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. 1055Hitler’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 lo￾cal infrastructure is relevant to mobilization costs and that the strength of local party organization is one important infrastructural factor. Apart from some re￾gional studies (Anheier 2003; Anheier, Neidhardt, and Vortkamp 1998), there is no systematic nationwide in￾formation 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 Min￾neapolis (Brustein 1998). The sample data include, in￾ter alia, information about the place and date that the members joined. The thorough description of the sam￾pling 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 calcu￾late time-varying weights. Auxiliary information about the annual development of national membership fig￾ures from Kater (1980) is used to generate election￾specific estimates. Details of the estimation procedure and descriptive statistics are given in Appendix F. Distance to nearest airfield. Of particular impor￾tance 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 dis￾tances to an airfield (in 100 km) for each municipal￾ity, county borough, and county. Figure G1 in the Ap￾pendix 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 cam￾paigners 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 elec￾tion (i.e., the first election in each election pair con￾sidered). Information on both variables is taken from Falter and Hänisch (1990). Goebbels’ appearances.To account for the eventual￾ity that Hitler’s campaign schedule was complemented or duplicated by those of other high-profile Nazi speak￾ers, we collect information about the public appear￾ances of Joseph Goebbels, the second most important Nazi speaker after Hitler. We first conduct an auto￾matic search of keywords12 using a digital version of his diaries (Goebbels 1992) and then manually code infor￾mation 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 in￾dicates whether Hitler previously (i.e., before the last election) visited a county to help control for unob￾served confounders, assuming that those factors had already affected past targeting decisions. Election￾specific summary statistics of all the variables and sup￾plementary 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 covari￾ates. Predictions from these models will then be used in the causal inference step to trim the sample to in￾clude 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, veranstal￾tung, 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