Ludwig Maximilians Universität München 22 th January, Determinants of R&D Financing Constraints: Evidence from Belgian Companies

Transcription

1 INNO-tec Workshop Ludwig Maximilians Universität München 22 th January, 2004 Determinants of R&D Financing Constraints: Evidence from Belgian Companies Prof. Dr. Michele Cincera Université Libre de Bruxelles DULBEA & CEPR

2 OBJECTIVES OF THE PAPER: Relationship between the possible existence of liquidity constraints and the decisions of Belgian private firms as regards their physical capital and R&D investments over the last decade. What are the main determinants of R&D financing constraints? OUTLINE OF THIS TALK: Motivations Data Econometric framework Empirical findings Conclusions

3 MOTIVATIONS: R&D intensity increasing across OECD... GERD as a % of GDP ( or nearest available years) Sweden Finland Japan United States Korea Switzerland Germany Iceland Total OECD France Denmark Netherlands Belgium Canada European Union Austria United Kingdom Australia Norway Czech Republic Ireland Italy Spain Hungary Portugal Poland Slovak Republic Greece Turkey

4 MOTIVATIONS: Financing constraints, i.e. credit rationing by lenders, may be more pronounced in the case of intangible investments such as R&D. Agency costs. Asymmetric information: risks and uncertainties of R&D; strategic considerations. High adjustment and sunk costs associated with R&D.

5 DATABASE: Sources: Frms annual accounts at the Central Balance Sheet Office (Belfirst); about private Belgian firms, Belgian biannual R&D surveys (OSTC); 548 firms, ; Belgian CIS-2; 1341 firms, All flow variables expressed in 1995 constant BEF and deflated with sectoral prices indices. Trimming and merging procedures. Variables extracted and constructed:

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7 DATABASE: Representativeness of Sample 1: Added value with respect to the national corresponding aggregate (in %) by industry sector Industries a # of firms Agriculture Energy product, water Metal and non metallic product Chemical products Machinery and equipment Transport equipment Food Textile Paper Rubber Wood and other manufacturing Construction Wholesale and retail trade Hotels et restaurants Transports and communications Financial intermediation Real estate and other business services Total Source: Institute for National Accounts (2001) and own calculation.

8 DATABASE: Representativeness of Sample 2: R&D expenditures (10^9 BEF of 1995) with respect to the national corresponding aggregate # of firms 1 Raw data set Sample BERD a / 2 % / 3 % Note: a) BERD = Total intramural business enterprise R&D expenditures. Source: Belgian and Office for Scientific, Technical and Cultural Affairs (2001) and own calculation.

9 ECONOMETRIC FRAMEWORK: Error correction investment equations (Hubbard & Petersen, 1988): C β β I it 1 3 R K 3 it CF C it it 1 I = η C it it 1 CF K it it 1 + β R = η K + β it 1 it CF C it 1 it 2 K + β CF 1 it 1 it 2 + β it 1 it 2 1 log + α + α ( Y ) + β log( Y ) + ρ( log( C ) log( Y )) i log i it + λ t 2 + ε First differenced and system IV-GMM estimators (Arellano and Bover, 1995; Blundell and Bond, 1998). it it 1 it 2 it 2 ( Y ) + β log( Y ) + ρ( log( K ) log( Y )) it + λ t + ε 2 it it 1 it 2 it 2 + +

10 MAIN FINDINGS: Error correction model for physical and R&D investments Model a physical capital R&D capital F.D. GMM b GMM SYS b F.D. GMM b GMM SYS b C.00 (.009).16 (.022)* C.000 (.0200).088 (.0646) I t-1 /C t-2.06 (.014)*.06 (.011)* R t-1 /K t (.0617).025 (.0304) CF t /C t-1.14 (.051)*.22 (.040)* CF t /K t (.0004).001 (.0003)* CF t-1 /C t-2.01 (.018).01 (.015) CF t-1 /K t (.0004)*.006 (.0003)* log(y t ) -.17 (.092)* -.11 (.067) log(y t ).105 (.0272)*.195 (.0119)* log(y t-1 ) -.13 (.092).03 (.024) log(y t-1 ) (.0179).000 (.0146) log(c t-2 )- log(y t-2 ) -.18 (.044)* -.01 (.014) log(k t-2 )- log(y t-2 ) (.0504)* (.0231)* log(y t-2 ) -.18 (.100) -.01 (.025) log(y t-2 ) (.0603)* (.0258)* Wald test of joint Wald test of joint 519 [.000] 3162 [.000] 191 [.000] 576 [.000] signif. signif. Wald test time Wald test time 33.3 [.000] 46.9 [.000] 14.4 [.044] 99.7 [.000] dummies dummies Wald test for CF 22.4 [.000] 53.7 [.000] Wald test for CF 115 [.000] 441 [.000] Sargan test 109 [.725] 183 [.058] Sargan test 26.6 [.541] 36.1 [.726] Test M [.000] [.000] Test M1-2.0 [.045] -1.7 [.098] Test M2.47 [.640] -.46 [.634] Test M2 1.6 [.112] 1.1 [.265] # of obs. (firms) (10049) 375 (160) Notes: a) Estimation performed using the DPD98 software (Arellano and Bond, 1998); all equations include time dummies; Heteroskedastic-consistent standard errors in bracket; P values in square brackets; M1 and M2: tests for first order and second order serial correlation in the first difference residuals. b) Two-step estimates; instruments used: observations dated t-2, t-3, t-4 and t-5 for X t (GMM F.D. and GMM SYS) and t-1 for X t (GMM SYS).

11 CAVEAT: Kaplan & Zingales (1997, 2000): Cash flow effects can be correlated with firms demand expectations; Not a direct measure of liquidity constraint! How can we disentangle between these two effects? Adding proxies of investment opportunities such as the Tobin s q for instance; Structural approach: Euler equations but results weak and mitigated; Estimation of forecasting equations to predict future sales with cash flow; This paper: Split the sample in subsamples of firms for whom financial constraints are more likely to be more (or less) important. Direct information on cash constraints: Barriers to innovation (CIS-survey).

12 ADDITIONAL FINDINGS: Long run effect of cash flow WAL VLA BXL AGE99 AGE20 AGE10 QUOTED NON QUOTED SUBSIDIARIES DOMESTIC LARGE MEDIUM SMALL

13 ADDITIONAL FINDINGS: Long run effect of cash flow AGRICULTURE VEHICLES HORECA CHEMICALS TEXTILE FOOD CONSTRUCTION OTHER MANUF ELECTRICALS PAPER RUBBER WHOLESALE TRANSPORT OTHER SERVICES METALS FINANCE

14 CAVEAT: Kaplan & Zingales (1997, 2000): Cash flow effects can be correlated with firms demand expectations; Not a direct measure of liquidity constraint! How can we disentangle between these two effects? Adding proxies of investment opportunities such as the Tobin s q for instance; Structural approach: Euler equations but results weak and mitigated; Estimation of forecasting equations to predict future sales with cash flow; This paper: Split the sample in subsamples of firms for whom financial constraints are more likely to be more (or less) important. Direct information on cash constraints: Barriers to innovation (CIS-survey).

15 CIS-2 2 BELGIUM: Factors hampering innovation

16 MAIN FINDINGS: Error correction model for R&D investment: firms that report a lack of appropriate sources of finance of their innovative activities in (CIS-2) Model a R&D capital R&D capital F.D. GMM b GMM SYS b F.D. GMM b GMM SYS b C.0076 (.0064).0372 (.0008)* C.0109 (.0030)*.0083 (.0016)* R t-1 /K t (.0556)*.0731 (.0137)* R t-1 /K t (.0429)* (.0188)* CF t /K t (.0002).0002 (.0001)* CF t-1 /K t (.0502)*.0028 (.0001)* CF t /K t-1 *LACK (.0740).2317 (.0107)* CF t-1 /K t-2 *LACK.1077 (.0004)* (.0187)* log(y t ) (.0202) (.0043)* log(y t ).0577 (.0143)*.0417 (.0029)* log(y t-1 ).0839 (.0285)*.2899 (.0018)* log(y t-1 ).0940 (.0206)*.1419 (.0077)* log(k t-2 )- log(y t-2 ) (.0603)* (.0082)* log(k t-2 )- log(y t-2 ) (.0415)* (.0147)* log(y t-2 ) (.0490)* (.0060)* log(y t-2 ) (.0284)* (.0110)* Wald test of joint Wald test of joint 1383 [.000] [.000] 390 [.000] [.000] signif. signif. Wald test for CF 63.1 [.000] 117 [.000] Wald test for CF 157 [.000] 2654 [.000] Sargan test 29.1 [.177] 1995 [.000] Sargan test 22.5 [.492] 1355 [.000] Test M [.143] [.616] Test M [.075] -.43 [.668] Test M2.045 [.964] [.608] Test M2.214 [.831] 3.11 [.002] # of obs. (firms) 214 (81) 214 (81) Notes: a) Estimation performed using the DPD98 software (Arellano and Bond, 1998); all equations include time dummies; Heteroskedastic-consistent standard errors in bracket; P values in square brackets; M1 and M2: tests for first order and second order serial correlation in the first difference residuals. b) Two-step estimates; instruments used: observations dated t-2, t-3, t-4 and t-5 for X t (GMM F.D. and GMM SYS) and t-1 for X t (GMM SYS).

17 MAIN FINDINGS: Error correction model for R&D investment Impact of permanent R&D Impact of publicly funded R&D Model F.D. GMM GMM SYS F.D. GMM GMM SYS C (.0229).079 (.1060) C (.0197).067 (.0904) R t-1 /K t (.0661).043 (.0383) R t-1 /K t (.0601).021 (.0376) CF t /K t (.0004).001 (.0004) CF t /K t (.0006).001 (.0004)* CF t /K t-1 *PERMA (.0064) (.0045)* CF t /K t-1 *PUBL.060 (.0386).096 (.0353)* CF t-1 /K t (.0006)*.006 (.0004)* CF t-1 /K t (.0004)*.005 (.0003)* CF t-1 /K t-2 *PERMA.001 (.0042).001 (.0034) CF t-1 /K t-2 *PUBL (.0189) (.0185)* log(y t ).152 (.0441)*.237 (.0365)* log(y t ).036 (.0373).120 (.0362)* log(y t-1 ) (.0209) (.0206) log(y t-1 ).005 (.0182) (.0195) log(k t-2 )- log(y t-2 ) (.0644)* (.0352)* Log(K t-2 )- log(y t-2 ) (.0581)* (.0345)* log(y t-2 ) (.0538)* (.0319)* Log(Y t-2 ) (.0495)* (.0278)* PUBL (.0189) (.0151)* Wald test of joint signif [.000] [.000] Wald test of joint signif. 754 [.000] [.000] Wald test time dummies 32.9 [.000] 35.5 [.000] Wald test time dummies 29.8 [.000] 44.4 [.000] Wald test for CF 11.8 [.003] 218 [.000] Wald test for CF.94 [.623] 252 [.000] Sargan test 24.9 [.527] 30.3 [.810] Sargan test 23.2 [.805] 29.1 [.786] Test M1-2.1 [.033] -1.5 [.122] Test M1-2.2 [.025] -2.0 [.051] Test M2 1.5 [.124] 1.1 [.262] Test M2 1.8 [.075] 1.4 [.171] # of obs. (firms) 375 (160) Notes: a) Estimation performed using the DPD98 software (Arellano and Bond, 1998); all equations include time dummies; Heteroskedastic-consistent standard errors in bracket; P values in square brackets. M1 and M2: tests for first order and second order serial correlation in the first difference residuals. b) Two-step estimates; instruments used: observations dated t-2and t-3 for X t (GMM F.D. and GMM SYS) and t-1 for X t (GMM SYS).

18 MAIN FINDINGS: Probit model with sample selection: determinants explaining the lack of appropriate sources of finance of firms innovative activities in (CIS-2 Belgium 1996)

19 CONCLUSIONS: Objectives: 3 newly constructed samples of Belgian private companies, Impact of financing constraints on both capital and R&D investments. R&D activities: more risky and less collateral. Higher financing constraints. High adjustment and sunk costs associated with R&D: firms will engage in R&D if they do not expect to be seriously affected by financial constraints. Cash flow effects tend to matter less for these firms investment decisions. Provision of public support to R&D may also interfere with the firm s investment decision.

20 CONCLUSIONS: Main empirical findings: positive impact of cash flow effects in the firms investment decisions; more important role for investment in physical capital than for R&D; results of investment accelerator specifications and of previous studies confirmed; firms with permanent R&D less subject to liquidity constraints; No true for firms that receive public aids. Additional results: large firms, firms listed on the stock exchange, subsidiaries of foreign MNE s less subject to liquidity constraints; younger and older firms appear to be more liquidity constrained; differentiated impacts of these constraints according to the firm s industry sector and region. Extension: endogeneize the public R&D funds analyse interactions between the level of: competition in the firms product market; outside finance (debt); managerial effort (Aghion, Dewatripont and Rey, 2000).

21 Danke für Ihre Aufmerksamkeit!