Back Matter
Author: Hui He, Ms. Nan Li, and Jing Fang1
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Appendix A. Constructing Firm-Specific Markups

Our construction of firm-specific markups closely follows De Loecker and Warzynski (2012). A firm i at time t produces output using the following production technology:

Qit=Qit(Kit,Lit,ωit)(18)

The only restriction we impose on Qit to derive an expression of markup is that Qit is continuous and twice differentiable with respect to its arguments.

Cost-minimizing producers consider the following Lagrangian function:

Lag(Kit,Lit,λit)=ritKit+witLit+λit(QitQit(.))(19)

where wit and rit denote a firm’s input cost for labor and capital, respectively. The first-order condition with respect to labor input is

LagitLit=witλitQit(.)Lit=0(20)

where the marginal cost of production at a given level of output is λit as LagitQit=λit. Rearranging terms and multiplying both sides by LitQit, we can express the labor elasticity, θi as:

θi=Qit(.)LitLitQit=1λitwitLitQit(21)

Define markup μ as the ratio of price over marginal cost, μ=Pitλit. Using this definition, we can rewrite equation (21) as

θi=μitwitLitPitQit(22)

Based on equation (22), once the labor elasticity, θi, is obtained from the production function estimation and the share of labor costs in total sales, witLitPitQit, is measured from data, firm’s markup can be constructed as follows:

μit=θitPitQitwitLit.(23)

Appendix B. The Analysis of Propensity-Score Matching

Our difference-in-difference analysis hinges crucially on the compariability between patenting and nonpatenting firms. To guarantee the comparison is meaningful, we have to make sure the treatment group (patenting firms) and control group (nonpatenting firms) are similar in terms of the major firm characteristics. Propensity-Score Matching (PSM) method serves this propose. Here we lay out the PSM procedure as follows.

For each firm i, define the treatment Di = 1 if the firm applies for at least one patent, and zero otherwise. We run the following logit model to estimate the propensity score:

Pr(Di=1|X)=G(size, age, industry dummy, year dummy)

where X = {size, age} and G(z) = exp(z)/(1 + exp(z)).

For firm i in the treatment group, we define pi(x) = Pr(Di = 1 | X = x). Under the common support condition, we have 0 < pi(x) < 1. We then take the nearest matching approach to pick the “matched” non-treated firm j for a treated firm i, based on the following criteria:

||pipj||=mink{D=0}||pipk||.

References

  • [1]

    Aghion, Philippe, Nick Bloom, Richard Blundell, Rachel Griffith and Peter Howitt (2005), “Competition and Innovation: An Inverted-U Relationship”, Quarterly Journal of Economics, 120(2): 701728.

    • Search Google Scholar
    • Export Citation
  • [2]

    Aghion, Philippe, Jing Cai, Mathias Dewatripont, Luosha Du, Ann Harrison and Patrick Legros (2015), “Industrial Policy and Competition”, American Economic Journal: Macroeconomics 7(4): 132.

    • Search Google Scholar
    • Export Citation
  • [3]

    Ackerberg, Daniel, Kevin Caves, and Garth Frazer (2015), “Identification Properties of Recent Production Function Estimators”, Econometrica, 83(6): 24112451.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [4]

    Balasubramanian, Natarajan and Jagadeesh Sivadasan (2011), “What Happens When Firms Patent? New Evidence From U.S. Economic Census Data”, Review of Economic Statistics, 93(1): 126146.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [5]

    Bloom, Nick, and Van Reenen J (2002), “Patents, Real Options and Firm Performance”. Economic Journal, 112:97116.

  • [6]

    Borensztein, Eduardo and Jonathan Ostry (1996), “Accounting for China’s Growth Performance”. American Economic Review, 86(2): 2242228.

    • Search Google Scholar
    • Export Citation
  • [7]

    Bloom Nick, Mark Schankerman and John Van Reenen (2013), “Identifying Technology Spillovers and Product Market Rivalry”, Econometrica, 81(4): 13471393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [8]

    Brandt, Loren, Johannes Van Biesebroeck and Yifan Zhang (2012), “Creative Accounting or Creative Destruction? Firm-Level Productivity Growth in Chinese Manufacturing”, Journal of Development Economics, 97:339351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [9]

    Brandt, Loren, Johannes Van Biesebroeck and Yifan Zhang (2014), “Challenges of Working With the Chinese NBS Firm-Level Data”, China Economic Review, 30: 339352.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [10]

    Brandt, Loren, Chang-tai Tsieh, and Xiaodong Zhu (2008), “Growth and Structural Transformation in China”, pp. 683729, in “China’s Great Economic Transformation” edited by Loren Brandt and Thomas G. Rawski, Cambridge University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]

    Brandt, Loren, and Xiaodong Zhu (2010), “Accounting for China’s Growth”, Working Paper DP No. 4764, IZA.

  • [12]

    Chang, Chun, Kaiji Chen, Daniel Waggoner and Tao Zha (2015), “Trends and Cycles in China’s Macroeconomy,NBER Macroeconomics Annual 2015, 30.

    • Search Google Scholar
    • Export Citation
  • [13]

    De Loecker, Jan, and Frederic Warzynski (2012) “Markups and Firm-Level Export Status”, American Economic Review, 102:243771.

  • [14]

    Du, Luosha, Ann Harrison and Gary Jefferson (2012). “Testing for Horizontal and Vertical Foreign Investment Spillovers in China, 1998-2007”, Journal of Asian Economics 23(3): 234243.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    Farre-Mensa, Joan and Alexander Ljungqvist (2013), “Do Measures of Financial Constraints Measure Financial Constraints?NBER Working Paper No. 19551.

    • Search Google Scholar
    • Export Citation
  • [16]

    Hall, Bronwyn, Adam Jaffe and Manuel Trajtenberg (2001), “The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools”, NBER working paper No. 8498.

    • Search Google Scholar
    • Export Citation
  • [17]

    Hall, Bronwyn, Adam Jaffe and Manuel Trajtenberg (2005) “Market Value and Patent Citations”, Rand Journal of Economics, 36, 1638.

    • Search Google Scholar
    • Export Citation
  • [18]

    Hsieh, Chang-Tai, and Zheng Song (2015), “Grasp the Large, Let Go of the Small: The Transformation of the State Sector in China”, forthcoming, Brookings Papers on Economic Activity.

    • Search Google Scholar
    • Export Citation
  • [19]

    Hu, Albert G. and Gary H. Jefferson (2009), “A Great Wall of Patents: What is Behind China’s Recent Patent Explosion?Journal of Development Economics, 90: 5768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [20]

    Klette, Jakob and Samuel Kortum (2004), “Innovating Firms and Aggregate Innovation”, Journal of Political Economy, 112(5): 9861086.

  • [21]

    Levinsohn, J. and A. Petrin (2003), “Estimating Production Functions Using Inputs to Control for Unobservables”, Review of Economic Studies 317342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [22]

    Li, Xi, Xuewen Liu, and Yong Wang (2015), “ A Model of China’s State Capitalism”, Hongkong University of Science and Technology, Working Paper.

    • Search Google Scholar
    • Export Citation
  • [23]

    Liu, Qing, Ruosi Lu, Yi Lu, and Tuan Anh Luong (2014), “Is Free Trade Good or Bad for Innovation?”, National University of Singapore, mimeo.

    • Search Google Scholar
    • Export Citation
  • [24]

    Olley, Steven and Ariel Pakes (1996), “The Dynamics of Productivity in the Telecommunications Equipment Industry”, Econometrica, 64(6): 12631297.

  • [25]

    Song, Zheng, Kjetil Storesletten and Fabrizio Zilibotti (2011), “Growing Like China,American Economic Review 101:202241.

  • [26]

    Thomas Reuters Report (2014), “China’s IQ: Trends in Patenting and the Globalization of Chinese Innovation”.

  • [27]

    Wei, Shang-jin, Zhuan XIe, and Xiaobo Zhang (2016) “From ‘Made in China’ to ‘Innovated in China’: Necessity, Prospect, and Challenges”, Journal of Economic Perspectives, forthcoming.

    • Search Google Scholar
    • Export Citation
  • [28]

    World Bank Report (2013), “ China’s Growth through Technological Convergence and Innovation? in China 2030: Building a Modern, Harmonious, and Creative Society”.

    • Search Google Scholar
    • Export Citation
  • [29]

    Zhu, Xiaodong (2012), “Understanding China’s Growth: Past, Present and Future”, Journal of Economic Perspective, 26(4): 103124.

1

We thank Luis Cubeddu, Daniel Garcia-Macia, Zheng Liu, Jianhuan Xu, Xiaodong Zhu, and participants from China Economics Annual 2015 and seminar in Wuhan University for helpful discussions and comments. We are grateful for Hanya Li for excellent research assistance. Hui He thanks research support by Shanghai Pujiang Program, and the Program for Professor of Special Appointment (Eastern Scholar) funded by Shanghai City Government.

1

Using the number of patents granted as a ratio of the number of patents applied and the foreign citations of Chinese patents, Wei, Xie and Zhang (2016) argues that this explosion of Chinese patents is not simply an outcome of easy approval or low-quality of patents in China,

3

Many have argued that part of China’s TFP gorwth may not reflect technical progress but rather an outcome of resource reallocation across sectors and across ownership forms (Borensztein and Ostry (1996)).

4

Novelty, in particular means that, before the date of filing, no identical invention or utility model has been publicly disclosed in publications or has been publicly used or made known to the public anywhere in the world. Furthermore, there should be no other earlier-filed Chinese applications, which describe the identical invention or utility model even if the publication date thereof is after the date of filing of the present case.

5

About 95% of firms from 1998 to 2007 are identified by the registration IDs, while the remainders are matched based on other information.

6

If a firm in SIE was established after 1998, the initial nominal capital stock is the book value of capital stock that the firm reports first time in SIE data. If a firm was established before 1998, initial capital stock is calculated by using information from the 1993 annual enterprise survey to construct estimates of the average rate of growth of the nominal capital stock between 1993 and the year that this firm appears in SIE first time. The real initial capital stock is then obtained by deflating with the investment de ator in that year.

7

The variance of the Negative Binomial is exp(xβ)+αexp(2xitβ), allowing for the variance to be larger than the mean (α is the over-dispersion measure). This relaxes the restrictions imposed by Poisson regression (i.e. α = 0). Given that the unconditional mean of patent count is much lower than its variance, Negative Binomial Model is more appropriate than the Poisson Model. In addition, we find that estimations based on a Poisson model yield qualitatively similar results and thus do not report them here.

8

As discussed in Blundell et al (1999), this method relaxes the strict exogeneity assumption required by the approach of Hausman, Hall and Griliches (1984).

9

Note that firm patenting is endogenous. Factors that contribute to more patents can simultaneously drive up firm size and productivity. Unfortunately, valid instruments are not available due to data limitation. Observations such as R&D tax subsidies or regulation concerning R&D only are not available at the firm or industry level.

10

The results are largely unchanged when more than one nonpatenting firms are matched with a given patenting firm as control groups

11

To test the robustness of the results of equations (11) and (12) to different quality of innovation, we run the regressions for the three different types of patent: invention, utility model and design. The coeffcients of all five TFP measures are positive and significant for invention and utility patent. The coeffcients of labor productivity and Solow residual of equation (12) are not significant for design patent.

12

Among the 142,717 firms in our merged patent-SIE sample, 20,737 of them have changed ownership, which account for 14.5% of the firms in the benchmark sample.

13

Also noted in Aghion et al. (2015) is a similar pattern in terms of the percentage of SOEs and POEs that received positive subsidies. It rose from 14% in 1998 to 25% in 2007 by SOEs, compared to 8% in 1998 to 12% in 2007 by POEs.

China’s Rising IQ (Innovation Quotient) and Growth: Firm-level Evidence
Author: Hui He, Ms. Nan Li, and Jing Fang