Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

scholarly journals BIASED TECHNICAL CHANGE, INTERMEDIATE GOODS, AND TOTAL FACTOR PRODUCTIVITY

2011 ◽  
Vol 16 (2) ◽  
pp. 184-203 ◽  
Author(s):  
Alessio Moro
Keyword(s):  
Total Factor Productivity ◽  
Production Function ◽  
Technical Change ◽  
Value Added ◽  
Intermediate Goods ◽  
Factor Productivity ◽  
Aggregate Production ◽  
Tfp Growth ◽  
Aggregate Production Function ◽  
The Impact

In this paper I show that the intensity at which intermediate goods are used in the production process affects aggregate total factor productivity (TFP). To do this, I construct an input–output model economy in which firms produce gross output by means of a production function in capital, labor, and intermediate goods. This production function is subject, together with the standard neutral technical change, to intermediates-biased technical change. Positive (negative) intermediates-biased technical change implies a decline (increase) in the elasticity of gross output with respect to intermediate goods. In equilibrium, this elasticity appears as an explicit part of TFP in the value added aggregate production function. In particular, when the elasticity of gross output with respect to intermediates increases, aggregate TFP declines. I use the model to quantify the impact of intermediates-biased technical change for measured TFP growth in Italy. The exercise shows that intermediates-biased technical change can account for the productivity slowdown observed in Italy from 1994 to 2004.

The Dynamic Correlation Between Capital Deepening and Total Factor Productivity in China

2020 ◽  
Vol 24 (4) ◽  
pp. 524-531
Author(s):  
Wuliu Zhang ◽  
Keyword(s):  
Human Capital ◽  
Total Factor Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
Linear Structure ◽  
Physical Capital ◽  
Influence Coefficient ◽  
Factor Productivity ◽  
Capital Deepening ◽  
The Impact

The impact of capital deepening on total factor productivity (TFP) is a significant and controversial issue. Based on the calculation of relevant indicators, this study adopts a Bayesian time-varying parameter model, Bayesian quantile regression, and adaptive Bayesian quantile models for in-depth statistical analysis. TFP was found to have a complex non-linear structure, and physical and human capital deepening indicators show a significant upward trend. The deepening of physical capital has a negative impact on TFP, while the deepening of human capital has a positive impact. In the capital deepening structure, the level of TFP has been improved and its structure optimized. Primary human and non-production physical capital deepening has no significant effect on TFP, while secondary human capital deepening has some significant effects on TFP. Tertiary and productive human capital deepening of TFP present two different forms of significant effect: the influence coefficient of the former declines in the increasing quantile and the change is larger, while the latter has a stable negative impact. The results of this study provide insights in terms of the improvement of China’s productivity.

scholarly journals DINAMIKA TOTAL FACTOR PRODUCTIVITY INDUSTRI BESAR DAN SEDANG INDONESIA

2014 ◽  
Vol 16 (3) ◽  
pp. 277-308
Author(s):  
Ndari Surjaningsih ◽  
Bayu Panji Permono
Keyword(s):  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Manufacturing Sector ◽  
Jel Classification ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Catching Up ◽  
Using Data

This paper calculates and decomposes the Total Factor Productivity (TFP) for large and medium scale industry in Indonesia covering the period of 2000-2009. By using Data Envelopment Analysis (DEA)  method, the result shows there is a shift of the supporting factors on the growth of TFP on manufacturing sector within the 2 (two) sample period. In the period of 2000-2004, efficiency change becomes the main contributor on the growth of TFP. Whereas in the period of 2005-2009, technical change becomes the main supporting factor of TFP,however it goes along with the growth of negative efficiency change or the decline of the company’s catching-up effect ability to adapt with the more advance technology. The grouping of the sample across subsectors, technical change and also efficiency change shows the declining amount of manufacture industry with superior productivity. Furthermore, the number of low and weakening catching-up industry is increasing.  Keywords: Indonesian manufacturing, total factor productivity, technical change, efficiency change, economic scale change, Data Envelopment Analysis JEL Classification: L6, M11

scholarly journals Distinction between Informal and Unorganized Sector: A Study of Total Factor Productivity Growth for Manufacturing Sector in India

2011 ◽  
Vol 3 (5) ◽  
pp. 296-310
Author(s):  
Indrajit Bairagya
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Informal Sector ◽  
Productivity Growth ◽  
Manufacturing Sector ◽  
Total Factor Productivity Growth ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Unorganized Sector

Since its very onset, the concept and definition of the informal sector has been a subject of debate both at the national and international levels. Existing literature uses the terms ‘informal sector’ and ‘unorganized sector’ interchangeably. However, in India, the characteristics of enterprises in the informal and non-informal unorganized manufacturing sectors are different and, thus, it is not justifiable to consider the informal and unorganized sector interchangeably for the manufacturing sector. Thus, the objective of this paper is to test the hypothesis on whether or not the total factor productivity growth (TFPG) of the informal manufacturing sector is different from the non-informal unorganized manufacturing sector. TFPG is decomposed into technical efficiency change and technological change. Later, technical efficiency change is further decomposed by pure efficiency change and scale efficiency change. Results show that the average TFPG of the non-informal sector is higher than the informal sector. The informal sector heavily concentrates in own account small enterprises, whereas the non-informal unorganized sector concentrates only in directory manufacturing enterprises (DME). Due to large in size, DME avails the advantages of economies of scale, which, in turn, helps the units for more growth in terms of total factor productivity growth. The main reason for productivity decrease of the enterprises, besides technology regress and the lack of adequate investments, is the limitation of activities and scale along with the optimal allocation of resources. This study provides a basis on how policies can be designed for enhancing the total factor productivity growth of the informal sector.

scholarly journals Technical Efficiency and Technical Change in Africa: The Role of Money from the Diasporas

2018 ◽  
Vol 10 (7) ◽  
pp. 177
Author(s):  
Gloria Clarissa O. Dzeha ◽  
Joshua Yindenaba Abor ◽  
Festus Ebo Turkson ◽  
Elikplimi Komla Agbloyor
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Seemingly Unrelated Regression ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Factor Productivity ◽  
Unrelated Regression ◽  
The Relationship

Based on evidence from the literature that the relationship between remittances and total factor productivity (TFP) is inconclusive, we employ the non-parametric Malmquist productivity index - Data Envelope Analysis to decompose total factor productivity (TFP) into technical change and technical efficiency and further investigate the effect of remittances on the technical change and technical efficiency. We employ the Seemingly Unrelated Regression estimation (SUR) technique in a panel of twenty-three African remittance recipient countries across a twenty-three-year period (1990-2013). We show that remittances received by households have a positive and significant impact on technical efficiency but no significant on technical change (innovativeness). We further show that remittances received by skilled labour is significant to technical efficiency but has a lowering effect on technical efficiency.

scholarly journals Total factor productivity of manufacturing sector in India: A regional analysis for the State of Haryana

2012 ◽  
pp. 97-118
Author(s):  
Shallu Sehgal ◽  
Suparn Sharma
Keyword(s):  
Total Factor Productivity ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Regional Analysis ◽  
The State ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Liberalization Policy

Using pooled data for the period of 1981-82 to 2007-08 for different categories of organized sector’s manufacturing industries for the sample state of Haryana, the present undertaking seeks to analyze the inter-temporal and inter-industry comparison of total factor productivity (TFP) measured by Malmquist productivity index (MPI), which is an application of DEA to panel data to calculate the indices of TFP change, technology change, efficiency change. The general development pattern observed by the Haryana is definitely not a healthy sign of structural change in the economy. The analysis of the discussion reflects that while the tertiary sectors have maintained its lion’s share in GDP of India and Haryana as well, the declining trend in the share of primary sector and more or less stable contribution of the secondary sector is noticeable. The study reveals that technical efficiency change is the key driver of TFPG in the manufacturing sector of Haryana during pre reforms period, however, the picture has turned around during the post reforms period. A positive impact of liberalization policy on technological advancement of the manufacturing sector of the state has been experienced. But, during the post reforms period the state has realized inefficiency in the utilization of resources in hand and it is really an alarming sign indicating that the incapability of manufacturing sector of the state in question to cope up with the technological advancement.DOI: http://dx.doi.org/10.3126/ejdi.v13i0.7213 Economic Journal of Development Issues Vol.13 & 14 2011, pp.97-118

scholarly journals Read All about It!! What Happens Following a Technology Shock?

2011 ◽  
Vol 101 (4) ◽  
pp. 1144-1179 ◽  
Author(s):  
Michelle Alexopoulos
Keyword(s):  
Information Technology ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Economic Activity ◽  
New Technologies ◽  
Technology Shocks ◽  
Factor Productivity ◽  
Technology Shock ◽  
Impact Of Technology ◽  
The Impact

Existing indicators of technical change are plagued by shortcomings. I present new measures based on books published in the field of technology that resolve many of these problems and use them to identify the impact of technology shocks on economic activity. They are positively linked to changes in R&D and scientific knowledge, and capture the new technologies' commercialization dates. Changes in information technology are found to be important sources of economic fluctuations in the post-WWII period, and total factor productivity, investment, and, to a lesser extent, labor are all shown to increase following a positive technology shock. (JEL E22, E23, E32, O33, O34, O47)

scholarly journals Economic Policy Uncertainty, Outward Foreign Direct Investments, and Green Total Factor Productivity: Evidence from Firm-Level Data in China

2021 ◽  
Vol 13 (4) ◽  
pp. 2339
Author(s):  
Yuegang Song ◽  
Feng Hao ◽  
Xiazhen Hao ◽  
Giray Gozgor
Keyword(s):  
Economic Policy ◽  
Total Factor Productivity ◽  
Industrial Development ◽  
Positive Impact ◽  
Western China ◽  
Factor Productivity ◽  
Firm Level ◽  
Chinese Firm ◽  
Level Data ◽  
Firm Level Data

This paper uses Chinese firm-level data to investigate the effect of China’s outward foreign direct investment (OFDI) on green total factor productivity (GTFP) under economic policy uncertainties (EPU). We found a significant positive impact of OFDI on GTFP. Moreover, an increase in EPU was shown to decrease GTFP. We also found that OFDI positively contributes to GTFP for private firms and foreign-invested firms in China. Technology-seeking OFDI contributes greater to GTFP than resource-seeking OFDI and market-seeking OFDI. These results remain robust when considering OFDI from firms in Central and East China as well as Western China. The findings are also robust with green labor productivity (GLP) substituting for GTFP using different econometric techniques. We also discuss potential implications in enhancing green innovation performance and sustainable industrial development in China.

scholarly journals KONSENTRASI SPASIAL, AGLOMERASI DAN PRODUKTIVITAS PERUSAHAAN INDUSTRI MANUFAKTUR INDONESIA

2021 ◽  
Vol 4 (2) ◽  
pp. 146-156
Author(s):  
Kusuma Wardani (Universitas Indonesia) ◽  
Muhammad Halley Yudhistira (Universitas Indonesia)
Keyword(s):  
Total Factor Productivity ◽  
Negative Impact ◽  
Positive Impact ◽  
High Tech ◽  
Factor Productivity ◽  
Localization Economies ◽  
Instrument Variable ◽  
Urbanization Economies ◽  
The Impact ◽  
The Relationship

AbstractThis study aims to analyze the impact of agglomeration in the form of localization economies and urbanization economies on the productivity of manufacturing industrial companies in Indonesia. Unlike previous studies, this study will look at the effect of technology level on the relationship between productivity and agglomeration by classifying research samples into low-tech and high-tech industries. In addition, this study also improves the estimation technique by addressing the endogeneity problem that has the potential to arise in estimating the relationship between productivity and agglomeration to be overcome by using instrument variable (IV). The study was conducted in two stages of estimation using company-level panel data from 2010 to 2014. First, productivity was measured at the company level using Total Factor Productivity (TFP). Then, the company productivity is estimated together with the company and industry characteristic variables, including the agglomeration measurement variable which represents localization economies and urbanization economies. The regression results show a positive impact from localization economies and a negative impact from urbanization economies.AbstrakPenelitian ini bertujuan menganalisis dampak aglomerasi berupa localization economies dan urbanization economies terhadap produktivitas perusahaan industri manufaktur di Indonesia. Berbeda dengan penelitian terdahulu yang juga meneliti dampak aglomerasi industri terhadap produktivitas perusahaan, pada penelitian ini akan melihat pengaruh tingkat teknologi terhadap hubungan produktivitas dan aglomerasi dengan mengklasifikasikan sampel penelitian ke dalam industri berteknologi rendah dan industri berteknologi tinggi. Selain itu, peneltian ini juga memperbaiki teknik estimasi dari penelitian sebelumnya dengan menangani masalah endogenitas yang berpotensi muncul dalam mengestimasi hubungan produktivitas dan aglomerasi akan diatasi dengan penggunaan instrument variable (IV). Penelitian dilakukan dalam dua tahap estimasi dengan menggunakan data panel level perusahaan dari tahun 2010 sampai 2014. Pertama, produktivitas diukur pada level perusahaan dengan menggunakan Total Factor Productivity (TFP). Kemudian, produktivitas perusahaan diestimasi bersama variabel karakteristik perusahaan dan industri, termasuk variabel pengukuran aglomerasi yang mewakili localization economies dan urbanization economies. Hasil regresi menunjukkan adanya dampak positif dari localization economies dan dampak negatif dari urbanization economies.

scholarly journals Total Factor Productivity Shock and Economic Growth in Selected Asean+3 Countries: A New Evidence Using a Panel Var

2021 ◽  
Vol 21 (3) ◽  
pp. 1366-1383
Author(s):  
Noorazeela Zainol Abidin ◽  
Ishak Yussof ◽  
Zulkefly Abdul Karim
Keyword(s):  
Economic Growth ◽  
Total Factor Productivity ◽  
Positive Impact ◽  
Production Costs ◽  
Panel Var ◽  
Factor Productivity ◽  
Data Set ◽  
Tfp Growth ◽  
The Impact ◽  
Panel Vector Autoregression

A comparison between countries shows that there is a difference in terms of economic growth achievement across nations. This difference is due to the contribution of capital growth, labor, and total factor productivity (TFP). Although the use of capital and labor plays a vital role in the production, the contribution of TFP growth is also indispensable, as it saves production costs. Nevertheless, in 1995-2000, most countries have experienced a negative growth of TFP in which can affect its contribution to economic growth. Therefore, the focal point of this study is to analyze the impact of TFP growth shock on economic growth in selected ASEAN+3 countries (i.e., Malaysia, Singapore, Thailand, Indonesia, Philippines, Cambodia, Vietnam, China, South Korea, and Japan), using the data set from 1981 to 2014. The study employed the panel vector autoregression (PVAR) method in analyzing the propagation of the shocks through impulse response function and variance decomposition. The main findings revealed that TFP growth shocks have a positive impact on economic growth. Besides, the results also showed that over the next ten years, the proportion of human capital variation would be more dominant in contributing to the economic growth for the selected ASEAN+3 countries. As the surge in TFP growth had a positive impact on economic growth, this finding indicated that each country needs to allocate more expenditure in the Research and Development (R&D) activities.

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