Convergence of labor productivity across the US states

Most studies of labor productivity convergence among US states base their analyses on state-level GDP per worker. Such aggregated analysis may hide important information about the role of sectors, and changes in the sectoral composition of these economies, in explaining economy-wide convergence results. Using highly disaggregated data for the period 1987–2015 from the US Bureau of Economic Analysis, we examine sectoral unconditional convergence in labor productivity in the US states. Our results demonstrate a general slowing down in the rate of convergence of labor productivity in the latter years of this period. The sectoral analysis also indicates that manufacturing was the primary driver of convergence during the 1987–1997 period, which had the highest rate of convergence; the role of this sector, however, has diminished in recent years. Several factors—such as the decline in interstate migration, rising housing costs in major cities, agglomeration, and structural changes in the US economy that have reduced the role of manufacturing—may have contributed to the rise in the labor productivity differential among US states.     

Scale and the origins of structural change

We consider broad patterns of structural change: (i) sectoral reallocations, (ii) rich movements of productive activities between home and market, and (iii) an increase in establishment size, especially in manufacturing. We extend these facts and develop a unified model explaining them. The crucial distinction across manufacturing, services and home production is the scale of the productive unit. In manufacturing, scale technologies lead to industrialization and marketization. In services, they lead to marketization and later demarketization of services. A later increase in the scale of services could yield a decline in industry and a rise in services, consistent with the data.     

Energy consumption and economic growth in the United States

We study the relationship between energy consumption and real GDP in the USA using a multivariate time-varying model [1973Q1–2014Q1]. We show that the combination of disaggregation into specific fuels and time variation gives more nuanced results than the alternatives for the USA. Specifically, we find that the Granger causal relationship between total energy and real US GDP is bi-directional through much of the 1990s, but unidirectional running from real US GDP to energy consumption in the 2000s. As for each fuel, similar patterns of change were observed in the causal relationship between coal consumption and real US GDP. Oil consumption largely shows a bi-directional relationship between consumption and US GDP, especially after 2009. And natural gas consumption shows a brief period in the early-to-mid 2000s where US GDP predicts energy consumption, but primarily shows that natural gas consumption and economic growth are independent.     

Monetary policy, oil shocks, and TFP: Accounting for the decline in US volatility

An equilibrium model is used to assess the quantitative importance of monetary policy for the post-1984 decline in US inflation and output volatility. The principal finding is that monetary policy played a substantial role in reducing inflation volatility, but a small role in reducing real output volatility. The model attributes much of the decline in real output volatility to smaller TFP shocks. We also investigate the pattern of output and inflation volatility under an optimal monetary policy counterfactual. We find that real output volatility would have been somewhat lower, and inflation volatility substantially lower, had monetary policy been set optimally.     

Can total factor productivity explain value added growth in services?

This paper examines the factors responsible for generating the services led growth witnessed in the Indian economy during 1980–2005. A sectoral growth accounting exercise shows that total factor productivity (TFP) growth was the fastest for services; moreover this TFP increase was significant in accounting for service sector value added growth. A growth model with agriculture, industry and services as three principal sectors is calibrated to Indian data using sectoral TFP growth rates. The baseline model performs well in accounting for the evolution of value added shares and their growth rates, but is unable to capture sectoral employment share trends. The performance of the model with respect to value added shares improves when the post 1991 increase in service sector TFP growth following the inception of market-based liberalization reforms is accounted for. A modified version of the model with public capital can better track trends in sectoral employment shares.     

The structural transformation and aggregate productivity in Portugal

We document the substantial process of structural transformation—the reallocation of labor between agriculture, manufacturing, and services—and aggregate productivity growth undergone by Portugal between 1956 and 1995. We assess the quantitative role of sectoral labor productivity in accounting for these processes. We calibrate a model of the structural transformation to data for the United States and use the model to gain insight into the factors driving the structural transformation and aggregate productivity growth in Portugal. The model implies that Portugal features low and roughly constant relative productivity in agriculture and services (around 22%) and a modest but growing relative productivity in manufacturing (from 44 to 110%). We find that productivity growth in manufacturing accounts for most of the reduction of the aggregate productivity gap with the United States and that a further closing of this gap can only be accomplished via improvements in the relative productivity of services. This paper was written while the authors were affiliated with the Federal Reserve Bank of Richmond. We would like to thank the editor, two anonymous referees, and participants at the Third Conference on Portuguese Economic Development in the European Context organized by the Bank of Portugal for their comments. All errors are our own.     

An Empirical Analysis of Short-run and Long-run Irish Export Functions: Does exchange rate volatility matter?

We analyse the long-run and short-run relationship between merchandise export volume and its determinants, foreign income, relative prices and exchange rate volatility, using the techniques of cointegration and error correction. The model was estimated for Irish exports and sectoral exports SITC 0-4 and SITC 5-8 to the EU using quarterly data for the period 1978-1998. The sectoral classification corresponds to the exports of mainly indigenous Irish firms and multinationals, respectively. We find that the exchange rate volatility has no effect on the volume of trade in the short-run but a significant positive effect in the long run. This is true in the aggregate and for our sectoral classifications. We can tentatively conclude that the decline in intra-EU exchange rate volatility associated with the single currency will lead to a long-run fall in Irish exports to the EU.     

Long-run sectoral development: Time-series evidence for the German economy

In economic development, long-run structural change among the three main sectors of an economy follows a typical pattern with the primary sector (agriculture, mining) first dominating, followed by the secondary sector (manufacturing) and finally by the tertiary sector (services) in terms of employment and value added. We reconsider the verbal theoretical work of Fourastié and build a simple model encompassing its main features, most notably the macroeconomic influences on the sectoral development. Estimation and analysis with German data for the period 1850–2001 show that this model is quite capable of replicating the empirical facts.     

China’s catch-up to the US economy: decomposing TFP through investment-specific technology and human capital

We implement a neoclassical growth model that incorporates investment-specific technology (IST) modifying capital investment in the law of motion of capital and bifurcates productivity into human capital and total factor productivity (TFP) in the production function. We focus on the role of changes in the quality-adjusted price of investment goods on China’s growth by comparing the effects of IST and human capital on the decomposition of US and Chinese productivity. The results show that both human capital and IST play an important role in the decomposition of US TFP. For China, human capital accounts for an increasingly higher portion of Chinese TFP for the period 1952–2009; however, IST contributes to the explanation of TFP only after the 1979 reforms. The analysis is extended by considering the impact of IST in the consumer’s investment decision and by projecting both countries’ GDP while modelling unbalanced Chinese growth using catch-up. Our model predicts that the Chinese economy will surpass the US economy in 2024.     

Explaining the declining energy intensity of the U.S. economy

This paper reconciles conflicting explanations for the decline in U.S. energy intensity over the last 40 years of the 20th century. Decomposing changes in the energy–GDP ratio into shifts in the structure of sectoral composition and adjustments in the efficiency of energy use within individual industries reveals that while inter-industry structural change was the principal driver of the observed decline in aggregate energy intensity, intra-industry efficiency improvements played a more important role in the post-1980 period. Econometric results attribute this phenomenon to adjustments in quasi-fixed inputs—particularly vehicle stocks, and disembodied autonomous technological progress, and show that price-induced substitution of variable inputs generated transitory energy savings, while innovation induced by energy prices had only a minor impact.     

Productivity and misallocation during a crisis: Evidence from the Chilean crisis of 1982

Measured total factor productivity often declines sharply during financial crises. In 1982, the Chilean manufacturing sector suffered a severe contraction in output, most of which can be accounted for by a falling Solow residual. This paper uses establishment data from the Chilean manufacturing census to examine the decline in measured TFP. To quantify the contribution of resource misallocation, I develop a measure of allocational efficiency along the lines of Hsieh and Klenow (2009) and derive the appropriate measure of aggregate productivity to which it should be compared. Across specifications, within-industry allocational efficiency either remained constant or improved in 1982, while a decline in between-industry allocational efficiency accounts for about one-third of the reduction in TFP. Industries more sensitive to domestic demand – durables and industries with low exports – experienced larger declines in measured TFP. This finding is consistent with large adjustment costs and underutilization of inputs. Reduced capital utilization played a substantial role, accounting for 25–50 percent of the decline in measured TFP.     

Evolutionary micro-dynamics and changes in the economic structure

The paper aims to account for the empirical stylized facts related to changes in sectoral structures that have led to the growth of services in most advanced countries over recent decades. A growth model with evolutionary micro–founded structural change is developed, which formalizes the role of technical change and changes in intermediate demand as they affect the evolution of the sectoral composition of the economy and macro-economic growth. The simulated results are based on the use of the actual data, including Input–Output coefficients in the case of Germany. Three scenarios are identified, which account for the effects of a set of key parameters on changes in the structure of the economy.     

Sectoral Capital–Labor Ratios and Total Factor Productivity: Evidence from Asia

This paper examines the relationship between sectoral capital–labor ratios and total factor productivity (TFP) for six Asian economies in the context of the Balassa–Samuelson model. A strong prediction of the model is that the capital–labor ratios in both the traded‐ and nontraded‐goods sectors depend on the TFP in the traded‐goods sector. Both single‐equation and panel cointegration tests support this implication of the model.     

The rise of service employment and its impact on aggregate productivity growth

This paper investigates how the employment shift from manufacturing toward service sectors affects the rate of economic growth when services play their role both in intermediate and in final demand. Our model includes as a special case both Baumol’s [Baumol, W.J., 1967. Macroeconomics of unbalanced growth: the anatomy of urban crisis. American Economic Review 57 (3), 415–426] model, in which services are produced only for final consumption, and Oulton’s [Oulton, N., 2001. Must the growth rate decline? Baumol’s unbalanced growth revisited. Oxford Economic Papers 53 (4), 605–627] model, in which services are entirely devoted to intermediate demand. We show that, given that the growth rate of productivity in the service sector is lower than that in the manufacturing sector, both the employment share in manufacturing and the rate of economic growth will decline in the long run irrespective of the size of the elasticity of substitution between labor and service input.     

An empirical analysis of the US stock market and output growth volatility spillover effects on three Anglo-Saxon countries

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1–2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.     

Why have business cycle fluctuations become less volatile?

This paper shows that a standard Real Business Cycle model driven by productivity shocks can successfully account for the 50% decline in cyclical volatility of output, its components, and labor input that has occurred since 1983. The model is successful because the volatility of productivity shocks has also declined significantly over the same time period. We then investigate whether the decline in the volatility of the Solow Residual is due to changes in the volatility of some other shock operating through a channel that is absent in the standard model. We therefore develop a model with variable capacity and labor utilization. We investigate whether government spending shocks, shocks that affect the household’s first order condition for labor, and shocks that affect the household’s first order condition for saving can plausibly account for the change in TFP volatility and in the volatility of output, its components, and labor. We find that none of these shocks are able to do this. This suggests that successfully accounting for the post-1983 decline in business cycle volatility requires a change in the volatility of a productivity-like shock operating within a standard growth model. We thank Stephen Parente, Ed Prescott, John Taylor, and two anonymous referees for helpful comments and suggestions.     

Structural transformation and the oil price

What part of the high oil price can be explained by structural transformation in the developing world? Will continued structural transformation in these countries result in a permanently higher oil price? To address these issues I identify an inverted-U shaped relationship in the data between aggregate oil intensity and the extent of structural transformation: countries in the middle stages of transition spend the highest fraction of their income on oil. I construct and calibrate a multi-sector, multi-country, general equilibrium growth model that accounts for this fact by generating an endogenously falling aggregate elasticity of substitution between oil and non-oil inputs. The model is used to measure and isolate the impact of changing sectoral composition in the developing world on global oil demand and the oil price in the OECD. I find that structural transformation in non-OECD countries accounts for up to 53% of the oil price increase in the OECD between 1970 and 2010. However, the impact of structural transformation is temporary. Continued structural transformation induces falling oil intensity and an easing of the upward pressure on the oil price. Since a standard one-sector growth model misses this non-linearity, to understand the impact of growth on the oil price, it is necessary to take a more disaggregated view than is standard in macroeconomics.     

Sectoral Patterns of Distribution in Slowly Growing Economies: The case of nine OECD countries in the 1980s and 1990s

Slow productivity growth has put pressure on the distribution of income between wages and profits in ways that are not amenable to a US versus Europe dichotomy but vary between sectors (especially between manufacturing and service activities) as well as among countries. Over the 1980s and the 1990s, wage flexibility and profit margin flexibility in service sectors-characterised, respectively, by high shares of total employment and high shares of total profits-outlined three growth patterns: countries adjusting to slow productivity growth by means of considerable flexibility in relative wages in services (as in the US, the UK and Germany); adjustment through flexibility in relative profit margins (as in France, Canada and Sweden); and finally, an absence of change in the sectoral structure of distribution (as in Japan, Italy and Belgium). These patterns may be associated with either changes in wage and profit rates or structural adjustments (such as changes in the share of part time jobs, or changes in the composition of the capital stock). They are shown to be an important influence on the prospect of a resumption of high productivity growth.     

Government intervention,sectoral productivity growth and structural transformation

This article examines the long-term impact of government intervention and sectoral productivity on structural transformation. We construct a multi-sector Dynamic General Equilibrium model that explicitly incorporates government intervention as a force of structural transformation. The government affects the economy through taxation and lump sum transfers. We show that in the steady state, a reduction in the tax rate and an increase in sectoral productivity will decrease the agricultural employment share, and when nonhomotheticity of preference is strong enough, these changes can also increase the share of services employment.     

The decline in German output volatility: a Bayesian analysis

Using Bayesian methods, we analyze whether a volatility reduction as documented for growth of U.S. gross domestic product (GDP) in the mid-1980s can also be detected for German GDP growth. Our analysis is based on different time series models allowing for alternative characterizations of output stabilization. Across all models we find empirical evidence for a decline in the output volatility around 1993. Furthermore, we assess competing explanations for reduced output volatility. Our empirical results suggest that the main source for the volatility reduction is an ongoing structural shift accelerated by the German reunification and accompanied by changes in the correlation structure between individual GDP components.