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

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ights enforcement on f<strong>in</strong>anc<strong>in</strong>g, R&D <strong>in</strong>put, and R&D output. The evidence suggests that<br />

facilitat<strong>in</strong>g f<strong>in</strong>anc<strong>in</strong>g and <strong>in</strong>vest<strong>in</strong>g <strong>in</strong> R&D are the channels through which better IP rights<br />

enforcement could affect, <strong>in</strong> the aggregate, the growth <strong>of</strong> the economy.<br />

4.1 The effect <strong>of</strong> IP rights enforcement on f<strong>in</strong>anc<strong>in</strong>g<br />

4.1.1 Access to new debt<br />

We exam<strong>in</strong>e the relationship between prov<strong>in</strong>cial-level IP rights enforcement and the <strong>in</strong>dividual<br />

firms’ ability to obta<strong>in</strong> external debt. We use a probit regression model for our 23,955 firm-year<br />

observations. To control for other factors that may affect these firms’ ability to obta<strong>in</strong> external debt,<br />

we <strong>in</strong>clude firm- and prov<strong>in</strong>cial-level characteristics. The model is specified as the follow<strong>in</strong>g:<br />

Y = α + β IP <strong>Rights</strong> <strong>Enforcement</strong> + β Firm Variables + β Prov<strong>in</strong>cial Variables<br />

i,t 1 i,t-1 2 i,t-1 3 i,t-1<br />

+ β Industry Dummies + ε<br />

5 i i,t<br />

(1)<br />

The dependent variable,<br />

Y<br />

i , t<br />

, is one if the net <strong>in</strong>crease <strong>in</strong> debt is at least 5% <strong>of</strong> total assets <strong>in</strong> year t,<br />

and zero otherwise. The set <strong>of</strong> firm-level variables <strong>in</strong>clude: patent dummy (which is coded as 1 if<br />

the company has any patents before the current year and 0 otherwise), R&D <strong>in</strong>tensity, sales growth<br />

rate, <strong>in</strong>tangible to total assets, return on assets (ROA), leverage, natural logarithm <strong>of</strong> total assets, and<br />

natural logarithm <strong>of</strong> firm age. The def<strong>in</strong>itions <strong>of</strong> these firm characteristics are listed <strong>in</strong> Appendix 3.<br />

We also <strong>in</strong>clude prov<strong>in</strong>cial bank<strong>in</strong>g system development and corruption control, prov<strong>in</strong>cial GDP<br />

growth, the number <strong>of</strong> universities <strong>in</strong> a prov<strong>in</strong>ce, as well as the metropolis dummy. All <strong>in</strong>dependent<br />

variables <strong>in</strong> the regression model are lagged by one year. We also add 21 <strong>in</strong>dustrial dummy variables<br />

accord<strong>in</strong>g to Industry Classification Standard <strong>of</strong> CSRC. The standard errors are corrected for<br />

heteroskedasticity and with<strong>in</strong>-prov<strong>in</strong>ce correlations.<br />

Insert Table 2<br />

Table 2 (Columns 1 and 2) reports the probit estimates <strong>of</strong> the marg<strong>in</strong>al effect <strong>of</strong> IPP1 (the<br />

21

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