4.2 Article

Diamond model and the export competitiveness of the agriculture industry from emerging markets: an exploratory vision based on a spatial effect study using a genetic algorithm

Journal

ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA
Volume 33, Issue 1, Pages 2427-2443

Publisher

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/1331677X.2019.1679212

Keywords

diamond model; export competitiveness; emerging markets; agriculture industry; genetic algorithm; spatial effect

Categories

Funding

  1. Central University of Finance and Economics
  2. National Social Science Foundation of China General Project [15BJL025]
  3. Research Base of Beijing Social Science Foundation General Project [14JDJGB046]
  4. National Social Science Foundation of China Key Project [14AZD118]
  5. Ministry of Education of China Key Project [15JZD022]
  6. National Natural Science Foundation of China General Project [71573023]
  7. Capital Normal University Research Project [ICS-2019-B-06]

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A deeper understanding of the comparative advantage of emerging markets in agricultural export can be gained by analysing the spatial connections of emerging markets through the framework provided by the diamond model. The geographic economics factors are transformed to interconnections of emerging markets by a genetic algorithm based on Mahalanobis distances. The spatial effect of geographic economics factors on export competitiveness of agriculture is further identified by spatial modelling. The irrigated land area, competitive labour cost, foreign direct investment (F.D.I.), and export market opportunity are important to further develop the export competitiveness of the agriculture industry from emerging markets based on spatial modelling. Also there is a spatial disturbances effect in agricultural export of emerging markets based on transformed interconnections structured by geographic economic factors. A fuzzy cluster analysis is further performed, and the stationary solution of clusters in dynamic transition across market segments is analysed by a Markov Chain. It is further found that the distribution of emerging markets with a higher level of export competitiveness can be more concentrated in clusters with lower levels of proportion. The findings of this research can offer support to global managers in further understanding the spatial effect of geographic economics factors on export competitiveness of agriculture from emerging markets.

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