Contribution Of Total Factor Productivity Research Articles

The real side of stock market exuberance: bubbles, output and productivity at the industry level

AbstractThere has been a growing interest in the theory of rational bubbles. Recent theories predict that bubbles are expansionary, but differ in the underlying mechanisms. This paper provides empirical evidence that helps us to assess different theories, and documents four main findings: stock market overvaluation is associated with (i) faster output and input growth, (ii) declining total factor productivity (TFP) growth, (iii) a greater contribution of labour and a declining contribution of TFP for output growth, (iv) an increase in the number of firms. Overall, these findings suggest that bubbly expansions are driven by increased factor accumulation (in particular labour), and not from higher productivity growth.

THE EDUCATIONAS A DETERMINANT FACTOR IN ECONOMIC GROWTH. THE ECUADORIAN CASE (1960-2019)

This paper studies the factors driving the Ecuadorian economic growth through the analysis of the education relevance measured as human capital for the period 1960-2019. By providing a characterization of the business cycle in Ecuador we were able to describe the economic growth trend, cyclical component and also the expansions and recessions periods. To this end, we also analyzed the labor share in the economy to observe the evolution of human capital importance in the income distribution.
 
 Secondly, a multiple regression model was designed to quantify the correlation between the economic growth rate and the evolution of human capital. The main findingsare that human capital and commodity prices have been relevant in explaining the economic growth, i.e., are the main source of increase in the GDP. To complement the analysis ant to stablish the importance of public policies for the betterment of education, a theoretical framework was designed to identify how the government spending and investment in education contribute for the human capital to increase over time.The results show that both government spending and investment are strongly correlated to increases in human capital index.
 
 Finally, we performed a statistical growth accounting exercise to quantify the relative importance of the different factors explaining output growth: inputs accumulation (extensive growth) and technological progress (intensive growth). By decomposing the labor between increase in workers and improving of human capital human capital we can isolate the effects of education in economic growth.
 
 Thus, for the period 1960-2019, for the Cobb-Douglas aggregate production function the average contribution of capital and labor were1.66%and 1.53% respectively points to the average annual output growth of 4.04%, whilehuman capital is accountable for 0.43%.It is also noticeable that TFP has been responsible for 0.40%, almost the same amount of the human capital. On the other hand, the augmented production function indicates that the isolated contribution of human capital was 0.25, while capital and labor were 1.14% and 1.26% respectively.The results obtained confirm the importance that the accumulation in physical capital and employment had played in explaining per capita output growth during the last decades, whereas the contribution of total factor productivity has been more modest. Human capital has also been a relevant factor in explaining the evolution of production, precisely because it is a developing economy that started from lower levels of human capital.

ECONOMIC GROWTH AND TOTAL FACTOR PRODUCTIVITY IN CENTRAL AND EASTERN EUROPEAN COUNTRIES BETWEEN TWO GLOBAL CRISES AND BEYOND

The aim of the article is to assess the factors of economic growth of the CEE countries over the 30-year history, the productivity of capital and human resources, the resilience of these countries to the negative impact of the global financial crisis. Methodology. The Solow growth model was used to estimate the growth rates of capital, labor and total factor productivity (TFP). The impact of macroeconomic indicators on GDP and TFP growth is assessed. The group of Central and Eastern European countries that joined the European Union was chosen for the analysis: Bulgaria, Romania, Poland, Hungary, Czech Republic, Slovakia, Slovenia, Estonia, Lithuania, Latvia, as well as post-Soviet European countries: Ukraine, Belarus, Russia and Moldova and Albania in the period from 1991 to 2019. Results. TFP makes a significant contribution to the economic growth of CEE countries. During the period of market reforms, TFP significantly decreased, and during the boom of 2000-2008 it fully ensured the growth of the CEE economies, after the crisis of 2008, the contribution of TFP decreased by 2 times. In the conditions of recovery, TFP growth is positively influenced by inflation, negative CA balance, and unemployment reduction. In the post-crisis period, a decrease in inflation, a positive CA balance, and an increase in unemployment had a positive impact on TFP growth. During a depression, the influence of capital becomes dominant. Restrictive monetary policy contributes to the efficiency of CEE economies. In the short run, unemployment increases, but in the long run it decreases significantly due to the growth of investment and exports. Practical implications. The analysis makes it possible to identify effective macroeconomic policies to stimulate the productivity of the economies of Central and Eastern Europe during the period of economic recovery and depression. Value/originality. A long-term study of the economic performance of CEE countries using the Solow methodology has revealed the behavior of total factor productivity in different periods of modern economic history and its contribution to economic growth.

Assessment of Green Total Factor Productivity on the Environmental Sustainability of Pakistan: Insights through Growth Accounting Framework

The research investigates the concept of green total factor productivity (TFP), with economic growth leadings towards the sustainable production. The paper investigates the CO2 interventions for total factor productivity for government, and policy makers to reduce carbon dioxide emissions and enhance productivity. The paper analysis is preceded using annual year’s data for gross domestic product (GDP), capital growth, labor growth, and carbon dioxide emanations. The data is grabbed from World Bank development Indicators (WDI) online database for year 1975 to 2019. The analysis is done by Growth Accounting Framework and MALMQUIST Luneberger Index. Total Factor Productivity is analyzed in two perspectives i.e. with carbon dioxide and without carbon dioxide emissions. The findings indicated that there is little contribution of TFP without carbon dioxide emissions which is -0.23. The value of TFP with carbon dioxide emissions gives an average per year value of -20.41 which is -0.41. The average growth in carbon dioxide is 2.94 averages per year. The highest carbon dioxide growth is 0.9. The highest value for growth development is -26.86. MALMQUIST Index concludes that Pakistan needs to work for the technological developments. The paper examined that green total factor productivity improves economic growth and reduces the carbon emission in the country. The policymakers and top management must employ the sustainable activities to retain the better growth with economic development in the near future.
 

THE CONTRIBUTION OF LABOR FORCE, MATERIAL CAPITAL, HUMAN CAPITAL AND TOTAL FACTOR PRODUCTIVITY TO ECONOMIC GROWTH BASED ON THE CHANGE OF EMOTIONAL BEHAVIOR

BackgroundPromoting the sustained and stable growth of the national economy is the eternal goal of economic theory researchers and policy makers. So, what is the fundamental source of promoting a country's sustainable economic growth?Research Objects and MethodsFrom the perspective of labor, physical capital, human capital and total factor productivity, this paper makes an empirical study on the contribution of various input factors to economic growth in 36 OECD countries from 1990 to 2017. In order to analyze the impact of this strategy on the change of livelihood capital on emotion, and provide effective measures and basis for alleviating psychological pressure. In this study, 500 subjects in Southwest China were investigated with Depression Anxiety Stress Scale (DASS-21).ResultsThe results show that: (1) the output elasticity of labor, physical capital and human capital input factors is statistically significant, indicating that input has an important impact on economic growth. (2) The total contribution share of investment accounts for about 1 / 3, of which labor force accounts for about 24%, and the proportion of physical capital and human capital accounts for 5% and 6% respectively is similar and relatively small. (3) Total factor productivity (TFP) is the most important driving force of economic growth, accounting for 2 / 3 of economic growth. (4) The contribution of labor and material capital showed a slow upward trend, the contribution of human capital was basically stable, and the contribution of total factor productivity showed a slow downward trend. (5) Exploring the sources of economic growth has important policy implications not only for OECD countries but also for developing countries. Taking the emotional grouping as the independent variable, the group conducted a one-way multivariate analysis of variance on the fluency and novelty of aut, box's M = 24.05, P < 0.001. The result shows that the data is not suitable for multivariate analysis of variance, so the emotional grouping is used as the independent variable to analyze the fluency and novelty of aut by one-way ANOVA respectively. Specifically, the main effect of emotion on aut fluency is significant, f (2, 99) = 6.43, P = 0.002, η 2 p= 0.12. Post hoc comparison showed that the anger group (M = 11.26, SD = 6.20) was higher than the neutral emotion group (M = 7.26, SD = 3.44, P = 0.002, Cohen's d = 0.80), with no other significant difference (see Figure 2D). The main effect of emotion on the novelty of aut was significant, f (2, 99) = 7.84, P < 0.001, η 2 p = 0.14. Post hoc comparison showed that the anger group (M = 15.29, SD = 8.85) was higher than the sadness group (M = 11.21, SD = 5.50, P = 0.042, Cohen's d = 0.55) and the neutral emotion group (M = 8.91, SD = 5.23, P = 0.001, Cohen's d = 0.88).ConclusionAll sectors of society should fully understand the importance of emotional factors of relevant groups in rehabilitation, actively improve the surrounding environment and the respective roles of both sides, pay special attention to their own virtual technology language and behavior, and shift from single display to multi-dimensional management, coordinator and collaborator. In the process of output, actively present personalized psychology to patients, flexibly organize activities, strive to create a relaxed and harmonious rehabilitation atmosphere, create a friendly and mutual aid business environment, encourage and guide patients to adjust their psychology to the best state.AcknowledgementsThe work is supported by Open Fund Project of Research Center for Economy of Upper Reaches of the Yangtse River of Chongqing Technology and Business University (KFJJ2018002). The author is highly grateful to the editor and anonymous referees for their valuable comments and constructive suggestions which help to improve the present form of this paper.

Contribution of TFP to economic growth of Dong Thap province: Current status and scenarios for the period 2021 - 2025

The study aims to analyze the contribution of Total Factor Productivity (TFP) in Dong Thap ’province’s economic growth using the Cobb-Douglas production function method, based on the data from 2000 - 2020. The study results show that the contribution of capital (ε_K) is 0.3524, the contribution of labor (ε_L) is 0.6476. Dong Thap province’s average TFP growth rate in each period of 05 years 2011 - 2015 and 2016 - 2020 is 2.31% per year and 1.93% per year, respectively, and ’TFP’s contribution to economic growth is 34.51% and 30.01%, respectively. Based on the status analysis of the period 2011 - 2015 and 2016 - 2020, the study proposes four growth scenarios associated with four productivity orientations for Dong Thap province in 2021 - 2025. This ’study’s results are the basis for organizational policies and orientations for socio-economic development, proposing provincial development guidelines and plans to 2025, oriented to 2030.

EVIDÊNCIAS SOBRE A EFICIÊNCIA PRODUTIVA BRASILEIRA: UMA ANÁLISE DA PRODUTIVIDADE TOTAL DOS FATORES (1950-2014)

This article had as its objective to study the behavior of Brazilian productivity between 1950-2014 and the impact on growth, also verifying the impact of labor and capital factors. This is important because productivity is considered a crucial determinant of economic growth in economic literature. In the country, the behavior of productivity has been declining since 1980, with some recovery periods, however short ones. For this, the study makes use of an econometric estimation based on the Vector Error Correction (VEC) model, with the intention of verifying the contribution of total factor productivity, as well as labor and capital inputs, to Brazilian production. The objective is to verify the contribution of productivity in growth, through the results of the model and the variance decomposition. Among the results, it was verified that the variables ​​contribute positively to growth and that productivity had greater elasticity among the inputs related to production, being 0.99, considering the long term. In addition, it was also found that productivity increased its contribution to variations in economic growth in the long term.

Understanding the mystery of continued rapid economic growth

This study examines the fixed output-elasticity assumption of the neoclassical economic growth model and introduces the time-varying elasticity production function model to measure China's time-varying factor income share. Combined with the dual economic theory, the phenomenon of China's “high investment rate-high savings rate-high growth rate” is explained. Meanwhile, the driving forces of economic growth are decomposed, and a total factor productivity (TFP) decomposition formula is given, which includes China’s unique factor structure change. We found that sufficient labor hinders the law of diminishing marginal factors, and the demographic dividend is the main reason for the mystery of China’s continued rapid economic growth; the “Krugman-Questioning” underestimated the contribution of TFP to its economic growth. The Cobb-Douglas production function cannot reflect structural changes in the process of China's economic growth but, the time-varying elasticity production function can reasonably describe it, which provides a new perspective for understanding developing countries' economic growth.

Analysis of Growth Accounting and Convergence in MENA Countries: Panel Cointegration Approach

The objective of this study is to investigate the sources of output growth and their convergence in the Middle East and North African countries over the period 1970–2017. Towards this end, the study employs Levin et al. (2002, Journal of Econometrics, vol. 108, pp. 1–24), Fisher-type (Choi, 2001, Journal of International Money and Finance, vol. 20, pp. 249–272) and Im et al. (2003, Journal of Econometrics, vol. 115, pp. 53–74) panel unit root tests and Pedroni (2004, Econometric Theory, vol. 20, pp. 597–625), Kao (1999, Journal of Econometrics, vol. 90, pp. 1–44) and Johansen–Fisher cointegration tests. After estimating the production function using random effects estimator to obtain the share of physical capital in output, we employed standard growth accounting approach to measure and decompose growth of total output into contributions from growth in physical capital, labour, human capital and total factor productivity (TFP). Further, the study discusses the existence of stochastic and deterministic convergence of real output per worker and its sources (physical capital per worker, human capital and TFP). The statistical results of the article can be summarized as follows: The contribution of physical capital to output growth is found to be positive and higher than the contribution of labour, whereas the contribution of TFP was negative across the region with the exception of Egypt, Morocco, Tunisia and Turkey. However, when the contribution of human capital is netted out, the contribution of TFP becomes negative in all the countries except for Tunisia. In addition, the study found no clear evidence of deterministic convergence in output per worker (but stochastic convergence), human capital and factor productivity. However, the statistical results provide overwhelming evidence for stochastic and deterministic convergence in physical capital per worker. JEL Classification: O4, O40, O47

Factor Accumulation and Total Factor Productivity Growth: ASEAN Economies

This study examines the contribution of factor accumulation on Total Factor Productivity (TFP) growth in the eight ASEAN countries with specific reference to short-term and long-term estimation effect. Using Mean Group (MG) estimator on data between 1990 and 2018, this study found a significant relationship between capital accumulation and TFP growth. Our analysis suggests that the positive growth on TFP in most ASEAN countries was due to the short-run effect of capital accumulation. This study also found that the ASEAN economies have a relatively lower rate of capital utilization that adversely affects TFP growth in the long run. The contribution of TFP to output growth is paramount for the sustainability of long-term economic growth. Thus, ASEAN countries need to accelerate capital accumulation and technology adoption in the short-term and utilize idle capital in the long-term.

Inclusive wealth, total factor productivity, and sustainability: an empirical analysis

Sustainability can be assessed by non-declining inclusive wealth, which refers to man-made capital, human capital, natural capital, and all other types of capital that are sources of human well-being. As the previous studies—including Arrow et al. (J Econ Perspect 18(3):147–172, 2004) and the Inclusive Wealth Report (2012 and 2014)—suggest, total factor productivity (TFP) is one determinant of inclusive wealth, because it is related to the resource allocation mechanism. TFP is one important component of sustainability. When considering the contribution of TFP toward inclusive wealth, attention needs to be paid to the improvement in the usage of human and natural capital as well as the traditional man-made capital. However, in the previous studies, only man-made capital and labor force have been considered. This study extends current measures of sustainability by capturing the efficient utilization of natural resources, giving us inclusive wealth-based TFP. Therefore, in contrast to conventional TFP measures, we consider both human and natural capital in addition to man-made capital. We examine 43 countries and find that a new indicator which asserts countries previously considered sustainable by earlier studies such as Arrow et al. (J Econ Perspect 18(3):147–172, 2004) as no longer sustainable.

EVALUATING THE CONTRIBUTION OF TOTAL FACTOR PRODUCTIVITY ON AGRICULTURAL GROWTH IN AN GIANG PROVINCE

By the growth regression approach, the research has identified that the investment capital contributed 1,939 and agricultural labor contributed 1,291 to the agricultural growth of An Giang province. More specifically, the contribution of TFP (Total Factor Productivity) to the agricultural growth in the period 2000 - 2004 was averagely 0,11%, in 2005 - 2010 was -5,03%, and in period 2011 - 2016 was 0,81%. The total factor productivity contributed to the agricultural growth slowly. In order to raise the contribution of TFP, the research represents 05 solutions including the increase of the effectiveness of using the investment capital, the increase of the quality of labor, the application of the science and technology into agricultural production, agriculturalrestructuring, and the increase of agricultural demand.

Three levels of education and the economic growth

ABSTRACTThe aim of this article is to solve the question how the three main stages of education contribute to the labour productivity growth in selected 125 countries in the period 1999–2014. The model is based on the neoclassical production function enhanced with human capital. The authors draw on the Penn World Tables 9.0 and UNESCO databases. The key benefit of this article is that human capital is characterized according to the returns to education from average number of years of formal schooling at the primary, secondary and tertiary level. Based on the panel data analysis, the contributions of capital and of the three levels of education to the growth of labour productivity are estimated. At the same time, the model allows to estimate the contribution of total factor productivity. The results of the analysis show that tertiary education has the strongest impact on labour productivity across the considered economies. At the same time, the breakdown of aggregate human capital by level of education leads to better clarification of the effects of human capital and physical capital on labour productivity. The conclusions also indicate a tendency towards rising returns to scale induced by the secondary and tertiary education.

Intangible capital in Chinese regional economies: Measurement and analysis

Intangible capital is treated as an important determinant of economic growth in the age of knowledge economy. It has however attracted less attention in China largely due to measurement impediment. This study aims to measure intangible investment and examine its contribution to regional economic growth in China. The results show that both the coastal and interior regions in China enjoyed high growth in intangible investment during 2003–2014, especially after the year 2008. Moreover, it is found that regional disparity in intangible capital is widening and that this trend is mainly driven by the enlarging gap in investment in computer software and architecture designs. In addition, it is observed that economic competency capital is neglected in both regions, implying generally poor enterprise management in China. When intangible capital is considered, growth accounting exercises show higher labor productivity growth together with a larger effect of capital deepening in both regions. Coastal regions tend to benefit more from intangible capital due to the advancement of computerization. If the contribution of computer software in the interior region was as large as that in the coast region, labor productivity growth of the interior would be 0.5 percentage points higher than its current level. Finally, the estimates of the contribution of total factor productivity to economic growth would be biased if intangible capital is not considered.

The Contribution of Total Factor Productivity in the Air Transport of Vietnam: The Case of Vietnam Airlines

The aim of this study is to determine the contribution of Total Factor Productivity (TFP) in the air transport of Vietnam over the case of Vietnam Airlines. Based on analyzing data of Vietnam Airlines from 2005 to 2015, this study showed that fixed assets, labor and TFP are factors that affecting the output elements of Vietnam Airlines as Available of Ton- Kilometers Conversion (ATKC), Performance of Ton-Kilometers Conversion (PTKC), revenue and value added. Among these factors, the contribution of fixed assets is higher than labor, especially in generating revenue and value added. In the period from 2006 to 2010, the growth of Vietnam Airlines was due to the increase in fixed assets, labor and TFP, but in the period from 2011 to 2015, this growth was mainly due to the increase of fixed assets. The results of this study suggest Vietnam Airlines and others airline in Vietnam can increase efficiency growth by investing capital in order to increase fixed assets, increase labor, innovate technology, improve management and improve labor skills.

Pakistan’s Productivity Performance and TFP Trends, 1980–2015: Cause for Real Concern

This paper reviews Pakistan’s productivity performance over the last 35 years (1980–2015) and identifies factors that help explain the declining trend in labor productivity and total factor productivity (TFP), both of which could have served as major drivers of productivity growth – as happened in East Asia and more recently in India. A key finding is that the maximum TFP gains and their contribution to economic growth are realized during periods of high-output growth. The lack of sustained growth and low and declining levels of investment appear to be the most important causes of the low contribution of TFP to productivity growth, which has now reached levels that should be of major concern to policymakers vis-à-vis Pakistan’s growth prospects.

Real GDI, Productivity, and the Terms of Trade in Canada

In this paper, a quarterly dataset of productivity is built for the Canadian business sector, and the Diewert and Yu () estimates of annual productivity growth are revised and updated to reflect changes in the new Canadian system of national economic accounts. The quarterly data are then used to study the contribution of total factor productivity and the terms of trade to growth of real gross domestic income. In most years of the 2000s, the contribution of the terms of trade became significant in real income growth, whereas that of total factor productivity growth was stagnant. Improvement in the terms of trade arises froma decline in the import price index and an increase in the export price index.

Drivers of tourism growth

The article has constructed the nonparametric decomposition framework to measure the growth drivers of tourism economy, and analyzed the drivers with the input and output data of the tourism industry in 30 provinces of China from 2005 to 2012. The results show that the contribution of total factor productivity (TFP) was gradually overtaking that of the factor inputs in this period and became the main driver of Chinese tourism output growth, with its corresponding contribution rate jumping from 48.56% in 2006 to 76.55% in 2012. Technological progress was the key to the escalation of such contribution, while technical efficiency, scale efficiency especially, had a relatively smaller, slipping contribution to the growth and the contribution of pure technical efficiency was negligible, sliding from 51.44% in 2006 to 23.45% in 2012. The drivers for tourism economy growth differed from region to region, with the middle and western regions growing faster than the eastern region. The rise in Central China was mainly driven by scale efficiency, while that in the western region was mostly fueled by pure technical efficiency that appears to manifest in two distinct phases: drive of factor input in 2005–2008 and TFP drive in 2008–2012. The tourism economy of China became increasingly intensive from 2006 to 2012, where the contribution of TFP surpassed that of factor inputs and the growth was ever more driven by TFP.

India’s Potential Output Revisited

Estimates of potential output have been revised downward across countries in the post-crisis period. In India, the debate on potential GDP and output gap has been intensified in the wake of revision in the GDP estimates with change in base year as well as the underlying methodology consistent with international best practices. In light of these, an attempt has been made for the first time in India to estimate potential GDP and output gap on a quarterly basis by using production function approach in addition to revisiting the estimates of potential output by conventional statistical methods for the period 1980Q2–2015Q4. The findings suggest that India’s potential growth, which had accelerated to around 8 % during 2003–2008, decelerated considerably in the aftermath of the global financial crisis to about 7 % during 2009–2015, mainly due to decline in contribution of total factor productivity and deceleration in the growth of capital stocks. The estimates further suggest that output gap, i.e. the percentage deviation of actual output from its potential level, has been negative since Q3 of 2012, though the gap is closing slowly. Key to accelerate growth as well as potential output in India lies with higher level of capital formation as its contribution dominates vis-a-vis the contribution of labour and total factor productivity.

Agricultural productivity growth in India: An analysis accounting for different land types

Agricultural endeavour in India has moved from being a traditional farming practice to a more modern and mechanized enterprise. One of the important prerequisite for raising agricultural production and mitigating supply constraints is enhancing agricultural productivity. Since traditional input resources are limited, an increase in agricultural production has to come from enhancing agricultural productivity. With the onset of Green Revolution in India, the contribution of modern technology towards raising agricultural production has been immense. Total Factor Productivity (TFP) growth in agriculture assesses the contribution of knowledge and efficiency towards raising agricultural production. For a comprehensive study of growth of the agriculture sector, TFP analyses should include the allied sectors like livestock, forestry and fishing as well. However, in estimating agricultural TFP growth, it is essential to account for the type of land being used for crop production. An addition of an acre of irrigated land will be much more productive than an addition of rainfed land. Similarly, an acre of rainfed land will yield more crop than an acre of pastureland, which is usually used for grazing purpose for livestock. Access to irrigated land protects farmers from the risks and uncertainties of vagaries of weather. In this study we derive weights for rainfed cropland, irrigated cropland and pastureland based on their relative productivity. Using the traditional Solow index method and accounting for different land type viz. rainfed land, irrigated land and pasture land, we measure TFP growth of the agriculture and allied sector in India from 1981 to 2008. Our results show that agricultural TFP growth in India for this period ranged between 1% during 1981-1990 to about 1.7% during 2000-2008. Growth in TFP was higher during the post reform period than during the pre reform period. We also find that usage in agricultural inputs has declined during the post reform period compared to the pre reform period. Our findings also show that, though contribution of TFP towards output growth was declining during the initial years, it however shows a rising trend towards explaining output growth during the last decade.