MIFIRA Framework Lecture 5 Supply responsiveness

1. MIFIRA FrameworkLecture 5Supply responsiveness Chris Barrett and Erin Lentz February 2012

2. Market integration • For those with less familiarity with price analysis techniques, review monitoring and analyzing data documentation from LRP Learning Alliance. • Lentz, E.C. (2011) “LRP: Monitoring and Analyzing Data 25 March 2011.doc”. Draft. • Lentz, E.C. (2011) “Lentz 11 LRP Price Analysis - How Prices Change.ppt” Draft. • Lentz, E.C. (2011) “Lentz 12 LRP Price Analysis – Approaches to Analysis.ppt” Draft. • The spreadsheet “Maize Kenya price series detrend and deseasonalize.xls” works through an example of how to deflate, deseaonsalize, and then correlate historical maize price series across several markets in Kenya

3. Market integration • Core question: how price elastic is supply and thus how much (unintended) price change might result from food security interventions? • Need to establish degree of spatial market integration. Main method: study patterns of price co-movement. • Prices that vary independently signal segmented markets (one market’s equilibrium is within the price band created by the central market price and transactions costs). • Prices should co-move (roughly one-for-one) in markets that are integrated (price bands are binding).

4. Supply Responsiveness MIFIRA questions we address: 1c. How much additional food will traders supply at or near current costs? 2a. Where are viable prospective source markets? 2b. Will agency purchases drive up food prices excessively in source markets? 2c. Will local or regional purchases affect producer prices differently than transoceanic shipments?

5. Slocal Price Paut Pint Sexternal Dlocal Quantity Why market integration? Need to understand how well integrated local market is with external markets in order to predict supply response and price changes post-intervention.

6. Theory of market integration Two distinct concepts of market integration: • Tradability: physical flows of commodity (or indifference to engaging in trade). This concept focuses on trade flows and not on prices or transactions costs. • Competitive spatial equilibrium: Inter-market price dispersion should be bounded by the commercial costs of arbitrage if competition drives the marginal returns to trade to zero. This approach focuses on prices (and sometimes transactions costs) and not on trade flows. If prices move independently, the local market is within the price band. If they comove, it’s integrated.

7. Demand Supply Pi Fixed cost Variable cost Nontradables price band Pc Px Quantity Price bands A “price band” is established by a central market price and the fixed and variable costs of transacting with that market. Local prices can fluctuate in equilibrium within that band.

8. Spatial Price Analysis • Step 1: Acquire and understand data • Find out how, where and when the data are collected. • Common errors: • - Not all wholesale price series. • - Raw vs. processed commodity prices. • - Not in common currency and physical unit (e.g., US$/MT) • - Time periods not matched correctly (weeks vs. months) • - Are data day-specific observations or period averages?

9. Spatial Price Analysis Statistical methods for estimating market integration First, plot the data and look for patterns and outliers. Second, look for common trends. Beware that bivariate correlation coefficients are a widely used by flawed measure due to the distortion caused by common trends.

10. Spatial Price Analysis Statistical methods for estimating market integration Instead, detrend the data to clean out common inflationary and seasonality patterns and then plot and correlate the series. Compute an index for each month, lumping all locations’ prices and years together, and then deflate each observation by the appropriate month’s index. At a minimum, clean out a common trend by first-differencing the series, then correlate Δp1 and Δp2 instead of levels.

11. Spatial Price Analysis Statistical methods for estimating market integration Now plot and visually examine the detrended and deseasonalized data series. Compute the bivariate correlation coefficient between the series. Where multiple markets’ series available, generate the full matrix of bivariate (detrended) correlation coefficients. Source: Barrett (2009), MIFIRA

12. Spatial Price Analysis A multivariate regression approach λ = partial correlation b/n p1 and p2 controlling for common exogenous factors If 0<λ≈1, then there is market integration.

13. Spatial Price Analysis Limitations to using price comovement analytic Simple spatial price analysis must be interpreted with caution. This is especially true when: - trade flows are discontinuous (as w/ “flow reversals”). - the costs of spatial arbitrage change significantly (and non-randomly) over time.

14. Parity Bounds Model (supplementary) Parity Bounds Model Statistically estimates how frequently prices consistent with: (i) market integration under spatial equilibrium (ii) market segmentation in equilibrium (iii) market disequilibrium Typically beyond the capacity of MIFIRA analysts due to data, computational and time constraints.

15. Parity Bounds Model (supplementary) Parity Bounds Model Example from Moser et al. (Agricultural Economics 2009): Some points worth noting: Market integration can vary dramatically across scales, depending on the distances and the nature of local competition among traders. Look for geographic heterogeneity in mkt integration.