Decomposition Of Total Factor Productivity Growth Research Articles

Total factor productivity growth decomposition and its determinants in Ethiopian manufacturing sector

The objective of this study is to examine the performance of the manufacturing firms in terms of productivity growth by accounting for the determining factors. The findings revealed that growth in TFP has an average rate of 4.87% per year and showed significant improvement over the three policy periods (1996–2003, 2004–2010, and 2011–2015), respectively 3.6%, 4.7%, and 6.5%. Total factor productivity has been decomposed into contributing sources of technical progress (1%), scale change (− 0.14%), and technical efficiency change (4.01%). Twelve factors are identified from the literature as determinants of TFP growth. Eight of them are found to statistically affect this growth. The periods from 2004 to 2010 (high economic growth period) and from 2011 to 2015 (GTP I) have a higher TFP growth rate relative to the base period (1996–2003) and similar private ownership. In addition, labor input growth, capital intensity, and growth in average wage contributed positively to TFP growth. Importers of raw material inputs have higher TFP growth rates compared with non-importers. Tone can conclude from this finding that Ethiopian large and medium-scale manufacturing firms are low in terms of TFP growth. There are factors that enhance productivity, and there are also others that hinder improvements. Thus, strategically designed, coordinated with the firms themselves, and focused interventions are required in order to raise the real sales and value added using the existing state-of-the art technology and input combination, along with advancing the technical know-how, improving labor quality, updating, and renovating the capital input.

Estimating and Decomposing the TFP Growth of Service-Oriented Manufacturing in China: A Translogarithmic Stochastic Frontier Approach

By constructing a translogarithmic stochastic frontier production model, this study explores the total factor productivity (TFP) of service-oriented manufacturing in 30 provinces in China during 2004–2020. We carried out decomposition analysis to understand in greater depth the potential drivers of TFP growth. The results show that the overall TFP of service-oriented manufacturing continuously improved during the sample period; however, the overall growth rate showed a significant slowing trend, and the contribution of TFP growth to output growth is still low. The industrial growth of service-oriented manufacturing is mainly driven by capital input, and the transformation of its growth mode from extensive to intensive has not yet been realized. Furthermore, there exists significant regional and sub-sectoral heterogeneity in the TFP growth of the industry. The decomposition of TFP growth shows that technological progress and technical efficiency are the main sources of TFP growth, but the growth rate of technological progress is declining gradually, and its driving effect on TFP is weakening. The deterioration of both scale and allocation efficiency hinders the improvement of TFP in service-oriented manufacturing, and there is still room for the industry to improve its TFP level by improving scale efficiency and allocation efficiency.

Assessing the total factor productivity growth decomposition: the transformation of economic growth momentum and policy choice in China.

How to maintain the momentum of sustained economic growth has always been an important issue of concern to all countries in the world. Using a three-level progressive analysis framework, the article provides an in-depth analysis of the evolution of supply-side economic growth drivers in China from 2003 to 2021 and the magnitude of their roles. The results show that in terms of the composition of growth drivers, total factor productivity makes the largest contribution to economic growth, while the contributions of capital scale and labor scale rank second and third, respectively; in terms of the industrial dimension, the changes in the contributions of each factor to GDP growth mainly originate from the influence of secondary and tertiary industries. From the decomposition of total factor productivity growth, industrial capital productivity and capital allocation structure both play a positive pulling effect on overall capital productivity, but have a weak downward trend. Industrial labor productivity and labor allocation structure both play a slight negative pulling effect on overall labor productivity; from the deep decomposition of growth momentum, industrial factor productivity and industrial factor allocation structure play a major positive pushing effect and a weak negative pushing effect on economic growth, respectively. The conclusion of this paper provides a valuable reference for developing countries on how to maintain the momentum of economic growth.

Transformation of Economic Growth Momentum and Policy Choice: A Case Study of Guangdong Province in China

The mystery of China’s economic growth has been widely concerned by the world. However, little attention has been paid to the transformation of the economic growth momentum of Guangdong Province, which has long occupied the top in China’s economic growth. This paper analyzes the evolution and the transformation of the momentum source of economic growth in Guangdong Province from 1979 to 2019, using Total Factor Productivity Growth Decomposition method. The results show that capital investment contributes the most to economic growth, the contributions of total factor productivity, and labor input rank second and third, respectively; the change of the contribution of each momentum source to GDP growth mainly comes from the impact of the secondary industry. Both industrial capital productivity and capital allocation structure play a negative role in driving the overall capital productivity, and both industrial labor productivity and labor allocation structure play a positive role in driving the overall labor productivity. Industrial factor productivity and industrial factor allocation structure play a major positive role and a weak negative role in promoting economic growth, respectively. Our findings provide some ideas on how to maintain economic growth in the new era especially for the developing countries.

How weather affects the decomposition of total factor productivity in U.S. agriculture

AbstractThis study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Region, Central Region, and Southern Plains of the United States, we estimate TFP change as the direct sum of multiple components, including a net weather effect. To assess the role of weather, we conduct a comparative analysis based on two distinct sets of input and output variables. A traditional set of variables that ignore weather variations, and a new set of “weather‐filtered” variables that represent input and output levels that would have been chosen under average weather conditions. From this comparative analysis, we derive biases in the decomposition of TFP growth from the omission of weather shocks. We find that weather shocks accelerated productivity growth in 12 out of 16 states by the equivalent of 11.4% of their group‐average TFP growth, but slowed down productivity by the equivalent of 6.5% of the group‐average TFP growth in the other four states (located in the Northern‐most part of the country). We also find substantial biases in the estimated contribution of technical change, scale effects, technical efficiency change, and output allocation effects to TFP growth (varying in magnitude and direction across regions) when weather effects are excluded from the model. This is the first study to present estimates of those biases based on a counterfactual analysis. One major implication from our study is that the official USDA's measures of TFP change would appear to overestimate the rate of productivity growth in U.S. agriculture stemming from technical change, market forces, agricultural policies, and other nonweather drivers.

Productivity growth and policy changes: A decomposition analysis of Indian metallic mining

Indian mining sector adopted the National Mineral Policy in 1993 and fully open up for foreign direct investment (FDI) in 2005. Prior to 1993, private participation in mining operation was restricted, and public sector played a dominant role in mineral exploration. Metallic minerals hold a vital position in the Indian mining sector and fulfil the input demand of industrial sector by supplying basic raw materials. This paper attempts to estimate and analyse the productive performance of the Indian metallic mining sector by decomposing the total factor productivity (TFP) growth into different components using firm‐level data for the period 1988–89 to 2014–15. Furthermore, the possibility of changes in productivity and its constituent components in different policy regimes have been unravelled. The analysis shows that the average TFP growth of the metallic mining is 0.07% during the study period, which is largely due to technological progress and technical efficiency change. However, TFP growth has declined from 0.28% in 1989–2005 to −0.33% in the period during complete allow of FDI. The decomposition of TFP growth using stochastic production frontier reveals that decline in productivity is due to change in scale efficiency and allocative efficiency components. It could be suggested that to improve the productivity growth further, metallic mining should upgrade to advance technology in operation and take the benefit of the scale factor.

A spatial productivity index in the presence of efficiency spillovers: Evidence for U.S. banks, 1992–2015

Abstract We present the methodology for a new spatial decomposition of total factor productivity (TFP) growth. The relevant literature is underdeveloped as there is just one short study which proposes a partial spatial TFP growth decomposition. We develop this literature in four respects. The first two developments are methodological to go from a partial decomposition to a complete one. First, we augment the partial decomposition with a cost efficiency spillover growth component. Second, we introduce own and spillover allocative efficiency growth components. Third, we provide a more detailed coverage of the spatial decomposition of TFP growth. Fourth, in contrast to the traditional application to geographical areas (e.g., countries) in the relevant literature, we apply our decomposition using firm level data, which suggests that there can be an important role for spatial productivity analysis in OR. Our application is to large U.S. banks over the period 1992–2015. Among other things, we find for the average large U.S. bank that TFP growth since the financial crisis has become much more dependent on the bank itself and less so on spatial spillovers.

Productivity growth and efficiency change: Comparing manufacturing- and service-based firms in India

This paper compares total factor productivity (TFP) growth and its components for both manufacturing- and service-based firms in India for the period 2008 to 2014. TFP growth based on data envelopment analysis shows higher productivity for the service sector as compared to the manufacturing sector. Further, decomposition of TFP growth results indicates that both the manufacturing and services sectors are driven mainly by technical change. Comparing TFP growth at the sub-sectoral level shows that mean TFP growth is highest in the case of IT firms as compared to the chemical, textile and trade industries. Further, the determinants of TFP growth using a feasible generalised least squares model indicate that capital intensity, capital turnover ratio and debt significantly affect productivity growth in the case of manufacturing. However, none of the determinants except capital intensity affect productivity growth in the services sector. From a policy standpoint, this paper suggests greater emphasis on both the service and manufacturing sectors despite some variability among firms.

Decomposing Total Factor Productivity Growth in Manufacturing and Services

Using the World Input–Output Database, this paper calculates total factor productivity (TFP) growth for a sample of 40 economies during the period 1995–2009 to show that TFP growth in Asian economies has been relatively strong. In a number of Asian economies, TFP growth in services has outpaced that in manufacturing. This paper presents a novel structural decomposition of TFP growth and shows that the main drivers of aggregate productivity growth, as well as differences in productivity growth between services and manufacturing, have been changing factor requirements. These effects tend to offset the negative productivity effect of a declining ratio of value added to gross output.

Nonparametric estimates of productivity and efficiency change in Australian Broadacre Agriculture

This paper computes and decomposes Färe-Primont indexes of total factor productivity of Australian broadacre agriculture by estimating distance functions. Using state-level data from 1990 to 2011, the empirical results show that TFP grew at an average rate of 1.36 per cent per annum in the broadacre agriculture over the period 1990–2011. There are variations of total factor productivity (TFP) growth across states and fluctuations over time within each state and territory. However, overall, there is a clear movement towards slower TFP growth across the sample period. Further decomposition of TFP growth shows that it is declining growth in technical possibilities (technological progress) that is the main driver of the declining trend in productivity growth in broadacre agriculture in Australia.

Decompositions of productivity growth into sectoral effects

The paper provides some new decompositions of labour productivity growth and Total Factor Productivity (TFP) growth into sectoral effects. These new decompositions draw on the earlier work of Tang and Wang (2004). The economy wide labour productivity growth rate turns out to depend on the sectoral productivity growth rates, output price effects and changes in sectoral labour input shares. The economy wide TFP growth decomposition is similar but some extra terms due to input price inflation make their appearance in the decomposition.

ENDOGENOUS SELECTION OF PRODUCERS, ASSET PRICES, AND PRODUCTIVITY SLOWDOWN*

This paper presents a simple heterogeneous agent model to show that shocks that reduce aggregate borrowing capacity of producers under borrowing constraints cause endogenous productivity slowdowns through declines in asset prices and biased selections of producers. These dynamics of the model replicate the qualitative features of the Japanese economy during the 1990s, including a decline in the within effect in total factor productivity growth decomposition as well as default on mortgage loans. Policy experiments demonstrate that foreclosure restrictions already in place mitigate an exogenous productivity slowdown, but that a tightening of foreclosure restrictions causes an endogenous productivity slowdown.

Convergence and divergence in regional agricultural productivity growth: evidence from Italian regions, 1951–2002

AbstractThis article analyzes long‐term agricultural Total Factor Productivity (TFP) growth at regional level by testing its time‐series properties and identifying factors associated with divergence as opposed to convergence. The empirical application concerns Italian regions over the 1951–2002 time period. TFP growth decomposition ultimately attributes the observed productivity growth performance to these contrasting (convergence vs. divergence) forces. We find that technological spillovers are the key convergence force regardless of how the spillover effects are computed. At the same time, forces favoring convergence are almost offset by divergence forces (mainly scale or learning effects). This decomposition may explain the persistence of TFP growth rate differences in Italian agriculture, and could be applicable elsewhere.

Estimation of total factor productivity in the Indian garment industry

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

The total factor productivity in China and India: new measures and approaches

PurposeThis 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.Design/methodology/approachTFP is measured using a non‐parametric Malmquist index which allows the decomposition of TFP growth into its components: efficiency and technical change.FindingsComparing 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.Originality/valueThe 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.

TFP and Economic Potential of the Tunisian Economy

In this paper we try to measure and to explain total factor productivity (TFP)growth in Tunisia over the period 1983-1996. We do not measure TFP growth by the conventional Solow residual. Instead we define TFP as the shift of the economy's production frontier, which we obtain year by year by a linear programming method, a sort of aggregate DEA analysis. We then decompose this aggregate TFP growth into a Solow residual, a terms of trade effect, and a shift in demand composition. We also proceed to a decomposition of TFP growth into individual factor productivity growth rates: those of labor, decomposed into five types, of capital and of the allowable trade deficit. We find that potential TFP has grown by 0.4 percent per year over the whole period. But, it is especially after 1991 that TFP has grown. Before that, it tended to display negative growth rates. Labor turns out to be the most important contributor to total factor productivity growth. Only in the last period did capital play an important role. The Solow residual was the main driver of TFP growth. Changes in the terms of trade and demand composition were detrimental to TFP growth.

The Conundrum of Economic Miracle: Manufacturing Growth without TFP Growth

The findings of low or even negative total factor productivity (TFP) growth in Singapore's manufacturing industries by Young (1995) and many others has been a controversial issue in view of its crucial role in the future sustainability of Singaporean manufacturing. This paper applies the varying coefficients frontier model to re-examine productivity growth in Singapore's manufacturing at the 3-digit industry level over the period 1970–1997. The results indicate that Singapore's manufacturing has on average experienced a –0.8 percent TFP growth per annum although the extent of TFP growth improved slightly in the 1990s. The decomposition of TFP growth into technical efficiency change and technological progress, found technological regress is responsible for the negative TFP growth. Factor accumulation remains the principal contributor to the economic miracle of Singapore's manufacturing industries.

Efficiency and productivity analyses of Indonesian manufacturing industries

This study estimates the technical efficiencies and total factor productivity (TFP) growths in food, textile, chemical and metal products industries from 1993 to 2000 in Indonesia by using the stochastic frontier model. Furthermore, the determinants of inefficiency are also analyzed and TFP growth is decomposed into technological progress, a scale component, and efficiency growth. The results reveal that the food, textile, chemical and metal products sectors are on average 50.79%, 47.89%, 68.65% and 68.91% technically efficient, respectively. It is noted that ownership contributed to technical inefficiencies in the food sector; location and size contributed to technical inefficiencies in the textile sector, whereas size, ownership and age contributed to inefficiencies in the chemical and metal products sectors. We note that productivity in food, textile, and metal products sectors decreased at the rate of 2.73%, 0.26%, and 1.65%, respectively, but increased at a rate of 0.5% in the chemical sector. The decomposition of TFP growth indicates that the growths are driven positively by technical efficiency changes and negatively by technological progress in all four sectors.

Total Factor Productivity Growth in the Canadian Life Insurance Industry: 1979-1989

This is the first paper to directly measure and decompose total factor productivity growth (TFPG) for the Canadian life insurance industry. TFPG averaged 1.0 percent per year over the period from 1979 to 1989, thereby outperforming many manufacturing industries. The rate of TFPG was 0.2 percent in the first half of the 1980's due to the depressed economy and 1.9 percent in the last half of the decade. Technological change was the major element contributing to TFPG. There was a large residual element in the decomposition of TFPG, reflecting possible adjustment costs associated with new information processing technologies.

Productivity growth and regulation in U.S. local telephony

Abstract The paper assesses the impact of alternative forms of regulation (price caps and incentive regulation) that are gradually substituting traditional rate-of-return regulation. A total factor productivity (TFP) growth decomposition exercise is undertaken for the period 1989–1994. A flexible translog cost function provides evidence of a non-homothetic cost function with slight diseconomies of scale and indicates labor saving technical change, corroborating previous stylized facts of the industry. The cost function estimation provided the necessary adjustment factors for the TFP growth decomposition. The evidence does not indicate differential efficiency levels accruing from alternative forms of regulation.