A Bottleneck Model of E-voting: Why Technology Fails to Boost Turnout

EUROPEAN UNIVERSITY INSTITUTE Department of Political and Social Sciences

A Bottleneck Model of E-voting.

Why Technology Fails to Boost Turnout Kristjan Vassil Till Weber

Abstract: Recent years have seen an increasing interest in internet voting in theory and practice. According to its proponents, e-voting modernizes the electoral process and boosts turnout. Less optimistic scholars object that citizens remain largely unaffected by the new technology. This study aims to fill the gap between these two claims. We argue that e-voting has a high impact on those citizens who are unlikely to use it in the first place; conversely, the impact is low on the bulk of typical e-voters. We test this hypothesis with new survey data from the 2007 general election in Estonia, the first country to have nationwide and legally binding elections on the internet. In a two-step model of individual behavior, we predict both the usage of e-voting and its impact on electoral participation. Our findings identify variables that increase the impact of evoting but simultaneously decrease the initial likelihood of usage. In particular, e-voting affects ‘peripheral’ citizens (in a demographic and political sense), but only few of these citizens vote on the internet. This bottleneck effect explains why e-voting has failed to boost aggregate turnout but also points to a role in reducing political inequality.

Paper presented at the Midwest Political Science Association Annual National Conference, Chicago April 2-5, 2009 Contact information: kristjan.vassil@eui.eu, till.weber@eui.eu

Introduction During the long decline of voter turnout in modern democracies, the question of how to motivate citizens to participate in elections has remained on the agenda of politics and political science. One rather recent attempt to address the issue is internet voting, i.e. the option to cast one’s vote over the internet in (otherwise) normal elections1. When internet voting was developed, hopes for a boost in turnout were great. However, the first experiences from Switzerland, the United Kingdom, the Netherlands and the U.S. did not confirm the expected effect.

Scholars put the blame on the failure of internet voting applications to overcome social divisions in conventional political participation. Usage of the new technology is not equally distributed across the population. It seems that instead of mobilizing disaffected or “peripheral” citizens, internet voting merely constitutes yet another channel of influence for the politically engaged. Traditional patterns of inequality in political participation seem to be reinforced, not transformed.

In this paper we further scrutinize the mobilization potential of internet voting applications. We highlight an analytical distinction that seems crucial to us but has as yet not attracted much attention in the literature: the mere usage of internet voting is different from its impact in terms of mobilization. Whereas we do not doubt that internet voting is mostly used by the politically engaged, we claim that the impact on participation is highest among peripheral citizens. Thus, internet voting does possess transformative potential; at the same time, however, this potential remains largely inactive due to a typical “bottleneck” effect.

The basic mechanism of the bottleneck effect is simple: peripheral citizens are unlikely to use internet voting, but those few who happen to do so are then exposed to strong mobilizing forces. More subtly, the effect is based on different motivations that lead to usage in the first place. Politically engaged citizens are generally experienced with computers and the internet. They use internet voting as a means to cast their ballot, but they are not overly interested in the application itself. Peripheral citizens are less computer literate. For them the application itself has a fascinating aspect, whereas the act 1

Throughout the text we use the terms internet voting and e-voting as synonyms.

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of voting is barely attractive in its own right. But by using the technology, peripheral citizens are brought in contact with politics and may then experience a more radical impact than engaged citizens who merely strive for convenience.

We test these propositions in the case of the 2007 general election in Estonia, the first time that internet voting has been used in a nationwide parliamentary election. The analysis follows a two-step model whose components are linked by the bottleneck mechanism. In the first step we predict individual usage of internet voting (as distinct from conventional voting and abstention) on the basis of demographic and attitudinal variables. This model is tested by multinomial probit regression with data from a general election survey of the Estonian population. In the second step we predict the impact of internet voting from similar variables by interval regression. To test this model we introduce a new survey of Estonian internet voters. Before we turn to empirical analysis, however, we will explicate our theory in more detail.

The Bottleneck Model In recent years the majority of scholars have become less optimistic about the internet’s ability to promote political participation in general and voter turnout in particular.

Although the last U.S. primaries demonstrated major novelties in web-campaigning, possibly contributing to differences in election outcomes, European e-democratic experiments have remained rather modest. Opposing the excessive cyber-optimism from the mid-nineties, the contemporary literature admits that in theory the internet may lower the costs of electoral participation, strengthen democratic practices and include the disengaged into civic life, but there seems to be little empirical support for these claims.

Internet applications have only weak impact on political participation and civic engagement.

The standard explanation for this finding is offered by theories of digital divide in general and political divide in particular: Online politics mirrors the patterns of inequality experienced in conventional politics and even increases the gap between the engaged and the disengaged (Alvarez & Nagler 2000; Wilhelm 2000; Putnam 2000; Margolis & Resnick 2000; van Dijk 2000; van Dijk 2005). Disparities in access to the internet based on income and education are still widespread. Online politics therefore tends to empower

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the wealthy and well-educated and to further marginalize the underprivileged (Mossberger, Tolbert & Stansbury 2003). The prime beneficiaries are elites with the resources and motivation to take advantage of internet applications, whereas the costs remain too high for less skilled citizens. The internet provides new opportunity structures for the elite rather than mobilizing the disengaged periphery. In this sense, promoting politics on the internet means preaching to the faithful.

Far from mobilizing the general public, the Internet may thereby function to increase division between the actives and apathetics within societies. /---/ But as the media of choice par excellence it is difficult to know how the Internet per se can ever reach the civically disengaged (Norris 2001, 231).

Recently, however, scholars have raised some doubts about the internet’s inability to reach the disengaged and bring them closer to politics. Based on studies of internet voting and Voting Aid Applications (VAA) – the two most tangible forms of online political participation – small but significant mobilization effects have been found. In particular, the results reported by Alvarez, Hall and Trechsel (2008) show that roughly one tenth of the internet voters in Estonia would not have turned out without the possibility to vote online. A mobilization effect of about the same magnitude was found

by Boogers (2006): One tenth of the users of Stemwijzer (the Dutch VAA) reported an increased motivation to cast their vote after obtaining the advice from the VAA. Kleinnijenhuis and van Hoof (2008) in their study of the usage of several Dutch VAAs observed that more people made a choice for a particular party after consulting the VAA. Although limited in cross-sectional and longitudinal terms, this evidence points toward some mobilization effects caused by VAA-usage and internet voting. An apparent question follows from here: Who is being mobilized and for what reason?

If online politics has any effect on participation at all, it is likely to occur among the converted citizens with particular attitudes and demographic characteristics: Young individuals with higher income, educational attainment, sense of political efficacy and positive attitudes toward politics are more likely to participate in online politics in general (Mossberger, Tolbert & Stansbury 2003; Norris 2001) and in e-voting in particular (Kersting & Baldersheim 2004; Alvarez & Nagler 2000; Solop 2001).

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A number of studies have established that the usage of internet voting is significantly skewed toward younger citizens. After all, it is the young who are exposed to the new media to a far greater extent than the elderly, and it is self-evident that internet voting is most conveniently accessible to those already familiar with new technologies.

These preconditions, combined with the fact that turnout has been generally low among young citizens (Franklin 2004; Wattenberg 2008), raise expectations that precisely the young will be mostly affected by internet voting (Kersting & Baldersheim 2004; Norris 2003; Alvarez, Hall & Trechsel 2008).

Considering voting behavior by age category, it becomes clear that above all younger people participated by voting over the Internet. Based on this finding, one can conclude that the introduction of voting by Internet seems to have significant impact on the participation of younger voters in elections. The use of internet voting mobilizes the generally underrepresented young persons, while it is more seldom used by older voters (Trechsel et al. 2007).

Building on these findings, we would like to highlight a distinction that has as yet not attracted much attention among scholars. The usage of internet voting often seems to be insufficiently differentiated from its impact. We argue that without making a conceptual distinction between the two, the analysis of internet voting may suffer from some degree of logical imprecision with implications for empirical analysis and theoretical interpretation. Namely, the act of using internet voting per se does not necessarily imply an effect on an individual’s propensity to turn out and may therefore not be an ideal indicator to measure mobilization. The proposition that the young are more likely to engage in e-voting due to their digital affinity may well hold, but we see no compelling reason for concomitant mobilization effects. It does not necessarily follow from the literature on usage that internet voting mobilizes particularly the young and affluent. Quite the contrary, we expect mobilization effects – if any – among the apathetic periphery. In particular, the greatest impact on the propensity to turn out should appear among those who are unlikely to use internet voting in the first place. Conversely, the impact on individual turnout should be low among typical internet voters.

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It is important to be aware of some ambiguity about the meaning of “turnout” in this respect. Previous studies tended to equate turnout with usage and impact, both in theory and measurement. We suggest that more precision is needed: Of course usage of e-voting implies turnout, but the mere act of usage (or turnout for that matter) does not imply impact. Impact may result from the mere availability of the option of e-voting and/or from the experience of using the application. Although the typical e-voter may be affected in both dimensions (availability and experience), we claim that impact decreases with the likelihood of usage.

Why should we expect such a pattern? The following thought experiment is meant to illustrate the difference between the usage of internet voting and its impact. Imagine internet voter “A” who is fluent with computers, politically engaged, interested in political news, discusses politics with his friends and family, and usually participates in elections. In terms of technology he is an active user of the internet and related applications. However, technology is so deeply rooted in his everyday life that he pays minimum attention to it. Technology for him is a means rather than a goal.

Also imagine internet voter “B”. He is much less computer literate, politically disengaged, rarely shows any interest in politics, and usually abstains in elections. In terms of technology he is no active internet user. Moreover, by default he rarely thinks of technology as an intrinsic part of his everyday life. However, when he happens to use it he finds technology somehow fascinating. For him, the usage of technology per se appears to be stimulating. For the same reason he finds the idea of casting his vote over the internet attractive, but he is attracted by the technology and not by the desire to vote.

By using internet voting, both ideal-type voters – “A” and “B” – may be positively affected in their propensity to turn out. If voter “A” finds that internet voting works smoothly and is indeed a comfortable alternative to the polling booth, he may be even more likely to turn out in the future. In this respect internet voting indeed reduces electoral costs (cf. Norris 2003). And if voter “B”’s fascination with technology brings him in contact with politics in the first place, he may develop some political interest and turn out with a higher probability as well. The effect, however, is rather superficial for Voter “A”, whereas Voter “B” may experience a more radical and potentially much stronger impact. For voters of type “B” e-voting is a major innovation, but for voters of

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type “A” it is a mere extension of a technology that they are long used to. The impact of e-voting then depends on the motive an individual had to use the application. However, this link serves to differentiate impact from usage, not to equate the two.

In summary, the peripheral citizens of type “B” are unlikely to use internet voting, but they are strongly affected by it once they manage to clear the first hurdle. Conversely, voters of type “A” use internet voting more frequently, but the impact on their propensity to turn out is limited. Similar “bottleneck” effects have been described previously by Lazarsfeld, Gaudet and Berelson (1944) and Zaller (1991) in the domain of political communication and its impact on individual preferences. /---/ the people who did most of the reading and listening not only read and heard most of their own partisan propaganda but were also most resistant to conversion because of their strong predispositions. And the people who were most open to conversion - the ones the campaign managers most wanted to reach - read and listened least. Those inter-related facts represent the bottleneck of conversion (Lazarsfeld, Gaudet, Berelson 1944: 95)

We employ the bottleneck metaphor in a similar fashion: the mobilization effect of internet voting would be strongest among disengaged citizens, but not many of these citizens manage to use it in the first place. And usage of internet voting is most common among active citizens, but these citizens do not experience high impact. The interplay of these two effects constitutes the bottleneck mechanism of internet voting.

If this line of reasoning holds, then usage is both conceptually and empirically decoupled from impact. By making a distinction between usage and impact we gain the conceptual clarity required for testing our core hypothesis: characteristics distinguishing the political periphery from the elite should decrease the probability of usage but increase impact. Figure 1 represents this hypothetical relationship.

[Figure 1 about here]

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Context and data We now turn to empirical evidence from the Estonian parliamentary elections in 2007.

This section briefly describes the main features of Estonian internet voting and introduces our datasets. On the basis of these data we then proceed to test the two-step model of usage and impact in the following sections.

Internet voting in Estonia In October 2005 Estonia became the first country to have statewide local elections where people could cast binding votes over the internet. This world premiere was followed by the national parliamentary elections in 2007 where the number of internet voters reached 5.4% of the total turnout2.

[Table 1 about here]

The general feasibility of e-voting in Estonia is based on the widespread use of electronic identification cards. Since 2002 more than one million of these credit-card size personal identification documents have been issued. For internet voters they allow to cast legally binding digital votes at a high security level. Participation in the electronic ballot requires a computer with an internet connection and a “smart-card reader�. For less than ten Euro these card readers are easily available at computer shops, supermarkets and bank offices. For users without personal computer or internet access, internet voting is accessible through a wide number of free internet access points in public libraries, community centers, etc.

The process of internet voting is an interaction with the website of the National Electoral Committee, www.valimised.ee (www.voting.ee). The user first inserts the IDcard into a card reader and opens the website. Then the user is required to identify himself/herself through a PIN-code associated to her/his ID-card. If the user is eligible to vote, the system displays the list of candidates by party in the user’s electoral district. The user chooses a candidate by clicking on the name and confirms the choice by using a

2

For further details see the reports of the Estonian National Electoral Committee (2005; 2007a; 2007b).

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second PIN-code. At the end of the process, the voter receives a confirmation that the vote has been cast3.

Internet voting is available during three days of advance voting (6-4 days before Election Day). To prevent coercion and fraud, internet voters are allowed to recast their electronic vote with their previous vote being deleted. For similar reasons, internet voters can dismiss their electronic vote altogether by casting a paper ballot on Election Day. Surveys Our study employs data from two Estonian surveys4. First, we use a representative population survey to explain individual usage of internet voting. Second, a new online survey of e-voters will shed light on the impact of e-voting on turnout.

The general population survey was carried out before the 2007 parliamentary elections between the 10th and 21st of February 2007 on a random sample of 803 adult Estonians (18 years and older). Data were gathered through interviewer-assisted questionnaires. The survey performed well is terms of demographic representativeness; minor deviations were adjusted by weighting.5 The e-voter survey was conducted online within one week’s time after Election Day between the 5th and 11th of March 2007. It reached a sample of 1206 respondents what is about 4% of the total e-voter population. The sample was recruited through a twostage snowball strategy. First a direct e-mail invitation was sent to more than 50 public mailing lists and about 100 individuals from academia and the public and private sector.

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For further details see the Election Assessment Mission Report of the OSCE/ODIHR (2007) and the

Overview of the Estonian E-voting System by the Estonian National Electoral Committee (2005).

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Both surveys were financed by the University of Tartu, Institute of Journalism and Communication,

supported by grants from the Estonian Ministry of Education and Science (grant nr. 0180017s07) and the Estonian Science Foundation (grant nr. 6526).

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The sample was weighted according to age, gender and place of residence. Reference values were

obtained from Statistics Estonia (2008). In both surveys missing values were handled by multiple imputation. The Amelia II program (King et al. 2001; Honaker, King & Blackwell 2006) was used to produce five imputed datasets, and all calculations were carried out for each of them. As proposed by Rubin (1987), the final point estimates simply represent the mean across the five datasets, and the final standard errors are based on the mean variance within the five datasets plus the variance across the five datasets (multiplied by a correction factor).

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Second, online advertisements appeared in the two largest national newspapers, Eesti Päevaleht and Postimees, for three days right after the election.

This method of data collection allowed us to reach a large sample of the e-voter population by efficient means what would be hard to achieve with other survey modes.

When it comes to impact, we need to analyze variation within the group of e-voters. To include a number of e-voters that is sufficient for this approach, conventional random sampling would require an enormous overall N. Our online strategy has clear advantages in this respect. However, it also entails two drawbacks: demographic nonrepresentativeness and potential self-selection bias.

Demographic non-representativeness results from the tendency of variables that predict usage of e-voting also to predict participation in the online survey. Table 2 compares the sample distribution of internet voters on four criteria – age, gender, place of residence, and vote choice – to the distribution of the whole e-voter population as available from official statistics (Statistics Estonia 2008; Estonian National Electoral Committee 2007a). We find that the younger, male and urban population is overrepresented in the survey. On the basis of the information in Table 2 we constructed post-stratification weights to bring the marginal distribution of our sample in line with that of the population. Estimation results remained robust throughout this procedure, indicating that although our sampling design affected average levels of some variables, their relationships (which are of interest here) are adequately represented.

[Table 2 about here]

Post-stratification also limits the role of potential self-selection bias: Table 2 shows some discrepancies between the population and the sample, but it also shows that no group is systematically excluded from the sample. There is sufficient variance on all criteria to generate a representative image of the e-voter population, especially given the large N of 1206. However, bias may go beyond these observable criteria if self-selection operates on the basis of some unobserved criterion. We suspect that two such criteria may play a role: citizens’ generalized attitudes toward technology and their experience of using technology. The survey may mostly attract people who are optimistic about e-

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voting as technological innovation in general (“euphoria” bias) or who have just had a pleasant experience using e-voting (“conversion” bias). These citizens would be likely to report relatively high impact. We thus have to be cautious about taking overall levels of impact at face value. However, our primary interest is not in estimating impact per se but in predicting its distribution across the electorate. The implications of self-selection for our hypothesis of high impact on peripheral citizens depend on which form of bias is present in which part of the population. Table 3 presents the possible scenarios.

[Table 3 about here]

Both forms of bias would lead to conservative estimates if they occur among the elite. We expect citizens who are likely to e-vote to experience low impact. If there is bias in this group of citizens in favor of higher impact, it becomes harder to confirm our hypothesis. Bias among the periphery is more complicated. Euphoria bias would mean we have sampled rather atypical “peripheral” citizens. The role of political and demographic characteristics that identify this group would be superseded by some unobserved criterion that unites all respondents in the sample. Then, the data would not possess structure and our variables would display only null findings. The only scenario that implies a liberal test of our hypothesis is conversion bias among the periphery. If we have primarily sampled peripheral citizens with a positive experience of e-voting, we may overestimate impact in this group. We cannot ultimately exclude this possibility, but it should be noted that even if liberal bias is present, conservative bias is likely to offset its effect (the question of overall levels mentioned above). And finally, the scenario of exact estimation with no bias in any of the groups is not ruled out by any of these considerations.

Explaining the usage of e-voting We now turn to the first component of our two-step model, namely the explanation of the usage of e-voting. Our aim is to go beyond the descriptive information presented above in three respects. We provide analytical leverage by using multivariate analysis; we consider

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attitudinal variables in addition to demographics; and we model the choice between abstention, conventional voting and e-voting.

Variables The dependent variable is the type of intended behavior in the parliamentary elections (abstention, conventional voting or e-voting) as derived from two survey questions6. The choice of independent variables follows from the theoretical discussion of reinforcement and mobilization effects above. Two main types – demographics and attitudes – will be treated separately in our analysis. This takes into account that attitudes can be partly derived from demographics; merging the two models would therefore lead to problems of collinearity and suppress certain effects that are of particular interest in our analysis.

The demographic variables are age (five groups), income (banded), place of residence (logged population), gender, and education (elementary, secondary and higher).

Attitudinal variables include political activity, trust, and media consumption. We aggregated these variables from multiple items, allowing us to capture a concept with adequate breadth and to reduce random error variance at the same time7.

Political activity is a scale of participation in political meetings, signing public petitions, contacting the media, and opinion leadership in politics8. Political trust is operationalized as two scales: one for trust in politics (government, politicians), and one for trust in the polity (the State, the President and the courts). Media consumption incorporates the perceived importance of print media, radio and television for obtaining campaign information. We also include self-reported computer literacy as a baseline

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Question 1: Are you planning to cast your vote in the coming Parliamentary elections (Answers provided:

Yes; Probably yes; Probably no; No. Collapsed to two categories.). Question 2: Do you, or does someone of your close friends, plan to cast your vote over the internet in the coming elections? (Relevant answer: Yes, I intend to).

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In general the items used here do not only differ in the particular aspect of a concept they measure, but

also in their position on the latent dimension (their “difficulty”). In such a case techniques like factor analysis often fail to identify the true latent structure. To account for varying difficulty between items, we applied polytomous Mokken scaling to derive the dimension of interest (Mokken 1971; Hemker, Sijtsma & Molenaar 1995; Hardouin 2007). All items were rescaled to the same range for this procedure.

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Question: Do other people ask frequently your opinion about the following subjects? (A battery where

“Politics” is one element).

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effect that allows us to evaluate the performance of the attitudinal variables over and above digital affinity.

These variables characterize citizens who should be likely to use internet voting according to the theories of digital and political divide discussed above. We expect evoters to be young, educated and wealthy males from urban areas. As regards attitudes, we expect a positive influence of political activity and media consumption. The impact of trust as conceptualized here should depend on the particular aspect: Trust in politics reflects a passive conception of the individual citizen’s role in democratic affairs; in comparison, trust in the polity attests to the belief that the individual citizen may play an active role in a functioning democratic order. We expect e-voters to have a strong sense of political efficacy and thus to trust the polity but not necessarily politics9.

Method The aim of our statistical approach is to model the choice between three nominal outcomes – non-voting, conventional voting, and e-voting. We estimate this model by multinomial probit regression. Probit is preferred over logit because the latter imposes the assumption of independence of irrelevant alternatives, i.e. the odds between each pair of alternatives do not depend on the inclusion of other alternatives (Maddala 1983: 61ff.). In our case this assumption is likely to be violated in two ways. First, voters may get used to the convenience of e-voting so that the odds of conventional turnout over abstention decrease. Second, e-voting may mobilize non-voters for whom the odds of conventional turnout over abstention increase. In both cases the alternatives are not independent and a model that does not impose this assumption (such as probit) is required10.

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The role of trust is based on the distinction between institutional trust and trust toward political actors

(Citrin 1974). We expect e-voters to be “critical citizens” (cf. Norris 1998) in that they trust the system (polity), but not necessarily the actors within the system (politics). It is this pattern of trust that should go along with political efficacy. While clearly defined in the case of “critical citizens”, however, one has to be cautious about the relation of trust and efficacy more generally (cf. Craig, Niemi & Silver 1990).

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Notwithstanding these theoretical considerations, multinomial logit regression produced similar results

for both models. Moreover, there was reason to test two other estimation strategies: First, the choice between abstention, conventional voting and e-voting could be modeled in a sequential manner: Citizens would first decide whether to turn out and then which method to use to cast their vote. Of course such a model would suppress potential mobilization effects of the availability of e-voting. Conversely, however, one might wonder whether multinomial probit as a simultaneous-choice model overly favors such effects.

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Multinomial probit coefficients are generally hard to interpret. To facilitate this task, we rescaled all independent variables to a range from 0 to 1 and calculated first differences in probabilities11. These estimates can then be interpreted as the effect of moving a variable from its minimum to its maximum value on the probability of a certain outcome (non-voting, voting, or e-voting). The results are presented in Table 4 and 5.

Results: usage [Table 4 about here]

The results of the demographic model in Table 4 confirm the basic expectations discussed in the literature. First, we find that the probability to turn out increases with age. However, this effect applies only to conventional voting whereas the probability to e-vote is similar for the first four age categories. In the oldest category we find a sharp decline of e-voting of more than 7%. This effect is quite substantial given that the sample contains only 9% of e-voters. With regard to age, e-voting ranks between abstention and conventional voting. Age plays an important role in explaining individual turnout regardless of which method is employed to cast one’s vote. In this sense, e-voters display the same characteristics common to all voters. However, among those who decide to vote, e-voters are more likely to be younger than conventional voters.

Among the other variables, urban residence increases the likelihood to e-vote.

Females are more likely to participate in voting, but not in internet voting. Income does not seem to play a significant role. Education initially increases the likelihood of e-voting and even more so of conventional voting. Interestingly, however, this order is reversed for higher education where e-voting prevails. Citizens with higher (as compared to secondary) education are not notably more likely to turn out in general, but they are more likely to prefer e-voting to conventional voting.

Therefore we also tested a Heckman selection model (Heckman 1976) with similar results. Second, a potential problem is the relatively low number of e-voters in the sample. We replicated our analysis using rare events logistic regression, a technique designed for dependent variables with a rare positive outcome (Tomz, King & Zeng 1999; King & Zeng 2001). Also this estimator confirmed the results reported here.

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This was done by averaging over 1,000 simulations drawn from the multivariate normal distribution

using an adapted version of Beber’s (2008) -qi- procedure in Stata.

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[Table 5 about here]

The results of the attitudinal model in Table 5 also confirm theoretical expectations. The general likelihood of turnout increases with political activity where the effect on e-voting is particularly strong. Trust in politics increases the likelihood to vote but decreases the likelihood to e-vote (although not significant). Trust in the polity increases the probability of both voting and e-voting. Thus, the impact of trust indeed depends on the particular aspect: Non-voters display low trust toward politics and even lower trust toward the polity; conventional voters display high levels in both dimensions; and e-voters tend to trust only the polity. With regard to the media, e-voters exhibit a level of consumption between the high value of conventional voters and the low value of non-voters. Finally, computer literate citizens are more likely to substitute e-voting for conventional voting.

In summary we find that most demographic and attitudinal variables perform in line with theoretical expectations. Age, education, urban residence, gender, political activity and computer literacy seem to play their expected role. Moreover, the impact of trust suggests that e-voters have a strong sense of political efficacy. Interestingly, media consumption does not increase the likelihood of e-voting. Additional analyses of items measuring information seeking behavior on the internet showed that e-voters generally replace traditional media with online sources.12 This seems to add to the image of the typical e-voter as an independent and demanding citizen.

Explaining the impact of e-voting We now turn to the second component of our two-step model, namely the explanation of the impact of e-voting. In particular, we want to know whether e-voting affects individual turnout and whether any such impact is distributed asymmetrically across the population.

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In order to avoid endogenous explanations of e-voting we have not included variables related to online

activity in our final models.

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We first report descriptive statistics of general mobilization effects and then proceed with multivariate analysis to predict these effects.

Table 6 and 7 present evidence from two survey questions on the impact of evoting. The first asks for impact on the motivation to turn out; the second concerns impact on behavior in the recent election. Each of the two items leans toward one of the dimensions of impact we have discussed above: past behavior primarily reacts to the availability of the option of e-voting, and present motivations primarily reflect the experience of using the application. Responses to both items document general mobilization effects, and motivations seem to be more strongly affected than behavior.

The size of the behavioral effect corresponds to others found in earlier studies (Trechsel et al. 2007; Boogers 2006). The size of the motivational effect indicates that future elections may see an even stronger impact on behavior.

[Table 6 about here] [Table 7 about here]

When interpreting these results one should be aware that we are dealing with subjective evaluations. Given that citizens are not always the best judges of their own motivations and behavior, we would prefer to also trace the impact of e-voting on the basis of long-term panel studies. Obviously, this option is not available shortly after the introduction of the technique in a single country. But of course the lack of alternatives does not eliminate the shortcomings of subjective evaluations. Below we will propose an estimation strategy to account for potential bias.

The descriptive findings indicate that the option of e-voting did indeed affect the propensity to turn out of a good part of our sample. We do not claim that this impact is representative in its entirety, but an effect of this magnitude is highly unlikely to be a mere artifact of subjective evaluation. Our next aim is to explain variation in impact: why are some e-voters affected by the new technology and others are not? Our bottleneck model suggests that usage and impact of e-voting are negatively related. Thus, we expect the impact to decrease with those variables predicting the probability that an individual used e-voting in the first place. Testing this hypothesis requires multivariate models that

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resemble the models we used to explain usage as closely as possible. Again, we expect variation by demographics and attitudes.

Variables To operationalize the dependent variable, we investigated how the two aspects of impact (motivations and behavior) are related to each other. As is evident from Table 6 and 7, the two items differ considerably in terms of difficulty (motivational impact is achieved more easily than behavioral impact). To assess scalability, we therefore subjected the two items to Mokken analysis (as explained above; with motivation reduced to a binary item) and achieved a strong Loevinger’s H of 0.67. This means that behavioral change does generally not occur without motivational change, but it indicates even stronger impact on top of changing motivations. The two aspects of impact represent the same latent phenomenon, and their scale will serve as our dependent variable.

Concerning independent variables, the demographics are the same as in the model of usage (i.e. age, income, residence, gender, and education)13. Attitudinal variables include political activity, sense of political efficacy, and perceived user friendliness of the e-voting system. Activity and efficacy are captured through self-assessment questions14.

The question on activity contains very similar elements as the scale used to predict usage.

Efficacy takes the role of trust in the model of usage. The e-voter survey does not contain a trust item, but the logic of the variable can be adequately represented. We considered trust in two distinct dimensions: trust in politics reflecting a passive role in democratic affairs, and trust in the polity reflecting an attitude of confidence in the functioning of democracy that is closely related to efficacy. This logic allows us to compare the effect of efficacy on impact with the effect of trust on usage. Finally, user friendliness is a scale of three items including an assessment of the website, difficulties in installing the smart-

13

Place of residence is limited to a dummy for the two biggest Estonian cities, Tallinn and Tartu.

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Question on activity: Could you please describe the level of your political activity - how often do you

participate in political events, talk about politics with your friends and family and follow political developments?

Question on efficacy: Do you think that your vote influences who is in power and how the country is governed?

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card reader, and the number of attempts needed to successfully cast an electronic vote.

PC literacy is again included as a baseline effect.

Method As mentioned above, predicting self-reported impact requires an adequate estimation strategy. Certain respondents will interpret the items constituting our dependent variable as if they were limited by an upper bound. Research on turnout shows that a part of the electorate votes as a matter of mere habit (Franklin 2004; Gerber, Green & Shachar 2003; Plutzer 2002). These voters have arguably reached some maximum level of electoral participation and it is unlikely that they will report an impact of e-voting on their past or future propensity to turn out. However, this effect is not owing to a “natural� upper limit in the propensity to turn out itself, but owing to the limited number of elections that citizens can participate in. This could only be avoided in a polity where dozens of elections are held every day. But with only one general election every four years (and just a few second-order elections in between) our dependent variable is effectively censored.

In fact, this form of bias is a symptom of the problem of subjective evaluation discussed above: people may be bad judges of their own behavior. We therefore apply an estimation technique that accounts for the constraints imposed on people by the limited number of elections. The impact of e-voting on turnout is treated as a latent variable that is not fully observable due to upper censoring. Censoring is expected for those respondents who report having turned out in all six previous elections (national, local and European). OLS regression would be inconsistent in this case because it takes censored point data at face value (Wooldridge 2002: 524f.). Instead, we estimate the model by interval regression. Censored responses are defined as elements of an interval having as lower bound the measured value of the dependent variable and as upper bound the maximum value applicable to all respondents. The model parameters can then be obtained by maximum likelihood estimation.

Again all independent variables were rescaled to a range from 0 to 1 so that the estimated coefficients can be interpreted as the effect of moving a variable from its minimum to its maximum value. Table 8 and 9 present the results.

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Results: impact [Table 8 about here]

The results of the demographic model confirm our core hypothesis of high impact on peripheral citizens. This is clearly the case with regard to older and rural voters. Less educated citizens also experience higher impact, but these effects are insignificant and relatively weak. This null finding, however, should be interpreted in light of our core hypothesis. We have argued that usage and impact of e-voting cannot be equated.

Moreover, we have seen above that education entails higher usage. The null hypothesis against which we test our model thus posits equality of usage and impact: if education leads to usage, it should also lead to impact. This is not what we find. Therefore, the effect of education supports the distinction of usage and impact. The insignificant effect of gender may be interpreted in a similar way.

[Table 9 about here]

The findings of the attitudinal model are coherent with the demographic model and our core hypothesis. Political activity decreases the impact of e-voting, but it increased the likelihood of usage. Political efficacy also reduces impact, an effect that should be related to the influence of trust on usage. Positive user experience leads to higher impact even while controlling for computer literacy. Again, the null effect of PC literacy should be contrasted with the expectation one would derive from an equation of usage and impact, namely a strong positive effect in this case.

In sum, we find that the impact of e-voting is indeed higher among peripheral citizens, but usage of e-voting is more likely among the well-educated elite. This implies that usage and impact should be treated separately, both conceptually and empirically.

The distinction can be demonstrated most clearly by contrasting the predictions from the two models. Figure 2 achieves this graphically. The vertical axis represents impact as predicted for the respondents of our online survey from the demographic and attitudinal models in Table 8 and 9. The horizontal axis represents the likelihood that these

19

respondents voted online. Note that of course all of them did so, but for some this was more likely than for others. These differences in the likelihood of usage are due to differences in demographics and attitudes. Each respondent features a combination of these characteristics that can be expressed as a propensity score: using the parameters of the two models of usage in Table 4 and 5, we predicted for each respondent of the online survey the propensity to be included in the e-voter population in the first place.15 Figure 2 plots the expected level of impact for the full range of propensity scores (where both values were averaged over the predictions of the demographic and the attitudinal model).

[Figure 2 about here]

According to our bottleneck model internet voting fails to increase turnout because its impact is highest among those citizens who are unlikely to use it. Figure 1 presented this logic in hypothetical form. The regression line in Figure 2 captures the same effect empirically: the higher the likelihood of e-voting, the lower the expected impact on turnout. The variables that gave rise to this prediction identify peripheral citizens as highimpact but low-probability users. These citizens seem to face many barriers in accessing e-voting, but once they manage to clear the first hurdle the impact on their propensity to turn out is high. Conversely (and perhaps somewhat counter-intuitively), it is not the young and educated who are being mobilized into political life by the new technology.

Frequent usage in this group does not lead to high impact.

Conclusion The image of a bottleneck is usually evoked to describe a process that is constrained by one single element while other elements are idling. We have modeled such a process to explain usage and impact of internet voting applications. Some scholars argued that the option to vote on the internet should lead to an increase in voter turnout. Others replied that such effects are unlikely because internet voting merely replicates existing patterns 15

Out-of-sample prediction requires comparable variables across the models of usage and impact. Two

variables required special attention. Efficacy serves to represent the diametrical effect of trust in politics and trust in the polity. Media consumption does not have a correspondent, so the variable was imputed by the mean value of the e-voters from the general population survey.

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of political participation. Our bottleneck model unites these claims in one framework.

Usage of internet voting is mostly restricted to the politically engaged, but the impact of this technology on the propensity to turn out is highest among peripheral citizens.

To all appearances, then, internet voting does not only increase turnout, but it also counteracts inequality in political participation. We do not doubt that as of today the new technology mostly benefits the political elite. However, in the long run the very reasons of this disparity may undermine their own short-term effects. The elite may well benefit from e-voting, but these benefits concern matters of mere convenience. More radical effects are expected mostly among peripheral citizens for whom e-voting may serve as a stepping stone toward political activity in general. In the long run, this mobilization effect should offset the pro-elite bias inherent to online politics. However, the pace at which this is possible critically depends on the narrow bottleneck of usage that restricts the impact of e-voting.

Our expectations of the future role of e-voting are mixed. Once the new technology achieves a critical amount of users, some laggards get carried along (Rogers 2003). The bottleneck will widen and let a higher impact pass. In the long run, then, overall turnout should increase. However, there is an upper limit for the rising tide to lift all the boats. The bottleneck model assumes that peripheral citizens become e-voters out of interest and curiosity with regard to technology. Once the technology loses its innovative character and becomes “domesticated� in everyday practices (Silverstone and Hirsh 1994), the impact of internet voting may disappear. The development of e-voting in Estonia, the case we have drawn on here, will shed light on these dynamics.

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Bibliography Alvarez, R.M., Hall, T.E. & Trechsel, A.H., 2009. Internet Voting in Estonia. PS: Political Science & Politics, forthcoming Alvarez, R.M. & Nagler, J., 2000. Likely Consequences of Internet Voting for Political Representation, The Loyola of Los Angeles Law Review, 34, 1115.

Beber, B. 2008. '-qi-: First Differences for Multinomial Probit Models in Stata', http://www.columbia.edu/~bhb2102/software.htm [Accessed January 31, 2009].

Boogers, M., 2006. Enquete bezoekers StemWijzer, Available at: http://www.publiekpolitiek.nl/Bestanden/Enquete-bezoekers-StemWijzer [Accessed May 20, 2008].

Citrin, J. 1974. The Political Relevance of Trust in Government. American Political Science Review. 68(3) pp. 973-988.

Craig, S., Niemi, R., Silver, G. 1990. Political Efficacy and Trust: A Report on the NES Pilot Study Items. Political Behavior, Vol. 12, No. 3, pp. 289-314 van Dijk, J., 2005. The Deepening Divide: Inequality In The Information Society, Sage. van Dijk, J., 2000. Widening information gaps and policies of prevention. Digital Democracy: Issues of Theory and Practice, 166-183.

Estonian National Electoral Committee, 2007a. Parliamentary elections 2007. Statistics of e-voting, Available at: http://www.vvk.ee/english/Ivoting_stat_eng.pdf [Accessed November 28, 2008].

Estonian National Electoral Committee, 2007b. Statistical overview and comparison of the results of I-voting in 2007 parliamentary elections and 2005 local elections, Available at: http://www.vvk.ee/english/Ivoting comparison 2005_2007.pdf [Accessed November 28, 2008].

Estonian National Electoral Committee, 2005. Estonian I-Voting System: General Description, Available at: http://www.vvk.ee/elektr/docs/Yldkirjeldus-eng.pdf [Accessed May 15, 2008].

Franklin, M.N., 2004. Voter Turnout and the Dynamics of Electoral Competition in Established Democracies Since 1945, New York: Cambridge University Press.

Gerber, A. S., Green, D.P., and Shachar, R., (2003) 'Voting May Be Habit-Forming: Evidence from a Randomized Field Experiment', American Journal of Political Science 47(3), 540-50.

22

Hardouin, J., 2007. Mokken Scale Procedures for Stata. Available at: http://www.freeirt.org/ [Accessed October 25, 2007].

Heckman, J., 1976. The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for such Models. Annals of Economic and Social Measurement, 5(4), 475-92.

Hemker, B.T., Sijtsma, K. & Molenaar, I.W., 1995. Selection of Unidimensional Scales From a Multidimensional Item Bank in the Polytomous Mokken IRT Model.

Applied Psychological Measurement, 19(4), 337-52.

Honaker, J., King, G. & Blackwell, M., 2006. Amelia II: A Program for Missing Data.

Available at: http://gking.harvard.edu/amelia/ [Accessed July 22, 2007].

Kersting, N. & Baldersheim, H., 2004. Electronic Voting and Democracy: A Comparative Analysis, Palgrave Macmillan.

King, G. et al., 2001. Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation. The American Political Science Review, 95(1), 49-69.

King, G. & Zeng, L., 2001. Logistic Regression in Rare Events Data. Political Analysis, 9(2), 137-163.

Kleinnijenhuis, J. & van Hoof, A., 2008. The Influence of Internet Consultants.

University of Antwerp.

Lazarsfeld, P. F., Berelson, B., and Gaudet, H., 1944. The People's Choice. Columbia University Press.

Maddala, G.S., 1983. Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press.

Margolis, M. & Resnick, D., 2000. Politics as Usual: The Cyberspace" revolution", Sage Publications.

Mokken, R.J., 1971. A theory and procedure of scale analysis with applications in political research, Mouton The Hague.

Mossberger, K., Tolbert, C.J. & Stansbury, M., 2003. Virtual Inequality: Beyond the Digital Divide, Georgetown University Press.

Norris, P.(ed) 1999. Critical Citizens: Global Support for Democratic Governance.

Oxford University Press.

23

Norris, P., 2001. Digital Divide: Civic Engagement, Information Poverty, and the Internet Worldwide, Cambridge University Press.

Norris, P., 2003. Will New Technology Boost Turnout? Evaluating Experiments in Evoting V. All-postal Voting Facilities in UK Local Elections. Available at: http://www.pippanorris.com/.

OSCE/ODIHR, 2007. OSCE/ODIHR Election Assessment Mission Report in the 2007 parliamentary elections in Estonia, Available at: http://www.vvk.ee/english/OSCE report_EST_2007.pdf [Accessed November 28, 2008].

Plutzer, E. (2002) 'Becoming a Habitual Voter: Inertia, Resources, and Growth in Young Adulthood', American Political Science Review 96(1), 41-56.

Putnam, R., 2000. Bowling alone, Simon & Schuster New York.

Rogers, E. M., 2003. Diffusion of Innovations, 5th ed. New York, NY: Free Press.

Rubin, D.B., 1987. Multiple Imputation for Nonresponse in Surveys. New York: J. New York: Wiley.

Silverstone, R. & Hirsch, E. (eds.) (1994). Consuming Technologies: Media and Information Domestic Spaces. London: Routledge.

Solop, F.I., 2001. Digital Democracy Comes of Age: Internet Voting and the 2000 Arizona Democratic Primary Election. PS: Political Science and Politics, 34(02), 289-293.

Statistics Estonia, 2008. Demographic indicators. Available at: http://www.stat.ee/ [Accessed September 11, 2008].

Tomz, M., King, G. & Zeng, L., 1999. ReLogit: Rare Events Logistic Regression.

Available at: http://gking.harvard.edu/stats.shtml [Accessed October 1, 2008].

Trechsel, A.H., Schwerdt, G., Breuer, F., Alvarez, M., Hall, T., 2007. Council of Europe and Estonian National Electoral Committee Report on Internet Voting in the 2007 parliamentary elections in Estonia, Available at: http://www.vvk.ee/english/CoE and NEC_Report E-Voting 2007.pdf [Accessed November 28, 2008].

Wattenberg, M. P. 2008. Is Voting for Young People? New York: Pearson Longman.

Wilhelm, A.G., 2000. Democracy in the Digital Age: Challenges to Political Life in Cyberspace, Routledge.

24

Wooldridge, J. M. (2002) Econometric Analysis of Cross Section and Panel Data, Cambridge: MIT Press.

Zaller, J., 1992. The Nature and Origins of Mass Opinion.

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Table 1. Main statistics of internet voting

Eligible voters Voter turnout E-votes counted E-votes among all votes

2005 1 059 292 47.4 % 9 287 1.9 %

Source: Estonian National Electoral Committee.

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2007 897 243 61.9 % 30 243 5.4 %

Table 2. Marginal distributions of internet voters Percentages Sample

Population

Difference

Gender Female Male

48.2 51.8

41.5 58.5

-6.7 6.7

11.2 32.0 32.7 18.2 5.9

16.4 46.4 24.3 10.9 2.0

5.2 14.4 -8.4 -7.3 -3.9

47.6 52.4

74.3 25.7

26.7 -26.7

34.5 26.7 13.3 10.7 9.1 3.6 2.1

30.2 33.8 18.1 13.1 2.2 1.8 0.8

-4.3 7.1 4.8 2.4 -6.9 -1.8 -1.3

Age (i) 18-24 25-34 35-49 50-64 65+ Residence Tallinn and Tartu Remaining country Vote choice Eesti Reformierakond Isamaa ja Res Publica Liit Sotsiaaldemokraatlik Erakond Eestimaa Rohelised Eesti Keskerakond Eestimaa Rahvaliit Others

Source of the population data: Estonian National Electoral Committee. (i) The last age category in the original population data starts at 60. Congruence with the survey categories was established by estimating density as a linear function of age and adjusting percentages accordingly.

Table 3. Potential forms of self-selection and their implications

Euphoria bias Conversion bias No bias

Periphery Null effects Liberal estimation Exact estimation

Elite Conservative estimation Conservative estimation Exact estimation

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Table 4. Probability of e-voting predicted from demographics Abstention Age (basis: 18-24) - 25-34 - 35-49 - 50-64 - 65+ Income Urban residence Gender (f) Education (basis: elementary) - Secondary - Higher

Voting

E-voting

.010 (.060) -.075 (.052) -.115** (.057) -.205*** (.055) -.093 (.067) .011 (.044) -.044 (.036)

.020 (.063) .077 (.057) .139** (.063) .276*** (.063) .094 (.072) -.057 (.048) .090** (.038)

-.030 (.036) -.002 (.037) -.024 (.038) -.071* (.041) -.001 (.037) .046* (.025) -.046** (.021)

-.212*** (.048) -.227*** (.041)

.145** (.057) .103 (.069)

.067* (.039) .124* (.064)

N Log pseudo-likelihood % correctly predicted Wald test

803 -626 69 60***

First differences from multinomial probit regression with robust standard errors in parentheses. * significant at .1 ** significant at .05 *** significant at .01

Table 5. Probability of e-voting predicted from attitudes Abstention Political activity Trust (politics) Trust (polity) Media consumption PC literacy

-.268*** (.059) -.139 (.098) -.337*** (.117) -.416*** (.059) .056 (.053)

N Log pseudo-likelihood % correctly predicted Wald test

Voting

E-voting

.113 (.093) .213** (.106) .218* (.129) .367*** (.066) -.214*** (.058)

.155** (.079) -.074 (.058) .119* (.063) .049 (.032) .158*** (.038)

803 -587 69 117***

First differences from multinomial probit regression with robust standard errors in parentheses. * significant at .1 ** significant at .05 *** significant at .01

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Table 6. Did the option of e-voting affect your motivation to participate in elections?

Negative effect No effect Positive effect

0.03% 53.06% 46.91%

Table 7. Would you have turned out without the option of e-voting?

I I I I

would would would would

have voted anyway rather have voted rather have abstained have abstained

62.48% 21.84% 8.41% 7.27%

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Table 8. Impact of e-voting on turnout predicted from demographics Age (basis: 18-24) - 25-34 - 35-49 - 50-64 - 65+ Income Urban residence Gender (f) Education (basis: elementary) - Secondary - Higher Constant N Log pseudo-likelihood Wald test

.088** .167*** .169*** .209*** .029 -.055** -.044

(.043) (.044) (.053) (.066) (.057) (.028) (.027)

-.021 -.029 .495***

(.049) (.044) (.056)

1206 -481 41***

Interval regression coefficients with robust standard errors in parentheses. * significant at .1 ** significant at .05 *** significant at .01

Table 9. Impact of e-voting on turnout predicted from attitudes Political activity Political efficacy User friendliness PC literacy Constant

-.081* -.152*** .175** -.003 .571***

N Log pseudo-likelihood Wald test

1206 -490 15***

(.047) (.050) (.085) (.068) (.086)

Interval regression coefficients with robust standard errors in parentheses. * significant at .1 ** significant at .05 *** significant at .01

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Figure 1. The hypothetical relation of usage and impact

Probability

Impact

Usage

Political periphery

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Figure 2. Impact of e-voting declining with likelihood of usage

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