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Are welfare state reforms electorally dangerous for governments? Political scientists have only recently begun to study this seemingly simple question, and existing work still suffers from two shortcomings. First, it has never tested the reform–vote link with data on actual legislative decisions for enough points in time to allow robust statistical tests. Secondly, it has failed to take into account the many expansionary reforms that have occurred in recent decades. Expansions often happen in the same years as cutbacks. By focusing only on cutbacks, estimates of the effects of reforms on government popularity become biased. This article addresses both shortcomings. The results show that voters punish governments for cutbacks, but also reward them for expansions, making so-called compensation, a viable blame-avoidance strategy. The study also finds that the size of punishments and rewards is roughly the same, suggesting that voters' well-documented negativity bias does not directly translate into electoral behavior.

Welfare state scholars debate whether reforms of the welfare state affect governments' popularity. We have collected yearly data on reforms of old age pensions and unemployment protection in the United Kingdom back to 1946, which allows us to statistically test if welfare reforms affect government support. It does. Cutbacks reduce support, and expansions increase it. Especially the last result is interesting because the literature mostly focuses on cutbacks. Our results suggest that there is an almost symmetrical relationship, meaning that cutbacks and expansions have roughly the same effect, but in opposite directions.

I velfærdsstatslitteraturen diskuteres det, om reformer påvirker opbakningen til den siddende regering. Vi har indsamlet årligt data på velfærdsreformer af pensions- og arbejdsløshedsområderne og meningsmålinger i Storbritannien helt tilbage til 1946. Det giver os mulighed for at teste statistisk, hvorvidt velfærdsreformer påvirker regeringens folkelige opbakning. Det viser sig, at det gør de. Nedskæringer fører til lavere støtte, men ekspansion fører til højere støtte. Ikke mindst det sidste er interessant, fordi man i litteraturen ofte fokuserer på nedskæringer. Vores analyse tyder på, at der er et næsten symmetrisk forhold, således at effekten af nedskæringer og ekspansion er nogenlunde lige stor, men altså i forskellige retninger.

Welfare state scholars debate whether reforms of the welfare state affect governments' popularity. We have collected yearly data on reforms of old age pensions and unemployment protection in the United Kingdom back to 1946, which allows us to statistically test if welfare reforms affect government support. It does. Cutbacks reduce support, and expansions increase it. Especially the last result is interesting because the literature mostly focuses on cutbacks. Our results suggest that there is an almost symmetrical relationship, meaning that cutbacks and expansions have roughly the same effect, but in opposite directions.

Die öffentlichen Schulden sind seit der Wiedervereinigung stark angestiegen. Was sind die Ursachen für die Schuldenentwicklung der 16 Bundesländer? Gibt es neben sozio-ökonomischen Einflussgrößen auch politisch-institutionelle Ursachen? Welche Konsequenzen kann die Identifikation verschiedener Determinanten der Staatsverschuldung für die Konsolidierung der öffentlichen Haushalte haben?

AbstractAlgorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio‐technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real‐world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.

This article addresses the question of appearance-based effects by looking at the U.S. House of Representatives election 2016. We broaden the focus beyond existing studies by offering a comprehensive and systematic analysis of the three traits attractiveness, competence, and likability while simultaneously taking into account confounding third variables and possible interactions. Corresponding to the comparative character of electoral competition in the districts, we developed a relative measure of the three traits which we apply in an online survey. This measure also takes into account the raters' latency times, that is, their clicking speed, as a weighting factor for their ambiguity in the ratings. With these data we test whether appearance matters for the electoral outcome. We find that attractiveness positively affects the vote share, whereas perceived likability and competence play no role. The study also tests to what extent the found appearance effects are conditioned by incumbency status, age, and gender of the contestants. Furthermore, it gives hints which aspects of their appearance candidates could change to perform better at the ballot box.

This article addresses the question of appearance-based effects by looking at the U.S. House of Representatives election 2016. We broaden the focus beyond existing studies by offering a comprehensive and systematic analysis of the three traits attractiveness, competence, and likability while simultaneously taking into account confounding third variables and possible interactions. Corresponding to the comparative character of electoral competition in the districts, we developed a relative measure of the three traits which we apply in an online survey. This measure also takes into account the raters' latency times, that is, their clicking speed, as a weighting factor for their ambiguity in the ratings. With these data we test whether appearance matters for the electoral outcome. We find that attractiveness positively affects the vote share, whereas perceived likability and competence play no role. The study also tests to what extent the found appearance effects are conditioned by incumbency status, age, and gender of the contestants. Furthermore, it gives hints which aspects of their appearance candidates could change to perform better at the ballot box.

AbstractAlgorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird's eye view of the different key decisions that are to be taken when state actors decide to use an algorithmic system, and illustrate these decisions with empirical examples from case studies. Building on these insights, we discuss the main pitfalls and promises of the use of algorithmic system by the state and focus on four levels: The most basic question whether an algorithmic system should be used at all, the regulation and governance of the system, issues of algorithm design, and, finally, questions related to the implementation of the system on the ground and the human–machine-interaction that comes with it. Based on our assessment of the advantages and challenges that arise at each of these levels, we propose a set of crucial questions to be asked when such intricate matters are addressed.