Total Factor Productivity: European and US Perspectives

Several global indices have been used to classify and to analyze the states of countries. Comparison can be performed not only based on country but also annually for each country. In this study, three prominent indices, the Global Competitiveness Index (GCI), the Global Innovation Index (GII) and the Human Development Index (HDI) were investigated to examine the relationships between them by employing the PLS-SEM method. According to the results, HDI has an influence on GII while GCI is affected by GII. The results also demonstrated that GII has a full mediating effect on the relationship between HDI and GCI. Moreover, findings indicated that countries should improve their innovativeness by taking human capital into consideration to gain competitive advantages.

This study assesses the simultaneous openness hypothesis that trade modulates foreign direct investment (FDI) to induce positive net effects on total factor productivity (TFP) dynamics. Twenty-five countries in Sub-Saharan Africa and data for the period 1980 to 2014 are used. The empirical evidence is based on the Generalized Method of Moments. First, trade imports modulate FDI to overwhelmingly induce positive net effects on TFP, real TFP growth, welfare TFP and real welfare TFP. Second, with exceptions on TFP and welfare TFP where net effects are both positive and negative, trade exports modulate FDI to overwhelmingly induce positive net effects on real TFP growth and welfare real TFP. In summary, the tested hypothesis is valid for the most part. Policy implications are discussed.

Economic Growth and Total Factor Productivity in the Czech Republic from 1992 to 2004

PurposeThis study investigates (1) the effect of foreign direct investment (FDI) on total factor productivity (TFP) and economic growth dynamics and (2) the relevance of value added from three economic sectors in modulating the established effect of FDI on TFP and economic growth dynamics.Design/methodology/approachThe geographical and temporal scopes are respectively 25 Sub-Saharan African countries and the period 1980–2014. The empirical evidence is based on non-interactive and interactive generalised method of moments.FindingsThe following main findings are established. First, FDI has a positive effect on gross domestic product (GDP) growth, GDP per capita and welfare real TFP. Second, the effect of FDI is negative on real GDP and TFP while the impact is insignificant on real TFP growth and welfare TFP. Third, values added to the three economic sectors largely modulate FDI to produce negative net effects on TFP and growth dynamics.Practical implicationsPolicy implications are discussed with particular emphasis on the need to complement added value across various economic sectors in order to leverage on the benefits of FDI in TFP and economic growth.Originality/valueTo the best of the authors’ knowledge, this is the first study to assess how value added from various economic sectors affect the relevance of FDI on macroeconomic outcomes.

The study examines the resources of economic growth in the Czech Republic in the course of years from 1992 until 2004. Using the growth accounting method, it analyses the contribution of individual factors to economic growth. Special attention is given to total factor productivity, which, apart from labour, also includes a fixed capital stock at constant prices. Compared to the previous period, the acceleration of the growth of total factor productivity decisively contributed to the speeding up of economic growth in the years 1999-2004. Furthermore, the study examines growth resources in six national economy sectors and analyses the contribution of individual sectors to the growth of macroeconomic total factor productivity. The analysis has shown that namely industry, transport, communications, and other services were involved in the speeding up of the growth of macroeconomic total factor productivity. A comparison of the dynamics of total factor productivity of the CR and EU-15 at the macroeconomic level has shown that while in 1992-1998, the growth of total factor productivity was slower in the CR, after 1998, it was faster (in 1999-2004, the average annual growth rate in the CR was 2.2% and 0.6% in EU-15). In the years 1996-2004, for which revised data are available for the CR, the average annual growth rate of total factor productivity in the CR was 1.5%, compared to 0.7% in EU-15. The analysis indicated that since 1999, total factor productivity in the CR has been converging to the EU-15 level, accelerating in 2003 and 2004, thereby achieving 63% of the EU-15 level in 2004.

This paper analyses the relationship between total factor productivity (TFP) and innovation-related variables during the second half of the twentieth century. We perform this analysis for several European countries (France, Germany, UK, and Spain) and the USA, extending Coe and Helpman’s (Eur Econ Rev 39:859–887, 1995) empirical specification to include human capital. We use a new dataset of patents data for the past 150 years to calculate the stock of knowledge using the perpetual inventory method. Our time series empirical analysis confirms the heterogeneous relationship between innovation variables (domestic stock of knowledge, imports of knowledge, and human capital) and productivity. Our results reveal the extent to which observed differences in technology adoption patterns and the levels of endowment of such resources can explain differences in TFP dynamics across countries. The estimated coefficients confirm the considerable gap that still exists between the European countries and the USA in innovation-related variables. Furthermore, we obtain a finding that may have important implications for innovation policies: the higher the levels of human capital and domestic knowledge stocks, the higher will be the response of TFP to a 1 % increase in any of the aforementioned variables.

The Total Factor Productivity (TFP) is now widely recognized as an important factor in both long-term economic growth and short-term growth fluctuations. Researchers of the International Monetary Fund came to the conclusion that the growth of the TFP was the most important long-term factor in raising the living standards. Therefore, the IMF and academics from different countries has been scrutinizing the reasons for the slowdown in TFP and investigating the underlying factors. The low rates of GDP grow in Ukraine call for finding the drivers, one of which is TFP growth. It raises the importance of analysis of the factors promoting this growth in Ukraine. The purpose of this work is to define TFP drivers, which would be most effective for Ukraine. TFP drivers in foreign countries are analyzed, TFP dynamics for Ukraine is calculated by use of Solow model, and TFP drivers over 2000–2017 are determined. The analysis of publications about TFP drivers at global level shows that they include: international transfer of knowledge and technologies, activities of small innovative fast-growing firms, the enhanced quality of quality of education, the increased expenditures on R&D and innovations, especially by business sector, the increased investments in intangible assets, the intensified patent activity, access of enterprises to lending. The TFP dynamics in Ukraine, calculated by the Solow model, is characterized by high growth rates by 2012, a sharp fall in 2013-2015, and a return to the growth path in 2016-2017, but, as in the whole world, by very moderate pace. The factors contributing to this return are capital investment in intangible assets, the increasing patent activity of Ukrainian researchers, the intensified innovation in the high-tech sector. Factors constraining the TFP and the contribution of innovation to economic growth are a significant proportion of technology transfer in the form of “know-how, agreements for the acquisition (transfer) of technologies”, which holds back the widespread introduction of cutting-edge technologies, and the reduction of funding for R&D and innovation. Further studies should be focused on searching for political decisions promoting implementation of structural reforms aimed to solve the existing problems and eliminate their consequences, especially in of the innovation and education field.

PurposeIn this paper, the authors study the long-run determinants of total factor productivity (TFP) in three major European economies over the period 1983–2017, namely Germany, France and Italy.Design/methodology/approachThe authors focus on the capital misallocation effects, scale effects and labor misallocation effects. To this end, the authors study how real interest rate shocks, real exchange rate shocks, real wage shocks and changes in labor regulation affected TFP in major European countries over the last decades. The authors employ a theoretical and an empirical model to investigate the issue. The empirical results are obtained using a VAR model for estimation.FindingsA stripped-down model of labor market in open economy with technology progress allows to identify the relevant variables affecting TFP. On the empirical ground, the authors find a positive relationship between TFP and real interest rate in the long run. Importantly, the authors detect a positive relationship between TFP and real exchange rate. Further, the authors show that the TFP can respond positively to a stricter labor market regulation and to a higher real compensation per employee. The results provide support to the idea that TFP has a positive relation with prices in the long run, while it may be biased along the cycle because of price rigidity.Research limitations/implicationsThe present model is stylized and may not capture all of the details of reality. The analysis should be extended to a larger number of countries. Technology progress could be proxied using different variables, as the R&D expenditure or the number of patents. Micro data, for specific sectors and industries, can improve the quality of the empirical investigation.Practical implicationsMainly the authors find that TFP has a positive relationship with price changes in the long run, while it may be biased along the cycle because of price stickiness. Capital misallocation and labor misallocation can negatively affect TFP. Thus, the observed divergences in European TFP can be traced back to the misallocation effects attributable to the decrease of real interest rate and real wages, together with the raising labor flexibility. Mainly, the authors detect a positive long-run relationship between TFP and real exchange rate. This outcome strengthens the supply-side view of the relationship between productivity and real exchange rate.Social implicationsThe authors believe that the present setup can be helpful to reflect critically on the nodes at the core of the productivity slowdown and asymmetries in the eurozone. The aim is to implement renewed policies in order to favor economic growth, convergence and stability in the euro area.Originality/valueThis research addresses the issue of asymmetries among European economies by focusing on the role played by real prices in the long run. Traditionally, the dynamics of TFP have been attributed only to technological components, human capital and knowledge. This work shows that the dynamics of prices such as the real interest rate, the real exchange rate and the real wage can also influence the technological process by pushing the production system toward choices that are not always optimal for economic growth. An interesting result of this research concerns the positive relationship between real exchange rates and TFP in the long term, evidence of an important supply-side effect on the technological process.

This paper draws on the 2019 to 2022 Annual Survey of Philippine Business and Industry to document new stylized facts on the post-pandemic dynamics of total factor productivity (TFP) in Philippine manufacturing. The estimates confirm the severe but heterogeneous productivity impact of Coronavirus disease 2019 (COVID-19) across sectors and regions, with low-tech industries suffering steep TFP declines. Recovery patterns were uneven: large manufacturers rebounded quickly after significant 2020 losses, medium-sized firms showed surprising resilience, while small firms struggled to regain their pre-pandemic productivity. Fixed-effects regressions show the significant and positive relationship of total hours worked, human capital, and tangible investment with TFP. In contrast, the productivity premia from research and development spending, financial access, and intangible investment are not robust after controlling for selection bias. This suggests that highly productive manufacturers compensated their reduced production capacity primarily through efficient labor utilization, skilled manpower, and capital deepening, which enabled agile business adjustments amidst pandemic shocks. Decomposition analysis also reveals the widening TFP gap between small and mediumsized firms, which accelerated between 2020 and 2022 due to increasing differences in endowment and persistent underlying traits. These findings underscore the constraints facing small manufacturers and the growing marginalization of their contribution to post-pandemic productivity growth.

Purpose. The purpose of this study is to model the dynamics of total factor productivity in the agricultural sectors of European Union countries and Ukraine using the DEA-based Malmquist productivity index (MPI), with its decomposition into technical efficiency change (EFFCH), technological change (TECHCH), and scale efficiency change (SECH), to identify the key drivers of productivity growth and assess cross-country differences in efficiency dynamics. Methodology. The study employs a non-parametric Data Envelopment Analysis (DEA) combined with the Malmquist productivity index to assess the dynamics of total factor productivity in the agricultural sectors of EU countries and Ukraine over 2015–2024. The index is decomposed into technical efficiency change, technological change, and, under variable returns to scale, pure (PECH) and scale efficiency change, enabling identification of key productivity drivers. The analysis is based on panel data for 28 decision-making units with multiple inputs and outputs. Estimation is conducted in R using the Benchmarking package, with average changes calculated via the geometric mean. Results. The results indicate a stable increase in total factor productivity in the agricultural sector, with the average Malmquist index exceeding unity for both EU countries (MPI = 1.025) and Ukraine (MPI = 1.021). Productivity growth is primarily driven by technological change (TECHCH > 1), while technical efficiency exhibits more moderate dynamics and declines during 2019–2021. Ukraine demonstrates a pattern where productivity gains are mainly driven by technological progress, accompanied by lower technical and scale efficiency, indicating the presence of structural inefficiencies. Originality. This study represents one of the first attempts to apply an integrated DEA-Malmquist framework to a unified panel of EU countries and Ukraine, enabling a comprehensive decomposition of productivity dynamics and identification of key drivers of total factor productivity growth. It identifies the dominant role of technological progress and reveals structural inefficiencies, particularly in Ukraine. Practical implications. The results provide a quantitative basis for improving agricultural policy and management decisions in EU countries and Ukraine. The findings highlight the need to complement technological modernisation with measures aimed at enhancing technical and scale efficiency, particularly in Ukraine. The study can be used by policymakers and stakeholders to design strategies for increasing productivity, optimising resource use, and strengthening competitiveness in the agricultural sector.

Technological progress and economic dynamics: Unveiling the long memory of total factor productivity

Introduction. The relationship between the Human Development Index, life expectancy and the level of innovative development of the economy as a whole for the countries of the world and separately for 85 subjects of the Russian Federation is considered. Materials and methods. The source of information for assessing the level of development of the innovative economy in the countries of the world was the Global Innovation Index for 2019, and in the regions of Russia – the data of the Association of Innovative Regions of Russia. The Human Development Report 2020 published by the United Nations Development Program was used to obtain information on Human Development Indices in the countries of the world. The source of information on Human Development Indices in the regions of Russia was the Analytical Note “Human Development Index in Russia: Regional Differences”, published by the Analytical Center under the Government of the Russian Federation in 2021. The source of information on the life expectancy of the population of 85 regions of Russia is the collections of Rosstat. Correlation and non-parametric analysis of variance was used to assess the relationship between the studied indicators. Results. It is shown that the Spearman correlation coefficient between the Global Innovation Index (GII) and the Human Development Index (HDI) according to the data for the countries of the world is 0.905 (significance level – <0.0001), the correlation coefficients between GII and life expectancy, both average and separately for men and women, are quite high: 0.834; 0.794 and 0.852 respectively. The correlation coefficient between the Innovative Economy Development Index (IEDI) and the HDI according to data for 85 regions of the Russian Federation is 0.578 (significance level is < 0.0001), the correlation coefficients between the IEDI and average life expectancy and separately for men are not statistically significant, but for the female population – 0.233 (significance level – 0.033). Conclusion. In the world, with the development of an innovative economy, the Human Development Index is growing, which, in turn, accelerates the development of the economy. In the world, with the growth of the level of development of the innovative economy, the average life expectancy is growing. In Russia, the relationship between the Human Development Index and the level of development of the innovative economy is rather linear, and the correlation coefficient between these indicators is much lower compared to the correlation coefficient for the countries of the world. In Russia, there is a significant positive correlation between the Innovative Economy Development Index and life expectancy only for the female population.

The potential for information technology penetration in sub-Saharan Africa (SSA) is very high compared to other regions. Unfortunately, productivity levels in the area are also deficient. This study investigates the importance of information technology in influencing the effect of foreign direct investment (FDI) on total factor productivity (TFP) dynamics. The focus is on 25 countries in SSA. Information technology is measured with mobile phone and internet penetration, while the engaged TFP productivity dynamics are TFP, real TFP, welfare TFP, and real welfare TFP. The empirical evidence is based on the Generalized Method of Moments. The findings show that except regressions about real TFP growth for which the estimations do not pass post-estimation diagnostic tests, it is apparent that information technology modulates FDI to positively influence TFP dynamics (i.e., TFP, welfare TFP, and welfare real TFP). Policy and theoretical implications are discussed.

We study the connection of innovation in 126 countries by different well-being indicators and whether there are differences among geographical regions with respect to innovation index score. We approach and define innovation based on Global Innovation Index (GII). The following well-being indicators were emphasized in the research: GDP per capita measured at purchasing power parity, unemployment rate, life expectancy, crude mortality rate, human development index (HDI). Innovation index score was downloaded from the joint publication of 2018 of Cornell University, INSEAD and WIPO, HDI from the website of the UN while we obtained other well-being indicators from the database of the World Bank. Non-parametric hypothesis testing, post-hoc tests and linear regression were used in the study.We concluded that there are differences among regions/continents based on GII. It is scarcely surprising that North America is the best performer followed by Europe (with significant differences among countries). Central and South Asia scored the next places with high standard deviation. The following regions with significant backwardness include North Africa, West Asia, Latin America, the Caribbean Area, Central and South Asia, and sub-Saharan Africa. Regions lagging behind have lower standard deviation, that is, they are more homogeneous therefore there are no significant differences among countries in the particular region.In the regression modelling of the Global Innovation Index, it was concluded that GDP per capita, life expectancy and human development index are significant explanatory indicators. In the multivariable regression analysis, HDI remained the only explanatory variable in the final model. It is due to the fact that there was significant multicollinearity among the explanatory variables and the HDI aggregates several non-economic indicators like GII. JEL Classification: B41, I31, O31, Q55

Continuous cotton ( Gossypium hirsutum L.) production was examined using data from Alabama's long‐term Old Rotation experiment (c. 1896). Index values were used to examine trends in productivity and sustainability for 95 yr. Treatments studied were those receiving (i) no N fertilizers and no winter legumes for 95 yr, (ii) only winter legumes as a source of N, and (iii) chemical fertilizer N. Three sets of index numbers were calculated from all inputs and outputs involved in the production systems: (i) total factor productivity (TFP), which accounts for all direct production inputs, but which does not consider production externalities; (ii) productivity relative to a base plot;and (iii) total social factor productivity (TSFP), which accounts for all direct production inputs as well as externalities of soil erosion and pesticide use. Viewed from the 95‐yr perspective of the Old Rotation experiment, all three treatments fulfill at least one criterion required for a system to be considered sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities are valued. None of the systems showed a linear trend in output or TFP over the life of the experiment;productivity cycles are present in all three systems, despite a positive overall trend. An average annual rate of TSFP growth of 1.8%/yr was attained. Accounting for erosion and pesticide externalities reduced the annual productivity growth rate by 0.2%/yr. The system that has neither an organic nor a chemical source of added N was less productive and less sustainable than the two other systems, with a 0.3%/yr TSFP growth rate. The plots using organic and chemical sources of N had similar productivity impacts. Valuing soil erosion and pesticide externalities had only a modest effect on measured productivity. The most dramatic single event to affect the productivity of cotton farming was the introduction of the mechanical cotton picker. The impact of this technology was powerful enough to offset the effect of many other changes in the system. Research Question Is cotton production in the southeastern USA sustainable? How do we measure sustainability of a crop that has been produced for almost 200 yr in the same region but has a reputation for depleting the soil of nutrients, extensive soil erosion, and high pesticide use? The objective of this study was to use input and output indexes and a calculation of total factor productivity (TFP) to determine if cotton production using different management strategies is sustainable over nearly a century of continuous production. Literature Summary Most researchers agree that a sustainable system should maintain or enhance agricultural production, reduce the level of production risk for the farmer, protect natural resources, be economically viable, and be socially acceptable. Measuring all of these attributes of a production system is very difficult. However, using the extensive data available from historical, long‐term experiments should provide insight as to sustainability of certain production systems. Alabama's Old Rotation (c. 1896) is the oldest continuous cotton experiment in the world. Input and output (yield) records and estimates allow calculation of TFP indexes over the 95‐yr history of continuous cotton production. Different cotton production systems can be compared. Study Description Three continuous cotton systems from the Old Rotation were chosen for comparison: (i) No N and no winter legumes since 1896 (No N), (ii) winter legumes (crimson clover and/or vetch) as the only source of N since 1896 (winter legumes), and (iii) no winter cover crop and 120 lb N/acre as ammonium nitrate since 1956 (N fertilizer). Where input records were not recorded (e.g., labor, costs, machinery, etc.), they were estimated from USDA, Alabama Agricultural Experiment Station, and Alabama Cooperative Extension Service publications. Soil erosion estimates for the three cropping systems on a Pacolet fine sandy loam, were made using Erosion Productivity Index Calculator modeling. Input, output, TFP, and total social factor productivity (TSFP) indexes for 95 yr were calculated. Total social factor productivity includes estimated values for the negative offsite effects of soil erosion and pesticide use. Applied Questions Is continuous cotton production sustainable? Viewed from the 95‐yr perspective of the Old Rotation, the no N, winter legume, and N‐fertilized continuous cotton plots all fulfill at least one criterion required for a system to be sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities (erosion and pesticides) are valued. The average growth rates on the No N plot are 0.5%/yr for TFP and 0.3%/yr for TSFP. On the winter legume plot, TFP and TSFP grew at a rate of 2.0%/yr and 1.8%/yr, respectively. The plots using organic and chemical sources of N had similar productivity records. None of the systems shows a linear trend in TFP over the history of the experiment. Productivity cycles are present in all three systems, despite the positive overall trend. An important focus of future research will be to explain whether these cycles are related to weather, technology, or changes in the resource base. As one would expect, the system that has neither an organic or a chemical source of added N is less productive than the two other systems. This system compares even more poorly when externality costs are assigned. Organic and chemical sources of N have similar productivity impacts. How have externalities such as soil erosion and the negative impact of pesticide use on the environment affected TFP? Soil erosion and pesticide externalities have had only a modest effect on measured productivity. The no N plot indexes are not changed at all; TFP on the legume and N‐fertilized plots decreased by 4 and 6%, respectively. The main conclusions of the previous question are therefore unaffected. How have technological advancements affected long‐term productivity/sustainability of continuous cotton production? The most dramatic single event to affect productivity was the introduction of the mechanical cotton picker around 1960. The impact of this technology is powerful enough to offset the effect of many other changes in the system. This advancement allowed cotton production to move from a labor‐intensive environment with increasing labor costs per pound of yield to an environment where harvesting costs were not seriously affected by increasing yields. Because technological advancements cannot be predicted into the future, predicting the long‐term sustainability of a system becomes very difficult.