Statistical Regularity and Free Will: L.A.J. Quetelet and P.A. Nekrasov

In the 19th century, causes of empirically observed stability of averages in settings relating to human behaviour were a topic of intense discussion in western Europe. This followed an extensive study of empirical stability by the founder of modern statistics (and of the International Statistical Institute) L.A.J. Quetelet, published in 1835, in what he called "Social Physics". The eminent mathematician of strong probabilistic and philosophical inclination and Russian Orthodox religious belief, P.A. Nekrasov, took up and modified Quetelet's Social Physics in 1902, with (social) independence seen as prime cause of statistical regularity. Our paper focuses on the role free will plays in the statistical writings of Quetelet and of Nekrasov. The work of the latter has remained little known in general, mainly for politico-ideological reasons.     

Offshoring and reshoring: U.S. insights and research challenges

The phenomenon of reshoring to the U.S. has received wide attention ever since it became a political platform for U.S. politicians during the last presidential election. The economic downturn, a heightened emphasis on sustainability, and increasing customer expectations for flexibility and improved cost performance drove firms to re-consider the appropriate “shoring” decision. The intent of this research note is to add a U.S.-centric perspective to the “shoring” debate.     

The Merchant and Mr. Reagan:

A BSTRACT . This paper brings to note for the historical record a facet of Reaganomics with a mercantilist hue. In particular, it is an examination of President Ronald Reagan's 1985 speech, "U.S. Trade Policy: Free and Fair Trade," President Reagan's major statement on trade policy. The address is shown to have been largely a retaliatory mercantilist tract.     

Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.