Peter Selb and Simon Munzert readily contain the geographic information necessary maintained by a dutch amateur historian that provides to do so.?In an independent data collection effort,we a daily account of Adolf Hitler's life compiled from var- retrieve the center coordinates of the municipalities ious sources.In sum.we are able to identify 566 Hitler and county boroughs included in Falter and Hanisch appearances in the specified period,455 of which were (1990)from the Google Maps API service(Google Inc public.s Figure 1 provides a timeline of events for the 2015).Whole counties are georeferenced in terms of entire observation period.Apparently,Hitler's cam- the center coordinates of their main towns or cities to paign activities had already been intense in the period account for the uneven population distribution within between the 1928 and 1930 elections(84 appearances) counties.Details and links to computer code can be and culminated in the run-up to the 1932 presiden- found in Appendix A. tial and Reichstag elections(243 appearances).In early Outcome variables.The primary outcome of interest 1933,as Hitler had already been appointed head of gov- is the number of NSDAP votes in a Reichstag election ernment,the NSDAP's strategy quickly switched from in an areal unit relative to the voting-eligible electorate. winning votes to repression (Evans 2005).Of the mea- The reason for standardizing against the number of ger 36 events recorded between November 7,1932 and eligible-not actual-voters is that the specific appor- March 5,1933,as many as 16 related to the January 15, tionment method used in Reichstag elections guaran- 1933 regional elections in Lippe.10 teed each party one seat per 60,000 votes (see Ap- To assess the effect of Hitler's appearances on lo- pendix E).Therefore,an increase in the number o cal election returns,we would ideally know whether or votes from one election to the next would increase a not (or rather:to what extent)the local population for party's chances of winning additional seats irrespec- which we observe the outcome had been exposed to tive of the vote gains and losses for other parties.We the event.This information is simply not available.Like also use the vote share of the communist party (Kom- other spatial ecological studies,we use spatial prox- munistische Partei Deutschlands.KPD)as the Nazi's imity to the events as a surrogate for exposure.First major adversary and the turnout rate as alternative Hitler appearances are point-referenced according to 4 outcomes.For the 1932 presidential elections,we use their location(municipality or borough center,possi- vote shares for Adolf Hitler and Ernst Thalmann, bly even the specific venue)using the Google Maps the KPD's candidate.as outcomes of interest.The rel- API service.We then classify those counties and county ative size of the areal units'voting-eligible popula boroughs (municipalities in the municipal-level anal- tion serves as a weighting variable in the difference- ysis)whose center coordinates were situated within a in-differences analysis.Given its vast variability across radius of ten kilometers from an event location at any units-county-level electoral populations range be- time during an interelection period as exposure units tween 184 and 871,764,with a mean of 41,358 Alternative specifications of the exposure variable with eligibles-appropriate weights are imperative for our varying geographical and temporal scope will be con- analysis(Ridgeway et al.2015).All election data are sidered in the section on robustness taken from Falter and Hanisch(1990) Competitiveness.The rational-actor perspective Exposure.Our main source of information about suggests that Hitler and the NSDAP focused their Hitler's public appearances is the multivolume edi- scarce campaign resources on close races-that is, tion"Hitler.Reden,Schriften,Anordnungen"(Hitler. on electoral districts where the stakes of winning 5795.801g Speeches,Writings,Directives)by the Institute for additional seats and of losing seats won in previous Contemporary History Munich (Hitler 1992,1994a, elections were high.To measure the NSDAP's loca 1994b,1994c,1995,1996,1997,1998).For each appear-- electoral stakes,we follow lines similar to Grofman ance within the specified period,we hand code its date and Selb (2009)and develop party-specific com- location,type of event(public or private)and,if avail- petition indices that are sensitive to the nature of able,attendance figures by source(police reports,press the automatic apportionment method used at that coverage,Nazi press coverage).As the edition only time (Schanbacher 1982).The indices are specified covers the period until Hitler's appointment as head at the level of the primary and secondary electoral of the national government on January 30,1933,we districts(Competitiveness 1 and 2,respectively).They consult Bruppacher(2012)to fill the gap until the reflect the minimum vote distance of the NSDAP Reichstag election on March 5,1933.For validation to winning an additional seat or to losing their final we use Bruppacher(2012)and Domarus (1973)and seat at the respective level at the previous election retrieve information from hitlerpages.com,a website relative to the highest possible vote distance to a seat gain or loss.An index value of one indicates maximum competitiveness,a value of zero indicates 7 O'Loughlin and colleagues(O'Loughlin,Flint,and Anselin 1994: the theoretical minimum.Details of the calculations O'Loughlin and Shin 1995)digitalize county boundaries for the 1930 Reichstag election based on U.S.military maps from World War II and descriptive statistics are given in Appendix E. However,this map does not contain geographic information about most county boroughs and about none of the communities.The for- L mer would lead to the exclusion of a substantial share of the voting eligible population from the analysis.The latter would prevent us s Most of the events we classified as nonpublic were speeches on the from disaggregating our study to the municipality level.Paul Thurner occasion of meetings with party officials. and his colleagues (2015)made a fresh geocoding effort,including A detailed description of the coding procedure and additional fig all the counties and county boroughs.For the last-mentioned reason. ures can be found in Appendix B. however,we do not use their materials either. 10 See the clustering of events in Figure B1 in the Appendix. 1054Peter Selb and Simon Munzert readily contain the geographic information necessary to do so.7 In an independent data collection effort, we retrieve the center coordinates of the municipalities and county boroughs included in Falter and Hänisch (1990) from the Google Maps API service (Google Inc. 2015). Whole counties are georeferenced in terms of the center coordinates of their main towns or cities to account for the uneven population distribution within counties. Details and links to computer code can be found in Appendix A. Outcome variables. The primary outcome of interest is the number of NSDAP votes in a Reichstag election in an areal unit relative to the voting-eligible electorate. The reason for standardizing against the number of eligible—not actual—voters is that the specific appor￾tionment method used in Reichstag elections guaran￾teed each party one seat per 60,000 votes (see Ap￾pendix E). Therefore, an increase in the number of votes from one election to the next would increase a party’s chances of winning additional seats irrespec￾tive of the vote gains and losses for other parties. We also use the vote share of the communist party (Kom￾munistische Partei Deutschlands, KPD) as the Nazi’s major adversary and the turnout rate as alternative outcomes. For the 1932 presidential elections, we use vote shares for Adolf Hitler and Ernst Thälmann, the KPD’s candidate, as outcomes of interest. The rel￾ative size of the areal units’ voting-eligible popula￾tion serves as a weighting variable in the difference￾in-differences analysis. Given its vast variability across units—county-level electoral populations range be￾tween 184 and 871,764, with a mean of 41,358 eligibles—appropriate weights are imperative for our analysis (Ridgeway et al. 2015). All election data are taken from Falter and Hänisch (1990). Exposure. Our main source of information about Hitler’s public appearances is the multivolume edi￾tion “Hitler. Reden, Schriften, Anordnungen” (Hitler. Speeches, Writings, Directives) by the Institute for Contemporary History Munich (Hitler 1992, 1994a, 1994b, 1994c, 1995, 1996, 1997, 1998). For each appear￾ance within the specified period, we hand code its date, location, type of event (public or private) and, if avail￾able, attendance figures by source (police reports, press coverage, Nazi press coverage). As the edition only covers the period until Hitler’s appointment as head of the national government on January 30, 1933, we consult Bruppacher (2012) to fill the gap until the Reichstag election on March 5, 1933. For validation we use Bruppacher (2012) and Domarus (1973) and retrieve information from hitlerpages.com, a website 7 O’Loughlin and colleagues (O’Loughlin, Flint, and Anselin 1994; O’Loughlin and Shin 1995) digitalize county boundaries for the 1930 Reichstag election based on U.S. military maps from World War II. However, this map does not contain geographic information about most county boroughs and about none of the communities. The for￾mer would lead to the exclusion of a substantial share of the voting eligible population from the analysis. The latter would prevent us from disaggregating our study to the municipality level. Paul Thurner and his colleagues (2015) made a fresh geocoding effort, including all the counties and county boroughs. For the last-mentioned reason, however, we do not use their materials either. maintained by a Dutch amateur historian that provides a daily account of Adolf Hitler’s life compiled from var￾ious sources. In sum, we are able to identify 566 Hitler appearances in the specified period, 455 of which were public.8 Figure 1 provides a timeline of events for the entire observation period.9 Apparently, Hitler’s cam￾paign activities had already been intense in the period between the 1928 and 1930 elections (84 appearances) and culminated in the run-up to the 1932 presiden￾tial and Reichstag elections (243 appearances). In early 1933, as Hitler had already been appointed head of gov￾ernment, the NSDAP’s strategy quickly switched from winning votes to repression (Evans 2005). Of the mea￾ger 36 events recorded between November 7, 1932 and March 5, 1933, as many as 16 related to the January 15, 1933 regional elections in Lippe.10 To assess the effect of Hitler’s appearances on lo￾cal election returns, we would ideally know whether or not (or rather: to what extent) the local population for which we observe the outcome had been exposed to the event.This information is simply not available.Like other spatial ecological studies, we use spatial prox￾imity to the events as a surrogate for exposure. First, Hitler appearances are point-referenced according to their location (municipality or borough center, possi￾bly even the specific venue) using the Google Maps API service.We then classify those counties and county boroughs (municipalities in the municipal-level anal￾ysis) whose center coordinates were situated within a radius of ten kilometers from an event location at any time during an interelection period as exposure units. Alternative specifications of the exposure variable with varying geographical and temporal scope will be con￾sidered in the section on robustness. Competitiveness. The rational-actor perspective suggests that Hitler and the NSDAP focused their scarce campaign resources on close races—that is, on electoral districts where the stakes of winning additional seats and of losing seats won in previous elections were high. To measure the NSDAP’s local electoral stakes, we follow lines similar to Grofman and Selb (2009) and develop party-specific com￾petition indices that are sensitive to the nature of the automatic apportionment method used at that time (Schanbacher 1982). The indices are specified at the level of the primary and secondary electoral districts (Competitiveness 1 and 2, respectively). They reflect the minimum vote distance of the NSDAP to winning an additional seat or to losing their final seat at the respective level at the previous election relative to the highest possible vote distance to a seat gain or loss. An index value of one indicates maximum competitiveness, a value of zero indicates the theoretical minimum. Details of the calculations and descriptive statistics are given in Appendix E. 8 Most of the events we classified as nonpublic were speeches on the occasion of meetings with party officials. 9 A detailed description of the coding procedure and additional fig￾ures can be found in Appendix B. 10 See the clustering of events in Figure B1 in the Appendix. 1054 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