Cristobal Young

In this age of globalization, many countries and U.S. states are worried about the tax flight of the rich. As income inequality grows and U.S. states consider raising taxes on their wealthiest residents, there is a palpable concern that these high rollers will board their private jets and fly away, taking their wealth with them. Many assume that the importance of location to a person's success is at an all-time low. Cristobal Young, however, makes the surprising argument that location is very important to the world's richest people. Frequently, he says, place has a great deal to do with how they make their millions. In The Myth of Millionaire Tax Flight , Young examines a trove of data on millionaires and billionaires―confidential tax returns, Forbes lists, and census records―and distills down surprising insights. While economic elites have the resources and capacity to flee high-tax places, their actual migration is surprisingly limited. For the rich, ongoing economic potential is tied to the place where they become successful―often where they are powerful insiders―and that success ultimately diminishes both the incentive and desire to migrate. This important book debunks a powerful idea that has driven fiscal policy for years, and in doing so it clears the way for a new era. Millionaire taxes, Young argues, could give states the funds to pay for infrastructure, education, and other social programs to attract a group of people who are much more mobile―the younger generation.

There are many ways of conducting an analysis, but most studies show only a few carefully curated estimates. Applied research involves a complex array of analytical decisions, often leading to a 'garden of forking paths' where each choice can lead to different results. By systematically exploring how alternative analytical choices affect the findings, Multiverse Analysis reveals the full range of estimates that the data can support and uncovers insights that single-path analyses often miss. It shows which modelling decisions are most critical to the results and reveals how data and assumptions work together to produce empirical estimates. Focusing on intuitive understanding rather than complex mathematics, and drawing on real-world datasets, this book provides a step-by-step guide to comprehensive multiverse analysis. Go beyond traditional, single-path methods and discover how multiverse analysis can lead to more transparent, illuminating, and persuasive empirical contributions to science.