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A measure for identifying substantial geographic concentrations

Author

Listed:
  • Chris van Egeraat
  • Edgar Morgenroth
  • Rutger Kroes
  • Declan Curran
  • Justin Gleeson

Abstract

Regional industrial policy emphasizes the notion of building on existing concentrations of competitive firms. A range of measures to identify such concentrations has been put forward in the literature. These measures, however, do not identify substantial concentrations which have the best potential for further development, tend to concentrate on scale measured by employment, and are applied using data for pre‐specified administrative boundaries. This paper presents a new concentration index that identifies substantial concentrations and utilizes information on both the number and size of plants. It also proposes a method for generating relevant industry‐specific spatial units. La política industrial regional hace hincapié en la noción de construir sobre las concentraciones existentes de empresas competitivas. En la literatura se ha presentado una serie de medidas para identificar tales concentraciones. Estas medidas, sin embargo, no identifican las concentraciones sustanciales que tienen el mejor potencial para un mayor desarrollo, tienden a concentrarse en la escala (medida por el empleo), y se aplican utilizando datos para límites administrativos especificados de antemano. Este artículo presenta un nuevo índice de concentración que identifica concentraciones sustanciales y utiliza información sobre el número y el tamaño de las plantas industriales. También propone un método para generar unidades espaciales relevantes específicas para la industria. 地域の産業政策は、競争力のある既存の企業の集積を拡大する意図を強調している。この集積を同定する様々な方法が論文で提唱されている。しかしながら、これらの方法は将来発展する可能性が最も高い大きな集積を識別せず、従業員数で測る規模に注目しており、予め設定された行政区域のデータを使用するときに適用できる。本稿では、大きな集積を識別し工場の数と規模の両方の情報を利用する新しい集積指数を提案する。また、重要な産業に特異的な空間単位を作り出す方法も提案する。

Suggested Citation

  • Chris van Egeraat & Edgar Morgenroth & Rutger Kroes & Declan Curran & Justin Gleeson, 2018. "A measure for identifying substantial geographic concentrations," Papers in Regional Science, Wiley Blackwell, vol. 97(2), pages 281-300, June.
    • Handle: RePEc:bla:presci:v:97:y:2018:i:2:p:281-300
    DOI: 10.1111/pirs.12241
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    Cited by:

    1. Pieter E. Stek, 2020. "Mapping high R&D city-regions worldwide: a patent heat map approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 279-296, February.
    2. Andrew Crawley & Max Munday, 2017. "Priority sectors in city regions? Some issues from a study of the Cardiff Capital Region," Local Economy, London South Bank University, vol. 32(6), pages 576-589, September.
    3. Barbara MARTINI & Marco PLATANIA, 2022. "Are The Regions With More Gender Equality The More Resilient Ones? An Analysis Of The Italian Regions," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 71-94, June.
    4. Egeraat Chris van & Doyle Eleanor, 2018. "Rethinking Irish cluster policy," Administration, Sciendo, vol. 66(1), pages 107-128, February.
    5. Pieter E. Stek, 2021. "Identifying spatial technology clusters from patenting concentrations using heat map kernel density estimation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 911-930, February.

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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