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- All HBS Web
(292)
- Faculty Publications (107)
- June 2019
- Article
Debt Traps? Market Vendors and Moneylender Debt in India and the Philippines
By: Dean Karlan, Sendhil Mullainathan and Benjamin Roth
A debt trap occurs when someone takes on a high-interest rate loan and is barely able to pay back the interest, and thus perpetually finds themselves in debt (often by refinancing). Studying such practices is important for understanding financial decision-making of... View Details
Keywords: Borrowing and Debt; Household; Personal Finance; Decision Making; Behavior; India; Philippines
Karlan, Dean, Sendhil Mullainathan, and Benjamin Roth. "Debt Traps? Market Vendors and Moneylender Debt in India and the Philippines." American Economic Review: Insights 1, no. 1 (June 2019): 27–42.
- May 2019 (Revised May 2020)
- Case
fidentiaX: The Tradable Insurance Marketplace on Blockchain
By: Alexander Braun, Lauren H. Cohen and Jiahua Xu
Three years ago, Alvin Ang and his partner founded fidentiaX in Singapore, with the ambition to create the world’s first marketplace for tradable insurance policies on blockchain. With a 26-page white paper, the start-up closed a successful fundraising round through an... View Details
Keywords: Blockchain; Insurance; Corporate Entrepreneurship; Technology Adoption; Business Strategy; Insurance Industry; Technology Industry; Singapore
Braun, Alexander, Lauren H. Cohen, and Jiahua Xu. "fidentiaX: The Tradable Insurance Marketplace on Blockchain." Harvard Business School Case 219-116, May 2019. (Revised May 2020.)
- 2019
- Article
Fair Algorithms for Learning in Allocation Problems
By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended).... View Details
Elzayn, Hadi, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth, and Zachary Schutzman. "Fair Algorithms for Learning in Allocation Problems." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 170–179.
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- October 2018 (Revised February 2018)
- Case
Masayoshi Son and the Vision Fund
In October 2016, SoftBank Group Corp., the Japanese conglomerate giant caused a significant shock to the worldwide market for venture capital and private equity by announcing the Vision Fund, the largest tech investment fund in the world at close to $100 billion. The... View Details
Nicholas, Tom, Ramana Nanda, and Benjamin N. Roth. "Masayoshi Son and the Vision Fund." Harvard Business School Case 819-041, October 2018. (Revised February 2018.)
- July 2018
- Article
Marketplaces, Markets, and Market Design
By: Alvin E. Roth
Marketplaces are often small parts of large markets, and both markets and marketplaces come in many varieties. Market design seeks to understand what marketplaces must accomplish to enable different kinds of markets. Marketplaces can have varying degrees of success,... View Details
Roth, Alvin E. "Marketplaces, Markets, and Market Design." American Economic Review 108, no. 7 (July 2018): 1609–1658.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- Article
Does Front-Loading Taxation Increase Savings?: Evidence from Roth 401(k) Introductions
By: John Beshears, James J. Choi, David Laibson and Brigitte C. Madrian
Can governments increase private savings by taxing savings up front instead of in retirement? Roth 401(k) contributions are not tax-deductible in the contribution year, but withdrawals in retirement are untaxed. The more common before-tax 401(k) contribution is... View Details
Beshears, John, James J. Choi, David Laibson, and Brigitte C. Madrian. "Does Front-Loading Taxation Increase Savings? Evidence from Roth 401(k) Introductions." Journal of Public Economics 151 (July 2017): 84–95.
- 2017
- Working Paper
Minimizing Justified Envy in School Choice: The Design of New Orleans' OneApp
By: Atila Abdulkadiroglu, Yeon-Koo Che, Parag A. Pathak, Alvin E. Roth and Oliver Tercieux
In 2012, New Orleans Recovery School District (RSD) became the first U.S. district to unify charter and traditional public school admissions in a single-offer assignment mechanism known as OneApp. The RSD also became the first district to use a mechanism based on Top... View Details
Keywords: Education; Decision Choices and Conditions; Marketplace Matching; Mathematical Methods; Design
Abdulkadiroglu, Atila, Yeon-Koo Che, Parag A. Pathak, Alvin E. Roth, and Oliver Tercieux. "Minimizing Justified Envy in School Choice: The Design of New Orleans' OneApp." NBER Working Paper Series, No. 23265, March 2017.
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of... View Details
Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- 2015
- Chapter
Is Experimental Economics Living Up to Its Promise?
By: Alvin E. Roth
The question that is the title of this essay already suggests that experimental economics has at least reached a sufficient state of maturity that we can try to take stock of its progress and consider how that progress matches the anticipations we may have had for the... View Details
Roth, Alvin E. "Is Experimental Economics Living Up to Its Promise?" Chap. 1 in Handbook of Experimental Economic Methodology, edited by Guillaume R. Frechette and Andrew Schotter, 13–42. Oxford University Press, 2015.
- 2014
- Working Paper
College Admissions as Non-Price Competition: The Case of South Korea
By: Christopher Avery, Alvin E. Roth and Soohyung Lee
This paper examines non-price competition among colleges to attract highly qualified students, exploiting the South Korean setting where the national government sets rules governing applications. We identify some basic facts about the behavior of colleges before and... View Details
Keywords: Competition; Higher Education; Policy; Government and Politics; Education Industry; South Korea
Avery, Christopher, Alvin E. Roth, and Soohyung Lee. "College Admissions as Non-Price Competition: The Case of South Korea." NBER Working Paper Series, No. 20774, December 2014.
- 2014
- Working Paper
Don't Take 'No' for an Answer: An Experiment with Actual Organ Donor Registrations
By: Judd B. Kessler and Alvin E. Roth
Over 10,000 people in the U.S. die each year while waiting for an organ. Attempts to increase organ transplantation have focused on changing the registration question from an opt-in frame to an active choice frame. We analyze this change in California and show it... View Details
Keywords: Decision Choices and Conditions; Health Care and Treatment; Philanthropy and Charitable Giving; Health Industry
Kessler, Judd B., and Alvin E. Roth. "Don't Take 'No' for an Answer: An Experiment with Actual Organ Donor Registrations." NBER Working Paper Series, No. 20378, August 2014.
- May 2014
- Article
Incorporating Field Data into Archival Research
By: Eugene F. Soltes
I explore the use of field data in conjunction with archival evidence by examining Iliev, Miller, and Roth's (2014) analysis of an amendment to the Securities Exchange Act of 1934. This regulatory amendment allowed depositary banks to cross-list firms without the... View Details
Soltes, Eugene F. "Incorporating Field Data into Archival Research." Journal of Accounting Research 52, no. 2 (May 2014): 521–540.
- 2013
- Article
Matching with Couples: Stability and Incentives in Large Markets
By: Fuhito Kojima, Parag A. Pathak and Alvin E. Roth
Accommodating couples has been a long-standing issue in the design of centralized labor market clearinghouses for doctors and psychologists, because couples view pairs of jobs as complements. A stable matching may not exist when couples are present. This article's main... View Details
Keywords: Market Design; Marketplace Matching; Balance and Stability; Jobs and Positions; Family and Family Relationships; Health Care and Treatment; Employment Industry; Health Industry
Kojima, Fuhito, Parag A. Pathak, and Alvin E. Roth. "Matching with Couples: Stability and Incentives in Large Markets." Quarterly Journal of Economics 128, no. 4 (November 2013): 1585–1632.
- Article
Unraveling Results from Comparable Demand and Supply: An Experimental Investigation
By: Muriel Niederle, Alvin E. Roth and M. Utku Ünver
Markets sometimes unravel, with offers becoming inefficiently early. Often this is attributed to competition arising from an imbalance of demand and supply, typically excess demand for workers. However this presents a puzzle, since unraveling can only occur when firms... View Details
Niederle, Muriel, Alvin E. Roth, and M. Utku Ünver. "Unraveling Results from Comparable Demand and Supply: An Experimental Investigation." Games 4, no. 2 (June 2013): 243–282. (Special Issue on Games and Matching Markets.)
- 2013
- Chapter
In 100 Years
By: Alvin E. Roth
Roth, Alvin E. "In 100 Years." Chap. 7 in In 100 Years: Leading Economists Predict the Future, edited by Ignacio Palacios-Huerta, 109–119. MIT Press, 2013.