The Electoral Geography of Weimar Germany:

Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party[1]

John O’Loughlin

Institute of Behavioral Science and

Department of Geography

University of Colorado

Campus Box 487

Boulder, CO. 80309-0487

Email: johno@colorado.edu

Acknowledgements

The research reported in this paper was supported by grants from the Geography and Regional Science Program of the National Science Foundation. Earlier versions of the paper were presented at “New Methodologies for the Social Sciences” conference at the University of Colorado, March 2000 and the Workshop on “Political Processes and Spatial Analysis” at Florida International University, March 2001. I received useful commentaries and questions from the participants at these meetings. The original Weimar data files were kindly provided by Ralph Ponemero of the Zentralarchiv für empirische Forschung of the Universität Köln. The original Weimar map was digitized by David Fogel and Steve Kirin of the Department of Geography of the University of California at Santa Barbara under the supervision of Luc Anselin. Other GIS and cartographic assistance was provided by Colin Flint, Michael Shin, Valerie Ledwith, Altinay Kuchukeeva, Jim Robb, Tom Dickinson and Frank Witmer. Helpful comments were provided by the Political Analysis reviewers and editorial team. Special thanks are due to Mike Ward and Luc Anselin (without implicating them) since they have been close collaborators in spatial analytical projects over the past 15 years.

Abstract

For over half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate Census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning that undermines any single predictive factor. Recent developments in geographic methodologies, based on GIS (Geographic Information Science) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods: global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping and barrier definition (wombling) are used in an exploratory spatial data analysis of the NSDAP vote. Key indicators are examined: the NSDAP vote percentage in 1930 (and two sets of estimates from King’s ecological inference method), the turnout of NSDAP voters in 1930, and the support for the NSDAP by Protestant voters. The results are consistent in showing a voting surface of great complexity with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate but is better characterized as a mosaic of support for “milieu parties”, mixed across class and other social lines and defined by a strong attachment to local traditions, beliefs and practices.
1 Introduction

Despite attempts to bridge the epistemological and methodological gaps between the disciplines of geography and political science recently, lack of awareness of developments in geographic techniques by political scientists is still evident.[2] Some reasons can be proffered for this neglect, not the least of which is the nature of the data deployed by political methodologists in their analyses. Over time, data collected from surveys of individuals have become the norm and, partly because of difficulties of inference across levels, political scientists have tended to eschew aggregate data collected for geographic units (King, 1997). The preponderance of individual-level data is of relatively recent vintage. A classic study of political behavior, V.O. Key’s (1949) Southern Politics in State and Nation, used aggregate electoral data, while Pollock’s (1944) study of Nazi party electoral success pointedly relied on a geographical analysis of the aggregate votes. King’s (1997) ecological inference methodology was recently the subject of a forum in the leading US geography journal, Annals of the Association of American Geographers (Vol. 90, no. 3, 2000). The reviews were generally favorable regarding the attempt to bridge the aggregate-individual scale, though important issues concerning the role of spatial autocorrelation still await resolution (see also Cho and Anselin, 2000 and Davies-Withers, 2001). It seems fair to assert that given the propensity of political scientists to rely on survey data of individuals and of geographers to rely on aggregate, often Census, data for small areal units, the gap between the preferred methodologies will likely continue.

The purpose of this paper, using the example of voting for the Nazi party in Weimar Germany, is to help bridge the gap by linking the methodological advances in geography and related environmental sciences to research questions in political science. While much of spatial autocorrelation is extended to spatial econometric modeling in a regression framework (Anselin, 1988), I confine my attention in this paper to descriptive and exploratory methods of spatial analysis. Extensive use of spatial econometric modeling to political data can be seen in O’Loughlin, Flint and Anselin (1994) and O’Loughlin, Kolossov and Vendina (1997). This paper is an exercise in exploratory spatial data analysis and therefore no inferential models are employed. Instead, attention is given to methods developed in the environmental sciences, especially environmental biology and physical geography, for uncovering underlying structures.

In examining the nature of aggregate data distributions and possible causal relationships, it emphasizes methods of exploratory spatial data analysis (ESDA – see Anselin, 1995), most of which have been developed in the geographical sciences and are increasingly available in specialized mapping and analyses software for the environmental sciences. Despite the addition of geographic modules to statistical software (such as the S-Plus module for ArcView GIS®), most of the users of such software seem to be environmental scientists (geologists, physical geographers, biologists, ecologists, engineers) interested in statistical data properties rather than social scientists with a bent towards the examination of aggregate data. Though survey data suffice nicely for most political topics, some research questions force the use of aggregate data. These include analysis of historical political questions that predate the arrival of reliable survey data (including the forces behind the electoral success of the Nazi party in Weimar Germany), political behavior in countries without national-level survey data but with acceptable census data (much of the world falls into this category), and questions that focus on the context of political decisions, forcing a consideration from the individual to the neighborhood and larger scales. Events data in international relations, gathered for countries and sub-state units, can also be analyzed using the spatial methodology (Murray et al;, 2002)

Spatial autocorrelation is the most fundamental concept in geography and integrates the growing set of spatial statistical approaches with the key elements of the discipline. A geographic truism, often known as the First Law of Geography (Tobler 1970, 236), states that “everything is related to everything else but near things are more related than distant things.” Across all specialized branches of geography and across all epistemological divides, spatial autocorrelation underpins geographic assumptions, methods and results. The (relative) order generated by spatial autocorrelative processes, the distribution of phenomena on the earth’s surface has been well documented in thousands of studies and simple observation, we know that clustering of like objects, people and places is the norm.

Geostatistical methods are typically configured for large samples and are used widely by environmental scientists. In order to introduce these methods to human geography, we need both larger datasets (many aggregate geographic units, also called polygons) than those to which we are accustomed, and a point sampling strategy. At a fine scale of resolution, every spatial distribution is discontinuous. The main difference between geostatistics and spatial autocorrelation is that the former deals with point sampling, usually on a grid, of a continuously geographic phenomenon (like a forest), the latter deals with a division of a geographic surface, thus producing an aggregation of geographic phenomena (Griffith and Layne, 1999, 457). With a large number of polygons, say approaching 1000 units, a centroidal or some other point sampling strategy offers a reasonable approximation of a continuous surface that can be modeled using geostatistical methods, like kriging (a statistical interpolation method that predicts values for unsampled locations on a surface) and trend surface analysis (fitting a linear or polynomial trend to a latitude, longitude and height surface).

In this paper, geostatistical methods are heavily used. Mantel correlation analysis (correlating distance and difference vectors) and variography -the process of pattern description and modeling using the variance of the difference between the values at two locations- are used to help understand the distribution of the Nazi party votes. Vector mapping (identifying local directional trends) and directional spatial correlograms (summary measures of association by major angles and distances) are added to the usual tools of spatial autocorrelation analysis- Morans I and G^*_i, measures of global and local spatial association- and GIS mapping in this paper. Wombling analysis (identification of statistically significant boundaries on a surface) is applied for the first time to a political geographic problem.

2 Weimar German Data and the Nazi Vote

Because of the use of methods based on point sampling, a dataset with many cases is preferred for analysis, and ideally it should also retain substantive interest. I chose the example of voting in Weimar Germany for this study. The issue of how the NSDAP (Nationalsozialistiche Deutsche Arbeiterpartei) or Nazi party came to electoral prominence has spurred hundreds of local and national-level studies over the past six decades. A data set available for aggregate analysis of Nazi support (German Weimar Republic Data, 1919-1933, no. 0042) is available from the ICPSR (www.icpsr.umich.edu), but users are cautioned that this dataset is replete with errors (Falter and Gruner, 1981). A cleaned version is available from the Zentralarchiv für empirische Forschung of the Universität Köln (see Hänisch, 1989 for an account of the data and levels of aggregations). The raw dataset consists of electoral and census data for Weimar Germany from 1919 to 1934 for 6,000 spatial units. However, the data are sparse for many individual units and must be aggregated to the same geographic basis for matching of census and electoral data. Previous works (O’Loughlin, Flint and Anselin, 1994; O’Loughlin, Flint and Shin, 1995) have used a dataset of 921 units for study of the key breakthrough election, that of 1930 when the NSDAP increased their vote share to 18.3%. However, in this current study over a longer time span (1924 to 1933), the data are aggregated to 743 units, including both Kreise (counties) and cities of Germany.[3]

Much is known about the NSDAP vote from a variety of authors (Childers, 1983; Falter, 1986, 1991; Kater, 1983; Küchler, 1992). Highly relevant to this paper, researchers have generally concluded that the geographic pattern is highly complex, with both strong local and regional elements, and that the correlation between the vote and compositional factors (e.g. religion, class, occupation, gender) is relatively weak. Until 1928, the NSDAP aimed its platform at blue-collar workers, but it had unexpected success in rural areas. Thereafter, the NSDAP targeted farmers, skilled workers, shopkeepers and civil servants, following a lower-middle class strategy that was bolstered by strong support for private property. Rural areas of Germany became bifurcated along the lines of inheritance traditions. In the Catholic areas of the south and west, where partible inheritance was common, the NSDAP platform fell on deaf ears while in the northern and northeastern rural sections, where impartible inheritance was the norm, the party found much success (Brustein, 1996). The composition of the NSDAP electorate additionally varied from region to region as a result of local economic circumstances and external pressures. Combining a model of economic interest with “political confessionalism” -attachment to a party based on social networks and historical traditions, such as the attachment of the urban and industrial working-classes to the Communists- most researchers accept that no one factor accounts for the success of the Nazi party. In May 1924, the NSDAP received 6.5% of the vote, decreasing to 3.0% in December 1924 and to 2.6% in 1928. The electoral breakthrough to 18.3% in 1930 was doubled to 37.4% in July 1932 after the economic collapse in Germany. The vote dropped to 33.1% in November 1932 before peaking at 43.8% in the last Weimar election in 1933, with the NSDAP never having never reached a majority.

For purposes of our earlier work, we divide Weimar Germany into six regions based on historical and cultural attachments; these regions overlap to some extent with the post-World War II Federal Länder that also were predicated on the notion of regional attachments. The regional boundaries are shown in Figure 1. In this present paper, these regions are not used as predictors, but reference is made to them in describing the map patterns and in probing the map’s spatial structure. The Nazi party took advantage of this regional mosaic by pushing a variegated appeal that was modified from locale to locale depending on local conditions (Heilbronner, 1998; Ault and Brustein, 1998; Brustein, 1990, 1996; Brustein and Falter, 1995; Kater, 1983; Stachura, 1980). The Weimar dataset is therefore satisfactory for detailed spatial analysis and offers a test of how far exploratory spatial data analysis can be carried to gain insights into a complex story that is still not fully understood, despite a massive effort by historians and social scientists. As shown by O’Loughlin, Flint and Anselin (1994), geographic-compositional models for the 1930 NSDAP vote need to take this spatial heterogeneity into account; the regression models with spatial autoregressive terms showed that different combinations of NSDAP supporters were distributed across the six regions.

Figure 1: The Six Historical-Cultural Regions of Weimar Germany (with key locales).

Since the main purpose of this paper is to describe and highlight the geographic elements in the support for the NSDAP, I will analyze a series of votes between 1924 and 1933 but I center the analysis on the 1930 Weimar parliamentary election. From just 2.6% in 1928, the NSDAP vote rose dramatically in 1930 to reach 18.3% of the total, making it the second largest party in the Reichstag (parliament) after the SPD (Sozialdemocratische Partei – social democrats). Therefore, 1930 is generally considered the “breakthrough election” for a party that had existed on the fringes of the parliamentary scene for a decade and directional analysis of the changes between the years 1924-28, 1928-1930, and 1930-32 allow for a better understanding of the spread of the party support, and these changes will be introduced during the directional correlation analysis.

The key dependent variable for analysis is the percentage of the 1930 valid vote received by the NSDAP in each of the spatial units. The distribution of the Nazi ratio of the 1930 vote is shown in Figure 2. While the map makes regional and local clusterings evident, it is lacking in wide bands of similar values. In general, the distribution of strong Nazi party support corresponds to the Protestant regions of the country, with largest values in East Prussia, Schleswig-Holstein, Oldenburg and Saxony. The Catholic areas of the Rhineland, Bavaria, Upper Silesia, as well as big cities, and industrial areas (notably Berlin, the Ruhr and Thuringia) were centers of opposition to the Nazi party. (In 1924, the party had received their strongest support in Bavaria, their center of initial mobilization and organization). However, within the North-Northeast versus West-Southwest-South divide, there are numerous islands of support and opposition distinguishing Catholic and Protestant areas; see the contrast between Upper and Lower Silesia or the eastern and central parts of the East Prussia exclaves. It is this cartographic complexity that makes the electoral map of Weimar Germany both a social science puzzle and a candidate for detailed spatial analysis.

3 The NSDAP in Weimar Germany

In this study I examine NSDAP support in Germany using six analytical steps: a) global indicators of spatial autocorrelation, b) distance and variance patterns, c) local indicators of spatial association, d) directional spatial autocorrelation analysis, d) vector mapping, and e) wombling (barrier identification). The percentage of the vote for the NSDAP is used throughout this study since it allows comparison to previous works and, in many ways it is the easiest indicator to both visualize and comprehend in the spatial analysis. The general indicator of the NSDAP vote is a conglomerate of the support of various constituencies for the Nazi party. One of several key correlates of Nazi party support have been identified in previous studies, I also use the ecological estimates for NSDAP voter turnout and Protestant population support for the NSDAP. To estimate the ratio for the 743 geographic units, I used the EzI version of the King program that does not require the use of the Gauss program (EzI: A(n Easy) Program for Ecological Inference by Kenneth Benoit and Gary King) available from http://gking.harvard.edu/stats.shtml.

Figure 2: Distribution (Quartiles) of the NSDAP 1930 Vote in Percentages

The EI (Ecological Inference) method has gained a great deal of press and familiarity in political science since it was first introduced by Gary King (1997). King has promoted his ecological inference technique as a method that allows disaggregation of the global (whole study region) estimates to the individual units that comprise the aggregate.[4] These estimates can be mapped, as King (1997, 25) illustrates for the white turnout in the 1990 New Jersey elections, and can also be the subject of further “second-order analysis”. In this study, the EI estimates are only considered in descriptive, exploratory spatial data analyses. King’s EI method, though now well known to political scientists, has only recently been introduced to geography. Though its potential is recognized (Fotheringham, 2000; O’Loughlin, 2000; Davies-Withers, 2001), no application of it designed to tackle key human geographic questions, has yet been published.

Using the EI methodology, I am interested in whether the group of interest, the Nazi party, showed a significant gain over its opponents in turning out its voters. Knowing the marginals (votes for the NSDAP and non-NSDAP parties, the turnout and the eligible voters), we can use EzI to estimate the NSDAP voter turnout using the accounting identity (King’s notation):

T_{i =} β_i^bX_i + β_i^w (1-X_i), (1)

where T_i is the proportion of NSDAP voters turning out to vote in each Kreisunit[5]; X_i is the proportion of the voters that picked the NSDAP; 1-X_i is the proportion of the vote for all other parties; β_i^b is the proportion of the NSDAP supporters that came to the polls; and β_i^w is the proportion of non-NSDAP supporters who came to the polls. The purpose of the EzI modeling is to estimate β^b (the aggregate turnout rate for Nazi voters for the whole country); one can also get estimates for the individual counties and cities (Kreisunits), b_i^b . Both T_i and X_i are known values, and β_i^b and β_i^w are the unobservable parameters of interest to be estimated using King’s ecological inference method. (Full details are available in King, 1997). Two key indicators -the estimated turnout of NSDAP voters and the estimated ratio of Protestants who voted for the NSDAP- are spatially examined in this study.

Table 1: EzI Estimates for Turnout of NSDAP Supporters in Reichstag Elections, 1924-1933

Election Date	No. of Cases	Ezi Estimate	Mean Turnout	+/- to NSDAP*
May 1924	930	.616	.743	-.127
December 1924	927	.899	.767	+.132
1928	940	.860	.759	+.101
1930	916	.809	.811	-.002
July 1932	924	.903	.818	+.085
November 1932	911	.882	.782	+.100
1933	883	.808	.870	-.062

* Gain and loss to the NSDAP calculated from the estimated NSDAP turnout compared to the mean turnout. The number of spatial units varies from election to election as a result of data availability in the Weimar German file.

The key comparative data for all Weimar Reichstag elections are shown in Table 1. The NSDAP voter turnout slipped below the national average in only the first and last elections (May 1924 and 1933). During the year of the rapid party growth and electoral surge, 1932, the turnout of NSDAP voters exceeded the national average by 8.5% and 10%, significantly boosting the party fortunes. The methods by which the NSDAP managed to activate its supporters are detailed in Brustein (1996), Grill (1983) and Hamilton (1982). In the turmoil of Weimar electoral politics, parties often matched an electoral strategy with promotion of street violence that targeted opponents’ electioneering. After 1930, paramilitary Nazi groups targeted supporters of opposing parties and prevented many from voting.

From previous research, it is clear that the key compositional predictor of the NSDAP vote in Weimar Germany is the Protestant ratio of the local population. After 1928, the NSDAP gained a large proportion of the support of the DNVP (Deutsche National Volkspartei – German National Peoples Party), a largely Protestant party in the north and east of the country whose vote was collapsing. The Catholics also had their own conservative party, the Zentrum (Center) party whose core support was in Bavaria. One of the main explanations of the rise to prominence of the NSDAP focuses on political confessionalism and the role of the religious loyalties in local communities that existed before the rise of a national electorate after 1945 (Passchier, 1980; Grill, 1986). The argument states that the NSDAP was relatively weak in Catholic areas because of the special nature of agricultural relations (the nature of inheritance) and social-cultural conflict about Catholic schools in the southern and western regions of the country that tied voters to the Zentrum party (Brustein, 1996; Stone, 1982; Heilbronner, 1998). Since the earliest work by Pollack (1944), the correlation of the NSDAP vote and the Protestant ratio has colored all subsequent studies.

EzI estimates indicate a 3.6% gain to the NSDAP from protestant voters in 1930, the breakthrough election for the party. By the July 1932 election, the advantage had risen to 9.0%. The advantage is calculated as the difference between the overall NSDAP vote ratio of 18.3% and the EzI estimate of Protestants voting for the NSDAP of 21.9%. In 1932, the respective figures were 37.4% and 46.4%. Data presented in table 2, however, suggest that German voting patterns were in fact quite complicated and that strong regional attachments remained. The comparisons to the national and regional means for the NSDAP clearly indicate the variegated nature of the core relationship.

Table 2: Regional Pattern of EzI Estimates for Protestant Ratio and NSDAP Vote 1930*

Region	Number of Cases	EzI Estimate	Protestant Ratio	NSDAP 1930 Ratio	Regional Gain/Loss	National Gain/Loss
Prussia	193	.216	.786	.214	+.002	+.033
Central Germany	144	.203	.829	.199	+.004	+.020
NorthWest Germany	74	.271	.837	.243	+.028	+.088
Rhineland	124	.211	.458	.155	+.056	+.028
Bavaria	150	.289	.270	.167	+.122	+.106
Baden-Württemburg	58	.174	.549	.152	+.022	-.009

*The mean national percentage for the NSDAP was 18.3% for a total number of cases of 743.

While caution is warranted for the estimates from Northwest Germany and Baden-Württemburg due to the small number of cases, the regional variation in the advantage to the NSDAP from the Protestant areas is large, from an advantage of only 0.2% in its core support region, Prussia, to 12.2% in Bavaria. In the two most Catholic regions (Bavaria and the Rhineland), Protestant support for the NSDAP was the strongest (regional advantage over the mean of 12.2% and 5.6% respectively). That the Protestant population’s support of the NSDAP was not uniformly similar across the country is undoubtedly connected to the tensions between the populations in mixed areas. For example, Heilbronner (1998) shows this for the Black Forest region of south-west Germany and Stone (1982) illustrates the same for Franconia (the northern part of Bavaria). In these mixed regions, the religiously based political parties acted as proponents of the confessional economic interests and politics took on a decidedly local, village-level, focus. Though the parties were competing nationally, the election can also properly be seen of thousands of local and regional contests for control. The Nazi party recognized this phenomenon in their appointment of Gauleiters (regional leaders), who in turn appointed local party organizers for the culturally defined divisions of the state (Freeman, 1995). Hitler’s speeches and the party flyers also tailored the Nazi party message to local circumstances (Brustein, 1996). As is evident from all the maps and statistics in this paper, the German electorate was highly disaggregated in a geographic manner, partly as a result of the splintered nature of the German Reich (only in existence for about 70 years), partly as a result of the strong culturally-defined effects that promoted distinct place-based uniqueness, and partly as a result of the electoral strategies of the parties.

The estimates for the 743 Kreisunits are derived from simulations, using a number of random samples from the distribution of values within the bounds of each Kreisunit that are set by the marginal totals of the cross-tabulations for each (King, 1997). The geographic distribution of these estimates for 1930 Weimar Germany are shown in Figure 3 (turnout of Nazi party voters in the 1930 election) and Figure 4 (support of Protestants for the NSDAP).

The comparative figure for the turnout of the Nazi party supporters is the estimated national mean of .811. Lowest values (below .75) are found in some of the regions of highest party support (eastern East Prussia, Oldenburg and Schleswig-Holstein) as well as in mostly Catholic or mixed religious regions in the West and South. Similarly, highest turnouts of Nazi party voters are in Lower Silesia (a Catholic coal-mining region), in Saxony and in Central Germany (Thuringia, Saxony). But, again the dominant feature of the map is its inchoate nature; Nazi party strength or weakness did not correspond to party turnout in any readily apparent way. The map of the estimates of the Protestant support for the NSDAP (Figure 4) is not cohesive; no macro-regional elements (and fewer localities) stand out in the map that highlights the extreme values. In the language of spatial analysis, this map has less spatial heterogeneity and more spatial dependence (O’Loughlin and Anselin, 1991). The mean value for Germany is .219; only scattered Kreisunits in northern Bavaria, East Prussia and Central Germany (mixed Protestant-Catholic regions) are evident as strongholds for Protestant support for the party. In contrast, in the Catholic areas of the Rhineland, Westphalia, and Württemburg, very low ratios of Protestants chose the NSDAP in the 1930 election.

Figure 3: EzI Estimates of the Turnout of NSDAP Voters, 1930

4 Global Indicators of Spatial Association

In spatial analysis, global summary measures of distributions are now as common as statistical distribution measures that are typically presented in the social sciences (Rogerson, 2000). The limitations of the usual mean and variance statistics are evident when a simple choropleth map of the distribution of the NSDAP vote shows regional clustering. Towards the goal of summarizing a geographic distribution, the Morans I measure is now most commonly presented, though there are alternative measures of spatial patterns (see Cliff and Ord, 1981; Bailey and Gatrell, 1995).

Figure 4: EzI Estimates of Protestant Support for the NSDAP, 1930

Morans I is derived from:

I = (N/S_o)