Total Factor Productivity Growth Rate Research Articles

Market Regulation, Cycles, and Growth Dynamics in a Monetary Union

AbstractWe build a two‐country currency union dynamic stochastic general equilibrium (DSGE) model with endogenous growth to assess the role of product market regulation (PMR) and labor market regulation (LMR) for growth and the adjustment to shocks. We show that with endogenous growth, there is no reason to expect real income convergence. Large shocks can lead to permanent changes of output and real exchange rates. Differences are exacerbated by different PMR and LMR. Less regulated economies have higher trend growth and recover faster from negative shocks. Results are consistent with higher inflation, lower employment, and disappointing total factor productivity (TFP) growth rates experienced in more regulated euro area members.

Decomposition and Driving Factors of Total Factor Productivity of Food Crops in the Yellow River Basin, China

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

New evidence of the driving forces behind Brazil's agricultural TFP growth—A stochastic frontier analysis with climatic variables and land suitability index

AbstractUsing panel data from 510 Brazilian micro‐regions in three census years (1995, 2006 and 2017), this study presents a productivity decomposition for the Brazilian agricultural sector using stochastic production frontier methods that account for the effects of rainfall, temperature and the land suitability index. We also calculated the total factor productivity (TFP) index and decomposed it into technical efficiency, technological change, scale efficiency and environmental efficiency. This article thus provides a new and more realistic assessment of recent Brazilian agricultural productivity growth. In recent decades, Brazilian agriculture has become widely known for presenting fast productivity growth; however, our results suggest that a lower TFP growth rate than previous estimates (1.96% per annum) and the overall effects of climate change could potentially compromise Brazilian agricultural TFP growth in the long run. Our findings might thus generate insights for agricultural and regional policies to increase efficiency in the sector and promote sustainable agricultural development in Brazil, which will contribute to the sector's competitiveness in international markets, the country's social and economic welfare, and environmental conservancy.

Can digital economy compensate the effect of aging on total factor productivity?

In China, the number of senior citizens has grown, along with the burden of old age, and aging has hampered economic growth. The advent of the digital age has led to the emergence of the digital economy as a new engine for economic growth. This paper uses DEA-Malmquist index model to measure the total factor productivity growth rate of 31 provinces in China from 2011 to 2021, and uses the moderating effects model to empirically investigate the relationship between the digital economy, aging and total factor productivity, and to verify whether the development of the digital economy can mitigate the negative impact of aging on total factor productivity. The results show that aging inhibits total factor productivity growth, and the digital economy can promote total factor productivity growth. Digital economy can alleviate the negative impact of aging on total factor productivity growth, and has a moderating effect. Digital economy plays a moderating role by improving the level of human capital and facilitating technological progress. The regional heterogeneity analysis shows that the moderating effect of the digital economy exists in the eastern and western regions and the southern region, but not in the central region and the northern region. Furthermore, the digital economy has a moderating effect on both the high and low aging groups. The research in this paper not only helps to evaluate the productivity effects of the digital economy, but also has important implications for finding ways to mitigate the negative effects of aging.

Total factor productivity gap between the “New” and “Old” Europe: an industry-level perspective

ABSTRACT The main goal of this article is to investigate the source of productivity growth for the Central and Eastern Europe (CEE) countries, at the industry level, based on the Schumpeterian creative destruction mechanisms. Our study provides valuable insights into productivity drivers for the CEE countries, which are vital for the policy makers. To measure productivity, we calculate the Total factor Productivity (TFP) measure based on the raw labour, at the industry level. There is a substantial productivity gap at the industry level between the CEE and core European countries (EU14). The result of econometric analysis suggests that the further a country lies behind the frontier, the higher is the rate of TFP growth, i.e. the convergence process.

Human capital and economic growth in Central Asia

ABSTRACT This study explores the long-term economic performance in former Soviet republics of Central Asia for the last three decades. We look at sources of economic growth based on the extended version of the neoclassical growth model. Our key focus is to see whether human capital in the form of education and health inputs has an impact on growth rates in the region. Results show that over the entire period under study, on average, the growth rates of total factor productivity (TFP) were not remarkable, ranging from 1.4% for Kazakhstan and 0.51% for Tajikistan to −1.13% for the Kyrgyz Republic. Kazakhstan as a frontier economy of the region maintained better economic results in almost all indicators. Since 2000, TFP growth rates were notably stronger in Tajikistan compared to Kazakhstan and the Kyrgyz Republic. However, a reduction in TFP in Tajikistan in the 1990s is attributed to the country’s dire civil war.

Growth in Agricultural Productivity

This study examines agricultural total factor productivity (TFP) from theoretical and empirical perspectives. Specifically, the measures, relevant data, and major sources of the TFP growth are discussed. Using the sector-level growth and productivity data from the EU KLEMS, EUROSTAT, FAOSTAT, and USDA databases, the TFP growth in the EU countries over 1996–2019 is considered. The sources of the TFP growth are analyzed. The results suggest that agricultural TFP increased in almost all EU countries over the period covered. TFP growth appears as an important component of labour productivity and value-added growth in the EU agriculture. The differences among the databases considered are noted in the sense of input and output levels and TFP growth rates.

Trade development and agricultural productivity change: Evidence from China

AbstractSince China joined the World Trade Organisation (WTO) in 2001, more Chinese agricultural products have been exported to various countries at low tariff rates. However, the impacts of WTO accession on China's agricultural productivity and technical efficiency are ambiguous. The aim of this study is to investigate the impacts of trade on China's agricultural production by analysing productivity changes before and after 2001. To understand the role trade plays in productivity and technical efficiency change, we introduce two trade indices to the multi‐input multi‐output distance function and technical inefficiency model to examine the export and import effects on technical efficiency and total factor productivity (TFP) change. Our findings indicate that the average growth rate of TFP is 5.8% per annum before joining the WTO, and it increases slightly with an annual rate of 8.5% after joining the WTO. Additionally, with the development of trade liberalisation, the traditional advantages (e.g., technology innovation, spillovers, FDI) of exports on agricultural productivity are being replaced by the competitive effect of imports.

Are State-Owned Enterprises Really Ineffective? An Empirical Study Based on Stochastic Frontier Analysis

Technical efficiency (TE) and total factor productivity (TFP) are important criteria to ensure the enhancement of the quality and efficiency of state-owned enterprises (SOEs) and function as important indicators to assess the quality of their accomplishments. The purpose of this study is to explore whether the efficiency of SOEs is higher or lower than that of private enterprises. Transcendental logarithmic production function and stochastic frontier analysis (SFA) are used to assess the TE and TFP of listed central SOEs, local SOEs, and private enterprises, the data of which were taken from 2006–2020. The results show that the sampled private enterprises had the highest average TE during the study period, followed by the central and local SOEs. The private enterprises also had the highest average TFP growth rate, followed by the local and central SOEs. The TFP decompositions show that the TE change (TEC) and technical change (TC) indices of the SOEs were lower than those of the private enterprises. The TC, TEC, and scale change (SC) are limiting the TFP growth rates of the SOEs in labor-intensive industries. The SC of the SOEs has changed less than that of private enterprises in the sampled capital-intensive industries. Northern and southern China had the highest rates of TE and TFP growth. Indeed, this paper measures and decomposes TFP, and analyzes the efficiency of SOEs and private enterprises in different industries and regions in an international context.

Productivity drivers of infrastructure companies: Network industries utilizing economies of scale in the digital era

AbstractWhat drives the productivity dynamics of infrastructure companies? Using a panel of firms in 14 countries, we study total factor productivity (TFP) enhancers of utility and network services companies. We find that moving TFP closer to the technological frontier drives productivity growth at higher speeds in Asian countries than in European countries. We also find that financial leverage exerts a positive effect on TFP growth for larger infrastructure firms and that more financially developed countries utilize economies of scale through better use of financial resources. Large utility and transportation companies display a higher rate of TFP growth, indicating that a competition policy to encourage M&As would be prudent for the utility/transportation sectors to maximize economies of scale. In contrast, we find diseconomies of scale for energy companies in some countries. Moreover, young network firms improve TFP growth faster than their peers in countries with fewer product market regulations. Therefore, policies should remove entry barriers while facilitating the departure of old and low‐productivity firms from network markets. Finally, policymakers should offer well‐targeted fiscal incentives for intangible investments to boost TFP because the accumulation of intangible assets such as digital technology promotes more scale economies through network effects.

A nonparametric analysis of climate change nexus on agricultural productivity in Africa: implications on food security

Abstract Earlier research largely ignored the effects of climate change on the growth of agricultural total factor productivity (TFP) in Africa. This study shows how climate inputs impact TFP growth in addition to other productivity growth indicators and metrics, as well as how they can impact overall input efficiency as productivity drivers. We use a panel of 42 African nations from 1999 to 2019 and a nonparametric data envelopment analysis-Malmquist technique. The non-parametric analysis revealed that the average growth rate of the non-climate-induced TFP estimates was 1.9%, while the average growth rate of the climate-induced TFP estimates was 2.4%. Accounting for temperature and precipitation separately, TFP grew by 2.3% on average. This growth rate (2.3%) is slightly less than the combined effect of temperature and precipitation (2.4%) but higher than the typical TFP growth rate (1.9%) that ignores climate variables, indicating that TFP growth in African agriculture risks being underestimated when climate inputs are ignored. We also find the distribution of the climate effects to vary across regions. In northern Africa, for example, the temperature-induced TFP growth rates were negative due to rising temperature in the region. Evidence from the decomposed TFP estimates indicates that climate variables also influence productivity determinants. However, technology improvement is fundamental to mitigating the effects of extreme weather inputs on TFP growth in Africa's agriculture. As a result, a few policy suggestions are provided to help policymakers deal with the effects of climate change on TFP growth in Africa's agriculture and ensure food security. The study advocated for a reevaluation of the climate–agriculture effect in order to fully comprehend the role of climate factors and their contributions to agricultural TFP growth in Africa.

China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model

The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.

How Powerfully and Rapidly Are Developing Countries Catching Up with the Advanced Economies?

The article, based on a number of author’s calculations and models, shows that if in the advanced economies (AEs), despite their preservation of leading positions in modern technologies, due to the loss of their demographic dividend, deindustrialization and contraction in the rate of capital formation, there is a cascade slowdown in the growth rate of per capita GDP and total factor productivity (TFP), the situation in developing countries (DCs) is on the whole more optimistic. Although (a) many DCS are experiencing acute social problems, (b) AEs are pursuing a policy of detainment of the growth of a number of DCs, they on the whole, thanks to the success of China, India, other, predominantly Asian DCs that have implemented pragmatic reforms and export-oriented industrialization, have made significant progress. In the last two decades DCs on the whole outperformed the AEs in terms of capital investment efficiency by 2.5 times, in average growth rates of industrial production and TFP - by five and two times, respectively. Their share in world GDP, which has risen by a factor of 1.5 to 3/5 over the past forty years, may, according to available forecasts, increase to ¾ by the middle of the century. In about three decades, the ratio of economic power of the countries of the East and the South and the countries of the West (in favor of the former) will quite possibly be approximately the same as it was in the planetary economy before the industrial revolution and colonial enslavement of the former by the latter.

Konvergencija i rast produktivnosti - dokazi iz Republike Srpske

The paper is mainly aimed at identifying the sources of total factor productivity (TFP) growth within the framework of convergence for the Republic of Srpska (RSRP). The main research question is what it is that drives technological progress for a small transition country. The current study focuses on the RSRP, as the follower, and the Republic of Serbia (RSRB), as the technological leader. The analysis carried out in this research study confirms the presence of convergence at the industry level, which means that the farther away from the technological frontier a country is, the higher the TFP growth rate. The research results enable policymakers to design and implement policies capable of enhancing domestic development and increase productivity growth.

The Role of Endogenous Technology in Making Sustained Economic Growth in Sub‒Saharan Africa: Panel GMM and VECM Approaches

This study examines the role of endogenous technology in making sustained economic growth in a panel of 36 Sub-Saharan Africa (SSA) countries over the period 1990-2020. We derive total physical capital, total employment productivity (TEP), total factor productivity (TFP) and terms of trade (TOT) growth rates using various concepts. In this sense, the panel of short-run vector error correction model (VECM) and panel of long–run generalized method of moments (GMM) are applied and the estimation results are obtained. The empirical results reveal that there are significantly positive impacts of the growth rates of TFP, TEP and terms of trade (TOT) on real GDP growth rate in the long -run. These are profoundly important indicators for SSA today that timely using endogenous technology can play the central role in succeeding economic growth. And hence, without a greater supply of home–grown talent in the areas of the economic sector, it may be hard to break down the vicious circle of poverty, build prosperous, inclusive and resilient economies that can be feeding the people and enabling the countries to compete with trade globally.

Alternative capital asset depreciation rates for U.S. capital and total factor productivity measures

The U.S. Bureau of Economic Analysis (BEA) and the U.S. Bureau of Labor Statistics (BLS) use estimates of depreciation rates for structures and equipment to construct estimates of capital stock from data on capital investments. The depreciation rates are based on research by Frank C. Wykoff and Charles R. Hulten from the 1980s. More recent studies by Statistics Canada, from 2007 and 2015, use Canadian data on used asset transactions from Canada’s Annual Capital and Repair Expenditures Survey of establishments. They found faster depreciation rates, especially for structures. Sheharyar Bokhari and David Geltner’s 2019 study of U.S. used asset prices also found faster depreciation rates for structures. To illustrate the potential effects of implementing these estimates from newer studies, we created a concordance to match Canadian to U.S. asset categories. We reestimated BEA capital stock measures and the BLS capital and total factor productivity (TFP) measures using depreciation rates based on the Canadian Annual Capital and Repair Expenditures Survey. Using these faster depreciation rates results in substantially lower estimates of net capital stocks and higher estimates of depreciation in BEA accounts but has minimal effects on growth rates of TFP in the BLS accounts.

Relative deprivation, time preference, and economic growth

AbstractWe study the growth process among a large group of economies where consumption relative to a reference group determines the discount factor of the household agents. We characterize all possible balanced growth paths and their stability properties. The model can explain why two economies having similar production technologies, preferences, and total factor productivity growth rates can differ in labor supply behavior and have diverging growth paths depending on their initial conditions. Numerical analysis of the model suggests that growth path divergence is plausible based on cross‐country differences in savings rates. History dependence on time preference also generates realistic transitional dynamics.

Agricultural Trade Liberalisation and Agricultural Total Factor Productivity Growth Convergence in Africa

Reducing income inequality in Africa rests on agricultural total factor productivity (TFP) growth and convergence. Liberalizing agricultural trade has emerged as a force of agricultural TFP growth convergence. Notwithstanding increasing agricultural trade, TFP in Africa is falling while the differences in TFP growth rates remain wide. We provide evidence on the impact of agricultural trade liberalization on agricultural TFP growth convergence. We examine trade by origin, disaggregated into intra-Africa, and rest-of-the-world trade. Also, we recognize the uniqueness of agricultural trade liberalization and analyze the effect of the removal of trade-distorting agriculture support. Using maize and rice data for the period 2005-2016, we apply a Feasible-Generalized- Least-Squares estimation of panel data models derived from Barro and Sala-i-Martin (1990). We find evidence for both absolute and conditional convergence, which is stronger for maize. Moreover, agricultural trade openness speeds up TFP growth convergence for both crops. Convergence speed is higher for intra-Africa trade. Estimations on domestic agriculture support suggest that reduction of support beyond distortion-free levels enhances TFP growth convergence. Our findings call for more agricultural trade liberalization. We appeal that the recently launched Africa Continental Free Trade Area prioritizes intra-Africa agricultural trade liberalization and further elimination of trade-distorting domestic agriculture support.
 Vol. 9, No. 2, August 2022: 71-88

Total factor productivity growth at the firm-level: The effects of capital account liberalization

This study provides firm-level evidence on the effect of capital account liberalization on total factor productivity (TFP) growth. We find that a one standard deviation increase in the capital account openness indicator constructed by Fernández et al. (2016) is significantly associated with a 0.18 standard deviation increase in firms’ TFP growth rates. The productivity-enhancing effects are stronger for sectors with higher external finance dependence and capital-skill complementarity, and are persistent five years after liberalization. Moreover, we show that potential transmission mechanisms include improved financing conditions, greater skilled labor utilization, and technology upgrades. Finally, we document heterogeneous effects across firm size and tradability, and threshold effects with respect to the country's institutional quality.

Projecting the Impact of Rising Temperatures: The Role of Macroeconomic Dynamics

We use theory and empirics to distinguish between the impact of temperature on transition (temporary) and steady state (permanent) growth in output per capita. Standard economic theory suggests that the long-run growth rate of output per capita is determined entirely by the growth rate of total factor productivity (TFP). We find evidence suggesting that the level of temperature affects the level of TFP, but not the growth rate of TFP. This implies that a change in temperature will have a temporary, but not a permanent, impact on growth in output per capita. To highlight the quantitative importance of distinguishing between permanent and temporary changes in economic growth, we use our empirical estimates and theoretical framework to project the impacts of future increases in temperature from climate change. We find losses that are substantial, but smaller than those in the existing empirical literature that assumes a change in temperature permanently affects economic growth.