Sectoral Total Factor Productivity Research Articles

The gains from changes in internal trade costs: A quantitative analysis of China

AbstractWe study how internal trade costs affect the Chinese economy. Combining regional and industry data with a multiregional multisector general equilibrium model, we quantify the magnitude of total trade costs and institutional trade costs, and the impact of their changes on aggregate, regional, and sectoral total factor productivity, gross domestic product, and welfare. Using unique data, we estimate the sectoral trade elasticity of internal trade in 16 tradable sectors. We use our calibrated model to perform a variety of counterfactual exercises. We find that the welfare gains are negative for a 5% reduction in total trade costs and for the elimination of institutional trade costs, which can be attributed to inherent inefficiencies in the economy. However, as total trade costs decrease further, the positive welfare effect significantly exceeds the impact of inefficiency. The regional and sectoral effects of changes in trade costs show that the spatial structure of the economy, input‐output linkages, and local factors together determine the heterogeneity of sectoral and regional consequences. Finally, we infer the relative changes in China's internal trade costs from 2012 to 2017, and calculate their economic effects.

Linkage, sectoral productivity, and employment spread

This study demonstrates how differences in the prefectural input–output (IO) multiplier and sectoral total factor productivity (TFP) translate into aggregate income differences across prefectures within a country. Furthermore, this study investigates the job creation generated from the IO multiplier and TFP relationship and considers expenditure in the high-income group.Our results show that aggregate multipliers substantially amplify average sectoral TFP by propagating through the IO network. However, as log TFP levels and multipliers are negatively correlated, sectors with high multipliers have below-average productivity levels, which reduces income per worker because key inputs to downstream sectors are expensive. The overall effect of the IO structure is dampened relative to the aggregate multiplier effect. For example, the commerce sector has large multipliers in many prefectures and is thus amplified through the IO network, but their TFPs are lower and do not significantly contribute to the increase in income. The TFPs of the information and communication equipment sector in certain prefectures are higher than those in Tokyo, but the benefits are not amplified well within the prefecture because their multipliers are low.Furthermore, sectors with high productivity generate fewer jobs than other sectors do. The sectors with a large influence on job creation in other sectors are not amplified well through the IO network because of the small multiplier. From the expenditure viewpoint, the high-income group increases their education consumption exponentially, but the multiplier is low and not widespread within the prefecture. The construction sector has a significant influence on job creation in other sectors. However, their economic activities do not spread widely within a prefecture, and the high-income group does not increase housing consumption in proportion to their income. Residents in prefectures with high multipliers tend to spend more money outside their residential prefectures, such as online shopping. Thus, expenditure outside residential prefectures reduces the effect of the multiplier that amplifies the average sectoral TFP and the difference in income across prefectures.This study contributes to the existing literature by providing an explicit understanding of these relationships. Our findings make people realize that promoting the industry alone is not enough to increase income and generate highly productive jobs. A spillover perspective is required.

Industry contributions to productivity growth in U.S. manufacturing: an application of alternative output concepts

To understand the performance of the U.S. manufacturing sector, in this article, we explore the performance of individual industries. We first use the U.S. Bureau of Labor Statistics published data to look at the influence of industries on sector-level performance and total factor productivity (TFP) growth. The underlying dynamics of production in any given industry determine its influence on the sector as a whole. Both an industry’s share of output and its individual TFP growth vary over time, and these changes jointly determine the industry’s contribution to performance in the manufacturing sector. Next, we trace the contribution of industries to manufacturing sector TFP by using three alternative output concepts—value-added output, sectoral output, and gross output. We examine the differences in industry contributions that result from the use of the alternative output measures, over 2000–21 and three different business cycles (2000–07, 2007–19, and 2019–21). We show that one must carefully deliberate before selecting a value-added-output, sectoral-output, or gross-output framework for TFP and contribution analysis.

POTENTIAL IMPACT OF ARTIFICIAL INTELLIGENCE APPLICATIONS ON AGRICULTURAL PRODUCTIVITY

Development of Artificial Intelligence (AI) methods and their applications are becoming important drivers of innovations which significantly affect all areas of economic activities including agriculture. The aim of the paper is to examine how AI solutions applied in agriculture can influence not only production practices, but the sector Total Factor Productivity (TFP). First, types of AI systems and areas of their use in agriculture and related activities are presented. Second, an attempt is made to indicate effects of such technological changes for agricultural TFP worldwide

The Effect of Educational Mismatch on The Turkish Manufacturing Industry

This article explores educational mismatch for twenty-three different Turkish industrial sectors using all available household surveys from 2004 to 2015. Our aim is to first evaluate the educational mismatch level in the Turkish industrial sectors and then analyze its effects on the sectoral total factor productivity, labor productivity, and wages. For the sectoral total factor productivity, our dynamic panel data estimations suggest that the modal value of education increases the growth rate of total factor productivity. Therefore, the widespread existence of educational mismatch implies the efficiency loss in Turkish manufacturing sector for the period of 2004-2015. For the sectoral labor productivity, the modal value of education and the mean value of over-education increases labor productivity whereas the mean value of under-education decreases it. These results clearly imply that the human capital theory overcomes the job satisfaction theory for the Turkish industrial industry because the mean years of over-education increases the sectoral labor productivity. For the sectoral wages, the mean value of over-education raises wages.

The impact of trade liberalisation on mining sector total factor productivity: Evidence from developing countries

While the paradox of plenty is given much weight on raging debates on resource endowment and growth path of the Southern Africa Development Community (SADC) countries. The study seeks to establish the effect of trade liberalisation on mining total factor productivity. The study employed panel data of selected seven countries from the SADC for the period 1990–2017. The countries in the sample include Botswana, South Africa, Tanzania, Namibia, Zimbabwe, the Democratic Republic of the Congo (DRC), and Zambia, and were chosen based on data availability. Hicks-Moorsteen productivity index was applied to generate the total factor productivity change. A panel auto regressive distributed lag model (PARDL) and pooled mean group (PMG) are the estimation techniques used. The inquiry is crucial to SADC because mining production is a source of foreign exchange that directly contributes to economic growth. However, with open economies of SADC study expects the easy flow and diffusion of technology to aid productivity in the mining sector (Griffith, Redding, & Van Reenen, 2014). Results indicate a positive and statistically significant long-run relationship between trade openness and total factor productivity change in the mining sector. The study recommends progressive trade openness in the mining sector, human capital development, research and development to augment technology transfer.

The impact of fiscal policy on total factor productivity growth in a developing economy: evidence from Botswana

Most empirical work on the nexus between fiscal policy and total factor productivity (TFP) is focussed on aggregate analysis and lack sector-specific evidence required to guide effective policy decisions. Our study addresses this gap by analysing the impact of fiscal policy on sectoral TFP growth in Botswana between 1984 and 2016 using the Autoregressive Distributive Lag method. The results suggest that focussing on aggregate TFP growth masks specific issues which are peculiar to various economic sectors. For instance, mineral tax, other tax revenue, and expenditure on social services were found to have a significant long-run positive impact on the TFP growth in the primary sector only, while a negative impact is witnessed in the secondary and tertiary sectors. SACU revenue has a positive impact on TFP growth in both the primary and tertiary sectors, while productive spending boosts TFP growth across all sectors. In addition, both the value-added tax and the non-mining income tax have a negative impact on TFP growth across all sectors. The speed of adjustment was found to be high in the primary sector compared to other sectors. The findings single out the importance of taking into account sector-specific fiscal policies in influencing sectoral TFP growth.

A New Method for Measuring Total Factor Productivity Growth Based on the Full Industry Equilibrium Approach: The Case of the Greek Economy

A new method of identifying the sources of output growth and measuring total factor productivity (TFP) is proposed, with an application to data from the Greek economy. The price accounting approach, based on the full industry equilibrium (FIE) framework introduced by Opocher and Steedman, where technical progress not only increases outputs relative to inputs but also reduces output prices relative to input rewards, is used. The contributions of this paper are that, first, it amends the FIE TFP measurement approach to account for heterogeneous labor inputs, imported inputs, and indirect taxes, and applies the method to real-world data from the Greek economy; second, it provides a comparison of the results with those found by the use of the neoclassical approach to TFP measurement arguing that the FIE approach measures better sectoral TFP change, and third, it provides an estimate of the effects of sectoral research and development (R&D) expenditures and R&D diffusion from other sectors on TFP change for the Greek economy.

Intangible capital and measured productivity

Because firms invest heavily in R&D, software, brands, and other intangible assets—at a rate close to that of tangible assets—changes in GDP, which does not include all intangible investments, understate the actual changes in total output. If labor inputs are more precisely measured, then it is possible to observe little change in measured total factor productivity (TFP) coincidentally with large changes in hours and investment. The output mismeasurement leaves business cycle modelers with large and unexplained labor wedges accounting for most of the fluctuations in aggregate data. To address this issue, I incorporate intangible investments into a multi-sector general equilibrium model and use data from an updated U.S. input and output table to parameterize income and cost shares, with intangible investments reassigned from intermediate to final uses. I employ maximum-likelihood methods and quarterly observations on sectoral gross outputs for the United States to estimate processes for latent sectoral TFPs that have common and sector-specific components. I do not use aggregate hours to estimate TFPs but find that the predicted hours series compares closely with the actual series and accounts for roughly two-thirds of its standard deviation. I find that sector-specific shocks and industry linkages play an important role in accounting for fluctuations and comovements in aggregate and industry-level U.S. data, and I find that at business-cycle frequencies, the model's common component of TFP is not correlated with the standard measures of aggregate TFP used in the macroeconomic literature. Adding financial frictions and stochastic shocks to financing constraints has a negligible impact on the results.

Revisiting real exchange rate volatility: non-traded goods and cointegrated TFP shocks

Abstract International real business cycle (IRBC) models predict a real exchange rate volatility that is much lower than the levels observed in the data. In this paper, we build a two-country IRBC model with both a traded and a non-traded goods sector, and calibrate it to UK-euro area (EA) data. We provide evidence on the existence of a cointegrating relationship between UK and EA traded sector total factor productivity (TFP) by estimating a vector error correction model (VECM). To account for this relationship, we incorporate non-stationary technology shocks in the traded sectors in our model, and show that then the model is able to match the observed volatility of the UK–EA real exchange rate. Our analysis points out that both the presence of non-traded sectors and non-stationary technology shocks are necessary to account for the observed volatility in the real exchange rate.

Evaluating research and education performance in Indian agricultural development

AbstractIndia's agricultural research spending has long been regionally uneven. Regional research intensity ratios, which account for size differences, indicate research spending has consistently favored southern and western states over northern, central, and eastern ones. Farm production patterns have, however, changed in recent years and regional output growth is being driven by new commodity mixes. In this article, we ask which region had the highest factor productivity growth and rate of return to investments in public agricultural research and higher education. Our analysis relies on a 1980–2008 Indian agricultural production and policy data set together with a dual heteroscedastic production frontier to decompose total factor productivity (TFP) growth into formal technical progress and efficiency elements. The model's regional flexibility affords region‐to‐region comparison of multiple research return rates, TFP growth, and the elements influencing them. Regardless of return‐rate measure or allocation scenario evaluated, the North has enjoyed the highest return to research spending, and the Central and East the lowest. Factor productivity growth has however been strongest in the South, primarily on account of efficiency gains. Overall, though, the technical progress and productivity growth easily attributable to government‐supported research has accounted for about one‐quarter of the sector's TFP growth.

Revisiting Real Exchange Rate Volatility: Nontraded Goods and Cointegrated TFP Chockse

International real business cycle (IRBC) models predict a real exchange rate volatility that is much lower than the levels observed in the data. In this paper, we build a two-country IRBC model with both a traded and a non-traded goods sector, and calibrate it to UK-euro area (EA) data. We provide evidence on the existence of a cointegrating relationship between UK and EA traded sector total factor productivity (TFP) by estimating a vector error correction model (VECM). To account for this relationship, we incorporate non-stationary technology shocks in the traded sectors in our model, and show that then the model is able to match the observed volatility of the UK-EA real exchange rate. Our analysis points out that both the presence of non-traded sectors and non-stationary technology shocks are necessary to account for the observed volatility in the real exchange rate.

Capital Allocation and Productivity in South Europe*

Abstract Starting in the early 1990s, countries in southern Europe experienced low productivity growth alongside declining real interest rates. We use data for manufacturing firms in Spain between 1999 and 2012 to document a significant increase in the dispersion of the return to capital across firms, a stable dispersion of the return to labor, and a significant increase in productivity losses from capital misallocation over time. We develop a model with size-dependent financial frictions that is consistent with important aspects of firms’ behavior in production and balance sheet data. We illustrate how the decline in the real interest rate, often attributed to the euro convergence process, leads to a significant decline in sectoral total factor productivity as capital inflows are misallocated toward firms that have higher net worth but are not necessarily more productive. We show that similar trends in dispersion and productivity losses are observed in Italy and Portugal but not in Germany, France, and Norway.

In search of structural change surplus and perpetual growth in Ethiopia

Purpose – The Ethiopian economy is characterized by erratic and poor performance with negative growth rates, seven times over the period 1981-2010. This trapped per capita income at 358 USD in 2010 staying far away from middle-income country status. A lot of unsolved debates regarding perpetual growth, structural change and sectoral allocation of resource emerged overtime. The purpose of this paper is to examine the alternative effects of induced sectoral total factor productivity and makes comparisons of various sectoral growth options. Design/methodology/approach – This study uses a recursive dynamic computable general equilibrium model based on neoclassical-structuralist thought. It also calibrates coefficients that capture the impacts of openness, imported capital and liberalization on sectoral total factor productivity growth using a model of vector auto-regressive with exogenous variables. Findings – Future economic growth rate is expected to grow at a declining trend and to be dominated by the service sector. If it keeps growing on the current path it will expose the economy to a severe structural change burden problem. Openness induced agricultural total factor productivity highly improves the welfare of households while imported capital goods induced industrial total factor productivity is also better in fostering structural change of the economy. The broad-based growth option that combines the induced total factor productivity of all sectors also enables the economy to achieve more sustainable growth, rapid structural change and welfare gain at the same time. Originality/value – There are intensive and charged debates regarding alternative sectoral growth options. However, the debate does not derive from a rigorous analysis and holistic economy-wide approach. It is rather affiliated with politics. Therefore, the paper is original and investigates these issues meticulously.

The evolution of comparative advantage: Measurement and welfare implications

Using novel estimates of sectoral total factor productivities for 72 countries across 5 decades we provide evidence of relative productivity convergence: productivity grew systematically faster in initially relatively less productive sectors. These changes have had a significant impact on trade volumes and patterns, and a non-negligible welfare impact. Had productivity in each country׳s manufacturing sector relative to the US remained the same as in the 1960s, trade volumes would be higher, cross-country export patterns more dissimilar, and intra-industry trade lower than in the data. Relative sectoral productivity convergence – holding average growth fixed – had a modest negative welfare impact.

What are the drivers of total factor productivity in the European Union?

ABSTRACTThis paper examines the evolution of total factor productivity (TFP) over time, as well as across countries and sectors, and investigates its determinants. To this end, a panel data set of 17 European Union (EU) countries and 13 sectors over the period 1995–2007 is used as part of a twofold approach. First, we estimate aggregate and sectoral TFP for 17 EU countries by means of the augmented mean group estimator to control for endogeneity, cross-section dependence and heterogeneous production technologies. Second, we investigate the relative importance of the drivers of predicted TFP using a dynamic ordinary least-squares estimator. The results indicate that rationalization, human capital and information and communication technologies are the main drivers of TFP.

Why is TFP growth sectorally concentrated?

Research shows that total factor productivity (TFP) growth is weak in European countries. This is inter alia attributed to the fact that substantial TFP growth is limited to a few industries. Because TFP growth is typically understood as technological progress, it is concluded that technology diffusion between sectors in Europe is hampered. We use EU KLEMS data sets to decompose sectoral TFP for nine European countries by means of a Malmquist approach in order to identify potential sources besides technical progress. Applying Harberger diagrams, we describe the sectoral distribution of TFP growth, efficiency gains and losses, economies of scale and technological progress. The analysis reveals that technological progress is quite evenly distributed across sectors in most European countries. The wide scattering of TFP growth is explained by deviating efficiency developments and the unused economies of scale. We conclude that the technology transfer between sectors in most European countries seems to work. Therefore, Europe in general does not need a new technology policy, but a further integration of the markets and a reduction of national market entry barriers. This requires further unification of pan-European standards in fields like trade and crafts codes or consumer protection policies.

Source of Service Sector TFP Growth in India: Evidence from Micro-data

India has seen service sector-led growth in recent years. High growth in the total factor productivity (TFP) has been the dominant source of growth in this sector. I use micro-data on the service sector companies to test the hypothesis if better factor reallocation has led to TFP gains in the service sector. I find that post reforms, the extent of resource misallocation has reduced in the service sector, thereby accelerating the pace of the TFP growth. Due to improved resource allocation, the information-technology industry operated at 84 per cent of its efficient TFP level in 2005. The communication industry and community services industry registered the fastest growth in terms of moving towards their efficient TFP levels. JEL Classification: 014, 040

CAPITAL MISALLOCATION AND AGGREGATE FACTOR PRODUCTIVITY

We propose a sectoral–shift theory of aggregate factor productivity for a class of multisector economies with AK technologies and a constant production possibilities frontier. Loans are partly secured by collateral and partly based on reputation. We find that both the growth rate and total factor productivity (TFP) respond to random and persistent endogenous fluctuations in the sectoral distribution of physical capital, which, in turn, responds to reversible exogenous shifts in relative sector productivities. Endogenous debt limits on secured and unsecured loans slow down capital reallocation, preventing the equalization of risk-adjusted equity yields across sectors. Economywide factor productivity and the aggregate growth rate are both negatively correlated with the dispersion of sectoral rates of return, sectoral TFP, and sectoral growth rates. We also find highly volatile limit cycles in economies with small amounts of collateral.

Determinants of Real Exchange Rates: An Empirical Investigation

The large and persistent deviations of nominal exchange rates from their purchasing power parities comprise a key stylized fact in international economics. This paper sheds light on these persistent deviations by combining two disparate strands of empirical work. The first strand focuses on real economic shocks such as sectoral technology shocks suggested by the celebrated Balassa-Samuelson model, whereas the second strand emphasizes monetary shocks which create persistent effects on both the real interest rate and the real exchange rate. We also hypothesize a third factor which may affect real exchange rates – shocks to the global financial system, which we proxy by the real price of gold.Although each factor in isolation has limited explanatory power, we find that these three factors in conjunction can successfully explain the medium to long run movements in 14 bilateral U.S. dollar real exchange rates from 1970 to 2006. The three factors are sectoral total factor productivity differentials, real interest rate differentials, and the real price of gold, representing real shocks, monetary shocks, and shocks to the global financial system, respectively. We document evidence suggesting that bilateral U.S. dollar real exchange rates are cointegrated with these three factors.