Fiscal Discipline as a Social Norm: The European Stability Pact

This paper reviews the arguments for and against the “Stability and Growth Pact.” We find the theoretical debate to be inconclusive, as both externality and credibility arguments can be used to yield opposite and plausible conclusions. Empirical evidence in favor of a Pact‐like rule is also scant. We therefore suggest the view that the Stability Pact is a public social norm, that countries obey in order to preserve reputation among the other members of the European Union. Using this extreme—but not implausible—hypothesis, we build a simple model similar in spirit to Akerlof's (1980) seminal work on social norms, and we show that reputation issues may cause the emergence of a stable but inferior equilibrium. Increased heterogenity generally has the effect of further reducing aggregate welfare; we conclude that the problems posed by the Pact/social norm are likely to increase following the enlargement, when a number of countries anxious to prove their “soundness” joined the club.     

Relative Valuation with Machine Learning

We use machine learning for relative valuation and peer firm selection. In out-of-sample tests, our machine learning models substantially outperform traditional models in valuation accuracy. This outperformance persists over time and holds across different types of firms. The valuations produced by machine learning models behave like fundamental values. Overvalued stocks decrease in price and undervalued stocks increase in price in the following month. Determinants of valuation multiples identified by machine learning models are consistent with theoretical predictions derived from a discounted cash flow approach. Profitability ratios, growth measures, and efficiency ratios are the most important value drivers throughout our sample period. We derive a novel method to express valuation multiples predicted by our machine learning models as weighted averages of peer firm multiples. These weights are a measure of peer–firm comparability and can be used for selecting peer-groups.     

The Gender Gap in Housing Returns

Using detailed transactions data across the United States, we find that single women earn 1.5 percentage points lower annualized returns on housing relative to single men. Forty-five percent of the gap is explained by transaction timing and location. The remaining gap arises from a 2% gender difference in execution prices at purchase and sale. Consistent with a negotiation channel, women list for less and experience worse negotiated discounts. The gender gap shrinks in tight markets, where negotiation is replaced by quasi-auctions. Overall, gender differences in housing explain 30% of the gender gap in wealth accumulation for the median household.     

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.     

House Price Markups and Mortgage Defaults

The transaction price of identical housing units can vary widely due to heterogeneity in buyer and seller preferences, matching, and search costs, generating what we term “markups” above or below the average market price. We measure markups for 3.4 million purchase-money mortgages and show that they can predict mortgage defaults and credit losses conditional on default even after accounting for collateral coverage (loan-to-value ratio) and a comprehensive set of other covariates. The findings suggest that standard collateral coverage estimation may be inaccurate, with implications for both individual and portfolio-level credit risk assessment.     

Standard Error Biases When Using Generated Regressors in Accounting Research

We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first-step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.     

Historical Patterns of Inequality and Productivity around Financial Crises

To understand the determinants of financial crises, previous research focused on developments closely related to financial markets. In contrast, this paper considers changes originating in the real economy as drivers of financial instability. To this end, I assemble a novel data set of long-run measures of income inequality, productivity, and other macrofinancial indicators for advanced economies. I find that rising top income inequality and low productivity growth are robust predictors of crises, and their slow-moving trend components largely explain these relations. Moreover, recessions that are preceded by such developments are deeper than recessions without such ex ante trends.