lecture 2: exporting, innovation and productivity h. vandenbussche brixen, september 2009

Lecture 2: Exporting, Innovation

and ProductivityH. Vandenbussche

Brixen, September 2009

Intro

• Research questions: – Does Innovation drive exporting?– Does Exporting drive innovation?

• How is Innovation measured? – Input measures: R&D expenditures; R&D department; Training– Output measures: Product innovation, Process Innovation

• Methodology: Probit models

• Empirical Evidence: Mixed

Hylke Vandenbussche Brixen, September 2009

Motivation

Motivation - Data - Econometric approach - Results - Conclusion

Early Literature• Melitz, 2003; Bernard and Jensen, 1999: productivity is a

random exogenous draw from a Pareto distribution.• Yeaple, 2005; Bustos, 2005; Constantini & Melitz 2007:

firms endogenously choose innovation.• Link between innovation and firm growth known in IO

Empirical evidence on innovation INPUT measures• Aw et al (2007): no link between R&D and probability to

start exporting for Taiwanese firms.• Cassiman & Martinez-Ros(2007): no link between R&D

and exporting for Spanish firms.


Motivation


Empirical literature on innovation OUTPUT measures• Cassiman & Martinez-Ros (2007): Product innovation not

process innovation affects exporting for Spanish firms• Caldera (2009): Product innovation ànd Process

innovation affect exporting for Spanish firms• Becker and Egger (2007): product innovation matters

more than process innovation to exporting for German firms. They do not isolate export starters which may lead to a simultaneity bias

• Damijan et al. (2008): no link between product nor process innovation and the decision to start exporting for Slovenian firms


Paper 1: Aw, Roberts and Whinston

• IO approach– Hopenhayn (’92) and Olley and pakes (1986) assume that a firm’s

productivity follows a Markov process and does NOT depend on investment:

– More general formulation here:

with r: spending on R&D and x: participation in export markets

• Data– Taiwanese electronics industry– Largest industrial sector: 25% of exports; 5% of GDP– Firm surveys 1986, 1991, 1996

• Innovation measure– R&D– Training

1( / )t tF

1( / , , )t t t tF r x

Transition Matrix of Investment Activities between Years t and

t + 1, 1986–1996 Number of firms (row proportion)

Investment Activity Year t Year (t + 1)

(number of firms in year t)

Start R&D/T Stop R&D/T Start Export Stop Export

No R&D/WT & No 24 (12.97) – 50 (27.03) –

Export (185)

Only R&D/WT (82) – 36 (43.90) 42 (51.22) –

Only Exporting (276) 73 (26.45) – – 52 (18.84)

R&D/WT & Export(530) – 156 (29.43) – 40 (7.55)

• Direction of causality not clear• Methodology: bivariate Probit model

i.e. takes two independent binary probit models and estimates them together but allows a correlation in the error term. This is to recognize that there may be unobserved variables that affect both binary choices. The model is estimated with maximum likelihood

Choice 1: R&D and Exporting

Choice 2: only Exporting

Choice 3: only R&D

3 32

0 1 2 3 4 5 6 7 1 11 1

Pr ( ) log( ) log( ) log( )it t it it it it it it k it k it it itk k

ob Exp a a a age a Enter a k a pwage a Multi plant a a f Choice l Choice x

3 32

0 1 2 3 4 5 6 7 1 11 1

Pr ( & ) log( ) log( ) log( )it t it it it it it it k it k it it itk k

ob R D a a a age a Enter a k a pwage a Multi plant a a f Choice l Choice r

Discrete Investment Activity Equation

Exporting R&D/WT

intercept −3.377 (0.647)* −6.749 (0.626)*

year dummy 0.137 (0.108) 0.023 (0.096)

entrant dummy 0.647 (0.162)* 0.593 (0.199)*

log(age) 0.128 (0.070) −0.209 (0.069)*

log(kit) 0.383 (0.038)* 0.496 (0.036)*

log(pwageit) −0.319 (0.104)* 0.114 (0.100)

multiplant dummy 0.067 (0.127) 0.035 (0.111)

productivity (ωit) 1.120 (0.356)* 0.524 (0.283)

productivity squared ()ωit 2 −0.631 (0.272)* −0.138 (0.215)

lagged Choice 1 dummy Exporting and R&D/WT 1.270 (0.297)* 0.711 (0.251)*

lagged Choice 2 dummy Exporting but not R&D/WT 0.921 (0.239)* 0.206 (0.263)

lagged Choice 3 dummy R&D/WT but not exporting −0.130 (0.423) 0.329 (0.425)

(ωit) * lagged Choice 1 dummy −0.036 (0.652) 0.193 (0.416)

(ωit) * lagged Choice 2 dummy 0.829 (0.464) −0.045 (0.415)

(ωit) * lagged Choice 3 dummy −0.599 (1.049) 0.246 (0.929)

Corr(εxit, εrit) 0.287 (0.059)*

Notes:

* Statistically significant at the α = 0.05 level.

Conclusion Aw et al.

• History of exporting matters• R&D does NOT matter for exporting• But! R&D and exporting together can put a firm on

a higher future productivity path!

Paper 2: Cassiman&Martinez-Ros

• Data– Spanish manufacturing firms– ’90-99– CIS for Spain: output measure of innovation– One way causality from innovation to exporting

• Theory– Vernon (1966) product life cycle i.e. firms invent new

product, first sell it at home and than abroad

Table 3a: Past Innovation and Exportst

Not Exportt Exportt Total

Not Innovate t-1 2807 (58%) 2070 (42%) 4877 (100%)

Innovate t-1 389 (16%) 2033 (84%) 2422 (100%)

Total 3196 (44%) 4103 (56%) 7299 (100%)

Table 3b: Past Product Innovation and Exports


No Product Innovation t-1 2799 (51%) 2720 (49%) 5519 (100%)

Product Innovation t-1 397 (22%) 1383 (78%) 1780 (100%)

Total 3196 (44%) 4103 (56%) 7299 (100%)

Table 3c: Past Process Innovation and Exports


No Process Innovation t-1 2505 (51%) 2405 (49%) 4910 (100%)

Process Innovation t-1 691 (29%) 1698 (71%) 2389 (100%)

Total 3196 (44%) 4103 (56%) 7299 (100%)

Table 7: Decision to Export at time t by Non-Exporters in t-1

Small and Medium Firms (<200 workers) Large Firms (> 200 workers)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Prod In (t-1) 0.208** 0.218* 1.112** 1.66** 0.55* 0.696** 0.696** 0.604 0.179 0.059

Proc Inn (t-1) 0.084 0.117 - 0.516 -0.966 -0.107 -0.044 -0.044 0.432 -0.092 -0.2

Size 0.017*** 0.027*** 0.018*** 0.029*** 0.029*** 0.001 0.001 0.001 0.001 0.001

SizeSq -0.100*** -0.163*** -0.104*** -0.173*** -0.172*** -0.001 -0.001 -0.001 -0.0004 -0.0005

Foreign 0.317 0.439 0.250 0.388 0.374 -0.511* -0.511* -0.365 -0.325 -0.325

Cap Int 0.00002 0.00004 0.00003 0.00005 0.00005 0.0001 0.0001 0.0001 0.0001 0.0001

Wage Int -1.248*** -1.478*** -1.210*** -1.522*** -1.467*** 0.1447 0.1447 0.132 0.110 0.138

Low Comp 0.001 0.003 0.001 0.002 0.002 0.010 0.010 0.014 0.011 0.012

Index -0.00003 0.00001 0.00003 0.00002 0.00001 0.00006 0.00006 0.00001 -0.00004 0.00001

Intercept -1.677*** -2.394*** -1.96*** -2.2*** -1.71 -2.50 -2.50 -1.852 -0.76 -1.501

Indy-Time Ds Included Included Included Included Included Included Included Included Included Included

Obs 2916 916 2916 2916 2916 140 140 140 140 140

Conclusion Cassiman-Martinez-Ros

• Product Innovation explains Exporting !• Especially in Small firms• Product Innovation suggest firm-specific demand

shocks

Paper 3: Damijan et al.

• Data

-Slovenian firm-level data

-CIS community industry survey ‘96-2002

-output measure of innovation• Methodology

– Bivariate Probit model on exporting and innovation– Allow for two way causality

Bivariate Probit model

A test for correlation between exporting to innovation i.e. “learning”:

Prob(Inov t = 1) = f(Inovt-2;Exp t-2;X t-2)

A test for correlation between innovation to exporting:

Prob(Export t = 1) = f(Exp t-2; Innov t-2;X t-2)

Table 4: Results of bivariate probit regressions (no matching, all exporters)

Export status

(1) (2) (3) (4) (5) (6)

Lagged innovation 0.129 0.054 0.096 -0.093 0.191 -0.041

Lagged exportstatus 1.876*** 2.281*** 2.128*** 2.443*** 2.421*** 2.401***

Lagged productivity 0.126* 0.145 -0.076 -0.067 -0.108 -0.050

Lagged employment 0.214*** 0.166*** 0.321*** 0.130* 0.177* 0.145*

Lagged capitalintensity 0.144*** -0.108** 0.067 -0.092* -0.029 -0.0640

Lagged R&DInvestment 0.004 0.025 0.0090.0260

FDI penetration in industry 0.151 0.114 -0.097-0.079

Industry dummies yes no yes no no no

Timedummies yes yes yes yes yes yes

N 3812 1551 1428 602 623 623

Rho 0.125 0.139 0.118 0.275 0.423 0.197

Prob rho=0 0.058 0.078 0.092 0.063 0.007 0.132

(1)-(4) Both product and process innovation considered, (5)only produc tinnovation is

considered and(6)only process innovation considered

Table5: Results of bivariate probit regressions (no matching, all innovators) Innovation status

• (1) (2) (3) (4) (5) (6) Lagged inn 1.226*** 1.396*** 0.631*** 0.891*** 0.912*** 0.463*** Lagged exports 0.223*** 0.332*** -0.053 0.536** 0.478** 0.254 Lagged productiv 0.167*** 0.171** 0.199** 0.072 0.092 0.208* Lagged employm 0.224*** 0.256*** 0.178*** 0.130** 0.134** 0.228*** Lagged capital int 0.069* -0.057 0.124* 0.049 -0.042 0.053 Lagged R&D Invest 0.077*** 0.051*** 0.057*** 0.049*** FDI penetration in sector 0.793*** 0.708*** 0.564** 0.651*** Sector dummies yes no yes no no noTime dummies yes yes yes yes yes yes N 3812 1551 1428 602 623 623

(1)-(4) Both product and process innovation considered (5)only product innovation is considered and (6)only process innovation considered

Conclusion Damijan et al.

• No evidence that R&D affects exporting• But evidence that Exporting affects innovation

(“learning”) for medium and large firms• Results are confirmed with matching techniques

Innovation, Exports and Productivity:

Firm-level evidence for Belgium

Ilke Vanbeveren (Lessius, KUL) and Hylke Vandenbussche (CORE-UCL & KUL-LICOS)

Brixen, September 2009

Intro

• Main research Q: Does innovation drive exporting?• Data: Belgium, Community Innovation Survey, 2 waves,

starters on export market vs. control group. • Methodology: Probit model. Dependent variable:

probability to start exporting. Independent variables: innovation variables and controls.

• Main findings: – It is the combination of product and process innovation

(not either of two in isolation) that increases firms’ probability to start exporting.

– Controlling for endogeneity of the innovation decision: no significant impact of innovation on probability to start exporting.

Ilke Van Beveren and Hylke Vandenbussche Brixen, September 2009

Road map

1. Motivation & related literature

2. Data

3. Econometric approach

4. Baseline results

5. Accounting for anticipation effects

6. Conclusion


Motivation


Three sources of endogeneity:• Simultaneity: Innovation and export decisions are

taken at the same time. – Possible solution: use lagged values of independent

variables.

• Causality: Past exporting history. – Possible solution: focus on starters versus non-exporters.

• Anticipation: Future prospect of exports.– Possible solution: use Instrumental Variable techniques.


Data


• Community Innovation Survey data Belgium.• 2 waves: 2000 (CIS3) and 2004 (CIS4).• Sampling is random in each period: 600 firms have

answered both questionnaires.• Information about:

– Firm-level innovation– Firm-level exports

• All sectors of the economy.• Accounting information of firms: Belfirst (2006).


Data


• Sample selection: 2 restrictions:– Simultaneity bias: we use (four-year) lagged firm-level

characteristics in the empirical analysis: we can only include firms that have answered both questionnaires (600 firms).

– Causality bias: To rule out the influence of past exporting history: we focus only on starters on the export market and compare these a group of non-exporters (189 firms).

• Innovation variables: dummy variables indicating whether firm engaged in a particular innovation activity.



Sector NNon-

exporters StartersMining (Nace 14) 2 1 1Food, beverages & tobacco (Nace 15-16) 6 1 5Textiles, clothing, leather (Nace 17-19) 6 2 4Wood (products) (Nace 20) 3 1 2Paper and publishing (Nace 21-22) 5 1 4Fuel and chemicals (Nace 23-24) 5 1 4Rubber and plastics (Nace 25) 4 1 3Non-metallic minerals (Nace 26) 4 1 3Basic and fabricated metals, machinery (Nace 27-29) 24 4 20Electrical, optical, medical instruments (Nace 30-33) 3 1 2Tranport equipment, manufacturing n.e.c. (Nace 34-37) 9 6 3Construction (Nace 45) 2 2 0Wholesale and retail trade (Nace 50-52) 35 16 19Transport and financial services (Nace 60-67) 40 30 10Real estate and business services (Nace 70-74) 41 24 17Total 189 92 97

Table 1: Sector distribution

Variable

Non-exporters Starters

Number of firms 92 97[Percentage of total] [48.68%] [51.32%]

Size 3.65 3.98***(Employment, fte, 2000) [1.28] [1.32]

Total factor productivity 1.00 2.25*(Törnqvist productivity index, 2000) [0.49] [9.11]

Reported values are means [standard deviations] in 2000 (except where the number of firms is reported). Starters are firms that start exporting in 2004, non-exporters do not export in 2000 and 2004. Significance levels

(*** p < 0.01; ** p < 0.05; * p < 0.10) refer to one-tailed test on the difference between the means for the starters compared to non-exporters.

Variables are defined in Appendix A.

Table 2: Comparing starters to non-exporters:



VariableNon-

exporters StartersN 92 97N_internal R&D 24 29N_external R&D 7 13N_product innovation 30 56N_process innovation 24 48N_product and process inn. 10 32

Table 3: Comparing starters to non-exporters: Innovation characteristics


[1] [2] [3] [4]

Internal R&D dummy 1External R&D dummy 0.4745 1Product innovation dummy 0.5176 0.3074 1Process innovation dummy 0.4805 0.3323 0.4428 1

Table 4: Correlations innovation measures

Econometric approach


• Estimation method: probit model.• Dependent variable: probability to start exporting.• Independent variables:

– Innovation dummies,– Sector dummies,– Firm-level control variables: Size and productivity.

4 4 4Pr 1 ln ,ln , ,it it it it iSTART f Size TFP INN I


Variables

Internal R&D

dummy

External R&D

dummyProduct

innovationProcess

innovation

Size 0.051* 0.044 0.030 0.031Total factor productivity 0.083** 0.077** 0.064* 0.077** Innovation measure -0.066 0.064 0.217*** 0.185**

Sector dummies Yes Yes Yes YesNumber of observations 189 189 189 189Pseudo R-square 0.166 0.165 0.190 0.182

Table 5: Regression results

Input measures Output measures

Probit regression. Dependent variable: probability to start exporting in 2004. Reported values are marginal effects. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are not reported here, can be found in

the paper.


Variables I II III IV V

Size 0.023 0.018 0.045 0.047 0.022Total factor productivity 0.066* 0.068* 0.075** 0.077** 0.073**

Product innovation 0.175** - - - -Process innovation 0.119 - - - -

Only product innovation 0.101 0.085 - -Only process innovation 0.029 - -0.028 -Product & Process innovation 0.301*** - - 0.285***

Sector dummies Yes Yes Yes Yes YesNumber of observations 189 189 189 189 189Psuedo R-square 0.196 0.201 0.167 0.165 0.196

Table 6: Regression results

Probit regression. Dependent variable: probability to start exporting in 2004. Reported values are marginal effects. Significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are not

reported here, can be found in the paper.


Accounting for anticipation effect


• How? IV estimation techniques.• Problem: IV probit is not possible when

endogenous variable is dummy.• Solution: Linear Probability Model (IV).• Requirements for good instruments:

– No direct impact on probability to start exporting.– Significant determinant of endogenous variable,

conditional on all other independent variables.


Dependent variable Onlyprod2000 Onlyproc2000 Bothinn2000

Size -0.005 -0.04 0.067** Total factor productivity -0.001 0.001 -0.001

Internal R&D 0.231** 0.213** 0.143* External R&D -0.105 -0.188 0.318** Training 0.107 0.153** 0.025

Sector dummies Yes Yes YesNumber of observations 189 189 189

Table A.1. First-stage regression results

Results of first-stage regression of the IV estimation reported in the last column of Table 6. Reported values are coefficients. Standard errors are unreported here, can be found in the paper. Dependent variable is given at the top of each column.Significance level: *** p<0.01, ** p<0.05, *

p<0.1.


IV IV IV

Size 0.008 0.043 0.041 0.077Total factor productivity 0.003** 0.002 0.002 -0.004

Only product innovation 0.107 -0.151 - -5.153Only process innovation 0.036 - 0.803 4.377Product and process inn. 0.275*** - - 1.207

Instruments (dummies, 2000)

- Internal R&D Training External R&DInternal R&D

Training

Sector dummies Yes Yes Yes YesR-square 0.236 0.163 0.161 0.178Number of observations 189 189 189 189

OLS or IV regression. Dependent variable: dummy, equal to one if the firms starts exporting in 2004. Reported values are coefficients. Significance level: *** p<0.01, **

p<0.05, * p<0.1. Standard errors are not reported here, can be found in the paper.

Table 7: Instrumental variables estimation

Variables

Baseline model LPM

Output measures


Conclusion

Motivation- Data - Econometric approach - Results - Conclusion

• It is not so much product or process innovation in isolation, but rather the combination of the two, that increases firms’ propensity to start exporting.

• After accounting for the potential endogeneity of the innovation decision in firms’ export decision: results suggest that firms self-select into innovation, i.e. they only invest in innovative activities if their future export prospects are good.


lecture 2: exporting, innovation and productivity h. vandenbussche brixen, september 2009

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