The Role of the IT Revolution in Knowledge Diffusion, Innovation … · 2019. 8. 15. · Explore...

Introduction Empirical Evidence Model Quantitative Analysis Conclusion

The Role of the IT Revolution in

Knowledge Diffusion, Innovation and Reallocation

Salome BaslandzeEinaudi Institute (EIEF)

November 24, 2015


Introduction

The IT Revolution: large penetration of Information andCommunications Technologies (ICT) over the last four decades.

What is the impact of the IT revolution on aggregate productivity andsectoral reallocation of economic activities in the U.S.?

• ”Direct Impact”: main focus of the literature.

• Cost reductions or changes in business organizations.

• ”Indirect Impact”: new channel in this paper.

• Diffusion of knowledge, flow of ideas.


Tradeoff from Knowledge Diffusion

Two opposing effects from increased knowledge diffusion:

• Positive (Information) “Learning effect”:knowledge flowing to you;

• Negative (Information) “Competition effect”:knowledge flowing to potential competitors.

Paper’s Hypothesis:The overall effect from ICT should depend on an industry’stechnological characteristic, external knowledge dependence(Ex: electronic test & measurement instruments vs fabrics industry)

I IT Revolution: Biased towards externally dependent industries.Induces sectoral reallocation.


This Paper

Study the impact of the IT revolution on reallocation and growth1. Empirics

• Explore knowledge diffusion with ICT using citations data.• Develop a novel measure of external knowledge dependence.• Document reallocation towards externally dependent industries.

2. Theory• General equilibrium model of technological change.• Firms heterogeneous with respect to knowledge dependence.• IT revolution increases information accessibility and competition

3. Quantitative Analysis• The IT revolution accounts for (1976-2003):

• at least 3/4 of the observed sectoral reallocation;• 76% of an observed increase in LP growth. Dominant sustainable

channel: ”indirect” impact from ICT.• Negative competition channel significantly dampens LP growth

from ICT.


Literature/Contribution• Empirical literature on the impact of ICT on productivity

(Stiroh (2002), Jorgenson, et al (2005), Kleis, et al (2012), Dedricket al (2003), Acemoglu, et al (2014), Brynjolfsson, Hitt (2000),among others).

• New empirical facts; study a new channel through knowledgediffusion.

• Endogenous growth and firm dynamics (Aghion, Howitt(1992), Klette, Kortum (2004), Akcigit, Kerr (2010), Lentz,Mortensen (2008), Aghion, et al (2014), Cai, Li (2012), amongothers ).

• Here, introduce technological interdependence, industryheterogeneity, knowledge diffusion.

• Structural change and transformation (Kongsamut, et al. (2001),Ngai, Pissarides (2007), Herrendorf, et al. (2014), Duarte,Restuccia (2010), Buera, Kaboski (2012), among others)

• Here, new IT technologies are biased towards externallydependent industries. Change in endogenous productivitiesarise due to differential impact by ICT.


Empirical Evidence


Data

• ICT data from BEA non-residential fixed capital dataset by typeof assets and industry. NIPA price indices for each asset.

• Patents and citations data from NBER, 1976-2003.


Patent: Compact Hand-Held Video Game System


Patent: Compact Hand Held Video Game System


Evidence for Knowledge Diffusion

Idea: Keep track of knowledge flows through patent citations:

Technology classes cite external patents more intensively over time.

Citations Input-Output Matrix

1976-1978

Cited Technology Class (ranked)

Citi

ng T

echn

olog

y C

lass

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7



Idea: Keep track of knowledge flows through patent citations:Technology classes cite external patents more intensively over time.


1976-1978


Citi

ng T

echn

olog

y C

lass

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7





1976-1978


Citi

ng T

echn

olog

y C

lass

Nuclear X-ray industry

Nuclear X-ray

Measuring & Testing

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7





1976-1978


Citi

ng T

echn

olog

y C

lass


Nuclear X-ray

Measuring & Testing

2001-2003


Citi

ng T

echn

olog

y C

lass


Nuclear X-ray

Measuring & Testing Organic Compounds

Biotech

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7





1976-1978


Citi

ng T

echn

olog

y C

lass

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7

Self-citations share ↓ by 16% (finer classes: ↓ 26% & # new classes cited ↑ 4.4 times)Finer classes Standardized Reg. ICT


Index of External Knowledge Dependence

Define External Knowledge Dependence for a class j (412 classes)based on 2001-2003 patents:

EKD Index =[# classes cited]∀ patent i∈j

N0

.51

1.5

2Density

0 .2 .4 .6 .8ExternalKnowledgeDependenceDistribution of External Knowledge Dependence Examples Alternatives


Evidence for Sectoral Reallocation in the Data

• Since the IT revolution, more externally dependent industries• have increased patenting activities,• have increased shares in real activities.

Log Patent Counts

-.50

.51

1976 1980 1985 1990 1995 2000

Coef

ficien

t on

Year

Dum

my

Year

Top 25% external dep.

Bottom 25% external dep.

Share of Value Added in GDP

910

1112

13S

hare

of V

alue

Add

ed

1975 1985 1995 2005year



Log Patn = β0 + βtYeart + jFE + ε OldClasses NoIT


Summary of the Empirics

1. Increased knowledge diffusion with ICT;

2. Heterogeneity of industries in their external knowledgedependence;

3. Reallocation of economic activities towards more externallydependent industries;

4. Increased competition with ICT; Details

5. Heterogeneous technological spillovers from ICT. Details


MODEL


Model. Overview

A new model of endogenous technological change:• Quality ladder;• Schumpeterian creative destruction.

New features:• R&D combines external knowledge with R&D expenses;

• Industry heterogeneity wrt knowledge dependence;

• ICT governing the access to external knowledge;

• Higher diffusion leads to:• ”Learning” effect,• ”Competition” effect.


Model

• Representative household with logarithmic utility

U =∞∫

0

exp(−ρt)logC(t)dt,

• Final good produced using continuum of intermediate goods

logYt =∫ 1

0logy(j, t)dj

• Perfect competition in the final good sector. Price of Y(t)normalized to 1.


Model. Intermediate Goods

• Intermediate good j is produced using a linear technology

y(j, t) = q(j, t)f (ICT(j, t))l(j, t),

bla

• Competing follower able to produce at q−i(j, t)⇒firm i produces if qi(j, t) > q−i(j, t).

• Limit pricing at p(j, t) = w(t)q−i(j,t)f (ICT−i(j,t))• Πi(j, t) = (p(j, t)−MCi(j, t))y(j, t)





q- endogenous labor productivity term







f (ICT)-direct productivity impact of ICT







l- labor input







text







text


• Limit pricing at p(j, t) = w(t)q−i(j,t)f (ICT−i(j,t))• Πi(j, t) = (1− q−iqi )Y(t)


Model. Productivity Evolution

– Law of motion upon innovation:

qi(j, t + ∆t) = λqi(j, t), where λ > 1

– i innovates n times after −i:

qi(j, t) = λnq−i(j, t)

– nj – technology gap.– Πi(j, t) = (1− λ−n)Y(t)







– n – technology gap.– Πi(j, t) = (1− λ−n)Y(t)







– n – technology gap.

– Πi(j, t) = (1− λ−n)Y(t)


Model. Types of R&D

• Innovation is a stochastic outcome from R&D activities.

• Two types of innovation:

• Vertical (xj) – own quality improvement:Tech. gap becomes nj(t + ∆t) = nj(t) + 1.

• Horizontal (zj) – improves quality in other line:Tech. gap becomes nl(t + ∆t) = 1.


Model. R&D

• To conduct R&D, firms combine external knowledgewith R&D expenditures.

• Product lines have heterogeneous knowledge productiontechnologies – defines their type s.

• Product line j has an external knowledge dependencedistribution with density fj distributed on (j−

δj2 , j +

δj2 ).


Technological Circle and Knowledge Dependence

Large

extern

al

depend

ence Small external

dependence

ε – exogenous knowledge diffusion parameter.

Ē(j, ε) =∫ j+ εt2

j− εt2fjdj



ε – exogenous knowledge diffusion parameter.

Ē(j, ε) =∫ j+ εt2

j− εt2fjdj



ε- exogenous knowledge diffusion parameter.

Ē(j, ε) =∫ j+ εt2

j− εt2fjdj – share of knowledge utilized.


Model. Vertical R&D

• For a firm of type s, to achieve a Poisson arrival rate of verticalinnovation x, it needs to invest:

Costverts,t =αs

Ē(s, t)φxγλ−n,

αs- technological efficiency of type s; Ē(s, t) ≤ 1.

• “Learning effect”: ICT increases knowledge diffusion ε –positive technological externalities: Ē(s, t)→ 1.


Model. Horizontal R&D• For a firm of type s, to achieve a Poisson arrival rate of

horizontal innovation z, it needs to invest:

Costhorizs,t =αsβ

Ē(s, t)ψzγ

αs- technological efficiency of type s; Ē(s, t) ≤ 1.• Innovate on a random product line i ∈ (j− ε2 , j +

ε2 ).

Replace incumbent. Sell the line for price p (bargaining).• As a result, each product line faces creative destruction

τjt =

j+ εt2∫j− εt2

zitεt

di

• “Competition effect”: τ increases with a rise in knowledgediffusion, ε.


Model. Steady State

• Finite number of industry types s ∈ 1...S having same δ and αs(so, equal Ē(s, t)).

• Assumption: all types distributed evenly on the technologicalcircle⇒ Location j is not a state variable.

• Consider a model in steady state.• (n, s) – state vector

• Denote a normalized value of a firm by v(n, s).


Model. Firm’s Value Function

ρv(n, s) = maxx(n,s),z(n,s)

π(n)− αsĒ(s, t)φ

x(n, s)γλ−n︸︷︷︸vertical R&D cost

− αsβĒ(s, t)ψ

z(n, s)γ︸︷︷︸horizontal R&D cost

+ x(n, s)(v(n + 1, s)− v(n, s))︸︷︷︸successful vertical innovation

+ z(n, s)p︸︷︷︸horizontal innovation

− τv(n, s)︸︷︷︸creative destruction


Model. Steady State

• Proposition: i) The value function is linearly separable:

v(n, s) = A(s)− B(s)λ−n

where

A(s) =1 + zsp− αsβĒ(s,t)ψ z

γs

ρ + τ,

B(s) =1 + xγs αsĒ(s,t)φ

ρ + τ + xs λ−1λ,


Model. Steady State

• Proposition: i) The value function is linearly separable:

v(n, s) = A(s)− B(s)λ−n

ii) Type-specific innovation: x(n, s) = x(s) and z(n, s) = z(s).

• Stationary distribution over the state space:

µ(n, s) =1S

(xs

xs + τ

)n−1τ

xs + τ

• Equilibrium aggregate growth:

g = logλ(

∑s xsS

+ τ

)+

∆F(ICT)F(ICT)


Quantitative Analysis


Setup

• Estimate technological parameter δ (triangular knowledgedependence distribution). Four types of industries, S = 4.

• Parameterize in two stages:• Outside the model;• Calibrate the model to the initial steady state in 1976-1978.

• Estimate diffusion series εt from the data.

• Feed in exogeneous series of εt and simulate the model overtime, 1976-2003.


Technological Distribution in the Data

• δj estimated based on the latest period, 2001-2003.

EKD Index =[# classes cited]∀ patent i∈j

N

0.5

11.5

2Density

0 .2 .4 .6 .8Delta

• 4 types: δ1, δ2, δ3, δ4 – quartiles of the distribution.


Estimated Direct Impact of ICT

• Recall the production function


• Functional form: f (ICT) = ICTκ.

• Convert to estimate the following regression:

gLPjt = 0.02gICTjt + gPatentsjt + controls(0.009)

• Point estimate comparable to the elasticities from the literature(Brynjolfsson and Hitt (2000), [0.01, 0.04]).


Moments and Estimates• Rest of the 9 parameters α1, α2, α3, α4, β, φ, ψ, γ, λ are calibrated

to match the following moments:

Moment Values

Targets Data Model

Aggregate Growth1976 1.31% 1.30%

Average HorizontalInnovation1976

0.22 0.19

R&D Intensity1976 0.09 0.05Vertical Innovation

Horizontal Innovation 1976 1.20 1.24[ Innov4Innov1

, Innov3Innov1 ,Innov2Innov1

]1976 [2.25, 1.15, 1.53] [2.31, 1.09, 1.53][ Innov4

Innov1, Innov3Innov1 ,

Innov2Innov1

]1977 [2.34, 1.12, 1.68] [2.33, 1.05, 1.61][ Innov4

Innov1, Innov3Innov1 ,

Innov2Innov1

]1978 [2.56, 1.37, 1.85] [2.38, 1.18, 1.72]


Moments and Estimates

• Resulting estimates are:

Calibrated Parameters

Parameters Meaning Value

[α1, α2, α3, α4] Scaling of vertical R&D [2.02, 0.5, 0.52, 0.05]

β Scaling of horizontal R&D 6.47

λ Step size of innovation 1.03

φ Vertical R&D curvatureto external knowledge

2.48

ψ Horizontal R&D curvatureto external knowledge

2.10

γ R&D cost curvature 2.98


Estimated Diffusion Parameter

Estimate knowledge diffusion as a function of ICT:

ε̄jt = β0 + β1ICTjt + β2ICTothersjt + pat stockjt + cap stockjt + j FE+ time+ ejt


Estimated Diffusion Parameter

Estimate knowledge diffusion as a function of ICT:

εt = Meanj(β̂0 + β̂1ICTjt + β̂2ICTothersjt )

.1.2

.3.4

.5.6

Ave

rage

Eps

ilon

1975 1980 1985 1990 1995 2000Year

Knowledge Diffusion over Time


Experiment. Reallocation of Innovation Activities

Total Innovation Trends by Type of Industry

01

23

4T

otal

Inno

vatio

n

1975 1980 1985 1990 1995 2000Year

Top 25% (model) Bottom 25% (model)Bottom 25% (data) Top 25% (data)

Time Change Data Model ExplainedTop 25%

Bottom 25% 2.35 times 1.75 times 74%


Experiment. Reallocation of Real Activities

Sharet(s) =Qt(s)ICTκt (s)∑n λ

−nµ(n, s)∑s Qt(s)ICTκt (s)∑n λ−nµ(n, s)

,

Share of Total Output by Type of Industry12

1416

18S

hare

of O

utpu

t (V

A)

1976 1981 1986 1991 1996 2001Year

Bottom 25% (model) Top 25% (model)Bottom 25% (data) Top 25% (data)


Growth of Labor Productivity

Growth of Labor Productivity. Data and Model

-20

24

6G

row

th R

ate

of L

abor

Pro

duct

ivity

1975 1980 1985 1990 1995 2000 2005Year

Model Data

Change in growth Data Model ExplainedBefore/After 1990 ↑ 45% ↑ 34% 75.6%


Growth Decomposition

Decomposition of ICT’s Impact

1.2

1.4

1.6

1.8

22.2

Growth

1975 1980 1985 1990 1995 2000Year

Before 1990 After 1990Average:28.7 bp

�Indirect 24%Direct 76%

Average:78 bp


1




1.2

1.4

1.6

1.8

22.2

Growth

1975 1980 1985 1990 1995 2000Year

Direct channel from ICT



Average:78 bp


1




1.2

1.4

1.6

1.8

22.2

Growth

1975 1980 1985 1990 1995 2000Year

Indirect channel from ICT

Direct channel from ICT



Average:78 bp


1


Counterfactuals

Counterfactual experiment to quantify negative competition effect:

Growth in 2003. Three scenarios.

No growthin ICT

Total impact of ICT(direct+indirect)

ICT withoutcompetitive spillovers

1.16% 2.02% 2.60%

Solow: ”You can see the computer age everywhere but in the productivitystatistics.”


Extension in Progress

Cross-Country Analalysis:• Increased divergence in growth between the U.S. and EU from

90s.• Could it be because of different effects the ICT technologies

played?• EUKLEMS cross-country data on output, productivities, ICT by

industries.• Look at the composition of industries in EU relative to the U.S.• Esimate evolution of εt in EU.• Similate the model under different scenarios: EU/US sectoral

composition and EU/US ICT evolution.• Results:

• contribution of different levels of ICT investment into differentialgrowth.

• contribution of different sectoral composition to differential growth.


Conclusion

• Study the role of ICT as a tool for knowledge dissemination inthe economy.

• Explore the impact of this new role on productivity growth andsectoral reallocation.

Other avenues:

• Endogenizing the IT revolution.

• Cross-country analysis.

• Analysis of industrial policies.


Appendix


Evidence for Knowledge DiffusionFiner technology categories- 412 (nclass)


1976-1978


Citi

ng T

echn

olog

y C

lass

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7

Note: Each row represents one of the 412 technology categories and each cell depicts a share of citations which are given bypatents in citing technology class to corresponding cited technology class. All shares in each row add up to one. Citedtechnology classes on the horizontal axis are ranked by citation shares received.

Back



Standardize citations tables using Sinkhorn-Knopp (1976)algorithm. Get same marginal distributions (2001-2003).

Citations Input-Output Matrix. Standardized

1976-1978


Citi

ng T

echn

olog

y C

lass

2001-2003


Citi

ng T

echn

olog

y C

lass

0

0.1

0.2

0.3

0.4

0.5

0.6

>0.7

Back


Knowledge Diffusion and ICT

Num. externalclasses cited

Externalcitations share

Log ICT0.151∗∗∗

(0.003)0.042∗∗∗

(0.012)Patent Stock X XCapital Stock X XNum. firms X XYear, Class FE X XObservations 11, 286 11, 286R2 0.86 0.85Method Poisson OLS

Notes: Industry (nclass) × Year regressions. Standard errors clustered bytechnology classes. *p < 0.10, **p < 0.05, ***p < 0.01 Back


External Knowledge Dependence0

.51

1.5

2D

ensi

ty

0 .2 .4 .6 .8External Knowledge Dependence

Textiles

Wire,fabrics,and,structure

Tuners

Metal,tools,andimplements,making

Railway,draft,appliances

Wireworking

,,,,,,,,,,,,,,,,,FertilizersSingle,generator,,systems

Compound,tools

BridgesBooks,,strips

Organic,compounds

ElectricityMetallurgical,apparatus

CryptographgyInduced,nuclear,reactions

AcousticsTelevision

Communications:,directive,radio,waves

Data,processing:Static,info,storage

Synthetic,resins

Electrical,Computers,and,othersX-ray,or,gamma,ray,systemsEducation,and,demonstration

Hidraulic,and,Earth,engineering

Measuring,and,testing

Data,processing:

Communications:,electrical

Molecular,biology

Semiconductors

Distribution of External Knowledge DependenceBack


Reallocation of Innovation. Old patent classes

• Identify some of the old classes from patents prior to 1960.• Distribution of external dependence for the old classes (245

classes): (quartiles 1-12%, 2-25%, 3-30%, 4-33%)

Log Patent Counts. Old Classes-.8

-.6-.4

-.20

.2.4

.6.8

1C

oeffi

cien

ts o

n Y

ear D

umm

ies

1976 1980 1985 1990 1995 2000Year



Back


Reallocation of Innovation. Without ICT classes

• Look at the trends excluding ICT technology classes.

Log Patent Counts. No ICT-classes.-.6

-.4-.2

0.2

.4.6

.8

1976 1980 1985 1990 1995 2000



Back


ICT and Heterogenous Technological Spillovers

Num. externalclasses cited

Externalcitations share


(0.004)−0.008∗∗(0.004)

Log ICT × Ext. Dep. 0.045∗∗∗

(0.007)0.028∗∗∗

(0.003)Patent Stock X XCapital Stock X XNum. firms X XYear, Class FE X XObservations 11, 286 11, 286R2 0.89 0.88Method Poisson OLS



ICT and Competition

# Entrants(new)

# Entrants(other incumb.)

Log (Herf index)


(0.020)0.550∗∗∗

(0.079)−0.389∗∗∗(0.028)

Num. incumb. X X XYear, Class FE X X XObservations 9, 682 9, 682 11, 286R2 0.76 0.74 0.86Method Poisson Poisson OLS



Alternative Indexes ofExternal Knowledge Dependence

• Benchmark definition on 1976-2003:Spearman′s corr = 0.98

• Benchmark definition on 1976-1978:Spearman′s corr = 0.81

• Alternative definition:

∑patent i∈j # classes citedi412×Nj

Spearman′s corr = 0.52Back


Equilibrium

Definition (Steady-State Equilibrium) Given the exogenous allocationof ICT across product lines and the parameter of the diffusion process, εt,an equilibrium of the economy consists of{x∗s , z∗s , µ(n, s)∗, p∗(n, s), y∗(n, s), Y∗, w∗, τ∗, g∗, r∗}s.t.: (i) Aggregate output Y∗t is given by final output specification;(ii) Intermediate goods prices and output p∗t (n, s), y

∗(n, s) satisfy demandand price setting equations;(iii) Wage w∗ clears the labor market;(iv) Innovation decisions x∗s , z∗s maximize a firm’s value;(v) Equilibrium creative destruction τ∗ is consistent with eq. horizontalinnovation;(vi) Distribution µ∗(n, s) is consistent with eq. inflow-outflow dynamics;(vii) r∗ satisfies the Euler equation;(viii) g∗ results from eq. horiz. and vert. innovations and ICT growth.


Vt(n, s) =

[Πt(n)− αsĒ(s,t)φ xt(n, s)

γλ−n

− αsβĒ(s,t)ψ zt(n, s)γ

]∆t + o(∆t)

+e−rt+∆t∆t

(xt(n, s)∆t + o(∆t))Vt+∆t(n + 1, s)

+(zt(n, s)∆t + o(∆t))(pt+∆t + Vt+∆t(n, s))+(τt∆t + o(∆t))× 0

(1− xt(n, s)∆t− zt(n, s)∆t− τt∆t− o(∆t))Vt+∆t(n, s)


r(t)Vt(n, s)− V̇t(n, s) = maxxt(n,s), zt(n,s)

Πt(n)− αsĒ(s,t)φ xt(n, s)

γλ−n

− αsβĒ(s,t)ψ zt(n, s)γ

+xt(n, s)(Vt(n + 1, s)−Vt(n, s))+zt(n, s)pt − τtVt(n, s)

IntroductionEmpirical EvidenceModelQuantitative AnalysisConclusion

The Role of the IT Revolution in Knowledge Diffusion, Innovation … · 2019. 8. 15. · Explore...

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