The Interaction between Business Cycles and Productivity Growth (original) (raw)
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Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations
1996
Using data for the G7 countries, Iestimate conditional correlations of employment and productivity, based on a decomposition of the two series into technology and non-technology components. The picturethatemerges is hardto reconcile with the predictions of the standardReal Business Cycle model. For a majority of countries the following resultsstand out: (a) technology shockt appear to induce a negative comovement between productivity and employment, counterbalancedby apositive comovement generatedby demandshocks, (b) the impulse responses show a persistent decline of employment in response to a positive technology shock, and (c) measured productivity increases temporarily in response to a positive demand shock. generally,the patternof economic fluctuationsattributedto technology shocks seems to be unrelatedto major postwarcyclical episodes. A simple model with monopolistic competition prices, and variable effort is shown to be able to account for the empirical findings.
Technology, demand, and productivity: What an industry model tells us about business cycles
Journal of Economic Dynamics and Control, 2021
In this paper, we study the relative importance of demand and technology shocks in generating business cycle fluctuations, both at the aggregate level and at the level of individual industries. We construct a New Keynesian DSGE model that is highly disaggregated at the industry level with an input-output network structure. Measured productivity in the model fluctuates in response to both technology and demand shocks due to endogenous factor utilization. We estimate the model by the simulated method of moments using U.S. industry data from 1960 to 2005. We find that the aggregate technology shock has zero variance. Exogenous shocks to technology are necessary for our model to fit the data, but these shocks are exclusively industry-specific, uncorrelated across industries. The bulk of the aggregate fluctuations, including those in aggregate measured productivity, are explained through shocks to aggregate demand. This shock structure is supported by a host of information from the disaggregate data. Our second finding is that about half of the decrease in the cyclicality of measured productivity in the U.S. after the mid-1980s can be explained by the reduction in the size of demand shocks, in line with the narrative of the great moderation.
Business cycle non-linearities and productivity shocks
2004
The recent empirical evidence documenting the presence of asymmetries in business cycles represents a challenge for the standard equilibrium models of real business cycle. These models successfully explain most ¿rst and second moments of the actual time series, but cannot replicate non-linear features of the data, unless a non-linear innovation is introduced. This paper aims at investigating the possible non-linearity in the technology shock, the basic innovation in Real Business Cycle models.
Okun's law, productivity innovations, and conundrums in business cycle dating
American Economic Review: Papers …, 2010
The NBER business cycle dating process has traditionally mixed together the fundamental component of business cycles, changes in real output, with changes of employment. This paper makes the case that these long and variable lags disqualify for business cycle dating all the components of labor market behavior, including aggregate hours of work and payroll employment. By definition real GDP is equal to output per hour times aggregate hours of work. This "output identity" can be extended to subdivide aggregate hours into the product of hours per employee, the employment rate, and the labor-force participation rate (LFPR). This paper provides a consistent analysis of the output identity, taking as its point of departure Okun's Law, a rough prediction that a cyclical deviation of output from trend would be divided between a 2/3 response of aggregate hours and a 1/3 response of productivity. In turn, the Okun characterization subdivided the 2/3 hours response into 1/3 for the employment rate, 1/6 for hours per employee, and 1/6 for the LFPR. The paper decomposes data on the components of the output identity into trends and deviations from trends, or "gaps". Its regression analysis reveals regular features of postwar business cycles, including lags of hours and employment behind output and leads of productivity changes ahead of output changes. While Okun's Law was roughly accurate until 1986, regressions for the post-1986 period turn Okun's Law on its head. The elasticity of changes in the hours gap to the output gap was 0.74 before 1986, close to Okun's 2/3, but rose after 1986 to 1.2. Productivity no longer exhibits procyclical fluctuations, rendering as obsolete both the Real Business Cycle model and those aspects of modern macro that include productivity shocks as an explanation of business cycles. Productivity grows slowest in the later stages of the business cycle expansion and most rapidly in the early phase of the business cycle recovery. And this is nothing new. The "end-of-expansion" slowdown in productivity growth and the "early recovery productivity bubble" were identified in economic research more than three decades ago. Hypotheses suggested here for changes in behavior focus on the role of collapses in profits and the stock market in 2000-02 and 2007-09 in creating unprecedented pressure on business executives (whose compensation more than before depends on stock options) to cut costs drastically. But the 2007-09 downturn was much bigger than 2000-02; for every deck chair thrown off the Titanic in 2000-02, three or four deck chairs were tossed overboard in 2007-09. The much larger response of labor hours to output fluctuations after 1986 is linked with other factors that have reduced labor's bargaining power and have increased income inequality. Increased responsiveness of hours may also reflect incentives to push employees into forced part-time work, as well as the role of the internet in making the labor market more flexible.
Labor Market Dynamics and the Business Cycle: Structural Evidence for the United States &ast
Scandinavian Journal of Economics, 2007
AbstractWe use a 12-dimensional VAR to examine the aggregate effects of two structural technology shocks and two policy shocks. For each shock, we examine the dynamic effects on the labor market, the importance of the shock for labor market volatility, and the comovement between labor market variables and other key aggregate variables in response to the shock. We document that
Productivity Shocks and the Business Cycle: Reconciling Recent VAR Evidence
2006
Gali (1999) used a VAR with productivity and hours worked to argue that technology shocks are negatively correlated with labor and are unimportant for the business cycle. More recently, Beaudry and Portier (2003) studied a VAR in productivity and stock prices. Remarkably, they found that the component which has a permanent impact on productivity is almost identical to that which
Spain has recently experienced more than a decade of price stability and economic growth however now is showing one of the most significant slowdowns in economic activity of the EU economies. There is a general consensus that this slowdown in economic activity is particularly important in Spain due to the low level and low rates of growth experienced by total factor productivity (TFP) during more than a decade. Among the key policy elements that could enhance TFP of manufacturing firms in Spain we find those related to human capital, foreign direct investment, and process innovations. We evaluate the effect of recessions on the productivity growth of firms with different level of productivity. We present evidence on the dynamic of firm’s TFP through the business cycle allowing for a differentiated behavior for technological leaders and followers. We observe lower persistence and faster convergence in TFP during recessions and, higher persistence and non convergence in TFP during exp...
Technology Shocks, Employment, and Labor Market Frictions
Recent empirical evidence suggests that a positive technology shock leads to a decline in labor inputs. However, the standard real business model fails to account for this empirical regularity. Can the presence of labor market frictions address this problem, without otherwise altering the functioning of the model? We develop and estimate a real business cycle model using Bayesian techniques that allows, but does not require, labor market frictions to generate a negative response of employment to a technology shock. The results of the estimation support the hypothesis that labor market frictions are the factor responsible for the negative response of employment.
Productivity Growth and Employment: Theory and Panel Estimates
2004
Theoretical predictions of the effect of TFP growth on employment are ambiguous, and depend on the extent to which new technology is embodied in new jobs. We estimate a model for employment, wages and investment with an annual panel for the United States, Japan and Europe and find that TFP growth increases employment. For the United States TFP growth explains the trend change in unemployment. We evaluate the model and find that creative destruction plays no part in aggregate unemployment dynamics. The model can explain up to half of the estimated impact of growth on unemployment.