ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

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

Determinants of Cattle Prices in Ethiopia

The views expressed in this paper are those of the author and do not represent the official position of his institution.

Presentation Outline

1. 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.

• 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

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)

• 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

Data description

5

Period

Terms of trade of cattle Vs grainsReal

prices of cattleOromia Somali SNNP Afar

Addis Ababa Average

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

Data description

6

7

Figure 1. Terms of trade of cattle versus grain price indices-4 regions.

2007m1

2007m4

2007m7

2007m10

2008m1

2008m4

2008m7

2008m10

2009m1

2009m4

2009m7

2009m10

2010m1

2010m4

2010m7

2010m10

2011m1

2011m4

2011m70

2

4

6

8

10

12

14

Oromiya Somalia SNNP Afar

Term

s of

trad

e

8

Figure 2. Terms of trade of cattle versus grain price indices- Addis Ababa.

2007m1

2007m4

2007m7

2007m10

2008m1

2008m4

2008m7

2008m10

2009m1

2009m4

2009m7

2009m10

2010m1

2010m4

2010m7

2010m10

2011m1

2011m4

2011m710

15

20

25

30

35

40

45

50

55

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.

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)

Janu

ary-

05

May

-05

Septe

mbe

r-05

Janu

ary-

06

May

-06

Septe

mbe

r-06

Janu

ary-

07

May

-07

Septe

mbe

r-07

Janu

ary-

08

May

-08

Septe

mbe

r-08

Janu

ary-

09

May

-09

Septe

mbe

r-09

Janu

ary-

10

May

-10

Septe

mbe

r-10

Janu

ary-

11500

1000

1500

2000

2500

3000

3500

4000

4500

Nominal prices Real prices

Pri

ce/R

ea

l pri

ce (

De

cem

be

r 2

00

6 p

rice

s)

Overall de-flation

Drought in lowlands?

Figure 3. Mean monthly nominal and real prices, January 2005-March 2011.

Period of rapid overall inflation

Strong recovery; high int. prices?

122005m1

2005m5

2005m12

2006m4

2006m8

2006m12

2007m4

2007m8

2007m12

2008m4

2008m8

2008m12

2009m4

2009m8

2009m12

2010m4

2010m8

2010m120

10

20

30

40

50

60

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Average nominal cattle Average real cattle Average nominal beef Average real beef

Pric

e of

a K

G o

f bee

f in

birr

/Dec

embe

r 200

6 bi

rr

Nom

inal

pric

es in

& r

eal p

rices

in (D

ec. 2

006)

Figure 4. Average nominal and real price of beef and cattle.

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:

• where • • is real price of cattle i at week t, • is continuous variable j associated with cattle i at time t, • 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

( ) ( )0

1 1

J K

it j jit k kit itj k J

P X D e

jitX

kitD

itP

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

Variable Coeff. Std. Error ElasticityBody mass index 3.26 0.25 0.84Age (immature is omitted) Young 2.07 0.11 0.44Mature 3.85 0.19 0.96

Male (female is omitted) 1.84 0.10 0.38Breed (Arussi is omitted) Boran 0.51 0.10 0.09Danakil -0.05c 0.10 Harar 1.8 0.16 0.37Mixed 0.75 0.10 0.14Raya Azebo 0.23b 0.12 0.04Zebu 0.75 0.12 0.14

Table 4.2 Nonlinear system price formation equations estimates of period, festival, and regional dummy

variables.

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 Meher6. Some demand-side seasonality effects with lower prices during

Orthodox fasting, but slightly higher prices during Ramadan7. 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

Variable Coeff. Std. Error ElasticityTotal monthly rainfall -0.01 0.01Festival dummies

Meher season 0.17 0.03 0.03End of meher season -0.22 0.04 -0.04Orthodox Christian fasting (March) -0.12b 0.05 -0.02Muslim Fasting 0.10b 0.05 0.02Fasika 0.10c 0.06Eid Alfetir -0.09c 0.12New year 0.00c 0.09

Region (Tigray is omitted) Afar 0.31 0.08 0.06Amhara -0.21 0.08 -0.04Oromia 0.02c 0.09Somali -1.17 0.14 -0.18SNNP -0.45 0.10 -0.08Addis Ababa 0.53 0.10 0.10Dire Dawa -0.06c 0.10

Urban center (rural towns omitted) 0.66 0.05 0.12

Table 4.2 continued

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

Sample All markets

Highland markets

only

Lowland markets

only

Variable Elasticity Elasticity Elasticity

Regional non-food price index 0.48 0.30 0.84

Sheep prices in market 0.04 Not significant 0.18

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 significant 0.12

Price of cattle in Australia 0.55 0.66 Not significant

Table 5.1 Explaining price changes over time

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