Volume 16, Issue 1

The academic literature is rife with analyses of the US Electoral College’s flaws, but proposals to improve the system often rely upon old ideas. For example, the idea of replacing the Electoral College with a nationwide vote originated in 1816, and the derivative concept underlying the National Popular Vote Interstate Compact dates to 1976. Similarly, numerous methods for retaining the College but modifying the manner in which individual states select electors were proposed during the nineteenth and twentieth centuries, but the only one that gained significant traction – the congressional-district system currently used by Maine and Nebraska – was initially described in the 1950s by Senator Karl Mundt and Representative Frederick Coudert. This article describes the County-Elector Plan, a new approach that maintains the Electoral College but allocates a state’s electoral votes to each county’s plurality winner, in an amount proportional to the county’s voter turnout. A candidate’s statewide electoral vote total is then the rounded sum of the electoral votes the candidate receives in each county. The County-Elector Plan would seismically transform presidential elections by shifting an election’s focus from a handful of battleground states to hundreds of battleground counties spread across both current battleground and spectator states. Retrospective application of the plan to the 2016 Trump-Clinton contest shows that each candidate would have received electoral votes from 41 states, and that Clinton would have won the election by 26 electoral votes. The County-Elector Plan could be implemented on a state-by-state basis, without requiring a constitutional amendment. The plan is gerrymandering-resistant and provides all voters in a state with equal voting power.

Individual and policy reactions to the coronavirus pandemic had disparate impacts on viral transmission and were heterogeneous in their influence on economic activity and personal outcomes (Kerpen, Phil, Stephen Moore, and Casey B. Mulligan. 2022. A Final Report Card on the States’ Response to Covid-19 . Working Paper 29928. National Bureau of Economic Research). Pandemic researchers struggle with choosing from multiple measurements of disease intensity. This paper is the first to suggest using a restricted data-rich dynamic factor model, generated from a variety of economic and pandemic data series to provide a comprehensive measurement of disease intensity. We use this approach to evaluate vaccination efficacy. We also provide future researchers with an open-source Python package that can run a restricted dynamic factor model with bespoke data input. By using the information generated by this specification, policy makers can choose how to respond to future pandemics with a deeper understanding of the costs and benefits of their choices. This paper concentrates on the United States, and exploits variation between U.S. states, but this approach is generalizable for any populations with similar data availability.

For many decades, Nigeria has been plagued by a consistently high rate of violent events, resulting in countless fatalities and the displacement of citizens. This study aimed to model the spatio-temporal patterns of these events and the associated fatality to gain insight into the chain of events and provide a basis for swift and strategic intervention. To this end, a Cox point process model through the stochastic partial differential equation was adopted, taking into account the location randomness exhibited by violent events occurrence. The data analyzed was derived from the Uppsala Conflict Data Program – Georeferenced Event Dataset (UCDP-GED) version 22. The results revealed that violent events are particularly prevalent in the country’s northeast region, with a probability of 0.42 of at least one death occurring per violent event. These findings suggest a need for urgent intervention through informed policymaking, impeding the influx of illegal arms and ammunition in porous borders, and strategically tackling poverty.

The aim of this article is to conduct a meta-analysis of existing research on the determinants of military expenditure. Using data from an initial screening of 179 studies, 15 studies were selected for meta-analysis, contributing 20,023 observations to analyze key variables influencing defense budget allocations. By employing a random-effects meta-analysis, this study synthesizes findings across diverse geopolitical and economic contexts, addressing inconsistencies in prior research and enhancing the reliability of conclusions. Our findings indicate that war, current military expenditure, and the presence of external threats (enemies) are significant drivers of military spending, while national conditions show a significant negative correlation with military expenditure. Other variables, including GDP, population, democracy, trade, FDI, arms exports, alliances, threats, and political regime type, do not show strong correlations. By combining data from multiple studies, this methodological approach not only generalizes results and improves the precision of effect estimates but also identifies gaps to guide future defense economics research. This research provides policymakers with a broader understanding of the factors shaping defense budgets, offering insights into how traditional budgeting methods might be complemented by a data-driven approach.