Inflation and Welfare in a Competitive Search Equilibrium with Asymmetric Information

We study an economy characterized by competitive search and asymmetric information. Money is essential. Buyers decide their cash holdings after observing the contracts posted by firms and experience match-specific preference shocks which remain unknown to sellers. Firms are allowed to post general contracts. In the baseline model with indivisible goods, we show that, when the number of potential buyers is fixed, inflation decreases markups. This, in turn, increases aggregate output and ex ante welfare. When goods are divisible the negative effect of inflation on markups holds for unconstrained agents but is ambiguous for constrained agents. Still, optimal monetary policy implies a positive nominal rate. When there is buyers' free entry, asymmetric information causes a congestion effect that can be corrected by monetary policy.     

How Does Monetary Policy Affect Welfare? Some New Estimates Using Data on Life Evaluation and Emotional Well-Being

Models on the optimal design of monetary policy typically rely on a welfare loss function defined over unemployment and inflation. We estimate such a function using two different dimensions of well-being. The first evaluates how close one is to “the best possible life” on a ladder scale. The second captures the emotional quality of everyday experiences. Our Gallup World Poll sample covers 1.5 million people in 141 nations from 2005 to 2019. Unemployment and inflation reduce well-being across all measures. The ratio of the unemployment-to-inflation effect is 6.2 for the “Ladder-of-Life.” It is lower for positive day-to-day experiences and higher for negative ones.     

Biased Auctioneers

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.     

Externalities of Financial Statement Fraud on the Incoming Accounting Labor Force

Financial statement fraud generates many negative effects, including reducing people's willingness to participate in the stock market. If it also stigmatizes accounting, it may similarly adversely affect the quantity and quality of workers willing to become accountants, thereby potentially creating negative effects for years to come. We examine the impact of fraud on the labor force entering the accounting profession, which is a key input into the production of accounting information (i.e., the output). Using data describing millions of college students across the United States, we find incoming students are actually more likely to major in accounting when local frauds occur during their formative years. These students are also more likely to have attributes desired by the accounting profession (e.g., high academic aptitude) and are more likely to subsequently serve in public accounting and become Certified Public Accountants. In the context of other fields (i.e., all college majors), we find that fraud similarly spurs interest in other business disciplines, but not in majors outside of business schools. Those attracted to other business disciplines, however, generally possess different traits. Specifically, students entering accounting are distinctively more likely to exhibit values espoused by the accounting profession, including a predisposition to public service and less commercial orientation. Thus, nonpecuniary motives appear to uniquely drive accounting student enrollment following fraud. Collectively, our findings suggest that, while fraud is unmistakably bad, it appears to have the positive unintended consequence of attracting labor into business disciplines and, in accounting, increasing the prevalence of desirable traits among entrants.     

The Effect of Client Industry Agglomerations on Auditor Industry Specialization

Prior research on auditor industry specialization documents fee premiums for local audit offices that are industry specialists. This research assumes that the effects of specialization are uniform across markets. We examine industry specialization based on the economic theory of industry agglomeration (geographic areas with high industry concentration). Agglomeration economies can facilitate access to knowledge for auditors serving a specific industry in those locations. We find that industry specialists in agglomerations earn a fee premium in excess of specialists in other markets. We find that nonspecialist offices in agglomerations also earn fee premiums in that industry when compared to nonspecialists in other markets even when controlling for these groups’ absolute share of the national market. We also address whether or not this expertise can be shared among offices in an agglomeration specialist's firm. We find that audit offices that have easy connections to a within-firm office in an agglomerated market can earn a fee premium relative to more distant offices, suggesting a benefit from knowledge transfer. This fee premium accrues to offices that would not be considered a specialist using traditional market share measures in a given industry. These findings indicate that the benefit of industry specialization depends on more than local market share.     

Model Comparison with Transaction Costs

Failing to account for transaction costs materially impacts inferences drawn when evaluating asset pricing models, biasing tests in favor of those employing high-cost factors. Ignoring transaction costs, Hou, Xue, and Zhang (2015, Review of Financial Studies, 28, 650–705) q-factor model and Barillas and Shanken (2018, The Journal of Finance, 73, 715–754) six-factor models have high maximum squared Sharpe ratios and small alphas across 205 anomalies. They do not, however, come close to spanning the achievable mean-variance efficient frontier. Accounting for transaction costs, the Fama and French (2015, Journal of Financial Economics, 116, 1–22; 2018, Journal of Financial Economics, 128, 234–252) five-factor model has a significantly higher squared Sharpe ratio than either of these alternative models, while variations employing cash profitability perform better still.