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Ethnic Minority Analysts’ Participation in Public Earnings Conference Calls
Get PDFWe investigate ethnic minority and non-minority sell-side analysts’ participation in public earnings conference calls. We find that minority analysts are underrepresented in conference call Q&A sessions, and minority analysts who do participate on the calls experience lower levels of prioritization than do nonminority analysts. Minority analysts’ lower participation rates are partially but not fully mediated by characteristics such as experience, work environment, and stock rating favorability. Additionally, firm and conference call fixed effects mediate approximately half the magnitude of lower minority participation rates. Extroverted minority analysts participate at higher rates, but the negative association between minority status and conference call participation is exacerbated when calls are more time constrained, when executive teams are less diverse, and when analysts are from less prestigious brokerage houses. Overall, we document the underrepresentation of minority analysts on earnings conference calls and provide evidence suggesting both analysts’ and managers’ choices influence minority analysts’ participation rates
Measuring the Expected Effects of the Global Tax Reform
Get PDFOver 140 countries agreed on a fundamental corporate tax reform in 2021 to be implemented in 2023 and beyond. To measure its potential effects, we study asset price changes within minutes of the reform announcements. We construct proxies for the reform's costs regarding U.S. companies' tax burdens and countries' public finances. Likely exposed companies exhibit significant negative stock returns. Our lower-bound estimates indicate total shareholder value losses of $112.6 billion one day after the reform announcements. Further, likely exposed countries experience increases in sovereign debt credit risk. Our findings inform the cost-benefit analysis of a historical international tax reform
Effects of Information Provision in Digital Markets
No full textDigitization of economic activities has transformed the way businesses operate. Continuous changes in digital markets, coupled with the explosion of available data, represent an exciting opportunity to develop a better understanding of the roles of different marketing tools in digital markets and its implications for consumer behavior, firm profits, and policy. Consequently, this dissertation is organized around two main themes: (1) platform economy and (2) online advertising.
The first chapter examines platform endorsement in the context of one of the world’s leading freelance platforms. Many digital platforms with large product assortments endorse a selected group of items to facilitate user choice. While it is intuitive that endorsed items may enjoy considerable benefits from increased sales, little is known about the effect of such platform endorsement on unendorsed items and on the platform. Using data from a field experiment conducted on an online freelance platform, I examine the effect of platform endorsement on user search and purchase behavior. I find that platform endorsement leads to an increase in search and purchases not only for endorsed services but also for unendorsed services. I find that this increase in search and purchases is mainly driven by an increase in overall quality perception of the services offered on the platform. I further explore heterogeneity in the effect of platform endorsement and find that the effect of platform endorsement on purchase is more pronounced for users with a higher propensity to purchase. I discuss implications for platforms, merchants, and regulators.
In the second chapter, I focus on a recent innovation in advertising – influencer marketing. The recent growth of the influencer marketing industry means brands are becoming more likely to contract with influencers. However, there is little empirical evidence regarding 1 consumer engagement with sponsored content relative to organic content. In this paper, I examine whether consumers engage less with sponsored content relative to organic content. Moreover, in light of the recent regulations across the globe mandating influencers to disclose advertising in sponsored content, I examine the effect of advertising disclosure in sponsored content on consumer engagement. I collect a dataset of 180,404 posts created by 510 Instagram influencers operating across ten categories. I identify sponsored posts in the dataset using advertising disclosure and supervised learning. Leveraging timing of regulatory actions and industry-level advertising trends as instrumental variables, I causally identify consumer engagement, measured by the number of likes, with sponsored content relative to organic content. I find that consumers engage less with sponsored content relative to organic content. However, the results show disclosure of advertising in sponsored content increases likes for sponsored content. Given the popularity of influencer marketing with brands, I further explore what characterizes successful influencer content. I rely on previous theory in consumer psychology and argue authenticity of content attenuates the negative effect of sponsorship on likes. I measure authenticity as – topic alignment of a post with other content shared by the influencer, influencer’s propensity to share brand related content, and the number of times a brand is mentioned in the post. I find that authenticity of content fully mitigates the negative effect of sponsorship on likes. My findings are relevant for regulators who are concerned about lack of advertising disclosure in influencer marketing and can also inform influencers and advertisers on their content creation strategies.
In the final chapter, I investigate systematic differences in online ratings based on gender in the context of an online labour market. Ratings have a direct effect on sales. I leverage a unique dataset from an online labour market that elicits both private and public ratings from buyers after completion of a job. While public ratings are displayed as star ratings on the website, the platform uses private ratings in internal evaluations. I find that conditional on having the same private rating, female freelancers receive lower public ratings compared to male freelancers. Further, I demonstrate that gender bias in rating is more pronounced 2 when buyers hail from locations with greater gender inequality. This suggests that buyers’ online behavior can reflect of their cultural biases. These results are important for platforms and merchants because systematic differences in consumer evaluation based on gender can lead to undesirable consequences for platform participants
The stability of MNL-based demand under dynamic customer substitution and its algorithmic implications
No full textWe study the dynamic assortment planning problem under the widely utilized multinomial logit choice model (MNL). In this single-period assortment optimization and inventory management problem, the retailer jointly decides on an assortment, that is, a subset of products to be offered, as well as on the inventory levels of these products, aiming to maximize the expected revenue subject to a capacity constraint on the total number of units stocked. The demand process is formed by a stochastic stream of arriving customers, who dynamically substitute between products according to the MNL model. Although this dynamic setting is extensively studied, the best known approximation algorithm guarantees an expected revenue of at least 0.139 times the optimum, assuming that the demand distribution has an increasing failure rate. In this paper, we establish novel stochastic inequalities showing that, for any given inventory levels, the expected demand of each offered product is “stable” under basic algorithmic operations, such as scaling the MNL preference weights and shifting inventory across comparable products. We exploit this sensitivity analysis to devise the first approximation scheme for dynamic assortment planning under the MNL model, allowing one to efficiently compute inventory levels that approach the optimal expected revenue within any degree of accuracy. The running time of this algorithm is polynomial in all instance parameters except for an exponential dependency on log Δ, where Δ=wmaxwmin stands for the ratio of the extremal MNL preference weights. Finally, we conduct simulations on simple synthetic instances with uniform preference weights (i.e., Δ=1). Using our approximation scheme to derive tight upper bounds, we gain some insights into the performance of several heuristics proposed by previous literature
Improving Dispute Resolution in Two-Sided Platforms: The Case of Review Blackmail
Get PDFWe study the relative merits of different dispute resolution mechanisms in two-sided platforms, in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. In our model, the seller takes into account the impact of consumer reviews on his future earnings; recognizing this, a malicious consumer may attempt to blackmail the seller by purchasing the product, posting a negative review, and demanding ransom to remove it. Without a dispute resolution mechanism in place, the presence of malicious consumers in the market can lead to a significant decrease in seller profit, especially in settings characterized by high uncertainty about product quality. The introduction of a standard centralized dispute resolution mechanism (whereby the seller can report allegedly malicious reviews to the host platform, which then judges whether to remove the review) can restore efficiency to some extent, but requires the platform's judgments to be both very quick and highly accurate. We demonstrate that a more decentralized mechanism (whereby the firm is allowed to remove reviews without consulting the platform, subject to ex post penalties for wrongdoing) can be much more effective, while simultaneously alleviating - almost entirely - the need for the platform's judgments to be quick. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution
Machine Learning and Fund Characteristics Help to Select Mutual Funds with Positive Alpha
Get PDFMachine-learning methods exploit fund characteristics to select tradable long-only portfolios of mutual funds that earn significant out-of-sample annual alphas of 2.4% net of all costs. The methods unveil interactions in the relation between fund characteristics and future performance. For instance, past performance is a particularly strong predictor of future performance for more active funds. Machine learning identifies managers whose skill is not sufficiently offset by diseconomies of scale, consistent with informational frictions preventing investors from identifying the outperforming funds. Our findings demonstrate that investors can benefit from active management, but only if they have access to sophisticated prediction methods
What Determines Consumer Financial Distress? Place- and Person-Based Factors
No full textWe use credit report data to study consumer financial distress in America. We report large, persistent disparities in financial distress across regions. To understand these patterns, we conduct a “movers” analysis. For collections and default, there is only weak convergence following a move, suggesting these types of distress are not primarily caused by place-based factors (e.g., local economic conditions and state laws) but instead reflect person-based characteristics (e.g., financial literacy and risk preferences). In contrast, for personal bankruptcy, we find a sizable place-based effect, which is consistent with anecdotal evidence on how local legal factors influence personal bankruptcy
Act Like a Leader, Think Like a Leader, Updated Edition of the Global Bestseller, With a New Preface
No full textA new edition of the bestseller that has helped aspiring leaders worldwide advance their careers and step up to larger leadership roles.
You aspire to lead with greater impact. The problem is you're busy executing on today's demands. You know you have to carve out time from your "day job" to build your leadership skills, but it’s easy to let immediate problems and old mindsets get in the way.
Herminia Ibarra--one of the world's foremost experts on leadership--shows how individuals at all levels can step up to leadership by making small but crucial changes in their jobs, their networks, and themselves. In Act Like a Leader, Think Like a Leader, Ibarra offers advice to:
Redefine your job in order to make more strategic contributions
Diversify your network so that you connect to, and learn from, a wider range of stakeholders
Become more playful with your self-concept, allowing your familiar--and possibly outdated--leadership style to evolve
Ibarra turns the usual leadership advice--generate insight about yourself through reflection and analysis of your strengths and weaknesses--on its head by arguing that you must first act and experiment your way into trying new things. The valuable external perspective you gain from direct experiences and experimentation--which Ibarra calls outsight--provides new and critical information on what kind of work is important to you, how you should invest your time, why and which relationships matter, and, ultimately, who you want to become.
Updated with new examples and self-assessments, this book gives you the tools to start acting like a leader and advancing your career to the next level
Online Assortment Optimization for Two-sided Matching Platforms
Get PDFMotivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers, and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests she has received and, based on her preferences, she chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers’ arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers’ preferences are described by the Multinomial Logit and Nested Logit choice models. We develop new forms of balancing algorithms, which we call preference-aware, that leverage structural information about suppliers’ choice models to design the associated discount function. In certain settings, these algorithms attain competitive ratios provably larger than the standard “barrier” of 1 − 1/e in the adversarial arrival model. Our results suggest that the shape and timing of suppliers’ choices play critical roles in designing online assortment algorithms for two-sided matching platforms