We demonstrate the importance of intertemporal marginal propensities to consume (iMPCs) in disciplining general equilibrium models with heterogeneous agents and nominal rigidities. In a benchmark case, the dynamic response of output to a change in the path of government spending or taxes is given by an equation involving iMPCs, which we call the intertemporal Keynesian cross. Fiscal multipliers depend only on the interaction between iMPCs and public deficits. We provide empirical estimates of iMPCs and argue that they are inconsistent with representative- agent, two-agent and one-asset heterogeneous-agent models, but can be matched by models with two assets. Quantitatively, models that match empirical iMPCs predict deficit-financed fiscal multipliers that are larger than one, even if monetary policy is active, taxation is distortionary, and investment is crowded out. These models also imply larger amplification of shocks that involve private borrowing, as we illustrate in an application to deleveraging.

The United States operates the world’s largest refugee resettlement program. However, there is almost no systematic evidence on whether refugees successfully integrate into American society over the long run. We address this gap by drawing on linked administrative data to directly measure a long-term integration outcome: naturalization rates. Assessing the full population of refugees resettled between 2000 and 2010, we find that refugees naturalize at high rates: 66% achieved citizenship by 2015. This rate is substantially higher than among other immigrants who became eligible for citizenship during the same period. We also find significant heterogeneity in naturalization rates. Consistent with the literature on immigration more generally, sociodemographic characteristics condition the likelihood of naturalization. Women, refugees with longer residency, and those with higher education levels are more likely to obtain citizenship. National origins also matter. While refugees from Iran, Iraq, and Somalia naturalize at higher rates, those from Burma, Ukraine, Vietnam, and Liberia naturalize at lower rates. We also find naturalization success is significantly shaped by the initial resettlement location. Placing refugees in areas that are urban, have lower rates of unemployment, and have a larger share of conationals increases the likelihood of acquiring citizenship. These findings suggest pathways to promote refugee integration by targeting interventions and by optimizing the geographic placement of refugees.

Bernard Malamud, Saul Bellow, Philip Roth, and their critics embraced the notion that their work displayed an affinity to Russian and Yiddish literature, especially to the work of Fyodor Dostoevsky, Nikolai Gogol, and Sholem Aleichem. Like these writers, the prominent American Jewish writers of the 1960s were understood as producing writing that emerged from their authentic, often negative emotions, work that voiced complaints. I first describe this generation's playful claiming of a Russian and Jewish genealogy, their definition of the Russian and Yiddish writers as a collective worthy of copying. I then use close readings of six passages to evaluate the American writers' assertions about their influence by the Russian and Yiddish ones. I compare the inset oral and written complaints in Roth and Bellow with those in Gogol, Dostoevsky, and Sholem Aleichem, both acknowledging their striking formal similarities and distinguishing the comic, satirically presented literary complaints of prerevolutionary Russia from the potentially more therapeutically oriented—albeit still satirical—literary complaints of postwar America. Finally, I look outside the literary texts to understand why it was appealing to 1960s American writers to think of themselves as influenced by prerevolutionary Russian and Yiddish verbal art. This article situates the American Jewish writers and their critics in an aural environment where Russian and Yiddish sounds were increasingly available in entertainment and where they were associated with authenticity and political opposition. In spite of the formal parallels among the American Jewish, Russian, and Yiddish literary complaints, and in spite of Roth and Bellow representing themselves compellingly as imitators, I argue that they need to be understood instead in their own national and temporal communicative context.

In 2008, when Michael McFaul was asked to leave his perch at Stanford and join an unlikely presidential campaign, he had no idea that he would find himself at the beating heart of one of today’s most contentious and consequential international relationships. As President Barack Obama’s adviser on Russian affairs, McFaul helped craft the United States’ policy known as “reset” that fostered new and unprecedented collaboration between the two countries. And then, as U.S. ambassador to Russia from 2012 to 2014, he had a front-row seat when this fleeting, hopeful moment crumbled with Vladimir Putin’s return to the presidency. This riveting inside account combines history and memoir to tell the full story of U.S.-Russia relations from the fall of the Soviet Union to the new rise of the hostile, paranoid Russian president. From the first days of McFaul’s ambassadorship, the Kremlin actively sought to discredit and undermine him, hassling him with tactics that included dispatching protesters to his front gates, slandering him on state media, and tightly surveilling him, his staff, and his family.

From Cold War to Hot Peace is an essential account of the most consequential global confrontation of our time.

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embed- dings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immi- gration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal anal- ysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.