Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We use time-series and cross-section methods to study long-term relationships between pairs of coffee prices, and assess how chemical, institutional and market factors affect the likelihood of finding stationary price differentials, the magnitude of such differentials, and their speed of adjustment. Using an empirical approach which does not require classifying coffee varieties as reference and non-reference, we find that varieties with chemical similarity have prices which are more similar, more likely to maintain stable long-term relationships, and more quickly to adjust after a shock.