patent premiumPatent Premium_revised empirical part with appendix-20190804 Lanhua

METHODSDataChina, especially its high-tech industries, is perhaps one of the best illustrates for our analytical settinga vast market yet notorious in weak IPR protection; economy drastically improved yet remarkably uneven sub-nationally; foreign firms became important players in this economy yet still facing myriads of institutional challenges. Data for this study come from two sources: one is China National Bureau of Statistics Annual Survey of Industrial Enterprises (ASIE) which is a census of all non-state-owned firms with more than RMB five million in revenue and all state-owned firms in China from 1998 to 2007. The dataset contains over 50 firm-level statistical indicators and covers the period of 1998-2007. The other data source is a publicly available patent dataset constructed by He, Tong, Zhang, and He (2016), which consists of all patent filings to CNIPA from 1998 to 2009. He, Tong, Zhang, and He (2016) adopted multiple calibration procedures and systematic manual checks to match assignee names of CNIPA patents to firm names in the ASIE database. Their data entail each patents details such as patent application, patent type, filing date and publication date, as well as an ASIE firm identifier that can be readily used to link the patent to a specific company in the ASIE database. By integrating these two datasets, we gathered a large database of firm-level patent, operational and accounting information in high-tech industries. The database allows us to compare the sample group of first-time patentees and another group of firms that had never applied for invention patents during the period of 1998-2007. Utility model patents are not included in this study because this type of patents can be granted with a lower level of technical complexity and a less rigorous examination process compared with invention patents. In addition, foreign firms filed 8,924 utility model patents in the observation period, much less than domestic firms (1,045,837 in total). The sharp contrast between the numbers of utility model patents filed by foreign and domestic firms reflects the difference in their perception of strength of the utility model, nature and complexity of technologies they developed and also patenting strategies these two types of firms usually pursue (Moga, 2012). Besides, we only include granted patents in our analyses since firms can only enforce their rights on granted patents., We dropped all firms that started to apply for patents before 1998 to ensure that patenting firms in our sample filed their first patents since 1998. Some descriptive statistics about the sample, such as the distribution of firms across years, different types of ownership and industries, can be found Appendix A1. Propensity score matching combined with difference-in-difference methodAs a key performance indicator for manufacturing firms, Levinsohn, 2003, Estimating Production Functions Using Inputs to Control for Unobservableswe use total factor productivity (TFP) to evaluate the performance impact when non-patenting firms started to enter into patenting activities. A central problem in quantifying the gain of TFP from patenting is that the decision to start patent filing may be endogenous, that is, s non-patenting firms with higher productivity might be more likely to file patents than those with lower productivity. A solution to this problem would be to compare the performance of a firm who started to file their first patent with the performance of the same firm had it not filed any patent application. However, the latter is not observable. To tackle this issue, we employed a propensity score matching method (Imbens & Wooldridge, 2009, Rosenbaum & Rubin, 1983) to construct a control group, consisting of firms that did not apply for patents but had nearly the same ex-ante likelihoods to enter into patenting activities as that of firms who started to file their first patent. Firstly, we rescale the time periods in such a way that all the patenting firms started patenting at the year t, and accordingly, the year t-1 is the pre-patenting year prior to the year when the firms first patent was filed. Thus, the ex-ante likelihood, also known as propensity score, can be predicted by a Probit model, based on multiple firm characteristics or covariates in the year t-1 that affect firms propensity to file their first patent in the year t. We match patenting firms with non-patenting firms using STATAs psmatch2 (Leuven & Sianesi, 2003) (please refer to some explanation with regard to the choice of matching method in Appendix A3 if needed). We conduct two types of widely used balancing tests after matching (Austin, 2009, Chang & Shim, 2015), i.e. pstest and hotelling balancing test, to make sure that treated group (patenting firms) and matched control group (non-patenting firms) are truly comparable with no statistical difference in the pre-patenting year t-1. Since only firms who exists at least for two consecutive years with non-missing covariates in the pre-patenting year t-1 and non-missing outcome variable (i.e. TFP) in the patenting year t can be used in propensity score matching, we can only include patenting firms who filed their first patent in any year during 1999 to 2007 and non-patenting firms during the same period in our final sample and get 1784 pairs of patenting firms and non-patenting firms matched one to one. After matching, we adopted a difference-in-difference approach to calculate TFP differences between treated firms and control firms. Comparing with a simple matching estimator without difference-in-difference analysis, our method allows us to utilize the panel nature of our dataset and control for within-firm unobservables. We measure a firms patent premium by its increased TFP with the first entry into patenting activities relative to its TFP without these patents (i.e., average treatment effect for treated firms, or ATT). We track TFP changes for three years after their first patent filings. Following Arnold and Javorcik (2009) and Chang and Shim (2015), we calculate patent premium as follows:Patent premiumk=1n(TFPt+kTreated-TFPt+kControl)-1n(TFPt-1Treated-TFPt-1Control) (1)where n is the number of matched pairs, year t-1 is the pre-patenting year prior to the filing year of a firms first patent, and k has a maximum value of three. We track firms patent premium for three years after firms first patent filing because the average grant lag of Chinese patents is around 3 years (SIPO, 2006, Yang, 2008). We use the following formula to calculate the standard errors (SE) and conduct t-test to evaluate the statistical significance of patent premium:SEk=1nVARTFPt+k-TFPt-1Treatment=1+1nVARTFPt+k-TFPt-1Control=1 , (2)Then to test the differential effects, we follow De Loecker (2007), Girma and Grg (2007)and Chang, Chung, and Moon (2013) to divide the sample firms into different groups by ownership types, the extent of institutional development and industrial competition intensity, and then examine whether patent premium would be greater in one group than in the other. Variables and MeasurementAlthough TFP can be estimated by a simple ordinary least square (OLS) estimator based on a Cobb-Douglas production function, this estimator suffers from serious simultaneity bias (Van Beveren, 2012), because firms adjust their input levels according to productivity shocks that are known to them but are unobservable to researchers. In this study, we use a semi-parametric method proposed by Levinsohn and Petrin (2003) to calculate TFP, which controls for the simultaneity bias by using an intermediate input as a proxy for unobserved productivity shocks. We provide a simple description of Levinsohn and Petrin (LP)s method in the appendix A2 and also demonstrate its advantages over other TFP estimation methods such as Olley and Pakes (1996)s (OP) productivity estimator.We carefully chose covariates to predict patenting propensity since adding redundant variables into matching procedure may cause potential bias of the matching estimator (Heckman & Navarro-Lozano, 2004). According to De Loecker (2007), the most important covariate is a patenting firms TFP in the year before it started to apply for patents, i.e. TFPt-1. Although we admit that R&D intensity is a significant predictor of firms patenting behavior, unfortunately, firm-level R&D statistic is incomplete in ASIE dataset. 我写在这里而没有写在最后的limitation部分，因为reviewer在读到covariates的选择时就会产生为什么没有加入研发费用的疑问以及对整个研究严谨性的质疑，此时回答正好消除他们的疑问。 As a remedy, we control for the impact of innovation output on a firms patenting propensity by adding New Productt-1, which is defined as the ratio of new product output to total output in the pre-patenting year, varying between 0 and 1. We also include Firm Sizet-1 and Firm Aget-1, which are the logarithm of number of employees and logarithm of firm age in the pre-patenting year, respectively, as these are two important factors affecting a firms patenting propensity (Brouwer & Kleinknecht, 1999, Holgersson, 2013, Patel & Pavitt, 1993). Following extant literature (Emodi, Murthy, Emodi, & Emodi, 2017), we also introduce Export intensityt-1 to control for firms export orientation and to what extent they focus on domestic market, which is calculated as export value to total sale in the year t-1. Finally, we control for firm ownership by adding two covariates: domestic firms and IJVs, both of which are dummy variables. The reference group in the Probit model is composed of WOSs. The detailed definitions of the above three ownership types can be found be Table A1-2 in Appendix A1. We also control for industry heterogeneity, regional and year differences by adding four-digit industry dummies, province and year dummies as covariates. Next, to investigate the difference in patent premium across firms of different ownership types, we partition the firms into domestic and foreign firms according to ASIEs classification. For foreign firms, the sample is further divided into IJVs and WOSs. The detailed classification of firms according to ASIEs ownership codes can be in Appendix A1. To test the moderating effects of subnational institutional development on patent premium, we employ a regional institutional development index compiled by the National Economics Research Institute (Fan, Wang, & Zhu, 2011), which measures province-level institutional development across China with five factors: (1) government-market relations, (2) development of non-state-owned enterprises, (3) development of product (and service) markets, (4) development of factor markets, and (5) level of development of market intermediaries and legal system. This regional marketization index has been widely used in management and economic studies (Jia, Huang, & Zhang, 2015, Qian, Wang, Geng, & Yu, 2017, Wang, Wong, & Xia, 2008). As shown in similar studies such as Chang et al., (2013), Chang and Shim (2015), using the median values of a specific variable to designate firms into two different groups and then test moderating effects is a common approach when employing matching and difference-in-difference estimator. Therefore, we divide all firms into a strong institution group and a weak institution group by the median value of the institutional development index of the region in which firms operated in. By such design, we can create two subsamples with roughly equal numbers, the one consisting of firms who operated in more institutional developed regions and the other comprising of firms located in less institutional developed regions. Table 1 provides the descriptive statistics and pairwise correlations for all the variables used in our analyses.RESULTSTable 2 presents the result of a Probit model, which is an intermediate step of propensity score matching to analyze whether a firm started to apply for patents in the year t. We only include patenting firms in their first patenting year (the year t) and all non-patenting firms from 1999-2007, which is also the sample we use in the matching. The dependent variable is patenting and the independent variables include all covariates. It shows that firms with higher TFP, larger size, a higher ratio of new product output and a lower level of export intensity at year t-1 are more likely to start filing patents in the following year. Domestic firms and IJVs are more likely to start patent applications in comparison to WOSs (the reference group in the regression). When looking at the separate result of domestic sample and foreign sample, we can find that exporting intensity has a stronger negative impact on firms patenting propensity for foreign firms, compared with their domestic counterparts. - Tables 1 & 2 go about here -Table 3 shows the result of balancing tests for the full sample, in order to evaluate to what extent the matching procedure helps to eliminate potential differences between non-patenting and patenting firms other than the patenting activity itself. We compare the before and post matching comparisons of means for the main covariates. As shown in the unmatched sample, there are statistically significant differences between patenting firms and non-patenting firms. For example, patenting firms had higher productivity, larger size, more innovation output and more export proportion in their sales than non-patenting firms did. After conducting PSM matching, differences in the means of these main variables between treatment group and control group are not statistically significant anymore, thus the self-selection bias among patenting firms and non-patenting are successfully eliminated. We also conduct a Hotelling test for balancing after every matching process, and the results present in the following tables also show that there is no significant difference between the treated and control group after matching. - Tables 3 go about here -Results for the full sample is presented in Table 4. Intuitively based the means of TFP reported in the table, we can find that with similar levels of productivity in the year t-1, patenting firms started to outperform matched non-patenting firms since the first-patenting in the year t and their TFP difference widens ever since. Then we look at the estimated patent premium for the full sample. Because the matching is always performed at the year t-1, the patent premium in the year t, t+1, t+2, t+3 can be understood as accumulated effects of entry into patenting on the productivity. Overall, high-tech firms in China, on average, enjoyed positive and statistically significant patent premium for four consecutive years since the year they filed the first patents. Specifically, the magnitude of the patent premium is 0.090 in the year t, i.e. (5.596-5.487)-(5.528-5.510) following the difference-in-differences estimation in Equation (1), statistically significant at 1 percent level. The size of patent premium can be revealed by the ratio of patent premium to average TFP value of the patenting firms (i.e., 0.090/5.596=0.016, where 5.596 is the average TFP value of the patenting firms). Therefore, in the year when firms started to file their first patents, their TFP is estimated to increase by 1.6 percent due to patenting. One year after first patent filing, patent premium climbed to 0.151, i.e. (5.689-5.465)-(5.587-5.514), significant at 1 percent level. Although we obtained 1784 pairs of matches after conducting PSM, the number of matches declined in the next three years. This is because we need to drop the matches with missing values of TFP t+1, TFP t+2 or TFP t+3 when calculating patent premium of the corresponding years according to the Equation (1).- Table 3 goes about here -Following the method used by Girma and Grg (2007), Chang, Chung, and Moon (2013) and Chang and Shim (2015), we compared patent premium received by domestic firms and foreign firms in Table 5. In order to simplify the presentation of the following results, we skip the means of TFP in each group used in the calculation (which can be provided upon request) and directly report the patent premium. We find that domestic firms patent premium is greater than foreign firms. Domestic firms achieved statistically significant positive patent premium in all four years, with the largest value 0.184 in year t+3 (i.e., 3.16 percent increase in average TFP of domestic firms due to patenting). However, foreign subsidiaries only obtained significant patent premium in the year t, with the value 0.052 which is much lower than the corresponding value of domestic firms in that year. These results lend support to Hypothesis H1.To shed light on the differential impact of institutional development on patent premium of domestic firms and foreign subsidiaries, we separately estimate their patent premium at different levels of institutional development. Table 6 demonstrates that domestic firms obtained significant patent premium in both weak institution environment and strong institution environment. However, in some years, such as the year t and the year t+2, domestic firms that operate in a weak institutional environment even obtained larger patent premium than those in a strong institutional environment. It may indicate institutional development failed to improve patent premium for domestic firms to a great extent. In contrast, foreign subsidiaries obtained statistically significant patent premium only in strong institution regions, but not in weak institution regions. Patent premium received by foreign firms in the strong institution group is 0.102 in the year t, significant at 1 percent level, and 0.126 significant at 5 percent level. Therefore, although with institutional development does not bring consistent increases on patent premium of domestic firms, patent premium of forei