Can Government Subsidies Promote the TFP of Enterprises? The Mediating Effect of R&D Decisions

2021 ◽  
pp. 097172182110307
Author(s):  
Zhen-Yu Qi ◽  
Si-Ying Yang
Keyword(s):  
Total Factor Productivity ◽  
Mediating Effect ◽  
Manufacturing Companies ◽  
Factor Productivity ◽  
Mediating Effects ◽  
Government Subsidies ◽  
Total Productivity ◽  
The Government ◽  
D Investment ◽  
Positive Effect

This article empirically analyses the effect of government subsidies on total factor productivity (TFP) based on the data of listed manufacturing companies in China. The results indicate that government subsidies increase total productivity directly as well as indirectly by increasing R&D investment. The positive effect of government subsidies on TFP is higher in non-state-owned enterprises (non-SOEs) than in state-owned enterprises (SOEs), higher in central SOEs than local SOEs and higher in enterprises with lower rather than higher TFP. Furthermore, the mediating effects of R&D decisions also differ among different enterprises. Therefore, the government should implement differentiated subsidy policies to promote enterprises’ TFP.

scholarly journals Study on the Effect of Environmental Regulation on the Green Total Factor Productivity of Logistics Industry from the Perspective of Low Carbon

2019 ◽  
Vol 12 (1) ◽  
pp. 175 ◽  
Author(s):  
Zijing Liang ◽  
Yung-ho Chiu ◽  
Xinchun Li ◽  
Quan Guo ◽  
Yue Yun
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Jiangsu Province ◽  
Spatial Effects ◽  
Low Carbon ◽  
Method Of Moment ◽  
Factor Productivity ◽  
Logistics Industry ◽  
Forward Logistics ◽  
The Government

Under the low-carbon background, with the aid of the Malmquist–Luenberger SBM (Slack-based Measure) model of unexpected output, the green total factor productivity (GTFP) of the logistics industry in Jiangsu Province, China, was measured and decomposed in this study based on the reality and experience of logistics industry development in 13 cities in three regions of Jiangsu Province in the years 2006–2018 by taking resource consumption into the input system and discharged pollutants into the output system. It is concluded that the environmental regulation (ER) has a significant positive effect on the growth of the GTFP of the logistics industry, and technological progress has become an important endogenous force that promotes the GTFP of the logistics industry in Jiangsu Province. On this basis, a dynamic GMM (Generalized method of moment) model and a Tobit model were constructed to further study the possible temporal and spatial effects of ER on the GTFP of the logistics industry. The research results reveal that the ER can exert both promoting and inhibitory effects on the GTFP of the logistics industry, and there is a temporal turning point for the effects. Besides, the effects notably differ spatially and temporally. Finally, some policies and advice for the green sustainable development of the logistics industry were proposed. For example, the government and enterprises should pay attention to the green and efficient development of the logistics industry and dynamically adjust the ER methods. They should consider the greening of both forward logistics links and reverse logistics system in the supply chain.

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 Macroeconomic Dynamics of the Total Factor Productivity of the Russian Economy

2021 ◽  
Vol 17 (3) ◽  
pp. 799-813
Author(s):  
Sergey A. Mitsek
Keyword(s):  
Total Factor Productivity ◽  
Public Investment ◽  
Labour Productivity ◽  
Aggregate Demand ◽  
Factor Productivity ◽  
Export Prices ◽  
Russian Economy ◽  
Economic Trends ◽  
Total Productivity ◽  
Economic Growth Rates

The growth rate of Russia’s total productivity has been slowing down significantly since 2008. The majority of relevant publications either describe an economic methodology or specifically focus on labour productivity. However, economic growth rates, as well as community welfare, largely depend on total factor productivity. The paper aims to determine the reasons for the slowdown in the growth of total factor productivity after 2008. This negative dynamics was assessed using a macroeconomic econometric model and estimates for Russian regions and types of economic activity. Elasticity of dependent variables was calculated based on econometric equations as well as multipliers of exogenous variables presented in the model. Ordinary and rank correlations between the variables were also examined. The calculations revealed that the stagnation of total factor productivity was caused by the misallocation of resources across industries and regions, de crease in aggregate demand, increase in capital goods prices (primarily due to rouble devaluation) and a slowdown in digital economy development. In turn, these trends were influenced by a decline in public investment and export prices, as well as a slowdown in population growth and liquidity. Simultaneously, growth of the world economy contributed to the demand for Russian export goods, preventing a decrease in productivity. The findings can be used for forecasting Russian economic trends and developing relevant policy measures. Further research will examine the role of human capital, energy intensity, climate and institutional factors in increasing the total productivity.

scholarly journals TOTAL FACTOR PRODUCTIVITY AND FRONTIER PRODUCTION FUNCTION = PRODUKTIVITAS FAKTOR PRODUKSI TOTAL DAN FUNGSI PERODUKSI FRONTIER

Agro Ekonomi ◽  
2016 ◽  
Vol 24 (2) ◽  
pp. 2
Author(s):  
Sri Widodo
Keyword(s):  
Total Factor Productivity ◽  
Production Function ◽  
Productivity Growth ◽  
Factor Productivity ◽  
Single Factor ◽  
Cross Sectional ◽  
Potential Output ◽  
Total Productivity ◽  
Cross Sectional Studies ◽  
Frontier Production Function

The total factor productivity became an interesting concept in the measurement of productivity growth. Productivity is a ratio of output to input. The most common measurement of productivity is single factor productivity or partial productivity such as of land, labor, or capital.A total (factor) productivity is a productivity of all factors of production where the factors are aggregated. In cross-sectional studies this total productivity is a ratio of actual to potential output where the potential output is estimated from ther frontier production function. One of the methods to estimate this frontier function is by using linear programming technique.The total productivity does not always coincide with a single factor productivity of land (yield), that in the study area the larger farms tend to have higher total productivity than yield

scholarly journals Research on Green Total Factor Productivity of Logistics Industry and its threshold effect of Environmental Regulation

2021 ◽  
Vol 235 ◽  
pp. 02022
Author(s):  
Wanchun Li
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Threshold Model ◽  
Intermediate Stage ◽  
Threshold Effect ◽  
Inhibitory Effect ◽  
Regulation System ◽  
Factor Productivity ◽  
Logistics Industry ◽  
The Government

This paper is based on the input-output panel data of logistics industry in 30 provinces and regions in China from 2005 to 2017, using nonparametric DEA model to evaluate the green total factor productivity of logistics industry, and build a panel threshold model to empirically test the nonlinear impact of environmental egulation. It is found that environmental regulation has a double threshold effect on green total factor productivity of logistics industry, the estimated threshold values are 89.85 and 211.27 respectively; when environmental regulation is at a low level below 89.85, environmental regulation has a positive effect of 2.09% on green total factor productivity of logistics industry, when environmental regulation is in the intermediate stage of 89.85 to 211.27, environmental regulation has a positive improvement effect of 6.41% on green total factor productivity of logistics industry; when environmental regulation is at a higher level than 211.27, environmental regulation has a negative inhibitory effect of 1.57% on green total factor productivity of logistics industry. Based on the empirical conclusion, this paper puts forward: First, using the performance assessment as the baton to urge the local government to establish an effective environmental regulation system; second, the government should plan to guide the green transformation and upgrading of the logistics industry to avoid “one size fits all” environmental regulation.

scholarly journals Analisis Keputusan Transfer Pricing Berdasarkan Pajak, Tunneling dan Exchange Rate

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Hani Sri Mulyani ◽  
Endah Prihartini ◽  
Dadang Sudirno
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Stock Exchange ◽  
Transfer Pricing ◽  
Tax Burden ◽  
Manufacturing Companies ◽  
Pricing Decisions ◽  
Pricing Decision ◽  
The Government ◽  
Positive Effect

Tax has two points of view, for the government tax is a source of state revenue that has the largest contribution, but for tax companies is a burden that must be paid. Often companies do tax planning strategies so that the tax burden that must be borne by the company becomes smaller. Companies usually exploit loopholes from the use of accounting methods allowed by accounting and taxation rules. Transfer Pricing is one of the ways companies take to reduce the tax burden. This study aims to determine and obtain empirical evidence about the effect of tax, tunneling and exchange rates on transfer pricing decisions both partially and simultaneously on manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2013-2017 period. The research method used is descriptive and verification analysis method. The population in this study were 144 manufacturing companies listed on the Indonesia Stock Exchange in the period 2013-2017. Sampling using a purposive sampling method and obtained a sample of 20 companies. The results of this study indicate that partially significant positive effect on transfer pricing decisions, tunneling does not significantly influence the transfer pricing and exchange rate decisions do not significantly influence the transfer pricing decision, but simultaneously the results of this study indicate that taxes, tunneling and exchange rates affect significant to the transfer pricing decision.

scholarly journals Environmental Regulation, Enterprises' Bargaining Power, and Enterprises' Total Factor Productivity: Evidence from China

2021 ◽  
Author(s):  
Shuwang Yang ◽  
Chao Wang ◽  
Hao Zhang ◽  
Tingshuai Lu ◽  
Yang Yi
Keyword(s):  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Environmental Economics ◽  
Bargaining Power ◽  
Factor Productivity ◽  
Firm Level ◽  
Capital Cities ◽  
Provincial Capital ◽  
The Government ◽  
The Relationship

Abstract The relationship between environmental regulation and enterprises' total factor productivity (TFP) has been a hot topic in the field of environmental economics, but the conclusions are still mixed. Employing a sample of 14,110 firm-year observations in China from 2010 to 2018, our research explores whether and when environmental regulation could trigger firms, to enhance TFP. The available evidence leads us to cautiously conclude that: 1) Environmental regulation notably improves enterprises' TFP, the conclusion still holds after a series of robustness tests. 2) Enterprises' bargaining power significantly weakens the influence of environmental regulation on enterprises' TFP. 3) Compared with non-state-owned enterprises and non-heavy-polluting industries, environmental regulation has a greater impact on state-owned enterprises and heavy-polluting industries; higher executive compensation does not motivate firms to improve TFP; compared with enterprises headquartered in non-provincial capital cities, environmental regulation has a greater impact on enterprises' TFP in provincial capital cities. Overall, the findings of our research are extremely relevant for the government, investor, and enterprise's manager, this paper provides micro-firm-level evidence for the Porter hypothesis in practice in China.

scholarly journals Research on the Impact of Green Technology Innovation on Energy Total Factor Productivity, Based on Provincial Data of China

2021 ◽  
Vol 9 ◽  
Author(s):  
Mengxin Wang ◽  
Yanling Li ◽  
Gaoke Liao
Keyword(s):  
Total Factor Productivity ◽  
Industrial Structure ◽  
Technology Innovation ◽  
Threshold Model ◽  
Threshold Effect ◽  
Green Technology ◽  
Mediating Effect ◽  
Factor Productivity ◽  
Influence Mechanism ◽  
The Impact

Against the background of carbon peaking and carbon neutralization, green technology innovation plays an important role in promoting the energy total factor productivity (TFP). This study verifies the impact of green technology innovation on energy TFP in a complete sample and the subsamples by region, by constructing a panel threshold model, and analyzes its influence mechanism on the basis of the mediating effect test based on annual provincial data of mainland China from 2005 to 2018. The empirical results reveal the following: first, with the level of economic development as the threshold variable, there is a threshold effect in the impact of green technology innovation on the energy TFP; second, green technology innovation has an impact on the energy TFP through industrial structure upgrading; that is, industrial structure has a mediating effect in the influence mechanism; and third, there is heterogeneity in the impact of green technology innovation on the energy TFP among different regions in China, and the threshold effect only exists in the western region, since the central and eastern regions have crossed a certain developmental stage.

scholarly journals Energy Efficiency, Policy and GTFP

2018 ◽  
Vol 53 ◽  
pp. 01033
Author(s):  
Fangqing Yi ◽  
Zenglian Zhang
Keyword(s):  
Economic Growth ◽  
Energy Efficiency ◽  
Environmental Regulation ◽  
Total Factor Productivity ◽  
Industrial Structure ◽  
Provincial Government ◽  
Factor Productivity ◽  
Linkage Mechanism ◽  
The Government ◽  
The Impact

The environmental and resource constraints on economic growth are increasingly evident. China urgently needs to reshape its economic growth momentum. The increase in green total factor productivity is particularly necessary for the growth of the quantity and quality of the economy. This paper selects the provincial panel data of 30 provinces in China from 2001 to 2015, and establishes a panel exchangeable errors model to analyze the impact of eight indicators on green total factor productivity (GTFP) and verifies its effectiveness. Empirical analysis shows that inter-provincial government competition, environmental regulation, energy consumption, and capital stock have a significant impact on green total factor productivity. The influence of foreign direct investment, industrial structure, and industrialization level on the total factor productivity of green is not significant. Therefore, the government should adopt suitable, flexible and diverse environmental regulation policies, promote energy-saving emission reduction and technology innovations through policies such as taxes and subsidies, strengthen the linkage mechanism between industrial structure upgrading and energy efficiency, to increase green total factor productivity.

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