Determinants of Cattle Prices in Ethiopia

ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE Determinants of Cattle Prices in Ethiopia Fantu Nisrane Bachewe and Derek Headey International Food Policy Research Institute (IFPRI) (Ethiopia Strategy Support Program, ESSP-II) Workshop theme: Food Price Dynamics and Policy Implications in Ethiopia 24 May 2012 Addis Ababa, EthiopiaThe views expressed in this paper are those of the author and do not represent the official position of his institution.

Presentation Outline1. General background2. Model: Hedonic price formation system of equations3. Data description4. Time-invariant determinants of cattle prices5. Determinants of cattle price changes over time6. Summary of findings.

General background• Beef is the most important source of animal-based protein in Ethiopian’s diet• However, cattle markets are thought to be characterized by a number of market failures and low productivity• In this paper we therefore have two objectives:• 1. To understand general price determination in cattle markets• 2. To specifically understand some of the drivers of price change over time• We use a hedonic price formation analysis (HPFA) for both objectives

General background• We use a very rich retail market cattle price data from ILRI, which includes detailed characteristics on breeds, body mass grades, age groups, for a large number of markets in both the lowlands and highlands• On objective 1 we try to understand how prices vary over these cattle characteristics, but also over space, over agricultural season and religious festivals• On objective 2 we merge this ILRI data with national and international data on input prices (feed, transport costs), general non-food inflation, international beef prices (Australia, Somalia)

Data description• Cattle are probably the most abundant resource in Ethiopia• However, prices grew fast over the period studied• Moreover, beef prices have risen very quick, and were even included in list of capped items in 2011• Growth in cattle prices faster than growth in grains prices – The terms of trade (TOT) of cattle versus grains increased over the entire period – Growth in TOT mostly dominated by growth in cattle prices 5

Data description Terms of trade of cattle Vs grains Real Addis prices of Period Oromia Somali SNNP Afar Ababa Average cattleOverall average 0.04 0.07 -0.18 0.97 0.82 0.43 2.20January 2007 to July 2008 -3.58 -3.37 -3.32 -0.94 -1.43 -2.24 -0.80August 2008 to Aug. 2010 2.92 2.86 3.23 2.33 2.35 2.55 1.48Sept. 2010 to July 2011 -0.57 -0.65 -2.77 1.00 1.02 -0.01 -3.58 6

Terms of trade 10 12 14 0 2 4 6 8 2007m1 2007m4 2007m72007m10 2008m1 Oromiya 2008m4 2008m72008m10 Somalia 2009m1 2009m4 2009m7 SNNP2009m10 2010m1 Afar 2010m4 2010m72010m10 2011m1 Figure 1. Terms of trade of cattle versus grain price indices-4 regions. 2011m47 2011m7

10 20 25 30 40 45 50 15 35 55 2007m1 2007m3 2007m5 2007m7 2007m92007m11 2008m1 2008m3 2008m5 2008m7 2008m92008m11 2009m1 2009m3 2009m5 2009m7 2009m92009m11 2010m1 2010m3 2010m5 2010m7 2010m92010m11 2011m1 2011m3 2011m58 Figure 2. Terms of trade of cattle versus grain price indices- Addis Ababa. 2011m7

Data description• We use powerful data set from ILRI: a panel cover time (01/2005- 03/2011), space (32 markets, 8 regions), and animal characteristics• Key points are:1. Good coverage of highlands and pastoralist areas2. Body mass measured as four grades, by breed and age3. Dataset measures volumes sold, not just prices4. Data is merged with CSA consumer price survey data on nonfood prices, animal transport costs, a proxy for feed by-product prices (cooking oil prices), a proxy for grazing land availability (rainfall), international beef prices, cattle prices in northern Somalia. * Note that we tested other variables (e.g. grain prices), but these were dropped because of insignificant results, or high degrees of correlation with the variables listed above.

Data description• Another point of note is how we deal with inflation issues• We deflate all price variables by the total CPI. Hence the dependent and independent prices variables are real price series• Figure below shows sharp increases in both nominal and real cattle prices in 2008 and 2010• We also note that one leading hypothesis for real cattle prices changes is strong international demand• Strong domestic demand (related to general inflation) could be another explanation, though it is more difficult to directly test• Irrespective of the hypothesis, the data suggest that increased demand is the main factor, although a limited supply response could also explain prices increases (e.g. constraints on feed & grazing land) 10

Price/Real price (December 2006 prices) 500 2000 2500 3000 3500 4000 4500 1000 1500 January-05 April-05 July-05 October-05 January-06 April-06 July-06 October-06 January-07 April-07 Period of rapid overall inflation July-07 October-07Nominal prices January-08 April-08 July-08 October-08 January-09Real prices April-09 July-09 October-09 Overall deflation January-10 April-10 July-10 Figure 3. Mean monthly nominal and real prices, January 2005-March 2011. October-10 January-11 high int. prices? Strong recovery; lowlands? Drought in

Price of a KG of beef in birr /December 2006 birr 0 10 20 30 40 50 2005m1 60 2005m4 2005m7 2006m1 2006m4 2006m7 2006m10 2007m1 2007m4 2007m7 Average nominal beef 2007m10 Average nominal cattle 2008m1 2008m4 2008m7 2008m10 2009m1 2009m4 2009m7 2009m10 Average real beef Average real cattle 2010m1 2010m4 2010m7 Figure 4. Average nominal and real price of beef and cattle. 2010m10 2011m1 0 500 1000 1500 2000 2500 3000 4000 4500 350012 Nominal prices in & real prices in (Dec. 2006)

Model: Hedonic price formation system of equations• HPFA assumes that prices of qualitatively different goods are a function of the sum total of consumers’ valuation of cattle attributes, as well as other variables affecting the market environment: J K• where Pit (  )   0    j X jit (  )    k Dkit  eit• j 1 k  J 1• Pit is real price of cattle i at week t,• X jit is continuous variable j associated with cattle i at time t,• Dkit is dummy variable k associated with cattle i at time t, and• Note that body mass is included in X but is treated as endogenous (i.e. a function of other factors). Hence we estimate both a price equation and a body mass equation• We also decompose source of price change over time

Results• Objective 1 – General price determination: 1. Prices heavily determined by body mass (elasticity of 1.04), but cannot test evidence of body mass improvements over time 2. Prices increase sharply with cattle age & gender (male): elasticities between 0.7 and 1.8 3. Harar breed easily attracts largest price premium, followed by Zebu, mixed breeds & Boran

Table 4.2 Nonlinear system price formation equations estimates of period, festival, and regional dummy variables.Variable Coeff. Std. Error ElasticityBody mass index 3.26 0.25 0.84Age (immature is omitted) Young 2.07 0.11 0.44 Mature 3.85 0.19 0.96Male (female is omitted) 1.84 0.10 0.38Breed (Arussi is omitted) Boran 0.51 0.10 0.09 Danakil -0.05c 0.10 Harar 1.8 0.16 0.37 Mixed 0.75 0.10 0.14 Raya Azebo 0.23b 0.12 0.04 Zebu 0.75 0.12 0.14 15

Results• Objective 1 – General price determination: 4. Rainfall variable had no impact in highlands (need better proxies for grazing land constraints), but . . 5. Agricultural seasonality effects were significant: slightly higher prices during Meher, and slightly lower prices at end of Meher 6. Some demand-side seasonality effects with lower prices during Orthodox fasting, but slightly higher prices during Ramadan 7. The main regional effect of importance was much lower prices in Somali region, even after for controlling for other factors. This warrant more investigation: imperfect competition, impacts of drought?

Table 4.2 continuedVariable Coeff. Std. Error ElasticityTotal monthly rainfall -0.01 0.01Festival dummies Meher season 0.17 0.03 0.03 End of meher season -0.22 0.04 -0.04 Orthodox Christian fasting (March) -0.12b 0.05 -0.02 Muslim Fasting 0.10b 0.05 0.02 Fasika 0.10c 0.06 Eid Alfetir -0.09c 0.12 New year 0.00c 0.09Region (Tigray is omitted) Afar 0.31 0.08 0.06 Amhara -0.21 0.08 -0.04 Oromia 0.02c 0.09 Somali -1.17 0.14 -0.18 SNNP -0.45 0.10 -0.08 Addis Ababa 0.53 0.10 0.10 Dire Dawa -0.06c 0.10Urban center (rural towns omitted) 0.66 0.05 0.12 17

Results• Objective 2: Price changes over time: 1. Non-food price inflation strongly associated with prices changes, but real non-food prices did not increase over time in highlands, so cannot explain increasing real cattle prices there. Yet non- food inflation does seem to explain cattle price increases in lowland markets (about 50% of price increase) 2. Significant but small effects of sheep prices in lowlands (explains 13% of price increase) 3. Cooking oil (feed proxy) and animal transportation prices also significant but do not explain price increases 4. In highlands international beef prices explain 10% of price increase, but in general most of the price increase in highlands is not explained by these variables

Table 5.1 Explaining price changes over time Highland Lowland markets marketsSample All markets only onlyVariable Elasticity Elasticity Elasticity Regional non-food price index 0.48 0.30 0.84 Sheep prices in market 0.04 Not 0.18 significant Locally produced cooking oil price 0.06 0.08 0.23 Animal transportation fare 0.18 0.198 0.25 Price of cattle in N. Somalia 0.07 Not 0.12 significant Price of cattle in Australia 0.55 0.66 Not significant 19

Summary of Findings• Our analysis of the ILRI data yields some important insights into market price determination in terms of cattle characteristics, and variation over space and seasons• Results on changes in prices over time are more complex: international factors provide a fairly strong explanation for part of the price increase, as does non-food inflation.• Yet much of the change is still unexplained

Determinants of Cattle Prices in Ethiopia

by essp2 on Jun 20, 2012

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