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Aggregate Implications of Changing Sectoral Trends
... Horvath (2000)]. Sectoral spillovers imply that sectors that are central in the input-output and capital flow networks are more responsive to developments in other sectors, as demonstrated by Foerster et al. (2021). Specifically, Bouakez et al. (2021) have shown that fiscal policy has larger effects in more central (or "upstream") sectors. ...
... Such rigidity of the government spending mix can explain our key findings because construction spending accounts for a large share of discretionary spending by state governments. Usually, construction spending Foerster et al. (2021). For both centrality measures, we set the attenuate factor to 0.5, following Carvalho (2014). ...
We document that the employment effects of financial aid to US states during the Great Recession were strongly unevenly distributed across sectors, the construction sector being the main beneficiary. State fiscal relief not only preserved a substantial number of jobs but it also fostered employment most strongly in the sectors hit hardest by the recession. Exploiting across-state differences, we conclude that the sectoral employment effects of state fiscal relief reflect the typical spending patterns of state and local governments, who usually spend large shares of their discretionary expenditures on construction.
... To study the demand for labor, capital, energy, and nonenergy intermediate inputs in the Quebec manufacturing industry, we use data on unit prices and quantities of each factor within each manufacturing SME. Firms databases often do not incorporate quantities and prices of factors and output but include only expenditures on inputs and revenues derived from output sales (Foerster et al. 2022). Data on input prices and quantities per manufacturing SME are constructed by incorporating bond yields using the calculation technique proposed by Harper et al. (1989) and assuming perfect competition in markets, allowing us to consider that the prices of manufacturing inputs/outputs are those of firms and that firms buy inputs or sell their outputs at these prices. ...
Investment in research and development (R&D) and information and communication technologies (ICT) enable firms to better organize their operations and effectively manage risks and uncertainties. This is particularly evident in capital-intensive industries such as those in the manufacturing sector in Quebec, where short-term flexibility is limited due to high adjustment costs and a recurrent labor shortage. By accounting for endogeneity in fixed capital stocks and variable inputs, we utilize unbalanced firm-level panel data from Quebec spanning from 2001 to 2018 to estimate input demand, substitution elasticities, and total factor productivity (TFP) growth. Results consistently indicate that TFP growth is declining due to high volatility in the demand for fixed capital, specifically for R&D and ICT. The effects of technical progress induced by R&D are very low and generally negative, implying that firms utilize investments in R&D to compensate for weak innovation or a lack of technical progress.
... 5 We cannot do justice here to this extensive literature, see e.g. Stock and Watson (1999), Kahn et al. (2002), McCarthy and Zakrajsek (2007), Galí andGambetti (2009), Sarte et al. (2015) and Foerster et al. (2022). ...
... Finally, a recent literature studies the role of investment networks in the propagation of sectoral productivity shocks. Foerster et al. (2022) and vom Lehn and Winberry (2022) show how investment and production networks interact to amplify trend sectoral shocks in the postwar US economy, while Casal and Caunedo (2023) provides novel and harmonized investment network data across countries and show how they change over the process of development. In our work, the investment network plays a central role in propagating the effect of sectoral policies through technology adoption, as the investment network affects the adoption cost of capital embedded technologies. ...
What is the role of production networks in inducing self‐fulfilling business cycles? We construct a multisector business cycle model that features both input–output linkages and credit constraints. Our theoretical framework demonstrates that a single aggregate financial multiplier is sufficient to characterize equilibrium determinacy, which hinges on the network structure. By quantitatively assessing the possibility of indeterminate equilibria in the United States from 2000 to 2020, we discover that the economy was prone to self‐fulfilling fluctuations in the period of pre‐2007.
Over the last 70 years, many advanced countries have experienced growing real house prices and an increasing housing wealth-to-income ratio. To explain these long-run patterns, this paper introduces a novel multi-sector growth model where housing services are produced using non-reproducible land and reproducible structures. Land is also employed in the non-housing sector. First, we identify two fundamental mechanisms driving the long-run increase in the real house price: (i) technological progress in the construction sector lags behind the technological progress of the rest of the economy and (ii) housing production is more land-intensive than non-housing production. Second, we study transitional dynamics for the US, UK, France, and Germany. Our calibrated model explains most of the observed increase in the housing wealth-to-income ratio since 1950. Counterfactual experiments identify initially low stocks of residential structures and non-residential capital as key exogenous drivers for this increase. The associated investment incentives led to a long-lasting construction boom and steadily increasing land scarcity, boosting residential land prices.
This paper provides a general framework to study the role of production networks in international GDP comovement. We first derive an additive decomposition of bilateral GDP comovement into components capturing shock transmission and shock correlation. We quantify this decomposition in a parsimonious multi-country, multi-sector dynamic network propagation model, using data for the G7 countries over the period 1978-2007. Our main finding is that while the network transmission of shocks is quantitatively important, it accounts for a minority of observed comovement under the estimated range of structural elasticities. Contemporaneous responses to correlated shocks in the production network are more successful at generating comovement than intertemporal propagation through capital accumulation. Extensions with multiple shocks, nominal rigidities, and international financial integration leave our main result unchanged. A combination of TFP and labor supply shocks is quantitatively successful at reproducing the observed international business cycle.
We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large connectedness tables lead to biased results and high estimation uncertainty, both of which are mitigated by regularization. To explore possible causes for the Great Moderation, we apply a cross‐validated estimator on sectoral spillovers of industrial production in the US from 1972 to 2019. We find that the spillover network has considerably weakened, which hints at structural change, for example, through improved inventory management, as a critical explanation for the Great Moderation.
We ask what specification of preferences can account for the changes in the expenditure shares of broad sectors that are associated with the process of structural transformation in the U.S. since 1947. Following the tradition of the expenditure systems literature, we first calibrate utility function parameters using NIPA data on final consumption expenditure. We find that a Stone-Geary specification fits the data well. While useful, this exercise does not tell the researcher what utility function to use in a model that posits sectoral production functions in value added form. We therefore develop a method to calculate the value added components of consumption categories that are consistent with value added production functions, and use these data to calibrate a utility function over sectoral consumption value added. We find that a Leontief specification fits the data well. Interestingly, the two specifications display very different properties: for final consumption expenditure income effects are the dominant force behind changes in expenditure shares whereas for consumption value added relative price effects are dominant.
Two issues related to mapping a multi-sector model into a reduced-form value-added model are often neglected: the composition
of intermediate goods, and the distinction between the productivity indices for value added and for gross output. We illustrate
their significance for growth accounting using the well known model of Greenwood etal. (in Am Econ Rev 87(3):342–362, 1997),
who find that about 60% of economic growth can be attributed to investment-specific technical change (ISTC). We investigate
the role of intermediate goods in their framework and find that, taking into account the composition of intermediates, ISTC
may well account for between 93 and 96% of post-war US growth.
From book synopsis:
The NBER Macroeconomics Annual presents pioneering work in macroeconomics by leading academic researchers to an audience of public policymakers and the academic community. Each commissioned paper is followed by comments and discussion. This year's edition provides a mix of cutting-edge research and policy analysis on such topics as productivity and information technology, the increase in wealth inequality, behavioral economics, and inflation.
The neoclassical growth model is used to identify the short-run effects of neutral technology shocks, which affect the production of all goods homogeneously, and investment-specific shocks, which affect only investment goods. The real equipment price, crucial for identifying the investment shocks, experiences an abrupt increase in its average rate of decline in 1982, so the analysis is based on a split sample. On the basis of the preferred specification, the two technology shocks account for 73 percent of hours' and 44 percent of output's business cycle variation before 1982, and 38 percent and 80 percent afterward. The shocks also account for more than 40 percent of hours' and 58 percent of output's forecast errors over a three- to eight-year horizon in both samples. The majority of these effects are driven by the investment shocks.
A typical two-digit industry in the United States appears to have approximately constant returns to scale. Three puzzles emerge, however. First, estimates rise at higher levels of aggregation. Second, apparent decreasing returns contradicts evidence of small economic profits. Third, estimates with value added differ substantially from those with gross output. A representative-firm paradigm cannot explain these puzzles but a simple story of aggregation over heterogeneous units can. The authors discuss implications of heterogeneity for calibrating one-sector macroeconomic models, showing that these models sometimes require firm-level parameters but at other times require the 'biased' aggregate parameters. Copyright 1997 by the University of Chicago.
We study a multisector model of growth with differences in TFP growth rates across sectors and derive sufficient conditions for the coexistence of structural change, characterized by sectoral labor reallocation and balanced aggregate growth. The conditions are weak restrictions on the utility and production functions. Along the balanced growth path, labor employed in the production of consumption goods gradually moves to the sector with the lowest TFP growth rate, until in the limit it is the only sector with nontrivial employment of this kind. The employment shares of intermediate and capital goods remain constant during the reallocation process. (JEL O41)
We argue that the network of investment production and purchases across sectors is an important propagation mechanism for understanding business cycles. Empirically, we show that the majority of investment goods are produced by a few “investment hubs” which are more cyclical than other sectors. We embed this investment network into a multisector business cycle model and show that sector-specific shocks to the investment hubs and their key suppliers have large effects on aggregate employment and drive down labor productivity. Quantitatively, we find that sector-specific shocks to hubs and their suppliers account for an increasing share of aggregate fluctuations over time, generating the declining cyclicality of labor productivity and other changes in business cycle patterns since the 1980s.
We develop a Bayesian latent factor model of the joint long-run evolution of GDP per capita for 113 countries over the 118 years from 1900 to 2017. We find considerable heterogeneity in rates of convergence, including rates for some countries that are so slow that they might not converge (or diverge) in century-long samples, and a sparse correlation pattern (“convergence clubs”) between countries. The joint Bayesian structure allows us to compute a joint predictive distribution for the output paths of these countries over the next 100 years. This predictive distribution can be used for simulations requiring projections into the deep future, such as estimating the costs of climate change. The model's pooling of information across countries results in tighter prediction intervals than are achieved using univariate information sets. Still, even using more than a century of data on many countries, the 100-year growth paths exhibit very wide uncertainty.
This paper shows that GDP growth volatility declines with production network diversification. To account for this evidence, I build a multisector model with CES technologies and a cost of complexity in the bundle of intermediates. Production network diversification decreases volatility when intermediate inputs and labor are substitute inputs. U.S. sectoral data suggest that labor and intermediates are substitutes in service sectors. Therefore, a calibrated model that then also matches each country's production network can quantitatively generate the empirical patterns. The model also explains why service-oriented countries are less volatile: service sectors have a more diversified set of suppliers.
We provide a nonlinear characterization of the macroeconomic impact of microeconomic productivity shocks in terms of reduced‐form nonparametric elasticities for efficient economies. We also show how microeconomic parameters are mapped to these reduced‐form general equilibrium elasticities. In this sense, we extend the foundational theorem of Hulten (1978) beyond the first order to capture nonlinearities. Key features ignored by first‐order approximations that play a crucial role are: structural microeconomic elasticities of substitution, network linkages, structural microeconomic returns to scale, and the extent of factor reallocation. In a business‐cycle calibration with sectoral shocks, nonlinearities magnify negative shocks and attenuate positive shocks, resulting in an aggregate output distribution that is asymmetric (negative skewness), fat‐tailed (excess kurtosis), and has a negative mean, even when shocks are symmetric and thin‐tailed. Average output losses due to short‐run sectoral shocks are an order of magnitude larger than the welfare cost of business cycles calculated by Lucas (1987). Nonlinearities can also cause shocks to critical sectors to have disproportionate macroeconomic effects, almost tripling the estimated impact of the 1970s oil shocks on world aggregate output. Finally, in a long‐run growth context, nonlinearities, which underpin Baumol's cost disease via the increase over time in the sales shares of low‐growth bottleneck sectors, account for a 20 percentage point reduction in aggregate TFP growth over the period 1948–2014 in the United States.
We develop inference methods about long-run comovement of two time series. The parameters of interest are defined in terms of population second moments of low-frequency transformations (“low-pass” filtered versions) of the data. We numerically determine confidence sets that control coverage over a wide range of potential bivariate persistence patterns, which include arbitrary linear combinations of I(0), I(1), near unit roots, and fractionally integrated processes. In an application to U.S. economic data, we quantify the long-run covariability of a variety of series, such as those giving rise to balanced growth, nominal exchange rates and relative nominal prices, the unemployment rate and inflation, money growth and inflation, earnings and stock prices, etc.
This paper studies the impact of state-level land-use restrictions on U.S. economic activity, focusing on how these restrictions have depressed macroeconomic activity since 2000. We use a variety of state-level data sources, together with a general equilibrium spatial model of the United States to systematically construct a panel dataset of state-level land-use restrictions between 1950 and 2014. We show that these restrictions have generally tightened over time, particularly in California and New York. We use the model to analyze how these restrictions affect economic activity and the allocation of workers and capital across states. Counterfactual experiments show that deregulating existing urban land from 2014 regulation levels back to 1980 levels would have increased US GDP and productivity roughly to their current trend levels. California, New York, and the Mid-Atlantic region expand the most in these counterfactuals, drawing population out of the South and the Rustbelt. General equilibrium effects, particularly the reallocation of capital across states, account for much of these gains.
U.S. output has expanded only slowly since the recession trough in 2009, even though the unemployment rate has essentially returned to a precrisis, normal level. We use a growth-accounting decomposition to explore explanations for the output shortfall, giving full treatment to cyclical effects that, given the depth of the recession, should have implied unusually fast growth. We find that the growth shortfall has almost entirely reflected two factors: the slow growth of total factor productivity, and the decline in labor force participation. Both factors reflect powerful adverse forces that are largely unrelated to the financial crisis and recession-and that were in play before the recession.
I quantify the contribution of sectoral shocks to business cycle fluctuations in aggregate output. I develop and estimate a multi-industry general equilibrium model in which each industry employs the material and capital goods produced by other sectors. Using data on US industries' input prices and input choices, I find that the goods produced by different industries are complements to one another as inputs in downstream industries' production functions. These complementarities indicate that industry-specific shocks are substantially more important than previously thought, accounting for at least half of aggregate volatility.
Using a dynamic factor model that allows for changes in both the long-run growth rate of output and the volatility of business cycles, we document a significant decline in long-run output growth in the United States. Our evidence supports the view that most of this slowdown occurred prior to the Great Recession. We show how to use the model to decompose changes in long-run growth into its underlying drivers. At low frequencies, a decline in the growth rate of labor productivity appears to be behind the recent slowdown in GDP growth for both the United States and other advanced economies. When applied to real-time data, the proposed model is capable of detecting shifts in long-run growth in a timely and reliable manner. © 2017 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.
After 2004, measured growth in labor productivity and total factor productivity slowed. We find little evidence that this slowdown arises from growing mismeasurement of the gains from innovation in information technology–related goods and services. First, the mismeasurement of information technology hardware is significant preceding the slowdown. Because the domestic production of these products has fallen, the quantitative effect on productivity was larger in the 1995–2004 period than since then, despite mismeasurement worsening for some types of information technology. Hence, our adjustments make the slowdown in labor productivity worse. The effect on total factor productivity is more muted. Second, many of the tremendous consumer benefits from the “new” economy such as smartphones, Google searches, and Facebook are, conceptually, nonmarket: Consumers are more productive in using their nonmarket time to produce services they value. These benefits raise consumer well-being but do not imply that market sector production functions are shifting out more rapidly than measured. Moreover, estimated gains in nonmarket production are too small to compensate for the loss in overall well-being from slower market sector productivity growth. In addition to information technology, other measurement issues that we can quantify (such as increasing globalization and fracking) are also quantitatively small relative to the slowdown. The things at which Google and its peers excel, from Internet search to mobile software, are changing how we work, play and communicate, yet have had little discernible macroeconomic impact…. Transformative innovation really is happening on the Internet. It’s just not happening elsewhere.
In the years since the Great Recession, many observers have highlighted the slow pace of productivity growth around the world. For the United States and Europe, we highlight that this slow pace began prior to the Great Recession. The timing thus suggests that it is important to consider factors other than just the deep crisis itself or policy changes since the crisis. For the United States, at the frontier of knowledge, there was a burst of innovation and reallocation related to the production and use of information technology in the second half of the 1990s and the early 2000s. That burst ran its course prior to the Great Recession. Continental European economies were falling back relative to that frontier at varying rates since the mid-1990s. We provide VAR and panel-data evidence that changes in real interest rates have influenced productivity dynamics in this period. In particular, the sharp decline in real interest rates that took place in Italy and Spain seem to have triggered unfavorable resource reallocations that were large enough to reduce the level of total factor productivity, consistent with recent theories and firm-level evidence.
The pace of business dynamism and entrepreneurship in the U.S. has declined over recent decades. We show that the character of that decline changed around 2000. Since 2000 the decline in dynamism and entrepreneurship has been accompanied by a decline in high-growth young firms. Prior research has shown that the sustained contribution of business startups to job creation stems from a relatively small fraction of high-growth young firms. The presence of these high-growth young firms contributes to a highly (positively) skewed firm growth rate distribution. In 1999, a firm at the 90th percentile of the employment growth rate distribution grew about 31 percent faster than the median firm. Moreover, the 90−50 differential was 16 percent larger than the 50−10 differential reflecting the positive skewness of the employment growth rate distribution. We show that the shape of the firm employment growth distribution changes substantially in the post-2000 period. By 2007, the 90−50 differential was only 4 percent larger than the 50−10, and it continued to exhibit a trend decline through 2011. The overall decline reflects a sharp drop in the 90th percentile of the growth rate distribution accounted for by the declining share of young firms and the declining propensity for young firms to be high-growth firms.
Analytical or coding errors in posterior simulators can produce reasonable but incorrect approximations of posterior moments. This article develops simple tests of posterior simulators that detect both kinds of errors, and uses them to detect and correct errors in two previously published articles. The tests exploit the fact that a Bayesian model specifies the joint distribution of observables (data) and unobservables (parameters). There are two joint distribution simulators. The marginal-conditional simulator draws unobservables from the prior and then observables conditional on unobservables. The successive-conditional simulator alternates between the posterior simulator and an observables simulator. Formal comparison of moment approximations of the two simulators reveals existing analytical or coding errors in the posterior simulator.
The relative price of services rises with development. A standard interpretation of this fact is that cross-country productivity differences must be larger in manufacturing (tradables) than in services (non-tradables). Using detailed data from the International Comparison Program we disaggregate the service sector and show that the behaviour of the relative price is markedly different across two broad classifications of services: traditional services such as government, health, and education featuring a rising relative price with development and non-traditional services such as communication, transportation, insurance and financial services featuring a declining relative price. Moreover, there is a structural transformation within services whereby the share of non-traditional services rises with development. What are the productivity implications of disaggregating the service sector? Using a simple devel-opment accounting framework and a model of the structural transformation within services, we find that cross-country productivity differences are large in non-traditional services, at least as large as those in manufacturing. Development requires also an emphasis on solving the productivity problem in non-traditional services in poor countries.
This paper studies the ability of input–output relationships to generate fluctuations in aggregate output in several multi-sector models, including that of Long and Plosser (1983). In these models, the manufacture of each sector’s output depends on other sectors for materials. These interconnections provide a mechanism for transmission of shocks across sectors and over time. We ask how aggregates from these multi-sector models behave relative to single-sector models. We provide conditions under which the second moment properties of aggregates in these models are the same as their single-sector counterparts. We further demonstrate that a broad class of input–output structures generate aggregates with identical second moment properties, and that the input–output structures in this class provide a poor amplification mechanism for sector shocks.
Long-run forecasts of economic variables play an important role in policy, planning, and portfolio decisions. We consider
forecasts of the long-horizon average of a scalar variable, typically the growth rate of an economic variable. The main contribution
is the construction of prediction sets with asymptotic coverage over a wide range of data generating processes, allowing for
stochastically trending mean growth, slow mean reversion and other types of long-run dependencies. We illustrate the method
by computing prediction sets for 10 to 75 year average growth rates of U.S. real per-capita GDP and consumption, productivity,
price level, stock prices and population.
We study the driving forces of fluctuations in an estimated New Neoclassical Synthesis model of the U.S. economy with several shocks and frictions. In this model, shocks to the marginal efficiency of investment account for the bulk of fluctuations in output and hours at business cycle frequencies. Imperfect competition and, to a lesser extent, technological frictions are the key to their transmission. Labor supply shocks explain a large fraction of the variation in hours at very low frequencies, but are irrelevant over the business cycle. This is important because their microfoundations are widely regarded as unappealing.
This paper presents the neoclassical model of capital accumulation augmented by choice of labor supply as the basic framework of modern real business cycle analysis. Preferences and production possibilities are restricted so that the economy displays steady state growth. Then we explore the implications of the basic model for perfect foresight capital accumulation and for economic fluctuations initiated by impulses to technology. We argue that the neoclassical approach holds considerable promise for enhancing our understanding of fluctuations. Nevertheless, the basic model does have some important shortcomings. In particular, substantial persistence in technology shocks is required if the model economy is to exhibit periods of economic activity that persistently deviate from a deterministic trend.
This paper presents a multisector dynamic general equilibrium model of business cycles with a distinctive feature: aggregate fluctuations are driven by independent sectoral shocks. The model hypothesizes that trade among sectors provides a strong synchronization mechanism for these shocks due to the limited, but locally intense, interaction that is characteristic of such input trade flows. Limited interaction, characterized by a sparse intermediate input-use matrix, reduces substitution possibilities among intermediate inputs which strengthens comovement in sectoral value-added and leads to a postponement of the law of large numbers in the variance of aggregate value-added. The chief virtue of this model is that reliance on implausible aggregate shocks is not necessary to capture the qualitative features of macroeconomic fluctuations. Building on Horvath, 1998, Review of Economic Dynamics 1, 781–808, which establishes the theoretical foundation for the relevance of limited interaction in the context of a stylized multisector model, this paper specifies a more general multisector model calibrated to the US 2-digit Standard Industrial Code economy. Simulations prove the model is able to match empirical reality as closely as standard one-sector business cycle models without relying on aggregate shocks.
We estimate a New-Neoclassical Synthesis business cycle model with two investment shocks. The first, an investment-specific technology shock, affects the transformation of consumption into investment goods and is identified with the relative price of investment. The second shock affects the production of installed capital from investment goods or, more broadly, the transformation of savings into the future capital input. We find that this shock is the most important driver of U.S. business cycle fluctuations in the post-war period and that it is likely to proxy for more fundamental disturbances to the functioning of the financial sector. To corroborate this interpretation, we show that it is closely related to interest rate spreads and that it played a particularly important role in the recession of 2008–2009.
Over the last 40 years, an increasing share of U.S. aggregate E&S investment expenditure has been allocated to capital-goods imports. While capital-goods imports were only 3.5 percent of E&S investment in 1967, by 2008 their share had risen tenfold to 36 percent. The goal of this paper is to measure the contribution of capital-goods imports to growth in U.S. output per hour using a simple growth accounting exercise. We find that capital-goods imports have contributed 20 to 30 percent to growth in U.S. output per hour between 1967 and 2008. More importantly, we find that capital-goods imports have been an increasing source of growth for the US economy: the average contribution of capital-goods imports to growth in U.S .output per hour has increased noticeably since 1967.
Real business cycles are recurrent fluctuations in an economy's incomes, products, and factor inputs - especially labor - that are due to nonmonetary sources. These sources include changes in technology, tax rates and government spending, tastes, government regulation, terms of trade, and energy prices. Most real business cycle (RBC) models are variants or extensions of a neoclassical growth model. One such prototype is introduced. It is then shown how RBC theorists, applying the methodology of Kydland and Prescott (Econometrica 1982), use theory to make predictions about actual time series. Extensions of the prototype model, current issues, and open questions are also discussed.
We develop a framework to assess how successfully standard time series models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle. Copyright 2008 The Econometric Society.
accumulation of capital in an earlier paper, and the adjustment is found nearly to double the importance of technical progress as a source of growth.3 The present paper studies the interaction of productivity change and intermediate input.4 The expansion in the production of intermediate goods occurring because of increased factor efficiency makes it important to distinguish between productivity change originating in a sector and the impact of productivity change on the sector. Productivity change in the first sense refers to the shift in the sectoral technology and is measured by the conventional productivity residual. Productivity change in the second sense measures the equilibrium response to the shifting sectoral technologies, and includes (a) the induced reallocation of factor input between sectors, and (b) the induced expansion in intermediate input, which serves to magnify the effect of technical change. In assessing the importance of productivity change as a source of growth, it is the second sense which is relevant, since it is the impact of productivity change which affects the evolution of the sector, and not the change in factor efficiency occurring within that sector. The distinction between the two aspects of productivity change is analogous to the distinction between nominal and effective tax incidence. An ad-valorem excise tax imposed at a given statutory rate may be regarded as shifting the commodity supply curve upward; the distributional impact of the tax depends on the equilibrium adjustment to this shift. The well-known Harberger (1962) model of tax incidence was, in fact, specifically intended to take these adjustments into account. In an essentially parallel vein, this paper develops the distinction between nominal and effective rates of technical change from the point of view of productivity analysis. Effective rates of productivity change are defined in this paper using the reduced form of an N-sector growth model, where the rate of change of total factor supply, and the rate of change of technological efficiency are assumed to be given exogenously. The growth rates of the endogenous variables-prices and quantities-are expressed as linear combinations 511
Can technology improvements cause productivity slowdowns?
- A Hornstein
- P Krusell