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Sources of productivity spillovers: panel data evidence from China

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Abstract

This paper assesses sources of productivity spillovers in China’s electric and electronic manufacturing industry using a rich panel data-set of 25,360 firms observed over the period 2004–2007. This industry is characterized by its important reliance on technology. In particular, the paper focuses on the role of other firms’ productivity as well as productivity shifters in affecting own firm-level total factor productivity. In addition, this paper examines the possible difference between spillovers from foreign-owned units and from units which participate at global markets through exporting in comparison to domestically-owned and non-exporting units. We find evidence of stronger spillovers from exporting firms than from non-exporting firms. This is true for foreign-owned as well as domestic exporters. The strength of the spillover effects differ across subsectors.

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Notes

  1. There is broad interest in China’s productivity growth. Earlier studies include, among others, Heshmati and Kumbhakar (2011) analyzing provincial data, Chen and Song (2008) analyzing county data, Lin (1992) and Wu (1995) analyzing sectoral data, and Brandt et al. (2012) analyzing firm level data.

  2. The vector \(\mu _{t}\) carries a time index only because of the entry and exit of some firms over time.

  3. Manufactures of electric equipment (industry code 39); Manufactures of communication equipment, computers, and other electronic equipment (industry code 40); and Manufactures of instruments, meters, and other office instruments (industry code 41).

  4. Recall that the dependent variable in this paper represents the (log) change of TFP (i.e., TFP growth). Accordingly, the between variation in the data is relatively small. The fraction of the time-invariant component in the disturbances is close to zero. Hence, estimation with firm fixed effects leads to a dramatic loss of information and degrees of freedom.

  5. See Diewert (1981), Caves et al. (1982), and Heshmati et al. (1999) for a broad discussion about measurement and estimation of TFP growth.

  6. Kogut and Zander (1993) suggest that multinational firms will generally induce less spillovers from their knowledge base, the more tacit and complex the knowledge is they transfer to subsidiaries abroad. Spillovers to third parties will be stronger if that knowledge is more general.

  7. Big cities consist of more than one 4-digit zip code region, while for smaller cities the zip code region may coincide with the city range. When looking at the whole city, Shenzhen in total hosts 1,789 firms in the considered sector. By comparison, Shanghai hosts 1,898 firms.

  8. These figures are suppressed to save space. However, they are available upon request from the authors.

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Acknowledgments

The authors gratefully acknowledge numerous helpful comments by the participants at the conference in honor of Lennart Hjalmarsson and by two anonymous reviewers and the editor.

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Authors and Affiliations

  1. Department of Economics and Center for Policy Research, Syracuse University, Syracuse, NY, 13244-1020, USA

    Badi H. Baltagi

  2. CEPR, CESifo, ETH Zurich, Zurich, Switzerland

    Peter H. Egger

  3. ETH Zurich, Zurich, Switzerland

    Michaela Kesina

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  1. Badi H. Baltagi
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  2. Peter H. Egger
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Correspondence to Michaela Kesina.

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Baltagi, B.H., Egger, P.H. & Kesina, M. Sources of productivity spillovers: panel data evidence from China. J Prod Anal 43, 389–402 (2015). https://doi.org/10.1007/s11123-014-0393-z

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