Firm Characteristics and Total Factor Productivity: Evidence from Indian Manufacturing Firms

2017 ◽  
Vol 11 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Lopamudra D. Satpathy ◽  
Bani Chatterjee ◽  
Jitendra Mahakud
Keyword(s):  
Total Factor Productivity ◽  
Ordinary Least Squares ◽  
Jel Classification ◽  
Raw Material ◽  
Firm Characteristics ◽  
Manufacturing Companies ◽  
Factor Productivity ◽  
Size Of Firms ◽  
Specific Factors

Measurement of the productivity of firms is an important research issue in productivity literature. Over the years, various methods have been developed to measure firm productivity across the globe. But there is no unanimity on the use of methods, and research on the identification of factors which determine productivity has been neglected. In view of these gaps, this study aims to measure total factor productivity (TFP) and tries to identify firm-specific factors which determine productivity of Indian manufacturing companies. The study is based on data of 616 firms from 1998–99 to 2012–13. To measure TFP, the Levinsohn–Petrin (L-P) method has been employed, and the fully modified ordinary least squares (FMOLS) method has been used to identify factors that affect TFP. The results reveal that embodied and disembodied technology plays a crucial role in the determination of productivity overall in manufacturing and other sub-industries. Similarly, the size of firms and intensity of raw material imports are also important for the determination of productivity across the sub-industries. JEL Classification: C14, C33, D24, L60

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 Evaluation of Vegetable Circulation Efficiency and Analysis of Influencing Factors in Henan Province

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueqiang Guo ◽  
Bingjun Li
Keyword(s):  
Panel Data ◽  
Dynamic Analysis ◽  
Static Analysis ◽  
Total Factor Productivity ◽  
Evaluation Model ◽  
Henan Province ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Specific Factors ◽  
Dea Method

In order to explore the deep-seated reasons affecting the development of vegetable circulation in Henan Province, combined with the panel data of Henan Province from 2014 to 2019, this paper first makes a static analysis on the vegetable circulation efficiency in Henan Province by using DEA method. Second, the Malmquist method is used to establish the total factor productivity evaluation model of vegetable circulation in Henan Province, and the dynamic analysis is carried out. The analysis results show that the main problem in the development of vegetable circulation in Henan Province is the low level of management and technology. Then, GM(1, N) model is established to further analyze the specific factors affecting the vegetable circulation efficiency in Henan Province. Finally, some reasonable suggestions are put forward for the development of vegetable circulation in Henan Province.

Total Factor Productivity and Most Favored Nations Tariffs: Evidence from Croatia

2017 ◽  
Vol 7 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Kladiola Gjini
Keyword(s):  
European Union ◽  
Trade Liberalization ◽  
Total Factor Productivity ◽  
Negative Relationship ◽  
Firm Characteristics ◽  
Factor Productivity ◽  
Present Evidence ◽  
Exporting Firms ◽  
Empirical Trade ◽  
Negative Link

Abstract One of the most important topics in empirical trade research is the link between productivity and trade liberalization. In this paper we will focus on the effect of MFN tariffs in the total factor productivity of Croatian firms over the period 2003-2012. This period is characterized by an increased openness toward European Union for Croatian firms. The aim of this paper is to present evidence on the negative link between productivity and tariffs by using the Levinsohn and Petrin (2003) method to estimate productivity of firms. Then we will use TFP as a dependent variable for firm characteristics and trade policy indicator (MFN tariffs). The results are in line with most other studies, confirming the negative relationship between TFP and tariffs. The results show that exporting firms have a higher productivity than non-exporting. We also conclude that up to a certain age productivity increases and then decreases.

scholarly journals How Does Industrial Digitalization Affect Enterprise Environmental Performance?

2021 ◽  
Author(s):  
Huwei Wen ◽  
Chien-Chiang Lee ◽  
Ziyu Song
Keyword(s):  
Total Factor Productivity ◽  
Environmental Performance ◽  
Jel Classification ◽  
Factor Productivity ◽  
Production Scale ◽  
Manufacturing Enterprises ◽  
Treatment Facilities ◽  
Production Technologies ◽  
The Relationship ◽  
Technology Effects

Abstract Despite the increasing use of digital technology in industrial production, how industrial digitalization affects the environmental performance of production activities remains unclear. This research contributes to the literature on the relationship between industrial digitalization and enterprise environmental performance by employing a large sample of Chinese manufacturing enterprises. Results indicate that the environmental performance of manufacturing enterprises has been significantly improved in the process of industrial digital transformation. Structural and technology effects are the influencing mechanisms. Industrial digitalization reduces the production scale of heavy polluting enterprises and improves product innovation and green total factor productivity, but it has an insignificant effect on total factor productivity. Moreover, industrial digitalization improves enterprise environmental performance by introducing front-end cleaner production technologies, rather than by increasing pipe-end pollutant treatment facilities.JEL Classification: Q56, O13, L86

Foreign Direct Investment and Total Factor Productivity: Any Nexus?

2020 ◽  
Vol 14 (2) ◽  
pp. 164-190
Author(s):  
Mohammed Abdullah ◽  
Murshed Chowdhury
Keyword(s):  
Foreign Direct Investment ◽  
Total Factor Productivity ◽  
Direct Investment ◽  
Growth Models ◽  
Generalized Method Of Moments ◽  
Spillover Effects ◽  
Jel Classification ◽  
Estimation Methods ◽  
Factor Productivity ◽  
The Impact

This study examines the impact of foreign direct investment (FDI) on the total factor productivity (TFP) of host countries. Extensions of the new growth theory provide a framework in which FDI increases the growth rate of a host country through technology transfer, diffusion and spillover effects. We construct four new series of TFP using the framework of neoclassical growth models. We also address the issue of endogeneity using the generalized method of moments. Our estimations using a balanced panel of 77 low- and middle-income countries suggest that FDI could not promote TFP in the countries studied. Our sensitivity analysis, in terms of alternative estimation methods, data, models and time period, reinforces the findings. We observe that the lack of absorptive capacity is likely to be an important reason for not having a direct relationship between FDI and TFP. JEL Classification: F21, F23, O33, F43, C33

scholarly journals How Heterogeneous Are the Determinants of Total Factor Productivity in Manufacturing Sectors? Panel-Data Evidence from Vietnam

Economies ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 57
Author(s):  
Quang-Thanh Ngo ◽  
Quang-Van Tran ◽  
Tien-Dung Nguyen ◽  
Trung-Thanh Nguyen
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Method Of Moments ◽  
Small Firms ◽  
Generalized Method Of Moments ◽  
Firm Characteristics ◽  
Factor Productivity ◽  
Total Capital ◽  
Manufacturing Sectors ◽  
The Government

One of the remaining challenges in explaining differences in total factor productivity is heterogeneity between sectors and within a specific sector in terms of labor and capital. This paper employs the generalized method of moments (GMM) to identify factors that affect total factor productivity across 21 manufacturing sectors and to clarify the heterogeneous determinants of total factor productivity within manufacturing sectors for the period 2010–2015. Our estimations show that large firms have significantly greater total factor productivity levels than small firms in some fragmentations of firms in terms of both labor and total capital and in some manufacturing sectors. It is suggested that firm characteristics should be considered by the government in establishing relevant policies for enhancing firm productivity.

scholarly journals Human Capital Spillovers, Productivity and Growth in the Manufacturing Sector of Pakistan

2009 ◽  
Vol 48 (2) ◽  
pp. 125-140
Author(s):  
Abdul Hamid ◽  
J. Hanns Pichler
Keyword(s):  
Developing Countries ◽  
Human Capital ◽  
Total Factor Productivity ◽  
Production Technology ◽  
Manufacturing Sector ◽  
Value Added ◽  
Jel Classification ◽  
Factor Productivity ◽  
Major Factors

Manufacturing is an important sector of Pakistan’s economy. The main focus of this paper is to analyse the major factors of value-added growth and productivity in the manufacturing sector by using Translog Production Technology over the period 1971-72 to 2004-05. The empirical findings show that the contribution of productivity and human capital is around one- third of the total value-added growth in manufacturing sector which is less than the contribution attributed to these factors in developed and many other developing countries. Conventional factors like capital and labour are still the mainstay in the value-added growth of Pakistan’s manufacturing sector. JEL classification: O1, O3, O4, O14, O15, O31 Keywords: Human Capital Spillovers, Total Factor Productivity, Absolute and Relative Shares

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