Publications

The supply chain for coffee involves multiple steps from cultivation to market. Farmers decide the coffee plant variant and the terroir, influencing the final flavor. After harvesting, the coffee cherry is milled to separate the husk, resulting in green beans, which are primarily exported to consuming countries for roasting. This roasting, blending, and packaging add significant value, mostly retained in consuming countries. Therefore, the supply chain creates challenges for equity, as growers receive minimal returns—typically just one to five percent of the final retail price. Efforts for greater equity face barriers like fluctuating commodity prices, lack of access to consumer markets, and climate change impacts on yields. Additionally, consolidation and trends towards single-portion coffee formats benefit roasters more than farmers, hampering the equitable distribution of profits.  However, the growth of the specialty coffee market presents opportunities for reform, including fostering direct trade relationships, promoting transparency in trading practices, encouraging sustainable farming methods, and developing origin-based branding.  What might be done to encourage these trends toward equity across the supply chain?

We illustrate an approach to measure demand externalities from co-location by estimating household level changes in grocery spending at a supermarket among households that also buy gas at a co-located gas station, relative to those who do not. Controlling for observable and unobserved selection in the use of gas station, we find significant demand externalities; on average a household that buys gas has 7.7% to 9.3% increase in spending on groceries. Accounting for differences in gross margins, the profit from the grocery spillovers is 130% to 150% the profit from gasoline sales. The spillovers are moderated by store loyalty, with the gas station serving to cement the loyalty of store-loyal households. The grocery spillover effects are significant for traditional grocery products, but 23% larger for convenience stores. Thus co-location of a new category impacts both inter-format competition with respect to convenience stores (selling the new category) and intra-format competition with respect to other supermarkets (selling the existing categories).

Do store brands aid store loyalty by enhancing store differentiation or merely draw price-sensitive customers with little or no store loyalty? This paper seeks to answer this question by empirically investigating the relationship between store brand loyalty and store loyalty. First, we find a robust, monotonic, positive relationship between store brand loyalty and store loyalty by using multiple loyalty metrics and data from multiple retailers and by controlling for alternative factors that can influence store loyalty. Second, we take advantage of a natural experiment involving a store closure and find that the attrition in chain loyalty is lower for households with greater store brand loyalty prior to store closure. Together, our results are consistent with evidence for the store differentiation role of store brands.

We study choice over prescription insurance plans by the elderly using government administrative data to evaluate how these choices are made and evolve over time. We find that there is large "foregone savings" from not choosing the lowest cost plan that has grown over time. We develop a structural framework that allows us to exactly decompose the changes in "foregone welfare" from inconsistent choices into supply and demand side factors. We find that foregone welfare increases over time due primarily to supply-side factors such as premiums and out-of-pocket costs; we estimate little learning at either the individual or cohort level.

We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals—an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors “waver” over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model’s distinctive predictions.