tle: “Predictors of AfD party success in the 2017 elections”
btitle: “A Bayesian modeling approach”
thor: |
Sebastian Sauer,
Sandra Sülzenbrück,
Yvonne Ferreira,
Rüdiger Buchkremer
te: “FOM
DGPs 2018”
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tput:
xaringan::moon_reader:
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nature:
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class: center, middle, inverse

Menace to society

Right-wing populism then and now

class: top, left # Causes of 20th century world wars

.small[.footnote[Source: Kershaw, I. (2016). To hell and back: Europe 1914-1949. New York City, NW: Penguin.]]

???

Image credit:Wikipedia, RIA Novosti archive, image #44732 / Zelma / CC-BY-SA 3.0

class: top, left

Right-wing populism varies greatly, but…

.footnote[Source: Decker, F. (2003). Der neue Rechtspopulismus. Wiesbaden: VS Verlag für Sozialwissenschaften. Nicole Berbuir, Marcel Lewandowsky & Jasmin Siri (2015) The AfD and its Sympathisers: Finally a Right-Wing Populist Movement in Germany?, German Politics, 24:2, 154-178, DOI: 10.1080/09644008.2014.982546]

AfD as a nucleus of the German right-wing movement

The AfD …

“Wenn der Staat die Bürger nicht mehr schützen kann, gehen die Menschen auf die Straße und schützen sich selber.”

.small[— Tweet by Markus Frohnmaier (@Frohnmaier_AfD) on August, 26th 2018 in reaction to Chemnitz riots]

.footnote[Source: Fuchs, C., & Middelhoff, P. (2018, May 12). Neue Rechte - Bis in den letzten, rechten Winkel. Retrieved from https://www.zeit.de/politik/deutschland/2018-05/neue-rechte-verteilung-deutschlandkarte]

class: top, left # Popular theories on AfD success

Populist party support is fueled by …

.footnote[Source: Franz, Christian; Fratzscher, Marcel; Kritikos, Alexander S. (2018) : German right-wing party AfD finds more support in rural areas with aging populations, DIW Weekly Report, ISSN 2568-7697, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin, Vol. 8, Iss. 7/8, pp. 69-79]

Our research model

.center[

]

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AfD -->\n<g id=\"node4\" class=\"node\">\n<title>AfD</title>\n<polygon fill=\"none\" stroke=\"#2f4f4f\" points=\"720,-360 504,-360 504,-324 720,-324 720,-360\"/>\n<text text-anchor=\"middle\" x=\"612\" y=\"-337.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#2f4f4f\">AfD</text>\n</g>\n<!-- unemployment&#45;&gt;AfD -->\n<g id=\"edge2\" class=\"edge\">\n<title>unemployment&#45;&gt;AfD</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M391.6608,-216.0919C437.208,-242.1188 521.2381,-290.1361 571.3293,-318.7596\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"569.8515,-321.9463 580.2705,-323.8689 573.3246,-315.8686 569.8515,-321.9463\"/>\n</g>\n<!-- foreigners -->\n<g id=\"node2\" class=\"node\">\n<title>foreigners</title>\n<polygon fill=\"none\" stroke=\"#2f4f4f\" points=\"468,-504 252,-504 252,-468 468,-468 468,-504\"/>\n<text text-anchor=\"middle\" x=\"360\" y=\"-481.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#2f4f4f\">foreigners</text>\n</g>\n<!-- foreigners&#45;&gt;AfD -->\n<g id=\"edge4\" class=\"edge\">\n<title>foreigners&#45;&gt;AfD</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M391.6608,-467.9081C437.208,-441.8812 521.2381,-393.8639 571.3293,-365.2404\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"573.3246,-368.1314 580.2705,-360.1311 569.8515,-362.0537 573.3246,-368.1314\"/>\n</g>\n<!-- east_west -->\n<g id=\"node3\" class=\"node\">\n<title>east_west</title>\n<polygon fill=\"none\" stroke=\"#2f4f4f\" points=\"468,-360 252,-360 252,-324 468,-324 468,-360\"/>\n<text text-anchor=\"middle\" x=\"360\" y=\"-337.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#2f4f4f\">east_west</text>\n</g>\n<!-- east_west&#45;&gt;AfD -->\n<g id=\"edge6\" class=\"edge\">\n<title>east_west&#45;&gt;AfD</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M468.3332,-342C476.679,-342 485.1303,-342 493.5367,-342\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" 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y=\"-571.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#2f4f4f\">immigration</text>\n</g>\n<!-- immigration&#45;&gt;foreigners -->\n<g id=\"edge3\" class=\"edge\">\n<title>immigration&#45;&gt;foreigners</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M209.7717,-539.653C240.3082,-528.7471 272.8178,-517.1365 299.7561,-507.5157\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"301.1746,-510.7257 309.4148,-504.0661 298.8202,-504.1335 301.1746,-510.7257\"/>\n</g>\n<!-- culture -->\n<g id=\"node7\" class=\"node\">\n<title>culture</title>\n<ellipse fill=\"none\" stroke=\"#2f4f4f\" cx=\"108\" cy=\"-342\" rx=\"108\" ry=\"108\"/>\n<text text-anchor=\"middle\" x=\"108\" y=\"-337.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#2f4f4f\">culture</text>\n</g>\n<!-- culture&#45;&gt;east_west -->\n<g id=\"edge5\" class=\"edge\">\n<title>culture&#45;&gt;east_west</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M216.3332,-342C224.679,-342 233.1303,-342 241.5367,-342\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"241.7583,-345.5001 251.7583,-342 241.7583,-338.5001 241.7583,-345.5001\"/>\n</g>\n</g>\n</svg>\n"

class: middle, center, inverse

AfD votes, and socioenomic factors at the Bundestagswahl 2017

class: top, left # AfD votes

Unemployment

Foreigners

class: middle, center, inverse

data analysis

class: top, left

Data preparation

Data were…

Bayes modeling

.footnote[Guideline: McElreath, R. (2016). Statistical rethinking. New York City, NY: Apple Academic Press Inc.]

Model specification

\[\begin{aligned} \text{AfD}_i &\sim \mathcal{N}(\mu_i, \sigma)\\ \mu_i &= \beta 0_{[east]} + \beta 1 \cdot \text{foreign_z} + \beta2 \cdot \text{unemp_z}\\ \beta0_{[east]} &\sim \mathcal{N}(0, 1)\\ \beta1 &\sim \mathcal{N}(0, 1)\\ \beta2 &\sim \mathcal{N}(0, 1)\\ \sigma &\sim \mathcal{N}(0, 1)\\ \end{aligned}\]

Model diagnosis: traceplot

class: middle, center, inverse

Results

Model diagnosis and coefficients

coefficient Mean StdDev lower.0.89 upper.0.89 n_eff Rhat
beta0[1] -0.45 5.74 -9.26 9.16 260.42 1
beta0[2] 1.43 5.75 -7.35 11.05 259.13 1
alpha 0.07 5.74 -9.54 8.84 260.09 1
beta1 -0.06 0.04 -0.13 0.02 520.19 1
beta2 -0.21 0.05 -0.28 -0.13 556.18 1
sigma 0.68 0.03 0.64 0.72 623.57 1

The whole shabeng: Multi level wins

model predictors WAIC pWAIC dWAIC weight SE dSE
m15_stan state+for+unemp (ML) -1356.32 21.73 0.00 1 34.91 NA
m13_stan state (ML) -1298.50 19.17 57.82 0 33.63 15.03
m14_stan east+for+unemp (ML) -1136.45 6.53 219.87 0 30.24 33.64
m12_stan area (ML) -951.15 111.74 405.16 0 31.27 37.11
m11c_stan unemp -894.09 4.02 462.23 0 37.03 39.86
m16_stan null (intercept) -885.56 3.35 470.76 0 38.15 40.53
m10_stan for+unemp+east -533.01 3.77 823.31 0 16.02 37.66
m11d_stan east -509.22 0.59 847.09 0 10.88 36.40
m9_stan for+unemp+east[] 625.56 6.92 1981.88 0 33.87 38.82
m9a_stan for+unemp 808.28 4.72 2164.60 0 34.09 39.30
m11a_stan for 813.89 4.14 2170.21 0 35.22 39.86

Model specification of most favorable model

\[\begin{aligned} \text{AfD}_i &\sim \mathcal{N}(\mu_i, \sigma)\\ \mu_i &= \beta 0_{[state]} + \beta 1 \cdot \text{foreign_z} + \beta2 \cdot \text{unemp_z}\\ \beta0_{[state]} &\sim \mathcal{N}(0, \sigma_2)\\ \beta1 &\sim \mathcal{N}(0, 1)\\ \beta2 &\sim \mathcal{N}(0, 1)\\ \sigma &\sim \mathcal{N}(0, 1)\\ \sigma_2 &\sim \mathcal{N}(0, 1) \end{aligned}\]

Coefficients of the most favorable model

Traceplot of most favorable model

Checking model additivity assumption

.footnote[Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.]

Posterior distributions of parameters

.footnote[Only level 1 parameters are shown.]

Comparing model predictions

Comparing observed and estimated AfD votes

class: middle, center, inverse

Conclusions

class: top, left

Theoretical implications

.footnote[Nicole Berbuir, Marcel Lewandowsky & Jasmin Siri (2015) The AfD and its Sympathisers: Finally a Right-Wing Populist Movement in Germany?, German Politics, 24:2, 154-178, DOI: 10.1080/09644008.2014.982546]

Statistical implications

Good textbook

class: middle, center, inverse

Thank you

Sebastian Sauer #### sebastiansauer #### https://data-se.netlify.com/ #### #### Sebastian Sauer

Get slides here:

http://data-se.netlify.com/slides/afd_dgps2018/afd_dgps2018.html#1

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.footnote[Built using R, RMarkdown, Xaringan. Inspiration from and thanks to Yihui Xie and Antoine Bichat, among others]