scholarly journals Agglomeration and Productivity: Evidence from Indian Manufactuaring

2020 ◽  
Vol 8 (1) ◽  
pp. 75-94
Author(s):  
Renjith Ramachandran ◽  
Ketan Reddy ◽  
Subash Sasidharan
Keyword(s):  
Total Factor Productivity ◽  
Positive Impact ◽  
Econometric Analysis ◽  
Industrial Agglomeration ◽  
Factor Productivity ◽  
Annual Survey ◽  
Level Data ◽  
Plant Level ◽  
The Impact ◽  
Productivity Gains

This study analyses the impact of industrial agglomeration on the total factor productivity (TFP) of Indian manufacturing. We employ plant-level data from the Annual Survey of Industries (ASI) to measure TFP and industrial agglomeration. Our econometric analysis discerns a positive impact of industrial agglomeration on plant productivity. In addition, we find that the larger plants are the beneficiaries of productivity gains associated with agglomeration. Further, our findings are robust to alternate measures of TFP.

scholarly journals Co-location of Formal and Informal Manufacturing and Firm’s Performance: Evidence from India

2021 ◽  
pp. 001946622110401
Author(s):  
Renjith Ramachandran ◽  
Subash Sasidharan
Keyword(s):  
Informal Sector ◽  
Positive Impact ◽  
National Sample ◽  
Sample Survey ◽  
Formal Sector ◽  
Manufacturing Plants ◽  
Annual Survey ◽  
Level Data ◽  
Plant Level ◽  
The Impact

This study analyses the impact of co-location between formal and informal manufacturing sectors on plant-level productivity. We employ a unique data obtained by merging plant-level data from Annual Survey of Industries (ASI 2011–2012) and Survey of Unorganised Manufacturing and Repairing Enterprises provided by National Sample Survey Office (NSSO 67th round). We find that formal and informal manufacturing plants gain from localisation. Further, co-location with informal enterprises has a positive impact on productivity of formal sector plants; however, we observe insignificant impact of co-location on informal sector enterprises. Additionally, we find evidence that informal sector enterprises benefit from industrially diversified regions. JEL Classifications: D24, R12, R3

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.

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 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.

scholarly journals Proximity and Investment: Evidence from Plant-Level Data *

2013 ◽  
Vol 128 (2) ◽  
pp. 861-915 ◽  
Author(s):  
Xavier Giroud
Keyword(s):  
Travel Time ◽  
Total Factor Productivity ◽  
Extensive Margin ◽  
Sample Period ◽  
Factor Productivity ◽  
Firm Level ◽  
Level Data ◽  
Plant Level

Abstract Proximity to plants makes it easier for headquarters to monitor and acquire information about plants. In this article, I estimate the effects of headquarters’ proximity to plants on plant-level investment and productivity. Using the introduction of new airline routes as a source of exogenous variation in proximity, I find that new airline routes that reduce the travel time between headquarters and plants lead to an increase in plant-level investment of 8% to 9% and an increase in plants’ total factor productivity of 1.3% to 1.4%. The results are robust when I control for local and firm-level shocks that could potentially drive the introduction of new airline routes, when I consider only new airline routes that are the outcome of a merger between two airlines or the opening of a new hub, and when I consider only indirect flights where either the last leg of the flight (involving the plant’s home airport) or the first leg of the flight (involving headquarters’ home airport) remains unchanged. Moreover, the results are stronger in the earlier years of the sample period and for firms whose headquarters is more time-constrained. In addition, they also hold at the extensive margin, that is, when I consider plant openings and closures.

scholarly journals Intelligence and Green Total Factor Productivity Based on China’s Province-Level Manufacturing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 4989
Author(s):  
Yining Zhang ◽  
Zhong Wu
Keyword(s):  
Total Factor Productivity ◽  
Manufacturing Industry ◽  
Positive Impact ◽  
Mechanism Analysis ◽  
Factor Productivity ◽  
Green Development ◽  
Central Regions ◽  
Intelligent Technology ◽  
Deep Integration ◽  
The Impact

The application of intelligent technology has an important impact on the green total factor productivity of China’s manufacturing industry. Based on the provincial panel data of China’s manufacturing industry from 2008 to 2017, this article uses the Malmquist–Luenburger (ML) model to measure the green total factor productivity of China’s manufacturing industry, and further constructs an empirical model to analyze the impact mechanism of intelligence on green total factor productivity. The results show that intelligence can increase the green total factor productivity of the manufacturing industry. At the same time, mechanism analysis shows that intelligence can affect manufacturing green total factor productivity by improving technical efficiency. However, the effect of intelligence on the technological progress of the manufacturing industry is not significant. In addition, the impact of intelligence has regional heterogeneity. It has significantly promoted the green total factor productivity in the eastern and central regions of China, while its role in the western region is not obvious. The research in this article confirms that intelligence has a significant positive impact on the green total factor productivity of the manufacturing industry, and can provide suggestion for the current further promotion of the deep integration of intelligence and the green development of the manufacturing industry to achieve the strategic goal of industrial upgrading.

scholarly journals Finance and Misallocation: Evidence from Plant-Level Data

2014 ◽  
Vol 104 (2) ◽  
pp. 422-458 ◽  
Author(s):  
Virgiliu Midrigan ◽  
Daniel Yi Xu
Keyword(s):  
Technology Adoption ◽  
Total Factor Productivity ◽  
Financial Frictions ◽  
Factor Productivity ◽  
Productivity Losses ◽  
Level Data ◽  
Adoption Decisions ◽  
Low Levels ◽  
Plant Level

We use producer-level data to evaluate the role of financial frictions in determining total factor productivity (TFP). We study a model of establishment dynamics in which financial frictions reduce TFP through two channels. First, finance frictions distort entry and technology adoption decisions. Second, finance frictions generate dispersion in the returns to capital across existing producers and thus productivity losses from misallocation. Parameterizations of our model consistent with the data imply fairly small losses from misallocation, but potentially sizable losses from inefficiently low levels of entry and technology adoption. (JEL E32, E44, F41, G32, L60, O33, O47)

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