Cueing quality: Unpacking country-of-origin effects on intentions to vaccinate against COVID-19 in Taiwan.

While existing studies have reported and recognized country-of-origin effects on the intentions to vaccinate against COVID-19 among individual citizens in some countries, the causal mechanism behind such effects to inform public health policymakers remain unexplored. Adding up a quality cue explanation for such effects to the existing literature, the authors argue that individual consumers are less willing to get a vaccine designed and manufactured by a country with a significantly lower quality perception than other countries. A survey experiment that recruited a nationally representative sample of Taiwanese adults (n = 1951) between December 13, 2020 and January 11, 2021 was designed and conducted to test the argument. We find that all else equal, Taiwanese respondents were on average less likely to express stronger willingness to take a vaccine from China than from the US, Germany, and Taiwan. Furthermore, even when the intrinsic quality of the vaccine was held constant by the experimental design, respondents still had a significantly lower quality perception of the vaccine from China, both in terms of perceived protection and severe side effects. Further evidence from casual mediation analyses shows that about 33% and 11% of the total average causal effects of the "China" country-of-origin label on vaccine uptake intention were respectively mediated through the perceived efficacy of protection and perceived risk of experiencing severe side effects. We conclude that quality cue constitutes one of many casual mechanisms behind widely reported country-of-origin effects on intention to vaccinate against COVID-19.

Using Facebook data to predict the 2016 U.S. presidential election.

We use 19 billion likes on the posts of top 2000 U.S. fan pages on Facebook from 2015 to 2016 to measure the dynamic ideological positions for politicians, news outlets, and users at the national and state levels. We then use these measures to derive support rates for 2016 presidential candidates in all 50 states, to predict the election, and to compare them with state-level polls and actual vote shares. We find that: (1) Assuming that users vote for candidates closer to their own ideological positions, support rates calculated using Facebook predict that Trump will win the electoral college vote while Clinton will win the popular vote. (2) State-level Facebook support rates track state-level polling averages and pass the cointegration test, showing two time series share similar trends. (3) Compared with actual vote shares, polls generally have smaller margin of errors, but polls also often overestimate Clinton's support in right-leaning states. Overall, we provide a method to forecast elections at low cost, in real time, and based on passively revealed preference and little researcher discretion.

Learning during a crisis: The SARS epidemic in Taiwan.

SARS struck Taiwan in 2003, causing a national crisis. Many people feared that SARS would spread through the health care system, and outpatient visits fell by more than 30% in the course of a few weeks. We examine how both public information and the behavior and opinions of peers contributed to this reaction. We identify a peer effect through a difference-in-difference comparison of longtime residents and recent arrivals, who are less socially connected. Although several forms of social interaction may contribute to this pattern, social learning is a plausible explanation for our finding. We find that people respond to both public information and to their peers. In a dynamic simulation based on the regressions, social interactions substantially magnify the response to SARS.