official confirmation that the complaint has been addressed. We see that the distribution peaks at eight days, i.e., the time the pothole has to be repaired by Moscow city ordinance. And 80 percent of all complaints are addressed in the two weeks’ time, indicating a highly efficient response rate.

We employ three popular measures of electoral competitiveness as our dependent variables.<sup>11</sup> The _total votes for the incumbent_ measure shows the degree of overall support for city authority among the voters. The _margin of victory_ measure (i.e., the difference between the votes for the election winner versus the runner-up candidate) indicates the city authority's electoral strength vis-à-vis opposition candidates. The _voter turnout_ measure (i.e., the percentage of eligible voters who cast the ballot) captures the degree of city residents’ overall electoral engagement.

Appendix Table A.1 shows the summary statistics for the variables employed in the empirical analysis.

We model the effect of digital public engagement by estimating the following empirical specification: + ∑ . (1) MM−KK The specification described by equation (1) is the spatial autoregressive model (SAR) for panel data ii,tt ii≠jj ii,jj jj,tt mm=MM−KK−3 ii,tt,mm ii tt ii,tt YY = ρρ∑ ww YY XX ββ + αα + γγ + εε (Elhorst 2014; LeSage and Pace 2009), where indices _{i,j}_ , _t_ and _m_ describe municipalities, years, and months before elections date, respectively; indicates measures of electoral competitiveness; indicates the degree of digital public engagement measured by cumulative pothole complaints ii,tt YY over the 3-months rolling window ii,tt,mm _M-K_ , where _K_ are months before the election month, _M_ ; is XX the element of the spatial weight matrix; and are parameters to be estimated with ii,jj ww indicating the spatial lag (i.e., the degree of spatial autocorrelation among the electoral ii tt ρρ, ββ, αα γγ ρρ competitiveness in neighboring municipalities), and and indicating municipality and time effects, respectively; and is the error term. We choose the SAR model as the base empirical ii tt αα γγ specification instead of the more commonly used linear panel data model with fixed effects because ii,tt εε

> 11 The use of electoral results from Russia in a study such as this inevitably raises the issue of electoral fraud that have tainted Russian electoral practices at various government levels at least since the 1996 presidential elections (Enikolopov et al. 2013; Kalinin and Mebane 2012; White 2016). There are several reasons why we can still rely on Moscow elections as reflecting, even if imperfectly, the popular will of the city residents. Moscow along with St. Petersburg are the cities with the highest number of independent election observers that limit opportunities for outright fraud, and their importance has increased during the last decade. The evidence is also accumulating that incumbents in Russia often revert to electoral manipulation strategies that are less visible than ballot-box stuffing and less costly in terms of public attention and fury: they manipulate institutions and electoral rules to prevent challengers from running in the first place (Szakonyi 2019). Indeed, the independent watchdog “Golos” agreed that Moscow 2018 elections did not experience fraud, though it questioned the fairness of the incumbent’s electoral strategies

> <u>(https://www.kommersant.ru/doc/3737769, [last accessed September 2020]).</u>

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