This paper studies the impact of career concerns on technological change by analyzing the adoption of digital cinematography in the U.S. motion picture industry. This setting allows us to collect rich data on the adoption of this new technology at the project level (i.e., movie) and on the career of the main decision-maker (i.e., director). We find that early-career directors played a leading role in the adoption of digital technology, an effect that appears to be explained by career concerns, rather than alternative motives we consider and analyze. Technological savviness also plays a role.

This study examines the determinants and implications of the volume of tax-related numbers reported in the financial statements. I document that the volume of tax numbers increases with tax reporting requirements and decreases with the complexity of the tax rate, implying that firms with greater proprietary costs decrease their numeric disclosures once they meet mandatory reporting requirements. With respect to implications, I document that firms reporting more tax numbers improve the transparency of the information environment, reducing the errors and dispersion of analysts’ implied effective tax rate forecasts. In contrast, greater emphasis on narrative tax disclosure does not reduce information frictions, highlighting an important trade-off between numeric detail and strategic narratives. Further investigation suggests that this relationship is driven by more tax numbers in the financial statement footnotes rather than in the face financial statements. These findings suggest firms’ tax information environment improves with a greater volume of numeric tax-related disclosures.

We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.

Cryptocurrency has been the subject of heightened regulatory and investor attention in recent years, and regulators and policymakers across the globe are deliberating on how to account for, regulate, tax, and oversee digital assets and cryptocurrency marketplaces. Yet researchers have a limited understanding of key attributes of those who deal in crypto assets, such as whether their financial sophistication differs from that of other investors. Using U.S. administrative data, we provide evidence on (i) the attributes of taxpayers reporting cryptocurrency sales to the IRS, (ii) how these attributes are evolving, and (iii) how investors treat cryptocurrency versus other financial assets in certain settings. The results suggest that average reporting cryptocurrency sellers exhibit demographic attributes generally associated with less financial sophistication and are more likely to trade in meme stocks. Overall, we provide timely evidence that can inform cryptocurrency policy deliberations by highlighting the characteristics of taxpayers who appear to report cryptocurrency sales.

Banks play a central role in the financial system and benefit the real economy by managing risk, and providing finance to households, small and medium-sized enterprises, large corporates and governments. However, their complexity, opacity and interconnectedness can elevate bank-level and systemic risks, posing dangers to the financial system and real economy. This was evident during the global financial crisis where taxpayer funded bailouts were used to rescue ailing banks, which in turn led to an overhaul of regulation and supervision. Consequently, safeguarding bank stability and addressing systemic risks via well designed regulations is essential for ensuring economic resilience and societal well-being. This study uses a quasi-natural experimental research design in the form of the Dutch Liquidity Balance Rule (LBR) to evaluate the impacts of liquidity regulation on bank-level stability and systemic risk. Our findings show that following the introduction of liquidity regulation, the stability of Dutch banks increases significantly relative to counterparts in neighbouring countries unaffected by the regulation. The observed reduction in risk stems from improved capitalization and reduced leverage, which contribute to greater financial stability. Systemic risk also decreases. Our findings have relevance beyond our research setting for policymakers tasked with implementing and monitoring the impacts of similar forms of liquidity regulation (such as bank liquidity coverage ratios) post global financial crisis.

Regulators around the world have begun to require investment companies to provide information regarding fossil fuel investments to external stakeholders. In this paper we examine whether such disclosures impact the investment portfolios and/or investment policies of the disclosing firms. Using a 2016 California disclosure mandate that required some U.S. insurance companies to disclose their fossil fuel investments on a public website, we find the disclosing insurers reduced their fossil fuel investments by approximately 20 % relative to the non-disclosers. Despite this on-average result, we note significant variation in changes to investment portfolios. We find insurers pressured by external stakeholders, including public shareholders and environmental activists, are more likely to divest. In contrast, enhanced Californian regulatory oversight power is unrelated to divesture. Even after the disclosure mandate is reversed, we find the disclosing insurers do not revert to their pre-policy holdings of fossil fuel investments, suggesting the impact created a longer-term change in investment behavior.

We estimate price elasticities of housing supply for U.S. cities by examining the impact of foreign purchases on housing prices and quantities. After other countries introduced foreign-buyer taxes beginning in 2011, both house prices and quantities increased more in locations with high foreign-born populations. An increase in global capital inflows, instrumented with tax policy changes scaled by immigrant exposure, increased prices and quantities over 2011–2018. We combine these estimates to construct new local supply elasticities, which average 0.26 and range from 0.06 to 0.9. Compared to prior estimates, our elasticities are more inelastic and change cities’ relative rankings.

This article documents adverse selection in Ginnie Mae issuers’ early buyout decisions. Conditional on default, we find a 1 percentage point increase in interest rate spread increases the probability of an early buyout by 7–9 percentage points. Issuers buy out higher interest rate spread loans because they generate greater economic gains when they reperform. We illustrate how issuers acquire private soft information that provides direct insight into the likelihood of reperformance. Although the soft information is ostensibly collected on behalf of investors during the delinquent loan servicing process, issuers can exploit the information in their early buyout decisions.

We study how analysts' inherited cultural attitudes to time orientation affect their production of long‐term information and the profitability of their stock recommendations. We find that analysts from long‐term‐oriented cultures exhibit a longer forecast horizon and issue more long‐term forecasts. They also produce more accurate long‐term forecasts and ask more long‐term‐focused questions during conference calls, eliciting greater long‐term disclosure from managers. In addition, they are more likely to use discounted valuation models that explicitly incorporate expectations about firms' long‐term prospects. Further, their stock recommendations are more profitable, consistent with their production of long‐term information enhancing valuation. Our findings highlight the role of cultural long‐term orientation in shaping analysts' information production in capital markets.