Empirical Detection and Quantification of Price Transmission in Endogenously Unstable Markets: The Case of the Global–Domestic Coffee Supply Chain in Papua New Guinea

Price transmission through global-domestic agricultural supply chains is a fundamental indicator of market integration and efficiency in policy analysis. Incomplete price transmission through poorly-integrated markets may lead to inefficient resource-use, production, and marketing decisions with consequent welfare loss. Conventional price-transmission econometrics test for a theory-based spatial-arbitrage restriction that long-run equilibrium prices in spatially distinct markets differ by no more than transaction costs. Price transmission is confirmed when cointegrated or Granger-causal price interactions are detected with linearly-stochastic VAR/ECM models imposing market equilibrium. The conventional approach is ill-equipped to test for price transmission when endogenously-unstable markets do not equilibrate due to systematic arbitrage-frustrating frictions including financial and institutional transaction costs and biophysical constraints. Such complex economic behavior—characteristic of nonlinear-deterministic dynamic systems—requires unconventional empirical methods. We propose a novel empirical framework using price data to test for market stability and price transmission along international-domestic supply chains incorporating nonlinear time series analysis and recently-emerging causal-detection methods from empirical nonlinear dynamics. We apply the framework to map-out and quantify price trans-mission through the global-exporter-processor-producer coffee supply chain in Papua, New Guinea. We find empirical evidence of upstream price transmission from the global market to domestic exporters and processors, but not through to producers.