TD MDAL Research Grant - 2023 awards

Faculty and PhD Student recipients include:

Faculty

Hyeun Lee, Assistant Professor of Strategic Management, “The Effects of Task Similarity in Closing the Performance Gender Gap.”

Scott Liao, Vice Dean, Undergraduate and Specialized Programs, “Debt contracting on director turnover” (with Andrea Down, Assistant Professor of Accounting in the Department of Management at UTSC, with a cross-appointment to the Accounting Area at the Rotman School of Management; Lulin Song, PhD student, Accounting; XijiangSu, PhD student Accounting)

Sheng Liu, Assistant Professor of Operations Management and Statistics, “Building A Flood Resilient Toronto with Data.”

Charles Martineau, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough, “How Important is Soft Information to Price Efficiency Following Earning Announcements?” (with Zissis Poulos, Postdoctoral Fellow; Vincent Gregoire, Associate Professor, Department of Finance, HEC Montreal)

Pamela Medina Quispe, Assistant Professor of Economics at the Department of Management, University of Toronto Scarborough and Rotman School of Management, “Time & Technology on the Job” (with Julieta Caunedo, Assistant Professor, Economics Analysis and Policy Area)

Yoshio Nozawa, Assistant Professor, Finance Area, University of Toronto Scarborough, “Data-Driven Forecast of Default Risk and Macroeconomy.”

Chay Ornthanalai, Associate Professor, Finance Area, “The Value of Private Information: Evidence from Affiliated Members of Options Clearing House” (with Xiang Zheng, Assistant Professor of Finance, School of Business, University of Connecticut)

Eugene Tan, Assistant Professor of Economic Analysis & Policy, “The Outside Options of Entrepreneurs” (with Attila Gyetvai, Research Economist, Bank of Portugal)

Irene Yi, Assistant Professor, Finance Area, “The Immigrant Wage Penalty: Evidence from Domestic and International MBA Graduates in Canada” (with Camille Hebert Assistant Professor of Finance, Department of Management, University of Toronto, Mississauga)

Marius Zoican, Assistant Professor of Finance, Department of Management, University of Toronto, Mississauga, “Building the Exchanges of Tomorrow:  Who Provides Liquidity on Decentralized Exchanges and Why?”

PhD students

Noemie Bucourt, PhD student, Finance, “Personal liability and pollution decisions” (Redouane Elkamhi, Associate Professor of Finance)

Zirou Chen, PhD student, Marketing, “Standing Out from the Crowd: Consumer Subscription and Gifting on Live Streaming Platform” (with Nitin Mehta, Professor of Marketing, Area Coordinator, Marketing Area, Ellison Professorship in Marketing; Clarice Zhao, PhD student, Marketing)

Olumurejiwa Fatunde, PhD fellow, Operations Management and Statistics, “Assortment Optimization Of Crowdsourcing Contests For Medical Knowledge Based On Revealed Motivations Of Participants” (with Gonzalo Romero, Associate Professor, Operations Management and Statistics)

Craig Fernandes, PhD student, Operations Research, “Income Pools for Superstar Markets” (with Timothy Chan, University of Toronto’s Associate Vice-President and Vice-Provost, Strategic Initiatives, Professor Industrial Engineering, Canada Research Chair in Novel Optimization and Analytics in Health and Associate Director of the Data Sciences Institute (DSI) and Director of Centre for Analytics and Artificial Intelligence Engineering (CARTE); Ningyuan Chen, Assistant Professor, Operations Management)

Xingchao Gao, PhD student, Accounting, “Diversify-or-Explain Disclosure and Stakeholder Monitoring” (with Ole-Kristian Hope, Deloitte Professor and Professor of Accounting)

Jordan Hutchings, PhD student, Marketing, “Does bike share drive business towards local retailers?” (with Avi Goldfarb, Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing)

Jialin Li, PhD student, Operations Management and Statistics, “Data Privacy in Pricing: Estimation Bias and Implications” (with Ningyuan Chen, Assistant Professor in Operations Management; Ming Hu, University of Toronto Distinguished Professor of Business Operations and Analytics and Professor of Operations Management; Sheng Liu, Assistant Professor of Operations Management and Statistics)

Junhao Liu, PhD student, Accounting, “Website Cookies and Voluntary Disclosure.”

Siyuan Liu, PhD student, Economic Analysis and Policy, “Entry Deregulation, Organizational Form, and Firm Performance” (with Zijun Cheng, PhD student, Marketing; Ruichi Xiong, PhD student, Economic Analysis and Policy; Nathaniel Baum-Snow, Professor of Economic Analysis and Policy)

Edna Lopez Avila, PhD student, Accounting, “Consequences of unsophisticated trades in the options market” (with Charles Martineau, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough)

Camilo Machado Goncalves, PhD student, Economic Analysis and Policy, “Gender-based price discrimination: an antitrust concern?” (with Maximiliano Machado, PhD student, Economic Analysis and Policy; El Hadi Caoui, Assistant Professor of Strategic Management, Department of Management, University of Toronto, Mississauga)

Mariana Oseguera Rodriguez, PhD student, Strategic Management, “Decredentialization of Work” (with Mitchell Hoffman, Associate Professor of Strategic Management)

Giulia Sargiacomo, PhD student, Accounting, “'If Not Certain Be Vague': How Uncertainty in Investors’ Preferences Shape Voluntary Climate-Change Disclosure” (with Ole-Kristian Hope, Deloitte Professor and Professor of Accounting)

Xinyi Xia, PhD student, Finance, “Financial Constraints, Monetary Policy, and Asset Prices” (with Jincheng Tong, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough)

Zhenghang Xu, PhD student, Operations Management, “Causal Models for Comparative Analytics in Queueing Systems” (with Opher Baron, Distinguished Professor of Operations Management; Dmitry Krass, Professor of Operations Management and Statistics and Sydney C. Cooper Chair in Business and Technology;Mark van der Laan, Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley; Arik Senderovich, Assistant Professor (tenure-track) at the School of Information Technologies (ITEC), York University)

Minwen Yang, PhD student, Marketing, “What types of cookie messages are more acceptable to consumers?” (with Claire Tsai, Professor of Marketing; Jordan Hutchings, PhD student, Marketing)

Zhoupeng Zhang, PhD student, Operations Management, “Riding Through Rallies: Will You Tip More?” (with Ming Hu, University of Toronto Distinguished Professor of Business Operations and Analytics, Professor of Operations Management; Wanjiang Deng, PhD student, Marketing, National University of Singapore)

Abstracts provided below:

Faculty

“The Effects of Task Similarity in Closing the Performance Gender Gap” 

Hyeun Lee, Assistant Professor of Strategic Management

Abstract: This study examines how task similarity can reduce the performance gender gap in male dominant organizational settings. I argue that task similarity with male peers, by structuring women in work of greater similarity to her male peers, fosters opportunities for interactions. By helping women to overcome isolation from male peers, which women frequently face in male-dominant organizations, task similarity can weaken the performance gender gap. To examine the research question, I examine sell-side securities analysts, majority of whom work in male-dominant organizational settings. Exploiting the accuracy of earnings estimates of each stock as objective task performance of an analyst and leveraging pairwise similarity of the stocks covered by each analyst and their male peer analysts, I find a significant reduction of performance gender gap in the presence of task similarity. From the mechanism tests, I reveal that task similarity closes the performance gender gap in male-dominant organizations, with an increase in the occupational representation of women in the task domain. I conclude that carefully designed work content can close the performance gender gap that women persistently face in male-dominant settings.

“Debt contracting on director turnover”

Scott Liao, Vice Dean, Undergraduate and Specialized Programs (with Andrea Down, Assistant Professor of Accounting in the Department of Management at UTSC, with a cross-appointment to the Accounting Area at the Rotman School of Management; Lulin Song, PhD student, Accounting; Xijiang Su, PhD student Accounting)

Abstract: The traditional view of corporate governance focuses extensively on how shareholders affect managerial decision-making and largely ignores the role that creditors play in the corporate governance process. In this proposed research, we fill this important void by investigating why lenders include proxy put clauses to deter board of director turnover and the consequences of including such provisions on the corporate governance of borrowing firms. Credit agreements often include a proxy put that allows lenders to accelerate their debt payment if a majority of the borrower’s board becomes comprised of “non-continuing directors” over a short period of time (usually one or two years). “Continuing Directors” are typically defined as persons who were on the board when the debt contract was issued or replacement directors nominated or elected by a majority of the directors who were in office when the debt contract was issued.

“Building A Flood Resilient Toronto with Data”

Sheng Liu, Assistant Professor of Operations Management and Statistics

Abstract: Toronto's resilience strategy identifies resilience to floods as a priority action for the city as it prepares to address climate impacts. Flood resilience entails being prepared for the worst-case scenario after considering the impacts of climate change on precipitation. This research program aims to develop a cross-disciplinary and data-driven decision framework for predicting worst-case rainfall events contingent on the city's infrastructure. We plan to apply unsupervised and supervised learning methods to properly cluster observed rainfall data and relate it to climate change features. Moreover, we will propose robust infrastructure planning strategies to hedge against the flooding risk amid climate change. We will also incorporate both physical and social vulnerability factors to ensure a fair resource allocation among different neighborhoods.

“How Important is Soft Information to Price Efficiency Following Earning Announcements?”

Charles Martineau, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough (with Zissis Poulos, Postdoctoral Fellow; Vincent Gregoire, Associate Professor, Department of Finance, HEC Montreal)

Abstract: Portfolio managers aim to rebalance at prices deemed ``efficient'' following firm-level news events. How efficient are stock prices following earnings announcements? While recent papers have shown that earnings surprises (i.e., hard information) are quickly reflected in stock prices, what about soft information? We show that soft information embedded in press releases and conference calls explains a more significant out-of-sample share of total stock returns on earnings announcement dates and that markets generally quickly incorporate soft information into stock prices. Nonetheless, ``inefficient'' price adjustment occurs following positive soft information news for smaller firms. Understanding the dynamics in price adjustments after conditioning hard and soft information following earnings announcements is essential for portfolio managers.

“Time & Technology on the Job”

Pamela Medina Quispe, Assistant Professor of Economics at the Department of Management, University of Toronto Scarborough and Rotman School of Management (with Julieta Caunedo, Assistant Professor, Economics Analysis and Policy Area)

Abstract: The rapid evolution of information technology and its impact on labor markets requires a deep understanding of job characteristics, the alignment of worker skills with technology, and the technologies employed in production. This understanding is crucial to the livelihood of workers and is a priority in designing policies that can mitigate or alleviate the effects of technological change on the labor market. However, data collection typically focuses solely on worker skills and is concentrated in developed countries.

This project aims to fill this gap by examining disparities in time allocation and technology usage across various occupations. We will collect data on job characteristics, including time spent on tasks, technology usage, and the specific skills required to operate these technologies. An innovative aspect of this pilot is compiling information on the technology and equipment used, achieved through photographs and AI image reading technology, to construct measures such as equipment characteristics, vintage, and market prices.

“Data-Driven Forecast of Default Risk and Macroeconomy”

Yoshio Nozawa, Assistant Professor, Finance Area, University of Toronto Scarborough

Abstract: This project utilizes machine learning techniques to improve default risk assessment and economic growth forecasting. Existing methods based on linear vector-autoregression and credit spreads suffer from inaccuracies and misspecification. To address these issues, the project employs advanced data analytics methodologies, assembling a comprehensive dataset with credit spreads, financial indicators, default events, and macroeconomic variables. Machine learning algorithms, such as logistic regression, support vector machines, or recurrent neural networks, will be applied, with feature selection techniques identifying influential variables. The model will be fitted to historical data on credit spreads and corporate defaults and the fitted values allows us to decompose credit spreads into the default risk and the residuals. The resulting decomposition provides more accurate predictions for default and economic growth, benefiting policymakers, practitioners, and academic researchers by enhancing risk assessment and offering valuable insights in uncertain economic times.

“The Value of Private Information: Evidence from Affiliated Members of Options Clearing House”

Chay Ornthanalai, Associate Professor, Finance Area (with Xiang Zheng, Assistant Professor of Finance, School of Business, University of Connecticut)

Abstract: We examine the value of private information for sophisticated traders in an increasingly competitive and highly regulated trading environment. We focus on options market due its meteoric rise in trading volume for professional and retail investors. Recent research finds that option trading activity by professional and proprietary trading firms, overall, are no longer profitable. These studies conclude that strict SEC oversights have been largely successful and suggests that the options market no longer serves as an important venue for price discovery. We show that sophisticated investors still make significant profits from trading on their private information in the options market. The novelty of our approach is how we identify the channel through which private information is transferred and potentially used in option trading. We examine trading activity of proprietary trading firms that are members of the Options Clearing Corporations (OCC). All option transactions in North America are cleared through the OCC by their clearing members. To identify the channel of information transfer, we focus on OCC clearing members that are affiliated with an investment bank that has helped a company raise capital within the past year (i.e., IPO, SEO, or debt issuance). We show that OCC clearing members make significant profits from trading equity options only when their affiliated investment bank has recently helped raised capital for the underlying firm. The economic magnitude is large after adjusting for standard risk factors.

“The Outside Options of Entrepreneurs”

Eugene Tan, Assistant Professor of Economic Analysis & Policy (with Attila Gyetvai, Research Economist, Bank of Portugal)

Abstract: This project investigates the outside options of entrepreneurs, in particular, the extent to which time and effort (which we broadly consider as "entrepreneurial human capital") invested into running a business is transferrable to paid work conditioned on business failure. To the extent that entrepreneurial human capital investment is specific to business ventures, and thus unrecoverable upon failure, potential entrepreneurs might start safer but less productive businesses or be deterred from entrepreneurship altogether. We propose a novel framework to quantify this barrier, and estimate it using administrative data covering the universe of work histories of entrepreneurs in Portugal and Hungary.

“The Immigrant Wage Penalty: Evidence from Domestic and International MBA Graduates in Canada”

Irene Yi, Assistant Professor, Finance Area (with Camille Hebert, Assistant Professor of Finance, Department of Management, University of Toronto, Mississauga)

Abstract: We study how the career dynamics of high-skilled workers differ by immigration status, by examining the career outcomes and paths of 3,144 MBAs from a top Canadian business school. The first salary of Canadian MBA graduates is on average $4,208-$12,358 higher than that of immigrant graduates, after controlling for factors that can explain variations in wages. We track the careers of the graduates and estimate their salary years after graduation using information from LinkedIn and Glassdoor. We find that the wage difference grows over time, consistent with path dependency. We find little support that language barriers explain the wage gap, and mixed evidence regarding the capability difference between Canadian and immigrant graduates. We find some evidence that Canadians and immigrant students self-select into different occupations and industries.

“Building the Exchanges of Tomorrow:  Who Provides Liquidity on Decentralized Exchanges and Why?”

Marius Zoican, Assistant Professor of Finance, Department of Management, University of Toronto, Mississauga

Abstract: The research proposal aims to investigate whether decentralized exchanges (DEXs) can become the dominant market design of future trading venues by analyzing the behavior of liquidity providers (LPs). The success of any trading venue depends on its ability to attract liquidity providers to connect buyers and sellers. Preliminary evidence shows that Ethereum gas fees have a significant impact on liquidity provision, leading to fragmentation across small (retail) and large (institutional) LPs due to different economies of scale in frequently adjusting positions. The current proposal aims to expand on this evidence and further explore the trade-offs faced by liquidity providers on DEXs. We aim to answer policy-relevant questions such as (i) whether just-in-time liquidity provision crowds out less sophisticated liquidity, (ii) how liquidity providers choose the underlying exchange blockchain protocol, and (iii) whether trading non-fungible tokens representing liquidity positions can bridge cheap and expensive blockchain protocols.

PhD students

“Personal liability and pollution decisions”

Noemie Bucourt, PhD student, Finance (Redouane Elkamhi, Associate Professor of Finance)

Abstract: I explore whether higher effective personal liability of executives and directors causes reduction in corporate pollution. This is not obvious because, in practice, there is no personal liability for corporate decision-makers: they pay litigation-related expenses with corporate funds or insurance paid by the company. Industrial pollution is a specific setting in which lawsuit settlements can be very high- and blow-up corporate funds or insurance coverage. To study the question, I look at a shock on personal liability for environmental lawsuits that occurred in Ontario, Canada, in 2012 and how it affected subsequent pollution decisions, insurance purchases, changes in governance structures of Canadian firms.

“Standing Out from the Crowd: Paid Subscription and Gifting on Live Streaming Platform”

Zirou Chen, PhD student, Marketing (with Nitin Mehta, Professor of Marketing, Area Coordinator, Marketing Area, Ellison Professorship in Marketing; Clarice Zhao, Marketing, McGill University)

Abstract: Livestreaming platforms typically have two monetization methods: user subscription and gifting to the content creators. In this paper, we explore the relationship between these two monetization tools. Are they substitutes or complements? Specifically, does subscribing increase or decrease consumers’ motivation to gift in terms of either frequency or monetary value? To answer these questions, we collect unique panel data of individual consumers’ activities and leverage the sequential roll-out of subscription features on TikTok Live for causal inference. Our empirical results show that subscription to live streaming channels significantly increases the frequency and amount of gifting. Furthermore, we examine the spillover effect of subscriptions on the gifting behavior of non-subscribers. We discuss the implications of our findings for both creators and the platform.

“Assortment Optimization Of Crowdsourcing Contests For Medical Knowledge Based On Revealed Motivations Of Participants”

Olumurejiwa Fatunde, PhD fellow, Operations Management and Statistics (with Gonzalo Romero, Associate Professor, Operations Management and Statistics)

Abstract: In this paper we study how contest offerings can be selected optimally to improve crowdsourcing, using data from a healthcare platform that runs rank-order tournaments to collect and aggregate diagnostic opinions. The platform owners can set contest offerings and prizes in order to attract users and improve accuracy. We borrow from the dynamic assortment planning literature by framing the contest offering decision as an assortment problem with independent search, perishable goods and non-independent demand. We use historical data to “learn” whether users are primarily driven by learning opportunities, monetary prizes, or affinity for competition. We define the optimal assortment, considering both motivation-specific user utility and platform aims. This paper applies assortment planning in a unique setting, incorporating the behavioral factors shaping demand.

“Income Pools for Superstar Markets”

Craig Fernandes, PhD student, Operations Research (with Timothy Chan, University of Toronto’s Associate Vice-President and Vice-Provost, Strategic Initiatives, Professor Industrial Engineering, Canada Research Chair in Novel Optimization and Analytics in Health and Associate Director of the Data Sciences Institute (DSI) and Director of Centre for Analytics and Artificial Intelligence Engineering (CARTE); Ningyuan Chen, Assistant Professor, Operations Management)

Abstract: To address income inequality in "Superstar Markets", we propose income pools - a contract where individuals agree to share a portion of their future earnings if they become successful. We develop the first mathematical model of income pools and prove that no finite-sized stable pool exists. In response, we consider bounded stable pools and epsilon-stable pools, proving their existence and Pareto properties. Our case study on professional baseball shows a 20%-30% welfare increase, most acutely benefiting the weakest agents.

“Diversify-or-Explain Disclosure and Stakeholder Monitoring”

Xingchao Gao, PhD student, Accounting (with Ole-Kristian Hope, Deloitte Professor and Professor of Accounting)

Abstract: Forthcoming

“Does bike share drive business towards local retailers?”

Jordan Hutchings, PhD student, Marketing (with Avi Goldfarb, Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing)

Abstract: Our work studies the complementary between transportation systems and retail in the domain of green transportation initiatives. Specifically, we quantify the lift felt by local retailers following the 2018-2019 expansion of the Boston bike share network. We formulate and test hypotheses of how bike users update their consideration sets as a result of bike share changing the flows of traffic throughout the city. We propose two channels of lift felt by nearby retailers; immediate increases in business from bike share riders who choose to visit the retailer after completing a trip, and sustained business from increased awareness of and decreased travel costs to retailer locations.

“Data Privacy in Pricing: Estimation Bias and Implications”

Jialin Li, PhD student, Operations Management and Statistics (with Ningyuan Chen, Assistant Professor in Operations Management; Ming Hu, University of Toronto Distinguished Professor of Business Operations and Analytics and Professor of Operations Management; Sheng Liu, Assistant Professor of Operations Management and Statistics)

Abstract: We study privacy protection mechanisms inspired by recent regulatory regimes, limited data retention and customer self-protection. Privacy protection impacts the estimation of the demand model, thereby influencing the pricing decisions. We find that, whether a customer group benefits depends on the product type, while the magnitude of the impact is determined by the level of historical personalization. Our theoretical findings are validated using a real dataset of online auto loans. This framework can be extended to accommodate nonlinear demand functions and duopoly scenarios.

Working paper available: https://papers.ssrn.com/abstract=4488404

“Website Cookies and Voluntary Disclosure”

Junhao Liu, PhD student, Accounting

Abstract: Using detailed website cookie data collected from U.S public firms' websites, I investigate the role of corporate collection and use of consumer data through website cookies in corporate voluntary disclosure. I argue that cookies infuse first-hand, granular, and real-time data into managers' information sets and enrich internal information about customers and sales operations. I show that the number of cookies is positively related to the frequency and the likelihood of issuing management sales forecasts. Using FinBERT-based measures, I find that cookies are also associated with a larger percentage of qualitative disclosure regarding customers, marketing, and products in 10-K filings. Further analyses suggest that cookies are more useful if they collect data of stronger relevance and larger volume. Additional analyses indicate that data analytic technology assist firms in exploiting cookie-collected data to enhance voluntary disclosure, while data privacy protection mechanisms impair the usefulness of cookie-collected data. Using a regulatory change, the California Consumer Privacy Act (CCPA), as a quasi-natural experiment, I provide additional evidence for the causal relation between cookies and voluntary disclosure. Overall, the paper sheds light on the role of corporate first-hand consumer data collected in financial reporting, highlights the usefulness of cookies in acquiring data to assist with disclosure, and speaks to the effects of data analytic technology as well as the potential impacts of data privacy regulations.

“Entry Deregulation, Organizational Form, and Firm Performance”

Siyuan Liu, PhD student, Economic Analysis and Policy (with Zijun Cheng, PhD student, Marketing; Ruichi Xiong, PhD student, Economic Analysis and Policy; Nathaniel Baum-Snow, Professor of Economic Analysis and Policy)

Abstract: Multi-unit firms often expand by either creating subsidiaries beyond their legal boundaries or opening establishments within them. This choice of organizational structure has direct implications for firm performance through various factors like profit allocation, liability, and taxation. However, prevalent entry regulations distort this choice by increasing the cost of setting up a subsidiary. Entry deregulation could potentially enhance firm performance by allowing firms to optimally restructure their organizational form. In this project, we utilize the 2005 Company Law reform in China, which reduced the required registered capital, to examine the impact of entry deregulation on firms' organizational structure and subsequent performance. We have constructed a unique matched firm-subsidiary-establishment dataset encompassing the entirety of Chinese firms for this purpose.

“Consequences of unsophisticated trades in the options market”

Edna Lopez Avila, PhD student, Accounting (with Charles Martineau, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough)

Abstract: This paper aims to explore the impact of retail trading on the options market. With the rise of online trading platforms and easy access to options trading, there has been a surge in retail participation in this market. My research will utilize cutting-edge data analysis techniques to examine the behavior and impact of retail traders in the options market. I will investigate the factors that influence their trading decisions, the impact of their trades on market dynamics, and the potential risks and benefits associated with their participation. The insights gained from this research will have significant implications for market regulators, financial institutions, and retail traders themselves.  

“Gender-based price discrimination: an antitrust concern?”

Camilo Machado Goncalves, PhD student, Economic Analysis and Policy (with Maximiliano Machado, PhD student, Economic Analysis and Policy; El Hadi Caoui, Assistant Professor of Strategic Management, Department of Management, University of Toronto, Mississauga)

Abstract: The term "pink tax" refers to the phenomenon in which goods marketed to women are priced higher than similar goods marketed to men (e.g., pink razors being more expensive than blue ones). If differences in prices cannot be attributed to higher costs associated with female-oriented products production, it suggests that firms are charging women higher markups. In this project, we investigate the pink tax in the personal care industry and whether antitrust policies should consider gender pricing. Firstly, we estimate a mixed logit demand model using individual data from NielsenIQ to address differences in preferences by gender. Using these estimates, we can recover marginal costs, which we use to study differences in markups. Secondly, we use the estimation results to simulate mergers between firms in the industry and see whether these practices can harm one gender more than the other, which could be relevant for the construction of antitrust policy.

“Decredentialization of Work”

Mariana Oseguera Rodriguez, PhD student, Strategic Management (with Mitchell Hoffman, Associate Professor of Strategic Management)

Abstract: This study investigates the phenomenon of "decredentialization" of work, where hiring companies eliminate college degree requirements and prioritize specific technical and soft skills. Via an audit study, I will evaluate whether the elimination of college degree requirements reduces socioeconomic inequality by increasing opportunities for job seekers with on the job experience, usually minorities. To establish external validity in the résumé design, I will use NLP and deep learning techniques to examine the most common characteristics of anonymized résumés in the professions of interest. I will conduct heterogeneity analysis using machine learning techniques such as causal decision trees and linear discrimination models to establish whether callback rates differ across races, states that have signaled openness to decredentialization, and labor market tightness. The study aims to contribute to the scholarship in management by integrating novel machine learning techniques into the data analysis of an audit study.

“If Not Certain Be Vague”: How Uncertainty in Investors’ Preferences Shape Voluntary Climate-Change Disclosure”

Giulia Sargiacomo, PhD student, Accounting (with Ole-Kristian Hope, Deloitte Professor and Professor of Accounting)

Abstract: Investors’ interest in the environmental practices of firms has experienced a remarkable surge in recent years, pushing companies to collect large volumes of information. Although firms’ voluntary reporting activities on ESG matters have also increased, the classic “unraveling” result does not always apply in equilibrium. Indeed, uncertainty about audience preferences could lead companies to reduce the amount of information released.

In this study, we exploit the unique setting of the CDP (previously Carbon Disclosure Project) annual questionnaires to study whether and how firms adjust voluntary climate-change disclosures when they are uncertain about their investors’ preferences. Specifically, using data-analytic techniques, we investigate how the level of uncertainty of audience preferences for environmental topics affects the managerial degree of vagueness in CDP questionnaire response.

“Financial Constraints, Monetary Policy, and Asset Prices”

Xinyi Xia, PhD student, Finance (with Jincheng Tong, Assistant Professor of Finance, Department of Management, University of Toronto Scarborough)

Abstract: This research is aimed at exploring the influence of high frequency monetary policy shocks on equity returns, specifically among the firms with different financial constraints. This research will use the dataset of firm fundamental information and equity returns for a sample of public firms in the United States in the recent 30 years. The analysis will explore cross sectional asset pricing implications and examine the underlining mechanism of why financial constraints matters for the monetary policy shocks response.

“Causal Models for Comparative Analytics in Queueing Systems”

Zhenghang Xu, PhD student, Operations Management (with Opher Baron, Distinguished Professor of Operations Management; Dmitry Krass, Professor of Operations Management and Statistics and Sydney C. Cooper Chair in Business and Technology; Mark van der Laan, Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley; Arik Senderovich, Assistant Professor (tenure-track) at the School of Information Technologies (ITEC), York University)

Abstract: Simulation is a powerful tool for comparative analysis of queueing models. With expert knowledge of underlying system structure, simulator can be constructed to predict intervention effects. However, such manual construction is time- and skill-demanding. It could also be subjective - if expert failed to note an important system feature (e.g. different customer types receiving different service priorities), the model will not be accurate. As an alternative, we propose a data-driven representation of system building blocks, justified by G-computation formula. We describe the queueing data generation process with structural equations and apply machine learning models to fit the equations. Through numerical experiments, we show that this approach can replace the explicit queueing dynamics and capture intervention effect in overtake-free queues.

“What types of cookie messages are more acceptable to consumers?”

Minwen Yang, PhD student, Marketing (with Claire Tsai, Professor of Marketing; Jordan Hutchings, PhD student, Marketing)

Abstract: Websites use cookies to display their features and provide consumers with personal, convenient website visits. However, the privacy issues of cookies have received considerable attention from both researchers and policymakers. While past research has mainly discussed how the visibility of cookie messages and the type of choices offered to consumers affect consumer privacy decisions, less is known of how the contents of cookie messages influence consumers’ willingness to share data. Our work advances this area of research by being the first to study how the framing of cookie content affects consumers’ privacy decisions. The current research findings will contribute to existing research on digital marketing and privacy and provide important insights to policymakers on how to best regulate cookie content and promote the welfare of consumers.

“Riding Through Rallies: Will You Tip More?”

Zhoupeng Zhang, PhD student, Operations Management (with Ming Hu, University of Toronto Distinguished Professor of Business Operations and Analytics, Professor of Operations Management; Wanjiang Deng, PhD student, Marketing, National University of Singapore)

Abstract: When there are large-scale movements going on calling for social justice (e.g., against racial discrimination), do consumers express more gratitude---and thus pay higher gratuities---towards innovative business models (e.g., Uber) that provide services to meet people’s basic needs (e.g., transportation services)? On the one hand, service providers may come from the very social class that demands more justice upon, and empathy can encourage consumers to tip more. On the other hand, however, social upheavals oftentimes disrupt business operations. Moreover, platforms like Uber allow service providers to set their own schedules; workers thus have the flexibility they need to participate in the social events they strongly sympathize with, which further undermines platforms’ capacities to provide quality services. In this research, we will leverage the Black Lives Matter protests as exogenous shocks to empirically investigate how passengers’ tipping behaviors change on ride-hailing platforms. We aim to contribute to the growing literature on the on-demand economy with a pioneering look into the service operations during challenging times.