Premature mortality exhibits strong spatial patterns in Great Britain. Local authorities that are located further North and West, that are more distant from its political centre London and that are more urban tend to have a higher premature mortality rate. Premature mortality also tends to cluster among geographically contiguous and proximate local authorities. We develop a novel analytical research design that relies on spatial pattern recognition to demonstrate that an empirical model that contains only socio-economic variables can eliminate these spatial patterns almost entirely. We demonstrate that socioeconomic factors across local authority districts explain 81 percent of variation in female and 86 percent of variation in male premature mortality in 2012–14. As our findings suggest, policy-makers cannot hope that health policies alone suffice to significantly reduce inequalities in health. Rather, it requires strong efforts to reduce the inequalities in socio-economic factors, or living conditions for short, in order to overcome the spatial disparities in health, of which premature mortality is a clear indication.
Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.
The entry and success of new parties has become a regular event in modern democracies. From the emergence of green to protest parties, new movements have entered the electoral arena. This article addresses one of the less studied aspects of new parties: the dynamic process of party exit and entry into politics. The article argues that changes to the party system, produced by the collapse of a political party, can lead to the successful entrance of new parties in the next election. The premise is that one party’s loss is a future one’s gain. The empirical results provide strong evidence that the size of the policy space created by a party collapse has a substantial impact on the level of new party’s success.
Firms in the USA rely on highly skilled immigrants, particularly in the science and engineering sectors. Yet, the recent politics of immigration marks a substantial change to US immigration policy. We implement a conjoint experiment that isolates the causal effect of nativist, anti-immigrant, pronouncements on where skilled potential-migrants choose to immigrate to. While these policies have a significantly negative effect on the destination choices of Chilean and UK student subjects, they have little effect on the choices of Indian and Chinese student subjects. These results are confirmed through an unobtrusive test of subjects’ general immigration destination preferences. Moreover, there is some evidence that the negative effect of these nativist policies are particularly salient for those who self-identify with the Left.
Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.
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