On the Effects of Technology Shocks overLabor Input at Business-Cycle Frequencies
An Empirical Note
Matheus Albergaria de Magalhaes1
Paulo Picchetti2
1Instituto Jones dos Santos Neves (IJSN) and FUCAPE Business School
2Escola de Economia de Sao Paulo - Fundacao Getulio Vargas (EESP-FGV)
Quarto Encontro de Economia do Espırito Santo (IV EEES)November 4t.h, 2013
Organization
Motivation
Contribution
Results
Conclusions
References
Motivation
I Question: what is the importance of technology shocks in theshort run?
I Economists have tried to understand the importance oftechnology for decades (e.g., Solow 1957).
I One strand of the literature considers technology shocks asthe main source of short-run fluctuations (e.g., Kydland and
Prescott 1982; Prescott 1986a,b).
I Other authors have posed important empirical challenges tosuch claims (e.g., Summers 1986; Mankiw 1989; Shea 1999).
Motivation
I Galı (1999) poses a challenge for first-generation RBC models.
I Author estimates a decomposition of productivity and hoursworked in: (i) techonology and (ii) non-technology(”demand”) components.
I Methodology: Structural Vector Autoregressions (SVAR)(Blanchard and Quah 1989).
Motivation
I Galı’s (1999) main results:
1. Estimated conditional correlations between labor input andproductivity measures have a: (i) negative sign for technologyshocks and (ii) positive sign for non-technology shocks.
2. Impulse response functions display a contractionary pattern forlabor input measures in response to technology shocks.
3. Productivity measures exhibit a pattern of temporary increasedue to positive non-technology shocks.
Motivation
Dynamic Responses of Macroeconomic Variables to a Technology Shock
First-Generation RBC Model
Source: Krueger (2007, Fig.11.2, p.91).
Motivation
Productivity and Hours Worked (Unconditional Correlations)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Galı (1999, Fig.1, p.260).
Motivation
Productivity and Hours Worked (Conditional Correlations)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Galı (1999, Fig.1, p.260).
Motivation
Productivity and Hours Worked (Correlation Estimates - SVAR Model)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Galı (1999, Table 1, p.259).
Motivation
Dynamic Effects of Technology and Nontechnology Shocks (SVAR Model)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Galı (1999, Fig.2, p.261).
Motivation
I Other authors have reached similar conclusions to Galı (1999).
I Shea (1999): working with R&D and patent data, concludesthat favorable technology shocks do not affect productivitymeasures at any horizon, except for a subset of industriesdominated by process innovations.
I Basu, Fernald and Kimball (2006): using modified Solowresiduals, authors uncover a result where input usage presentsa contractionary response to technology shocks.
Motivation
I There were disagreements related to the main results reportedby Galı (1999).
I Christiano, Eichenbaum and Vigfusson (2003) (CEV): laborinput’s dynamic response may depend on the way one modelsits Data-Generating Process (DGP).
I If hours worked are specified as levels (I(0) process), laborinput displays a positive response to technology shocks in theshort run.
I Francis and Ramey (2005) (FR): sensitivity tests reject CEV’smain findings and confirm the contractionary response oflabor input to technology shocks.
Contribution
I My goals today:
1. Revisit the technology-employment debate (emphasis onconditional correlations and impulse-response functions derivedfrom SVAR’s estimation).
2. Use different datasets related to the American economy.
3. Discuss the main results reported in the literature.
Contribution
I Empirical Strategy:
1. Employ Galı’s (1999), FR’s (2005) and CEV’s (2003) originaldatasets.
2. Reestimate alternative SVAR specifications (hours worked infirst-differences or levels).
3. Run Granger-causality tests relating identified technologyshocks and demand measures (Hall-Evans InvarianceProperty).
Contribution
I Two Contributions of this paper:
1. Use of different data sources (robustness) (e.g., Whelan 2009).
2. Results are sensitive to the way labor input is modelled(first-differences or levels).
Results
Impulse Response Functions for Galı’s (1999) Data (First-Differenced Hours)
Results
Impulse Response Functions for Galı’s (1999) Data (Hours in Levels)
Results
Impulse Response Functions for FR’s (2003) Data (First-Differenced Hours)
Results
Impulse Response Functions for FR’s (2003) Data (Hours in Levels)
Results
Impulse Response Functions for CEV’s (2003) Data (First-Differenced Hours)
Results
Impulse Response Functions for CEV’s (2003) Data (Hours in Levels)
Results
Exogeneity Tests - First-Differences Specifications
Results
Exogeneity Tests - Levels Specifications
Conclusions
I Results confirm Galı’s and FR’s findings...
I ...at the same time that they go against CEV’s results.
I Conclusion: estimation results depend on how one specifieslabor input’s DGP.
Conclusions
I Observations:
1. There are RBC models where labor input can display a negativeresponse to technology shocks (e.g., Collard and Dellas 2004).
2. The adequacy of RBC models should not be solely based onthe dynamic behavior of labor input measures (narrowcriterium) (Wang and Weng 2007).
Conclusions
I Future Research:
1. Inclusion of investment-specific techonology shocks (Fisher
2006).
2. New technology measures (Alexopoulos 2011).
3. Importance of heterogeneous inputs for SVAR’s long-runrestrictions (Bocola, Hagedorn and Manovskii 2011).
I More work is still needed to demonstrate which theoreticalapproach (flexible or rigid price settings) should be preferredwhen studying the effects of technology shocks in the shortrun.
References
ALEXOPOULOS, M. Read all about it!! What happens following a technology shock?
American Economic Review, v.101, n.4, p.1144-1179, Jun.2011.
BASU, S.; FERNALD, J.G.; KIMBALL, M. Are technology improvements
contractionary? American Economic Review, v.96, n.5, p.1418-1448, Dec.2006.
BLANCHARD, O.J.; QUAH, D.T. The dynamic effects of aggregate demand and
supply disturbances. American Economic Review, v. 79, n. 4, p. 655-673, Sep.1989.
BOCOLA, L.; HAGEDORN, M.; MANOVSKII, I. Identifying technology shocks in
models with heterogeneous inputs. University of Pennsylvania, Mimeo., Mar.2011,
36p.
References
CHRISTIANO, L.J.; EICHENBAUM, M.; VIGFUSSON, R. What happens after a
technology shock? Northwestern University, Mimeo., May 2003, 52p.
COLLARD, F.; DELLAS, H. Supply shocks and employment in an open economy.
Economics Letters, v.82, p.231-237, 2004.
FISHER, J.M. The dynamic effects of neutral and investment-specic technology
shocks. Journal of Political Economy, v.114, n.3, p.413-452, Jun.2006.
FRANCIS, N.; RAMEY, V.A. Is the technology-driven real business cycle hypothesis
dead? Shocks and aggregate fluctuations revisited. Journal of Monetary Economics,
v.52, n.8, p.1379-1399, Nov.2005.
References
GALI, J. Technology, employment and the business cycle: do technology shocks
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KYDLAND, F.; PRESCOTT, E.C. Time to build and aggregate fluctuations.
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MANKIW, N.G. Real business cycles: a new keynesian perspective. Journal of
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PRESCOTT, E.C. Theory ahead of business cycle measurement. Federal Reserve
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PRESCOTT, E.C. Response to a skeptic. Federal Reserve Bank of Minneapolis
Quarterly Review, v.10, n.4, p.28-33, Fall 1986 [1986b].
References
SHEA, J. What do technology shocks do? In: BERNANKE, B.S.; ROTEMBERG, J.
(Eds.). NBER Macroeconomics Annual 1998, v.13, Jan.1999, p.275-322.
SOLOW, R.M. Technical change and the aggregate production function. The Review
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Reserve Bank of Minneapolis Quarterly Review, v.10, n.4, p.23-27, Fall 1986.
WANG, P.; WEN, Y. A defense of RBC: understanding the puzzling effects of
technology shocks. Federal Reserve Bank of Saint Louis, Mimeo., Jun.2007, 34p.
WHELAN, K. Technology shocks and hours worked: checking for robust conclusions.
Journal of Macroeconomics, v.31, n.2, p.231-239, Jun.2009.
Thank You
Matheus Albergaria de Magalhaes
http://www.sites.google.com/site/malbergariademagalhaes