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Q: Risk, rents, or growth?
No full textWe document that the increasing polarization in Tobin’s Q within industries is closely connected to the growing divergence in rents and the emergence of superstar firms over the past four decades, while discount rates and growth rates did not exhibit the same increasing dispersion. We explain these industry polarization trends in an estimated general equilibrium model where each industry consists of large superstar oligopolists and small monopolistically competitive firms with endogenous transitions between them. Small firms make investments in speculative innovation to increase their probability of becoming superstars. Our model estimation finds that rising entry barriers in both small and superstar firms contribute to rising polarization in markups, but the rising barriers to creating small firms and increasing tastes for goods produced by superstars account for most of the divergence in Q. Stunting the creation of small firms generates greater incentives for speculative innovation, magnifying the impact of market power dispersion on industry polarization in Q
Motivating Support for Workplace Diversity Policies: A Mindsets Framework
No full textDiversity policies designed to foster more equitable work environments are widespread, but not necessarily widely supported. In this review, we advance a fixed-growth mindsets approach to understand people’s support for, or resistance to, diversity policies in the workplace. We theorize that people’s mindsets, or their fundamental beliefs about the malleability of attributes, underlie their diversity support via multiple mechanisms: (1) effort, (2) bias, (3) attributions, and (4) worldview threat. We expand upon each theorized mechanism, draw on established evidence to substantiate our arguments, and offer exciting new questions to guide future research. Because mindsets are amenable to change, we argue that our motivational framework to understanding diversity support offers a novel path forward for both scholarship and organizations that want to generate a greater consensus of support for their diversity policies
A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design
Full text linkNetwork design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version where operational costs are uncertain due to fluctuating demand and estimated as a sample average from historical data. This problem is computationally challenging, and instances with as few as 100 nodes often cannot be solved to optimality using current decomposition techniques. We propose a stochastic variant of Benders decomposition that mitigates the high computational cost of generating each cut by sampling a subset of the data at each iteration and nonetheless generates deterministically valid cuts, rather than the probabilistically valid cuts frequently proposed in the stochastic optimization literature, via a dual averaging technique. We implement both single-cut and multi-cut variants of this Benders decomposition, as well as a variant that uses clustering of the historical scenarios. To our knowledge, this is the first single-tree implementation of Benders decomposition that facilitates sampling. On instances with 100–200 nodes and relatively complete recourse, our algorithm achieves 5-7% optimality gaps, compared with 16-27% for deterministic Benders schemes, and scales to instances with 700 nodes and 50 commodities within hours. Beyond network design, our strategy could be adapted to generic two-stage stochastic mixed-integer optimization problems where second-stage costs are estimated via a sample average
Adaptive optimization for prediction with missing data
Full text linkWhen training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then compute predictions. In this paper, we view prediction with missing data as a two-stage adaptive optimization problem and propose a new class of models, adaptive linear regression models, where the regression coefficients adapt to the set of observed features. We show that some adaptive linear regression models are equivalent to learning an imputation rule and a downstream linear regression model simultaneously instead of sequentially. We leverage this joint-impute-then-regress interpretation to generalize our framework to non-linear models. In settings where data is strongly not missing at random, our methods achieve a 2–10% improvement in out-of-sample accuracy
After Shocks: Humble Leadership Improves Employee Adjustment Following Shock Events
No full textShock events are highly disruptive, threatening employees’ performance and increasing the risk that they quit. Yet, little research has focused on how leaders can help employees adjust in the wake of shock events. We draw on the socialization literature to build theory about how leaders can help employees successfully adjust and adapt following shock events. We propose that
humble leaders – because they are open to learning from and seeing value in employees’ shock-related experiences – will be more likely to use adjustment behavior that reduces employee
turnover and promotes employee performance. Focusing on the COVID-19 pandemic as a nearly universal shock event, we find evidence for our hypothesized effects across two multi-source
field studies (N = 2,392). Specifically, we find that humble leadership is positively related to affirming employees’ shock-related experiences and giving employees autonomy over how they approach work following shock, ultimately reducing turnover and enhancing employee performance
Are CEOs Rewarded for Luck? Evidence from Corporate Tax Windfalls
Full text linkFocusing on the one-off tax gains and losses (i.e., windfalls) associated with the 2017 Tax Cuts and Jobs Act, we reexamine whether CEOs are rewarded for luck. We find that weakly monitored CEOs are compensated for the windfall tax gains but not penalized for the corresponding tax losses. No such pattern is observed for CEOs facing greater pay scrutiny. The pay for windfalls cannot be explained as rewards for CEOs’ efforts, talents, political activities, or as firms sharing their tax gains with all executives. The results are more consistent with rent extraction by CEOs facing weak pay scrutiny
Reaching for Yield: Evidence from Households
Full text linkThe literature has documented “reaching for yield”—the phenomenon of investing more in risky assets when interest rates drop—among institutional investors. We analyze detailed transaction data from a large brokerage firm to provide direct field evidence that individual investors also exhibit this behavior. Consistent with models of portfolio choice with labor income, reaching for yield is more pronounced among younger and less-wealthy individuals. Consistent with prospect theory, reaching for yield is more pronounced when investors are trading at a loss. Finally, we observe and discuss the phenomenon of “reverse reaching for yield.
Gender Gap in Startup Recruiting: Evidence from Changes in Termination Costs
No full textWomen remain underrepresented not only as startup founders but also as nonfounding employees (“joiners”). Yet there is limited understanding of how to address this disparity. Whereas traditional explanations have emphasized individual employment preferences, we shift the focus to employers’ decision making. We propose that weaker employment protection—which reduces termination costs—increases women’s likelihood relative to men of being hired by startups rather than incumbent firms, as it enhances employers’ flexibility to experiment with traditionally overlooked candidates. We further hypothesize that this effect is less pronounced when information cues counteract stereotypical beliefs about women hires, such as when (a) women have experience as founders, or (b) the focal startup operates in an industry with a higher representation of female employees or female startup employees. Using Portuguese registry data on new hires from 2009 to 2013 and a regression discontinuity in time (RDiT) design, we provide empirical support for these predictions. Our study highlights gender disparities in hiring between startups and incumbent firms and shows that weaker employment protection can help reduce this gender gap
Forest through the Trees: Building Cross-Sections of Stock Returns
No full textWe build cross-sections of asset returns for a given set of characteristics, that is, managed portfolios serving as test assets, as well as building blocks for tradable risk factors. We use decision trees to endogenously group similar stocks together by selecting optimal portfolio splits to span the stochastic discount factor, projected on individual stocks. Our portfolios are interpretable and well diversified, reflecting many characteristics and their interactions. Compared to combinations of dozens (even hundreds) of single/double sorts, as well as machine-learning prediction-based portfolios, our cross-sections are low-dimensional yet have up to three times higher out-of-sample Sharpe ratios and alphas
Designing Layouts for Sequential Experiences: Application to Cultural Institutions
No full textA fundamental issue faced by experience providers—ranging from retail to culture—is displaying a collection of items for physical and digital interactions. The arrangement of the exhibits in different locations, which we call the layout, affects the visitors’ choices over time and space, thereby driving their engagement with the offered experience. In a collaboration with the Van Gogh Museum (Netherlands), we develop a predict-then-optimize framework to inform such operational decisions. First, we propose a sequential choice model, called pathway multinomial logit, that represents visitor activity as a sequence of conditional logit outcomes influenced by the layout. Estimation on large-scale visitor activity logs recorded on multimedia guides reveals that increase in spatial distances and search distances on the multimedia guide interface are strongly correlated with a reduction of transition propensity between artworks, while also uncovering relationships with artwork characteristics and contextual features. Counterintuitively, in response to more congestion, visitors may interact with more exhibits, including less prominent artworks. Our model predicts the next visitor transition with an out-of-sample accuracy of 63%. We test the predictive accuracy of our model against several benchmarks and modified layouts. Finally, we formulate the layout optimization problem, where the goal is to assign artworks to different locations to maximize the expected length of visitors’ paths. We establish a strong inapproximability result for this new optimization setting. Our simulations suggest that optimized layouts might lift visitor engagement by improving proximity and retention exerted by the layout