Finance Division
Babson College
224 Tomasso Hall
231 Forest Street
Babson Park, MA 02457
781-239-5060
lcdstein@babson.edu
http://lukestein.com
This paper studies how access to financial services among a previously unbanked group affects human capital, labor market, and wealth outcomes. We use novel data from the Freedman’s Savings Bank — created following the American Civil War to serve free Blacks — employing an instrumental variables strategy exploiting the staggered rollout of bank branches. Families with accounts are more likely to have children in school, be literate, work, and have higher occupational income, business ownership and real estate wealth. Placebo effects are not present using planned but unbuilt branches, or for Whites, suggesting significant positive effects of financial inclusion.
Active corporate executives are a popular source of independent directors. Although their knowledge, expertise, and network can bring value to firms on whose boards they sit, independent executive directors may be more likely to be distracted than other directors due to their outside executive roles. Using newly constructed data linking independent directors to their employers, we identify periods when employers’ poor performance may distract them from board service. We find that firms with distracted independent executive directors have lower performance and value, higher CEO compensation, reduced CEO turnover-performance sensitivity, lower earnings quality, and lower M&A performance. These adverse effects are mainly driven by distracted directors who sit on relevant committees, and are stronger for small boards.
We examine the effect of race on market outcomes by selling iPods through local online classified advertisements throughout the United States. Each advertisement features a photograph including a dark- or light-skinned hand, or one with a wrist tattoo. Black sellers receive fewer and lower offers than white sellers, and the correspondence with black sellers indicates lower levels of trust. Black sellers’ outcomes are particularly poor in thin markets (suggesting that discrimination may not “survive” competition among buyers) and those with the most racial isolation and property crime (consistent with channels through which statistical discrimination might operate).
with Laura Lindsey
Revision requested, The Journal of Financial Economics
This paper examines the effects of a shock to angel finance on entrepreneurial activity and employment. Using U.S. Census data, we estimate the state-level fraction of households that lost accreditation status from Dodd–Frank’s elimination of housing wealth in determining accreditation. A larger reduction in the pool of potential investors reduces firm entry and employment at small entrants, particularly in areas with alternate sources of financing. Employment increases at small and young incumbents, and relative wages for the startup sector decline, especially for high-skilled workers and industries. These results suggest that angels are an important source of entrepreneurial finance to high-quality, competitive firms.
with Ran Abramitzky, Jacob Conway, and Roy Mill
We study differences in economic outcomes by perceived skin tone among African Americans using full-count U.S. decennial census data from the late-19th and early-20th centuries. Comparing children coded as “Black” or “Mulatto” by census enumerators and linking these children across population censuses, we first document large gaps in educational attainment and income among African Americans with darker and lighter perceived skin tones. To disentangle the drivers of these gaps, we identify all 36,329 families in which enumerators assigned same-gender siblings different Black/Mulatto classifications. Relative to sisters coded as Mulatto, sisters coded as Black had lower educational attainment, were less likely to marry, and had lower-earning, less-educated husbands. These patterns are consistent with more severe contemporaneous discrimination against African-American women with darker perceived skin tones. In contrast, we find similar educational attainment, marital outcomes, and incomes among differently-classified brothers. Men perceived as African Americans of any skin tone faced similar contemporaneous discrimination, consistent with the “one-drop” racial classification rule that grouped together individuals with any known Black ancestry. Lower incomes for African-American men perceived as having darker skin tone in the general population were driven by differences in opportunities and resources that varied across families, likely reflecting the impacts of historical or family-level discrimination.
(This paper subsumes portions of (and supersedes) an earlier working paper coauthored with Roy Mill)
with Donald E. Bowen III, S. McKay Price, and Ke Yang
We conduct an audit study of loan approval and interest rate decisions suggested by large language models (LLMs). Using a dataset of real loan applications and experimentally manipulated race and credit scores, we find that LLMs recommend denying more loans and charging higher interest rates to Black applicants than otherwise-identical white applicants. This racial bias is largest for lower-credit-score applicants and riskier loans, but present across the credit spectrum. Surprisingly, simply instructing the LLM to make unbiased decisions eliminates the racial disparity in approvals and moderates the interest rate disparity. LLM recommendations correlate strongly with real lenders’ decisions, despite having no fine-tuning or specialized training, no macroeconomic context, and access to only limited data from each loan application. A number of different leading LLMs produce racially biased recommendations, although the magnitudes and patterns vary. Our results highlight the critical importance of auditing LLMs and demonstrate that even basic prompt engineering can help reduce LLM bias.
with Spencer Barnes
In the presence of behavioral biases, prices can diverge from fundamentals, and the effects of racial/ethnic bias are evident in many financial and non-financial markets. We investigate the determinants and consequences of discrimination in parimutuel horse betting by assessing return differences across horses whose trainers have racially/ethnically distinctive surnames, which bettors may see as a proxy for quality (accurately or inaccurately) or a source of non-pecuniary returns (due to animus). Bets on horses with nonwhite-named trainers earn higher risk-adjusted returns, and these differences are especially pronounced among riskier bets, which receive lower average returns under the well-known “favorite–longshot” bias. Racial/ethnic return differences are stronger overall and especially among longshots-for “win” than “place” and “show” bets, among horses with poor prior performance, in low-stakes races with “fast” conditions, and in the U.S. South. These results are consistent with the effects of discrimination being strongest among less-informed and less-sophisticated bettors.
with Charles C.Y. Wang
In the presence of managerial short-termism and asymmetric information about skill and effort provision, firms may opportunistically shift earnings from uncertain to more certain times. We document empirically that when financial markets are less certain about a firm’s future value, the firm reports more negative discretionary accruals. Stock-price responses to earnings surprises are moderated when firm-level uncertainty is high, consistent with performance being attributed more to luck rather than skill and effort, which can create incentives to shift earnings toward lower-uncertainty periods. We document that the resulting opportunistic earnings management is concentrated in CEOs, firms, and periods where such incentives are likely to be strongest: (1) where CEO wealth is sensitive to change in the share price, (2) where announced earnings are particularly likely to be an important source of information about managerial ability and effort, and (3) before implementation of Sarbanes-Oxley made opportunistic earnings management more challenging. Our evidence highlights a novel channel through which uncertainty affects managerial decision making in the presence of agency conflicts.
with Oliver Boguth
Investors have a choice over when to incur taxes on individual investments, and typically benefit from delaying the realization of capital gains while harvesting losses. This option implies that the effective tax rate on capital losses exceeds the one on capital gains, resulting in a convex after-tax payoff. Convexity creates a demand for idiosyncratic volatility (IVOL) within a well-diversified portfolio, and can therefore explain the puzzling negative relation between IVOL and expected stock returns. A simple model with tax-timing options predicts that the demand for idiosyncratic volatility increases with the tax rate, the nominal interest rate, and unrealized capital gains, and we show that all three measures predict the IVOL premium in the time-series. In the cross-section, we show that the magnitude of the IVOL premium increases with investors’ average tax exposure.
with Elizabeth Stone
There is wide debate over the impact of uncertainty on firm behavior, due to the difficulty both of measuring uncertainty and of identifying causality. This paper takes three steps that attempt to address these challenges. First, we develop an instrumental variables strategy that exploits firms’ differential exposure to energy and currency prices and volatility. For example, airlines are negatively affected by high oil prices while oil refiners benefit from them, but both are sensitive to oil price volatility; retailers, in comparison, are not particularly sensitive to either the level or volatility of oil prices. Second, we use the expected volatility of stock prices as implied by equity options to obtain forward-looking measures of uncertainty over firms’ business conditions. Finally, we examine how uncertainty affects a range of outcomes: capital investment, hiring, research and development, and advertising. We find that uncertainty depresses capital investment, hiring, and advertising, but encourages R&D spending. This perhaps-surprising result for R&D is consistent with a theoretical literature emphasizing that long investment lags create valuable real put options which offset the effects of call options lost when projects are started. Aggregating across our panel of Compustat firms, we find that rising uncertainty accounts for roughly a third of the fall in capital investment and hiring that occurred in 2008–10.
Recipient of Deans’ Award for Excellence in Graduate Teaching (Babson College’s annual award to one faculty member for ‘‘excellence and innovative practices in teaching’’)
Recipient of 2022 Thomas Kennedy Award for Teaching Excellence (Babson College’s annual “Graduate Faculty of the Year” awarded annually by graduating class to one faculty member “who personifies teaching excellence at the graduate level and whose personal standards of quality and caring extend beyond the classroom”)
Recipient of 2019 Huizingh Outstanding Undergraduate Teacher Award (ASU W.P. Carey School of Business’ annual award to an instructor “dedicated to inspiring students through excellence in teaching and mentoring”)
Recipient of 2011–12 Gores Award (Stanford University’s “highest award for excellence in teaching”)
Instructor, Babson College
Financial Data Analysis and Practice (Finance 6200)
Finance for Entrepreneurs (Finance 6110)
M.B.A. Finance (Finance 7800)
Introduction to Financial Management (Finance 7200)
Instructor, Arizona State University
Managerial Finance (Finance 302)
Identification Strategies in Corporate Finance (Finance 791)
Instructor, Stanford University
Intermediate Microeconomics (Economics 50)
Microeconomic Theory for Non-Economics Ph.D. Students (Economics 202N) [slides]
Online High School Microeconomics (EPGY OHS Economics 20)
Teaching Assistant, Stanford University
Introduction to Financial Economics (Economics 140)
First-Year Ph.D. Macroeconomics (Economics 210) [notes]
Managerial Economics for MBAs (GSB Management Economics 200)
Economics for Sloan Fellows (GSB Management Economics 209)
Emergency Medical Technician Training (Surgery 111a/211a)
Anthony Rice (Ph.D. 2021), Chinese University of Hong Kong
Sean Flynn (Ph.D. 2017), Cornell University via Colorado State University
Hong Zhao (Ph.D. 2017), NEOMA Business School
Yung-Ling Chi (Ph.D. 2016), National Chung Hsing University
Qi Dong (Ph.D. 2015), King Fahd University of Petroleum and Minerals
Chadwick Ali’varius (B.S. 2020), Max Bamford (B.S. 2020), Alexander Doughty (B.S. 2020), Neil Jha (B.S. 2020), Zach Leibovit-Reiben (B.S. 2020), John Charette (B.S. 2019), Harshit Thacker (B.S. 2019), Hamza Amjad (B.S. 2018), Gurkaran Chotalla (B.S. 2017), Landon Gagner (B.S. 2017), Matthew Klein (B.S. 2017), John Lauro (B.S. 2017), Michael Muscheid (B.S. 2017), Aaron Chavez (B.S. 2016), A. J. Gilman (B.S./B.A. 2016), Steven Kaye (B.S. 2016), Samir Reddy (B.S. 2016)
“Teaching from home” technology recommendations
See Teaching section above for notes and slides
Notes summarizing Stanford Economics’ first-year Ph.D. core courses (and source code)
Stata/LaTeX Workflows, including table gallery with code samples
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