Accepted Manuscript Sectoral dynamics and business cycles Manjola Tase
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Economics Letters
Received date : 27 August 2018 Revised date : 30 November 2018 Accepted date : 9 December 2018 Please cite this article as: M. Tase, Sectoral dynamics and business cycles. Economics Letters (2018), https://doi.org/10.1016/j.econlet.2018.12.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Highlights
There is evidence of asymmetry in different phases of business cycles. Recessions are associated with larger changes in sectoral composition than expansions.
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Sectoral Dynamics and Business Cycles
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Manjola Tase∗
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November 30, 2018
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Abstract
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I construct an index of sectoral dynamics to characterize changes in the sectoral composition of economic activity. There is evidence of asymmetry in different phases of business cycles with recessions being associated with larger changes in sectoral composition than expansions. Since the 1990s, while sectoral changes appear to be smaller and spread across more sectors, the correlation between sectoral dynamics and GDP growth is higher. I find that recession duration in the impulse response functions from a VAR of sectoral dynamics and GDP growth rate is similar to recession duration observed in the data.
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Keywords: Structural changes, business cycles
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JEL Classification: E32, E24
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∗
Board of Governors of the Federal Reserve System. Email:
[email protected]. Address: Board of Governors of the Federal Reserve System, Mailstop 85, 20th Street and Constitution Avenue N.W., Washington, DC 20551. I would like to thank an anonymous reviewer, George Hall, Zeynep Senyuz, David Miller and seminar participants at Brandeis University and the Board of Governors of the Federal Reserve System for helpful comments. The analysis and conclusions set forth are my own and do not necessarily reflect the views of the Board of Governors or the staff of the Federal Reserve System. Declarations of interest: none.
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Introduction
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This paper investigates changes in the allocation of economic activity across sectors over time and
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explores how these sectoral dynamics relate to business cycles. I construct a simple index of sectoral
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dynamics based on changes in economic activity across sectors. The index is constructed for various
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measures of economic activity, levels of sectoral disaggregation, coverage, and time frequency. The
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larger the index, the more pronounced are the changes in the sectoral composition of the economy.
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I document the following facts about sectoral dynamics and business cycles.
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First, there is evidence of asymmetry in different phases of business cycles with recessions be-
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ing associated with larger changes in sectoral composition than expansions. For example, over
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the period 1948–2010, sectoral shifts were about 1.5 times larger in recessions than in expansions.
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Furthermore, the larger the changes in sectoral composition, the more severe the recessions were.
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Second, starting from the 1990s, there is a strengthening of the correlation between sectoral dy-
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namics and aggregate output. Third, while until the 1990s, business cycles were characterized by
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large cyclical changes in the share of the Durables sector, afterwards, sectoral changes appear to
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be smaller and spread across more sectors.
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I also perform a simple simulation exercise to replicate the stylized facts on the relationship be-
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tween sectoral dynamics and business cycles. I find that recession duration in the impulse response
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functions from a VAR of sectoral dynamics and GDP growth rate is similar to recession duration
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observed in the data.
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I use industry-level data on output, value added and employment from different sources as described
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in the Appendix with sectoral coverage, disaggregation, time frame and frequency varying across
Index of sectoral dynamics and stylized facts
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datasets. I measure sectoral dynamics as the average change in the sectoral shares of total output
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over two consecutive periods across all sectors, as shown in ( 1):
SecDynamicst =
1X |ωi,t − ωi,t−1 |, n
(1)
i
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where ωi,t denotes the detrended sector i share of total output at time t.
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The sector shares are detrended as the sectors which are more sensitive to business cycle condi-
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tions have also been shrinking over time. As such using non detrended sector share could mechan-
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ically produce a negative correlation between the index of sectoral dynamics and GDP growth.
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The index presented in this paper is in the spirit of Lilien (1982). Lilien (1982) constructs a
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measure of structural shifts within the labor market and argues that sectoral shifts are represented
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by a positive correlation between the dispersion of the employment growth rate across sectors and
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the level of the unemployment rate. The index in this note is based on various measures sectoral
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economic activity including not only employment but value added and output as well and it is not
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affected by changes in labor intensity in a given sector.
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Figure 1 plots the index for various levels of sectoral disaggregation using value added or
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employment data. Summary statistics for the index over stages of the business cycles and over time
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are shown in Table 1. Both Figure 1 and Table 1 show that recessions are associated with larger
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values of this index than expansions, suggesting that most of the reallocation of economic activity
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across sectors occurs during recessions. Furthermore, the larger the value of the index, the larger
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is the drop in GDP growth. Focusing on recession periods only, the negative correlation suggests
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that the larger the sectoral dynamics, the more pronounced the recessions are. These results are 1
I am indebted to an anonymous reviewer for pointing this out. Indeed, calculating the index using the non detrended sector shares shows that about half or more of the correlation between the index and aggregate growth arise from trends in sector shares.
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robust across a variety of levels of disaggregation as well as the basis for the construction of sectoral
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shares.
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Since the 1990s, there is a strengthening of the correlation between sectoral dynamics and
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aggregate output across all specifications, suggesting an increased contribution of sectoral dynamics
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to GDP growth. This is in line with Foerster et. al (2011) who find a considerable increase in the
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role of the idiosyncratic shocks. .5
percentage points
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.3
.2
.1
0 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 Sectoral Dynamics(value added, 15)
Sectoral Dynamics(value added, 22)
Sectoral Dynamics(value added, 35)
Sectoral Dynamics(employment, 14)
Figure 1: Index of Sectoral Dynamics. The numbers in parentheses correspond to the number of sectors. The data for (value added, 15) and (value added, 22) from BEA’s Industry Accounts, (value added, 35) is from Dale Jorgenson’s 35-sectors KLEMS, (employment,14) is from BLS’s Current Employment Statistics. Details are provided in the data appendix. All sector shares are detrended prior to calculating the indexes.
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While recessions are associated with large sectoral shifts, the magnitude and distribution of
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these shifts have been changing over time. Figure 2 shows that the range of the change in the
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sectoral shares was wider before the mid–1980s. However, the standard deviation of the change in 4
mean(Index | recession) mean(Index | expansion) mean(Index|recession) mean(Index|expansion)
corr(Index, growth) corr(Index, growth)|recession corr(Index, growth)|before 1990 corr(Index, growth)|after 1990 mean(Index pre 90s) mean(Index post 90s)
SecDyn(35) 0.116 0.077 1.5 -0.387 -0.325 -0.442 -0.516 0.093 0.070
SecDyn(22) 0.152 0.111 1.4 -0.189 -0.602 -0.118 -0.713 0.126 0.112
SecDyn(15) 0.204 0.148 1.4 -0.183 -0.565 -0.136 -0.644 0.170 0.148
SecDyn(CES) 0.153 0.102 1.5 -0.331 -0.473 -0.348 -0.674 0.118 0.095
Table 1: Summary Statistics.SecDyn(35), SecDyn(22) and SecDyn(15) and SecDyn(CES) correspond to the index of sectoral dynamics using linearly detrended sector shares. For SecDyn(35), SecDyn(22) and SecDyn(15) sector shares are the sector’s share of value added to total output. The numbers in parentheses correspond to the number of sectors. Sector shares in SecDyn(CES, 16) are based on employment. The time coverage for SecDyn(35) is 1960–2005; for SecDyn(22) and SecDyn(15) 1947–2010; and, for SecDyn(CES, 16) 1939–2013. Details are provided in the data appendix. All sector shares are detrended prior to calculating the indexes.
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sectoral shares is largely unchanged.
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In the period before the mid–1980s, the largest changes in the sectoral shares were concentrated
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in the “Durables goods” sector, with the share of Durables shrinking in recessions and increasing in
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expansions. During the period 1948–1983, there were 16 recession years and 19 expansion years.2
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Durables had the largest drop in sectoral share in 10 out of the 16 recession years and the largest
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increase in 10 out of the 19 expansion years which were also preceded by an expansion year. The
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period 1984–2010 shows a different picture. More sectors exhibited large changes in both recessions
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and expansions. Out of the 6 recession years in the period 1984–2010, Durables had the largest
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drop in 3 years, followed by Construction and Mining. During the expansions years, Finance and
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Insurance, Professional Services and Transportation were the sectors with the largest increases.
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The period 1984–2010 shows a different picture. More sectors exhibited large changes in both 2 NBER’s dating of business cycles is provided as a quarterly and monthly date, while the sectoral data used is at an annual frequency. I have labeled a year as a recession year if the economy was in recession for any period during that year.
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2 Recessions Min(Change in Sectoral Shares) Max(Change in Sectoral Shares) Sd(Change in Sectoral Shares)
1.5
percentage points
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0.5
0
−0.5
−1
−1.5
−2 1940
1950
1960
1970
1980
1990
2000
2010
2020
Figure 2: Range and Standard Deviation of the Change in Sectoral Share of GDP. BEA’s Industry Accounts, 22-sector disaggregation.
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recessions and expansions. While during recessions, Durables, Construction and Mining, had the
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largest drops, during expansions, Finance and Insurance, Professional Services and Transportation
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were the sectors with the largest increases. Hence, even though the range of changes in the sectoral
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share is narrower in the period after the mid–1980s, more sectors exhibit changes in their share of
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GDP. While before the mid–1980s the cycles were mostly mirroring the change in the manufacturing
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activity, in the later period, distinct sets of sectors drove recessions and expansions suggesting
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a larger role for structural changes. These structural shifts can provide an explanation for the
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stagnant employment during the recoveries since the 1990s, also known as jobless recoveries, as
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discussed in (Groshen and Potter (2003) and Panovska (2016)).
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Empirical testing
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In this section, I present a VAR analysis of aggregate growth and sectoral dynamics. The purpose
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of this exercise is to test whether we can match the duration of recessions in the data with the ones
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generated by impulse responses to shocks in SecDynamicst . The variables in the VAR are based
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on the seasonally adjusted Industrial Production (IP) quarterly data for the period 1972q1-2013q4.
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The impulse response functions are shown in Figure 3.3 Also plotted is the 95 percent confidence
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interval for each of the impulse responses. IRF: SecDyn −> IPgrowth
IRF: SecDyn −> SecDyn
1
1
.5 (%)
(%)
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0 0
−.5 −.5 0
5 lag
10
0
IRF: IPgrowth −> SecDyn
5 lag
10
IRF: IPgrowth −> IPgrowth
1 1
.5 (%)
(%)
.5
0
0
−.5
−.5 0
5 lag
10
0
5 lag
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Figure 3: Impulse Responses. The results were computed from a recursive VAR with 3 lags and over the 1972q1–2013q4 sample period.
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An increase in the index of sectoral dynamics by 1 percentage points leads to an immediate
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decline in IP growth by 0.5 percentage point. As a reference, the mean and the standard deviation
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are 1.78 and 0.73 for the index of sectoral dynamics and 0.55 and 1.55 for IP growth, where the units
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are percent. The decline in IP lasts for two quarters and then it fades away after the third quarter. 3 The standard lag-length selections criteria recommend a recursive VAR with three lags. The stability conditions are satisfied, and the errors are not correlated.
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This result is similar to the duration of recession in the data. During the period 1972q1–2013q4, the
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length of a recession varied from 2 quarters (the 1980 recession) to 6 quarters (the Great Recession),
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with an average of 4 quarters. Since World War II, the average recession duration has been 3.7
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quarters.
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This paper presents an index of structural dynamics that captures changes in the sectoral compo-
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sition of economic activity. These sectoral shifts are associated with an aggregate downturn like
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what we would observe in the case of an aggregate productivity shock. In this regard, sectoral
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shifts can be considered as an additional mechanism that generates business cycles.
Concluding remarks
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References
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[1] Foerster Andrew T. , Pierre-Daniel G. Sarte, and Mark W. Watson. 2011. “Sectoral versus
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Aggregate Shocks: A Structural Factor Analysis of Industrial Production”. Journal of Political
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Economy, 119(1),1-38.
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[2] Groshen, Erica L. and Simon Potter. 2003. “Has Structural Change Contributed to a Jobless
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Recovery?”. Federal Reserve Bank of New York Current Issues in Economics and Finance 9(8).
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[3] Lilien, David M. 1982. “Sectoral Shifts and Cyclical Unemployment”. The Journal of Political
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Economy, 90(4), 777–93. [4] Panovska, Irina. 2016. “What Explains the Recent Jobless Recoveries?” Macroeconomic Dynamics, http://dx.doi.org/10.1017/S1365100515000656.
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