate productivity in Brazil, considering the few disaggregated studies on productivity. In addition, based on the estimates, a growth decomposition was performed to verify the contribution of productivity to economic growth. Additionally, econometric methodologies were used to calculate productivity in order to verify whether the results obtained were similar, using panel estimates with pooled data, fixed effects, and a dynamic panel in level and differences. Among the results, it was verified that there was higher productivity growth in services and that there was a decrease in industry. The regions that achieved better performance were the North, Northeast, and Center-West, depending on the sector analyzed. In addition, considering all the regions, only industry contributed negatively to growth, except in the Center-West. Regarding econometrics, the pooled data model presented the best results.