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Investment in Information and Communication Technologies (ICT): The Role of Geographic Distance and Industry Proximity

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  • Oliviero A. Carboni

    (of Sassari and CRENoS, Italy)

Abstract

Employing data from Italian manufacturing firms, this paper attempts to check the existence of geographic and industry distance effects on the investment in information and communication technology (ICT). Geographic distance is defined by the Euclidean distance between each possible pair of locations (municipalities) according to their geographical coordinates. Industry proximity is measured by the firms’ industry distance according to the trade intensity between sectors. The model specified here refers to the combined spatial autoregressive model with autoregressive disturbances (SARAR), which are modelled simultaneously. The results show that both geographical and industry proximity have positive effects on the amount of ICT investment by firms. Furthermore, an econometric analysis shows that productivity, R&D activity, subsidies, reorganization, and labor composition are jointly correlated to ICT investment.

Suggested Citation

  • Oliviero A. Carboni, 2013. "Investment in Information and Communication Technologies (ICT): The Role of Geographic Distance and Industry Proximity," The Review of Regional Studies, Southern Regional Science Association, vol. 43(2,3), pages 191-212, Winter.
    • Handle: RePEc:rre:publsh:v:43:y:2013:i:23:p:191-212
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    2. repec:cup:judgdm:v:14:y:2019:i:3:p:299-308 is not listed on IDEAS
    3. Luigi Aldieri & Maria Carmela Aprile & Concetto Paolo Vinci, 2017. "R&D Spillover Effects on the Strategic Behavior of Large International Firms during the World Financial Crisis," International Journal of Business and Management, Canadian Center of Science and Education, vol. 12(11), pages 141-141, October.
    4. Aldieri, Luigi & Sena, Vania & Vinci, Concetto Paolo, 2018. "Domestic R&D spillovers and absorptive capacity: Some evidence for US, Europe and Japan," International Journal of Production Economics, Elsevier, vol. 198(C), pages 38-49.
    5. Oliviero A. Carboni & Paolo Russu, 2018. "Measuring and forecasting regional environmental and economic efficiency in Italy," Applied Economics, Taylor & Francis Journals, vol. 50(4), pages 335-353, January.
    6. Miao He & Guibing He & Jiaxin Chen & Yuan Wang, 2019. "Sense of control matters: A long spatial distance leads to a short-term investment preference," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(3), pages 299-308, May.

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    More about this item

    Keywords

    ICT; spatial weights; spatial dependence; spatial models;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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