No abstract

The SARS-CoV-2 virus was first identified in Wuhan, China, in late December 2019, and it quickly spread to many countries. By March 2020, the virus had triggered a global pandemic (World Health Organization, 2020). In response to this crisis, governments have implemented unprecedented public health measures. The success of these policies will largely depend on the public's willingness to comply with new rules. A key factor in citizens’ willingness to comply is their understanding of the data that motivate government action. In this study, we examine how different ways of presenting these data visually can affect citizen's perceptions, attitudes and support for public policy.

The international tax system is a complex regime composed of thousands of bilateral tax treaties. These agreements coordinate policies between countries to avoid double taxation and encourage international investment. I argue that by solving this coordination problem on a bilateral basis, states have inadvertently created opportunities for treaty shopping by multinationals. These opportunities, in turn, reduce the potency of fiscal policy, put pressure on governments to change their domestic tax laws, and ultimately constrain state autonomy. This constraint is theoretically distinct from the usual race-to-the-bottom story and it generates different testable implications. I use a motivating case study to show how multinationals leverage the structure of the treaty network to reduce their tax burden. Then, I develop a new measure of treaty-shopping opportunities for firms in 164 countries. Where the proliferation of tax treaties allows multinationals to engage in treaty shopping, states' fiscal autonomy is limited, and governments tend to maintain lower tax rates.

Multiple imputation (MI) is often presented as an improvement over listwise deletion (LWD) for regression estimation in the presence of missing data. Against a common view, we demonstrate anew that the complete case estimator can be unbiased, even if data are not missing completely at random. As long as the analyst can control for the determinants of missingness, MI offers no benefit over LWD for bias reduction in regression analysis. We highlight the conditions under which MI is most likely to improve the accuracy and precision of regression results, and develop concrete guidelines that researchers can adopt to increase transparency and promote confidence in their results. While MI remains a useful approach in certain contexts, it is no panacea, and access to imputation software does not absolve researchers of their responsibility to know the data.

How does the institutional design of a state's bureaucracy affect foreign policy? We argue that institutions can moderate bureaucrats’ incentives to act in accordance with an Executive's diplomatic preferences. Where the Executive can influence budgets or career paths, bureaucrats face incentives to adopt her diplomatic goals as their own. Where agencies are shielded from Executive influence, bureaucrats are free to act independently in a bid to enhance their autonomy and their reputation for competence. To test these expectations, we develop a new measure of bureaucratic independence for the 15 aid‐giving agencies in the US government. We analyze how independence affects foreign aid allocation patterns over the 1999–2010 period. We find that in “dependent” agencies, foreign aid flows track the diplomatic objectives of the president. In “independent” agencies, aid flows appear less responsive to presidential priorities and more responsive to indicators of need in the recipient country. Our results highlight limits on the diplomatic use of foreign aid and emphasize the importance of domestic institutional design. Our findings yield insight into a broad range of policy domains—including international finance, immigration, and the application of economic sanctions—where multiple government agencies are in charge of implementing foreign policy.