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Received wisdom posits that sterilized foreign exchange intervention can be effective by altering the currency composition of assets held by the public. This paper proposes an alternative channel: sterilized intervention may (or may not) have real effects because it changes the net credit position of the central bank vis a vis financial intermediaries, thereby affecting external debt limits. This argument is developed in the context of an open economy model with domestic banks subject to occasionally binding collateral constraints. Intervention has real effects if and only if it occurs when the constraints bind; at such times, a sterilized sale of official reserves relaxes the constraints by reducing the central bank's debt to domestic banks, freeing resources for the latter to increase the supply of credit to domestic agents. The analysis yields several noteworthy implications for intervention policy, official reserves accumulation, and the interaction between intervention and monetary policy.

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We have seen in the past decade a sharp increase in the extent that companies use data to optimize their businesses. Variously called the `Big Data' or `Data Science' revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of an Artificial Intelligence (AI) that works by combining many ML algorithms together - each targeting a straightforward prediction task - to solve complex problems. We will define a framework for thinking about the ingredients of this new ML-driven AI. Having an understanding of the pieces that make up these systems and how they fit together is important for those who will be building businesses around this technology. Those studying the economics of AI can use these definitions to remove ambiguity from the conversation on AI's projected productivity impacts and data requirements. Finally, this framework should help clarify the role for AI in the practice of modern business analytics and economic measurement.

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Significance tests are probably the most common form of inference in empirical economics, and significance is often interpreted as providing greater informational content than non-significance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are typical and even prevalent in economics, where data sets are large (and becoming larger) and where there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. In consequence, we advocate a visible reporting and discussion of non-significant results in empirical practice.

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This paper examines whether job suburbanization caused declines in black employment rates from 1970 to 2000. I find that black workers are less likely than white workers to work in observably similar jobs that are located further from the central city. Using evidence from establishment relocations, I find that this relationship at least in part reflects the causal effect of job location. At the local labor market level, I find that job suburbanization is associated with substantial declines in black employment rates relative to white employment rates. Evidence from nationally planned highway infrastructure corroborates a causal interpretation.

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Over the past four decades, government debt as a fraction of GDP has been on an upward trajectory in advanced economies, approaching levels not reached since World War II. While normative macroeconomic theories can explain the increase in the level of debt in certain periods as a response to macroeconomic shocks, they cannot explain the broad-based long-run trend in debt accumulation. In contrast, political economy theories can explain the long-run trend as resulting from an aging population, rising political polarization, and rising electoral uncertainty across advanced economies. These theories emphasize the time-inconsistency in government policymaking, and thus the need for fiscal rules that restrict policymakers. Fiscal rules trade off commitment to not overspend and flexibility to react to shocks. This tradeoff guides design features of optimal rules, such as information dependence, enforcement, cross-country coordination, escape clauses, and instrument vs. target criteria.