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This study proposes an analytical framework towards behavioral political economy of institutional change. It considers institutional changes as central government's choices under uncertainty, which are largely driven by the strategic outcomes in a behavioral coordination game between local officials and private businessmen. With field facts in China, this study suggests that institutional changes begin with pro-competition policies, then a better protection of property rights, followed by a possible standstill or even worse rule of law.

We conduct an online survey to explore how Chinese people living in Germany perceive and react to group criticism in the context of the debate on the Wuhan Diary, a chronicle about life during the lockdown in Wuhan. We find that the majority rating of the book is a lukewarm "neither like nor dislike." Most participants are open to criticism in principle and do not agree that the book only spreads so-called "negative-energy". However, many participants were skeptical about the objectivity of the book and concerned about its potential use by so-called anti-China forces, even though the degree of blind patriotism is relatively low in our sample. The factors influencing the book's evaluation are intriguing: perceived Western sentiment, media exposure and uncritical patriotism all affect COVID-19-related conspiracy beliefs, which in turn lead to a more negative evaluation of the book. A cluster analysis reveals two groups which differ in terms of properties like blind patriotism, belief in certain conspiracies, and also demographic parameters. Our results shed light on identity politics, motivated beliefs, and collective narcissism.

The worldwide COVID-19 pandemic puts countries and their governments in an unprecedented situation. Strong countermeasures have been implemented in most places, but how much do people trust their governments in handling this crisis? Using data from a worldwide survey, conducted between March 20th and April 22nd, 2020, with more than 100,000 participants, we study people's perceptions of government reactions in 57 countries. We find that media freedom reduces government trust directly as well as indirectly via a more negative assessment of government reactions as either insufficient or too strict. Higher level of education is associated with higher government trust and lower tendency to judge government reactions as too extreme. We also find different predictors of perceived insufficient reactions vs. too-extreme reactions. In particular, number of COVID-19 deaths significantly predicts perceived insufficient reactions but is not related to perceived too-extreme reactions. Further survey evidence suggests that conspiracy theory believers tend to perceive government countermeasures as too strict.

In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initiating event can be retrospectively confirmed and the occurrence of the second failure event is observed subject to right censoring. In a cancer data application, the initiating, first and second events could correspond to birth, cancer onset and death. The fact that the data are collected conditional on the first failure event occurring within a time interval induces bias. Interval sampling is widely used for collection of disease registry data by governments and medical institutions, though the interval sampling bias is frequently overlooked by researchers. This paper develops statistical methods for analysing such data. Semiparametric methods are proposed under semi-stationarity and stationarity. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes. We apply the proposed methods to ovarian cancer registry data.