The Role of Demand Information for Price‐Setting Newsvendors: A Monotone Comparative Statics Approach

ABSTRACT

In this paper, we examine the role of demand forecast information for price‐setting newsvendors. In our model, the newsvendor faces stochastic, price‐sensitive demand, has discretion over the retail price, and receives an informative signal that aids the quantity decision. Focusing on a class of signal structures that satisfy the scale‐location property, We analyze the model using a monotone comparative statics approach, which does not require uniqueness of the solution and substantially relaxes the regularity conditions commonly assumed in the literature, and we show that as the quality of demand forecasts improves, the optimal retail price may increase, decrease, or remain unchanged. We provide sufficient conditions on the primitives under which each type of monotonicity arises. We offer a comprehensive analysis across a broad spectrum of information structures, whereas the existing literature focuses primarily on the comparison between full‐information and no‐information benchmarks. Finally, we also discuss applications to food waste in the Grab‐and‐Go food market and illustrate our results using several commonly adopted demand distributions.