1. 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, Ethiopia
The views expressed in this paper are those of the author and do not represent the official position of his institution.
2. Presentation Outline
1. General background
2. Model: Hedonic price formation system of equations
3. Data description
4. Time-invariant determinants of cattle prices
5. Determinants of cattle price changes over time
6. Summary of findings.
3. 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
4. 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)
5. 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
6. Data description
Terms of trade of cattle Vs grains
Real
Addis prices of
Period Oromia Somali SNNP Afar Ababa Average cattle
Overall average 0.04 0.07 -0.18 0.97 0.82 0.43 2.20
January 2007 to July 2008 -3.58 -3.37 -3.32 -0.94 -1.43 -2.24 -0.80
August 2008 to Aug. 2010 2.92 2.86 3.23 2.33 2.35 2.55 1.48
Sept. 2010 to July 2011 -0.57 -0.65 -2.77 1.00 1.02 -0.01 -3.58
6
7. Terms of trade
10
12
14
0
2
4
6
8
2007m1
2007m4
2007m7
2007m10
2008m1
Oromiya
2008m4
2008m7
2008m10
Somalia
2009m1
2009m4
2009m7
SNNP
2009m10
2010m1
Afar
2010m4
2010m7
2010m10
2011m1
Figure 1. Terms of trade of cattle versus grain price indices-4 regions.
2011m4
7
2011m7
9. 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 areas
2. Body mass measured as four grades, by breed and age
3. Dataset measures volumes sold, not just prices
4. 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.
10. 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
11. 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-07
Nominal prices
January-08
April-08
July-08
October-08
January-09
Real 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
12. 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
3500
12
Nominal prices in & real prices in (Dec. 2006)
13. 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
14. 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
15. Table 4.2 Nonlinear system price formation equations
estimates of period, festival, and regional dummy variables.
Variable Coeff. Std. Error Elasticity
Body mass index 3.26 0.25 0.84
Age (immature is omitted)
Young 2.07 0.11 0.44
Mature 3.85 0.19 0.96
Male (female is omitted) 1.84 0.10 0.38
Breed (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
16. 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?
17. Table 4.2 continued
Variable Coeff. Std. Error Elasticity
Total monthly rainfall -0.01 0.01
Festival 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 0.09
Region (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 0.10
Urban center (rural towns omitted) 0.66 0.05 0.12
17
18. 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
19. Table 5.1 Explaining price changes over time
Highland Lowland
markets markets
Sample All markets only only
Variable 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
20. 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
Editor's Notes
Previous works that used HPFA include Teklewold et. al. (2009) for cattle and Ayele et. al. (2006) for shoats and the recent work by Kassie et. al. (2011) uses heteroscedasticity efficient estimation and random parameters logistic models to estimate the implicit prices of indigenous cattle traits.
NumberHighlandLowlandTotalNumber of markets25732Proportion of markets7822100Number of observations 7,532 4,055 11,587 Proportion of observations6535100
There appear to be several distinct periods of price trends, During:The January 2005-March 2007 period, average real and nominal prices grew at monthly rates of 0.6 and 1.9 percent,The April 2007-January 2009 period, when real and nominal prices grew at 1.8 and 4.1 percent (a period of high food inflation in general)The January-November 2009 period: real and nominal price declines in cattle at 3.7 % and other food typesDecember 2009-September 2010: strong recovery (high international prices?) October 2010 onwards: decline in cattle prices (drought in pastoralist areas? government price controls?)This growth in prices was followed by a relatively faster growth in the number of cattle sold in an average market of 23 percent. However, this growth was heavily influenced by the 594 % growth in Nov -Dec 05, by the 208 % growth in Sept-Oct 06, by the 171 % growth in April-may 07, by the615 % growth in August-Sep 07, by the150 % growth in Nov-Dec 08, and by the107 % growth in March-April 2010. Excluding the six fastest growths and decline rates, average monthly volume of sales grew at only 1.4 percent.
Figure 3. Average nominal and real price of beef and cattle.
Note that body mass is included in D but is a function of other factors (i.e. we account for reverse causality problem between body grade and prices
Elasticity of cattle prices WRT real international beef price was 0.9 – so international price transmission has been very important in recent years!
Elasticity of prices of cattle with respect to (WRT) live animal and meat exports was 0.056, Cattle prices increase by 5.6 cents for a 1 birr increase in value of exports.Elasticity of cattle prices WRT real international beef price was 0.9 – so international price transmission has been very important in recent years!Cattle prices increase with swine prices, although its effect was small Elasticity WRT cattle transportation fares is 0.2; A bull worth 1,000 birr gets priced 1,200 when real price of transportation grow by 1 birr, but its overall effect was small Cattle prices increase with price of locally produced cooking oil,Estimated coefficient of 6-month lagged average monthly rainfall implies with sufficient rains prices increase slightly,Elasticity WRT body mass index was 0.45, a cattle that is fat/moderate sold at 45 percent larger price than moderate/thinReal prices of cattle in a given age category were 54 percent larger relative to ones in immediately younger category. Male cattle sold at prices 36 percent larger than female cattle. The Harar, Boran, and mixed breed sold at larger prices than Arussi; Danakil and Raya Azebo breeds sold lower, and Zebu and Arussi were not different.
Elasticity of cattle prices WRT real international beef price was 0.9 – so international price transmission has been very important in recent years!
Estimated coefficient of 6-month lagged average monthly rainfall implies that if there were sufficient rains that do not force farmers distress sell their cattle, prices increase slightly,
Relative to Tigray, real prices lower in Dire Dawa and larger in all regions except Somali, which is not different from Tigray,Real prices in urban center markets were 25 percent larger, May explain urban center markets that appear to have integrated prices due to other reasons than through actual trade flows.The Harar, Boran, and mixed breed sold at larger prices than Arussi; Danakil and Raya Azebo breeds sold lower, and Zebu and Arussi were not different. Relative to 2005, real prices of cattle were larger during the 2006-2008 period, lower in 2011, and were no different in 2009 & 2010.
External prices and factors accounted for 1.21 percent,Increases in value of live animal and meat exports accounted for 0.80 percent, increase in prices of local cooking oil (0.016),Increase in transport cost (0.2), global beef (0.2), & swine prices (-0.08)Improvements in cattle attributes accounted for 1.24 percentImprovements in the body mass condition (0.15), increase in the number of young (0.12), mature (0.72), and male cattle (0.24) soldWhile the 0.6 percent average annual growth in prices implied by year dummies can be an upper bound for increase in real prices of cattle not captured by factors included in the analysis, The 0.14 percent that is unexplained by the factors included in the analysis can serve as a lower bound.
