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Anti-liberal, anti-establishment or constituency-driven? Spatial econometric analysis of polish parliamentary election results in 2015

100%
Lasoń A., Torój A.
European Spatial Research and Policy
|
2019
|
vol. 26
|
issue 2
199-236
EN
We investigated the spatial variation patterns of voting results in Polish parliamentary election in 2015 across 380 regions. That election was a milestone event in Polish politics that substantially affected Poland’s internal and foreign policy directions and promoted two emerging political parties as runners-up against the well-established ones. While socio-economic, cultural and geographical factors such as economic activity, historical legacies (post-Russian East vs post-German West) and economic dichotomies (cities vs the countryside) explain most variations for most parties, they do not appeared to fit as determinants of the new parties’ support, especially of right-wing populists. Demographic target groups of individual parties appear to be relatively unresponsive to their pre-election offerings. The spatial specification of econometric models considerably improves their statistical properties. We also examined mixed-W models to account for the unobservable spatial effects stemming from the construction of constituencies. Their distinctive sets of candidates added significantly to the explanation of the spatial variation in voting.
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How Regional Business Cycles Diffuse through Space and Time: Evidence from Spatial Markov Model of Polish NUTS-3 Regions

81%
Rabiej A., Sikora-Kruszka D., Torój A.
Central European Journal of Economic Modelling and Econometrics
|
2023
|
issue 4
345-383
EN
In a national economy, are individual subnational regions business cycle takers or setters? We address this important regional policy question by investigating regional business cycles at NUTS-3 granularity in Poland (N = 73), using two metrics in parallel: GDP dynamics and unemployment. To extract the business cycle, we use a spatial Markov switching model that features both idiosyncratic business cycle fluctuations across regions (as a 2-state chain), as well as spatial interactions with other regions (as spatial autoregression). The posterior distribution of the parameters is simulated with a Metropolis-withinGibbs procedure. We find a clear division into business cycle setters and takers, the latter being largely (but not only) non-metropolitan regions.
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