Demand heterogeneity and the estimation of firm-level markup

YIN Heng1,2 ZHANG Ziyao2

(1.National Academy of Development and Strategy, Renmin University of China)
(2.The School of Finance, Renmin University of China)

【Abstract】De Loecker and Warzynski (DLW, 2012) put forward a new method to estimate firm-level markup, and this method was widely accepted as a concise tool for the estimation and analysis of markup. However, the estimation may be seriously biased due to incomplete consideration of demand heterogeneity. We develop a structural model with richer demand heterogeneity to estimate the firm-level markup and contrast it with the three-step DLW method by means of ten Chinese manufacturing industries in China Industrial Enterprises Database during 1998–2013. There are indeed huge differences between these two methods. In all the ten industries, our estimation distributes more compactly with a smaller variation. The level of estimated markup is far smaller than that of DLW’s estimations. Markup decreases steadily during this period, which means Chinese manufacturing marker is becoming more competitive. Export market is more competitive than domestic market, and firms located in the inland area enjoy higher markup than coastal firms. The results from the DLW method stand sharply against this picture. Further analysis shows that there are distinct outcomes between our method and DLW method in multiple dimensions, such as markup level, dispersion, and dynamic characteristic. This contrast gives obvious evidences that the three-step DLW method’s bias cannot be ignored and it may give misleading answers in many important empirical fields.

【Keywords】 firm-level markup; firm heterogeneity; production function estimation; structure estimation;

【DOI】

【Funds】 National Natural Science Foundation of China (71673305) National Natural Science Foundation of China (71873132)

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(Translated by YANG xuehong)

    Footnote

    [1]. ① Jaumandreu and Yin (2017) stated clearly that the heterogeneity at the firm’s demand end was even greater than that (productivity) at the production end. [^Back]

    [2]. ① Grieco and McDevitt (2016) dealt with it in the same way. [^Back]

    [3]. ① For the concrete influencing factors, see Doraszelski and Jaumandreu (2018) and Jaumandreu and Yin (2017). [^Back]

    [4]. ① Jaumandreu and Yin (2017) found that even though the observable demand movement factors were controlled, the remaining demand heterogeneity was still very obvious. However, vertical heterogeneity is controlled here, and such treatment is acceptable. [^Back]

    [5]. ① This method is widely used in trade and empirical industrial organizations, such as Berry et al. (1995), Olley and Pakes (1996), Levinsohn and Petrin (2003), Ackerberg et al. (2015), Wooldridge (2009), and Doraszelski and Jaumandreu (2013, 2018). It also includes the sieve estimation proposed by Ai and Chen (2003, 2007). [^Back]

    [6]. ② Firms above designated size refer to the ones with an annual main business income (sales volume) of more than CNY 5 million (included). State-owned enterprises with an annual main business income of less than CNY 5 million are not included after 2006. After the adjustment in 2011 (included), industrial enterprises whose main business income exceeds CNY 20 million (included) can just be included in the investigation scope. [^Back]

    [7]. ① Industrial Classification for National Economic Activities (GB/T4754-2002) issued on May 10, 2002 and Industrial Classification for National Economic Activities (GB/T4754-2011) issued on April 29, 2011. The corresponding industry codes change in China Industrial Enterprises Database began in 2003 and 2013. [^Back]

    [8]. ② For the definition and construction of variables, see the appendix on the website of China Industrial Economics (http://www.ciejournal.org). [^Back]

    [9]. ③ For special standards, see the appendix on the website of China Industrial Economics (http://www.ciejournal.org). [^Back]

    [10]. ④ Olley and Pakes (1996) pointed out that in the estimation of production function, there were often errors that the capital elasticity coefficient was too low. [^Back]

    [11]. ① The data of the tariff rate derive from the online data set on the paper page of Brandt et al. (2017) on the official website of AEA. Output tariff refers to the tariff rate of the industry to which the firm belongs. Input tariff refers to the weighted average tariff rate of raw materials used by the firm. The input tariff rate is the weighted mean of the output tariff rate corresponding to raw materials used by the firm. The weight is taken from the three-digit industry input-output table in 2002. For concrete calculation, see Brandt et al. (2017). [^Back]

    [12]. ① Lu and Yu (2015) used the three-digit industries as the analysis objects. From the perspective of research design, it is more reasonable to use more detailed four-digit industry data. Lu and Yu (2015) also supported this conclusion. [^Back]

    [13]. ② For Ellison-Glaeser Index, see Ellison and Glaeser (1997). [^Back]

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This Article

ISSN:1006-480X

CN: 11-3536/F

Vol , No. 12, Pages 60-77

December 2019

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Article Outline

Abstract

  • 1 Introduction
  • 2 Estimation of markups at the manufacturing end and problems in the DLW method
  • 3 Integrated estimation of production function parameters and markups
  • 4 Data and results of basic estimation
  • 5 Comparison with the existing results of China’s manufacturing markup analysis
  • 6 Conclusions
  • Footnote

    References