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The racial gap in entrepreneurship and opportunities inside established firms
No full textRacial disparities in entrepreneurship have been widely discussed in the literature, with most studies focusing on mechanisms that amplify such disparities. However, less attention has been devoted to factors that promote inclusion. We propose that intrapreneurship—launching and operating new ventures inside established organizations—represents a more inclusive entrepreneurial pathway than entrepreneurship involving a standalone venture. We predict that relative to White employees, Black employees will (1) be more likely to engage in intrapreneurship than entrepreneurship and (2) achieve greater financial performance as founders of internal ventures than standalone ventures. Using data on a representative sample of American new business founders in 2005, we found supportive evidence for our theory. We show that when racial stereotypes become more prominent as an evaluative heuristic, such as in the presence of high levels of discrimination or when other quality signals are absent, disparities between Black and White employees are less likely to increase in intrapreneurship than in entrepreneurship. Our study thus highlights the importance of intrapreneurship in leveling the playing field for racial minorities pursuing entrepreneurial activitie
Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance
Get PDFMissing data is a common issue in real-world datasets. This paper studies the performance
of impute-then-regress pipelines by contrasting theoretical and empirical evidence. We establish the asymptotic consistency of such pipelines for a broad family of imputation methods. While common sense suggests that a ‘good’ imputation method produces datasets that are plausible, we show, on the contrary, that, as far as prediction is concerned, crude can be good. Among others, we find that mode-impute is asymptotically sub-optimal, while mean-impute is asymptotically optimal. We then exhaustively assess the validity of these theoretical conclusions on a large corpus of synthetic, semi-real, and real datasets. While the empirical evidence we collect mostly supports our theoretical findings, it also highlights gaps between theory and practice and opportunities for future research, regarding the relevance of the MAR assumption, the complex interdependency between the imputation and regression tasks, and the need for realistic synthetic data generation models
Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices
No full textMaking sense of massive, individual-level data is challenging: marketing researchers and analysts need flexible models that can accommodate rich patterns of heterogeneity and dynamics, work with and link diverse data types, and scale to modern data sizes. Practitioners also need tools that can quantify uncertainty in models and predictions of consumer behavior to inform optimal decision-making. In this paper, we demonstrate the promise of probabilistic machine learning (PML), which refers to the pairing of probabilistic modeling and machine learning methods, in pushing the frontier of combining flexibility, scalability, interpretability, and uncertainty quantification for building better models of consumers and their choices. Specifically, we overview both PML models and inference methods, and highlight their utility for addressing four common classes of marketing problems: (1) uncovering heterogeneity, (2) flexibly modeling nonlinearities and dynamics, (3) handling high-dimensional and unstructured data, and (4) addressing missingness, often via data fusion. We also discuss promising directions in enriching marketing models, reflecting recent developments in representation learning, causal inference, experimentation and decision-making, and theory-based behavioral modeling
The Digital Lives of the Poor: Entertainment Traps and Information Isolation
Get PDFSmartphones have enabled the delivery of life-improving information services to base-of-the-pyramid (BOP) consumers. However, little is known about how the poor interact with the digital world. Through a novel app we developed to investigate real-time smartphone usage, we identify an unnoticed barrier to digital information access by the poor—data shortages. By analyzing over 9.4 million minutes of smartphone usage data from 929 residents of a Mumbai settlement, we find that entertainment consumes 61% of their phone time. Our data reveal that under universally adopted monthly data plans, low-income individuals binge on YouTube and social media, resulting in data shortages and information isolation in the late-plan period. We offer a practical operational solution to this problem—shorter data-replenishment cycles—which serve as a commitment device to curb binge usage. We randomly assign participants to a “capped plan”—with daily data usage caps—or a standard (monthly) plan. Assignment to the capped plan increases late-plan access of invites to health camps sent via WhatsApp, increases attendance at these in-person camps by 27%, and reduces social media binge usage. Most participants (particularly those with low self-control and high fear of missing out) prefer the capped plan, even when costlier—clearly signaling demand. Because capped plans are inherently cheaper to provide, offering them could enable providers to increase BOP customer value and expand access. Our results suggest an opportunity to amplify the impact of life-improving services targeted at the poor by leveraging users’ interactions with smartphone technology
The Political Development Cycle: The Right and the Left in People's Republic of China from 1953
No full textWe quantify the effects of the political development cycle—the fluctuations between the Left (Maoist) and the Right (pragmatist) development policies—on growth and structural transformation of China in 1953–1978. The left policies prioritized structural transformation toward nonagricultural production and consumption at the expense of agricultural development. The right policies prioritized agricultural consumption through slower structural transformation. The imperfect implementation of these policies led to large welfare costs of the political development cycle in a distorted economy undergoing a structural change
Corruption and Firm Growth: Evidence from around the World
No full textWe empirically investigate the relationship between corruption and growth using a firm-level dataset that is unique in scale, covering almost 88,000 firms across 141 economies in 2006–20, with wide-ranging corruption experiences. The scale and detail of our data allow us to explore the corruption-growth relationship at a very local level, within industries in a relatively narrow geography. We report three empirical regularities. First, firms that make zero informal payments tend to grow slower than bribers. Second, this result is driven by non-bribers in high-corruption countries. Third, among bribers, growth is decreasing in the amount of informal payments—in both high- and low-corruption countries. We suggest that this set of results may be reconciled with a simple model in which endogenously determined higher bribe rates lead to lower growth, while non-bribers are often excluded entirely from growth opportunities in high-corruption settings
ESG Disclosures in the Private Equity Industry
Get PDFThis paper offers the first systematic evidence on environmental, social, and governance (ESG) disclosures provided by a large global sample of private equity (PE) firms. Using historical websites from 2000 to 2022, we develop and validate a novel dictionary-based measure of voluntary PE firm ESG disclosures. Descriptive statistics reveal an increasing time trend in these disclosures, with social topics becoming as important as environmental topics recently. Multivariate analyses show that the demand for ESG information from fund investors is a significant determinant of PE firms’ ESG disclosures. Leveraging data on PE firms’ portfolio companies, we document that more PE firm ESG disclosures are associated with better ESG outcomes at the portfolio company level, suggesting that voluntary ESG disclosures align with real actions for the average PE firm
Seller-Orchestrated Inventory Financing under Bank Capital Regulation
Get PDFTo help small firms secure bank financing, large sellers often orchestrate joint finance programs, linking their small dealers with major banks that lend to all participating dealers based on the information the seller provides. We examine supply chain decisions (pricing and inventory) and lending terms under such seller-orchestrated financing programs. In loan pricing, we highlight a form of financial friction that is of particular importance under such schemes – bank capital regulation. Banks are globally mandated to maintain regulatory capital to mitigate unforeseen loan losses, using either the standardized approach (where regulatory capital is a fixed percentage of the loan amount) or the internal rating-based (IRB) approach (where it depends on the loan's Value-at-Risk). We consider a game-theoretic model consisting of a large seller and multiple capital-constrained newsvendor-type dealers, who obtain financing from banks who are subject to capital regulation. The seller decides the wholesale price and whether to orchestrate a joint finance program for its dealers by collaborating with a bank, and the dealers choose their inventory level and the financing channel. We find that a seller should only orchestrate the joint financing program when the bank adopts the IRB approach and the dealers are of low risk. Such a program is more profitable to the seller when the demand correlation among dealers is low, and there is a large number of dealers. Although always benefiting the seller, these programs may hurt dealers with intermediate risk. Facing dealers with varying financial situations, the terms under the joint finance program should be designed as if the financially strong dealers subsidize the weak ones. Finally, allowing the seller to share part of the loan loss could further enhance the performance of joint financing, but only when the seller's opportunity cost of capital is low. Our findings provide guidance to large sellers on how to orchestrate joint finance schemes, and to small dealers on making their corresponding operational decisions
The Business Case for Diversity Backfires: Detrimental Effects of Organizations’ Instrumental Diversity Rhetoric for Underrepresented Group Members’ Sense of Belonging
Get PDFMany organizations offer justifications for why diversity matters, i.e., organizational diversity cases. We investigated their content, prevalence, and consequences for underrepresented groups. We identified the “business case” (BC), an instrumental rhetoric claiming that diversity is valuable for organizational performance, and the “fairness case” (FC), a non-instrumental rhetoric justifying diversity as the right thing to do. Using an algorithmic classification, Study 1 (N=410) found that the BC is far more prevalent than the FC among the Fortune 500. Extending theories of social identity threat, we next predicted that the BC (vs. FC, or control) undermines underrepresented groups’ anticipated sense of belonging to, and thus interest in joining organizations – an effect driven by social identity threat. Study 2 (N=151) found that LGBTQ+ professionals randomly assigned to read an organization’s BC (vs. FC) anticipated lower belonging, and in turn, less attraction to said organization. Study 3 (N=371) conceptually replicated this experiment among female (but not male) STEM job seekers. Study 4 (N=509) replicated these findings among STEM women, and documented the hypothesized process of social identity threat. Study 5 (N=480) found that the BC (vs. FC and control) similarly undermines African American students’ belonging. Study 6 (N=1,019) replicated Study 5 using a minimal manipulation, and tested these effects’ generalizability to Whites. Together, these findings suggest that despite its seeming positivity, the most prevalent organizational diversity case functions as a cue of social identity threat that paradoxically undermines belonging across LGBTQ+ individuals, STEM women, and African Americans, thus hindering organizations’ diversity goals