Volume 7, Issue 1-2

This article puts the self-interest hypothesis to an empirical test by analysing the 2004 referendum on fiscal equalisation in Switzerland. That vote put forth a series of reforms which created regional winners and loser in terms of having to pay or receiving unconditional funding. Although Switzerland is usually portrayed as a paradigmatic case in terms of inter-regional solidarity and national integration, we show that rational and selfish cost-benefit calculations strongly mattered for the end-result. We rely on a multi-level model with referendum and other data on more than 2700 municipalities and all 26 cantons. More broadly, our findings confirm that rational choice theory works well for voting on straightforward monetary issues with a clearly defined group of winners and losers. However, symbolic interests such as party strength and cultural predispositions against state intervention and in favour of subsidiarity also matter and need to be taken into account alongside.

Exponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients may be. In this research, we examine one violation of the asymptotic assumptions of ERGMs – perfectly measured social networks. Using several Monte Carlo simulations, we demonstrate that even randomly distributed measurement errors in networks under study can cause considerable attenuation in coefficients from ERGMs, and do real harm to subsequent hypothesis tests.

The following article analyzes violent political conflicts. Starting from theoretical debates on the determinants of violent conflicts, it focuses on intrastate conflicts. The main explanatory factors which are analyzed from different perspectives are demographic factors. Not only the size of the population or the population density is relevant, but also the so-called youth bulge. Youth bulges, i.e. large cohorts of young people, are the main driving forces of war. Especially variants like male youth bulges are highly significant in the empirical analysis. The article uses different data sets for the analysis, like the Global Peace Index and the CONIS data. Various statistical methods are used in the analysis. The article also draws attention to intervening factors, which might intensify or lessen the impact of the youth bulge.

Many undergraduate degrees require the completion of an introductory statistics course, but it is unclear to what extent taking a statistics course improves statistical literacy. We conducted an online survey with a simple random sample of undergraduate students at the University of Waterloo, Canada. We then compared students who have completed at least one statistics course to those who have completed none and found that taking a statistics course did not improve statistical literacy on the questions asked (causation versus correlation, margin of error, and others). Introductory statistics courses may want to address statistical literacy as a learning outcome more explicitly for a better understanding of public policies.