This paper analyzes the post-crisis slump in 30 European economies during the 2008Q1–2014Q4 period using the business cycle accounting (BCA) method à la [Chari, V. V., P. Kehoe, and E. McGrattan. 2007. “Business Cycle Accounting.” Econometrica 75 (3): 781–836]. We find that the deterioration in the efficiency wedge is the most important driver of the European Great Recession and that this adverse shock persists throughout our sample. Moreover, we find that countries with higher growth in nonperforming loans feature a smaller decline in efficiency wedges. These findings support the emerging literature on resource misallocation triggered by financial crises.
We are grateful to the editor, anonymous referee, Sylvain Barde, Pedro Brinca, Jagjit Chadha, Patrick Minford, Claudio Morana, Jakub Muck, and participants of the University of Kent MaGHiC PhD workshop, the 47th MMF Annual Conference, and the SCE 22nd International Conference on Computing in Economics and Finance and seminars at the University of Barcelona, RWI Essen and Keio University for their helpful comments.
A Sensitivity analysis
A.1 Government wedges
In CKM ( 2007) and BCKM ( 2016) the estimation is conducted using data of output, investment, labor and government wedges. The main reason why we use consumption data instead of the government wedge data for estimation is because for Latvia and Romania there are periods in which the government wedge turns negative due to a large trade deficit. Since we cannot take logs of negative numbers, we use the consumption data which is always positive. We also prefer using the consumption data to decompose the fluctuation in consumption in the same way as the other endogenous variables.
Technically speaking, the consumption data series can be reproduced from the linearized resource constraint up to the linearization error. Therefore, the difference in estimation results should be coming from the linearization error. When the fluctuations in government wedges and consumption are large, the linearization error might become large enough to affect the accounting results.
Figure 8 presents the simulation results for output using government wedges as an observable for countries except for Latvia and Romania. In order to compute the cross-country mean, we used the benchmark results for Latvia and Romania. The results show that using government wedges as observables increases the importance of investment wedges in accounting for the post crisis slump. The general reason behind this result is that when we use the government wedge as an observable the linearized consumption series in the model drops less than that in the data. As a result, the role of investment wedges which encourage consumption over investment is overstated. However, the quantitative impact is not substantial.
A.2 Adjustment costs
In this section, we investigate the impact of investment adjustment costs on the accounting results. We follow BCKM ( 2016) and assume quadratic adjustment costs in the capital accumulation equation:
where Φ = Λ − (1 − δ).
The impact of investment adjustment costs have been discussed in CKM ( 2007). They show that adjustment costs should systematically increase the contribution of investment wedges on output fluctuation. We find that this is true in our sample as well.
Figure 9 presents the simulation results for output from the model with investment adjustment costs. We follow CKM ( 2007) and BCKM ( 2016) and set the adjustment cost parameter ϕ for each country such that the marginal Tobin’s q is equal to 1/4. The results show that the contribution of investment wedges on the post crisis slump is indeed greater when investment adjustment cost is included in the model. Nonetheless, the efficiency wedges remain the most dominant wedge in accounting for the post crisis slump in Europe.
B Decomposition of consumption, investment and labor
In this section, we conduct the BCA decomposition for consumption, investment and labor. Table 9 and Table 10 show that efficiency wedges contribute significantly to the drop in consumption and investment in all countries except for Malta. Table 11 shows that in Germany, Slovakia, Czech Republic, Sweden and United Kingdom labor is growing relative to the pre-crisis trend. However, there is no clear pattern regarding the contributions of each wedge to the changes in labor.
|Country||Consumption drop (%)||Wedge Contributions (%)|
Source: Authors calculation
|Country||Investment drop (%)||Wedge Contributions (%)|
|Country||Labor Drop (%)||Wedge Contributions (%)|
C Population weighted results
Figure 10 presents the detrended data of each country weighted by its population. It is clear that the population weighted average of each variable falls less than the benchmark simple mean of them. This is because the countries that experienced the largest economic down turn such as Greece, Estonia and Latvia are small in terms of population while those that experienced a much smaller economic down turn such as Germany and France are much larger in terms of population.
Figure 11 presents the population weighted wedges. The population weighted average efficiency and labor wedge decline less than their benchmark simple mean counterparts. Government wedges increase less in the population weighted average than in the benchmark simple mean. The interesting result is the investment wedge. The population weighted average investment wedges gradually returns to the trend level while the benchmark simple mean continues to fall. This implies that investment market distortions in large countries gradually resolved while those in smaller countries remain.
Figure 12 presents the population weighted average of the accounting results for output. The results show that the main reason that the population weighted average output dropped less than the benchmark simple mean is because the efficiency wedges decline less and investment wedges recover to the trend level in the population weighted results.
D Country groups
The countries in each regional group are listed in Table 12.
|Eastern Europe||Bulgaria, Estonia, Latvia, Lithuania, Slovakia, Slovenia,|
|Czech Republic, Hungary, Poland, Romania|
|Southern Europe||Cyprus, Greece, Italy, Portugal, Spain|
|Euro||Austria, Belgium, Cyprus, Estonia, Finland, France, Germany,|
|Greece, Ireland, Italy, Latvia, Luxembourg, Netherlands, Portugal, Slovakia, Slovenia, Spain|
|Nordic Countries||Denmark, Finland, Iceland, Norway, Sweden|
|BeNeLux||Belgium, Luxembourg, the Netherlands|
|British Isles||Ireland and the United Kingdom|
Source: Authors' definition
Adjemian, S., H. Bastani, M. Juillard, F. Mihoubi, G. Perendia, M. Ratto, and S. Villemot. 2011. Dynare: Reference Manual Version 4.Dynare Working Paper Series, 1.Search in Google Scholar
Bernanke, B., M. Gertler, and S. Gilchrist. 1999. “The Financial Accelerator in a Quantitative Business Cycle Framework, ch. 21.”. In Taylor, J., and M. Woodford (Eds.), Handbook of Macroeconomics. Vol. 1C, 1341–1393. Amsterdam, The Netherlands: Elsevier.10.1016/S1574-0048(99)10034-XSearch in Google Scholar
Brinca, P., V. V. Chari, P. Kehoe, and E. McGrattan. 2016. “Accounting for Business Cycles.”. In Taylor, J., and H. Uhlig (Eds.), Handbook of Macroeconomics. Vol. 2A, 1013–1063. Amsterdam, The Netherlands: Elsevier.10.1016/bs.hesmac.2016.05.002Search in Google Scholar
Brinca, P., N. Iskrev, and F. Loria. 2017. On Identification Issues in Business Cycle Accounting Models. mimeo.Search in Google Scholar
Buera, F., and B. Moll. 2015. “Aggregate Implications of a Credit Crunch: The Importance of Heterogeneity.” American Economic Journal: Macroeconomics 7 (3): 1–42.10.1257/mac.20130212Search in Google Scholar
Carlstrom, C., and T. Fuerst. 1997. “Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis.” American Economic Review 87 (5): 893–910.10.26509/frbc-wp-199602Search in Google Scholar
Chakraborty, S. 2009. “The Boom and the Bust of the Japanese Economy: A Quantitative Look at the Period 1980–2000.” Japan and the World Economy 21 (1): 116–131.10.1016/j.japwor.2008.01.001Search in Google Scholar
Chakraborty, S., and K. Otsu. 2013. “Business Cycle Accounting of the BRIC Economies.” B.E. Journal of Macroeconomics 13 (1): 381–413.Search in Google Scholar
Cho, D., and A. Doblas-Madrid. 2013. “Business Cycle Accounting East and West: Asian Finance and the Investment Wedge.” Review of Economic Dynamics 16 (4): 724–744.10.1016/j.red.2012.10.003Search in Google Scholar
Cole, H., and L. Ohanian. 2004. “New Deal Policies and the Persistence of the Great Depression: A General Equilibrium Analysis.” Journal of Political Economy 112 (4): 779–816.10.1086/421169Search in Google Scholar
Gertler, M., and N. Kiyotaki. 2010. “Financial Intermediation and Credit Policy in Business Cycle Analysis.”. In Friedman, B., and M. Woodford (Eds.), Handbook of Monetary Economics. Vol. 3, 547–599. Amsterdam, The Netherlands: Elsevier.10.1016/B978-0-444-53238-1.00011-9Search in Google Scholar
Gertler, M., N. Kiyotaki, and A. Queralto. 2012. “Financial Crises, Bank Risk Exposure and Government Financial Policy.” Journal of Monetary Economics 59: S17–S34.10.1016/j.jmoneco.2012.11.007Search in Google Scholar
Inaba, M., and K. Nutahara. 2009. “The Role of Investment Wedge in the Carlstrom-Fuerst Economy and Business Cycle Accounting.” Economics Letters 105: 200–203.10.1016/j.econlet.2009.07.011Search in Google Scholar
Khan, A., and J. Thomas. 2013. “Credit Shocks and Aggregate Fluctuations in an Economy with Production Heterogeneity.” Journal of Political Economy 121 (6): 1055–1107.10.3386/w17311Search in Google Scholar
Klein, A., and K. Otsu. 2013. Efficiency, Distortions and Factor Utilization During the Interwar Period.University of Kent School of Economics Discussion Papers, 2013, KDPE-1317.Search in Google Scholar
Otsu, K. 2010. “A Neoclassical Analysis of the Asian Crisis: Business Cycle Accounting for a Small Open Economy.” B.E. Journal of Macroeconomics 10 (1):. Article 17.10.2202/1935-1690.1980Search in Google Scholar
Otsu, K. 2012. “How Well Can Business Cycle Accounting Account for Business Cycles?” Economics Bulletin 32 (2): 1774–1784.Search in Google Scholar
Uhlig, H. 2001. “A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily.”. In Computational Methods for the Study of Dynamic Economies. , edited by Marimon, R., and A. Scott (Eds.), Oxford UK: Oxford University Press 30–61.10.1093/0199248273.003.0003Search in Google Scholar
©2018 Walter de Gruyter GmbH, Berlin/Boston