A spatial analysis of the impact of West German television on protest mobilization during the East German revolution

Abstract

Formal models of revolutionary collective action suggest that ‘informational cascades’ play a crucial role in overcoming collective action problems. These models highlight how information about the aggregate level of participation in collective action conveys information about others’ political preferences, and how such informational cues allow potential participants to update their beliefs about the value of participating in antiregime collective action. In authoritarian regimes, foreign mass media are often the only credible source of information about antiregime protests. However, limited robust evidence exists on whether foreign media can indeed serve as a coordination device for collective action. This article makes use of a detailed dataset on protest events during the 1989 East German revolution and exploits the fact that West German television broadcasts could be received in most but not all parts of East Germany. Across a wide range of Cox proportional hazards models and conditional on a rich set of observables, it finds that the availability of West German television did not affect the probability of protest events occurring. The evidence presented here does not support the widely accepted ‘fact’ that West German television served as a coordination device for antiregime protests during the East German revolution. More broadly, it also calls into question strong claims about the effects of communication technology on revolutionary collective action.

Keywords

authoritarian regimes collective action democratization East Germany mass media natural experiment

Introduction

Overthrowing an authoritarian regime is incredibly difficult. It requires ordinary citizens to overcome collective action problems and to coordinate their antiregime behavior (Lichbach, 1995). Such coordination is complicated both by the prevalence of preference falsification (Kuran, 1989, 1991), which makes it difficult to accurately gauge the strength of antiregime sentiments among the citizenry, and the absence of independent domestic media in authoritarian regimes. The theoretical literature thus stresses the importance of foreign media in transitions to democracy, in particular during the third wave of democratization (Huntington, 1991: 100–106; Schmitter, 1996: 34–35; Whitehead, 1996: 5–8). However, the state of empirical research in this area can still be described as inchoate. Only a handful of studies have empirically examined the question of whether foreign mass media can indeed facilitate revolutionary collective action. Our article contributes to this literature, and the literature on media effects in authoritarian regimes more generally, by examining the role played by West German television (WGTV) during the East German revolution.¹ Because of its historical significance and the unparalleled wealth of primary sources that has become available after German reunification, the East German revolution is a prominent case in the literature on social movements and revolutionary collective action (e.g. Hirschman, 1993; Lohmann, 1994; Pfaff & Kim, 2003; Pfaff, 2006). According to this literature, WGTV played a key role during the East German revolution by broadcasting news about the escalating political crisis directly into East German living rooms. The literature suggests that by spreading knowledge of successful protests and the unexpected vulnerability of the East German dictatorship, WGTV was able to alter East Germans’ perceptions of political opportunity and to facilitate the activation and diffusion of protest (Kuran, 1991: 37; Opp, Voss & Gern, 1993: 254–255; Opp & Gern, 1993: 675–676; Hirschman, 1993: 198; Jarausch, 1994: 44; Grix, 2000: 32–33).

In this article, we evaluate this widely held claim that WGTV served as a coordination device for protest activities during the East German revolution. Our research design is based on a natural experiment, as we make use of the fact that WGTV broadcasts could be received in most but not all parts of East Germany (Kern & Hainmueller, 2009: 380–382). Relying on a detailed micro-level dataset of more than 2,700 protest events that took place between September 1989 and March 1990, we use Cox proportional hazards models to model the risk of protest events at the county-level conditional on the availability of WGTV and an unusually rich set of covariates. In the next section, we begin by providing some historical background on the East German revolution. We then review the literature on mass media and revolutionary collective action. Following that, we introduce our research design and statistical model. We then present and discuss our results. The last section concludes.

The 1989 East German revolution

Until its collapse in 1989, the German Democratic Republic (GDR) was seen as one of the most stable communist regimes in Eastern Europe. Western observers were impressed with East Germany’s economic performance and regarded it as a socialist success story (Kopstein, 1997: 1–13). The Socialist Unity Party of Germany (Sozialistische Einheitspartei Deutschlands, SED) ruled East Germany through an implicit social contract that rewarded political acquiescence with steadily improving living standards (Pollack, 2000: 35–40; Dale, 2005: 59–81). As the 1980s progressed, however, the East German regime became increasingly incapable of buying off its people. During the 1970s, East German economic growth had been spurred by easy access to international credit and economic aid from the Soviet Union. The 1970s oil shocks, rising interest rates, and reduced Soviet subsidies revealed the structural weaknesses of East Germany’s centrally planned economy. With its focus on heavy industry and the extensive use of factor inputs, the East German economy was ill equipped to participate in the technological revolution that was propelling growth in capitalist economies (Stiglitz, 1994). By the late 1980s, stagnation had become evident (Kopstein, 1997). Even though nominal wages continued to rise, inflation, frequent consumer goods shortages, and the lack of adequate housing left many East Germans with the impression that their living standards were stagnating, if not declining (Schneider, 1996). The gap between West and East Germany had never been as plainly visible as in the late 1980s, as the availability of WGTV and visits to and from the West facilitated direct comparisons of living standards (Dale, 2005: 82–97).

Economic discontent was not the only reason for the communist regime’s lack of popular support. The appalling state of the environment also undermined its legitimacy. The East German economy was geared towards maximizing output regardless of the ecological consequences. East Germany’s large chemical industry and the use of brown coal for electrical power production, compounded by the lack of effective pollution abatement technology, led to dramatic ecological damages. East Germany’s emission rates of pollutants such as particulate matter, sulfur dioxide, and nitrogen oxides were among the highest in Europe. Indeed, per capita sulfur dioxide and particulate matter emissions were more than 15 times as high as in West Germany (Kuhrt, Buck & Holzweissig, 1996).

As in other communist countries, political dissent was not tolerated in East Germany. Lacking the opportunity to voice their concerns, many dissatisfied East Germans left their country, either legally after they had received exit visas (after a waiting period that could last up to ten years) or illegally. Dissidents who remained could only find support within the confines of the Protestant church. A number of clergymen supported small groups of political activists concerned with issues of peace, sustainable development, the environment, and human rights by providing them with access to modest resources such as telephones, copying machines, and meeting space. Some activists dismissed from their state jobs also found employment with the church. In the late 1980s, a tiny East German samizdat press emerged that attempted to create a public sphere independent of the regime (Neubert, 1998; Pollack, 2000: 197–200; Dale, 2005: 103–107).

The majority of East Germans, however, found neither the political program nor the lifestyle of these political activists attractive. Most East Germans valued higher living standards and the freedom to travel to the West more than the ‘socialism with a human face’ that dissidents attempted to bring about (Pollack, 2000: 205–208; Dale, 2005: 98–119). Opposition groups remained on the fringes of popular political activity, neither organizing nor playing a key role in the protests that erupted in September and October 1989 (Opp, Voss & Gern, 1993; Opp & Gern, 1993). The first ‘Monday demonstrations’ in Leipzig were predominantly led by East Germans who had applied for exit visas and were hoping that public protest would make them enough of a nuisance to the regime to be allowed to leave the country (Pollack, 2000: 209–234).

In contrast to Hungary and Poland, no significant impetus for reform arose from within the ruling party. Following Mikhail Gorbachev’s accession to the leadership of the Soviet Communist Party in 1985, relations between the GDR and the Soviet Union swiftly soured, with East Germany distancing itself from Gorbachev’s reform program. Most East Germans in contrast welcomed Gorbachev’s reforms and hoped for similar changes in East Germany (Süss, 1996).

The East German regime’s situation became increasingly difficult when Hungary and Poland both effectively left the Soviet bloc at the beginning of 1989. In January, non-communist parties were legalized in Hungary. In February, round-table talks began in Poland and in June, the Polish Communist Party relinquished its hold on power. In May, Hungary started to dismantle its border fortifications with Austria. Throughout the summer, thousands of East Germans vacationing in Hungary made use of what seemed like a once-in-a-lifetime opportunity to escape to the West. Others occupied West German embassies in Prague, Warsaw, and Budapest to force their emigration to West Germany. The East German regime eventually relented and allowed all East Germans occupying West German embassies to leave for West Germany (Zelikow & Rice, 1995: 63–101). The regime’s demand that the sealed trains carrying them to West Germany had to cross East German territory so that they could be formally stripped off their citizenship and expelled from East Germany backfired, however. Many East Germans learned about this compromise solution from WGTV. Along the train route, large-scale riots broke out between the police and desperate East Germans who attempted to board the trains. The East German leadership, focused on the celebration of the 40th anniversary of the GDR on 7 October, had seriously misread the public mood. While East German media remained silent about the riots, WGTV broadcast dramatic footage of the arrival of thousands of East German emigrees in West Germany. This mass exodus and the way it was mismanaged by the East German regime significantly fueled public protests (Hirschman, 1993: 186–193; Pfaff & Kim, 2003: 415–416).

Mass media and revolutionary collective action

According to relative deprivation theory (Gurr, 1970), people rebel when they perceive a discrepancy between their material expectations and the regime’s ability to ensure the living standards to which they feel entitled. Such a gap undoubtedly existed for most East Germans, who coveted Western consumer goods and wished for the freedom to travel to the exotic places shown on WGTV.² Yet relative deprivation can hardly account for the outbreak of the East German revolution (or any of the East European revolutions). After all, living conditions in West Germany had been significantly better than in East Germany throughout the existence of the two German states, yet mass protests did not break out until summer 1989 (with the exception of the June 1953 uprising, which was quickly suppressed by Soviet troops and tanks).³

The theory of political opportunity structure (Tarrow, 1998) is based on the notion that people do not rebel when they are most discontent, but when a formerly closed system of political opportunities for change opens up. This was undoubtedly the case in East Germany. Gorbachev’s domestic reforms sowed discontent among East Germans when the East German regime refused to implement similar reforms in East Germany. More importantly, the reforms also changed East Germans’ perceptions of the Soviet commitment to guarantee the survival of the East German regime against domestic challenges. The failed 1953 uprising had taught East Germans that regime change would not be possible against the wishes of the Soviet Union. Yet Gorbachev’s reforms and his remark during a WGTV interview in East Berlin on 6 October that ‘Those who are late will be punished by life itself’ were interpreted by East Germans as at least tacit approval of political reform in East Germany.⁴ Yet while political opportunity theory correctly highlights the (external) conditions that were necessary for the overthrow of the East German regime, it does not explain the process of micro-level mobilization of antiregime protest.

The literature on bandwagon processes and behavioral cascades explicitly addresses the micro-level problem of protest participation by acknowledging that the decision of one individual to join collective action depends upon the decisions of others (Granovetter, 1978; Schelling, 1978). Kuran (1989, 1991) develops a model for participation in revolutionary collective action. He posits that every individual has two preferences over regime type: a private preference and a public preference. The private preference corresponds to one’s true preference, whereas the public preference is the preference one chooses to publicly reveal. Preference falsification denotes the situation in which private and public preferences diverge. Whether an individual joins the opposition depends on the trade-off between two payoffs, one external and the other one internal. The external payoff captures the incentives (e.g. future personal rewards if the opposition is successful) and disincentives (e.g. risking one’s life) for publicly declaring one’s support for the opposition movement. The larger the existing opposition movement, the smaller are the disincentives for participation, since the likelihood of punishment for siding with the opposition is decreasing in the size of the opposition. If many people participate in antiregime protests, the regime will be unable to punish most of them. If only a few protest, in contrast, the regime will be able to severely punish all of them. The internal payoff is rooted in the psychological costs of preference falsification stemming from loss of personal autonomy and sacrifice of personal integrity. The larger the divergence between an individual’s public and private preferences, the larger the psychological costs of ‘living a lie’ (Havel, 1985: 20).

Now assume that someone is opposed to the regime. Holding his public and private preferences constant, as the opposition movement grows, there comes a point at which the psychological costs of publicly supporting a secretly despised status quo outweigh the costs of joining the opposition, and so the individual joins the opposition movement. This point is called his revolutionary threshold: the opposition size required to change his behavior from feigned support for the status quo to open support for the opposition. A ‘revolutionary bandwagon’ ensues when someone joins the opposition, and that leads others to join the opposition as well. Because of such revolutionary bandwagons, authoritarian regimes that once appeared unshakeable can see their support crumble in no time. Whether such a cascade occurs depends on the unobservable distribution of revolutionary thresholds, which is why revolutions are easy to explain ex post but impossible to predict ex ante (Kuran, 1989, 1991).

Lohmann (1993, 1994) offers a slightly different model in which turnout at protest events among political moderates provides information about others’ political preferences. Both Kuran (1991) and Lohmann (1994) use their respective models to shed light on protest mobilization during the East German revolution, stressing how protest participation depended on credible information about others’ political preferences. Neither their formal models nor empirical analyses, however, contain detailed discussions of the channels through which information about the size of the opposition movement (Kuran, 1989, 1991) or aggregate turnout of moderates in protest events (Lohmann, 1993, 1994) is transmitted. The widely cited article by Lohmann (1994), for example, succeeds admirably in describing how the Monday demonstrations in Leipzig helped to change public perceptions of the vulnerability of the East German regime. It also notes that mass demonstrations in Leipzig ‘triggered a wave of political protest throughout the GDR’ (Lohmann, 1994: 42). Her account, however, does not explain how East Germans learned about protest events that the vast majority of them could not directly observe. This omission is particularly critical in the context of regimes with heavily censored, state-owned mass media such as East Germany.

A number of authors have stressed WGTV’s role during the East German revolution. Kuran (1991: 37) for example writes with respect to the demonstrations that took place in East Berlin during the celebrations of the 40th anniversary of the GDR that WGTV ‘immediately played these events back to the rest of East Germany. The scenes alerted disgruntled citizens in every corner of the country to the pervasiveness of discontent, while the government’s weak response revealed its vulnerability.’ Opp & Gern (1993: 675–676) note that ‘the [Leipzig] Monday prayers, together with the demonstrations, contributed to the emergence of protest in other East German cities. People were informed, primarily on WGTV, about the events in Leipzig, and the expectation formed that citizens in each city would meet spontaneously on the city square for Monday demonstrations.’ Jarausch (1994: 44) similarly notes that ‘Western television coverage enabled acts of symbolic defiance to reach a wider audience, spreading unrest.’ With regard to the exiting crisis, Hirschman (1993: 198) writes that ‘pictures of the exodus soon flooded the TV screens, with the result of not just causing established critics […] to sharpen their criticism but also of making activists out of long-passive average citizens’. Opp, Voss & Gern (1993: 254–255, 260) and Grix (2000: 32–33) similarly stress WGTV’s role in disseminating political information not available from the state-controlled East German media.

In the social movements literature, empirical research on mass media as a facilitator of collective action has largely been limited to the impact of domestic mass media in democratic societies (e.g. Andrews & Biggs, 2006; Roscigno & Danaher, 2001; Myers, 2000). Work on the impact of domestic mass media in authoritarian settings in contrast is in its infancy. Yanagizawa-Drott (2014) finds that radio broadcasts served as a coordination device for collective violence during the Rwandan genocide. Enikolopov, Petrova & Zhuravskaya (2011), in an article on media effects in Russia, demonstrate the strong impact of the only national television channel independent of the government on voting for opposition parties during the 1999 parliamentary elections. Adena et al. (2013) document the role that radio propaganda played in undermining the democratic institutions of the Weimar Republic and the rise of nazism in pre-WWII Germany.

Recent research has also examined the role of horizontal, ‘social’ communication technologies such as cell phones, Facebook, and Twitter as opposed to older vertical communication technologies (i.e. newspapers, radio, and television) (Warren, 2014, 2015). Shapiro & Weidmann (2015), for example, show that the availability of cell phone networks reduced collective violence in Iraq between 2004 and 2009 by lowering the transaction costs of cooperating with the government. Pierskalla & Hollenbach (2013) in contrast present cross-national evidence for 27 African countries that suggests that the availability of cell phone technology facilitates violent collective action.

The recent literature on the Arab Spring and the Color Revolutions likewise discusses the impact of social communication technologies, although studies often fall short of offering credible causal identification strategies. Some scholars claim that social media either cause (Shirky, 2011) or play a role in causing revolutions and transitions to democracy (Howard, 2010; Hussain & Howard, 2013). Others avoid explicit causal claims but argue that social media are powerful tools that the opposition can use to disseminate information, circumvent censorship, and organize and coordinate collective action (Chebib & Sohail, 2011; Eltantawy & Wiest, 2011; Khamis & Vaughn, 2011; Goldstein, 2007; Lynch, 2011, 2012: 10–11).

Most directly related to our research are articles by Kern (2011) and Grdĕsić (2014) that both look at WGTV’s role as a coordination device for protest activities during the East German revolution. Kern (2011) compares counties without WGTV to a matched comparison group of counties with WGTV, but does not find any evidence that WGTV affected the speed or depth of protest diffusion. Grdĕsić (2014) argues, based on vector autoregressions and Granger causality tests, that WGTV news reports about East German protests ‘Granger caused’ protests in East Germany the following day.

Research design

Our research design takes advantage of the fact that WGTV broadcasts could be received in most but not all parts of East Germany. Specifically the northeastern part of East Germany and the Dresden district in the southeast were by and large cut off from WGTV broadcasts due to East Germany’s topography and their distance from West German broadcast transmission towers. Historical maps of WGTV’s over-the-air signal strength used in previous research (Kern & Hainmueller, 2009: Figures 1 and 3) allow us to distinguish between broad areas of East Germany with and without WGTV. However, these maps have a serious drawback in that they are not detailed enough to allow us to reliably determine the availability of WGTV at the county level. We therefore use the Longley-Rice electromagnetic signal propagation model in conjunction with terrain data and data on the location and technical characteristics of WGTV broadcast transmitters to model WGTV’s signal strength across East Germany (see Figure 1).⁵

Figure 1.

Signal strength of WGTV in East Germany as predicted by Longley-Rice model

We then discretize this continuous measure of WGTV signal strength to distinguish between East German counties with and without WGTV. We set the threshold value as the modeled average signal strength in the center of the city of Dresden. For a county to have access to WGTV, we require that at least 50% of the county receives a signal equal to or above that threshold (see Figure 2). While the map in Figure 2 closely reproduces the overall pattern of the historical maps shown in Kern & Hainmueller (2009), our approach classifies a number of counties differently than Kern (2011). In the online appendix, we show that our results are unaffected when we use Kern’s (2011) classification instead. Moreover, there we also show that our results are entirely unaffected by the exact signal strength threshold

Figure 2.

East German counties with and without WGTV based on Longley-Rice model and Dresden cutoff level

used to separate counties with WGTV from counties without WGTV.

Our identifying assumption is that access to WGTV was idiosyncratic conditional on covariates. In other words, we assume that whether a county had WGTV or not was uncorrelated with unobserved county characteristics related to the risk of protest, conditional on observed characteristics. Kern (2011) already noted that counties with and without WGTV differed in terms of a number of covariates, several of which are plausibly related to the risk of protest.

Table I shows balance statistics, comparing counties with WGTV to counties without WGTV. The first set of covariates captures broad socio-economic differences between counties in terms of log(population size), population density, sector shares, the shares of skilled and unskilled labor, and the proportion of the population that holds a college degree, is female, and of working age. A district capital dummy denotes the capitals of the 15 East German districts (Bezirke), which constituted the urban centers of East Germany and might therefore have been more prone

Figure 3.

Number of protests per county, East Germany, 4 September 1989–18 March 1990

to see antiregime protests. Other covariates capture relative deprivation in dimensions that were particularly salient in East Germany (Kuhrt, Buck & Holzweissig, 1996): housing quality (housing space per capita and the proportion of apartments with bathrooms, interior toilets, and modern heating), the provision of public goods (residents per medical doctor and residents per dentist), and air pollution (nitrogen oxides, sulfur dioxide, and particulate matter emissions). We also measure the percentage population change over the preceding decade, which we regard as a rough proxy for the overall desirability of living in a county.⁶

While the most important aspect of political opportunity structure, the change in Soviet policy with regards to its Eastern European satellites, was constant across East Germany,

Table I.

Balance

Covariate	WGTV	Non-WGTV	Standardized	Variance	t-test	KS-test
	mean	mean	difference	ratio	p-value	p-value
Log(population size)	11.008	10.989	0.030	0.704	0.896	0.670
Population density	350.236	474.475	–0.149	1.128	0.478	0.517
$%$ industry	0.357	0.314	0.302	1.146	0.154	0.127
$%$ agriculture	0.174	0.191	–0.112	0.557	0.636	0.652
$%$ crafts and construction	0.094	0.097	–0.118	1.044	0.578	0.567
$%$ services and transportation	0.357	0.379	–0.274	0.770	0.228	0.278
$%$ skilled	0.625	0.613	0.412	0.824	0.071	0.307
$%$ unskilled	0.130	0.135	–0.248	1.346	0.224	0.273
$%$ college	0.201	0.206	–0.167	0.717	0.465	0.599
$%$ female	0.519	0.515	0.523	0.636	0.031	0.003
$%$ working age	0.642	0.641	0.054	0.510	0.812	0.069
Housing space (m²)	27.342	26.152	0.568	0.415	0.028	0.000
$%$ bathroom	0.819	0.795	0.323	0.574	0.178	0.029
$%$ interior toilet	0.739	0.730	0.057	0.831	0.799	0.824
$%$ modern heating	0.427	0.422	0.031	0.487	0.898	0.075
Residents per medical doctor	596.342	616.240	–0.100	1.053	0.637	0.082
Residents per dentist	1,549.489	1,573.600	–0.076	0.459	0.754	0.540
Nitrogen oxides (tons/km²)	6.235	7.630	–0.089	0.925	0.683	0.569
Sulfur dioxide (tons/km²)	81.554	86.248	–0.024	1.327	0.905	0.327
Respirable dust (tons/km²)	33.095	34.122	–0.016	2.227	0.929	0.542
District capital	0.068	0.080	–0.046	0.828	0.834	0.835
% change in population size 1980–88	–0.017	–0.013	–0.073	0.500	0.762	0.680
June 1953 protests	0.189	0.299	–0.304	0.487	0.215	0.753
Exit visas (per 1,000 residents)	3.269	3.274	–0.002	0.628	0.993	0.076

The table shows covariate balance for 217 East German counties with and without WGTV. The first two columns show means, the third column shows differences in means standardized by the square root of the average variance, the fourth column shows variances in the WGTV group divided by variances in the non-WGTV group, the fifth column shows p-values from two-sample t-tests, and the last column shows p-values from bootstrapped Kolmogorov-Smirnoff tests of equality of distributions.

we also include two covariates that capture county-level variation in social capital and social embeddedness. Informed by work on the long-term persistence of patterns of cooperation and social capital (Putnam, 1993; Guiso, Sapienza & Zingales, 2008), we control for historical antiregime collective action during the June 1953 East German uprising. We regard this covariate as an (admittedly imperfect) proxy for social capital in 1989, although it might also capture time-invariant grievances not captured by our socio-economic and relative deprivation measures. The June 1953 protest data are taken from Kowalczuk (2003) and represent the fraction of municipalities in a county that experienced protests, strikes, or attacks on representatives of the East German regime during the uprising. Finally, we also control for emigration levels just before the East German revolution (January–August 1989) using data from Grundmann (1997) to address the argument advanced by Pfaff & Kim (2003) that there was a curvilinear relationship between exit (emigration) and voice (protest) (see also Hirschman, 1993).

The first two columns of Table I show covariate means for counties with and without WGTV, the third column shows differences between covariate means standardized by the square root of the average variance, the fourth column shows p-values from two-sample t-tests and the last column shows p-values from bootstrapped Kolmogorov-Smirnoff tests of equality of distributions. For several of these covariates we see larger differences between the two groups of counties than one would expect by chance alone. Clearly, our natural experiment, while providing us with variation in access to WGTV, only imperfectly approximates a randomized experiment. Our analyses will adjust for these systematic differences.

Protest event data are taken from Schwabe (1999), which provides a detailed country-wide compilation of 2,734 county-level protest events between 4 September 1989 and 18 March 1990, the date of the first free election in East Germany. This compilation is based on records of the East German Ministry of the Interior, which assembled daily crisis reports submitted by local police forces, the records of the Ministry of State Security, and numerous published secondary sources. Our outcome data thus avoid common problems with the coding of protest events from news reports (Mueller, 1997; Baum & Zhukov, 2015); we are confident that systematic measurement error is negligible here.

Figure 3 shows a heat map of protest counts. On average, counties with WGTV experienced about $12.9$ protest events, while counties without WGTV experienced about $10.6$ protest events. The raw data thus seem to provide some face validity for the claim that WGTV increased the likelihood of protest events. Of course, given the systematic differences between counties with and without WGTV documented in Table I, this difference hardly represents a credible estimate of the causal effect of WGTV.

Given the spatial and temporal dimensions of our data, a simple non-parametric comparison of counties with and without WGTV is not feasible. Any robust analysis of the impact of WGTV on the occurrence of protest during the East German revolution will need to account for both time- and space-dependency. Moreover, it will also have to account for systematic differences between counties with and without WGTV. We therefore rely on a spatial survival model explaining the risk and timing of protest events as a function of access to WGTV, conditional on covariates. A critical feature of the East German revolution was that East German counties were at risk of experiencing multiple protests over the course of the revolution, and, in fact, typically did. As a consequence, a repeated events modeling approach that accounts for the clustered nature of the data is needed. Following the advice given by Box-Steffensmeier & De Boef (2006), Box-Steffensmeier, De Boef & Joyce (2007), and Box-Steffensmeier, Linn & Smidt (2014), we employ a conditional frailty gap time Cox model that allows us to simultaneously address event dependence and heterogeneity.⁷

Moreover, in some models we will also investigate the extent to which our results are robust to spatial dependency (Darmofal, 2009). We do so through the inclusion of a conditional spatial lag predictor variable. Specifically, we include a temporally lagged spatial lag variable that measures the proportion of county i’s neighbors that experienced a protest event during the preceding three days, during the preceding seven days, or during the preceding two weeks. To the best of our knowledge, ours is the first analysis that simultaneously accounts for spatial dependence, event dependence, and heterogeneity within a repeated events framework.

Results

Table II presents estimates from five Cox model specifications. The model in column 1 contains a dummy variable for access to WGTV but no covariates or frailty terms. Column 2 adds the covariates listed in Table I, and column 3 also adds frailty terms to account for unobserved heterogeneity across counties. Coefficient estimates for covariates and frailty terms are not shown. Exponentiating the coefficient estimate for WGTV gives us the estimate of WGTV’s multiplicative effect on the probability of a protest event occurring conditional on covariates and frailties.⁸ Standard errors are shown in brackets. Figure 4 graphically presents the estimates of WGTV’s multiplicative effect based on the models in Table II with 95% confidence intervals. In none of the three models is the impact of WGTV statistically significant at the $0.05$ level, and only in the model without covariates or frailties is it significant at the $0.10$ level.

This null result could be due to our focus on the East German revolution in its entirety. If WGTV did have an effect on protest activities, we would expect its impact to decrease over time as East German media became more independent from the East German regime and better able to provide unbiased coverage of the mounting regime crisis (Kern, 2011; Grdĕsić, 2014). Such a pattern of decreasing WGTV effects would provide strong evidence for the impact of WGTV, perhaps even more convincing than if we had found a positive effect of WGTV throughout the entire East German revolution.

Models 4 and 5 in Table II look at over-time heterogeneity in the effect of WGTV. The model in column 4 includes an interaction term between WGTV and a dummy variable for the time period before the fall of the Berlin Wall. The model in column 5 allows the effect of WGTV to vary even more finely, by including interaction terms between WGTV and dummy variables for three time periods: 4 September to 18 October (Through 18 October), 19 October to 31 October (Late October), and 1–9 November (Early November). (The omitted category is the period after the fall of the wall.) These time periods roughly correspond to periods of increased media freedom in East Germany (Kern, 2011: 1185).

Table II.

Effect of WGTV on probability of protest event from Cox models

	(1)	(2)	(3)	(4)	(5)
WGTV	${0.113}^{†}$ $[0.067]$	$0.115$ $[0.075]$	$0.130$ $[0.173]$	${0.222}^{*}$ $[0.102]$	${0.224}^{*}$ $[0.100]$
Wall				${1.728}^{* *}$ $[0.152]$
WGTV $\times$ Wall				$- 0.099$ $[0.151]$
Through 18 October					${1.719}^{* *}$ $[0.283]$
Late October					${1.815}^{* *}$ $[0.218]$
Early November					${1.688}^{* *}$ $[0.197]$
WGTV $\times$ Through 18 October					$- 0.131$ $[0.305]$
WGTV $\times$ Late October					$- 0.058$ $[0.222]$
WGTV $\times$ Early November					$- 0.128$ $[0.202]$
Covariates	no	yes	yes	yes	yes
Frailties	no	no	yes	yes	yes
Number of events	2,598	2,598	2,598	2,598	2,598
Number of observations	40,365	40,365	40,365	40,365	40,365
Log/I-likelihood	−9,077	−8,843	−8,741	−8,583	−8,586

The table shows coefficient estimates from five Cox models, with standard errors in brackets. Wall corresponds to the period before the fall of the Berlin Wall. ^† p < 0.1, *p < 0.05, **p < 0.01.

Figure 4.

WGTV impact estimates from Models 1–5 in Table II and 95% confidence intervals

Note that neither model shows heterogeneity in the effect of WGTV.⁹ In both models, only the impact estimates for the period after the fall of the wall are statistically significant, which is the opposite of what we would expect. Overall, the results presented so far do not support the hypothesis that WGTV facilitated antiregime protest in East Germany, in particular during the early stages of the revolution.

Robustness

In this section of the article we subject our results to a series of robustness checks. We start by investigating the sensitivity of our results to spatial diffusion processes. In areas of East Germany that had access to WGTV, both WGTV and social networks could have served as coordination devices for collective action by providing East Germans with political information not available from the state-controlled East German media. Survey data collected by Opp and his colleagues (Opp & Gern, 1993; Opp, Voss & Gern, 1993) indeed show that social networks played a role in mobilizing the Leipzig protests. It thus stands to reason that social networks could have been an even more important source of political information in parts of East Germany that did not have access to WGTV. If social networks served as a substitute for WGTV when WGTV was not available, the estimated effect of WGTV would be biased downwards. We at least partially control for this possibility by including temporally lagged spatial lag variables that measure the extent to which protest events triggered subsequent protests in neighboring counties. This strategy does not directly capture the existence of social networks, a task for which we would need individual-level network data. However, it does allow us to control for one important effect of social networks, the spatial diffusion of collective action (Gould, 1991).

Table III shows impact estimates from five Cox models. Model 1 adds a temporally lagged spatial lag variable to the baseline model in column 3 of Table II. This spatial lag variable measures the proportion of a county’s neighboring counties that experienced a protest event

Table III.

Effect of WGTV on probability of protest event from Cox models: Accounting for spatial dependence and alternative sources of political information

	(1)	(2)	(3)	(4)	(5)
WGTV	0.105 [0.170]	0.091 [0.171]	0.103 [0.174]	0.097 [0.168]	0.103 [0.182]
Spatial lag three days	yes	no	no	yes	no
Spatial lag seven days	no	yes	no	yes	no
Spatial lag 14 days	no	no	yes	yes	no
Poland border dummy	no	no	no	no	yes
Trains dummy	no	no	no	no	yes
Covariates	yes	yes	yes	yes	yes
Frailties	yes	yes	yes	yes	yes
Number of events	2,598	2,598	2,598	2,598	2,598
Number of observations	40,365	40,365	40,365	40,365	40,365
I-likelihood	–8,693	–8,683	–8,724	–8,669	–8,740

The table shows coefficient estimates from five Cox models, with standard errors in brackets. ^† p < 0.1, *p < 0.05, **p < 0.01.

Table IV.

Effect of WGTV on probability of protest event from Cox models: Subsets of counties or events

	(1)	(2)	(3)	(4)	(5)	(6)
WGTV	0.103 [0.175]	0.062 [0.254]	0.195 [0.237]	0.065 [0.186]	0.204† [0.122]	–0.582* [0.347]
Omit East Berlin	yes	no	no	no	no	no
Omit southeast	no	yes	no	no	no	no
Omit northeast	no	no	yes	no	no	no
Omit neighbors	no	no	no	yes	yes	no
Best subset	no	no	no	no	yes	no
First events only	no	no	no	no	no	yes
Covariates	yes	yes	yes	yes	yes	yes
Frailties	yes	yes	yes	yes	no	yes
Number of events	2,521	2,458	2,476	2,340	585	207
Number of observations	40,170	38,220	37,830	36,465	9,165	11,700
I-likelihood	–8,599	–8,163	–8,179	–7,592	–1,070	–842

The table shows coefficient estimates from six Cox models, with standard errors in brackets. ^† p < 0.1, *p < 0.05, **p < 0.01.

on the preceding three days. Model 2 adds such a spatial lag variable for the preceding seven days, and Model 3 does the same for the preceding 14 days. Model 4 simultaneously includes all three spatial lag variables. Across all four models, the estimated impact of WGTV remains small and statistically insignificant.

Model 5 controls for two alternative sources of political information.¹⁰ First, the riots that took place when the trains carrying embassy fugitives passed through East Germany could have had a particularly strong impact on East Germans’ awareness of the weakness of the communist regime. Second, in Poland the communist party was ousted in early 1989. East Germans living close to the Polish border might have been better informed about these events than the average East German, which might have increased their awareness of the weakness of communist regimes more generally. We thus include dummy variables for counties through which the trains passed as well as counties bordering Poland. Neither of these predictors has a substantively or statistically significant impact on protest events (results not shown); more importantly, the effect of WGTV remains small and statistically insignificant.

Table IV shows that our results do not change when we focus on specific subsamples of counties or when we estimate the effect of WGTV on time to first event. Model 1 in Table IV omits East Berlin from the sample. This does not affect the WGTV estimate or its statistical significance. Model 2 drops the non-WGTV counties in the southeast from the sample and identifies the effect of WGTV only using the non-WGTV counties in the northeast. Model 3 does the reverse, dropping the non-WGTV counties in the northeast from the sample but keeping the non-WGTV counties in the southeast. Even though the counties in the agrarian northeast are quite different from the counties in the industrialized southeast, both control groups generate small and statistically insignificant WGTV effect estimates. Following Kern (2011), Model 4 omits all WGTV counties that border at least one non-WGTV county (22 counties in total). The effect estimate remains small and statistically insignificant.

Model 5 uses a reduced sample chosen to maximize the comparability of WGTV and non-WGTV counties given the imbalances we saw earlier in Table I. As in Model 4, we first dropped all WGTV counties bordering non-WGTV counties. We then estimated propensity scores using a probit model with all covariates entered linearly and dropped all counties for which there was no overlap in propensity score distributions (see Figure A4 in the online appendix). This left us with 25 non-WGTV counties and 34 WGTV counties. We then evaluated all $(\begin{matrix} 34 \\ 25 \end{matrix}) \approx 52.5$ million samples that can be generated by choosing (without replacement) 25 WGTV counties from the pool of 34 available WGTV counties in terms of the maximum absolute standardized difference in means across covariates. The sample with the best balance between WGTV and non-WGTV counties was used to estimate Model 5.¹¹ In this sample of highly comparable WGTV and non-WGTV counties, the estimated WGTV effect is positive and statistically significant at the $10 %$ level.¹²

Finally, Model 6 restricts the sample to the first event in each county, which addresses the concern that WGTV might only have facilitated the first protest event in each county. The WGTV estimate is statistically significant at the $10 %$ level but negative, implying that availability of WGTV increased the time to first protest event.

Overall, based on the results presented in Tables II–IV, we conclude that there is no robust empirical support for the hypothesis that WGTV facilitated antiregime protests, in particular during the early stages of the East German revolution. This finding is robust to variation in the time periods we looked at, spatial diffusion, the measurement of WGTV availability, and the specific samples of counties or events used in the estimations.

Discussion

Our finding that WGTV had no discernible impact on protests during the East German revolution raises the question of how protest was mobilized and spread so swiftly across East Germany. One possibility that we already alluded to earlier is social networks. Research has shown that friendship and family ties (McAdam, 1986; Gould, 1991) are important for protest mobilization. For the East German revolution, Opp, Voss & Gern (1993) and Opp & Gern (1993) have illuminated the role social networks played in both increasing awareness of the Monday demonstrations and motivating potential participants to join. Moreover, formal work on high-risk collective action suggests that merely having access to politically relevant information, such as was provided by WGTV, is not sufficient to convince people to participate. When the personal risks associated with participation in collective action are high, as they undoubtedly were during the early stages of the East German revolution, resolving coordination dilemmas requires multiple interpersonal contacts who reinforce the normative importance of taking action (Centola & Macy, 2007). WGTV did supply the political information that the literature on ‘revolutionary bandwagons’ has identified as a crucial ingredient in revolutionary collective action. But perhaps the impersonal supply of information by mass media cannot rival friendship and family ties that not only transmit information about successful protests in other places but also persuade individuals to risk their lives in support of a common cause.

In many respects, WGTV in East Germany was a ‘most likely’ case for the impact of foreign media on antiregime collective action in authoritarian regimes. For one, there were no significant language or cultural barriers between East and West Germans. Moreover, WGTV, which was extremely popular in East Germany (Kern & Hainmueller, 2009), devoted a great deal of attention to East German politics even before the outbreak of the East German revolution. It is therefore not surprising that WGTV has been heralded as a prime example of the influence of foreign mass media on transitions to democracy. Yet given our inability to find empirical support for the claim that WGTV played an important role in mobilizing antiregime protests in this case, we are somewhat wary of broad claims that communication technology can facilitate collective action in other cases. At the same time, recent work by Adena et al. (2013) on the effects of Nazi radio propaganda and Shapiro & Weidmann’s (2015) research on the impact of cellular networks in Iraq convincingly demonstrate that horizontal and vertical communication technologies can sometimes affect collective action (in perhaps unexpected ways). While calls for further research have become a cliche, we do believe that the impact of communication technology on collective action is one area in which such calls are justified and where further research is needed. Even though arguments linking communication technology to collective action are plausible enough, the question probably cannot be settled by relying on cross-national evidence alone. Detailed case studies with credible causal identification strategies are needed to pin down the effects, or lack of effects, of communication technologies on collective action.

Footnotes

Replication data

All statistical analyses were done using R $3.0.3$ . The online appendix as well as the dataset, codebook, and do-files for the empirical analyses in this article can be found at .

Acknowledgements

We are grateful to Siegfried Grundmann and Steven Pfaff for sharing data. We also thank William Lynn Shirley and Kevin Remington of the Department of Geography at the University of South Carolina and Yosef Bodovski of the Population Research Institute at Penn State University for providing invaluable GIS assistance. Giovanni Zambotti of the Center for Geographic Analysis at Harvard University graciously allowed us to use the Communication System Planning Tools ArcGIS extension. Julie Kubrick of the Institute for Telecommunication Sciences helped direct our efforts at an early stage. Ben Olken and Ruben Enikolopov generously shared advice and software. Janet Box-Steffensmeier, Kyle Joyce, and Suzanna Linn provided extremely helpful advice with regards to our spatial survival model. Allan Dafoe, Jason Lyall, and Nils B Weidmann offered extremely useful comments on earlier drafts of this article. Any remaining errors are our own. All authors contributed equally and are listed alphabetically.

Notes

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