Publications

Objectives Previous studies have reported inconsistent findings regarding the relationship between COVID-19 restrictions and suicide rates, particularly concerning business and social restriction policies. This study aimed to address this gap by analyzing detailed US county-level restriction and suicide death data. Study Design Data from the US Centers for Disease Control and Prevention (CDC) were obtained for county-level suicide rates by race, sex, and age from 2016 to 2023. Yale School of Management-Tobin Center State and Local COVID Restriction Database provided data on COVID-19 social and business restrictions. These datasets were combined with other relevant data on county-level demographics, gross domestic product (GDP), unemployment, and population density. Methods Poisson interrupted time-series regression was employed to assess whether these restrictions were associated with changes in suicide rates during pandemic (2020 and 2021) and post-pandemic (2022 and 2023) periods. Results During the pandemic restriction era of 2020–2021, stricter business capacity limits were linked to lower suicide rates overall (Poisson coefficient: -0.90 [95% CI -1.54, -0.25, p = 0.006]), and in particular among males (Poisson coefficient: -1.13 [-1.94, -0.32, p = 0.006]). The estimated coefficient was not statistically significant for females. Among age groups, individuals aged 25–34 and 35–44 experienced lower suicide rates in counties with tighter restrictions, while other age and sex groups did not show similar trends. Additionally, no statistically significant correlations were found across racial groups. In contrast, social gathering restrictions had a less consistent relationship with suicide rates; while those aged 15–24 experienced an increase in rates under tighter restrictions, those aged 25–34 had a decrease. No other demographic groups yielded statistically significant coefficient estimates. Conclusions Results underscore the importance of considering differential effects of business and social restrictions on suicide rates, and to tailor interventions to address the unique needs of specific populations during public health crises.

Local, state, and federal governments offer firms combinations of tax incentives and financial intermediation to help attract or retain jobs and investment for their constituents. Tax breaks are often implemented as place-based policies (PBPs), for which firms must allocate resources to a particular locality to maximize subsidy amounts. Tax instruments underlying PBPs can take many forms, including tax breaks for specific firms in critical sectors, broad-based subsidies for hiring and capital expenditures, industrial policies which operate through intergovernmental development plans, and local revitalization programs targeting neighborhoods which appear to be distressed based on measures such as unemployment and poverty rates. While there is a large body of research examining the equity-efficiency tradeoffs inherent in PBPs based on aggregated real economic outcomes and via quantitative spatial models, less is known about how firms alter their production processes and corporate strategy in responding to policy nudges. Data limitations, especially in contexts with small, privately held firms, prevent comprehensive analyses of these margins of adjustment. On the labor side, firms can use subsidies to engage in labor hoarding; for multi-plant firms, PBPs induce firms to shift the spatial distribution of worker skills within the firm's internal network, with implications for regional inequality. Firms also alter their investment plans over time, across space, and between physical and intangible capital inputs; funds obtained through place-based programs may substitute for external financing sources. Metrics for scoring PBPs aimed at firms range from ex post partial equilibrium cost per job or general equilibrium NPV calculations to ex ante criteria based on compatibility of firms' incentives with policymakers' objectives and the scope for welfare losses from inter-jurisdictional tax competition. Large variation in the same metric across existing studies focusing on the same type of corporate tax instrument underscores challenges in extrapolating the successes and failures of any one PBP into general policy design principles.

Public disclosure regimes seek to align regulated agents' incentives and improve downstream decision-making, but adverse signals can conflate useful information with noise arising from luck and circumstance. We study this tradeoff in the context of registered advisers' personal financial distress disclosures, examining their labor-market effects and informational value. We find greater job separation following disclosures (13.9% and 17.6% increases relative to baseline separation rates following bankruptcy and other financial disclosures, respectively), comparable to the effects of adviser misconduct disclosures, suggesting that downstream actors infer broad performance concerns even from weakly informative signals. Using FOIA-obtained termination-reason data, we show that these distress-related separations largely reflect involuntary discipline rather than voluntary exit. Exploiting the regime's fixed ten-year disclosure limit, we find a discrete improvement in advisers' labor-market mobility once older disclosures cease to be publicly visible, suggesting that ongoing public visibility shapes labor-market outcomes. We also find evidence that these disclosures contain some information about adviser quality but also substantial noise arising from external shocks. Financial disclosures predict future misconduct, but much more weakly than misconduct disclosures, and distress events linked to medical emergencies and prior job loss have little predictive value despite substantial labor-market penalties. Consistent with this interpretation, increases in local housing wealth reduce disclosure likelihood, highlighting the role of shocks in generating distress. Black and Hispanic advisers exhibit higher disclosure rates and greater sensitivity of disclosure risk to housing-wealth shocks, implying unequal exposure to the costs of mandatory disclosure while benefits accrue more broadly to clients and markets. Taken together, our findings suggest that disclosure regimes can generate collateral consequences when beneficiaries cannot fully distinguish informative signals from distress arising from luck and circumstance.

Do corporate control transactions discipline the labor force? Consistent with synergies, new disclosures of employee misconduct in the investment advisory industry drop by between 17 and 22 percent following mergers. Both targets and acquirers have better pre-merger misconduct records than the industry’s average firm and, within the subsample of merging firms, there is assortative matching on misconduct. Merger events facilitate further reductions in misconduct through separations of target firm employees with high misconduct. Many of these employees remain in the industry, suggesting that consolidation plays an important role in the redistribution of misconduct across firms.

Pandemic restrictions targeted non-essential businesses that required in-person contact to operate. We document the impact of these restrictions county-by-county on directly regulated businesses and on businesses not subject to restrictions. Through 2023, a one standard deviation increase in restrictions increased business closures by 3% and reduced net job creation by 18%. These adverse effects extended beyond regulated firms to those exempt from regulations. We attribute this transmission at least in part to the loss of face-to-face interactions.

Nature provides essential inputs to the economy through ecosystem services. In Asia and the Pacific, accelerating biodiversity loss is eroding these services, increasing vulnerability to shocks, and creating new risks for investors and governments.

Should central banks’ inflation targets remain set in stone? We study a dynamic mechanism
design problem between a government (principal) and a central bank (agent). The central
bank has persistent private information about structural shocks. Firms learn the state from the
central bank’s reports and form inflation expectations. A dynamic inflation target implements the
full-information commitment allocation. The central bank is delegated the authority to adjust
the level and flexibility of its target as long as it does so one period in advance. All history
dependence of the mechanism is summarized by the current period’s target. We show that
a declining natural interest rate and a flattening Phillips curve imply opposite optimal target
adjustments. We leverage our framework to study longer-horizon time consistency problems
and speak to practical policy questions of inflation target design.

The 2020 revisions to the Federal Reserve’s monetary policy framework included a shift in the Fed’s policy focus to shortfalls (rather than deviations) from maximum employment and a commitment to “flexible average inflation targeting.” The new framework, and the associated guidance and asset purchases with which it was implemented, were tested by the surge in inflation in 2021 and 2022. We consider the lessons learned from this experience. We conclude that the changes to the framework were too focused on the experience following the financial crisis and hence were not robust in the face of unexpected changes in economic circumstances. We also argue that the Fed made mistakes with the calibration and communication of the tools used to implement the framework—the forward guidance on the policy rate and the asset purchase program. We recommend a broad framework that would be appropriate in a wide range of policy environments, with the specific policy approach to be taken in any given circumstance to be communicated through forward guidance and asset purchase announcements. We suggest ways in which the Fed could implement these tools with better calibration and communication, in order to avoid having its policy commitments exacerbate costly economic outcomes.

The credit-implied volatility (CIV) surface is introduced as an organizing framework for analysis of credit spreads, providing a description of CDS spreads for firms across the credit spectrum, of varying maturities, and at all points throughout the credit cycle.

We model the optimal resolution of insolvent firms in general equilibrium. Collateral- constrained banks lend to (i) solvent firms to finance investments and (ii) distressed firms to avoid liquidation. Liquidations create negative fire-sale externalities. Liquidations also re- lieve bank balance-sheet congestion, enabling new firm loans that generate positive collateral externalities by lowering bank borrowing rates. Socially optimal interventions encourage liqui- dation when firms have high operating losses, high leverage, or low productivity. Surprisingly, larger fire sales promote interventions encouraging more liquidations. We study synergies be- tween insolvency interventions and macroprudential regulation, bailouts, deferred loss recog- nition, and debt subordination. Our model elucidates historical crisis interventions.

The origin of the modern joint-stock company is typically traced to the concomitant appearance of large-scale maritime trading companies in England and the Netherlands in the early seventeenth century. Highlighting medieval cases in southern Europe, we claim that the joint-stock company emerged earlier in history. These prior appearances support the theory of convergent evolution towards the joint-stock company. We document alternative and largely independent developmental paths that suggest the joint-stock company can emerge in a variety of legal, political and socioeconomic contexts. This evidence has implications for identifying the necessary background underlying the emergence of the joint-stock company, and for the debate regarding the link between business institutions and economic growth.

We examine whether and how granular, real-time predictive models should be in- tegrated into central banks’ macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between imple- menting models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a set- ting in which private portfolios react endogenously to the regulator’s model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is op- timally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.

We analyze survey data on ESG beliefs and preferences in a large panel of retail investors linked to administrative data on their investment portfolios. The survey elicits investors’ expectations of long-term ESG equity returns and asks about their motivations, if any, to invest in ESG assets. We document four facts. First, investors generally expected ESG investments to underperform the market. Between mid-2021 and late-2022, the average expected 10-year annualized return of ESG investments relative to the overall stock market was –1.4%. Second, there is substantial heterogeneity across investors in their ESG return expectations and their motives for ESG investing: 45% of survey respondents do not see any reason to invest in ESG, 25% are primarily motivated by ethical considerations, 22% are driven by climate hedging motives, and 7% are motivated by return expectations. Third, there is a link between individuals’ reported ESG investment motives and their actual investment behaviors, with the highest ESG portfolio holdings among individuals who report ethics-driven investment motives. Fourth, financial considerations matter independently of other investment motives: we find meaningful ESG holdings only for investors who expect these investments to outperform the market, even among those investors who reported that their most important ESG investment motives were ethical or hedging reasons.

We examine whether and how granular, real-time predictive models should be in- tegrated into central banks’ macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between imple- menting models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator’s optimal policy in a set- ting in which private portfolios react endogenously to the regulator’s model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data—a graph transformer—and we discuss why it is op- timally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.

How does market power in the corporate banking sector influence the effects of interest rate cap policies on credit allocation? We study this question using administrative credit registry data in Bangladesh, where the Central Bank capped the interest rate on corporate loans at 13% in 2009, relative to a pre-reform average interest rate of 14.5%. We apply a difference-in-differences design with variation in pre-regulation, branch-level interest rates as an exposure measure and find that a one percentage point cap-induced drop in rates increased lending amounts by 30% over the two years of the cap regime. This increase in lending is not driven by banks’ costs to supplying credit, as proxied by the riskiness of the borrower pool or deposit funding costs. Our results point to substantial credit under-provision due to banks’ market power in an emerging markets context, even in the presence of relationship lending.

We empirically characterize how China is internationalizing its bond market by staggering the entry of different types of foreign investors into its domestic market and propose a dynamic reputation model to explain this strategy. Our framework rationalizes China's strategy as trying to build credibility as a safe issuer while reducing the cost of capital flight. We use our framework to shed light on China's response to episodes of capital outflows.

Multiple extensions of the federal student loan forbearance program that began in March 2020 resulted in a temporary payment pause that lasted more than 3 years. We examine the impact of long-term forbearance on the evolution of borrowing by distressed individuals. Compared to distressed borrowers not in forbearance, we observe a 13.4-point credit score increase within a year of forbearance, followed by 12.3% more credit card debt and 4.6% more auto loans, but significantly less total mortgage debt. By year 3, student loan balances are 12.1% higher for the forbearance sample and delinquencies on nonstudent debt are also higher. In the absence of policy interventions, our results suggest that the extended breathing room that the program allowed could accelerate post-forbearance financial distress.

Machine learning tools have been remarkably successful at using published anomalies for creating portfolios with extremely high returns. However, the underlying economic mechanisms behind their performance remains unclear. This paper proposes a theory-based dynamic arbitrage trading strategy to interpret how neural networks select among anomalies over time. Using 153 firm characteristics (anomalies), this strategy ranks them similarly to neural networks and absent the use of microcaps explains nearly 40% of their monthly performance. When unpublished anomalies and microcap stocks are excluded, an economic model based algorithm fully explains almost all of the neural network’s return performance and largely duplicates the anomaly selection. Additionally, we show how the performance of neural networks can be further improved by incorporating aspects of economic principles.

We study the socially optimal level of illiquidity in an economy populated by house- holds with taste shocks and present bias with naive beliefs. The government chooses mandatory contributions to accounts, each with a different pre-retirement withdrawal penalty. Collected penalties are rebated lump sum. When households have homoge- neous present bias, β, the social optimum is well approximated by a single account with an early-withdrawal penalty of 1 − β. When households have heterogeneous present bias, the social optimum is well approximated by a two-account system: (i) an account that is completely liquid and (ii) an account that is completely illiquid until retirement.

Disruptions to government contracts traditionally arise during federal shutdowns when Congress fails to appropriate necessary funding. However, recent shifts in federal real estate policy, marked by lease cancellations and non-renewals, challenge the long-standing perception of federal leases as a secure and stable investment. We investigate how federal lease cancellations impact the pricing of government contract risk premia. Using unanticipated Department of Government Efficiency (DOGE) cancellations as a shock to commercial mortgage default risk, we find that first-loss CMBS bond tranches directly linked to DOGE-notified leases experience a persistent 4% drop in price, with large, negative spillover effects to bond prices, delinquency rates, and rental cash flows tied to nearby public and private-tenant leases. These results reflect that early termination options were previously perceived by investors as a dormant clause. Applying arbitrage pricing models of commercial lease contingencies confirms the underpricing of risk associated with government tenants.