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Donald Trump entered the presidency in 2017 with an electoral mandate to reduce US military involvement around the world and to abandon the trade and investment treaties that empowered global corporations. Yet he mostly continued the foreign policies adopted by previous administrations. In recent decades, those policies have increasingly served particularistic elite interests at the expense of the US ruling class as a whole, and they have also been unsuccessful in stemming the decline of US imperial power. This chapter explores the factors that explain this continuity of policy. In analyzing the reasons for policy stasis, it offers an analytical basis to evaluate what might change under President Biden. It also assesses what strategies might be most effective for those who hope to resist US militarism and to undermine the US capacity to enforce a hegemony based on rapacious capitalism.

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.