Social and emotional learning (SEL) programs that target disruptive students aim to improve their classroom behavior. Small-scale programs in high-income countries have demonstrated positive effects. Using a randomized experiment, we show that a nationwide SEL program in Chile has no effect. Very disruptive students seem to reduce the program’s effectiveness. With attention deficit hyperactivity disorder being more prevalent in middle- than high-income countries, very disruptive students may be more present there, which could diminish the effectiveness of SEL programs. Moreover, implementation fidelity seems lower in this program than in the small-scale ones considered earlier, which could also explain the program’s null effect.

We provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature’s standard approach. The standard approach takes a given model of normal accruals for granted and neglects any uncertainty about the model and its parameters. By contrast, our Bayesian approach allows incorporating parameter and model uncertainty into the estimation of normal accruals. This approach can increase power and reduce false positives in tests for opportunistic earnings management as a result of better estimates of normal accruals and more robust inferences. We advocate the greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages.

ABSTRACT We study the economic impact of private equity (PE) investments on local governments, which are important corporate stakeholders. Examining over 11,000 deals and private firm data in Europe, we document that target firms' effective tax rates and total tax expenses decrease by 15% and 13% after PE deals. At the same time, target firms expand their capital expenditures and firm boundaries, but do not increase employment. Using administrative data on the public finances of German municipalities and exploiting the geographical and time‐series variation in PE deals, we document that PE activity is negatively associated with local governments' tax revenues and spending. This result is likely driven by reduced tax payments of PE portfolio firms, accompanied by only modest positive spillovers of PE investments on regional economic growth. Collectively, our findings suggest that corporate tax efficiency serves as a cost‐cutting channel in the PE sector and constrains the finances of local governments.

We investigate how a change in regulatory oversight affects bank risk, using the passage of the Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018 as a setting. Using a sample of bank holding companies (BHCs) covering the period 2015Q1 through 2020Q1, we find that risk increases for large BHCs affected by a change in regulatory oversight. In addition to increasing bank level risk, affected BHCs increase their respective contribution to the systemic risk. These BHCs also experience higher profitability, increased market valuation and reduced compliance costs.

We examine the choice of organizational structure for VC-backed startup firms. These firms overwhelmingly organize as C-corporations rather than as tax advantaged limited liability companies (LLCs). This results in foregone tax savings of $43.9 billion, or 4.9% of the total equity invested in the sample firms. The decision is puzzling, given plausible estimates of the direct costs involved, but appears related to “hassle” and other transition costs generated by participants implementing a new form. Firms with more employees and investors are likely to choose the C-corporation. VCs appear to prefer the C-corporation form, as receiving VC money is associated with most LLC firms switching to a C-corporation within 30 days. Greater VC preferences for C-corporations are linked to a preference for familiarity, and less attention to taxes.

ABSTRACT We develop FinBERT, a state‐of‐the‐art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts. Using a sample of researcher‐labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short‐term memory, in sentiment classification. Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text. We find that FinBERT's advantage over other algorithms, and Google's original bidirectional encoder representations from transformers model, is especially salient when the training sample size is small and in texts containing financial words not frequently used in general texts. FinBERT also outperforms other models in identifying discussions related to environment, social, and governance issues. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT. Our results have implications for academic researchers, investment professionals, and financial market regulators.

This study examines the relation between CEO tournament incentives, proxied by the difference between CEO pay and the median pay of the senior executives of a given firm, and corporate debt contracting. We find negative relations between CEO pay gap and the cost of debt and default risk, and a positive relation between CEO pay gap and debt maturity. Further analysis indicates that the results are stronger for firms with near-retirement CEOs, which are more likely to run CEO tournaments. Our evidence suggests that creditors view tournament incentives favorably and are willing to provide better debt terms.

To determine the welfare implications of price changes in demand data, we introduce a revealed preference relation over prices. We show that the absence of cycles in this relation characterizes a consumer who trades off the utility of consumption against the disutility of expenditure. Our model can be applied whenever a consumer’s demand over a strict subset of all available goods is being analysed; it can also be extended to settings with discrete goods and non-linear prices. To illustrate its use, we apply our model to a single-agent data set and to a data set with repeated cross-sections. We develop a novel test of linear hypotheses on partially identified parameters to estimate the proportion of the population who are revealed better off due to a price change in the latter application. This new technique can be used for non-parametric counterfactual analysis more broadly.

ABSTRACT This study critically evaluates research published by Contemporary Accounting Research ( CAR ) between 1984 and 2021 using bibliometric analysis. We examine the following: (i) CAR 's publication quality and the factors associated with its citations and (ii) CAR 's scope regarding research diversity, methods, authors geographical dispersion, and collaborative networks. The methodology permits observation of finer collaboration details and research patterns not apparent by simply categorizing the data. We use tools such as performance analysis, coauthorship analysis, bibliographic coupling, and regression analysis. The bibliometric analysis shows improvement in CAR 's CiteScore and source‐normalized impact per paper over time, consistent with publishing high‐quality research. Our analysis reveals that authors' geographical affiliations, research subject areas, and research methods are not systematically associated with citations across our various subsamples. A notable exception is that research on audit topics generates more citations than studies examining financial accounting topics. Other factors significantly and positively associated with citations include article age, article length, number of authors, order of author names, and number of references. We also show that CAR has become more diverse regarding author affiliations, subject areas, and research methods than most leading accounting journals. Only Accounting, Organizations and Society emerges as more diverse, thereby serving as a benchmark for CAR in the future. CAR should consider focusing on high‐interest areas to boost citations and tightening its acceptance criteria.