Does Enforcement of Intellectual Property Rights Matter in China ...

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Greater corruption (less corruption control) is associated with lower informal financing ratio. The reason is that greater corruption is found to be associated with more funding from banks (Fan, Rui, and Zhao, 2008) and thus, by the substitution effect, a lower ratio of informal financing relative to bank loans. GDP growth and provincial number of universities have positive and significant coefficients. The regressions using IPP2 as the measure of IP enforcement generate similar results (Column 4 and Column 5). In the instrumental variable regressions, the impact of IPP1 (IPP2) is greater (smaller) than those reported in OLS regressions. The two instruments are strong determinants of IP enforcement, and the overidentification tests further confirm they are valid instruments. 4.2 The effect of IP rights enforcement on R&D input We estimate the effect of IP rights enforcement on firm-level R&D investment. We hypothesize that firms in provinces with better IP rights enforcement are more inclined to invest in R&D, and thus are more willing to allocate greater shares of funds to R&D. The OLS regression takes the following form: R&D Intensity = α + β Sources of Funds + β (Sources of Funds IP Right Enforcement ) i,t 1 i,t 2 i,t i,t-1 3 i,t -1 4 i,t -1 5 i ε i,t + β IP Rights Enforcement + β Provincial Variables + β Industry Dummies + (2) We define the dependent variable, R&D Intensity, as R&D expenditures by firm i in year t divided by its total assets at the beginning of the year. R&D intensity is estimated as a function of IP rights enforcement, the newly raised funds from three sources (debt, external equity and internal financing), and the interactions of the funding sources with IP rights enforcement. 15 The funding sources are scaled by total assets (the detailed description is given in Appendix 3). Also included are provincial characteristics, and industry dummies. All independent variables, except for the new funding 15 Internal financing is profit minus dividend, adjusted for withdrawal of equity. 26
variables, are lagged by one year. The standard errors are robust to corrections for heteroskedasticity and within-province correlations. Insert Table 5 The coefficients and z-stat obtained from the OLS estimation of Equation (2), using IPP1 as the measure of IP enforcement, are presented in Column 1 in Table 5. The coefficients on the interaction terms between IP rights enforcement and funding from new debt, and between IP rights enforcement and internal financing are positive and significant. The results suggest that in provinces with better IP rights enforcement, high tech companies will invest a significantly higher proportion of new debt and new internal financing in R&D. When moving from the lowest-IPP1 province to highest-IPP1 province, the percentage of new debt invested in R&D more than doubles, from 3.1% to 8.5%, while the percentage of new internal financing invested in R&D rises from 9.2% to 20.6%. The results above are robust to controlling for other provincial characteristics (Column 2). We also re-estimate the basic model in Column 1 by controlling for the interactions between the provincial characteristics and the three funding sources. The estimated coefficients of the interaction terms between IP rights enforcement and new debt, and between IP rights enforcement and new internal finance, remain positive and statistically significant (not tabulated). The results are robust when we use IPP2 to measure IP enforcement (Column 4 and Column 5). The results do not change qualitatively in the instrumental variable regressions (Column 3 and Column 6). 4.3 The effect of IP rights enforcement on R&D output We now investigate the impact of IP rights enforcement on R&D output. The first measure of R&D output is the number of innovation patents. The number of innovation patents is predicted to 27
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