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Dynamic implications of production shocks and policy on livestock markets and household welfare a sectoral and economy wide analysis
- 1. Dynamic Implications of Production Shocks and Policy on Livestock Markets and Household Welfare: A Sectoral and Economy-Wide Analysis IFPRI-ILRI-EDRI Livestock Initiative Informal seminar, April 20, 2011
- 7. Figure x. Overview of the research components and their linkages with each other Household demand analysis of livestock General equilibrium model Analysis of livestock prices & values chains GIS (spatial) analysis of livestock sector Integration of markets; effect of shocks on prices; modeling scenarios Patterns of production and market access Spatial data on rainfall & forage shocks Elasticities of own price, cross-price & income demand Partial equilibrium model of livestock dynamics Improved herd dynamics
- 9. An attempt to be diagnostic Beef Shoats Poultry Dairy Comments Feed XXX XXX X XXX Regional & seasonal variation based on farming systems Disease XX XX XXX X Particualrly acute for chickens, but a problem for all sub-sector Marketing, processing, etc XX X X XXX Dairy perishable, so market access critical; export constraints for beef & shoats Breed quality XX XX XX XX Cross-breeding possible over medium/long run Others: labor, credit, extension . . . X X X X Difficult to judge
- 13. Changing composition of livestock exports – fall in hides, big rise in live bovine
- 14. Big rise in live animal export values, almost certainly related to increase international prices
- 15. But note the changing composition of trade routes and end markets: Djibouti down and Somalia and Sudan up (formal trade replacing informal trade?). UAE way, way up!!
- 19. Data Description … contd.
- 21. Data Description … contd.
- 22. Data Description … contd.
- 34. Results (cont’d) – Compensated own-Price and cross-price Elasticities Beef Mutton & Goat meat Other meat & animal products Dairy products Beef Total -0.733*** 0.391*** 0.083*** 0.259*** [0.0233] [0.0193] [0.0073] [0.0105] Urban -0.665*** 0.382*** 0.136*** 0.147*** [0.0235] [0.0176] [0.0071] [0.0087] Rural -0.793*** 0.378*** 0.031 0.386*** [0.0786] [0.0577] [0.0266] [0.0331] Mutton & Goat meat Total 1.554*** -1.465*** 0.069 -0.158* [0.1003] [0.1144] [0.0426] [0.0846] Urban 1.889*** -1.882*** 0.184*** -0.192*** [0.1103] [0.1144] [0.0378] [0.0613] Rural 2.822*** -2.008*** -0.292 -0.523** [0.6603] [0.4221] [0.2907] [0.2565] Other meat & animal products Total 0.403*** 0.395*** -0.996*** 0.194*** [0.0285] [0.0337] [0.0229] [0.0336] Urban 0.71*** 0.181*** -0.988*** 0.071** [0.0476] [0.0386] [0.0228] [0.0289] Rural -0.056 0.289 -1.039*** 0.806*** [0.233] [0.1926] [0.0698] [0.0989] Dairy products Total 0.405*** -0.249*** 0.28*** -0.669*** [0.0244] [0.0344] [0.0152] [0.0344] Urban 0.06 0.062 0.304*** -0.523** [0.098] [0.0868] [0.0289] [0.2613] Rural 0.134 -0.009 0.247*** -0.151** [0.1256] [0.0618] [0.0577] [0.0746]
Editor's Notes
- Research Objectives Being part of a larger project that is aimed at examining a sector little studied this section of the study is aimed at assessing if and how regional and central markets are integrated, and Identify factors that play significant role in affecting livestock price formation and study how these factors influence livestock pricing and possibly study if and how they affect trade routes To achieve these objectives we propose: Integration of markets is intended to be investigated using threshold autoregression model We are gathering data relevant to study price formation and surveying what econometric model to use
- The basic idea behind applying TAR to analyze market integration, which somebody called “a fancy way of doing correlations”, is to see if and how prices in spatially separated markets co-move and how fast price shocks are transmitted among integrated markets TAR differs from simple correlation as it acknowledges the existence of thresholds, which are created by transaction costs, that price differentials must exceed before equilibrating price adjustments that lead to market integration occur. Simple representation of a TAR model for two spatially separated livestock markets ( i and j ) with prices P i t and P j t , both of which are unit root AR(1) is: The regime switching framework can be characterized as: where c is the threshold value that causes a regime switch. Specifically, when the lagged price differential is below the threshold value lambda 1 =1, implying that the parity relationship follows a random walk when there are small deviations of price differences. However, a large deviation, such as a shock to the price in either market, will trigger the condition j ~ Pt1j > c, causing = (2). Under the assumption that a stable equilibrium between prices at the two spatially separated locations exists, lambda 2 < 1, implying that the price differential process is stationary and shocks to P i t or P j t will die out over time.
- For MI analysis although there were longer time series CSA data that we found less reliable and ILRI data that was rich in terms its detail in livestock quality with short time series and lacking information on many markets we preferred to use monthly CSA price data that balances both quality and time span. The data spans from July 2001 through January 2011 (115 months). It comprises price data on 4 categories of livestock, goats, sheep, cock, and hen. Although we are working on developing some weight for markets the data shows that un-weighted average real prices have increased by an average annual rate of 3.4, 1.9, 3.5, 2.3, 1.1, 2, 3.4, and 3.5 percent for bulls, cows, oxen, heifer, sheep, goats, cocks, and hen, respectively.
- As can be expected the story is different when considering nominal prices. The same 8 categories had average annual growth in nominal prices of 17.6, 16.1, 17.8, 16.4, 14.8, 15.9, 17.8, and 17.7 percent.
- While correlations with real prices are weak, nominal prices of livestock are strongly correlated with almost all of the variables