ELECLAW is based on the resources of GLOBALCIT. The original material was gathered by country experts, who were asked to collect the electoral legislation in force in their country, to summarise the relevant legal provisions in a questionnaire, and to write a narrative report on access to electoral rights. However, the resulting databases were compiled centrally by our team. The coding of ELECLAW was also centralised: one specially assigned collaborator took the lead, while two additional collaborators checked the coding and discussed entries where there were uncertainties or disagreements. In addition, most collaborators were involved in crafting the coding rules, which helped ensure that they were strictly adhered to or modified on the go to capture unanticipated and relevant real-world complexities. This centralisation strategy, together with our focus on de jure regulations, minimises the interpretative bias which arises when the task of evaluating the data is delegated to national experts who are often less acquainted with the broader comparative picture.Footnote 10
ELECLAW measures the degree of electoral inclusion of a given legal provision using a scale ranging from a minimum of 0 to a maximum of 1. In case of eligibility restrictions, this usually translates into theoretical minima and maxima of 0 for “generally disenfranchised” and 1 for “generally enfranchised”. For all other indicators, the determination of the minima and maxima is empirically determined, though these minima and maxima often reflect what is theoretically possible. For instance, automatic registration is arguably the least cumbersome form of accessing the ballot and thus coded 1.
ELECLAW scales are ordinal. The number of points between the 0 and 1 endpoints varies depending on the relevant qualitative distinctions that can be drawn based on the data. The 0–1 scale is split up into equal intervals between the points to yield an intuitive ordinal construct. Distances between points on different scales therefore may vary and are not strictly comparable across individual indicators. Still, this method allows for plausible aggregation as long as the underlying measurement level is adequately considered in subsequent analyses.
We illustrate the logic of measurement in ELECLAW by discussing three examples. The first is voting rights restrictions for resident citizens based on mental disability (see Table 2). It was obvious from the start of our coding process that no disenfranchisement would be the most inclusive regulation, while a blanket disenfranchisement of all legally incapacitated persons would be the most restrictive. In order to construct our mid-point scales, our attention was drawn to an influential 2010 judgement of the European Court of Human Rights that found that an absolute ban on legally incapacitated persons violated the right to free elections enshrined in the Convention.Footnote 11 A key distinction arises between those electoral regulations applying to a general category of persons and those providing for a separate assessment of the functional abilities that are relevant for the act of voting (Raad et al. 2009: 624; see also Beckman 2014). We therefore drew on this aspect to construct an empirically informed four-point scale that measures how severe the exclusion is in a fine-grained way (cf. Table 2). It captures whether the disenfranchisement must be subjected to a proportionality test, and if it is not, whether it applies indiscriminately to all persons found to be fully legally incapacitated or hospitalised in a mental institution or to specific categories only. Accordingly, France, where, since 2007, a judge must rule on the retention or abolition of the right to vote of a protected person when ordering or renewing a measure of curatorship, was coded 0.67. By contrast, countries that disenfranchise all persons who were found legally incapacitated by a court decision, such as Argentina, Belgium, Poland, or Portugal, were coded 0.33.
Table 2 Voting rights restrictions for resident citizens based on mental disability The second example concerns voting rights for non-resident citizens. This is to illustrate the importance of considering not only eligibility, but also access. Obviously, these restrictions are especially relevant for non-resident citizens: any attempt to measure their electoral inclusion without considering this dimension would miss a crucial institutional aspect, which is in fact often used by governments to constrain the influence of the diaspora (Turcu 2018; Arrighi and Lafleur 2017). Greece is a case in point. Although the right to vote from abroad is constitutionally enshrined, it has never been implemented, thus effectively disenfranchising the Greek diaspora unless voters travel back to the country on Election Day. This aspect is captured in our five-point eligibility indicator, which attributes a score of 0.25 to those elections where the enfranchisement of non-resident citizens exists in principle but was not implemented, and our voting method indicator, where the combination of in-country voting and non-subsidised travel corresponds to the most exclusive category and is thus coded 0 (cf. Table 3).
Table 3 Voting rights restrictions for non-resident citizens based on voting methods Spain provides a more subtle example. In 2011, the introduction of the “voto rogado”, whereby Spanish citizens abroad must activate their right to vote through a cumbersome procedure, considerably decreased turnout among the external electorate in subsequent elections (Merino 2015). Again, this administrative hurdle is accounted for in our coding. On the one hand, in the absence of past-residence or professional restrictions on the right to vote from abroad, Spain was given the maximum score 1 on our eligibility indicator (i.e. generally enfranchised). On the other hand, the combination of an active registration procedure with frequent renewal and postal voting was translated into a lower score in our access indicator.
More generally, we treat the electronic or online voting method as the most inclusive. One could argue that making available only this method excludes all those who are not digital natives. However, as there is no case we are aware of that allows for electronic voting without also supplying another method in parallel, this categorisation is viable. We therefore only code the most inclusive provision in case there are multiple options. The result is a four-point scale that captures how cumbersome the voting method is (see Table 3).
The third example is that of voting rights for non-citizen residents. Here, we were confronted with the differential treatment of various groups. The first distinction is that of EU Second Country Nationals (SCN) and Third Country Nationals (TCN) across EU member states. We developed separate indicators to measure their inclusion. The indicator for TCN also serves as a general indicator for non-citizen residents. Thus, when comparing EU states with non-EU states, users can choose to either use only the TCN indicators or the aggregated indicator that takes into account that all EU states must grant voting rights to EU citizens in local legislative elections.
Our strategy for dealing with potential complexities is simple. We use the average score in case of multiple codes. For instance, in Nordic countries, Nordic non-EU citizens (from Iceland and Norway) have a lower residence requirement for local voting rights than other TCN. The score for residence-based eligibility restrictions for TCNs is thus the average of the score for Nordic TCN and all other TCN. We can also accommodate more complex cases such as local legislative elections in Portugal, where Brazilians, Cape-Verdean, and citizens of countries with reciprocity agreements have different residence conditions. This highlights the flexibility and fine-grained nature of ELECLAW.