Hitler's Speeches and the Rise of the Nazi Party FIGURE 1.Timeline of Hitler's public appearances,March 1927 to March 1933. Elections(R=Reichstag,P=Presidential) 5 3 2 1927 1928 1929 1930 1931 1932 1933 Organizational strength.It has been argued that lo- ers.Therefore,we include the number of eligibles (in cal infrastructure is relevant to mobilization costs and 100,000)and the vote share of the NSDAP (for the that the strength of local party organization is one 1932 presidential election:Hitler)at the previous elec- important infrastructural factor.Apart from some re- tion (i.e.,the first election in each election pair con- 4号 gional studies (Anheier 2003;Anheier,Neidhardt,and sidered).Information on both variables is taken from Vortkamp 1998),there is no systematic nationwide in- Falter and Hanisch(1990). formation about the local organizational strength of Goebbels'appearances.To account for the eventual- 'asn the NSDAP and its development over time.As a proxy ity that Hitler's campaign schedule was complemented for organizational strength,we estimate county-level or duplicated by those of other high-profile Nazi speak- party membership totals (in 1,000)based on samples ers,we collect information about the public appear- drawn from the two original NSDAP member files ances of Joseph Goebbels,the second most important archived at the Berlin Document Center by teams of Nazi speaker after Hitler.We first conduct an auto- researchers in Berlin(Falter and Kater 1993)and Min. matic search of keywords12 using a digital version of his neapolis(Brustein 1998).The sample data include,in- diaries(Goebbels 1992)and then manually code infor- ter alia,information about the place and date that the mation on places,dates,and types of speeches(public members joined.The thorough description of the sam or private).Finally,we geocode the appearances using pling procedures in Schneider-Haase (1991)allow us the Google Maps API service.In total,we are able to to calculate appropriate design weights.Unfortunately, collect data on 200 public speeches,an overwhelming 575.1018 the researchers used fixed yearly quotas for entries in majority of which(110)were held in Berlin.Figure B2 the period 1930-1933 so that it is impossible to calcu- in the Appendix maps the locations of Goebbels' late time-varying weights.Auxiliary information about appearances. the annual development of national membership fig- Previous appearances.In addition to the matching ures from Kater (1980)is used to generate election- variables listed,we include a binary variable that in- specific estimates.Details of the estimation procedure dicates whether Hitler previously (i.e.,before the last and descriptive statistics are given in Appendix F. election)visited a county to help control for unob- Distance to nearest airfield.Of particular impor- served confounders,assuming that those factors had tance for mobilization costs as of 1932 is the distance already affected past targeting decisions.Election- to the nearest airfield.We consult several Wikipedia specific summary statistics of all the variables and sup- entries and a privately run website!to identify a total plementary maps are given in Appendix H. of 70 civilian airfields in operation at that time.We use the Google Maps API service to geocode the airfields, which provides the basis for calculating minimum dis- PREDICTING HITLER'S APPEARANCES tances to an airfield (in 100 km)for each municipal- In the first stage of our empirical analysis,we model ity,county borough,and county.Figure Gl in the Ap- the election-specific probability of a Hitler visit to a pendix maps the location of the airfields as well as the community or county as a function of the above covari- minimum distances. ates.Predictions from these models will then be used Number of eligibles and previous vote share.Our in the causal inference step to trim the sample to in- theoretical considerations suggest that rational cam- clude as controls only those counties and communities paigners primarily target locales with large numbers of eligibles and a high expected share of supportive vot- 12 The list of keywords includes(root)words related to speeches and rallies:sprech,gesprochen,rede,kundgebung,ansprache,veranstal- 11 http://www.forgottenairfields.com/. tung,vortrag. 1055
Hitler’s Speeches and the Rise of the Nazi Party FIGURE 1. Timeline of Hitler’s public appearances, March 1927 to March 1933. Organizational strength. It has been argued that local infrastructure is relevant to mobilization costs and that the strength of local party organization is one important infrastructural factor. Apart from some regional studies (Anheier 2003; Anheier, Neidhardt, and Vortkamp 1998), there is no systematic nationwide information about the local organizational strength of the NSDAP and its development over time. As a proxy for organizational strength, we estimate county-level party membership totals (in 1,000) based on samples drawn from the two original NSDAP member files archived at the Berlin Document Center by teams of researchers in Berlin (Falter and Kater 1993) and Minneapolis (Brustein 1998). The sample data include, inter alia, information about the place and date that the members joined. The thorough description of the sampling procedures in Schneider-Haase (1991) allow us to calculate appropriate design weights. Unfortunately, the researchers used fixed yearly quotas for entries in the period 1930–1933 so that it is impossible to calculate time-varying weights. Auxiliary information about the annual development of national membership figures from Kater (1980) is used to generate electionspecific estimates. Details of the estimation procedure and descriptive statistics are given in Appendix F. Distance to nearest airfield. Of particular importance for mobilization costs as of 1932 is the distance to the nearest airfield. We consult several Wikipedia entries and a privately run website11 to identify a total of 70 civilian airfields in operation at that time. We use the Google Maps API service to geocode the airfields, which provides the basis for calculating minimum distances to an airfield (in 100 km) for each municipality, county borough, and county. Figure G1 in the Appendix maps the location of the airfields as well as the minimum distances. Number of eligibles and previous vote share. Our theoretical considerations suggest that rational campaigners primarily target locales with large numbers of eligibles and a high expected share of supportive vot- 11 http://www.forgottenairfields.com/. ers. Therefore, we include the number of eligibles (in 100,000) and the vote share of the NSDAP (for the 1932 presidential election: Hitler) at the previous election (i.e., the first election in each election pair considered). Information on both variables is taken from Falter and Hänisch (1990). Goebbels’ appearances.To account for the eventuality that Hitler’s campaign schedule was complemented or duplicated by those of other high-profile Nazi speakers, we collect information about the public appearances of Joseph Goebbels, the second most important Nazi speaker after Hitler. We first conduct an automatic search of keywords12 using a digital version of his diaries (Goebbels 1992) and then manually code information on places, dates, and types of speeches (public or private). Finally, we geocode the appearances using the Google Maps API service. In total, we are able to collect data on 200 public speeches, an overwhelming majority of which (110) were held in Berlin. Figure B2 in the Appendix maps the locations of Goebbels’ appearances. Previous appearances. In addition to the matching variables listed, we include a binary variable that indicates whether Hitler previously (i.e., before the last election) visited a county to help control for unobserved confounders, assuming that those factors had already affected past targeting decisions. Electionspecific summary statistics of all the variables and supplementary maps are given in Appendix H. PREDICTING HITLER’S APPEARANCES In the first stage of our empirical analysis, we model the election-specific probability of a Hitler visit to a community or county as a function of the above covariates. Predictions from these models will then be used in the causal inference step to trim the sample to include as controls only those counties and communities 12 The list of keywords includes (root) words related to speeches and rallies: sprech, gesprochen, rede, kundgebung, ansprache, veranstaltung, vortrag. 1055 Downloaded from https://www.cambridge.org/core. Shanghai JiaoTong University, on 26 Oct 2018 at 03:56:49, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0003055418000424
Peter Selb and Simon Munzert TABLE 1. Probit estimates of Hitler appearances by election.Standard errors in parentheses. Sep 1930 Sep 1930(mun.) Apr1932(P) Jul 1932 Nov 1932 Mar 1933 Competitiveness 1 0.311 0.737* -0.653* -0.294 1.251* (0.291) (0.153) (0.386) (0.424) (0.748) Competitiveness 2 0.668* 0.770* 0.333* -0.256 0.094 (0.226) (0.125) (0.179) (0.209) (0.310) Organizational strength -0.704 -0.616* -0.166 -0.106 -0.257* -0.240 (0.513) (0.239) (0.260) (0.190) (0.155) (0.173) Distance to nearest airfield -0.323 0.050 -1.943* -0.334* -0.367 -3.574** (0.282) (0.164) (0.612) (0.190) (0.235) (0.780) Number of eligibles 0.695* 0.736* 0.196 1.200** 0.593* 0.5774 (0.164) (0.103) (0.216) (0.270) (0.214) (0.240) Previous NSDAP vote share 3.394 2.135* 0.579 0.989* 0.158 (2.146) (0.945) (0.847) (0.589) (1.088) Previous Hitler vote share -5.544* (1.823) Previous appearance 0.844** 0.886* 5.719 0.985* 0.967* 0.418 (0.185) (0.086) 240.510) (0.129) (0.129) (0.199) Goebbels appearance 1.077* 1.189* 7.513 0.708* 1.165* 1.618* (0.200) (0.112) (6.609.109) (0.207) (0.381) (0.293) (Intercept) -2.269* -2.715* -4.750 -1.161 -1.714** 2.337* (0.249) (0.140) (240.510) (0.328) (0.425) (0.750) Mc-Fadden's Pseudo R2 0.31 0.27 0.51 0.24 0.23 0.44 Observations 1.000 3.864 685 1.000 953 953 Log Likelihood 229.763 .712.633 63.317 398.459 252.176 118.828 Akaike Inf.Crit. 477.526 1.443.267 140.633 814.918 522.353 255.655 Note:"p<0.1;“p<0.05;**p<0.01 that are similar to exposure units in terms of their es- strength of local party organizations and the probabil- timated propensity score (while also being geographi- ity of a visit.An ad hoc interpretation of this finding cally distant enough from exposure units).The results would be that Hitler appearances were targeted at ar- 是 from the propensity score estimation are interesting in eas lagging behind in terms of organizational develop- their own right,too,since they provide rare system- ment to increase party membership(see Bytwerk 1981 atic insight into early Nazi campaign strategy.While 16).Unfortunately,the available samples from the NS- there are numerous studies on the organization of Nazi DAP member files are too small to detect local changes propaganda(e.g.Anheier 2003;Rosch 2002)and the in membership in the immediate aftermath of Hitler manipulative techniques employed(e.g.Anheier,Nei- appearances.What additional analyses show,however, dhardt,and Vortkamp 1998;Paul 1990),little is known is that local organizational development in the whole about the targeting of candidate appearances. legislative period following an election did not sys- Table 1 reports probit estimates and their standard tematically differ between exposure and nonexposure errors(clustered by primary electoral district)for each units.13.The significantly positive coefficient associated election separately.As one would expect,the size of the with Goebbels'appearances suggests that Goebbels' eligible voting population in a county turns out to be a campaign schedule tended to duplicate Hitler's.Thus,if consistent predictor of Hitler's campaign appearances we ignored Goebbels'activities,there would be a risk in across elections.Also in line with our expectations,the the subsequent analyses of erroneously ascribing cam- distance to the nearest airfield is a significant predictor paign effects to Hitler,whereas they actually trace back of Hitler visits as of the 1932 elections-Hitler's first to Goebbels.Finally,previous campaign appearances campaign trip by plane did not occur before April 3. prove to be useful predictors of current events,indicat- 1932(Bruppacher 2012,265).The exceptionally large ing that there are factors relevant to(past and current) coefficient on airfield distance referring to the 1933 targeting choices that are not appropriately taken into election is due to the extraordinarily intense regional account in our model specification.4 Nevertheless,the election in Lippe in January 1933 and its proximity to the airports of Bielefeld and Hannover-Vahrenwald (see Figure H2 in the Appendix).The campaign trail 13 See Table 139 in the Appendix. also tended to stop where the NSDAP did well in 14 The inflated SEs for"Previous appearances"and the intercept in the previous election-although the coefficients are,at the presidential election model are due to the fact that all 21 appear- most,weakly significant-and where the last election ances(32 units affected using a 10 km radius)took place in counties that had been exposed to earlier appearances.For this election,an was close from the party's viewpoint.The parameter earlier visit was-empirically speaking-a necessary but not suffi- estimates indicate a negative relationship between the cient condition for an appearance. 1056
Peter Selb and Simon Munzert TABLE 1. Probit estimates of Hitler appearances by election. Standard errors in parentheses. Sep 1930 Sep 1930 (mun.) Apr 1932 (P) Jul 1932 Nov 1932 Mar 1933 Competitiveness 1 0.311 0.737∗∗∗ − 0.653∗ − 0.294 1.251∗ (0.291) (0.153) (0.386) (0.424) (0.748) Competitiveness 2 0.668∗∗∗ 0.770∗∗∗ 0.333∗ − 0.256 0.094 (0.226) (0.125) (0.179) (0.209) (0.310) Organizational strength − 0.704 − 0.616∗∗∗ − 0.166 − 0.106 − 0.257∗ − 0.240 (0.513) (0.239) (0.260) (0.190) (0.155) (0.173) Distance to nearest airfield − 0.323 0.050 − 1.943∗∗∗ − 0.334∗ − 0.367 − 3.574∗∗∗ (0.282) (0.164) (0.612) (0.190) (0.235) (0.780) Number of eligibles 0.695∗∗∗ 0.736∗∗∗ 0.196 1.200∗∗∗ 0.593∗∗∗ 0.577∗∗ (0.164) (0.103) (0.216) (0.270) (0.214) (0.240) Previous NSDAP vote share 3.394 2.135∗∗ 0.579 0.989∗ 0.158 (2.146) (0.945) (0.847) (0.589) (1.088) Previous Hitler vote share − 5.544∗∗∗ (1.823) Previous appearance 0.844∗∗∗ 0.886∗∗∗ 5.719 0.985∗∗∗ 0.967∗∗∗ 0.418∗∗ (0.185) (0.086) (240.510) (0.129) (0.129) (0.199) Goebbels appearance 1.077∗∗∗ 1.189∗∗∗ 7.513 0.708∗∗∗ 1.165∗∗∗ 1.618∗∗∗ (0.200) (0.112) (6,609.109) (0.207) (0.381) (0.293) (Intercept) − 2.269∗∗∗ − 2.715∗∗∗ − 4.750 − 1.161∗∗∗ − 1.714∗∗∗ − 2.337∗∗∗ (0.249) (0.140) (240.510) (0.328) (0.425) (0.750) Mc-Fadden’s Pseudo R2 0.31 0.27 0.51 0.24 0.23 0.44 Observations 1,000 3,864 685 1,000 953 953 Log Likelihood − 229.763 − 712.633 − 63.317 − 398.459 − 252.176 − 118.828 Akaike Inf. Crit. 477.526 1,443.267 140.633 814.918 522.353 255.655 Note: *p<0.1; **p<0.05; ∗∗∗p<0.01 that are similar to exposure units in terms of their estimated propensity score (while also being geographically distant enough from exposure units). The results from the propensity score estimation are interesting in their own right, too, since they provide rare systematic insight into early Nazi campaign strategy. While there are numerous studies on the organization of Nazi propaganda (e.g. Anheier 2003; Rösch 2002) and the manipulative techniques employed (e.g. Anheier, Neidhardt, and Vortkamp 1998; Paul 1990), little is known about the targeting of candidate appearances. Table 1 reports probit estimates and their standard errors (clustered by primary electoral district) for each election separately.As one would expect, the size of the eligible voting population in a county turns out to be a consistent predictor of Hitler’s campaign appearances across elections. Also in line with our expectations, the distance to the nearest airfield is a significant predictor of Hitler visits as of the 1932 elections—Hitler’s first campaign trip by plane did not occur before April 3, 1932 (Bruppacher 2012, 265). The exceptionally large coefficient on airfield distance referring to the 1933 election is due to the extraordinarily intense regional election in Lippe in January 1933 and its proximity to the airports of Bielefeld and Hannover-Vahrenwald (see Figure H2 in the Appendix). The campaign trail also tended to stop where the NSDAP did well in the previous election—although the coefficients are, at most, weakly significant—and where the last election was close from the party’s viewpoint. The parameter estimates indicate a negative relationship between the strength of local party organizations and the probability of a visit. An ad hoc interpretation of this finding would be that Hitler appearances were targeted at areas lagging behind in terms of organizational development to increase party membership (see Bytwerk 1981, 16). Unfortunately, the available samples from the NSDAP member files are too small to detect local changes in membership in the immediate aftermath of Hitler appearances. What additional analyses show, however, is that local organizational development in the whole legislative period following an election did not systematically differ between exposure and nonexposure units.13. The significantly positive coefficient associated with Goebbels’ appearances suggests that Goebbels’ campaign schedule tended to duplicate Hitler’s.Thus,if we ignored Goebbels’ activities, there would be a risk in the subsequent analyses of erroneously ascribing campaign effects to Hitler, whereas they actually trace back to Goebbels. Finally, previous campaign appearances prove to be useful predictors of current events, indicating that there are factors relevant to (past and current) targeting choices that are not appropriately taken into account in our model specification.14 Nevertheless, the 13 See Table I39 in the Appendix. 14 The inflated SEs for “Previous appearances” and the intercept in the presidential election model are due to the fact that all 21 appearances (32 units affected using a 10 km radius) took place in counties that had been exposed to earlier appearances. For this election, an earlier visit was—empirically speaking—a necessary but not sufficient condition for an appearance. 1056 Downloaded from https://www.cambridge.org/core. Shanghai JiaoTong University, on 26 Oct 2018 at 03:56:49, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0003055418000424