Venture capital investment, intellectual property rights protection and firm innovation: evidence from China

Figures & data

Table 1. Distribution of VC-backed firms by industry

Industry	2-digit SIC	# Of Firms	Percentage
Motor Vehicles and Parts	36	11	4.1
Special Equipments	35	17	6.34
Medical Products	27	23	8.58
Computers, Communication and Other Electronic Products	39	27	10.07
Chemical Material and Chemical Products	26	31	11.57
Meter and Related Devices	40	49	18.28
Other industries		110	41.06
Total		268	100

This table presents the industry distribution of the VC-backed companies in our sample. Industries are presented in 2-digit standard industry codes (2-digit SIC). We report the total number of firms in an industry (# of Firms) and the percentage of firms of an industry in the total sample (Percent).

Table 2. Summary statistics of VC-backed firms

Variables	Obs	Mean	SD.	Min	Max
Sales (RMB10mil)	1,613	45.353	79.750	0.508	407.524
Dif_Sales (RMB10mil)	1,415	10.388	20.492	−15.791	91.399
Exports (RMB10mil)	1,386	10.211	24.975	0	121.603
Dif_Exports (RMB10mil)	1,012	2.698	13.151	−100.018	135.501
New_Prdct (RMB10mil)	1,386	8.795	22.707	0	105.683
Dif_New_Prdct (RMB10mil)	1,012	2.042	6.796	−7.678	29.093
Patent	1,613	7.440	103.235	0	3442
Citation	1,613	0.352	0.598	0	2
VC_Entry	1,613	0.464	0.499	0	1
Firm size (1000)	1,613	1.015	1.491	0	7.500
Leverage	1,613	0.536	0.210	0.032	1.388
Firm age	1,613	10.444	11.910	0	100
State Ownership	1,613	0.095	0.293	0	1

This table reports summary statistics for the 268 venture-backed companies in our sample for the entire period examined. We present the number of observations (Obs), mean (Mean), standard deviation (SD.), minimum (Min) and maximum (Max) for the variables we are interested in.

Table 3. Differences between VC-backed and non-VC-backed firms in innovation#

	Non-VC-backed Obs (all years)		VC-Backed Obs (all years)
Variables	Obs	Mean	Obs	Mean	Diff	T-stats
Exports	5056	2.869	1,386	10.211	−7.342***	−10.402
Dif_Exports	3601	0.365	1,012	2.698	−2.333***	−5.562
New_Prdct	5056	1.247	1,386	8.795	−7.548***	−11.949
Dif_New_Prdct	3601	0.233	1,012	2.042	−1.809***	−8.555
Patent	5869	0.166	1,613	7.440	−7.274***	−2.830
Citation	5869	0.008	1,613	0.352	−0.344**	−2.358
	Non-VC-backed Obs in the Entry year		VC-backed Obs in the Entry year
Dif_Exports	842	0.584	182	6.104	−5.520***	−3.463
New_Prdct	1174	1.055	246	10.406	−9.351***	−5.561
Dif_New_Prdct	842	0.213	182	3.342	−3.130***	−5.276
Patent	1189	0.193	268	3.892	−3.699***	−2.708
Citation	1189	0.003	268	0.299	−0.295**	−2.302
	Non-VC-backed Obs in the Entry+1 year		VC-backed Obs in the Entry+1 year
Exports	498	2.621	139	14.519	−11.898***	−4.124
Dif_Exports	483	0.101	118	2.472	−2.371	−1.310
New_Prdct	498	1.118	139	10.142	−9.023***	−4.338
Dif_New_Prdct	483	0.131	118	1.828	−1.670***	−2.804
Patent	557	0.110	153	6.739	−6.629*	−1.727
Citation	557	0.007	153	0.810	−0.803*	−1.640

This table shows the differences in innovation output between VC-backed firms in our sample and non-VC-backed firms in our randomly drawn control group. We report the total number of observations (Obs), as well as the average (Mean) of exports (Exports), increase of exports (Dif_Exports), new product sales (New_Prdct), increase of new product sales (Dif_New_Prdct), the number of newly granted patents (Patent), and the increase in patent citations (Citation) for each group of firms over the entire period examined, the year of VC entry, and the year after VC entry. We also present the differences between the two groups of firms (Diff) and the T-statistics. Exports, Dif_Exports, New_Prdct and Dif_New_Prdct are RMB 10 mil. ***: 1% significance; **: 5% significance; *: 10% significance.

Table 4. Venture Investment and Innovation Outputs of Firms

	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
VC_Dummy	2.385***	1.698***	1.695***	2.507***
	(0.419)	(0.164)	(0.124)	(0.269)
Firm size	0.737***	0.346***	0.075***	0.137***
	(0.095)	(0.038)	(0.019)	(0.039)
Leverage	−0.238	−0.297	0.024	−0.987**
	(0.517)	(0.236)	(0.203)	(0.500)
Ln (Firm age)	−0.440**	0.046	0.111*	0.059
	(0.198)	(0.088)	(0.066)	(0.177)
State Ownership	0.382	0.266	−0.193	−1.124**
	(0.484)	(0.221)	(0.165)	(0.475)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−5.018**	−1.302	−4.572***	−5.708***
	(2.159)	(0.875)	(0.256)	(0.616)
N	4607	4607	7475	7475
R-sq	0.0537	0.0966
Wald Chi-sq			448.10	168.86
P-value	0.000	0.000	0.000	0.000

This table reports the relationship between VC investment and firm innovation estimations. Models (1) and (2) are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. The dependent variables are the increase of exports of a firm in a given year (Dif_Exports), the increase of new product sales of a firm in a given year (Dif_New_Prdct), the number of newly granted patents of a firm in a given year (Patent) and the increase in patent citations of a firm in a year (Citation). VC_Dummy is a dummy variable equal to 1 if the firm is backed by VC and 0 if otherwise. Ln (Firm Age) is the age of a firm in a given year in a natural logarithm format. Firm size is measured by the total number of employees of the firm in a given year in a natural logarithm format. Leverage is the total liability divided by the total assets of a firm in a given year. State Ownership is the ratio of state-owned stake divided by the total equity of a firm in a given year. Province fixed effects, Industry fixed effects, and Year fixed effects are controlled. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 5. Two-Stage panel regressions on VC investment and innovation outputs

	(1)	(2)	(3)	(4)
Panel A	VC_Dummy	VC_Dummy	VC_Dummy	VC_Dummy
Genealogies	−0.076***	−0.076***	−0.019***	−0.019***
	(−5.450)	(−5.450)	(−6.401)	(−3.891)
Constant	−0.708***	−0.708***	0.226***	0.226***
	(−29.819)	(−29.819)	(10.927)	(10.927)
N	4607	4607	4037	4037
P-value	0.000	0.000	0.000	0.000
Panel B	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
VC_Dummy	1.996***	1.570***	2.262***	2.731***
	(3.935)	(5.957)	(6.538)	(45.828)
Firm size	0.570***	0.335***	0.005	−0.111***
	(9.654)	(11.037)	(0.182)	(−4.566)
Leverage	0.095	−0.131	0.105	0.340***
	(0.222)	(−0.596)	(1.099)	(3.251)
ln(Firm age)	−0.358**	0.018	0.038	−0.026
	(−2.306)	(0.224)	(1.244)	(−0.817)
State Ownership	−0.040	0.198	−0.045	−0.177**
	(−0.107)	(1.037)	(−0.542)	(−2.074)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−0.955	0.433	−2.301***	−2.267***
	(−1.139)	(1.006)	(−9.961)	(−8.580)
N	4607	4607	4037	4037
R-sq	0.154	0.165
Wald Chi-sq			1011.44	4112.33
P-value	0.000	0.000	0.000	0.000

This table presents the two-stage estimations for the effects of VC investment in firm innovation output. Panel A reports the first stage of estimations, in which the dependent variables across the four models are VC_Dummy, a dummy variable equal to 1 if the firm is backed by VCs, and 0 if otherwise. The instrumental variable is the total number of genealogies that appeared in a city before 1949 in logarithm format (Genealogies). Panel B reports the second stage estimations, in which the independent variables are the instrumented VC_Dummy and the dependent variables are measurements for firm innovation output, i.e. the increase of exports of a firm in a given year (Dif_Exports), the increase of new product sales of a firm in a given year (Dif_New_Prdct), the number of newly granted patents of a firm in a given year (Patent) and the increase in patent citations of a firm in a year (Citation). The control variables of estimations shown in Panel A are the same as those in Panel B. Ln (Firm Age) is the age of a firm in a given year in a natural logarithm format. Firm size is measured by the total number of firm employees in a given year in a natural logarithm format. Leverage is the total liability divided by the total assets of a firm in a given year. State Ownership is the ratio of state-owned stake divided by the total equity of a firm in a given year. Province fixed effects, Industry fixed effects, and Year fixed effects are controlled. Values in parentheses are t-statistics; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 6. Logit regressions for VC investment selection

	(1)	(2)	(3)	(4)
	VC_Dummy	VC_Dummy	VC_Dummy	VC_Dummy
Dif_Exports	0.048**
	(0.021)
Dif_New_Prdct		0.356***
		(0.115)
Patent			0.564***
			(0.127)
Citation				1.362***
				(0.465)
Firm size	0.482***	0.778***	0.361***	0.457***
	(0.179)	(0.219)	(0.099)	(0.070)
Leverage	−1.636**	−1.734**	−1.837***	−1.213***
	(0.764)	(0.801)	(0.521)	(0.324)
Ln (Firm age)	0.828**	0.694*	0.389**	0.091
	(0.356)	(0.368)	(0.172)	(0.109)
State Ownership	−1.721*	−1.969*	−0.534	−0.884*
	(1.007)	(1.054)	(0.541)	(0.456)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−4.234	−4.192	−2.096	−2.700
	(2.887)	(3.060)	(2.045)	(1.693)
N	300	300	632	632
Pseudo R-sq	0.3381	0.3978	0.2896	0.1894
P-value	0.000	0.000	0.000	0.000

This table reports the Logit regressions for VC investment selection. The dependent variables across the four models are VC_Dummy, a dummy variable equal to 1 if the firm is backed by VCs, and 0 if otherwise. The independent variable is the increase of exports of a firm in a given year (Dif_Exports), the increase of new product sales of a firm in a given year (Dif_New_Prdct), the number of newly granted patents of a firm in a given year (Patent) and the increase in patent citations of a firm in a year (Citation) in Models (1), (2), (3) and (4), respectively. Ln (Firm Age) is the age of a firm in a given year in a natural logarithm format. Firm size is measured by the total number of firm employees in a given year in a natural logarithm format. Leverage is the total liability divided by the total assets of a firm in a given year. State Ownership is the ratio of state-owned stake divided by the total equity of a firm in a given year. Province fixed effects, Industry fixed effects, and Year fixed effects are controlled. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 7. The PSM Sample

Variables		Obs		Mean		SD.		Min		Max
Panel A: Summary Statistics of PSM sample
Dif_Exports		4,047		0.947		5.579		−14.012		40.745
Dif_New_Prdct		4,047		0.506		4.376		−13.032		36.229
Patent		7,002		1.730		26.806		0		1161
Citation		7,002		0.088		1.587		0		76
Firm size		7,002		0.936		2.293		0.015		14.471
Leverage		7,001		0.488		0.256		0.009		1.127
Ln (Firm age)		6,999		2.172		0.836		0		4.605
State Ownership		7,002		0.065		0.246		0		1
Panel B: T-test in one year before the VC Entry year for PSM sample
	Non-VC-backed Obs				VC-backed Obs
Variables	Obs		Mean		Obs		Mean		Diff		T-stats
lnSales	988		12.070		201		12.087		−0.017		−0.184
Dif_Sales	988		0.597		201		0.666		−0.069		−0.962
Dif_Exports	667		1.734		135		2.030		−0.296		−0.336
Dif_New_Prdct	659		0.599		135		0.549		0.051		0.347
Patent	1,095		2.995		224		4.746		−1.750		−0.787
Citation	1,095		0.349		224		0.353		−0.004		−0.086

This table presents the basic information for the PSM control group. Panel A reports the summary statistics of the PSM control group during the entire examination period. We report the total number of observations (Obs), mean (Mean), standard deviation (SD.), minimum (Min) and maximum (Max) for all the variables of interest. Panel B presents the t-tests (T-stats) of the difference (Diff) in terms of variables of interest for the VC-backed firms and the PSM control group one year before the VC entry. Firm innovation is measured by the increase of exports (Dif_Exports), the increase of new product sales (Dif_New_Prdct), the number of newly granted patents (Patent), and the increase in patent citations (Citation) of a firm in a given year. Firm Size is measured by the total number of employees of a firm in a given year in natural logarithm format; Ln (Firm Age) is the age of a firm in a given year in natural logarithm format; Leverage is the total liability divided by the total assets of a firm in a given year; State Ownership is the ratio of state-owned stake divided by the total equity of a firm in a given year.

Table 8. Ex-post effects of VC investment (PSM sample)

	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
VC_Post	1.145***	1.213***	0.385*	−0.393
	(0.442)	(0.383)	(0.235)	(0.325)
VC_Dummy	0.710**	1.033***	2.388***	2.676***
	(0.340)	(0.294)	(0.167)	(0.320)
VC_Entry	−0.453*	−0.081	0.242*	0.084
	(0.235)	(0.204)	(0.142)	(0.253)
Firm size	0.554***	0.325***	0.511***	0.229***
	(0.039)	(0.034)	(0.026)	(0.022)
Leverage	0.736**	0.047	−0.115	−0.262
	(0.354)	(0.306)	(0.234)	(0.427)
ln(Firm age)	−0.383***	−0.006	0.364***	0.311**
	(0.122)	(0.106)	(0.070)	(0.154)
State Ownership	−0.137	0.402	−0.282	−2.232***
	(0.353)	(0.305)	(0.209)	(0.427)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−0.171	−2.750**	−2.405***	−5.329***
	(1.813)	(1.175)	(0.183)	(0.584)
N	4954	4954	8565	8565
R-sq	0.081	0.063
Wald Chi-sq			642.44	249.03
P-value	0.000	0.000	0.000	0.000

This table reports the regression estimations for the ex-post effects of VC investment on firm innovation based on the PSM sample. Models (1) and (2) are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. VC_Dummy is a dummy variable equal to 1 if the firm is backed by VC and 0 if otherwise. VC_Entry equals 0 for the period before the VC investment is made and 1 for the period after the investment is made. The value of this variable for the firms in the control group is determined by their pairs in the VC-backed group. VC_Post is the interaction term of VC_Entry and VC_Dummy. Dependent variables and control variables are the same as those in Table 4. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 9. Sensitivity of VC effects to IPR protection

	(1)		(2)	(3)	(4)
	Dif_Exports		Dif_New_Prdct	Patent	Citation
Panel A: IP protection is measured by Infringement Filed
VC_Dummy	2.797***		1.987***	1.930***	2.009***
	(0.517)		(0.228)	(0.149)	(0.118)
Infringement_Filed	0.794		0.504	−0.125	−0.547
	(0.663)		(0.375)	(0.372)	(0.398)
VC_Dummy*Infringement_Filed	−2.903**		−1.311**	−0.756*	0.341
	(1.160)		(0.636)	(0.448)	(0.485)
Firm size	0.791***		0.279***	0.073***	0.136***
	(0.114)		(0.050)	(0.022)	(0.020)
Leverage	−0.135		−0.265	−0.093	−0.152
	(0.584)		(0.291)	(0.227)	(0.202)
ln(Firm age)	−0.617***		0.018	0.148**	0.153**
	(0.233)		(0.111)	(0.076)	(0.065)
State Ownership	0.811		0.411	−0.173	−0.150
	(0.573)		(0.289)	(0.190)	(0.190)
Province FE	Y		Y	Y	Y
Year FE	Y		Y	Y	Y
Industry FE	Y		Y	Y	Y
Constant	−5.095		−2.167	−4.282***	−3.367*
	(3.900)		(1.641)	(0.303)	(2.037)
N	3427		3427	5683	5683
R-sq	0.079		0.093
Wald Chi-sq				390.84	740.25
P-value	0.000		0.000	0.000	0.000
	(1)	(2)		(3)		(4)
	Dif_Exports	Dif_New_Prdct		Patent		Citation
Panel B: IP protection is measured by Infringement_Closed
VC_Dummy	2.797***	1.987***		1.930***		2.009***
	(0.517)	(0.228)		(0.149)		(0.118)
Infringement_Closed	1.166	0.580		−0.069		−0.625
	(0.850)	(0.481)		(0.461)		(0.499)
VC_Dummy*Infringement_Closed	−3.333**	−1.666**		−1.290**		0.234
	(1.469)	(0.806)		(0.584)		(0.615)
Firm size	0.793***	0.279***		0.074***		0.137***
	(0.114)	(0.050)		(0.022)		(0.020)
Leverage	−0.139	−0.264		−0.100		−0.150
	(0.584)	(0.291)		(0.227)		(0.202)
ln(Firm age)	−0.616***	0.017		0.152**		0.154**
	(0.233)	(0.111)		(0.075)		(0.065)
State Ownership	0.816	0.412		−0.173		−0.150
	(0.573)	(0.289)		(0.189)		(0.190)
Province FE	Y	Y		Y		Y
Year FE	Y	Y		Y		Y
Industry FE	Y	Y		Y		Y
Constant	−5.059	−2.170		−4.261***		−3.736*
	(3.898)	(1.641)		(0.306)		(2.175)
N	3427	3427		5683		5683
R-sq	0.079	0.093
Wald Chi-sq				391.85		740.26
P-value	0.000	0.000		0.000		0.000

This table reports the regression estimations for the sensitivity of VC effects to IPR protection. Models (1) and (2) are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. VC_Dummy differentiates the treated and control groups (between groups). IPR protection is measured by two different variables. Infringement Filed is the ratio of the total number of patent infringement cases filed over the total number of patents granted in a province each year, and Infringement Closed is the ratio of the total number of patent infringement cases closed over the total number of patents granted in a province each year. The higher the ratios are, the lower the IPR protection is in a region. The interaction term of IPR variables and VC_Dummy (VC_Dummy*IPR) is also included. Dependent variables and control variables are the same as those in Table 4. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 10. The effects of VCFs’ experience on innovation outputs

	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
Panel A
VC_Age1	1.636***	1.240***	1.627***	2.648***
	(0.551)	(0.214)	(0.145)	(0.317)
VC_Age2	3.108***	2.141***	1.767***	2.383***
	(0.543)	(0.212)	(0.147)	(0.307)
Firm size	0.736***	0.347***	0.072***	0.138***
	(0.095)	(0.038)	(0.019)	(0.039)
Leverage	−0.238	−0.295	0.021	−0.997**
	(0.516)	(0.236)	(0.203)	(0.504)
ln(Firm age)	−0.437**	0.048	0.116*	0.051
	(0.198)	(0.088)	(0.066)	(0.178)
State Ownership	0.365	0.255	−0.196	−1.092**
	(0.483)	(0.220)	(0.165)	(0.476)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−4.991**	−1.288	−4.573 ***	−5.677***
	(2.156)	(0.872)	(0.256)	(0.619)
N	4607	4607	7475	7475
R-sq	0.0553	0.0996
Wald Chi-sq			448.06	168.24
P-value	0.000	0.000	0.000	0.000
Panel B
VC_Comp1	2.116***	1.370***	1.813***	2.922***
	(0.566)	(0.219)	(0.145)	(0.324)
VC_Comp2	2.622***	1.995***	1.572***	2.148***
	(0.535)	(0.210)	(0.147)	(0.313)
Firm size	0.739***	0.349***	0.079***	0.142***
	(0.095)	(0.038)	(0.019)	(0.039)
Leverage	−0.236	−0.292	0.011	−1.082**
	(0.517)	(0.236)	(0.203)	(0.510)
ln(Firm age)	−0.440**	0.047	0.102	0.033
	(0.199)	(0.088)	(0.066)	(0.180)
State Ownership	0.375	0.253	−0.188	−1.030**
	(0.484)	(0.221)	(0.165)	(0.473)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−4.990**	−1.259	−4.579***	−5.738***
	(2.161)	(0.875)	(0.255)	(0.622)
N	4607	4607	7475	7475
R-sq	0.0542	0.0982
Wald Chi-sq			452.25	170.29
P-value	0.000	0.000	0.000	0.000

This table reports the effects of VCFs’ experience on firm innovation. Panel A presents the results in which VCFs’ experience is measured by the age of a VCF. VC_Age1 equals one if the lead VCF is younger than 10 years when the VC-backed firm gained its first VC investment and equals zero if otherwise. VC_Age2 equals one if the lead VCF is older than ten years when the VC-backed firm gained its first VC investment and equals zero if otherwise. Panel B reports the results in which VCFs’ experience is measured by the number of deals closed by the VCF. VC_Comp1 equals one if the lead VCF has closed fewer than 100 venture deals when the investment in a given entrepreneurial firm has been concluded and zero if otherwise. VC_Comp2 equals one if the lead VCF has closed 100 or more venture deals when the investment in a given entrepreneurial firm has been concluded and equals zero if otherwise. Dependent variables and control variables are the same as those in Table 4. Models (1) and (2) for both panels are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Table 11. The effects of VCFs’ ownership on innovation outputs

	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
Panel A
PVCF	3.156***	1.683***	1.683***	2.539***
	(0.520)	(0.185)	(0.141)	(0.303)
GVCF	1.816**	1.496***	1.877***	3.120***
	(0.875)	(0.304)	(0.208)	(0.399)
Firm size	0.585***	0.396***	0.110***	0.213***
	(0.101)	(0.037)	(0.024)	(0.047)
Leverage	−0.307	−0.240	0.091	−0.946*
	(0.515)	(0.224)	(0.211)	(0.535)
ln(Firm age)	−0.363*	0.060	0.104	−0.032
	(0.201)	(0.084)	(0.070)	(0.187)
State Ownership	−0.131	0.124	−0.075	−0.001
	(0.494)	(0.215)	(0.179)	(0.443)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−4.309*	−2.109**	−4.558***	−5.704***
	(2.221)	(0.829)	(0.269)	(0.675)
N	4408	4408	7146	7146
R-sq	0.052	0.102
Wald Chi-sq			394.58	153.76
P-value	0.000	0.000	0.000	0.000

This table reports the effects of VCFs’ ownership on firm innovation. GVCF is a dummy variable that equals one if more than 50% of the VCF is owned by the state and zero if otherwise. PVCF is also a dummy variable that equals one if the state owns less than 50% of the VCF and zero if otherwise. Dependent variables and control variables are the same as those in Table 4. Models (1) and (2) are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.

Ex-ante project assessment criteria of VCs in China

Project selection criteria: Likert Scale: 0–4	Mean	SD
Group I: Personality of entrepreneur. The entrepreneur:
1. is honest enough.	3.68	0.475
2. is capable of sustained intense effort.	3.65	0.485
3. is able to evaluate and react to risks well.	3.35	0.544
4. articulates in discussing venture.	2.88	0.409
5. attends to details.	2.38	0.551
6. has a personality compatible with mine.	1.97	0.870
7. has a rich social network.	3.03	0.388
Group II: Capability of the entrepreneur. The entrepreneur:
8. is thoroughly familiar with the market targeted by the project.	3.74	0.448
9. has demonstrated leadership ability in the past.	3.21	0.410
10. has a track record relevant to the venture.	2.91	0.514
11. was referred to me by a trustworthy source.	2.26	0.618
12. has overseas educational and working experience.	2.09	0.933
13. I am already familiar with the entrepreneur reputation.	2.03	0.627
Group III: Characteristics of the product or service
14. The product is proprietary or can otherwise be protected.	2.94	0.629
15. The product enjoys demonstrated market acceptance.	3.26	0.511
16. The product has been developed to the point of a functioning prototype.	2.18	0.576
17. The product may be described as ‘high tech’.	2.15	0.702
18. The product has excellent export potential.	2.03	0.460
19. The product or service is complementary to our other portfolios.	2.09	0.621
Group IV: Characteristics of the market of the product or service
20. The target market enjoys a significant growth rate.	3.71	0.462
21. The venture will stimulate an existing market.	2.35	0.485
22. The venture is in an industry with which I am familiar.	2.06	0.547
23. There is little threat of competition during the first three years.	2.82	0.387
24. The venture will create a new market.	1.94	0.489
25. The market size is scalable.	3.18	0.576
Group V: Financial considerations with this project
26. I require a return equal to at least ten times my investment within 5–10 years.	3.24	0.606
27. I require an investment that can easily be liquid (e.g. taken public or acquired).	2.94	0.422
28. I require a return equal to at least ten times my investment within at least five years.	2.76	0.431
29. I will not be expected to make a subsequent investment.	1.94	0.600
30. I will not participate in later rounds of investment.	1.24	0.606
31. It is easy to find further investors or bank loans for the project.	2.38	0.511
Group VI: Geographical considerations with this project
32. The project is located in the capital city or other major cities in China.	2.26	0.511
33. The project is located within 50 miles of my office.	1.24	0.431
34. It is easy to access needed human resources in the location.	3.00	0.492
35. Local public policy is friendly to SMEs and the venture industry.	2.97	0.460
Group IV: Features of the management team. (Please score 1 for the single item below that you suggest the essential one for the venture to go forward.)
36. The project is initiated by one person, and he/she has relevant experience on the idea.	5.9%
37. The project is initiated by more than one person, all having similar relevant experience.	8.8%
38. The venture is initiated by more than one person, constituting a functionally balanced management team.	58.8%
39. None of the above factors are essential for the venture to go forward.	26.5%

This table reports the results of interviews on project selected criteria reported by VCs in China. Thirty-four interviewees from 37 VCFs were asked to rank each criterion on a Likert scale from 0 to 4 expressing the level of agreement.

Ten essential project selection criteria identified by VCs

	Number	%
1. The entrepreneur is thoroughly familiar with the market targeted by the project.	25	73.5
2. The entrepreneur is capable of sustained intense effort.	23	67.6
3. The entrepreneur is honest enough.	22	64.7
4. The target market enjoys a significant growth rate.	20	58.8
5. The entrepreneur is able to evaluate and react to risks well.	13	38.2
6. I require a return equal to at least ten times my investment within 5–10 years.	11	32.4
7. The product enjoys demonstrated market acceptance.	10	29.4
8. The market size is scalable.	9	26.5
9. The product is proprietary or can otherwise be protected.	6	17.6
10. Local public policy is friendly to SMEs and the venture industry.	4	11.8

This table reports the interviews with the essential project selection criteria identified by the VCs. Thirty-four interviewees from 37 VCFs were asked to list the essential criteria without which they will reject projects regardless of other aspects. In total, ten essential criteria were identified.

Sensitivity of VC effects to IPR protection (IPR protection is measured by IPR_Index)

	(1)	(2)	(3)	(4)
	Dif_Exports	Dif_New_Prdct	Patent	Citation
VC_Dummy	2.063***	1.007***	1.471***	1.907***
	(0.558)	(0.227)	(0.161)	(0.132)
IPR_Index	−0.014	−0.011	0.000	0.002
	(0.028)	(0.014)	(0.009)	(0.008)
VC_Dummy*IPR_Index	0.029	0.065***	0.020**	0.014*
	(0.032)	(0.015)	(0.009)	(0.009)
Firm size	0.733***	0.340***	0.072***	0.133***
	(0.095)	(0.038)	(0.019)	(0.018)
Leverage	−0.216	−0.267	0.038	−0.010
	(0.518)	(0.236)	(0.203)	(0.184)
ln(Firm age)	−0.434**	0.049	0.103	0.101*
	(0.199)	(0.088)	(0.065)	(0.057)
State Ownership	0.405	0.302	−0.185	−0.260
	(0.485)	(0.221)	(0.164)	(0.163)
Province FE	Y	Y	Y	Y
Year FE	Y	Y	Y	Y
Industry FE	Y	Y	Y	Y
Constant	−4.981**	−1.216	−4.281***	−3.613***
	(2.163)	(0.875)	(0.279)	(0.119)
N	4607	4607	7475	7475
R-sq	0.054	0.101
Wald Chi-sq			479.83	942.80
P-value	0.000	0.000	0.000	0.000

This table reports the regression estimations for the sensitivity of VC effects to IPR institutions. Models (1) and (2) are linear regressions, while Models (3) and (4) are Negative Binomial regressions for panel data. VC_Dummy equals one if the firm is a VC-backed one and zero if otherwise. IPR_Index is the score indicating the weighted average for the number of patents filed and approved per R&D professionals in a province in a given year. The interaction term of IPR_Index and VC_Dummy (VC_Dummy*IPR_Index) is also included. Dependent variables and control variables are the same as those in Table 4. Values in parentheses are standard errors; * = p < 0.1; ** = p < 0.05; *** = p < 0.01.