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Fed Communication, News, Twitter, and Echo Chambers
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
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data
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.
More than Words: Twitter Chatter and Financial Market Sentiment
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
Optimal Bidder Selection in Clearing House Default Auctions
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.
Recovery of 1933
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
The Dual U.S. Labor Market Uncovered
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.
Endogenous Bargaining Power and Declining Labor Compensation Share
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
A Financial Stress Index for a Small Open Economy: The Australian Case
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
The Effects of Volatility on Liquidity in the Treasury Market
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
Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor 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
Less Bank Regulation, More Non-Bank Lending
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
Implications of Student Loan COVID-19 Pandemic Relief Measures for Families with Children
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.
The Role of Property Assessment Oversight in School Finance Inequality
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
Workplace Automation and Corporate Liquidity Policy
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
The Role of Wages in Trend Inflation: Back to the 1980s?
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
Stressed Banks? Evidence from the Largest-Ever Supervisory Review
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
It's Not Who You Know—It's Who Knows You: Employee Social Capital and Firm Performance
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
Private Equity and Debt Contract Enforcement: Evidence from Covenant Violations
We document the importance of a financial sponsor when a borrower violates a covenant, providing creditors the opportunity to enforce debt contracts. We identify PE-sponsored borrowers in the Shared National Credit Program (SNC) data and find that they violate covenants more often than comparable non-PE borrowers. Yet, compared to non-PE, PE-backed borrowers experience smaller reductions in credit commitment upon violation, suggesting lenders are more lenient with PE sponsors. This leniency effect is also stronger among financially healthier lenders. We show that our results are consistent with a repeated-deals mechanism, as lenders frequently interact with financial sponsors and choose to preserve relationship rent. Consistent with this mechanism, we find little evidence that PE-sponsored loans eventually underperform relative to non-PE-sponsored loans following covenant violations. Our findings have important implications for understanding heterogeneity in debt contract enforcement and credit constraints faced by distressed borrowers with financial sponsors.
Keywords: Private Equity Funds, Covenants, Debt Contract Enforcement, Bank Lending
Racial Unemployment Gaps and the Disparate Impact of the Inflation Tax
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
Contagion in Debt and Collateral Markets
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
Breaks in the Phillips Curve: Evidence from Panel Data
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
Bank Relationships and the Geography of PPP Lending
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
Household, Bank, and Insurer Exposure to Miami Hurricanes: a flow-of-risk analysis
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
Early Joiners and Startup Performance
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
IT Shields: Technology Adoption and Economic Resilience during the COVID-19 Pandemic
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
Does Private Equity Over-Lever Portfolio Companies?
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
Recession Signals and Business Cycle Dynamics: Tying the Pieces Together
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.
Who Pays For Your Rewards? Redistribution in the Credit Card Market
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.
Monetary Policy and Home Buying Inequality
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.
Beyond "Horizontal" and "Vertical": The Welfare Effects of Complex Integration
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
Earnings Business Cycles: The Covid Recession, Recovery, and Policy Response
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
The Information Value of Past Losses in Operational Risk
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
Welfare and Spending Effects of Consumption Stimulus Policies
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.
Bad News, Good News: Coverage and Response Asymmetries
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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