Factor Demand Linkages, Technology Shocks and the Business Cycle

We investigate the business cycle effects of imperfect transmission of technology shocks within a basic real business cycle (RBC) model along two dimensions. First, we assume that agents cannot distinguish a temporary increase in productivity growth from a sustained increase in the underlying growth rate of productivity and instead must conduct signal extraction exercises and update beliefs about the source of aggregated shocks. Second, we propose a technology adjustment cost resulting in the slow diffusion of technological innovations into the production process. Both of these impediments to the transmission of technology result in a large initial wealth effect, increasing investment and hours less, relative to the usual RBC model without these frictions. Furthermore, each of these features is capable of producing a decline in hours on impact of the technology shock matching the negative response in hours found in the data by such works as Gali [American Economic Review89(1), 249–271 (1999)].

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Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?

The American Economic Review ◽

10.1257/aer.89.1.249 ◽

1999 ◽

Vol 89 (1) ◽

pp. 249-271 ◽

Cited By ~ 782

Author(s):

Jordi Galí

Keyword(s):

Simple Model ◽

Business Cycles ◽

Business Cycle ◽

Monopolistic Competition ◽

Real Business Cycle ◽

Technology Shocks ◽

Aggregate Fluctuations ◽

G7 Countries ◽

Technology Shock ◽

The Business Cycle

I estimate a decomposition of productivity and hours into technology and non-technology components. Two results stand out: (a) the estimated conditional correlations of hours and productivity are negative for technology shocks, positive for nontechnology shocks; (b) hours show a persistent decline in response to a positive technology shock. Most of the results hold for a variety of model specifications, and for the majority of G7 countries. The picture that emerges is hard to reconcile with a conventional real-business-cycle interpretation of business cycles, but is shown to be consistent with a simple model with monopolistic competition and sticky prices. (JEL E32, E24)

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The Contribution of Large and Small Employers to Job Creation in Times of High and Low Unemployment

The American Economic Review ◽

10.1257/aer.102.6.2509 ◽

2012 ◽

Vol 102 (6) ◽

pp. 2509-2539 ◽

Cited By ~ 108

Author(s):

Giuseppe Moscarini ◽

Fabien Postel-Vinay

Keyword(s):

Business Cycle ◽

Cross Sections ◽

Job Creation ◽

The United States ◽

Differential Growth ◽

Factor Demand ◽

The Business Cycle ◽

Small Employers ◽

Creation Rate ◽

Differential Growth Rate

We document a negative correlation, at business cycle frequencies, between the net job creation rate of large employers and the level of aggregate unemployment that is much stronger than for small employers. The differential growth rate of employment between initially large and small employers has an unconditional correlation of —0.5 with the unemployment rate, and varies by about 5 percent over the business cycle. We exploit several datasets from the United States, Denmark, and France, both repeated cross sections and job flows with employer longitudinal information, spanning the last four decades and several business cycles. We discuss implications for theories of factor demand. (JEL D22, E23, E32, J23, L25)

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Technology shocks, structural breaks and the effects on the business cycle

Economics Letters ◽

10.1016/j.econlet.2008.03.011 ◽

2008 ◽

Vol 100 (3) ◽

pp. 392-395 ◽

Cited By ~ 4

Author(s):

Vincenzo Atella ◽

Marco Centoni ◽

Gianluca Cubadda

Keyword(s):

Business Cycle ◽

Structural Breaks ◽

Technology Shocks ◽

The Business Cycle

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Decomposition of the Business Cycle Shock and the Default Rate of SMEs in Korea

Journal of Derivatives and Quantitative Studies ◽

10.1108/jdqs-04-2019-b0002 ◽

2019 ◽

Vol 27 (4) ◽

pp. 401-423

Author(s):

Jaesung James Park ◽

Joonkyo Hong ◽

Sumi Na

Keyword(s):

Business Cycle ◽

Capital Accumulation ◽

Structural Var ◽

Technology Shocks ◽

Empirical Results ◽

Long Run ◽

Default Rate ◽

Technology Shock ◽

The Business Cycle ◽

The Impact

This paper, through a structural VAR identified by a long-run restriction which is imposed by a neoclassical growth model, decomposes the real price index of capital accumulation (= deflator for fixed capital accumulations/consumption expenditure deflator), labor productivity (= real GDP/total employee hours), and total employee hours into three business cycle shocks: (i) investment-specific technology shock, (ii) neutral technology shock, and (iii) non-technology shock, and explores which shock has played a significant role in contributing to decreases in the default rate of SMEs. Empirical results drawn from Korean data spanning from 2000:Q1 to 2016:Q2 indicate that when the two technology shocks arise by 1%p, the default rate decreases by 0.03%p to 0.05%p permanently. In contrast, the impact of the non-technology shock on the default rate is highly transitory : the default rate decreases by 0.02%p in response to the 1%p increment in non-technology shock but turns back to its initial level after about three quarters. These imply the technology shocks could account for the most of variations in the default rate of SMEs. Our empirical results, therefore, deliver the policy implication that SME financing should focus on innovative firms with aggressive funding.

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