R&D Investment, Exporting, and Productivity Dynamics

Transcription

1 R&D Investment, Exporting, and Productivity Dynamics Bee Yan Aw, Mark J. Roberts, Daniel Yi Xu NASM 2009

2 Motivation Does openness to trade promote productivity? Exports and firm productivity are correlated Selection: robust finding Learning-by-Exporting: mixed evidence A reflection of a new mechanism: endogenous innovation? Bustos (2007), Lileeva and Trefler (2007): trade liberalization induces more innovation. Aw, Roberts, and Winston (2007): R&D and exporting correlated.

3 Motivation Recent macro/trade models of joint decisions of R&D and export: Atkeson and Burstein (2008), Costantini and Melitz (2007). Interdependent R&D and exporting at firm-level Exporting larger market more R&D incentive Selection R&D higher expected future productivity re-inforce selection market size effect depend on domestic/exporting profitability innovation process costs assoicated with each activity

4 Goal of Our Paper Document firm-level R&D and export dynamics in Taiwanese Electronics Industry. Estimate an empirical structural model and quantify how Optimal R&D and export decisions depend on expectation of future productivity/export demand fixed/sunk costs assoicated with choices Decompose correlated R&D and Export dynamics by selection on unobservables state dependence To be done: quantify how large is the market size effect, i.e. how does R&D respond to trade cost reduction?

5 Preview of Results Productivity in response to both R&D and exporting. Impact of R&D is larger. But, relatively low exporting cost makes it a more important channel. The interdependence of R&D and exporting is dominated by selection: stable export demand. To be done: what if a trade liberalization?

6 Taiwan Electronics Industry Data Taiwan Annual Manufacturing Survey. Product classes: consumer electronics, telecommunication equipment, computers and storage equipment, electronics parts and components. Most dynamic industry in Taiwanese manufacturing sector Export participation.39 - compete with low-margins R&D performers.17 - major focus on process innovation Significant cross-sectional heterogeneity in productivity and activities. Sustained productivity growth, 3.6% annual in 80s and 90s. Key variables: Revenue-domestic and export, Physical capital stocks (size), R&D expenditure, Variable costs-material, labor, energy

7 Transition pattern of R&D and exporting Status Year t+1 Status year t Neither only R&D only Export Both All Firms Neither only R&D only Export Both Persistence in the status: (1) high sunk costs (2) high degree of persistence in the underlying profit heterogeneity. Exporting is more common than R&D investment. Undertaking one of the activities in year t more likely to add the other in year t + 1, less likely to drop the other in year t + 1

8 Theoretical Model - Static Decisions Technology: Short-run marginal cost: lnc it = lnc(k it, w t ) x it = β 0 + β k lnk it + β w lnw t x it k it capital stock, w t variable input price, x it productivity Differs across firms, but not a function of output Two sources of heterogeneity: capital-observable, productivity-not observable by researchers.

9 Demand: Demand for the firm s output in domestic market (Dixit -Stiglitz) q D it = Q D t (p D it /P D t ) η D = I D t P D t ( pd it P D t ) η D = Φ D t (p D it ) η D All aggregates are combined into Φ D t. Similarly, demand for the firm s output in export market: q X it = z it I X t P X t ( px it P X t ) η X = Φ X t z it (p X it ) η X z it : firm-specific demand shock in export market. Heterogeneity between export and domestic market for each firm.

10 These assumptions imply domestic and export revenue function as: ln r X it ln r D it η D = (η D + 1) ln( η D + 1 ) + ln ΦD t + (η D + 1)lnc it η X = (η X + 1) ln( η X + 1 ) + ln ΦX t + (η X + 1)lnc it + z it Profits: directly relate revenue to unobservables x it and z it. π D it = ( 1/η D )r D it (Φ D t, k it, x it ) π X it = ( 1/η X )r X it (Φ X t, k it, x it, z it ) Finally, total cost tvc it = r D it (1 + 1 η D ) + r X it (1 + 1 η X )

11 Transition of State Variables Productivity x it evolves endogenously, depending on R&D d it 1 and exporting e it 1 : x it = g(x it 1, d it 1, e it 1 ) + ξ it d it 1 : learning-by-investing. e it 1 : learning-by-exporting. d, e: discrete (0/1) or continuous. Export demand shock z it evolves exogenously as a first order markov process: z it = ρ z z it 1 + µ it, µ it N(0, σ 2 µ) Firm size measure capital k i : short time series dimension with very little variation over time.

12 Theoretical Model - Dynamic Decisions Sources of dynamics: e and d affect evolution of future x, z is persistent over time Beginning each activity involves one-time sunk cost. Sequence of Information and Decisions: 1 Begin period t with productivity and export demand shock (x it, z it ). 2 Random fixed cost γ F it of exporting and sunk cost γs it export decision. 3 Maximize static profits π D it and, if exporting, π X it. 4 Random fixed cost of R&D γ I it and sunk cost γd it R&D decision. 5 End of period t, new states (x it+1, z it+1 ) realized.

13 Dynamic Decisions - Value Functions Let s it = (z it, x it, k i, e it 1, d it 1, Φ t ) Firm s integrated value function in year t V it (s it ) = πit D + max e it (0,1) {πx it e it 1 γit F (1 e it 1 )γit S + Vit E, Vit D }dg γ Firm s future value of exporting: Vit E (s it ) = max {δe tv it+1 (s it+1, e it = 1, d it = 1) γitd I it 1 d it (0,1) γit D,D γi (1 d it 1 ), δe t V it+1 (s it+1, e it = 1, d it = 0)}dG Firm s future value when it chooses not to export: Vit D (s it ) = max {δe tv it+1 (s it+1, e it = 0, d it = 1) γitd I it 1 d it (0,1) γit D,D γi (1 d it 1 ), δe t V it+1 (s it+1, e it = 0, d it = 0)}dG

14 Finally, the expected future value conditional on different choices e it and d it : E t V it+1 = V it+1(s it+1)df (x x it, e it, d it )df (z z it )dg(φ Φ t ) Φ z x Three mechanisms that exporting and R&D are correlated: Selection: probability of e it and d it increasing in x it and z it. MBD(s it ) = E t V it+1 ( e it, d it = 1) E t V it+1 ( e it, d it = 0) export sunk cost γit S knowledge production g(x it, e it, d it ). MBE(s it ) = πit X + V it E (, d it 1) Vit D(, d it 1) R&D sunk cost γit D knowledge production g(x it, e it, d it ).

15 Empirical Model and Estimation Set of parameters: Domestic Demand and Cost Parameters (Φ D, β 0, β k ): domestic revenue equation-recover unobserved productivity x it. productivity evolution equation x it = g(x it 1, d it 1, e it 1 ) + ξ it x it = α 0 + α 1 x it 1 + α 2 (x it 1 ) 2 + α 3 (x it 1 ) 3 (η X, η D ): total cost equation Dynamic Parameters +α 4 d it 1 + α 5 e it 1 + α 6 d it 1 e it 1 + ξ it (ρ z, σ µ, Φ X ): export revenue equation-only observed for exporters. G γ : firm s conditional choice probabilities

16 Demand, Cost, and Productivity Evolution Parameters Parameter Discrete R&D Continuous R&D 1 + 1/η D.8432 (.0195).8432 (.0195) 1 + 1/η X.8361 (.0164).8361 (.0164) β k (.0052) (.0051) α (.0198).0866 (.0194) α (.0519).5982 (.0511) α (.0915).3777 (.0912) α (.0585) (.0588) α (.0099).0067 (.0012) α (.0046).0197 (.0045) α (.0115) (.0014) SE(ξ it ) sample size Markups average 1.18 for domestic and 1.20 for exports Steady state values of x (relative to d = e = 0): Export only 1.34, R&D only 2.00, Both 2.22.

17 Dynamic Parameter Estimates Dynamic Parameter Estimates Model 1 Model 2 Parameter Estimate St Error Parameter Estimate St Error γ I (Innov FC) γi 1 (size 1) γi 2 (size 2) γ D (Innov SC) γd 1 (size 1) γd 2 (size 2) γ F (Export FC) γf 1 (size 1) γf 2 (size 2) γ S (Export SC) γs 1 (size 1) γs 2 (size 2) Φ X (Export Rev Intercept) Φ X ρ Z (Export Rev AR process) ρ Z log σ µ (Export Rev Std Dev) log σ µ

18 Exporting: Productivity, Costs, and History Marginal Benefit of Exporting (millions of NT dollars) Vt E Vt D MBE = πt X + Vt E Vt D x t d t 1 = 1 d t 1 = 0 d t 1 = 1 d t 1 = 0 d t 1 = 1 d t 1 = increasing in productivity: selection V E > V D : sunk costs/lbe MBE(d t 1 = 1) < MBE(d t 1 = 0): sunk R&D is overwhelmed by tech subsititution (α 6 )

19 Costs of Exporting and R&D Costs of Exporting and R&D (millions of NT dollars) Mean Exporting Costs a Mean R&D Costs b x t Fixed Sunk Fixed Sunk a. For plants with d t 1 = 1 b. For plants with e t = 1

20 Conclusion Extensions of this framework (subject to better data): Quality improvement vs. cost reduction Industry Equilbrium Knowledge externality