In the context of leveraged buyouts (LBOs), this paper empirically studies the relation between pre-buyout credit market conditions and the post-buyout behavior of target companies, employing a supervisory dataset to overcome limited data availability for post-buyout target financial information. We propose an LBO-specific measure of (changes of) credit market conditions--the short-term (6-month) change of credit spreads leading up to buyout close. Using this proposed measure, we show that loosening pre-LBO credit market conditions, which are related to higher buyout leverage consistent with the literature, are associated with poor post-LBO (operating) performance of the target company. These results support the narrative of agency costs of debt such as risk shifting and debt overhang but are inconsistent with theories of disciplinary effects of debt. We provide further evidence supportive of the theories of agency costs of debt and some results favorable to the risk shifting story.
Keywords: Private equity, leveraged buyout, credit market condition, agency cost
This paper documents new facts on the modification of bank loans using FR Y-14Q regulatory data on C&I loans. We find that loan-level modifications of key contractual terms, such as interest and maturity, occur at least once for 41 percent of loans. Cross sectional differences in modifications are substantial and amplified by borrower distress. Relative to single-lender loans, syndicated loans are 1.5 times more likely to be modified and interest rate changes are twice as likely. Our findings call into question whether 1) creditor dispersion makes loan modifications more challenging and 2) relationship lending between banks and small borrowers creates more scope for flexibility when borrower-level conditions change.
Keywords: Corporate debt, Renegotiation, SME lending, Relationship lending
We develop a multisector, open economy, New Keynesian framework to evaluate how potentially binding capacity constraints, and shocks to them, shape inflation. We show that binding constraints for domestic and foreign producers shift domestic and import price Phillips Curves up, similar to reduced-form markup shocks. Further, data on prices and quantities together identify whether constraints bind due to increased demand or reductions in capacity. Applying the model to interpret recent US data, we find that binding constraints explain half of the increase in inflation during 2021-2022. In particular, tight capacity served to amplify the impact of loose monetary policy in 2021, fueling the inflation takeoff.
Keywords: Monetary policy, goods constraints, import constraint, inflation, occasionally binding constraint, supply chain constraints
By using realized and survey-based expected exchange rate data, the paper presents five key findings regarding the Uncovered Interest rate Parity (UIP) and related puzzles in an Emerging Market (EM). First, Fama regressions, when not accounting for shifts in the UIP relationship, yield slopes that are statistically identical to one, irrespective of whether survey-based expected exchange rates or realized exchange rates are used. Second, caution is necessary however, as our analysis identifies three distinct sub-periods within each exchange rate measure, each exhibiting varying levels of puzzling behavior. Third, under realized exchange rates, expectation errors can introduce both downward and upward biases or no bias at all, depending on the sub-period. On the other hand, currency risk premiums consistently lead to a downward bias. Under expected exchange rates, currency risk premiums continue to exert a downward bias at varying degrees across sub-periods. Fourth, responses to interest rate differential shocks by expectation errors are pivotal in inducing both downward and upward biases or removing biases altogether when utilizing realized exchange rate data. Fifth, evidence concerning overshooting and reversal puzzles, as well as their link to the UIP puzzle, varies depending on the specific sub-period and the choice of exchange rate measurement, making it more intricate than the previous literature has documented.
Keywords: UIP Puzzle, FX Rate Overshooting Puzzle, Predictability Reversal Puzzle, Fama Regression, Expectations
Borrowing and Spending in the Money: Debt Substitution and the Cash-out Refinance Channel of Monetary Policy
We show that the strong negative effect of higher mortgage rates on cash-out refinancing reflects substitution into other borrowing products, not large changes in total new household borrowing. We exploit an exogenous increase in long-term rates to show that, in the cross-section of outstanding mortgage rates, changes in cash-out and alternative borrowing are offsetting. Additionally, we instrument using monetary policy surprises to show that, over the period from 2006-2021, changes in cash-out refinancing are offset by alternative borrowing. Our results suggest that debt substitution substantially weakens the cash-out refinance channel of monetary policy and reduces its path-dependence.
This paper reviews the literature examining how the introduction of a retail CBDC would affect the banking sector and financial stability. A CBDC has the potential to improve welfare by reducing financial frictions, countering market power in deposit markets and enhancing the payment system. However, a CBDC also entails noteworthy risks, including the possibility of bank disintermediation and associated contraction in bank credit, as well as potential adverse effects on financial stability. The recycling of the new CBDC liability through asset purchases or lending by the central bank plays an important role in determining the economic consequences of the introduction of a CBDC. A CBDC also raises important questions regarding the footprint of central banks in the financial system. Ultimately, the effects of a CBDC depend critically on its design features, of which remuneration is the one discussed most often in the literature.
Keywords: Central bank digital currency, bank disintermediation, financial stability, central bank balance sheet, payment system
We document the existence of a regulatory premium in the federal funds market related to the implementation of the Liquidity Coverage Ratio (LCR). We use difference-in-differences analysis and confidential bank level data on borrowing in the fed funds and Eurodollar markets to compare the interest rates paid by banks subject to daily reporting of their liquidity profile (daily reporters) relative to other banks. We find that, after the implementation of LCR, daily reporters paid a higher rate compared to other banks when borrowing in the fed funds market given the LCR-favorability of many of the lenders in this market. In addition, on the days that banks borrowed in both the fed funds and Eurodollar markets, daily reporters paid a higher rate than other banks for their borrowing in the fed funds market but not for their borrowing in the Eurodollar market.
Keywords: Federal funds, Eurodollars, Liquidity Coverage Ratio, Market segmentation
Hawkish or Dovish Fed? Estimating a Time-Varying Reaction Function of the Federal Open Market Committee's Median Participant
This paper estimates a time-varying reaction function of the median participant of the Federal Open Market Committee, using a Taylor rule with time-varying coefficients estimated on one- to three-year ahead median forecasts of the federal funds rate, inflation, and the unemployment rate from the Summary of Economic Projections (SEP). We estimate the model with Bayesian methods, incorporating the effective lower bound on the median federal funds rate projections. The results indicate that the monetary policy rule has become significantly more persistent after the pandemic than in the years prior, and it currently reacts strongly to inflation, at more than twice the responsiveness estimated prior to 2020. Our proposed policy rule produces accurate predictions of the median federal funds rate projections in real time for given SEP forecasts of inflation and the unemployment rate, suggesting that the median participant's reaction function is well-represented by our assumed Taylor rule with time-varying coefficients. Our results show that the median participant's reaction function becomes less persistent and less responsive to inflation yet more responsive to the output gap in anticipation of tighter monetary policy conditions, measured by a steeper yield curve. We also find that labor market activity, inflation, and macroeconomic uncertainty correlate significantly with the evolution of the time-varying coefficients of the rule. Finally, we show that in times of a less persistent policy rule or more responsiveness to inflation, markets perceive nominal bonds as better macroeconomic hedges.
Keywords: Summary of Economic Projections, Reaction function, Taylor rule, FOMC communications, Time-varying coefficients, Censored regression
The deep deterioration in the labor market during the Great Recession, the subsequent slow recovery, and the missing disinflation are hard to reconcile for standard macroeconomic models. We develop and estimate a New-Keynesian model with financial frictions, search and matching frictions in the labor market, and endogenous intensive and extensive labor supply decisions. We conclude that the estimated combination of the low degree of nominal wage rigidities and high degree of real wage rigidities, together with the small role of pre-match costs relative to post-match costs, are key in successfully forecasting the slow recovery in unemployment and the missing disinflation in the aftermath of the Great Recession. We find that endogenous labor supply data are very informative about the relative degree of nominal and real wage rigidities and the slope of the Phillips curve. We also find that none of the model-based labor market gaps are a sufficient statistic of labor market slack, but all contain relevant information about the state of the economy summarized in a new indicator for labor market slack we put forward.
Keywords: Great Recession, labor force participation, labor supply, missing disinflation, search and matching
We propose a parsimonious framework to understand how the issuance of central bank digital currency (CBDC) might affect the financial system, the Federal Reserve’s balance sheet, and the implementation of monetary policy. We show that there is a wide range of outcomes on the financial system and the Federal Reserve’s balance sheet that could reasonably occur following CBDC issuance. Our analysis highlights that the potential effects on the financial sector depend critically on how the Fed manages its balance sheet. In particular, CBDC could in principle put substantial upward pressure on the spread of the federal funds rate and other wholesale funding rates over the interest rate on reserves unless the Fed expanded its balance sheet to accommodate CBDC issuance.
Keywords: Central bank digital currency, monetary policy implementation, bank disintermediation, central bank balance sheet
Financial intermediaries manage myriad interest rate risk exposures. We propose a new method to measure financial intermediaries' residual interest rate risk using high-frequency financial market data. Our method exploits all available high-frequency information and is valid under extremely weak assumptions. Applying the method to U.S. life insurers, we find their interest rate risk management strategies are generally effective. However, life insurers are more sensitive to changes in long-term interest rates than property and casualty insurers. We show that the term premium helps to explain the difference in sensitivities between the two types of insurer.
Keywords: Financial Institutions, Interest Rate Risk Management, High-Frequency Financial Econometrics, Subsampling, Life Insurers
This paper examines the provision of official flood risk information in the United States and its distributional impacts on residential flood insurance take-up. Assembling all flood maps produced after Hurricane Katrina, I document that updated maps decreased the number of properties zoned in high-risk floodplains and incorrectly omitted five million properties, primarily in neighborhoods with more Black and Hispanic residents. Leveraging the staggered timing of map updates, I estimate they decreased flood insurance take-up and exacerbated racial disparities in insurance coverage. Correcting flood maps could increase welfare by $20 billion annually, but past map updates distorted risk and price signals.
Keywords: Applied Econometrics, Climate Adaptation, Disaster Insurance, Environmental Inequalities, Flood Risk, Information Provision
Black workers experience a higher unemployment rate, as well as more volatile employment dynamics, than white workers, and the racial unemployment rate gap is largely unexplained by observable characteristics. We develop a New Keynesian model with search and matching frictions in the labor market, endogenous separations, and employer discrimination against Black workers to explain these outcomes. The model is consistent with key features of the aggregate economy and is able to explain key labor market disparities across racial groups. We then use this model to assess the effects of the Federal Reserve's new monetary policy framework--interest rates respond to shortfalls of employment from its maximum level rather than deviations--on racial inequality in the labor market. We find that shifting from a Deviations interest rate rule to a Shortfalls rule reduces the racial unemployment rate gap and the model-based measures of labor market discrimination but increases the average in ation rate. From a welfare perspective, we find that the Shortfalls approach does not do much to reduce racial inequality in our model economy.
Keywords: Unemployment, Monetary Policy, Racial Inequality, Discrimination
Major central banks remunerate reserves at negative rates (NIR). To study the long-run effects of NIR, we focus on the role of reserves as intertemporal stores of value that are used to settle interbank liabilities. We construct a dynamic general equilibrium model with commercial banks holding reserves and funding investments with retail deposits. In the long run, NIR distorts investment decisions, lowers welfare, depresses output, and reduces bank profitability. The type of distortion depends on the transmission of NIR to retail deposits. The availability of cash explains the asymmetric effects of policy-rate changes in negative vs positive territory.
Keywords: Monetary policy, interest rates, money market, negative interest rate
We examine whether the adoption of the current expected credit losses (CECL) model, which reflects forward-looking information in loan loss provisions (LLP), improves banks’ information production. Consistent with better information production, we find changes in CECL banks' financial reporting and operations. First, these banks' loan loss provisions become timelier and better reflect future local economic conditions. Second, CECL banks disclose longer, more forward-looking, and more quantitative LLP information. Lastly, they have fewer loan defaults after adopting CECL. These improvements are greater for banks that invest more in CECL-related information systems and human capital and even more salient for larger banks. Our findings suggest that banks' information production is improved under a more forward-looking accounting standard. However, these improvements are greater for banks with more resources to invest in related technology and human capital.
Keywords: Current Expected Credit Losses (CECL); Banks; Information Production; Loan Loss Provisioning
This paper presents a theory of safe asset creation and the interactions between systemic risk and aggregate demand. The creation of private safe assets by financial intermediaries requires them to take leverage, which generates a risk of future crisis (systemic risk) in which intermediaries liquidate assets to service their debt. In contrast, the creation of public safe assets by the government does not generate systemic risk as the government’s power to tax allows it to better absorb losses. The level of systemic risk determines the neutral rate of interest through households’ precautionary saving and aggregate demand. The model features a two-way interaction between systemic risk and aggregate demand. Monetary and fiscal policy can stabilize aggregate demand and reduce systemic risk by altering the mix of private and public safe assets held by savers. When monetary policy is constrained, the economy can enter a risk-driven stagnation trap in which economic stagnation arises due to excessive systemic risk. Macroprudential policies which reduce systemic risk can stimulate aggregate demand.
This study examines the forecasting performance of inflation swaps and survey-based expectations for one-year inflation. Conducting this exercise helps determine if one set of expectations can provide a cleaner signal about future inflation. The study finds that, overall, inflation swaps more frequently provide better forecasts of future inflation. Previous studies that found poor performance of swaps were strongly influenced by liquidity issues during the financial crisis and the pandemic. When these periods are excluded, swaps have superior predictive ability. Our analysis suggests that combining the two expectations can lead to even better forecasts. The optimal static combination is roughly an equal weighting of swaps and surveys. Alternatively, a dynamic smooth-transition regime switching model can also lead to superior performance and provide a clearer signal on expectations of future inflation. Recently, this measure has implied the Federal Reserve is expected to be closer to its inflation target over the next year than the surveys would suggest.
Keywords: Inflation Expectations, Inflation Swaps, Surveys, Forecasting
In this paper we outline tokenization, which is a new and rapidly growing financial innovation in crypto asset markets, and we discuss potential benefits and financial stability implications. Tokenization refers to the process of constructing digital representations (crypto tokens) for non-crypto assets (reference assets). As we discuss below, tokenizations create interconnections between the digital asset ecosystem and the traditional financial system. At sufficient scale, tokenized assets could transmit volatility from crypto asset markets to the markets for the crypto token's reference assets.
Keywords: Financial stability and risk, cryptoassets, financial innovations, interconnections, tokenization
Data privacy in digital asset systems is of sustained importance to end users. However, there can be disconnect between an end user’s expectations of privacy while using a digital asset payment system and the system’s actual treatment of collected, stored, and used data. This paper provides foundational primer on data privacy alongside qualitative and technical assessments of various approaches to data privacy frameworks and strategies relevant to the early stages of a digital asset system’s design. Analysis relies initially on an outlay of foundational data privacy concepts, including anonymity, confidentiality, and full disclosure, alongside three differing approaches to data privacy frameworks. Analysis finds that some concepts, such as a desire for “cash-like anonymity,” are based on false underlying assumptions. The paper moves away from a likely unattainable standard of anonymity and instead focuses on a hybrid approach to data privacy, inclusive of Cavoukian’s privacy-by-design and popular applications of privacy-by-policy. This hybrid approach is visualized with a technical comparison of privacy-enhancing technologies (PET) across architectural layers, detailing both popular and emerging PETs relevant to digital asset systems which prioritize a hybrid approach to confidentiality. The paper further finds that a particular combination of popular and emerging technologies may provide as-yet untested but novel benefits to maintaining strong confidentiality – and possibly end users’ expectations of privacy – while data is under audit. A nuanced approach, rather than a reliance on a singular novel PET or dubious assurances of anonymity, may best facilitate strong confidentiality with sustainable end-user privacy protections for digital asset system users.
Keywords: Computer privacy, Digital assets, Digital signatures, Distributed ledger technology, Privacy, Privacy-by-design, Technology planning
Are Real Assets Owners Less Averse to Inflation? Evidence from Consumer Sentiments and Inflation Expectations
Using data from the University of Michigan Surveys of Consumers, we document a significant negative association between consumer sentiment and inflation expectations, controlling for prevailing inflation in the economy. We further show that consumer sentiments of homeowners and stockowners are more sensitive to expected inflation than those of other consumers, a disparity at odds with the notion that owning such assets provides hedges against inflation. Leveraging data from the Survey of Consumer Expectations, we find three factors that help account for this difference. First, assets owners' outlook for the broad economy seems to be more sensitive to their inflation expectations than other consumers' outlook. Second, assets owners appear to expect income growth to lag spending growth by a wider margin than other consumers and that margin widens with inflation expectations. Third, homeowners' inflation expectations tend to be less variable and less volatile than those of renters, which may allow the former to have a greater bearing on consumer sentiments.
Keywords: Consumer Sentiments, Home Ownership, Inflation Expectations, Inflation Targeting, Rational Inattention, Stock Ownership
Personal loans used for a variety of purposes, such as debt consolidation, medical bills, vacations, or the payment of a large ticket item, reached $356 billion or about 10 percent of nonrevolving consumer credit at the end of 2022. Although depository institutions such as banks, thrifts, and credit unions dominate the personal loan market, finance companies, institutions that typically lend to nonprime consumers, hold nearly a fourth of these loans. This paper provides an overview of this nascent but relatively understudied sector of the United States credit market.
Keywords: Consumer credit, Installment loans, Personal loans
Previous research has found evidence suggesting that financial technology (FinTech) lenders seek out opportunities in markets that have been underserved by mainstream banks. The research focuses primarily on the effect of bank market structure, limited income, and economic hardship in attracting FinTech companies to underserved markets. This paper expands the scope of FinTech research by investigating the role of interest rate regulation of consumer credit and institutional risk segmentation in FinTech lenders’ efforts to solicit new customers in the personal loan market. We find that strategic partnerships between FinTech companies and specialist banks target marginal-risk, near-prime, and low-prime consumers for credit card and other debt consolidation loans. These FinTech-bank partnerships especially target marginal consumers in states with low interest rate ceilings. Mainstream banks largely avoid higher-risk consumers, and low rate ceilings inhibit consumer finance company lending, which historically has been the major source of personal loans for higher risk consumers and may compete with banks at the margin. In partnering with the specialist banks, the FinTech lenders are able to take advantage of federal preemptions from state rate ceilings to lend profitably to higher-risk consumers in stateswith lowrate ceilings to compete in these markets.
Keywords: Consumer Credit, Access to Credit, Interest Rate Cap, Financial Regulation, FinTech
This study examines the market-implied premiums for bearing default clustering risk by analyzing credit derivatives contracts on the CDX North American Investment Grade (CDX.NA.IG) portfolio between September 2005 and March 2021. Our approach involves constructing a time series of reference tranche rates exclusively derived by single-name CDS spreads. The default clustering risk premium (DCRP) is captured by comparing the original and reference tranche spreads, with the former exceeding the latter when investors require greater compensation for correlated defaults at the portfolio level. The fitted DCRP level significantly increased in response to the 2007-9 global financial crisis and remained relatively stable for a period, followed by a gradual decline beginning in 2016. Notably, the COVID-19 shock caused another sharp rise in the DCRP level. Our empirical analysis finds that the estimated DCRP has significant implications for asset pricing, particularly in affecting the investment opportunities available to U.S. stock investors during times of instability in the financial system.
Keywords: Credit Default Swap (CDS), CDS Index (CDX), Reference Tranche Rate, Default Clustering Risk Premium
The identification of disaster risk has remained a significant challenge due to the rarity of macroeconomic disasters. We show that the interbank market can help characterize the time variation in disaster risk. We propose a risk-based model in which macroeconomic disasters are likely to coincide with interbank market failure. Using interbank rates and their options, we estimate our model via MLE and filter out the short-run and long-run components of disaster risk. Our estimation results are independent of the stock market and serve as an external validity test of rare disaster models, which are typically calibrated to match stock moments.
Keywords: economic disasters, extended Kalman filter, interbank rate options, interbank rates, maximum likelihood estimation, time-varying disaster risk
The empirical option valuation literature specifies the pricing kernel through the price of risk, or defines it implicitly as the ratio of risk-neutral and physical probabilities. Instead, we extend the economically appealing Rubinstein-Brennan kernels to a dynamic framework that allows pathand volatility-dependence. Because of low statistical power, kernels with different economic properties can produce similar overall option fit, even when they imply cross-sectional pricing anomalies and implausible risk premiums. Imposing parsimonious economic restrictions such as monotonicity and path-independence (recovery theory) achieves good option fit and reasonable estimates of equity and variance risk premiums, while resolving pricing kernel anomalies.
Keywords: maximum likelihood estimation, option pricing, price of risk, pricing kernel, risk premium
Of all major industries, construction is the only one to have registered negative average productivity growth since 1987. One might suspect measurement error to have biased growth downward since the deflators for this sector, which are used to translate nominal construction spending into the real quantity of structures, have risen much faster than those for other sectors. We find evidence of an upward bias in these deflators related to unobserved improvements in structure quality, but the magnitude is not large enough to alter the view that construction-sector productivity growth has been weak. We also find only small contributions from other potential sources of measurement error. We conclude that productivity growth may well have been quite low in construction, even if it has not been as low as implied by official statistics.
Keywords: Housing and real estate, Productivity
We revisit the role of long-term nominal corporate debt for the transmission of inflation shocks in the general equilibrium model of Gomes, Jermann, and Schmid (2016, henceforth GJS). We show that inaccuracies in the model solution and calibration strategy lead GJS to a model equilibrium in which nominal long-term debt is systematically mispriced. As a result, the quantitative importance of corporate leverage in the transmission of inflation shocks to real activity in their framework is 6 times larger than what arises under the rational expectations equilibrium.
Keywords: Corporate leverage, Debt overhang, Generalized Euler equation, Nominal long-term debt
Since the mid-1990s, the number of listed firms in the U.S. has halved, and their public disclosure has become opaquer. To explain these trends, we develop a general equilibrium model where the choices of going public or private and the transparency of voluntary disclosure are characterized analytically. In the equilibrium, the stock market with directed search and the private equity market with random search co-exist. According to the estimation, stricter disclosure regulation and increased intangible capital share are the key drivers of the observed patterns. Lastly, we characterize a policymaker’s trade-off between welfare and productivity and analyze the optimal policy.
Keywords: Intangible capital, corporate disclosures, technology diffusion
We provide evidence on the effect of the slope of the yield curve on economic activity through bank lending. Using detailed data on banks’ lending activities coupled with term premium shocks identified using high-frequency event study or instrumental variables, we show that a steeper yield curve associated with higher term premiums (rather than higher expected short rates) boosts bank profits and the supply of bank loans. Intuitively, a higher term premium represents greater expected profits on maturity transformation, which is at the core of banks’ business model, and therefore incentivizes bank lending. This effect is stronger for ex-ante more leveraged banks. We rationalize our findings in a portfolio model for banks.
Keywords: predictive power of the yield curve; term spread; term premium; bank lending; bank profitability; event study; instrumental variable.
Chetty et al. (2022a) introduced an array of social capital measures derived from Facebook friendships and found that one of these indicators, economic connectedness (EC), predicted upward income mobility well. Bricker and Li (2017) proposed the average credit score of a community's residents as an indicator of local social trust. We show in this paper that the average credit scores are robustly correlated with EC, negatively correlated with the friending-bias measure introduced in Chetty et al. (2022b), and predict economic mobility to a comparable extent after controlling for EC. The consistency and complementarity between these two indicators, despite being derived from individuals' activities in distinct contexts, underscore trust as a crucial component of social capital and provide insights that are useful for understanding the formation and accumulation of social capital.
Keywords: Social trust, social capital, economic mobility, credit score
Affording Degree Completion: An Experimental Study of Completion Grants at Accessible Public Universities
To improve college affordability and graduation rates, universities are increasingly allocating “completion grants” to students who are nearing the finish line but facing financial challenges. Using an experimental design and common program model across 11 broad-access public universities in ten states, we assessed the impact of a completion grants averaging $1,200 distributed among more than 14,000 students. We find that, despite university expectations that most students were near completion, only two-thirds of students eligible to receive a completion grant graduated within the academic year. Receiving a completion grant did not improve that rate. However, nearly all eligible students (95%) graduated within three years or were still working on their degrees. While completion grants are intended to enhance equity, we do not find evidence that they exerted positive impacts for marginalized groups as designed in this study. Moreover, while there was some program implementation variation across universities, it did not lead to differences in program impact.
Keywords: Higher education, affordability, graduation, financial aid, inequality
To meet the rising need for food and nutrition assistance during the pandemic in the United States, all states were approved to provide Emergency Allotments (EA) to households enrolled in the Supplemental Nutrition Assistance Program (SNAP). In this analysis, we use the Census Bureau’s Household Pulse Surveys and exploit staggered state-level variation in dissolution of the SNAP EA payments to study whether the end of EA is associated with food-related challenges and economic hardships. Our findings indicate that EA termination is followed by a decrease in the likelihood that adult survey respondents had sufficient food for consumption and an increase in the probability of experiencing difficulty in paying meeting with usual household expenses. These findings provide policy-relevant insights into the potential impact of the nationwide termination of the EA payments that came into effect in early 2023.
Keywords: Emergency Allotments, Pandemic, SNAP, Staggered difference-in-differences
A few sufficient statistics can identify the aggregate effects of distortions to firm investment in a class of general equilibrium models that can accommodate rich general equilibrium effects including endogenous firm entry. This result does not depend on the microfoundation of the distortion; one can generate inferences about aggregate effects that apply for multiple microfoundations or in cases where a fully specified model is difficult to solve. To demonstrate the relevance of the methodology, we use it to quantify the aggregate consequences of costly external equity financing and a manager-shareholder friction, relying on estimates from the corporate finance literature to identify the sufficient statistics. The results elucidate differences between partial and general equilibrium findings and demonstrate how labor supply elasticities, complementarities in production, and firm entry interact with the different firm-level distortions.
Keywords: Agency Costs, Costly External Finance, Firm Entry, General Equilibrium, Heterogeneous Firms, Sufficient Statistics
The emerging world of decentralized finance (DeFi), facilitated by smart contracts operating on blockchain networks, has been notable both for its rapid growth and the high-profile collapses of several of its largest participants. In this paper, we provide a technical account of the financial mechanisms which facilitated the growth and eventual collapse of the Terra Network. From this analysis, we outline a generalizable economic theory of blockchains which aims to differentiate the economics of blockchains as programmable environments from blockchains as accounting ledgers for crypto-assets. This adds to the existing literature on crypto-assets, which largely focuses on the financial characteristics of the crypto-assets themselves rather than their underlying blockchains. We argue that DeFi is structured so as to offer consumers distinct blockchain networks as competing choices differentiated by several key characteristics. We test several implications of this theory using Terra’s collapse as a natural experiment, finding evidence that bridges between programmable blockchain networks create increased risk of spillover effects to other blockchains’ programmable environments in the wake of a major shock event like Terra’s collapse. Specifically, blockchains suffered a time-bound loss of market share and the likelihood of this loss grew approximately 40% for each additional bridge that was deployed in common with Terra at the time of Terra’s collapse.
We provide the first evidence on the role of college networks in the re-employment of displaced workers. An extensive literature examines the consequences of layoffs, but the factors which facilitate re-employment are relatively under-studied. Using administrative data and a cross-cohort design, we find that network connections with actively-hiring employers increase the re-employment rate. This result is driven by re-employment at contact’s firms suggesting that a stronger network does not improve worker quality more broadly. These results suggest that college has the potential to improve employment outcomes beyond improved human capital and signaling.
We revisit predictability of forecast errors in macroeconomic survey data, which is often taken as evidence of behavioral biases at odds with rational expectations. We argue that to reject rational expectations, one must be able to predict forecast errors out of sample. However, the regressions used in the literature often perform poorly out of sample. The models seem unstable and could not have helped to improve forecasts with access only to available information. We do find some notable exceptions to this finding, in particular mean bias in interest rate forecasts, that survive our out-of-sample tests. Our findings help narrow down the set of biases that merit closer attention of researchers in behavioral macroeconomics.
Keywords: Behavioral Bias, Forecasting, Out-of-sample prediction, Rational expectations, Survey Data
I show that the decline in interest rates and corporate tax rates over the past three decades accounts for the majority of the period’s exceptional stock market performance. Lower interest expenses and corporate tax rates mechanically explain over 40 percent of the real growth in corporate profits from 1989 to 2019. In addition, the decline in risk-free rates alone accounts for all of the expansion in price-to-earnings multiples. I argue, however, that the boost to profits and valuations from ever-declining interest and corporate tax rates is unlikely to continue, indicating significantly lower profit growth and stock returns in the future.
Keywords: long-run prediction, stock returns, equity premium, corporate profits, interest rates, corporate taxes
This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support, providing both an optimal grid and transition probability matrix. The method can be used for multivariate processes, as well as non-stationary processes such as those with a life-cycle component. The method is based on minimizing the information loss between a Hidden Markov Model and the true data-generating process. We provide sufficient conditions under which this information loss can be made arbitrarily small if enough grid points are used. We compare our method to existing methods through the lens of an asset-pricing model, and a life-cycle consumption-savings model. We find our method leads to more parsimonious discretizations and more accurate solutions, and the discretization matters for the welfare costs of risk, the marginal propensities to consume, and the amount of wealth inequality a life-cycle model can generate.
Keywords: Numerical methods, Kullback–Leibler divergence, misspecified model, earnings process
This paper uses summaries of the Federal Open Market Committee’s (FOMC’s) meetings to identify its operational targets and map those to operating regimes. We find that operational targets were more often discussed in the earlier part of the FOMC’s 85-year history, but recent years have seen a resurgence in discussions. We identify distinct operating regimes and find that regimes with discussions of multiple targets, usually rate and quantity pairs, are more common than regimes dominated by discussions of single targets. We document that the current period (the 2007-2009 financial crisis to today) is a notable break in operational targets from earlier periods. We also show that shifts in operational targets occur during recoveries, or after a significant downturn in the macroeconomy.
Keywords: FOMC, monetary policy implementation, asset purchases, federal funds market, repo market
We study the relation between inflation and real activity over the business cycle. We employ a Trend-Cycle VAR model to control for low-frequency movements in inflation, unemployment, and growth that are pervasive in the post-WWII period. We show that cyclical fluctuations of inflation are related to cyclical movements in real activity and unemployment, in line with what is implied by the New Keynesian framework. We then discuss the reasons for which our results relying on a Trend-Cycle VAR differ from the findings of previous studies based on VAR analysis. We explain empirically and theoretically how to reconcile these differences.
Keywords: Inflation, real activity, business cycles, trend-cycle VAR
We study how the transmission of monetary policy to firms' investment and credit spreads depends on their financial conditions, finding a major role for their excess bond premia (EBPs), the component of credit spreads in excess of default risk. While monetary policy easing shocks compress credit spreads more for firms with higher ex-ante EBPs, it is lower-EBP firms that invest more. We rationalize these findings using a model with financial frictions in which lower-EBP firms have flatter marginal product of capital curves. We also show empirically that the cross-sectional distribution of firm EBPs determines the aggregate effectiveness of monetary policy.
Keywords: Monetary Policy, Investment, Credit Spreads, Excess Bond Premium, Firm Heterogeneity.
We estimate monetary policy surprises (sentiment) from the perspective of three different textual sources: direct central bank communication (FOMC statements and press conferences), news articles, and Twitter posts during FOMC announcement days. Textual sentiment across sources is highly correlated, but there are times when news and Twitter sentiment substantially disagree with the sentiment conveyed by the central bank. We find that sentiment estimated using news articles correlates better with daily U.S. Treasury yield changes than the sentiment extracted directly from Fed communication, and better predicts revisions in economic forecasts and FOMC decisions. Twitter sentiment is also useful, but slightly less so than news sentiment. These results suggest that news coverage and Tweets are not a simple echo chamber but they provide additional useful information. We use Sastry (2022)’s theoretical model to guide our empirical analysis and test three mechanisms that can explain what drives monetary policy surprises extracted from different sources: asymmetric information (central bank has better information than journalists and Tweeters), journalists (and Tweeters) have erroneous beliefs about the monetary policy rule, and the central bank and journalists (Tweeters) have different confidence in public information. Our empirical results suggest that the latter mechanism is the most likely mechanism.
Keywords: Monetary policy, public information, price discovery
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like unemployment insurance claims provided an imperfect signal of job loss. Additionally, we find that our Twitter job loss indicator provides incremental information in predicting official unemployment flows in a given month beyond what weekly unemployment insurance claims offer.
Keywords: Job Loss, Natural Language Processing, Neural Networks.
We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next day stock market returns. Most notably, we show that the index contains information that helps forecast changes in the U.S. monetary policy stance: a deterioration in Twitter financial sentiment the day ahead of an FOMC statement release predicts the size of restrictive monetary policy shocks. Finally, we document that sentiment worsens in response to an unexpected tightening of monetary policy.
Keywords: Financial Market Sentiment, Monetary policy, Natural Language Processing, Stock Returns, Twitter
Default auctions at central counterparties (or 'CCPs') are critically important to financial stability. However, due to their unique features and challenges, standard auction theory results do not immediately apply. This paper presents a model for CCP default auctions that incorporates the CCP's non-standard objective of maximizing success above a threshold rather than revenue, the key question of who participates in the auction and the potential for information leakage affecting private portfolio valuations. We show that an entry fee, by appropriately inducing members to participate or not, can maximize the probability the auction succeeds. The result is novel, both in auction theory and as a mechanism for CCP auction design.
When Roosevelt abandoned the gold standard in April 1933, he converted government debt from a tax-backed claim to gold to a claim to dollars, opening the door to unbacked fiscal expansion. Roosevelt followed a state-contingent fiscal rule that ran nominal-debt-financed primary deficits until the price level rose and economic activity recovered. Theory suggests that government spending multipliers can be substantially larger when fiscal expansions are unbacked than when they are tax-backed. VAR estimates using data on "emergency" unbacked spending and "ordinary" backed spending confirm this prediction and find that primary deficits made quantitatively important contributions to raising both the price level and real GNP after 1933. VAR evidence does not support the conventional monetary explanation that gold revaluation and gold inflows, which raised the monetary base, drove the recovery independently of fiscal actions.
Keywords: Great Depression; monetary-fiscal interactions; monetary policy; fiscal policy; government debt
Aggregate U.S. labor market dynamics are well approximated by a dual labor market supplemented with a third, predominantly, home-production segment. We uncover this structure by estimating a Hidden Markov Model, a machine-learning method. The different market segments are identified through (in-)equality constraints on labor market transition probabilities. This method yields time series of stocks and flows for the three segments for 1980-2021. Workers in the primary sector, who make up around 55 percent of the population, are almost always employed and rarely experience unemployment. The secondary sector, which constitutes 14 percent of the population, absorbs most of the short-run fluctuations, both at seasonal and business cycle frequencies. Workers in this segment experience six times higher turnover rates than those in the primary tier and are ten times more likely to be unemployed than their primary counterparts. The tertiary segment consists of workers who infrequently participate in the labor market but nevertheless experience unemployment when they try to enter the labor force. Our individual-level analysis shows that observable demographic characteristics only explain a small part of the cross-individual variation in segment membership. The combination of the aggregate and individual-level evidence we provide points to dualism in the U.S. labor market being an equilibrium division of labor, under labor market imperfections, that minimizes adjustment costs in response to predictable seasonal as well as unpredictable business cycle fluctuations.
Keywords: Dual labor markets, Hidden Markov Models, machine learning.
Workhorse search and matching models assume constant bargaining weights, while recent evidence indicates that weights vary across time and in cross section. We endogenize bargaining weights in a life-cycle search and matching model by replacing a standard Cobb-Douglas (CD) matching function with a general constant elasticity of substitution (CES) matching function and study the implications for the long-term labor share and bargaining power in the U.S. The CES model explains 64 percent of the reported decline in the labor share since 1980, while the CD model explains only 28 percent of the decline. We then use the model to recover changes in bargaining power and find that workers' bargaining power has declined 11 percent between 1980 and 2007 because of a decline in tightness.
Keywords: CES matching function, Endogenous bargaining power, Labor share, Search and matching
We construct a Financial Stress Index (FSI) for a small open economy, which aims to provide clear and timely signals of financial market strains. This can be used in developing appropriate responses to address these adverse events. To do so, we use the principal component framework and apply it to Australian monthly data on interest rates, spreads, exchange rates, house price growth and inflation expectations. Decomposing the index into foreign and domestic components, we find that the foreign factors can explain more than half (57.4%) of our Australian Financial Stress Index (AFSI). To determine the information content of our index, we run a series of Granger causality tests on several economic and financial observables. We also estimate whether including the AFSI can improve the prediction of the different economic and financial outcomes relative to a specification that uses only its own previous data. We find that including the AFSI improves the forecasts for future retail sales growth and bank credit growth. Finally, we show that financial stress can have non-linear effects on bank credit growth. In particular, an increase in financial stress affects credit growth more adversely if AFSI is high. This result further highlights the importance of an accurate and timely measure of financial stress in an economy for researchers and policy makers.
Keywords: financial stress index, financial stability, small open economies
We study the relationship between volatility and liquidity in the market for on-the-run Treasury securities using a novel framework for quantifying price impact. We show that at times of relatively low volatility, marginal trades that go with the flow of existing trades tend to have a smaller price impact than trades that go against the flow. However, this difference tends to diminish at times of high volatility, indicating that the perceived information content of going against the flow is less when volatility is high. We also show that market participants executing trades aggressively using market orders will experience larger increases in price impact than those executing trades passively using limit orders as volatility increases. And times of low market depth are associated with increased risk of high price impact and high sensitivity to volatility in future, perhaps because liquidity is more reliant on high-speed quote replenishment and is therefore more fragile.
Keywords: liquidity, Treasury market, market depth, volatility, order execution, hidden Markov model
Through the lens of a nonlinear dynamic factor model, we study the role of exogenous shocks and internal propagation forces in driving the fluctuations of macroeconomic and financial data. The proposed model 1) allows for nonlinear dynamics in the state and measurement equations; 2) can generate asymmetric, state-dependent, and size-dependent responses of observables to shocks; and 3) can produce time-varying volatility and asymmetric tail risks in predictive distributions. We find evidence in favor of nonlinear dynamics in two important U.S. applications. The first uses interest rate data to extract a factor allowing for an effective lower bound and nonlinear dynamics. Our estimated factor coheres well with the historical narrative of monetary policy. We find that allowing for an effective lower bound constraint is crucial. The second recovers a credit cycle. The nonlinear component of the factor boosts credit growth in boom times while hinders its recovery post-crisis. Shocks in a credit crunch period are more amplified and persist for longer compared with shocks during a credit boom.
Keywords: Interest rates, effective lower bound, credit cycle, asymmetric dynamics, predictive distributions, tail risk
Bank deregulation in the form of the repeal of the Glass-Steagall Act facilitated the entry of non-bank lenders into the market for syndicated loans during the pre-2008 credit boom. Institutional investors disproportionately purchase tranches of loans originated by universal banks able to cross-sell loans and underwriting services to firms (as permitted by the repeal). A shock to cross-selling intensity increases loan liquidity at origination and over time. The mechanism is that non-loan exposures ensure monitoring even when banks retain small loan shares. Our findings complement the conventional view that regulatory arbitrage caused the rise of non-bank lenders.
Keywords: Non-bank lending, bank deregulation, credit supply, loan liquidity, industrial organization of financial markets
The initial years of the COVID-19 pandemic and the resulting economic fallout likely posed particular financial strain on U.S. households with children, who faced income disruptions from widespread jobs and hours cuts in addition to new childcare and instruction demands. One common expense for many such households is their student loan payment. The Coronavirus Aid, Relief, and Economic Security (CARES) Act included provisions to curb the impacts of these payments, which have been extended several times. These measures were not targeted and thus applied independent of need. This chapter analyzes two nationally representative datasets and finds: 1) families with children were more likely to benefit from pandemic student loan relief than those without children, but this relief was concentrated among higher-income and White families and 2) there were larger improvements in overall credit health and an increased use of other credit among families with student loan debt that was eligible for relief relative to those with student loan debt that was not.
This paper explores an under-studied channel for school finance inequality: property assessment. School districts have historically relied on local tax revenues (typically property taxes) to fund schools, which can generate disparities in funding across districts. Many states passed school finance reforms that give more state funding to poorer districts. These formulas typically discourage school districts from offsetting state funding by reducing local tax rates ("crowd out"). However, many reforms have not adequately addressed another source of inequality: property assessment accuracy and equity. Moreover, state reform can unintentionally subsidize property underassessment. We analyze a state government intervention to address property assessment inequities within and across school districts. We use difference-in-differences and county- and school district-level administrative data to find the intervention boosted assessments by 32 percent. Assessment equity improved substantially and local property revenues temporarily increased by 17 percent. Local fiscal and institutional capacity played a role in assessment inequity pre-reform. Our results suggest that underassessment can compound funding inequality across districts in states that rely on property wealth to fund schools. Therefore, effective state oversight in property assessments is needed to ensure the integrity of funding systems that distribute state funding to districts on the basis of their assessed property wealth.
Keywords: Property assessment, school finance reform, inequality
Using an occupational probability of computerization, we measure a firm’s ability to replace labor with automated capital. Our evidence suggests that the potential to automate a workforce enhances operating flexibility, allowing firms to hold less precautionary cash. To provide evidence for this mechanism, we exploit the 2011–2012 Thailand hard drive crisis as an exogenous shock to the cost of automation. In addition, the negative relation between prospective automation and cash holdings is greater for firms with a lower expected cost of worker displacement and greater labor-induced operating leverage.
Keywords: Automation, Corporate liquidity policy, Labor-induced operating leverage, Operating flexibility, Substitutability of labor with automated capital
This paper examines whether the measurement of trend inflation can be improved by using wage data in a dynamic factor model of disaggregated prices and wages for the United States. The model features time-varying coefficients and stochastic volatility. An estimate of trend inflation is a time-varying distributed lag of prices and wages, where the weight on a series depends on its time-varying volatility, persistence, and comovement with other series. The results show that wages inform estimates of trend inflation. The weight on wages was highest around 1980, drifted down through the 2000s, and returned to its 1980s value by 2022. In addition, inflation in the 2020s appears to have unmoored moderately from the 2 percent range that prevailed for decades, as the role of the persistent component of inflation increased in recent year. However, accounting for wages lowers the model's view of the increase in the volatility of trend inflation.
Keywords: Factor Model, Price Inflation, Unobserved Components Model, Wage Inflation
We study short-term and medium-term changes in bank risk-taking as a result of supervision, and the associated real effects. For identification, we exploit the European Central Bank’s asset-quality-review (AQR) in conjunction with security and credit registers. After the AQR announcement, reviewed banks reduce riskier securities and credit supply, with the greatest effect on riskiest securities. We find negative spillovers on asset prices and firm-level credit availability. Moreover, non-banks with higher exposure to reviewed banks acquire the shed risk. After the AQR compliance, reviewed banks reload riskier securities but not riskier credit, resulting in negative medium-term firm-level real effects. These effects are especially strong for firms with high ex-ante credit risk. Among these non-safe firms, even those with high ex-ante productivity experience negative real effects. Our findings suggest that banks’ liquid assets help them to mask risk from supervisors and risk adjustments banks make in response to supervision have persistent corporate real effects.
Keywords: Corporate real effects from bank credit, Asset quality review, Stress tests, Supervision, Risk-masking, Costs of safe assets
We show that the social capital embedded in employees’ networks contributes to firm performance. Using novel, individual-level network data, we measure a firm’s social capital derived from employees’ connections with external stakeholders. Our directed network data allow for differentiating those connections that know the employee and those that the employee knows. Results show that firms with more employee social capital perform better; the positive effect stems primarily from employees being known by others. We provide causal evidence exploiting the enactment of a government regulation that imparted a negative shock to networking with specific sectors and provide evidence on the mechanisms.
Keywords: Social capital, Social networks, Labor and finance
The Long-Run Real Effects of Banking Crises: Firm-Level Investment Dynamics and the Role of Wage Rigidity
I study the long-run effects of credit market disruptions on real firm outcomes and how these effects depend on nominal wage rigidity at the firm level. Exploiting variation in firms' refinancing needs during the global financial crisis, I trace out firms' investment and growth trajectories in response to a credit supply shock. Financially shocked firms exhibit a temporary investment gap for two years, resulting in a persistent accumulated growth gap six years after the crisis. Shocked firms with rigid wages exhibit a significantly steeper drop in investment and an additional long-run growth gap relative to shocked firms with flexible wages.
Keywords: Financial Crises, Bank Lending, Real Effects, Firm Investment, Wage Rigidity
Using the Shared National Credit supervisory data, we find Private Equity (PE) sponsored firms violate loan covenants more often than comparable non-PE firms. However, upon covenant violation, PE-sponsored borrowers experience relatively smaller reductions in credit commitments, suggesting lenders are more lenient with these borrowers. This limited-punishment effect exists in both covenant-heavy and covenant-lite loans but is stronger for banks with relatively higher capital. Limited punishment is driven by repeated deals and sponsor reputation, as well as the higher bargaining power of sponsors in loan renegotiation. Our results indicate sponsors generate financial flexibility by dampening debt contract enforcement for distressed borrowers.
Keywords: Private Equity; Covenants; Loan Renegotiation; Syndicated Loans
We study the nonlinearities present in a standard monetary labor search model modified to have two groups of workers facing exogenous differences in the job finding and separation rates. We use our setting to study the racial unemployment gap between Black and white workers in the United States. A calibrated version of the model is able to replicate the difference between the two groups both in the level and volatility of unemployment. We show that the racial unemployment gap rises during downturns, and that its reaction to shocks is state-dependent. In particular, following a negative productivity shock, when aggregate unemployment is above average the gap increases by 0.6pp more than when aggregate unemployment is below average. In terms of policy, we study the implications of different inflation regimes on the racial unemployment gap. Higher trend inflation increases both the level of the racial unemployment gap and the magnitude of its response to shocks.
Keywords: unemployment, discrimination, racial inequality, monetary policy, inflation
This paper investigates contagion in financial networks through both debt and collateral markets. We find that the role of collateral is mitigating counterparty exposures and reducing contagion but has a phase transition property. Contagion can change dramatically depending on the amount of collateral relative to the debt exposures. When there is an abundance of collateral (leverage is low), then collateral can fully cover debt exposures, and the network structure does not matter. When there is an adequate amount of collateral (leverage is moderate), then collateral can mitigate counterparty contagion, and having more links in the network reduces contagion, as interlinkages act as a diversifying mechanism. When collateral is not enough (leverage is high) and agents in the network are too interconnected, then the collateral price can plummet to zero and the whole network can collapse. Therefore, we show the importance of the interaction between the level of collateral and interconnectedness across agents. The model also provides the minimum collateral-to-debt ratio (haircut) to attain a robust macroprudential state for a given network structure and aggregate state.
Keywords: collateral, financial network, fire sale, systemic risk
We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns of instability and to examine lead-lag patterns in how individual inflation series change. We find evidence of a marked flattening in the Phillips curves for US sectoral data and among EU countries, particularly poorer ones. Conversely, evidence of a flattening is weaker for MSA-level data and for the wage Phillips curve. US regional data and EU data point to a kink in the price Phillips curve which remains relatively steep when the economy is running hot.
Keywords: Bayesian analysis, Inflation, Panel data, Phillips curve, Structural breaks, Unemployment
I study how bank relationships affected the timing and geographic distribution of Paycheck Protection Program (PPP) lending. Half of banks' PPP loans went to borrowers within 2 miles of a branch, mostly driven by relationship lending. Firms near less active lenders shifted to fintechs and other distant lenders, resulting in delays receiving credit but only slightly lower loan volumes. I estimate a structural model to fit the observed relationship between branch distance, bank PPP activity, and origination timing. I find that banks served relationship borrowers 5 to 9 days before other borrowers, an effect in line with reduced-form estimates using a sample of PPP borrowers with previous SBA lending relationships.
Keywords: Banks, credit unions, and other financial institutions, COVID-19, Paycheck Protection Program (PPP), Relationship Lending
We analyze possible future financial losses in the event of hurricane damage to Miami residential real estate, where the hurricane's destructiveness reflects climate-change. We focus on three scenarios: (i) a business-as-usual scenario, (ii) a Hurricane-Ian-spillovers scenario, and (iii) a cautious-markets scenario. We quantify bank exposures and loss rates, where exposures are proportional to the size of real estate markets and loss rates depend on post-hurricane devaluations and insurance coverage. This quantitative methodology could complement modeling of local economy impacts, stress on public finances, asset market losses, and other financial developments that will also affect banks.
Keywords: Climate-related risk, Financial stability, Flow of risk, Real estate loans
We show that early joiners—non-founder employees in the first year of a startup—play a critical role in explaining firm performance. We use administrative employee-employer matched data on all US startups and utilize the premature death of workers as a natural experiment exogenously separating talent from young firms. We find that losing an early joiner has a large negative effect on firm size that persists for at least ten years. When compared to that of a founder, losing an early joiner has a smaller effect on firm death but intensive margin effects on firm size are similar in magnitude. We also find that early joiners become relatively more important with the age of the firm. In contrast, losing a later joiner yields only a small and temporary decline in firm performance. We provide evidence that is consistent with the idea that organization capital, an important driver of startup success, is embodied in early joiners.
Using U.S. Business Registry Data to Corroborate Corporate Identity: Case Study of the Legal Entity Identifier
This paper offers a fresh perspective on fundamental issues in using official incorporation records to corroborate the identity of corporate entities by comparing two publicly-available sets of information, namely, business registry incorporation records and reference data from the Legal Entity Identifier (LEI) system, with some focus on the monitoring function performed by LEI issuers as agents for LEI data users. Three modes of analysis are used to consider these issues, high-level analysis of LEI system data about U.S. entities with LEIs, interviews conducted with U.S. business registries, and entity-level comparisons of business registry and LEI records for entities with LEIs incorporated in the states of Ohio and Massachusetts. The fresh perspective provided here includes attention to key comparison issues such as truncation of Legal Names in official records; significant state-level variation in requirements to provide business address information in incorporation records or periodic reports; recognition that some key business register data may not be readily available or available only at a cost; whether in this context enhancements can be made to the expectations for, and disclosures by, LEI issuers in their monitoring role; and to what extent the high incidence of non-renewal of LEIs might play a role in the quality of LEI reference data. The paper develops measures of scope and degree for many key issues that can arise in using business registry information within an identity-corroboration context. The exceptional transparency of the LEI system allows for detailed comparisons that connect its data quality and value proposition with its sources and methods.
Keywords: anti-money laundering, corporations, counterparty risk, data mapping, financial supervision and regulation
We study the labor market effects of information technology (IT) during the onset of the COVID-19 pandemic, using data on IT adoption covering almost three million establishments in the US. We find that in areas where firms had adopted more IT before the pandemic, the unemployment rate rose less in response to social distancing. IT shields all individuals, regardless of gender and race, except those with the lowest educational attainment. Instrumental variable estimates–leveraging historical routine employment share as a booster of IT adoption– confirm IT had a causal impact on fostering labor markets’ resilience. Additional evidence suggests this shielding effect is due to the easiness of working-from-home and to stronger creation of digital jobs in high IT areas.
Keywords: Unemployment Rate, Technology, IT Adoption, Inequality, Skill-Biased Technical Change
Detractors have warned that Private Equity (PE) funds tend to over-lever their portfolio companies because of an option-like payoff, building up default risk and debt overhang. This paper argues PE-ownership leads to substantially higher levels of optimal (value-maximizing) leverage, by reducing the expected cost of financial distress. Using data from a large sample of PE buyouts, I estimate a dynamic trade-off model where leverage is chosen by the PE investor. The model is able to explain both the level and change in leverage documented empirically following buyouts. The increase in optimal leverage is driven primarily by a reduction in the portfolio company’s asset volatility and, to a lesser extent, an increase in asset return. Counterfactual analysis shows significant loss in firm value if PE sub-optimally chose lower leverage. Consistent with lower asset volatility, additional tests show PE-backed firms experience lower volatility of sales and receive greater equity injections for distress resolution, compared to non PE-backed firms. Overall, my findings broaden our understanding of factors that drive buyout leverage.
Keywords: Private Equity; Capital Structure; Default Risk; Trade-off Theory
Examining a parsimonious, yet comprehensive, set of recession signals yields three lessons. First, signals from financial markets, leading indicators of activity, and gauges of the macroeconomic environment are each useful at different horizons, with leading indicators and financial signals informative at short horizons and the state of the business cycle at medium horizons. Second, approaches emphasizing the yield curve overstate the recession signal from the term spread if other factors are not considered; given correlations among indicators, these differences are often small, but were large in 2022. Finally, simulations of a reduced-form vector autoregression of unemployment and financial conditions, which captures the time-series properties of the series well, suggest the patterns are consistent with a typical hump-shape characterization of business cycle dynamics; this synthesis tightens the connections of the recession prediction literature with the business-cycle literature.
We study credit card rewards as an ideal laboratory to quantify redistribution between consumers in retail financial markets. Comparing cards with and without rewards, we find that, regardless of income, sophisticated individuals profit from reward credit cards at the expense of naive consumers. To probe the underlying mechanisms, we exploit bank-initiated account limit increases at the card level and show that reward cards induce more spending, leaving naive consumers with higher unpaid balances. Naive consumers also follow a sub-optimal balance-matching heuristic when repaying their credit cards, incurring higher costs. Banks incentivize the use of reward cards by offering lower interest rates than on comparable cards without rewards. We estimate an aggregate annual redistribution of $15 billion from less to more educated, poorer to richer, and high to low minority areas, widening existing disparities.
Does monetary policy influence who becomes a home owner? Home purchases by low- and moderate- income households may be particularly sensitive to mortgage interest rates, as these households' budgets are tighter and they more frequently come up against binding payment-to-income ratio constraints in credit decisions. Exploiting the timing of high-frequency observations of mortgage applicants locking in their interest rates around monetary policy shocks, I find that a 1 percentage point policy-induced increase in mortgage rates lowers the presence of low-income households in the population of home buyers by 1 percentage point, and of low- and moderate-income households by 2 percentage points, immediately following the shock. Effects are substantially stronger among first-time home buyers, and persist for approximately one year.
We study the welfare impacts of mergers in markets where some firms are already vertically integrated. Our model features logit Bertrand competition downstream and Nash Bargaining upstream. We numerically simulate four merger types: vertical mergers between an unintegrated retailer and an unintegrated wholesaler, downstream "horizontal" mergers between an unintegrated retailer and an integrated retailer/wholesaler, upstream "horizontal" mergers between an unintegrated wholesaler and an integrated retailer/wholesaler, and integrated mergers between two integrated retailer/wholesaler pairs. We find that mergers that have both horizontal and vertical characteristics typically harm consumers. We apply the model to the Republic/Santek merger as a real-world example.
Keywords: bargaining models, merger simulation, vertical markets, vertical mergers
Using a panel of tax data, we follow the earnings of individuals over business cycles. Compared to prior recessions, the Covid policy response and recovery were far more progressive. Among workers starting in the bottom quintile, median real earnings including fiscal relief increased 66 percent in 2020 and earnings increases offset relief decreases in the 2021 recovery. After the prior two recessions, this measure had decreased by 24 percent. Among those starting in the top quintile, median and average real earnings were approximately unchanged. This difference from prior recessions is largely attributable to larger Covid-era stimulus payments and unemployment insurance.
Keywords: Covid-19, wages, earnings, stimulus checks, unemployment insurance, countercyclical policy, government transfers
Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models allows banks’ management to make more informed risk decisions by better matching economic capital and risk appetite, and allows regulators to enhance their understanding of banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank.
Keywords: Banking; Operational Risk; Risk Management
Using a heterogeneous agent model calibrated to match measured spending dynamics over four years following an income shock (Fagereng, Holm, and Natvik (2021)), we assess the effectiveness of three fiscal stimulus policies employed during recent recessions. Unemployment insurance (UI) extensions are the clear "bang for the buck" winner, especially when effectiveness is measured in utility terms. Stimulus checks are second best and have the advantage (over UI) of being scalable to any desired size. A temporary (two-year) cut in the rate of wage taxation is considerably less effective than the other policies and has negligible effects in the version of our model without a multiplier.
We study the dynamic link between economic news coverage and the macroeconomy. We construct two measures of media coverage of bad and good unemployment figures based on three major US newspapers. Using nonlinear time series techniques, we document three facts: (i) there is no significant negativity bias in economic news coverage. The asymmetric responsiveness of newspapers' coverage to positive and negative unemployment shocks is entirely explained by the effects of these shocks on unemployment itself; (ii) consumption reacts to bad news, but not to good news; (iii) bad news is more informative to the agents and affects their expectations more than good news.
Keywords: News Coverage, Agents' Information, Business Cycles, Asymmetry, Threshold-SVAR.
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