We study the implications of rising cyber risk for macroeconomic outcomes and the ability of policy to mitigate them. We develop a general equilibrium model that, novel to the literature, features strategic interactions between heterogeneous firms that invest in cybersecurity and attackers who direct their search toward the most attractive targets, taking these investments into account. Attackers trade-off the higher expected bounty from attacking a larger firm against the lower likelihood of overcoming their cybersecurity defenses. Cyberattacks generate a negative externality by reducing aggregate productivity, motivating a role for policy. We discipline the model using new firm-level evidence on cybersecurity investment, documenting that cybersecurity employment as a share of total employment rises steeply with firm size and that mid-sized firms have increasingly become the targets of attacks over the past two decades. Introducing cyber risk to the economy reduces firm entry by 4 percent, aggregate productivity by 0.6 percent, and total output by 1.7 percent. Using the model, we evaluate two policy interventions—subsidies for cybersecurity investment and bailouts for attacked firms. While subsidies can raise aggregate output, bailouts reduce it, with effects that depend crucially on the severity and nature of cyber risk.

This paper quantifies the effect that regulatory capital requirements have on bank lending and real economic activity. Exploiting a change in capital requirements by the Federal Reserve at the onset of the pandemic recession, it establishes causally that looser requirements increased bank credit provision to consumers. On average, banks who received relatively more balance sheet space from the policy change passed this along to their customers in the form of relatively higher credit limits from Q2 2020 to Q1 2021, which also led to relatively higher credit card borrowing among these customers. Using a general equilibrium quantitative model calibrated to match the empirical findings, the paper shows that absent the Federal Reserve policy change, consumption would have fallen by an extra 2.7% in the three years following the pandemic recession. Motivated by these estimates, this paper evaluates the efficacy of countercyclical capital requirements and finds that such policy could lower consumption volatility over the business cycle by as much as 12%

We quantify the contribution of changes to the structure of the U.S. labor market to wage stagnation over the past 40 years. Using a rich structural model of wage and employment dynamics estimated on Current Population Survey data, we reach three main conclusions. First, upward job mobility has declined by half between the 1980s and 2010s. Second, this decline is not driven by weaker aggregate labor demand. Instead, we identify three key structural forces: increased mismatch between job openings and job seekers, rising employer concentration that limits job shopping opportunities, and reduced job search among the employed—potentially due to the growing use of non-compete agreements. Third, by curbing upward mobility, these structural shifts have lowered aggregate real wage growth by four percentage points since the 1980s, corresponding to approximately 40 percent of the decline of the aggregate labor share.

Why has worker mobility in the United States declined so much over the past decades? While previous work attributes this decline to reduced labor market dynamism, this paper reveals that one third of this decline is due to increased educational attainment among workers. Higher education affects labor mobility in two ways. First, having a larger share of young workers in school rather than in the labor market precludes these very workers, who are typically the most mobile, from switching jobs and occupations. Second, education provides workers an alternative to learning about their ''type'' making educated workers less reliant on experimenting with new jobs.

We analytically characterize optimal monetary policy in a multisector economy with menu costs and contrast it with the textbook Calvo model. Following a sectoral productivity shock, the textbook model prescribes stable inflation, providing a formal justification for inflation targeting. In contrast, under menu costs, policy should "look through" such shocks and allow inflation to move inversely with output. We provide sharp intuition: stabilizing inflation causes shocks to spill over across sectors, forcing firms to pay menu costs unnecessarily. In a quantitative model, a simple look-through policy — stabilizing nominal wages rather than inflation — improves welfare via lower menu costs.

I study how wealth impacts workers' job-switching behavior and earnings through a precautionary job-keeping motive. All else equal, low-wealth workers are less willing to switch jobs because such moves increase their short-term risk of job loss. I quantify this channel using a search and matching model where wages are determined by a generalized alternating offer bargaining protocol that accommodates risk aversion, wealth accumulation, and on-the-job search. Precautionary job-keeping accounts for 43% of the earnings gap between low- and high-wealth workers after the Great Recession. The pandemic stimulus weakened this motive, fueling the strong recovery in job-switching in the United States.

Data, data, data… Economists know their importance well, especially when it comes to monitoring macroeconomic conditions - the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before so-called big data became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.