The Impact of Policy Reforms on Labour Productivity, Price Cost Margin and Total Factor Productivity: The Nepalese Experience

2004 ◽  
Vol 5 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Kishor Sharma
Keyword(s):  
Total Factor Productivity ◽  
Labour Productivity ◽  
Policy Reforms ◽  
Factor Productivity ◽  
The Impact

Productivity performance of the Canadian pulp and paper industry

1990 ◽  
Vol 20 (6) ◽  
pp. 825-836 ◽  
Author(s):  
David L. Frank ◽  
Asghedom Ghebremichael ◽  
Tae H. Oum ◽  
Michael W. Tretheway
Keyword(s):  
Total Factor Productivity ◽  
Pollution Control ◽  
Capital Investment ◽  
Paper Industry ◽  
Labour Productivity ◽  
Pulp And Paper Industry ◽  
Pulp And Paper ◽  
Point Estimate ◽  
Factor Productivity ◽  
The Impact

This paper analyzes the productivity performance of the Canadian pulp and paper industry for the 1963–1984 period. The industry's productivity is first measured, then its sources are analyzed. Total factor productivity is used to measure industry productivity, and statistical estimation of neoclassical cost functions are used to determine sources of the productivity changes. In addition to decomposing the productivity changes into technical changes and changing the scale of the industry's output and capacity utilization, an attempt is also made to assess the impact of pollution control expenditures. The paper finds that although labour productivity grew at 2.5% per year (modest in comparison to other industries), the gross total factor productivity grew only by 1.2% per year. This is largely due to the fact that capital investment raises labour productivity but retards overall productivity. Of the 1.2% growth in total factor productivity, 0.88% was due to the increased scale of the industry output and 0.32% to technical change. Although there is no statistically conclusive evidence, the point estimate indicates that pollution control expenditures may have retarded productivity growth. However, this subject requires further investigation.

scholarly journals Empirical Cost of Electricity Outage on Labour and Capital Productivity in Nigeria

2022 ◽  
Vol 3 (1) ◽  
Author(s):  
Osinachi Iroh ◽  
Ijeoma Kalu ◽  
Alwell Nteegah
Keyword(s):  
Total Factor Productivity ◽  
Electricity Generation ◽  
Labour Productivity ◽  
Negative Relationship ◽  
Series Data ◽  
Electricity Pricing ◽  
Factor Productivity ◽  
Power Outages ◽  
Power Outage ◽  
The Impact

This study empirically examined the impact of electricity power outages on Nigeria’s capital and labour productivity.  The emphasis is on how frequent electricity outage reduces labour and capital effectiveness and other factors of production.  To achieve the above objective, annual time series data on Total Factor Productivity - a proxy for Nigeria’s factors productivity, Power Outage (electric power transmission and distribution losses as % of output), and other controlled variables were used to estimate the relationship and all data were from World Bank Development Indicators (WDI). The Fully Modified Ordinary Least Square (FOLS) technique was adopted for analysis.  The empirical results showed a negative relationship between power outages and factor productivity.  The result also reveals that electricity pricing has a significant negative impact on the factor productivity while both electricity generation and population have a significant positive impact on Nigeria’s total factor productivity.  The implication is that the substitution effect between labour and capital is positive, meaning that Nigeria exhibits a labour-intensive production function. In conclusion, the study is of the opinion that power outage and electricity pricing negatively impact factors productivity while electricity generation and population have a positive relationship with factors productivity in Nigeria.

Examining the Impact and Influencing Channels of Emission Trading Pilot Markets in China

2020 ◽  
Vol 11 (1) ◽  
pp. 136-136
Author(s):  
Qiong Wu ◽  
Kanittha Tambunlertchai ◽  
Pongsa Pornchaiwiseskul
Keyword(s):  
Total Factor Productivity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Emission Trading ◽  
Chinese Government ◽  
Synergy Effect ◽  
Factor Productivity ◽  
Plan Period ◽  
Carbon Emission Trading ◽  
The Impact

The global warming has become a serious issue in the world since the 1980s. The targets for the first commitment period of the Kyoto Protocol cover emissions of the six main greenhouse gasses (GHGs). China is the world's largest CO2 emitter and coal consumer and was responsible for 27.3 percent of the global total CO2 emission and 50.6 percent of the global total coal consumption in 2016 (BP, 2017). As China plays an important role in the global climate change, China has set goals to improve its environmental efficiency and performance. In 2011, the Chinese government for the first time announced an intent to establish carbon emission trading market in China. Eight regional emission trading schemes have been operating since 2013 (seven pilot markets during the 12th Five Year Plan period and one pilot market during the 13th Five Year Plan period) including provinces of Guangdong, Hubei, and Fujian, and cities of Beijing, Tianjin, Shanghai, Shenzhen, and Chongqing. The goal of these regional emission trading pilot markets is to help the government establish an efficient carbon emission trading scheme at national level. Some researchers have been focused on examining the impact of emission trading schemes in China using CGE model by constructing different scenarios and ex-ante analysis using data prior to emission trading pilot markets implementation. While this paper tries to conduct an ex-post analysis with data of 2005-2017 to evaluate the impact of emission trading pilot markets in China at provincial level using difference-in-difference (DID) model. By including both CO2 and SO2 as undesirable outputs to calculate Malmquist-Luenberger (ML) Index to measure green total factor productivity, this paper plans to evaluate the impact of carbon emission trading pilot markets in China via emission reduction, regional green development, synergy effect and influencing channels. This paper tries to answer the following research questions: (1) Do emission trading pilot markets reduce CO2 emission and increase regional green total factor productivity? (2) Is there any synergy effect from emission trading pilot markets? (3) What are the influencing channels of emission trading pilot markets? Keywords: Emission trading, CO2 emissions, Different-in-difference

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.

Impact of Nanotechnology Patents on Green Development of China's Building Industry

2020 ◽  
Vol 14 (2) ◽  
pp. 141-152
Author(s):  
Xialing Sun ◽  
Rui Zhang ◽  
Xue Chen ◽  
Pengpeng Li ◽  
Jin Guo
Keyword(s):  
Total Factor Productivity ◽  
Inhibitory Effect ◽  
Building Industry ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Technological Support ◽  
Green Development ◽  
Panel Data Regression ◽  
The Impact ◽  
Building Engineering

Background: The sustainable development of the building industry has drawn increasing attention around the world. Nanomaterials and nanotechnology play an important role in the processes of energy saving and reducing consumption in the building industry. Nanotechnology patents provide key technological support for the green development of the building industry. Based on patent data in China, this paper quantitatively analyzed the application of nanotechnology patents in the building industry and the time trend, regional differences, and evolution of China's nano-patent applications in the building field. Methods: In this study, the environmental total factor productivity of the building industry considering carbon constraints was determined and then used as the dependent variable to measure the green development of the building industry. On this basis, a panel data regression model was constructed to determine the impact of nano-patents on the green development of the building industry. Results: Nanotechnology patents in the building industry can significantly improve total factor productivity. From the perspective of patent composition, technology-based patents that focus on substantial innovation can significantly promote the green development of the building industry, whereas strategic patents show a significant inhibitory effect. Regionally, the western region of China has the advantage of being less developed and thus more efficient than the central and eastern regions in the application of new nano-products. Finally, the research also showed a significant lag in the application of China's nanotechnology patents and low implementation efficiency. Conclusion: Nano patents can promote green development in the building industry, but there is room for improvement in the speed with which laboratory inventions are transformed into building engineering applications.

scholarly journals Impact of Migrant Workers on Total Factor Productivity in Chinese Construction Industry

2019 ◽  
Vol 11 (3) ◽  
pp. 926 ◽  
Author(s):  
Gui Ye ◽  
Yuhe Wang ◽  
Yuxin Zhang ◽  
Liming Wang ◽  
Houli Xie ◽  
...  
Keyword(s):  
Construction Industry ◽  
Total Factor Productivity ◽  
Migrant Workers ◽  
Annual Report ◽  
Learning By Doing ◽  
Construction Sector ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Statistical Yearbook ◽  
The Impact

Total factor productivity (TFP) is of critical importance to the sustainable development of construction industry. This paper presents an analysis on the impact of migrant workers on TFP in Chinese construction sector. Interestingly, Solow Residual Approach is applied to conduct the analysis through comparing two scenarios, namely the scenario without considering migrant workers (Scenario A) and the scenario with including migrant workers (Scenario B). The data are collected from the China Statistical Yearbook on Construction and Chinese Annual Report on Migrant Workers for the period of 2008–2015. The results indicate that migrant workers have a significant impact on TFP, during the surveyed period they improved TFP by 10.42% in total and promoted the annual average TFP growth by 0.96%. Hence, it can be seen that the impact of migrant workers on TFP is very significant, whilst the main reason for such impact is believed to be the improvement of migrant workers’ quality obtained mainly throughout learning by doing.

scholarly journals The Impact of Participation in PPP Projects on Total Factor Productivity of Listed Companies in China

2021 ◽  
Vol 13 (14) ◽  
pp. 7603
Author(s):  
Xiangdong Liu ◽  
Guangxi Cao
Keyword(s):  
Total Factor Productivity ◽  
High Speed ◽  
Operation Mode ◽  
Listed Companies ◽  
Financing Constraints ◽  
Eastern Region ◽  
Time Varying ◽  
Factor Productivity ◽  
The Impact ◽  
China’S Economy

The key to transforming China’s economy from high-speed growth to high-quality development is to improve total factor productivity (TFP). Based on the panel data of China’s listed companies participating in PPP (Public–Private Partnerships) projects from 2010 to 2019, this paper constructs the time-varying DID method to test the impact of participation in PPP projects on the company’s TFP empirically, explore the mechanism of the effect of participation in PPP projects on the company’s TFP, and then conduct heterogeneous analysis from four perspectives: region, industry, ownership form, and operation mode. The empirical results show that participation in PPP projects can significantly promote the growth of the company’s TFP, which mainly comes from the promotion of the innovation level of listed companies and the alleviation of financing constraints by participating in PPP projects. In addition, participation in PPP projects has a significant impact on TFP of listed companies in the eastern region, listed companies in the secondary and tertiary industries, state-owned listed companies, and listed companies participating in PPP projects under the BOT mode.

Two sides, but not of the same coin: digitalization, productivity and unemployment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oxana Krutova ◽  
Pertti Koistinen ◽  
Tuuli Turja ◽  
Harri Melin ◽  
Tuomo Särkikoski
Keyword(s):  
Total Factor Productivity ◽  
Job Loss ◽  
Labour Productivity ◽  
Research Problem ◽  
Quality Of Work Life ◽  
Macro Level ◽  
Factor Productivity ◽  
Content Type ◽  
Technological Unemployment ◽  
Factors Of Production

PurposeThis paper aims to examine how input from the digital restructuring of the workplace and productivity affects the risk of job loss and unemployment.Design/methodology/approachRelying on the concepts of technological unemployment and the productivity paradox as well as the theory of skills-biased technological change, the analysis incorporated micro-level individual determinants of job loss, macro-level economic determinants of input and the contribution from traditional (machinery and equipment) vs innovative (ICT) factors of production. The model has been also controlled for “traditional” indicators of “outsiderness” in the labour market. The Quality of Work Life Survey, which is a broad-based national interview survey produced by Statistics Finland, for 2018, the latest year available (N = 4,110) has been used in the analysis. Binomial logistic regression has been applied in order to estimate the effects of individual- and macro-level factors on the risk of job loss.FindingsThe results support arguments for the divergence between effects from labour- vs total-factor productivity on the risks of job loss, as well as the divergence between effects for temporary (layoff) vs permanent job loss (dismissal or unemployment). While the contribution from “traditional” factors of production to labour productivity potentially decreases the risk of permanent job loss, input from “innovative” factors of production on total-factor productivity potentially causes adverse effects (e.g. growing risks of permanent job loss).Originality/valueThe paper contributes to the theoretical discussion about technological unemployment and productivity by means of including two different concepts into a single econometric model, thus enabling examination of the research problem in an innovative way.

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 Construction of Spatiotemporal Difference Image of the Impact of Financial Structure on China’s Total Factor Productivity

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Yu
Keyword(s):  
Total Factor Productivity ◽  
Evaluation Method ◽  
Financial Structure ◽  
Model Verification ◽  
Factor Productivity ◽  
Analysis Model ◽  
Dea Model ◽  
Dea Analysis ◽  
Low Efficiency ◽  
The Impact

My country’s current research on the influencing factors of total factor productivity has problems such as single evaluation method, low efficiency, and poor overall level in terms of evaluation methods and evaluation efficiency. Based on this, this study divides the financial structure into three traditional sections, banking, securities, and insurance, and uses the DEA model to study the temporal and spatial differences of the financial structure’s influence on the total factor productivity of the four major political and economic regions of China’s eastern, western, central, and northeastern China. First, establish a DEA model based on data mining algorithms, combine financial data comparisons over the years, to achieve a quantitative analysis of the financial structure’s impact on China’s total factor productivity, calculate financial efficiency, and then combine the DEA analysis data model with the grey correlation method. Analyze its internal influence rules, and design experiments for model verification analysis. The results show that the DEA analysis model can realize 8 iterations of data on the impact of financial structure on China’s total factor productivity, and its evaluation accuracy can reach more than 96.2%.

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