Decomposition Of Productivity Growth Research Articles

DEKOMPOSISI PERTUMBUHAN PRODUKTIVITAS INDUSTRI MANUFAKTUR INDONESIA: PENDEKATAN STOCHASTIC FRONTIER

Productivity growth can be decomposed into its sources, namely technical changes, technical efficiency changes, and business scale changes. The role of each of these sources is important to be studied because Indonesia experienced a decline in total factor productivity (TFP) growth in 1990-2015 which indicates a productivity issue. The results of previous studies by Margono Sharma (2006) and Ikhsan (2007) regarding the decomposition of productivity growth in the manufacturing industry sector in Indonesia using a stochastic frontier approach, there are still disagreement about the contribution of technical changes and technical efficiency changes to TFP growth. This is thought to be caused by differences in the specifications of the technical efficiency models used, namely Battese Coelli (1992) and Lee Schmidt (1993). The two models have different estimation methods, Battese Coelli (1992) use maximum likelihood estimate, while Lee Schmidt (1993) use non-linear least squares. Hence the scalar parameters that determine the pattern of technical efficiency over time (η in Battese Coelli (1992) and θ in Lee Schmidt (1993)) are different.This study uses data for the period of 2003-2014 in the manufacturing industry sector. The results of this study indicate that different technical efficiency model specifications produce a different relative contribution of TFP growth sources. Therefore, choosing a technical efficiency model to decompose TFP growth should be thorough if the results will be used in important policy making.

The Decomposition of Productivity Growth for India: Before and After 1991

We analyse how structural shifts affected India’s productivity growth from 1983 to 2017. The analysis is carried out within a broad three-sector level of disaggregation of the aggregate economy. We show that about 29% of the aggregate productivity growth of India during this period comes from structural change. While this contribution has declined since 2007, the remaining 71% of the productivity growth originates within the individual sectors, especially within the tertiary sector, which alone explains about 30% of this productivity growth. About a quarter of the total productivity growth that occurred prior to economic liberalization in 1991 is attributed to the tertiary sector. We found that between 2007 and 2017, the tertiary sector was responsible for almost 38% of India’s total productivity growth. This contribution is much larger than the contribution made by structural change, and, therefore, the significance of the tertiary sector in Indian economic expansion is growing. Our findings also indicate that the most structural change occurred between 1998 and 2007, which may have contributed to productivity increase in the decade.

Decomposition of productivity growth among the cooperative banks in Jammu and Kashmir (India)

This paper uses the non-parametric Malmquist productivity index to investigate the total productivity change among the cooperative banks in Jammu and Kashmir from 2004 to 2019. Analysing the peculiarities of cooperative banks’ productivity is essential because if productivity has increased, it should be reflected in their performance, lower client pricing, and enhanced quality services. To examine productivity changes when choices of inputs and outputs are varied, we used two distinct approaches, the intermediation approach and the income approach. Our findings show that average TFP estimates varied widely between the cooperative banks over the years. Compared to the intermediation approach, TFP growth was significantly higher than the income approach. Further, the productivity gains were driven mainly by efficiency change rather than the technological change component. Our findings have important policy implications that can serve important use to the policymakers and regulators, allowing them to devise effective methods for the cooperative banks so that they can remain competitive and sustainable.

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.

DEKOMPOSISI PERTUMBUHAN PRODUKTIVITAS INDUSTRI MANUFAKTUR INDONESIA: PENDEKATAN STOCHASTIC FRONTIER

Productivity growth can be decomposed into its sources, namely technical changes, technical efficiency changes, and business scale changes. The role of each of these sources is important to be studied because Indonesia experienced a decline in total factor productivity (TFP) growth in 1990-2015 which indicates a productivity issue. The results of previous studies by Margono Sharma (2006) and Ikhsan (2007) regarding the decomposition of productivity growth in the manufacturing industry sector in Indonesia using a stochastic frontier approach, there are still disagreement about the contribution of technical changes and technical efficiency changes to TFP growth. This is thought to be caused by differences in the specifications of the technical efficiency models used, namely Battese Coelli (1992) and Lee Schmidt (1993). The two models have different estimation methods, Battese Coelli (1992) use maximum likelihood estimate, while Lee Schmidt (1993) use non-linear least squares. Hence the scalar parameters that determine the pattern of technical efficiency over time (η in Battese Coelli (1992) and θ in Lee Schmidt (1993)) are different.This study uses data for the period of 2003-2014 in the manufacturing industry sector. The results of this study indicate that different technical efficiency model specifications produce a different relative contribution of TFP growth sources. Therefore, choosing a technical efficiency model to decompose TFP growth should be thorough if the results will be used in important policy making.

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.

Estimation and Decomposition of Productivity Growth of the Organized Manufacturing Industries of Transport Equipments in India: An Interstate Analysis

Estimation and Decomposition of Productivity Growth of the Organized Manufacturing Industries of Transport Equipments in India: An Interstate Analysis

Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition

Analyzing green transformation of energy use and pollutant emissions in China’s “Three Regions and Ten Urban Agglomerations” (TRTAs) allows effectively promoting sustainable development in the country. This paper applies Data Envelopment Analysis, namely the Bounded-adjusted Measure (BAM) relying on the additive structure, to measure the technical inefficiency and productivity change across TRTAs in China. The technical inefficiency and productivity change observed for TRTAs are 0.29 and 2.29% respectively. The decomposition results indicate that industrial energy consumption, industrial sulfur dioxide (SO2), and industrial soot & dust emissions are the main variables causing TRTAs’ industrial operation inefficiency. Spatially, environmental inefficiency of the above-mentioned three variables in North China Urban Agglomeration (NA) is higher than those in Northwest, Yangtze and Pearl River Delta Urban Agglomerations (NWA, YRDA, and PRDA respectively). Decomposition of the Gini coefficient shows that the performance associated with the three variables varies among the regions with intra-regional differentiation of the PRDA being the highest. Given the regional patterns in productivity change, more efforts should be directed towards improving technical progress on industrial energy conservation in NWA. Furthermore, regulations on industrial air pollutant emissions, joint mitigation and monitoring of the key indicators in PRDA should also be emphasized.

What drives decarbonization of new passenger cars?

Transition towards a low-carbon transport sector fundamentally depends on decarbonization of the passenger car fleet. Therefore, it is critically important to understand the driving factors behind decreasing CO2 emissions of new passenger cars. This paper develops a new decomposition method to break down the change in the average CO2 emissions of new passenger cars into components representing changes in available technology, carbon efficiency of consumer choices, vehicle attributes, fuel mix, and the gap between type-approval and on-road CO2 emissions of passenger cars. Our decomposition draws insights from the traditional index decomposition analysis and frontier-based decomposition of productivity growth. It satisfies such desirable properties as factor reversal, time reversal, and zero-value robustness. An empirical application to a unique data set that covers all registered passenger cars in Finland sheds light on why and how the CO2 emissions of new cars decreased from year 2002 to year 2014.

한국 수산가공산업의 생산성 분해에 관한 연구: 확률적 변경모형을 이용

한국 수산가공산업의 생산성 분해에 관한 연구: 확률적 변경모형을 이용

What is the impact of problem loans on Japanese bank productivity growth?

AbstractThis paper examines for the first time the impact of problem loans on Japanese productivity growth. We exploit a new data set of Japanese problem loans classified into two categories: bankrupt and restructured loans. We opt for a novel and flexible productivity growth decomposition that allows to measure the direct impact of these problem loans on productivity growth. The results reveal that Japanese bank productivity growth was severely constrained by bankrupt and restructured loans early in 2000s, whilst some persistence of the negative impact of problem loans on productivity growth is observed in the late 2000s. Thereafter, there is only some partial recovery in the productivity growth from 2012 to 2015. Further, we also perform cluster analysis to examine convergence or divergence across regions and over time. We observe limited convergence, though Regional Banks seem to form clusters in some regions.

TRADE LIBERALIZATION AND FIRM PRODUCTIVITY: ESTIMATION METHODS MATTER

In this paper, we show that the relationship between trade liberalization and firm productivity is sensitive to the method used to estimate the production function. We estimate the productivity of Colombian manufacturing plants using the methods of Levinsohn and Petrin, Ackerberg et al., and Gandhi et al. and at times come to surprisingly different conclusions about firm productivity growth after the liberalization. Results from a growth decomposition exercise and from a quantile regression model reinforce the dissimilarity of results across methods. (JEL F13, 14, D24, C14)

The Interplay Between Problem Loans and Japanese Bank Productivity

This paper examines for the first time the impact of problem loans on Japanese productivity growth. We exploit a new data set of Japanese problem loans classified into two categories: bankrupt and restructured loans. We opt for a novel and flexible productivity growth decomposition that allows to measure the direct impact of these problem loans on productivity growth. The results reveal that Japanese bank productivity growth was severely constrained by bankrupt and restructured loans early in 2000s, whilst some persistence of the negative impact of problem loans on productivity growth is observed in the late 2000s. Thereafter, there is only some partial recovery in the productivity growth from 2012 to 2015. Further, we also perform cluster analysis to examine convergence or divergence across regions and over time. We observe limited convergence, though Regional Banks seem to form clusters in some regions.

Productivity Growth Decomposition in the Manufacturing Industries of Food, Beverages, and Tobacco Products in India : A Stochastic Frontier Approach

The study applied stochastic production frontier approach to decompose the sources of total factor productivity growth (TFPG) of the organized manufacturing industries of food, beverages, and tobacco products in India into technological progress, changes in technical efficiency, economic scale effect, and allocative efficiency effect during the period from 1981-82 to 2010-11 during the entire period, during the pre- and post-reform period (1981-82 to 1990-91 and 1991-92 to 2010-11), and also, during two decades in the post-reform period (1991-92 to 2000-01 and 2001-02 to 2010-11) at the three-digit level of disaggregation of industries by NIC'04 and NIC'98. According to the estimated results, technological progress and allocative efficiency effect were the major contributors to TFPG of the organized manufacturing industries of food, beverages, and tobacco products in India during the study period from 1981-82 to 2010-11. Further, TFPG of the organized manufacturing industries of food, beverages, and tobacco products in India declined during the post-reform period (1991-92 to 2010-11), which is mainly accounted for by the decline in TP in this period, as our study revealed.

Decomposition of Productivity Growth of Water and Sewerage Companies: An Empirical Approach for Chile

Evaluating the productivity growth of water and sewerage companies (WaSCs) is useful to ensure their long-term sustainability and to protect the interests of customers. Previous studies that assessed the WaSCs’s productivity provided an aggregate productivity index without accounting for the specific contribution of the variables involved in the analysis. To overcome this limitation, and as a pioneering approach, this study provides a comprehensive analysis of the effects that inputs and outputs have on WaSCs´ overall productivity change. In doing so, a methodological approach based on the weighted directional distance model was applied. An empirical application was carried out for the Chilean water industry from 2005 to 2014. Results evidenced that inputs, desirable outputs and undesirable outputs contributed negatively to productivity regression. The negative change of the efficiency change was caused mainly by the regression of the non-revenue water. From a policy and managerial perspective, this study reveals the importance of quantifying the contribution of inputs and outputs in productivity growth in order to support decision-making.

Perubahan Struktur Ekonomi, Dekomposisi Sumber Pertumbuhan Output, dan Pertumbuhan Total Factor Productivity (TFP): Analisis Lanjutan Tabel Input-Output Provinsi Kalimantan Selatan, 2000-2010

Economic growth can be seen from the aggregate demand side, namely consumption, investment, government spending, exports and imports, and the aggregate supply side namely labor growth, capital growth and the growth of TFP (Total Factor Productivity). This study uses South Kalimantan’s Input-Output Tables of 2000, 2005 and 2010. The results showed that although the structure of the value-added of the mining sector remains the main economic pillar, but this sector has low linkages with other sectors. Based Multiplier Product Matrix, manufacturing industry suggest a leading role in the economy so that it can be said that the province of South Kalimantan are heading toward a change in the economic structure. Decomposition of sources of growth based on the Chenery’s model (1960) showed that the main source of economic growth in South Kalimantan is exports by 67 percent in the period 2000-2005 and 73.72 percent in the period 2005-2010, especially the export of coal. Decomposition of productivity growth made by Namura and Kuroda’s model (2004) and suggests that TFP growth and capital have a strong linear relationship and significant Output growth, while labor productivity have no significant correlation with Output growth. Finally, the wealth of the abundant natural resources, industry-oriented economic growth and sustainable development in South Kalimantan Province is agriculture-based industries and mining-based industries with the main strategy is to import substitution and investment in capital and technology. Keywords : Input-Output Tables, Multiplier Product Matrix, Decomposition of output growth, Total Factor Productivity JEL Classification: C67, O47

Bounded learning-by-doing and sources of firm level productivity growth in colombian food manufacturing industry

This paper models the bounded learning concept with the learning progress function characterized by the degree of efficiency and the specification of the learning progress as a logistic function capturing both the slow start-up and the limit in learning progress. We differentiate learning efficiency from the technical efficiency. The endogeneity corrected stochastic frontier model is then used to decompose the factor productivity growth into components associated with technological change, technical efficiency, scale, and learning. This productivity growth decomposition provides useful information and policy level insight in firm-level productivity analysis. Empirical results based on plant-level panel data on the Colombian food manufacturing industry for the period 1982–1998 suggest that productivity growth not only stems from technical progress, technical efficiency change, and scale but also from significant learning effect. The relative importance of the productivity growth components provides perspective for efficient resource allocation within the firm.

Assessing regional eco-efficiency from the perspective of resource, environmental and economic performance in China: A bootstrapping approach in global data envelopment analysis

Eco-efficiency has been receiving increasing attention across the world. This paper proposes an extended data envelopment analysis model, which combines global benchmark technology, directional distance function and a bootstrapping approach to investigate the dynamic trends of regional eco-efficiency in China from 2003 to 2014. Moreover, a developed slacks-based measure is utilized to decompose the performance fluctuations into resource, environmental and economic efficiency. Then, the key factors responsible for the changes in eco-efficiency are explored using the global Malmquist–Luenberger index. The empirical results demonstrate an upward trend of eco-efficiency in China. During the study period this trend was high in eastern and northern areas but low in northwestern areas. However, it should be noted that some eco-efficient regions still consume much land, water and energy and emit much environmental pollutants in absolute terms. The whole of China performs well on the economic front, while resource and environmental performances are not encouraging, particularly on environmental efficiency. The eastern and northern regions have experienced the greatest advances in both resource and environmental efficiency, while the undeveloped areas have not shown much progress, which further widens the gaps between developed areas and undeveloped areas. The decomposition of productivity growth indicates that technical progress is the decisive factor in promoting China's eco-efficiency, while decreasing management level is the major obstacle hampering the improvement in eco-efficiency.

Trade Policy Reform and Firm-Level Productivity Growth: Does the Choice of Production Function Matter?

This paper considers whether a fairly well-established empirical relationship between liberalized trade and firm productivity growth is sensitive to the choice of an identification strategy for production function estimation. We estimate the productivity of Colombian manufacturing plants using the methods of Levinsohn and Petrin (2003), Ackerberg, Caves, and Frazer (2006), and Gandhi, Navarro, and Rivers (2012), and at times come to surprisingly different conclusions about the country's experience with trade policy reform during the 1980s. Results from a quantile regression model and a productivity growth decomposition exercise tend to vary as we experiment with different specifications of the production function. Research that is concerned with the short and medium-term impact of trade liberalization on domestic manufacturing industries should therefore pay close attention to issues of robustness to alternative strategies for estimating the productivity of firms.

Structural Change and Industrial Policy in Turkey

ABSTRACTWe present evidence on structural change in Turkey and provide an overview of the evolution of industrial policy in the past three decades Turkey has experienced substantial growth in labor productivity in the past decade. About two-thirds of the increase in aggregate labor productivity arises from reallocation of employment from low- to high-productivity sectors and one-third from productivity increases within sectors. Decomposition of productivity growth using microdata also reveals an important contribution from reallocation. We also document substantial change in the composition of exports. We argue that structural change was not a direct result of selective industrial policy simply because the incentive system displayed little sectoral selectivity during the period when major structural change took place.