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Agglomeration and firm-level productivity: A Bayesian spatial approach

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  • Yoshihiro Hashiguchi
  • Kiyoyasu Tanaka

Abstract

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.
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  • Yoshihiro Hashiguchi & Kiyoyasu Tanaka, 2015. "Agglomeration and firm-level productivity: A Bayesian spatial approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 95-114, November.
    • Handle: RePEc:bla:presci:v:94:y:2015:i::p:s95-s114
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    File URL: http://hdl.handle.net/10.1111/pirs.12147
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    Cited by:

    1. Carlos Carreira & Luís Lopes, 2018. "Regional knowledge spillovers: a firm-based analysis of non-linear effects," Regional Studies, Taylor & Francis Journals, vol. 52(7), pages 948-958, July.
    2. Zhaoying Lu, 2021. "Human capital spillovers from Special Economic Zones: evidence from Yangtze Delta in China," Discussion Papers in Economics and Business 21-02, Osaka University, Graduate School of Economics.
    3. Yang, Chih-Hai & Lin, Hui-Lin & Li, Hsiao-Yun, 2013. "Influences of production and R&D agglomeration on productivity: Evidence from Chinese electronics firms," China Economic Review, Elsevier, vol. 27(C), pages 162-178.
    4. Zhaoying Lu, 2022. "Human Capital Spillovers from Special Economic Zones: Evidence from Yangtze Delta in China," Economies, MDPI, vol. 10(5), pages 1-15, April.
    5. Cheng, Zhonghua & Li, Xiang & Zhu, Yeman & Wang, Meixiao, 2023. "The effects of agglomeration externalities on urban green total-factor productivity in China," Economic Systems, Elsevier, vol. 47(2).
    6. Neng Shen & Yuqing Zhao & Qunwei Wang, 2018. "Diversified Agglomeration, Specialized Agglomeration, and Emission Reduction Effect—A Nonlinear Test Based on Chinese City Data," Sustainability, MDPI, vol. 10(6), pages 1-22, June.
    7. Carlos Carreira & Luís Lopes, 2016. "Collecting new pieces to the regional knowledge spillovers puzzle: high-tech versus low-tech industries," GEMF Working Papers 2016-06, GEMF, Faculty of Economics, University of Coimbra.
    8. Mauricio Sarrias, 2020. "Random Parameters and Spatial Heterogeneity using Rchoice in R," REGION, European Regional Science Association, vol. 7, pages 1-19.
    9. Meihong Dai & Xianneng Li & Yuduo Lu, 2017. "How Urbanization Economies Impact TFP of R&D Performers: Evidence from China," Sustainability, MDPI, vol. 9(10), pages 1-17, September.
    10. Tanaka, Kiyoyasu & Hashiguchi, Yoshihiro, 2017. "Agglomeration economies in the formal and informal sectors : a Bayesian spatial approach," IDE Discussion Papers 666, Institute of Developing Economies, Japan External Trade Organization(JETRO).

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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