08/0

4/20

23

1

THE ROLE OF LIVESTOCK IN THE KENYAN ECONOMY: A DYNAMIC CGE ANALYSIS

Ayele GelanErmias Engida

PRESENTATION TO RESAKSS STAFFNAIROBI, KENYA

JUNE 28, 2011

TOPICS OF DICUSSION Study contexts and motivations

Approaches and methods

Overview of the existing model and

modifications

Future extensions

08/0

4/20

23

2

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

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

Young female

Sale of live animals

costs of keeping young animals

+ +

SCHEMATIC PRESENTATION OF HERD DYNAMICS AND PRODUCTIVITY

Production and economic flows (off-take, in-takes and others) Reproduction and growth (growth, births, deaths)

Immature female

Mature female

Births

Young male

Immature male

Off-takes

Sales of products

=

=

Yields/animal

+

TR

Other economic

uses

+

Mature male

costs of keeping immature animals

costs of keeping mature animals

Female deaths

Male Deaths

TC

-

=

Gross margin

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 Two types of chicken (Ingenious and Commercial)

08/0

4/20

23

6

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;

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)

08/0

4/20

23

8

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)

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

SIMULATION SCENARIOS We simulate Total Factor

Productivity (TFP) shocks to various subsectors

Base growth follows the previous years’ trend

Additional shocks will be applied as in Thurlow and Benin 2008 (which is based on the CAADP framework)

Simulation Shocks

BASE All Ag commodities grow at the previous trend

CEREAL Cereals + vegetable/fruit + enset grow faster

CASH CROP Cash crops and pulses + oilseeds grow faster

LIVESTOCK Livestock activities grow faster

CAADP All Ag commodities grow faster

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

And the other task is definitely dealing with the simulation

08/0

4/20

23

12

THANK YOU!

08/0

4/20

23

13