1) The document analyzes the effects of technology shocks on labor input at business-cycle frequencies. It revisits the debate on whether technology shocks are the main drivers of short-run fluctuations in economic activity. 2) The author aims to contribute to this debate by re-estimating structural vector autoregression (SVAR) models using different US economic data sources and specifications for labor input. 3) The results confirm previous findings that labor input displays a contractionary response to technology shocks, depending on how labor input is modeled in the SVAR. Estimation results are sensitive to whether labor input is specified in first-differences or levels.

1. On the Effects of Technology Shocks over
Labor Input at Business-Cycle Frequencies
An Empirical Note
Matheus Albergaria de Magalh˜es1
a
Paulo Picchetti2
1 Instituto
2 Escola
Jones dos Santos Neves (IJSN) and FUCAPE Business School
de Economia de S˜o Paulo - Funda¸˜o Get´lio Vargas (EESP-FGV)
a
ca
u
Quarto Encontro de Economia do Esp´
ırito Santo (IV EEES)
.
November 4th , 2013

2. Organization
Motivation
Contribution
Results
Conclusions
References

3. Motivation
Question: what is the importance of technology shocks in the
short run?
Economists have tried to understand the importance of
technology for decades (e.g., Solow 1957).
One strand of the literature considers technology shocks as
the main source of short-run fluctuations (e.g., Kydland and
Prescott 1982; Prescott 1986a,b).
Other authors have posed important empirical challenges to
such claims (e.g., Summers 1986; Mankiw 1989; Shea 1999).

4. Motivation
Gal´ (1999) poses a challenge for first-generation RBC models.
ı
Author estimates a decomposition of productivity and hours
worked in: (i) techonology and (ii) non-technology
(”demand”) components.
Methodology: Structural Vector Autoregressions (SVAR)
(Blanchard and Quah 1989).

5. Motivation
Gal´ (1999) main results:
ı’s
1. Estimated conditional correlations between labor input and
productivity measures have a: (i) negative sign for technology
shocks and (ii) positive sign for non-technology shocks.
2. Impulse response functions display a contractionary pattern for
labor input measures in response to technology shocks.
3. Productivity measures exhibit a pattern of temporary increase
due to positive non-technology shocks.

6. Motivation
Dynamic Responses of Macroeconomic Variables to a Technology Shock
First-Generation RBC Model
Source: Krueger (2007, Fig.11.2, p.91).

7. Motivation
Productivity and Hours Worked (Unconditional Correlations)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Gal´ (1999, Fig.1, p.260).
ı

8. Motivation
Productivity and Hours Worked (Conditional Correlations)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Gal´ (1999, Fig.1, p.260).
ı

9. Motivation
Productivity and Hours Worked (Correlation Estimates - SVAR Model)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Gal´ (1999, Table 1, p.259).
ı

10. Motivation
Dynamic Effects of Technology and Nontechnology Shocks (SVAR Model)
United States, 1948:01-1994:04 (Quarterly Data)
Source: Gal´ (1999, Fig.2, p.261).
ı

11. Motivation
Other authors have reached similar conclusions to Gal´ (1999).
ı
Shea (1999): working with R&D and patent data, concludes
that favorable technology shocks do not affect productivity
measures at any horizon, except for a subset of industries
dominated by process innovations.
Basu, Fernald and Kimball (2006): using modified Solow
residuals, authors uncover a result where input usage presents
a contractionary response to technology shocks.

12. Motivation
There were disagreements related to the main results reported
by Gal´ (1999).
ı
Christiano, Eichenbaum and Vigfusson (2003) (CEV): labor
input’s dynamic response may depend on the way one models
its Data-Generating Process (DGP).
If hours worked are specified as levels (I(0) process), labor
input displays a positive response to technology shocks in the
short run.
Francis and Ramey (2005) (FR): sensitivity tests reject CEV’s
main findings and confirm the contractionary response of
labor input to technology shocks.

13. Contribution
My goals today:
1. Revisit the technology-employment debate (emphasis on
conditional correlations and impulse-response functions derived
from SVAR’s estimation).
2. Use different datasets related to the American economy.
3. Discuss the main results reported in the literature.

14. Contribution
Empirical Strategy:
1. Employ Gal´ (1999), FR’s (2005) and CEV’s (2003) original
ı’s
datasets.
2. Reestimate alternative SVAR specifications (hours worked in
first-differences or levels).
3. Run Granger-causality tests relating identified technology
shocks and demand measures (Hall-Evans Invariance
Property ).

15. Contribution
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).

16. Results
Impulse Response Functions for Gal´ (1999) Data (First-Differenced Hours)
ı’s

17. Results
Impulse Response Functions for Gal´ (1999) Data (Hours in Levels)
ı’s

18. Results
Impulse Response Functions for FR’s (2003) Data (First-Differenced Hours)

19. Results
Impulse Response Functions for FR’s (2003) Data (Hours in Levels)

20. Results
Impulse Response Functions for CEV’s (2003) Data (First-Differenced Hours)

21. Results
Impulse Response Functions for CEV’s (2003) Data (Hours in Levels)

22. Results
Exogeneity Tests - First-Differences Specifications

23. Results
Exogeneity Tests - Levels Specifications

24. Conclusions
Results confirm Gal´ and FR’s findings...
ı’s
...at the same time that they go against CEV’s results.
Conclusion: estimation results depend on how one specifies
labor input’s DGP.

25. Conclusions
Observations:
1. There are RBC models where labor input can display a negative
response to technology shocks (e.g., Collard and Dellas 2004).
2. The adequacy of RBC models should not be solely based on
the dynamic behavior of labor input measures (narrow
criterium) (Wang and Weng 2007).

26. Conclusions
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-run
restrictions (Bocola, Hagedorn and Manovskii 2011).
More work is still needed to demonstrate which theoretical
approach (flexible or rigid price settings) should be preferred
when studying the effects of technology shocks in the short
run.

27. 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.

28. 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.

29. References
GAL´ J. Technology, employment and the business cycle: do technology shocks
I,
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].

30. 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.

31. Thank You
Matheus Albergaria de Magalh˜es
a
matheus.albergaria.magalhaes@gmail.com
http://www.sites.google.com/site/malbergariademagalhaes