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The potential for industrial activity among EU regions: An empirical analysis at the NUTS2 level

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  • Gornig, Martin
  • Werwatz, Axel

Abstract

In the last decade, many parts of the world experienced severe increases in agricultural land prices. This price surge, however, did not take place evenly in space and time. To better understand the spatial and temporal behavior of land prices, we employ a price diffusion model that combines features of market integration models and spatial econometric models. An application of this model to farmland prices in Germany shows that prices on a county-level are cointegrated. Apart from convergence towards a long-run equilibrium, we find that price transmission also proceeds through short-term adjustments caused by neighboring regions.

Suggested Citation

  • Gornig, Martin & Werwatz, Axel, 2019. "The potential for industrial activity among EU regions: An empirical analysis at the NUTS2 level," FORLand Working Papers 13 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    • Handle: RePEc:zbw:forlwp:132019
    DOI: 10.18452/20909
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    References listed on IDEAS

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    2. Falk, Barry L., 1991. "Formally Testing the Present Value Model of Farmland Prices," Staff General Research Papers Archive 11093, Iowa State University, Department of Economics.
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    5. Juan Carmona & Joan R. Rosés, 2012. "Land markets and agrarian backwardness (Spain, 1904-1934)," European Review of Economic History, European Historical Economics Society, vol. 16(1), pages 74-96, February.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Agricultural land markets; price diffusion; spatial dependence; ripple effect;
    All these keywords.

    JEL classification:

    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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