Aggregate productivity and aggregate technology

Aggregate productivity and aggregate technology are meaningful but distinct concepts. We show that a slightly modified Solow productivity residual measures changes in economic welfare, even when productivity and technology differ because of distortions such as imperfect competition. Our results imply that aggregate data can be used to measure changes in welfare, even when disaggregated data are needed to measure technical change. We then present a general accounting framework that identifies several new non-technological gaps between productivity and technology, gaps reflecting imperfections and frictions in output and factor markets. Empirically, we find that these gaps are important, even though we abstract from variations in factor utilization and estimate only small average sectoral markups. The evidence suggests that the usual focus on one-sector DSGE models misses a rich class of important propagation mechanisms that are present only in multi-sector models.     

Sources of productivity growth: an empirical analysis with German sectoral data

Many theoretical analyses of the sources of economic growth focus on knowledge spillovers and scale economies to explain growth endogenously. The contribution of this paper is to shed some light on these arguments by an empirical investigation based on a production function framework. Sectoral production functions are estimated with annual German data of 51 sectors from 1960–90. The estimates reveal that both a pure Solow growth model and a Solow model augmented with human capital cannot account for the observed productivity increases. The model should be extended by allowing for inter-industry spillovers and scale economies at the aggregate level, as well as for scale economies associated with human capital at the sectoral level. The business cycle affects observed productivity changes in the short run and in the long run.     

Sectoral and Aggregate Technology Shocks:Is There a Relationship?

We analyze sector-specific shocks in productivity and demand in 19 manufacturing sectors of the Austrian economy. Based on a structural vector autoregressive model (SVAR) with long-run restrictions developed by Galí (1999) we extract technology and non-technology shocks from sectoral and aggregate data. We study their patterns and relationship by means of a principal components analysis and find a close association of sectoral and macroeconomic non-technology shocks but only a very weak association for technology shocks. Impulse response analysis indicates that for almost all manufacturing sectors as well as the aggregate Austrian economy productivity growth rates experience an immediate increase due to positive technology shocks while hours worked decline. Thereby we confirm Galís results on the level of Austrian manufacturing industries. From regression analysis, we find that our shocks are closely associated to employment growth and output growth but not to investment growth and that the reaction is different for the aggregate economy and manufacturing industries.JEL codes: D24, E23, E32, O30We thank Werner Müller and the participants of the 2004 conference of the Austrian Economic Association (NOeG) for helpful comments. We would like to thank an anonymous referee for many helpful comments that led to a substantial improvement of the paper. The usual disclaimer applies. This research project was supported by a research Grant (Project Nr. 9800) of the Jubiläumsfonds der Österreichische Nationalbank (OeNB).     

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.     

ESTIMATING PRODUCTIVITY GROWTH IN THE KOREAN ECONOMY WITHOUT RESTRICTIVE ASSUMPTIONS

This study eliminates the effects of markup, returns to scale, and capital utilization from the Solow residual (SR) for Korea to derive an alternative measure for productivity. Empirical results show that markup generates significant bias in the SR, and that the alternative productivity measure is greater than the residual. Furthermore, money supply Granger‐causes the SR but does not Granger‐cause the alternative measure, suggesting that the new productivity measure is consistent with the neutrality of money. The results contradict the presumption that the SR with variable capital utilization represents true technology shocks because it is orthogonal to demand shocks.(JEL C32, E32, O47)     

Sectoral shocks and business cycles: a disaggregated analysis of output fluctuations in the UK

In this paper we first show that it is possible to modify linear real business cycle models to allow for disaggregate (industry-specific) factors in the generation of macroeconomic fluctuations. We then try to determine the relative importance of aggregate and sectoral shocks by doing principal components analysis on the residuals from a VAR of output growth rates in 19 UK industrial sectors. We find that a significant percentage of the innovations in sectoral output growths can be accounted for by a single unobserved component. However, since the model only sets an upper bound to the explanatory power of aggregate impulses, the importance of using sectoral data to extract additional information to bear on the analysis of economic fluctuations is confirmed by our findings.     

Technological leadership and sectorial employment growth: A spatial econometric analysis for U.S. counties

This paper studies the determinants of technological catch‐up considering spatial and sectoral aggregation of industries. We investigate how geographical and technological proximity to the technology leader impact regional employment growth. We model technological progress by means of a hierarchical process of catch‐up to the technology leader. We also incorporate measures for knowledge spillover effects to test the roles of competition, specialisation, and diversity at the industry level. Empirical results using data at the county level for different economic sectors (2‐dig NAICS) for the United States indicate that human capital plays a crucial role in promoting sectoral employment growth. The association between technological/geographical distance to the technology leader and employment growth varies across sectors.     

The implications of heterogeneous resource intensities on technical change and growth

Abstract We analyze the long‐term dynamics of an economy in which sectors are heterogeneous with respect to the intensity of natural resource use. It is shown that heterogeneity induces technical change to be biased towards resource‐intensive sectors. Along the balanced growth path, the sectoral structure of the economy is constant as the higher resource dependency in resource‐intensive sectors is compensated by enhanced research activities. Resource taxes have no impact on dynamics except when the tax rate varies over time. Research subsidies and the sectoral provision of productivity‐enhancing public goods raise growth and provide an effective tool for structural policy.     

The utilization of capital and the growth of output in a developing economy: The case of South Korean manufacturing

This paper presents the utilization rate of capital equipment in the S. Korean manufacturing industry during 1962–1971. The aggregate utilization rate is shown to have increased at an annual rate of about 8%. The level of utilization in S. Korea is found to be generally lower even in 1971 than in the U.S. in 1962, both at the aggregate and sectoral levels. The rise in the utilization rate is shown to have contributed nearly as much as investment has in the growth of manufacturing output. The influence of the residual component declines from 36% to 8% of the recorded growth of output when the increase in the utilization rate is properly allowed for.     

The Impact of Euro on Sectoral Equity Returns and Portfolio Risk

This paper examines the implications of the adoption of the euro and the resulting monetary policy integration for investors in the Euro area in terms of stock market diversification. In particular, we study the difference between investment strategies based on country indices and on sector indices. In addition, we use GARCH-M to model return and volatility for the daily sectoral euro equity indices from 1992 to 2009 to analyze how and to what extent volatility in the sector equity index is driven by shocks occurring in the US, aggregate European equity index, aggregate Euro Zone equity index, and the global equity index. We find strong evidence that diversification over sectors yields more efficient portfolios than diversification over countries and that the volatility spillover of the aggregated Euro zone equity return index on the sectoral equity return index has increased after the launch of the euro.     

Cyclicality and Sectoral Linkages: Aggregate Fluctuations from Independent Sectoral Shocks

The traditional argument against the relevance of sector-specific shocks for the aggregate phenomenon of business cycles invokes the law of large numbers: positive shocks in some sectors are offset by negative shocks in other sectors. This paper hypothesizes that cancellation of sector-specific shocks via the law of large numbers is affected by interactions among producing sectors. The analysis is performed within the context of a multisector model similar in spirit to that of Long and Plosser [J. Polit. Econ.91(1983), 39–69]. It is shown that the rate at which the law of large numbers applies is controlled by the rate of increase in the number offullrows in the input-use matrix rather than by the rate of increase in the total number of sectors. Investigations of actual input-use matrices from the U.S. economy reveal that the number of full rows increases much slower than the total number of rows upon disaggregation, and when these input-use matrices are used to parameterize the model, aggregate volatility from sectoral shocks declines at less than half the rate implied by the law of large numbers. This finding leaves open the possibility that a sizeable portion of aggregate volatility is caused by “smaller” shocks to individual sectors. Simple statistics calculated from the model indicate that as much as 80% the volatility in U.S. gross domestic product growth rates could be the result of independent shocks to two-digit Standard Industrial Code sectors.Journal of Economic LiteratureClassification Numbers: E1, E32, C67.     

Productivity and convergence across U.S. States and industries

We examine the sources of aggregate labor productivity movements and convergence in the U.S. states from 1963 to 1989. Productivity levels vary widely across sectors and across states, as do sectoral output and employment shares. The main finding is the diverse performance of sectors regarding convergence. Using both cross-section and time series methods, we find convergence in labor productivity for both manufacturing and mining. However, we find that convergence does not hold for all sectors over the period. Decomposing aggregate convergence into industry productivity gains and changing sectoral shares of output, we find the manufacturing sector to be responsible for the bulk of cross-state convergence.     

THE TRANSMISSION OF MONETARY POLICY IN A MULTISECTOR ECONOMY*

This article constructs and estimates a sticky‐price, Dynamic Stochastic General Equilibrium model with heterogeneous production sectors. Firms in different sectors vary in their price rigidity, production technology, and the combination of material and investment inputs. In particular, firms buy inputs from all sectors using the actual Input–Output Matrix and Capital Flow Table of the U.S. economy. By relaxing the standard assumption of symmetry, this model allows idiosyncratic sectoral dynamics in response to monetary policy shocks. The model is estimated by the Generalized Method of Moments using sectoral and aggregate U.S. time series.     

Wage and price differences,technology gap and labor flow dynamics

The relationship between wage difference and relative employment is a very important issue in the field of economic structural change. An M-sector economic growth model is constructed in this paper to investigate the relationship between wage difference, price difference, technology gap, relative employment and sectoral dynamic change from the perspective of technologies. Labor flow is regarded as a decision-making behavior to maximize the benefits of economic agents. The benefits of labor flow mainly come from sectoral wage difference, and the costs of labor flow mainly come from the social resource expenditure during labor flow process. Our model illustrates that: (i) the relative employment is positively correlated with the real wage difference and technology gap; (ii) the sectoral dynamic changes coexist with the aggregate economic growth; (iii) increases in technology gap, and price and wage differences will stimulate labor migration, exacerbate the unbalanced growth of sectoral economies, and lead to faster and more effective aggregate economic growth.     

Sectoral Employment Dynamics in Australia and the COVID-19 Pandemic

We develop a multivariate time series model of employment in 19 sectors for Australia. We use this model to determine the long-run effect of a 1% increase in economic activity in any chosen sector on aggregate employment. Our findings point to manufacturing and construction sectors as those that generate the largest positive spillovers for the aggregate economy. Moreover, we provide an interactive web-based app that produces our model's forecasts based on any user-specified scenario. As the restrictions associated with the COVID-19 pandemic evolve, the sectoral employment multipliers together with these interactive tools will provide useful information for policymakers.     

中国铁路行业产出变化的驱动因素——基于2005—2012年面板数据的实证研究

基于"索洛余值"法建立扩展型索洛模型,利用灰色关联分析方法计算模型的弹性系数,利用2005—2012年中国铁路行业的面板数据,计算了技术进步、政府投资、非政府投资和劳动力投入等对铁路行业产出增长的贡献率,并分析了中国《中长期铁路网规划》颁布实施以来铁路行业产出变化的驱动因素。得出结论:近年来中国铁路行业的巨大成就与技术进步和政府投资密不可分,政府的大规模投资和支持已帮助中国铁路企业步入以技术进步和创新为主导的内生驱动式发展阶段,技术进步已成为影响中国铁路行业产出增长的最重要因素。     

Hours and employment variation in business cycle theory

Summary Previous business cycle models have made the assumption that all the variation in the labor input is either due to changes in hours per worker or changes in number of workers, but not both. In this paper, both vary. We think this is a better model for estimating the contribution of Solow technology shocks to aggregate fluctuations. We find that about 70% of the variance of U.S. postwar cyclical fluctuations is induced by variations in the Solow technology parameter.This material is based on work supported by the National Science Foundation under Grant Numbers SES-8722451 and SES-8909361. The Government has certain rights to this material.Any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and not necessarily those of the National Science Foundation, the University of Minnesota, the Federal Reserve Bank of Minneapolis, or the Federal Reserve System.     

Dynamics of sectoral business cycle comovement

Sectoral comovement accounts for a considerable share of the variance of aggregate variables. However, little is known about its time-varying aspects by now. In this article, a multivariate DCC- GARCH framework is employed to study dynamics of sectoral comovement across manufacturing sectors both in the United States and in Germany. To account for possible nonlinearities, asymmetric effects in conditional volatilities as well as in conditional correlations are being assessed. We find that comovement across sectors is not stable but shows irregular movements. Particularly, contractions tend to be more synchronized than expansions in manufacturing sector. Moreover, we examine the role of various aggregate factors for the fluctuations in conditional correlations. Our findings reveal that both the non-constant variability of common factors and the changes in the effects of these factors play role for the fluctuations in sectoral comovement.     

The Effects of Technology Changes on Sectoral Trade Patterns*

We develop a model in which sectoral trade patterns depend on both the technology common to all sectors and the technologies specific to each sector. Changes in the common technology level affect sectoral trade patterns through their impact on intertemporal optimization behavior, while changes in the sector‐specific technology levels affect sectoral trade patterns by influencing comparative advantage. The model shows: (1) unexpected increases in the common technology level worsen sectoral trade balances, but expected increases in the common technology level improve them; and (2) given other countries’ sectoral technology levels, an increase in a sector‐specific technology level relative to other sectors improves sectoral trade balances through its operation on comparative advantage. Using Japanese data, the empirical results reported in this paper support the model’s predictions.     

Convergence Across the EU Regions: Economic Composition and Structural Transformation

This paper refers to 1995–2006 and analyzes the role and sources of within- and between-sectors convergence in the context of the economic catching-up process across the European Union regions. The perspective adopted here overcomes the prevailing theoretical and empirical contributions referred to as the one-sector economic growth model by Solow that neglects the implications of economic composition and structural transformation on regional aggregate growth and catching-up. Further, a comparison between the results from the fixed effects panel data model and cross-regions ordinary least square estimates allow consideration of the technological differences in the economic and sectoral speeds of convergence. The growth-accounting approach integrates these results and underlines the role of structural composition and change on the process of economic and between-sectors catching-up.