The September 24, 2017 German federal elections saw the far right, anti-immigrant and euro-skeptical party “Alternative for Germany” (“Alternative für Deutschland” – AfD) surging to third place behind chancellor Merkel’s center-left CDU/CSU and the Social Democrats. With the AfD’s success the focus of most observers, an interesting phenomenon has been largely overlooked, namely the fact that the AfD disproportionately benefited from increased turnout. We analyzed some of the available data at the level of electoral districts and found that the AfD was particularly strong in those districts that saw the largest increase in voter turnout.
The following table shows results from a regression analysis that models vote share as a function of a number of covariates, including the change in electoral participation (the turnout variable). Focussing on model 3 where the AfD’s vote share is the dependent variable, we find that the AfD massively benefitted from an increase in turnout. Increasing the change in turnout (i.e. mobilization) from its mean by one standard deviation leads to a one percentage point increase in AfD votes.
Determinants of Electoral Success, German Federal Elections 2017
|
|
CDU/CSU |
SPD |
AfD |
|
(1) |
(2) |
(3) |
|
| Change in Turnout from 2013 |
0.214* |
-0.872*** |
0.510*** |
|
(0.116) |
(0.119) |
(0.103) |
|
|
|
|
| Population Density |
-0.000*** |
-0.000*** |
-0.000*** |
|
(0.000) |
(0.000) |
(0.000) |
|
|
|
|
| Disposable income per capita |
0.000* |
-0.000*** |
0.000 |
|
(0.000) |
(0.000) |
(0.000) |
|
|
|
|
| Higher Education (percentage Abitur) |
-0.002*** |
0.001*** |
-0.002*** |
|
(0.000) |
(0.000) |
(0.000) |
|
|
|
|
| Unemployment |
-0.006*** |
0.011*** |
0.003** |
|
(0.001) |
(0.001) |
(0.001) |
|
|
|
|
| Percentage migration background |
-0.002*** |
-0.001*** |
0.001*** |
|
(0.000) |
(0.000) |
(0.000) |
|
|
|
|
| East |
-0.073*** |
-0.134*** |
0.112*** |
|
(0.008) |
(0.008) |
(0.007) |
|
|
|
|
| Constant |
0.427*** |
0.286*** |
0.107*** |
|
(0.032) |
(0.033) |
(0.028) |
|
|
|
|
| Observations |
299 |
299 |
299 |
| R2 |
0.718 |
0.724 |
0.729 |
| Adjusted R2 |
0.711 |
0.717 |
0.723 |
| Residual Std. Error (df = 291) |
0.032 |
0.033 |
0.028 |
| F Statistic (df = 7; 291) |
105.830*** |
108.835*** |
111.919*** |
|
| Notes: |
***Significant at the 1 percent level. |
|
**Significant at the 5 percent level. |
|
*Significant at the 10 percent level. |
This is substantively important, given that the AfD fell short of clearing the threshold for parliamentary representation by just 0.3 percent in the 2013 federal elections. On the other hand, the SPD massively lost from an increase in turnout, showing that the party was not able to speak to its voters.
Of the other variables, we observe a number of significant effects. Most importantly the AfD is stronger in the east. On average, going from a Western district to one in the East increases AfD vote share by more than 11 percent.
Secondly, the AfD is stronger in less populated areas, i.e. outside the big cities. The AfD is also stronger where voters are less educated and where unemployment is higher.
Finally, the AfD is stronger in districts where the share of the population with a “migration background” is higher. It is important to note that the Federal Returning Office defines “migration background” as follows: “Migration background means foreign nationals plus all those Germans who came to Germany after 1955 plus all those Germans with at least one parent who came to Germany after 1955” – “Als Personen mit Migrationshintergrund werden alle zugewanderten und nicht zugewanderten Ausländer sowie alle nach 1955 auf das heutige Gebiet der Bundesrepublik Deutschland zugewanderten Deutschen und alle Deutschen mit zumindest einem nach 1955 auf das heutige Gebiet der Bundesrepublik Deutschland zugewanderten Elternteil definiert.” (Federal Returning Office). Immigrants from Russia who came to Germany in the early 1990s and who disproportionately support the AfD could explain this effect. (see, for example, here.)
How important was each of these variables in relation to each other? To see this, we can turn to z-score standardized coefficients (beta coefficients) and their graphic representation on the following figure:
The strongest predictor in relative terms is the East dummy, then the education variable, followed by population density, the change in turnout from 2013, the percentage of people with migration background, and finally the unemployment variable. Note that the variable that captures disposable income is not significant.
We can conclude by pointing out that the AfD success cannot be explained by economic grievances. The variable that captures income is not significant and the unemployment variable is a poor predictor once other factors have been taken into account. What matters most is the division between Eastern und Western districts. In the East, many voters seem to be alienated from established parties and possibly from the system as a whole. The AfD with their anti-system rhetoric was able to mobilize these voters. The geographic and educational divide of the electorate is reminiscent of the situation in other countries, for example in the US.
