This paper studies how banks compete amid digital disruption and the resulting distributional effect across consumers. Digital disruption increases the geographic coverage of banking services, bringing new entrants to local markets. However, as digital customers shift from branches to digital services, banks close branches, and the remaining branching banks gain market power among non-digital customers that rely on branches. Consequently, digital customers benefit from the intensified bank competition at the cost of non-digital customers who pay higher prices for branch services and face the risk of financial exclusion. We provide empirical evidence by exploiting the staggered expansion of 3G networks, instrumented by regional distribution of lightning strike frequency. Using a structural model, we further quantitatively decompose the benefit and costs of digital disruption resulting from banks’ pricing, branching, and entry decisions. The results highlight the role of banks’ endogenous responses to digital disruption in widening the gap in access to banking services. Jiang is from the University of Southern California, Marshall School of Business. erica.jiang@marshall.usc.edu. Yu is from Singapore Management University. gloriayu@smu.edu.sg. Zhang is from UCLA Anderson School of Management. jinyuan.zhang@anderson.ucla.edu. Runtong Cai provided outstanding research assistance. We are grateful to Kenneth Ahern, Greg Buchak, Matteo Benetton, Philip Bond, Marco Di Maggio, Andrea Eisfeldt, Valentin Haddad, Barney Hartman-Glaser, Gerard Hoberg, Rawley Heimer, Gregor Matvos, Shaun McRae, Clemens Otto, Amit Seru, Anthony Lee Zhang and seminar and conference participants at UCLA Anderson, USC Marshall, Singapore Management University, University of Washington at Seattle, University of Technology Sydney, IIOC 2022 for comments. Yu thanks the support from Sim Kee Boon Institute for Financial Economics at SMU. First version: Feb, 2022.
The robust multi-product pricing problem is to determine the prices of a collection of products so as to maximize the worst-case revenue, where the worst case is taken over an uncertainty set of demand models that the firm expects could be realized in practice. A tacit assumption in this approach is that the pricing decision is a deterministic decision: the prices of the products are fixed and do not vary. In this paper, we consider a randomized approach to robust pricing, where a decision maker specifies a distribution over potential price vectors so as to maximize its worst-case revenue over an uncertainty set of demand models. We formally define this problem -- the randomized robust price optimization problem -- and analyze when a randomized price scheme performs as well as a deterministic price vector, and identify cases in which it can yield a benefit. We also propose two solution methods for obtaining an optimal randomization scheme over a discrete set of candidate price vectors based on constraint generation and double column generation, respectively, and show how these methods are applicable for common demand models, such as the linear, semi-log and log-log demand models. We numerically compare the randomized approach against the deterministic approach on a variety of synthetic and real problem instances; on synthetic instances, we show that the improvement in worst-case revenue can be as much as 1300%, while on real data instances derived from a grocery retail scanner dataset, the improvement can be as high as 92%.
Problem definition: We examine how market and economic factors influence the occurrence of forced labor in supply chains and how buying companies can develop optimal contracts to prevent forced labor in the presence of information asymmetry between the buyer and the agent. Methodology/results: We develop a game-theoretic model of a labor supply chain comprising a socially aware buyer and a profit-maximizing labor agent. Our equilibrium analysis shows that the audit cost affects the extent to which the buyer can extract surplus from the agent. In the asymmetric information case, we design a menu of contracts and show that the difference in the agent’s earnings dictates how an unconstrained optimal contract can be adjusted to be incentive compatible. Our result suggests that it is optimal for the buyer to leave a surplus to agents with high recruitment capability (measured in terms of the labor pool size) regardless of the audit cost. As we extend our analysis to the multiagent case, we develop a “sequential” menu of contracts that ensures no coercion and maximum buyer profit. We apply our model to a data set of labor agents for recruiting foreign agricultural workers in the United States. Managerial implications: To implement incentive-compatible contracts that deter coercive labor outcomes, the buyer may need to allocate informational rents to the agent by foregoing a portion of its surplus. We find that information asymmetry regarding the agent’s true recruitment capability necessitates that the buyer offers a “menu of contracts” to prevent the risk of forced labor. This menu benefits exactly one type of agent, depending on the earnings differential between the available contracts, but always leaves a financial surplus for agents with strong recruitment capabilities. When multiple potential agents are available, the buyer can employ a reverse auction mechanism to select one agent. Though the core insights from contracting with a single agent continue to apply, their impact diminishes as the number of potential agents grows. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0438 .
I find both U.S. and international evidence that aggregate corporate cash savings strongly negatively predict future excess market returns. In the U.S. stock market, market timing based on aggregate cash savings yields annual returns 2.55% higher than an all-equity-all-the-time strategy. The strong negative correlation between cash changes and future returns holds at the firm level, and aggregates to the portfolio and economy levels. I provide theoretical and empirical evidence that the time variation in risk premia arising from economic uncertainty at least in part drives the predictive power of cash savings. These findings also shed light on the puzzling positive correlation between accruals and future returns in the aggregate: after controlling for aggregate cash savings and aggregate capital investment, aggregate accruals have a near-zero relation with future market returns.
Since 2011, there has been a substantial decline in the number of students graduating from college with accounting majors. We help explain this decline by empirically showing that technological advancement is associated with lower relative demand from employers for accounting majors compared to other business majors, especially finance. Specifically, corporate software investment is associated with lower relative wages and employment for accounting majors. Cross-sectional tests point to these findings resulting from differences in demand for human capital associated with specific college majors. As the wage gap between finance and accounting majors widens, fewer students subsequently choose an accounting major and more choose a finance major, consistent with selection models going back to Roy (1951). Our findings illustrate the utility of incorporating human capital demand factors into analyses of accounting labor markets and their connection to college major choice.
