The Role of the IT Revolution in Knowledge Diffusion, Innovation and Reallocation

Transcription

1 The Role of the IT Revolution in Knowledge Diffusion, Innovation and Reallocation Salome Baslandze Einaudi Institute (EIEF) November 24, 2015

2 Introduction The IT Revolution: large penetration of Information and Communications Technologies (ICT) over the last four decades. What is the impact of the IT revolution on aggregate productivity and sectoral reallocation of economic activities in the U.S.?

3 Introduction The IT Revolution: large penetration of Information and Communications Technologies (ICT) over the last four decades. What is the impact of the IT revolution on aggregate productivity and sectoral reallocation of economic activities in the U.S.? Direct Impact : main focus of the literature. Cost reductions or changes in business organizations.

4 Introduction The IT Revolution: large penetration of Information and Communications Technologies (ICT) over the last four decades. What is the impact of the IT revolution on aggregate productivity and sectoral 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.

5 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.

6 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 s technological characteristic, external knowledge dependence (Ex: electronic test & measurement instruments vs fabrics industry) IT Revolution: Biased towards externally dependent industries. Induces sectoral reallocation.

7 This Paper Study the impact of the IT revolution on reallocation and growth 1. 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 ( ): 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.

8 Literature/Contribution Empirical literature on the impact of ICT on productivity (Stiroh (2002), Jorgenson, et al (2005), Kleis, et al (2012), Dedrick et al (2003), Acemoglu, et al (2014), Brynjolfsson, Hitt (2000), among others). New empirical facts; study a new channel through knowledge diffusion. Endogenous growth and firm dynamics (Aghion, Howitt (1992), Klette, Kortum (2004), Akcigit, Kerr (2010), Lentz, Mortensen (2008), Aghion, et al (2014), Cai, Li (2012), among others ). Here, introduce technological interdependence, industry heterogeneity, 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 externally dependent industries. Change in endogenous productivities arise due to differential impact by ICT.

9 Empirical Evidence

10 Data ICT data from BEA non-residential fixed capital dataset by type of assets and industry. NIPA price indices for each asset. Patents and citations data from NBER,

11 Patent: Compact Hand-Held Video Game System

12 Patent: Compact Hand Held Video Game System

13 Patent: Compact Hand Held Video Game System

14 Evidence for Knowledge Diffusion Idea: Keep track of knowledge flows through patent citations:

15 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 Citing Technology Class Citing Technology Class > Cited Technology Class (ranked) Cited Technology Class (ranked) 0

16 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 Citing Technology Class Nuclear X-ray industry Citing Technology Class > Measuring & Testing Nuclear X-ray Cited Technology Class (ranked) Cited Technology Class (ranked) 0

17 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 Nuclear X-ray industry Nuclear X-ray industry >0.7 Citing Technology Class Citing Technology Class Measuring & Testing Measuring & Testing Organic Compounds Nuclear X-ray Cited Technology Class (ranked) Nuclear X-ray Cited Technology Class (ranked) Biotech 0

18 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 Citing Technology Class Citing Technology Class > Cited Technology Class (ranked) Cited Technology Class (ranked) Self-citations share by 16% (finer classes: 26% & # new classes cited 4.4 times) Finer classes Standardized Reg. ICT 0

19 Index of External Knowledge Dependence Define External Knowledge Dependence for a class j (412 classes) based on patents: EKD Index = [# classes cited] patent i j N Density External Knowledge Dependence Distribution of External Knowledge Dependence Examples Alternatives

20 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.

21 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 Share of Value Added in GDP Coefficient on Year Dummy Top 25% external dep. Bottom 25% external dep Year Share of Value Added Bottom 25% external dep. Top 25% external dep year Log Patn = β 0 + β t Year t + jfe + ε OldClasses NoIT

22 Summary of the Empirics 1. Increased knowledge diffusion with ICT; 2. Heterogeneity of industries in their external knowledge dependence; 3. Reallocation of economic activities towards more externally dependent industries; 4. Increased competition with ICT; Details 5. Heterogeneous technological spillovers from ICT. Details

23 MODEL

24 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.

25 Model Representative household with logarithmic utility U = 0 exp( ρt)logc(t)dt, Final good produced using continuum of intermediate goods logy t = 1 0 logy(j, t)dj Perfect competition in the final good sector. Price of Y(t) normalized to 1.

26 Model. Intermediate Goods Intermediate good j is produced using a linear technology bla y(j, t) = q(j, t)f (ICT(j, t))l(j, t), Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = w(t) q i (j,t)f (ICT i (j,t)) Π i (j, t) = (p(j, t) MC i (j, t))y(j, t)

27 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), q- endogenous labor productivity term Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = w(t) q i (j,t)f (ICT i (j,t)) Π i (j, t) = (p(j, t) MC i (j, t))y(j, t)

28 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), f (ICT)-direct productivity impact of ICT Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = w(t) q i (j,t)f (ICT i (j,t)) Π i (j, t) = (p(j, t) MC i (j, t))y(j, t)

29 Model. Intermediate Goods Intermediate good j is produced using a linear technology l- labor input y(j, t) = q(j, t)f (ICT(j, t))l(j, t), Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = w(t) q i (j,t)f (ICT i (j,t)) Π i (j, t) = (p(j, t) MC i (j, t))y(j, t)

30 Model. Intermediate Goods Intermediate good j is produced using a linear technology text y(j, t) = q(j, t)f (ICT(j, t))l(j, t), Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = w(t) q i (j,t)f (ICT i (j,t)) Π i (j, t) = (p(j, t) MC i (j, t))y(j, t)

31 Model. Intermediate Goods Intermediate good j is produced using a linear technology text y(j, t) = q(j, t)f (ICT(j, t))l(j, t), Competing follower able to produce at q i (j, t) firm i produces if q i (j, t) > q i (j, t). Limit pricing at p(j, t) = Π i (j, t) = (1 q i q i )Y(t) w(t) q i (j,t)f (ICT i (j,t))

32 Model. Productivity Evolution Law of motion upon innovation: q i (j, t + t) = λq i (j, t), where λ > 1 i innovates n times after i: q i (j, t) = λ n q i (j, t) n j technology gap. Π i (j, t) = (1 λ n )Y(t)

33 Model. Productivity Evolution Law of motion upon innovation: q i (j, t + t) = λq i (j, t), where λ > 1 i innovates n times after i: q i (j, t) = λ n q i (j, t) n technology gap. Π i (j, t) = (1 λ n )Y(t)

34 Model. Productivity Evolution Law of motion upon innovation: q i (j, t + t) = λq i (j, t), where λ > 1 i innovates n times after i: q i (j, t) = λ n q i (j, t) n technology gap. Π i (j, t) = (1 λ n )Y(t)

35 Model. Types of R&D Innovation is a stochastic outcome from R&D activities. Two types of innovation: Vertical (x j ) own quality improvement: Tech. gap becomes n j (t + t) = n j (t) + 1. Horizontal (z j ) improves quality in other line: Tech. gap becomes n l (t + t) = 1.

36 Model. R&D To conduct R&D, firms combine external knowledge with R&D expenditures. Product lines have heterogeneous knowledge production technologies defines their type s. Product line j has an external knowledge dependence distribution with density f j distributed on (j δ j 2, j + δ j 2 ).

37 Technological Circle and Knowledge Dependence Large external dependence Small external dependence ε exogenous knowledge diffusion parameter. Ē(j, ε) = j+ ε t 2 j ε t f j dj 2

38 Technological Circle and Knowledge Dependence ε exogenous knowledge diffusion parameter. Ē(j, ε) = j+ ε t 2 j ε t f j dj 2

39 Technological Circle and Knowledge Dependence ε- exogenous knowledge diffusion parameter. Ē(j, ε) = j+ ε t 2 j ε t f j dj share of knowledge utilized. 2

40 Model. Vertical R&D For a firm of type s, to achieve a Poisson arrival rate of vertical innovation x, it needs to invest: Cost vert s,t = α s Ē(s, t) φ xγ λ n, α s - technological efficiency of type s; Ē(s, t) 1. Learning effect : ICT increases knowledge diffusion ε positive technological externalities: Ē(s, t) 1.

41 Model. Horizontal R&D For a firm of type s, to achieve a Poisson arrival rate of horizontal innovation z, it needs to invest: Cost horiz s,t = α sβ Ē(s, t) ψ zγ α s - technological efficiency of type s; Ē(s, t) 1. Innovate on a random product line i (j ε 2, j + ε 2 ). Replace incumbent. Sell the line for price p (bargaining). As a result, each product line faces creative destruction τ jt = j+ ε t 2 j ε t 2 z it ε t di Competition effect : τ increases with a rise in knowledge diffusion, ε.

42 Model. Steady State Finite number of industry types s 1...S having same δ and α s (so, equal Ē(s, t)). Assumption: all types distributed evenly on the technological circle 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).

43 Model. Firm s Value Function ρv(n, s) = max x(n,s),z(n,s) π(n) α s Ē(s, t) φ x(n, s)γ λ n }{{} vertical R&D cost α sβ z(n, s)γ Ē(s, t) ψ }{{} horizontal R&D cost + x(n, s)(v(n + 1, s) v(n, s)) }{{} successful vertical innovation + z(n, s)p }{{} τv(n, s) }{{} horizontal innovation creative destruction

44 Model. Steady State Proposition: i) The value function is linearly separable: v(n, s) = A(s) B(s)λ n where A(s) = 1 + z sp ρ + τ α sβ Ē(s,t) ψ z γ s, B(s) = 1 + xγ s α s Ē(s,t) φ ρ + τ + x s λ 1 λ,

45 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).

46 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) = 1 S ( xs ) n 1 τ x s + τ x s + τ Equilibrium aggregate growth: ( ) s x s g = logλ S + τ + F(ICT) F(ICT)

47 Quantitative Analysis

48 Setup Estimate technological parameter δ (triangular knowledge dependence distribution). Four types of industries, S = 4. Parameterize in two stages: Outside the model; Calibrate the model to the initial steady state in Estimate diffusion series ε t from the data. Feed in exogeneous series of ε t and simulate the model over time,

49 Technological Distribution in the Data δ j estimated based on the latest period, EKD Index = [# classes cited] patent i j N Density Delta 4 types: δ 1, δ 2, δ 3, δ 4 quartiles of the distribution.

50 Estimated Direct Impact of ICT Recall the production function y(j, t) = q(j, t)f (ICT(j, t))l(j, t), Functional form: f (ICT) = ICT κ. Convert to estimate the following regression: g LPjt = 0.02g ICTjt + g Patentsjt + controls (0.009) Point estimate comparable to the elasticities from the literature (Brynjolfsson and Hitt (2000), [0.01, 0.04]).

51 Moments and Estimates Rest of the 9 parameters α 1, α 2, α 3, α 4, β, φ, ψ, γ, λ are calibrated to match the following moments: Moment Values Targets Data Model Aggregate Growth % 1.30% Average Horizontal Innovation R&D Intensity Vertical Innovation Horizontal Innovation [ Innov4 Innov 1, Innov 3 Innov 1, Innov 2 [ Innov4 Innov 1, Innov 3 Innov 1, Innov 2 Innov 1 ]1976 [2.25, 1.15, 1.53] [2.31, 1.09, 1.53] Innov 1 [2.34, 1.12, 1.68] [2.33, 1.05, 1.61] ]1977 [ Innov4 Innov 1, Innov 3 Innov 1, Innov 2 Innov 1 [2.56, 1.37, 1.85] [2.38, 1.18, 1.72] ]1978

52 Resulting estimates are: Moments and Estimates Calibrated Parameters Parameters Meaning Value [α 1, α 2, α 3, α 4 ] 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 ψ Vertical R&D curvature to external knowledge Horizontal R&D curvature to external knowledge γ R&D cost curvature 2.98

53 Estimated Diffusion Parameter Estimate knowledge diffusion as a function of ICT: ε jt = β 0 + β 1 ICT jt + β 2 ICTjt others + pat stock jt + cap stock jt + j FE + time + e jt

54 Estimated Diffusion Parameter Estimate knowledge diffusion as a function of ICT: ε t = Mean j ( ˆβ 0 + ˆβ 1 ICT jt + ˆβ 2 ICT others jt ) Average Epsilon Year Knowledge Diffusion over Time

55 Experiment. Reallocation of Innovation Activities Total Innovation Trends by Type of Industry Total Innovation Year Top 25% (model) Bottom 25% (data) Bottom 25% (model) Top 25% (data) Time Change Data Model Explained Top 25% Bottom 25% 2.35 times 1.75 times 74%

56 Experiment. Reallocation of Real Activities Share t (s) = Q t(s)ict κ t (s) n λ n µ(n, s) s Q t (s)ict κ t (s) n λ n µ(n, s), Share of Total Output by Type of Industry Share of Output (VA) Year Bottom 25% (model) Bottom 25% (data) Top 25% (model) Top 25% (data)

57 Growth of Labor Productivity Growth of Labor Productivity. Data and Model Growth Rate of Labor Productivity Year Model Data Change in growth Data Model Explained Before/After % 34% 75.6%

58 Growth Decomposition Decomposition of ICT s Impact Growth Year Average: 28.7 bp Before 1990 After 1990 Indirect 24% Average: Direct 76% 78 bp Indirect 70% Direct 30%

59 Growth Decomposition Decomposition of ICT s Impact Growth Direct channel from ICT Year Average: 28.7 bp Before 1990 After 1990 Indirect 24% Average: Direct 76% 78 bp Indirect 70% Direct 30%

60 Growth Decomposition Decomposition of ICT s Impact Growth Direct channel from ICT Indirect channel from ICT Year Average: 28.7 bp Before 1990 After 1990 Indirect 24% Average: Direct 76% 78 bp Indirect 70% Direct 30%

61 Counterfactuals Counterfactual experiment to quantify negative competition effect: Growth in Three scenarios. No growth in ICT Total impact of ICT (direct+indirect) ICT without competitive spillovers 1.16% 2.02% 2.60% Solow: You can see the computer age everywhere but in the productivity statistics.

62 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 ε t 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 differential growth. contribution of different sectoral composition to differential growth.

63 Conclusion Study the role of ICT as a tool for knowledge dissemination in the economy. Explore the impact of this new role on productivity growth and sectoral reallocation. Other avenues: Endogenizing the IT revolution. Cross-country analysis. Analysis of industrial policies.

64 Appendix

65 Evidence for Knowledge Diffusion Finer technology categories- 412 (nclass) Citations Input-Output Matrix Citing Technology Class Citing Technology Class > Cited Technology Class (ranked) Cited Technology Class (ranked) 0 Note: Each row represents one of the 412 technology categories and each cell depicts a share of citations which are given by patents in citing technology class to corresponding cited technology class. All shares in each row add up to one. Cited technology classes on the horizontal axis are ranked by citation shares received. Back

66 Evidence for Knowledge Diffusion Standardize citations tables using Sinkhorn-Knopp (1976) algorithm. Get same marginal distributions ( ). Citations Input-Output Matrix. Standardized Citing Technology Class Citing Technology Class > Cited Technology Class (ranked) Cited Technology Class (ranked) 0 Back

67 Knowledge Diffusion and ICT Num. external classes cited External citations share Log ICT (0.003) (0.012) Patent Stock Capital Stock Num. firms Year, Class FE Observations 11, , 286 R Method Poisson OLS Notes: Industry (nclass) Year regressions. Standard errors clustered by technology classes. *p < 0.10, **p < 0.05, ***p < 0.01 Back

68 External Knowledge Dependence Back Density Railway,draft,appliances Wireworking Metal,tools,and implements,making Textiles Wire,fabrics,and, structure Tuners Bridges Books,,strips,,,,,,,,,,,,,,,,,Fertilizers Single,generator,,systems Compound,tools Organic,compounds Electricity Metallurgical,apparatus Cryptographgy Induced,nuclear,reactions Acoustics Television Communications:,directive,radio,waves Synthetic,resins Data,processing: Static,info,storage Electrical,Computers,and,others X-ray,or,gamma,ray,systems Education,and,demonstration Hidraulic,and,Earth,engineering Molecular,biology External Knowledge Dependence Distribution of External Knowledge Dependence Communications:,electrical Semiconductors Data,processing: Measuring,and,testing

69 Reallocation of Innovation. Old patent classes Identify some of the old classes from patents prior to Distribution of external dependence for the old classes (245 classes): (quartiles 1-12%, 2-25%, 3-30%, 4-33%) Log Patent Counts. Old Classes Coefficients on Year Dummies Top 25% external dep. Bottom 25% external dep Year Back

70 Reallocation of Innovation. Without ICT classes Look at the trends excluding ICT technology classes. Log Patent Counts. No ICT-classes Top 25% external dep. Bottom 25% external dep Back

71 ICT and Heterogenous Technological Spillovers Num. external classes cited External citations share Log ICT (0.004) (0.004) Log ICT Ext. Dep (0.007) (0.003) Patent Stock Capital Stock Num. firms Year, Class FE Observations 11, , 286 R Method Poisson OLS Notes: Industry (nclass) Year regressions. Standard errors clustered by technology classes. *p < 0.10, **p < 0.05, ***p < 0.01 Back

72 ICT and Competition # Entrants # Entrants (new) (other incumb.) Log (Herf index) Log ICT (0.020) (0.079) (0.028) Num. incumb. Year, Class FE Observations 9, 682 9, , 286 R Method Poisson Poisson OLS Notes: Industry (nclass) Year regressions. Standard errors clustered by technology classes. *p < 0.10, **p < 0.05, ***p < 0.01 Back

73 Alternative Indexes of External Knowledge Dependence Benchmark definition on : Spearman s corr = 0.98 Benchmark definition on : Spearman s corr = 0.81 Alternative definition: Spearman s corr = 0.52 Back patent i j # classes cited i 412 N j

74 Equilibrium Definition (Steady-State Equilibrium) Given the exogenous allocation of ICT across product lines and the parameter of the diffusion process, ε t, an equilibrium of the economy consists of {xs, zs, µ(n, s), p (n, s), y (n, s), Y, w, τ, g, r } s.t.: (i) Aggregate output Yt is given by final output specification; (ii) Intermediate goods prices and output pt (n, s), y (n, s) satisfy demand and price setting equations; (iii) Wage w clears the labor market; (iv) Innovation decisions xs, zs maximize a firm s value; (v) Equilibrium creative destruction τ is consistent with eq. horizontal innovation; (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.

75 V t (n, s) = +e r t+ t t [ Πt (n) α s x Ē(s,t) φ t (n, s) γ λ n ] α sβ z Ē(s,t) ψ t (n, s) γ t + o( t) (x t (n, s) t + o( t))v t+ t (n + 1, s) +(z t (n, s) t + o( t))(p t+ t + V t+ t (n, s)) +(τ t t + o( t)) 0 (1 x t (n, s) t z t (n, s) t τ t t o( t))v t+ t (

76 r(t)v t (n, s) V t (n, s) = max x t (n,s), z t (n,s) Π t (n) α s x Ē(s,t) φ t (n, s) γ λ n α sβ z Ē(s,t) ψ t (n, s) γ +x t (n, s)(v t (n + 1, s) V t (n, s)) +z t (n, s)p t τ t V t (n, s)