Late payments, higher prices? An experimental investigation of competitive procurement
Matthew J. Walker & Kyle Hyndman
Abstract
The decision to pay one's supplier late is commonplace across global supply chains and, arguably, a key challenge for many businesses. In a multiple‐methods study, we contribute to the literature by documenting important empirical and anecdotal features about the likelihood and severity of late payments, formulating and solving a theoretical model grounded in these features, which suggests a policy tool to reduce late payments and then validating the model through a rigorous laboratory experiment. Grounded in our direct interactions with buyer‐side and supplier‐side organizations and our analysis of regulatory payment reports, we construct a game‐theoretic model to analyze the qualitative effect through which uncertainty about the date payment will be received influences suppliers’ pricing decisions. We then examine whether an economic incentive (penalty for late payment) deters buyers from reneging upon an announced payment term. The effectiveness of introducing a penalty for late payment is not obvious because some buyers may choose to circumvent the penalty by announcing a longer payment term up front, and the penalty may influence suppliers’ bidding strategies. We find analytically and validate experimentally that setting the highest penalty for late payment is an effective deterrent mechanism and leads to the highest expected buyer profit through its effect on supplier competition. Thus, establishing a credible commitment to pay one's suppliers within a shorter payment term is a cost‐effective managerial strategy for buyers. However, a welfare loss arises if the penalty is not set high enough because it fails to align incentives for a subset of buyers.
2 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.25 × 0.4 = 0.10 |
| M · momentum | 0.55 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.