Measurement of competitiveness in smallholder
livestock systems and emerging policy advocacy:
an application to Botswana
Mainstreaming Livestock Value Chain : Bringing research
to bear on impact assessment, policy analysis and
advocacy for development, 5-6 Nov. 2013, Accra-Ghana
Sirak Bahta1 and P. Malope2
1International Livestock Research Institute (ILRI)
2Botswana Institute of Development Policy Analysis

Outline
 Introduction and objectives
 Literature Review
 Methodological Approach
 Results and discussion
 Conclusion and Policy Implications

Introduction
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Botswana agric. dominated by livestock production
Beef dominant within the Botswana livestock sector
EU market access has justified massive investment in beef
for export
Dualistic structure of production, with communal
dominating
Productivity low esp. in the communal sector
Not clear as to whether beef production is competitive
Studies have relied on household budget analysis and
limited household data
Others have concentrated on productivity of agriculture

Objectives
Measure competitiveness of beef production using
household data
Specifically the study seeks to:
• Identify the determinants of profitability
• Identify efficiency drivers
• Measure overall profit efficiency of beef production
• Come up with policy recommendations to improve
competitiveness of beef production
• Identify gaps between this application of household
analysis and the information needed for policy
advocacy and implementation

Literature-definition
• Competitiveness has many definitions
• Competitiveness can be measured
levels, macro; meso and micro-levels
at
three
• Study measure competitiveness at micro level
• Definition at micro level relate to profitability
• “the ability to sell products that meet demand
requirements in terms of price, quality & quantity and
at the same time ensure profits”

Literature Review - determinants
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Internal factors
Size of the farm
Organisational structure of the farm
Social capital
External factors
Government policy
Public expenditure in research, extension and
Infrastructure
Location of farms
6

Study Area
7

Approach
•
•
•
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Household data, collected by survey
Translog profit frontier function
Dependent variable = profit per beef equivalent
Independent variable = weighted output price, Input
prices per beef equivalent (feed, veterinary and Labor),
Fixed costs per beef equivalent (Fixed capital, family labor
and Land)
• Efficiency drivers: household characteristics (Age,
Education, Gender, non-farm income, access to crop farm
income) and transaction cost variables (distance to
markets, access to agriculture/market information) and
location variable (FMD zone)
8

Results: Descriptive statistics
Variables
Mean
Value of beef Cattle output (Pula per year)
5955
Beef cattle price (Pula)
1993.04
Feed cost (Pula per year)
605.57
Vet. cost (Pula per year)
650.89
Labour Cost (Pula per Month)
237.78
Cost of other inputs (Pula per year)
350.5
Value of fixed capital (Pula)
Crop land area (Hectares)
Family labour (hours per month)
131779.5
6.19
210.34
9

Results: Descriptive statistics
Variables
Mean
Age of household head (Years)
59.79
Education of Household head (years)
4.95
Household Off farm income (Pula per year)
54815.57
Distance to commonly used market(Km)
39.65
Herd size (Beef cattle equivalent)
23.86
Gender (% female farmers)
22%
Information access (Yes=1, No=2)
76.79%
FMD disease zone (Yes=1, No=2)
42.80%
Crop income (Yes=1, No=2)
50.03%
10

Results:Stochastic profit frontier estimates
OLS
MLU
Variables
Coeff. t-values
Constant
Ln (Average Beef cattle price)
Ln (Feed prices)
Ln (Veterinary prices)
Ln (Labor prices.)
Ln (fixed capital)
Ln (Family labour Hrs)
Ln (Crop land area)
sigma-squared
gamma
log likely hood function
LR test of the one-sided error
-34.87
5.01
-0.15
-0.12
0.08
-0.02
-0.06
0.55
-1129.60
-26.31
28.23
-3.61
-2.97
0.24
-0.64
0.28
2.95
Coeff. t-values
-38.12 -32.49
5.51 34.85***
-0.13 -3.11***
-0.09 -2.46**
-0.79 -1.93**
0.02
0.53
0.46
1.82*
0.28
1.70*
7.87 6.03***
0.80 11.09***
-1093.43
72.32

Results: Efficiency drivers
Variables
Coefficient
t-values
Constant
-11.86
-2.47**
Age of household head
-1.26
-3.44***
Education of Household head
0.043
0.14
Annual household non-farm income
0.26
2.64***
Distance market (commonly used)
0.56
2.42**
Herd size
2.48
4.92***
Gender (% female farmers)
-2.82
-2.43***
Information access (Yes=1, No=0)
4.15
2.80***
FMD disease zone (Yes=1, No=0)
-4.56
-2.31
-3.84***
-2.94***
Crop income (Yes=1, No=0)

Results: efficiency scores
Efficiency scores (Mean 0.56)
172
Firms
160
120
26% less than 0.5
efficiency score
140
92
73
80
40
28
26
15
10
0
<.20
0.2-0.3
0.3-0.4
0.4-0.5
0.5-0.6
0.6-0.7
0.7-0.8
0.8-0.9
0
0.9-1
Efficiency scores
13

Conclusion and policy implications
• The mean efficiency of 0.56 implies that there is a
substantial loss of profit due to inefficiency.
• Profits could be increased through reduction in
inputs costs, increase in output price achieved
and improved access to crop land.
• Presence of inefficiency in the study reminds that
production models that assume absolute
efficiency could lead to misleading conclusions.
14

Conclusion and policy implications
• Policies to improve farm profit should be
directed at
Enhancing producer prices as well as ways to
reduce input prices
improving infrastructure such as roads and
collection points of livestock
Improving access to crop land and
Encouraging farmers to engage in crop
farming, particularly in feed production.
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Conclusion and policy implications
• Presence of inefficiency is largely ignored by the
multi-market and CGE models used in policy
analysis. Results of a policy change will be
measured differently by models if they:
– Serve to improve efficiency (which models may miss)
– Increase production or consumption (which may
preserve and even magnify inefficiencies)
• Ideas for including inefficiencies in models are
needed
16

Thank you!