The Role of Livestock in the Kenyan Economy: A Dynamic CGE Analysis, presented by Ermias Engeda and Ayele Gelan at ReSAKSS-ECA Internal Seminar

1. THE ROLE OF LIVESTOCK IN THE KENYAN
ECONOMY: A DYNAMIC CGE ANALYSIS
Ayele Gelan
Ermias Engida
PRESENTATION TO RESAKSS STAFF
06/07/2012
NAIROBI, KENYA 1
JUNE 28, 2011

2. TOPICS OF DICUSSION
 Study contexts and motivations
 Approaches and methods
 Overview of the existing model and
modifications
 Future extensions
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3. IMPORTANCE OF LIVESTOCK IN A
DEVELOPING ECONOMY
 Livestock’s macro roles are not often recognized
• “Livestock revolution” - Growing demand for meat and dairy
products
• Crop-livestock interactions (e.g., draft power, manure, crop
residue feed, etc)
• Livestock products and agro-processing (e.g., dairy, leather, etc)
 How high are macro multipliers from livestock
sector growth?
• How much income growth and poverty reduction can we
generate with livestock sector growth?
• General equilibrium analysis needed to capture these

4. POLICY AND RESEARCH PRIORITIES
 NEPAD (2006) recognized the importance of
integrating the livestock sector into the CAADP
framework
 Diao and Pratt (2008) conclude that “growth in
staples is the priority for poverty reduction”
• Combining growth in staples and livestock has high economic multipliers
& strong poverty reduction gains in food deficit areas
 Dorosh and Thurlow (2009) - poverty-growth
elasticities
• Cereals have highest rural poverty reduction potential

5. Male
Deaths
Young Immature Mature Other
economic
male male male uses
+
Births Sale of live
Off-takes animals
+
Young Immature Mature Yields/ Sales of
female female female animal products
=
Female
TR
deaths
-
costs of keeping
costs of keeping + costs of keeping +
= TC
mature animals
young animals immature animals
=
Production and economic flows (off-take, in-takes and others)
Reproduction and growth (growth, births, deaths) Gross margin

6. CURRENT STUDY – DATA ISSUES
 Livestock module specification mostly guided by
available data
- Kenya Population Census
- Behnke (2012) - IGAD-LPI consultancy report
- FAO and ILRI documents from the web sites
 Livestock type and product are guided by available
data
 Cattle
 Shots
 Camel
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 Two types of chicken (Ingenious and Commercial)
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7. CURRENT STUDY – DATA ISSUES……
 Livestock products
 Live cattle
 Milk (cattle, goat, camel)
 Egg
 Spatial dimensions
 8 provinces of Kenya
 3 agro-ecological zones in each province (highlands, semi-
arid, arid)
 We tried to prepare a data set with livestock
 numbers by type, sex and age;
- prices by type, sex and age and also prices of their
products;
- Birth, offtake and death rates by type, sex and
age;

8. CURRENT STUDY – HERD DYNAMICS
 Herd dynamic module mostly followed the
structure of the Ethiopian model
• Numbers by Sex and age
• Birth, off-takes and death rates by type, sex and age
 More data is desperately needed
• Livestock age and sex composition
• Prices of livestock (live) and their products
• Factor quantities ….(labour, land, and sectoral capital output ratios)
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9. MODEL SPECIFICATION
 Milk production is estimated (in value) for each livestock type
and egg production estimated as well for both types of chickens
(indigenous and commercial).
 Stock change is calculated (in value terms) for each type, sex
and age.
 Coupling the herd dynamics with the economy-wide
model
 Integrating the biological and the economic processes
 Establishing stock-flow relationships in existing economy-wide
models (e.g. livestock as capital and livestock products)
 Revising and improving the system of economic accounts in
existing models (e.g., breeding stocks as capital in livestock, etc)

10. DYNAMIC CGE MODEL FOR KENYA
» We use Thurlow and Benin’s (2008) model
(“Agricultural Growth and Investment Options for Poverty Reduction in
Kenya”)
• General equilibrium: the model represents different
markets, all reaching equilibrium
• Dynamic: the model is solved recursively
 Model is calibrated for Kenya using 2007 Kenyan
Social Accounting Matrix
• 3 AEZs, 143 AEZ specific activities, 53commodities,
19 factors, and 45 households

11. SIMULATION SCENARIOS
Simulation Shocks
 We simulate Total
Factor Productivity
(TFP) shocks to various BASE All Ag commodities grow at
the previous trend
subsectors
CEREAL Cereals + vegetable/fruit +
 Base growth follows the enset grow faster
previous years’ trend CASH CROP Cash crops and pulses
+ oilseeds grow faster
 Additional shocks will
be applied as in LIVESTOCK Livestock activities grow
faster
Thurlow and Benin
2008 (which is based on CAADP All Ag commodities grow
the CAADP framework) faster

12. FUTURE EXTENSIONS
 As we mentioned earlier the major problem in the
this task is getting data as detail as the model needs.
So future works of the model concentrate more on
getting those data into use
- By sex and age detail livestock figures for the
module
- Factor quantities for the main model
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And the other task is definitely dealing with the simulation

13. THANK YOU!
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