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How large is congressional dependence in agriculture. Bayesian inference about ‘scale’ and ‘scope’ in measuring a spatial externality

Holloway, G. ORCID:, Lacombe, D. and Shaughnessy, T. (2014) How large is congressional dependence in agriculture. Bayesian inference about ‘scale’ and ‘scope’ in measuring a spatial externality. Journal of Agricultural Economics, 65 (2). pp. 463-484. ISSN 0021-857X

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To link to this item DOI: 10.1111/1477-9552.12054


The political economy literature on agriculture emphasizes influence over political outcomes via lobbying conduits in general, political action committee contributions in particular and the pervasive view that political preferences with respect to agricultural issues are inherently geographic. In this context, ‘interdependence’ in Congressional vote behaviour manifests itself in two dimensions. One dimension is the intensity by which neighboring vote propensities influence one another and the second is the geographic extent of voter influence. We estimate these facets of dependence using data on a Congressional vote on the 2001 Farm Bill using routine Markov chain Monte Carlo procedures and Bayesian model averaging, in particular. In so doing, we develop a novel procedure to examine both the reliability and the consequences of different model representations for measuring both the ‘scale’ and the ‘scope’ of spatial (geographic) co-relations in voting behaviour.

Item Type:Article
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of Agri-Food Economics & Marketing
ID Code:35259
Uncontrolled Keywords:Bayesian model averaging;Bayesian spatial probit;Congressional vote dependence;Markov chain Monte-Carlo methods;PAC contributions and their effectiveness;political economy;spatial correlations


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