Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information | Zendy

Peter Selb | Zendy; Simon Munzert | Zendy

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Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information

Author(s) -

Peter Selb,

Simon Munzert

Publication year - 2011

Publication title -

political analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.953

H-Index - 69

eISSN - 1476-4989

pISSN - 1047-1987

DOI - 10.1093/pan/mpr034

Subject(s) - computer science , german , small area estimation , exploit , representation (politics) , bayesian probability , population , survey data collection , data mining , econometrics , politics , statistics , geography , political science , artificial intelligence , mathematics , computer security , sociology , archaeology , estimator , law , demography

Measures of constituency preferences are of vital importance for the study of political representation and other research areas. Yet, such measures are often difficult to obtain. Previous survey-based estimates frequently lack precision and coverage due to small samples, rely on questionable assumptions or require detailed auxiliary information about the constituencies' population characteristics. We propose an alternative Bayesian hierarchical approach that exploits minimal geographic information readily available from digitalized constituency maps. If at hand, social background data are easily integrated. To validate the method, we use national polls and district-level results from the 2009 German Bundestag election, an empirical case for which detailed structural information is missing.

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