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

explain aggregate fluctuations? American Economic Review, v.89, n.1, p.249-271,

Mar.1999.

KYDLAND, F.; PRESCOTT, E.C. Time to build and aggregate fluctuations.

Econometrica, v.50, n.6, p.1345-1370, 1982.

KRUEGER, D. Quantitative Macroeconomics: an introduction. University of

Pennsylvania, Mimeo., 2007, 101p.

MANKIW, N.G. Real business cycles: a new keynesian perspective. Journal of

Economic Perspectives, v.3, n.3, p.79-90, Summer 1989.

PRESCOTT, E.C. Theory ahead of business cycle measurement. Federal Reserve

Bank of Minneapolis Quarterly Review, v.10, n.4, p.9-22, Fall 1986 [1986a].

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

of Economics and Statistics, v.39, n.3, p.312-320, Aug.1957.

SUMMERS, L.H. Some skeptical observations on real business cycle theory. Federal

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

matheus.albergaria.magalhaes@gmail.com

http://www.sites.google.com/site/malbergariademagalhaes