Richard Grice

Assistant Professor of Marketing
at
NUS Business School

About

I am an Assistant Professor of Marketing at the National University of Singapore. My research lies at the intersection of quantitative marketing and industrial organisation, and I make use of both empirical and analytical methods. I am particularly interested in strategic decision-making, retail competition and complexity, which means I get excited by things like game-playing AIs, algorithmic pricing, and the cognitive processes involved in economic decision-making.I completed my PhD in Marketing with distinction at INSEAD under the guidance of Maria Ana Vitorino and Paulo Albuquerque. I also hold an MA in Economics from the University of Rochester, have been awarded First Class Honours in Economics by the University of New South Wales, and have a combined Bachelors of Economics and Commerce from the University of Western Australia.Prior to graduate school I spent some time working as an economic consultant in Sydney with HoustonKemp Economists, Charles River Associates and Endgame Economics. In these positions I provided advice on the regulation of natural monopolies — particularly those arising in the electricity and gas sectors — and produced analyses supporting expert testimony in litigation. My analyses have been relied upon in matters before Australian state and federal courts, and in dispute settlement proceedings before the World Trade Organisation.Outside of my research I enjoy travel, reading and coffee, as well as bouldering — having been lucky enough to spend my PhD years in Fontainebleau — and scuba diving.

Research

My research focuses on the network structure of retail competition and its implications for strategic decision-making and pricing. In my job market paper I used high-frequency price data from Australian gasoline stations to estimate the network structure of station competition and study how it mediates the propagation of prices across a market. I develop and apply the idea of networked competition more broadly in my other work. My plan going forward is to continue combining ideas from microeconomics and complex systems to advance our understanding of retail markets and dynamic competition.

Working Papers

Learning Competitors' Identities from the Timing of Pricing Decisions: An Application to Retail Gasoline
It is becoming clear that it is not just consumers who limit their consideration when faced with broad product assortments. Managers also restrict their attention to narrow subsets of potential competitors. How then can we identify who managers are actually treating as their competitors, and thus the relationships mediating price competition? I propose an approach exploiting information on the identities of competitors encoded in the timing of pricing decisions. The primary advantage of this approach is that it identifies who managers treat as competitors — not who consumers consider substitutes — and thus who constrain their price-setting. I found the method in the decision problem of a pricing manager within the ABBE continuous-time retail competition framework (Arcidiacono et al. 2016. Rev. Econ. Stud.). I derive from this structural foundation a 'reduced-form' expression characterising the hazard-rate of a manager's price-change decisions as a function of their competitors' prices. Estimating this expression with l1–norm regularisation exploits the consistent model selection properties of the LASSO to identify true competitors from potential competitors. In addition, the linearity of the expression makes implementation of the method a straight-forward and efficient application of popular, highly-optimised machine learning packages. I demonstrate the method with an application to high-resolution price data from gasoline stations in Australia. The application reveals that stations from Sydney's suburbs to its surrounding regions all compete and influence each other within a networked competition structure, rather than distinct geographic markets. This finding motivates the need for further research into the effect of such network structures on the dynamics of competition and corresponding market outcomes.

Chain-Linked Markets
In this paper, I question the typical characterisation of oligopoly markets as distinct sets of firms who all compete. Motivated by the observation that many retail markets have sparse substitution patterns, I argue markets can include firms who do not directly compete, but still influence each other through shared competitors — a situation Chamberlin (1933) called the "chain-linking" of markets. I present and study an oligopoly model with sparse substitution patterns to quantify (1) the influence of indirect competitors and (2) the magnitude of inferential biases possible when assuming markets are distinct and all firms within them compete. The model micro-founds sparse substitution in the demand of consumers who have heterogeneous consideration and convex preferences. The sparse substitution connects firms in a network, and I prove that their price competition over this network has a unique pure-strategy equilibrium for any pattern of consumer consideration. The influence of direct and indirect competitors can be measured by firms' centrality in the demand structure when the relative intensity of pairwise substitution is measured by diversion ratios. Comparative statics analysis reveals how conclusions about competition based on prices can be biased when markets are chain-linked. Numerical analysis shows the size of errors possible when ignoring the chain-linking of markets. Overall, the findings offer useful insights into the complex nature of retail markets and highlight the importance of accounting for indirect competition in empirical studies.

Mutual Fund Market Structure and Company Fee Competition: Theory and Evidence
with Ahmed Guecioueur
We investigate whether competition between the fund companies that offer mutual funds constrains individual fund fees. We document that over half of individual fund fee variation is explained by company-wide components. Moreover, we show using SEC prospectus download data that company-level attributes influence investors' consideration of companies. We connect these facts with a model of fee competition between co-considered fund companies, characterising the competitive landscape and associated equilibrium fees. Calibrating the model, we derive a testable prediction for competitively constrained fees. The prediction successfully explains cross-sectional variation in the company fee components, identifying the influence of company competition on fees.

Curriculum Vitae

A PDF version of my CV can be found here.