Creative Destruction and Uncertaintyover the Business Cycle
Petr Sedlacek
Macro Discussion Group, BonnOctober 2014
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 1 / 30
Uncertainty rises during recessions
1960 1970 1980 1990 2000 20100.75
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25
Source: Jurado, Ludvigson, Ng (2013)
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 2 / 30
Why?
Bloom (2009)
an exogenous increase in uncertainty
→ higher value of waiting
→ firms freeze hiring and investment
→ real activity drops
uncertainty is “bad”
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 3 / 30
But what is uncertainty?
(micro-) uncertainty: changes in the volatility of idiosyncratic shocks
dispersion of sales and employment growth rates (Bloom, 2012)
dispersion of firm-level TFP, sales (Christian and Bachmann, 2011)
dispersion in firm-level investment (Christian and Bachmann, 2014)
(macro-) uncertainty: changes in the volatility of aggregate shocks
stock market volatility (Bloom, 2009)
time-varying volatility in fiscal policy rules (Keith et al., 2013)
time-varying volatility in unforecastable component of time-series(Jurado et al., 2013)
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 4 / 30
So what if causation runs the other way?
some shock (not uncertainty)
→ different firms respond differently
→ the dispersion of firm-level outcomes increases
But, also need that
→ real activity drops
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 5 / 30
What will this paper be about?
“Creative destruction (Schumpeterian)” shocks
a positive “neutral technology” shock
→ increases productivity in the long-run
but, in short-run it reduces output and employment (Michelacci,Lopez-Salido, 2007)
These shocks are like vintage technology shocks
→ some firms become very productive
→ some firms become obsolete
→ dispersion in TFP (and hence employment growth) increases
All the above looks like a technology shock!
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 6 / 30
What will this paper be about?
endogenize uncertainty fluctuations
main channel: propagation of creative destruction shocks
I these are recessionary because reallocation (of labor) takes time
I in the process, measured uncertainty increases
I but in the medium- to long-run, output and productivity rise
uncertainty is not “bad”
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 7 / 30
Paper
Empirical part
identify neutral technology shocks
does uncertainty respond to Schumpeterian shocks?
Model part
build model with Schumpeterian shocks and slow reallocation
investigate extent of uncertainty changes due to such shocks
incorporate possible feedback:
I new technology arrives as a result of R&D
I higher payoffs (positive shock) encourages more R&D
I more aggregate R&D speeds up creative destruction process ?
I creates persistence (possibly magnification) of initial shock
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 8 / 30
Empirical evidence
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 9 / 30
Methodology
use a structural VAR
identify neutral technology shocks using long-run restrictions (Gali,1999; Fisher, 2001)
investigate IRFs of other variables to shock
I measures of uncertainty
I unemployment
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 10 / 30
Structural VAR
Yt = ΠYt−1 + εt
Yt = [∆at, ut, qt]′
at is log labor productivity
ut is log of unemployment
qt is log of a measure of uncertainty
E[εt, ε′t] = Σ
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 11 / 30
Identifying Schumpeterian shocks
Reduced-form VAR
Yt = εt + C1εt−1 + ... = C(L)εt
Structural VAR
Yt = A0υt +A1υt−1 + ... = A(L)υt
where εt are reduced-form shocks
where υt are structural shocks
E[υtυ′t] = Ω
υt = A−10 εt
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 12 / 30
Identifying Schumpeterian shocks
given the above → A0ΩA′0 = Σ
w.l.g. assume that first element of υt = υNt and Ω = I
A0A′0 = Σ
Identifying assumption:
only υNt affects at in long-run
→∑∞
i=0Ai(1, 1) 6= 0 and∑∞
i=0Ai(1, j) = 0 for j > 1
finally,∑∞
i=0Ai = CA0
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 13 / 30
Estimation
labor productivity: output per hour in non-farm business sector
unemployment from BLS
uncertainty measures: Bloom (2009), Jurado et al. (2013)
sample from 1962Q3 (1960Q2 or 1970Q2) to 2011Q2
deterministic trend with breaks in 1973Q2, 1997Q1 (Fernald, 2007)
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Impulse response: labor productivity
0 5 10 15 20 250
0.02
0.04
0.06
0.08
0.1
0.12labor productivity
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 15 / 30
Impulse response: unemployment
0 5 10 15 20 25-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6unemployment rate
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 16 / 30
Impulse response: uncertainty (Bloom, 2009)
0 5 10 15 20 25-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6uncertainty
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 17 / 30
Impulse response: uncertainty (other)
0 5 10 15 20 25-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
stock marketmacro-levelfirm-level
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 18 / 30
Granger causality tests
H0: uncertainty does not Granger-causes neutral technology shocks?
I cannot be rejected
H0: neutral technology shocks do not Granger-causes uncertainty?
I rejected
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 19 / 30
Before moving on to model...
Proposed channel also depends on R&D
1 2 3 4 5 6 7 8 9 10-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12 R&D investment
consistent with Stein and Stone (2013)
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 20 / 30
Model
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Model features
vintage technologies
I technological frontier is exogenous and stochastic
I with some probability firms can catch up to frontier
search and matching model with endogenous “entry and exit”
I similar to Michelacci, Lopez-Salido (2007)
I delivers “recessionary” response to neutral tech. shocks in short run
endogenize probability of catching up to frontier
I firms spend resources on R&D
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Technology
technology frontier evolves according to
zt = z + zt−1 + ηt, ηt ∼ N(0, σ2z)
z > 0 constant drift term
at any time, an individual firm can catch up with probability I(σi,t)
where σi,t = ri,t/yi,t is research intensity
Define technology gap as γi,t = zt − zi,tnot catching up means that firm moves “down the ladder”
let i denote the number of periods a firm does not “update” itstechnology
An individual firm also gets iid productivity shocks pi,t
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Value functions
ex-post job value for firm:
Ji,j,t = exp(γi,t)pj,t − wi,j,t − c/2σ2i,j,t+ (1)
β(1− ρ)[I(σi,j,t)(J0,t+1 − Ji+1,t+1) + Ji+1,t+1
]
cost of R&D similar to Klette and Kortum (2004)
ex-post job value for worker:
Wi,j,t =wi,j,t+ (2)
β(1− ρ)[I(σi,j,t)(W0,t+1 − Wi+1,t+1) + Wi+1,t+1 − Ut+1
]+ βUt+1
x = E[x|pj,t > pi,t]
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 24 / 30
Value functions
value of unemployment:
Ut =b+ Ftβ(1− ρ)[W0,t+1 − Ut+1
]+ βUt+1
value of a vacancy:
0 =κ+Qtβ(1− ρ)J0,t+1
Ft is job finding rate of unemployment
Qt is job filling rate of firms
new firms start with frontier technology (for now)
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 25 / 30
Wages
Under Nash bargaining:
wi,j,t = η(exp(γi,t)pj,t − c/2σ2i,j,t + κθ) + (1− η)b
η is bargaining power of workers
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 26 / 30
Optimal R&D expenditure
assume that I(σ) = ιrµt γ1−µt , similar to Klette and Kortum (2004)
I probability of innovation is increasing in R&D expenditure
I but it also depends on “knowledge capital” summarized by gap fromfrontier
σi,t =
[β(1− ρ)µι
(1− η)cγi,t(J0,t+1 − Ji+1,t+1)
]1/µ
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 27 / 30
Number of firms
this is a one-firm-one-worker setup
→ number of firms = employment
endogenous separations happen if surplus of a match ¡ 0
Si,j,t = Ji,j,t +Wi,j,t − Ut < 0
the above defines a cutoff pi,t
“exit” happens when pi,j,t < pi,t
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 28 / 30
Creative destruction
a positive neutral technology shock
→ frontier firms more productive
I induces more R&D expenditure
→ more mass shifted towards less productive technologies
→ firm exit increases
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 29 / 30
Issues
seems that R&D intensity increases with distance from frontier?
I include distinction between innovation and imitation?
I incentives to innovate higher at frontier?
no feedback into the speed of creative destruction?
I Klette and Kortum (2004) → more aggregate R&D → higherprobability of being pushed down the ladder
I also solved by imitation? → more frontier firms → less firms toimitate?
I markups? Somehow more frontier firms → lowers markups → lessproductive firms exit?
Sedlacek Schumpeter and Uncertainty Bonn, October 2014 30 / 30
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