Decomposing Producer Price Risk: An Analysis of Livestock Markets in Northern Kenya Christopher B. Barrett and Winnie K. Luseno Cornell University and RTI International
Motivation • Producer price volatility in agricultural markets • a significant disincentive to market participation and agricultural production • particularly important in settings where producers have little or no access to financial markets … cause consumption crises • Price stabilization - a real concern for producer groups and policymakers • Locating the source(s) of price variability is important for identifying effective intervention strategies
Objectives • To introduce a simple method to determine the extent to which producer price risk is attributable to • volatile inter-market margins • intra-day variation • intra-week (day of week) variation, or • seasonality • To apply method to livestock markets in northern Kenya, which suffer significant inefficiencies due to • high transactions costs • difficulties in contract enforcement • physical insecurity • poor infrastructure
Method Decompose price for any individual transaction as follows: i = individual transaction t = individual day w =specific week p = price in one market (e.g., the source market) p* = price in another market (e.g., destination/terminal mkt)
Intuition Behind Decomposition • It represents intraday variation due to prevailing transactional institutions and associated information advantages (e.g., auctions versus dyadic exchange) • Mt captures intra-week variability due to market thickness and day-of-week effects. • Bt reflects variation in intermarket spreads (i.e., volatility in basis). • St captures seasonality and world market cycle effects. • p* reflects mean central market price.
Variance Decomposition Just take the variance of this expression and rearrange terms to isolate 4 sources of producer price variability (each term is variance plus covariances, e.g., BR =V(B)+ ΣCOV(B,.) • The proportion of total transactions price risk attributable to each component is then just the individual risk component divided by V(pit): • irt = IRt/V(Pit) mrt = MRt/V(Pit) • brt = BRt/V(Pit) srt = SRt/V(Pit)
Data Two source markets in the north: Marsabit and Moyale Main terminal markets in Nairobi (Dagoretti/Kariobangi). Several thousand observations on livestock transactions collected from January 1996 to December 1997 by staff from the GTZ-Marsabit Development Project. Data were collected opportunistically, therefore not a random sample. Data includes information on negotiated price, gender, species and body condition.
Main Results • 1) Seasonality accounts for a negligible proportion of producer price risk. Panseasonal pricing would not reduce producer price risk appreciably. Indeed, b/c seasonality covaries negatively with other components, typically price stabilizing in northern Kenya markets. • 2) Size, condition and species determine the destination market to which an animal is moved: • intermarket basis risk most important for animals destined for slaughter in terminal markets (males, fair/poor females) • trade in good condition females is mainly for stock replacement and breeding purposes, so local market effects dominate.
Other Results • inter-market basis risk covaries positively with inter-day differences within the week in source markets; • intra-day/intra-market risk is effectively unrelated to the other three sources of risk; • controlling for product quality is an important guard against aggregation bias
Conclusions When trade is highly localized, price variability emerges naturally from weakness in local markets. Local markets’ institutional design (e.g., auctions) are then key. When trade occurs between regions, high and volatile intermarket margins drive producer price volatility. Transport infrastructure and physical security are the key factors for such markets.