Creative Destruction and Uncertaintyover the Business Cycle

Petr Sedlacek

Macro Discussion Group, BonnOctober 2014

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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)

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Why?

Bloom (2009)

an exogenous increase in uncertainty

→ higher value of waiting

→ firms freeze hiring and investment

→ real activity drops

uncertainty is “bad”

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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)

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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

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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!

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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”

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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

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Empirical evidence

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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

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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

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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

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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

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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

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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

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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)

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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]

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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

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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

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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

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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

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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?

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