Measurement of competitiveness in smallholder livestock systems and emerging policy advocacy:

an application to Botswana

Sirak Bahta1 and P. Malope2

1International Livestock Research Institute (ILRI)2Botswana Institute of Development Policy Analysis

Mainstreaming Livestock Value Chain : Bringing research to bear on impact assessment, policy analysis and

advocacy for development, 5-6 Nov. 2013, Accra-Ghana

Outline

Introduction and objectives

Literature Review Methodological Approach Results and discussion Conclusion and Policy Implications

Introduction 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 at three levels, macro; meso and micro-levels

• 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”

6

Literature Review - determinants

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

7

Study Area

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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)

Approach

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) 131779.5

Crop land area (Hectares) 6.19

Family labour (hours per month) 210.34

Results: Descriptive statistics

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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%

Results: Descriptive statistics

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Results:Stochastic profit frontier estimates

VariablesOLS MLU

Coeff. t-values Coeff. t-values

Constant -34.87 -26.31 -38.12 -32.49Ln (Average Beef cattle price) 5.01 28.23 5.51 34.85***Ln (Feed prices) -0.15 -3.61 -0.13 -3.11***Ln (Veterinary prices) -0.12 -2.97 -0.09 -2.46**Ln (Labor prices.) 0.08 0.24 -0.79 -1.93**Ln (fixed capital) -0.02 -0.64 0.02 0.53Ln (Family labour Hrs) -0.06 0.28 0.46 1.82*Ln (Crop land area) 0.55 2.95 0.28 1.70*sigma-squared 7.87 6.03***

gamma 0.80 11.09***log likely hood function -1129.60 -1093.43LR test of the one-sided error 72.32

Results: Efficiency drivers

Variables Coefficient t-valuesConstant -11.86 -2.47**Age of household head -1.26 -3.44***Education of Household head 0.043 0.14Annual 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 -3.84***Crop income (Yes=1, No=0) -2.31 -2.94***

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Results: efficiency scores

<.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 0.9-10

40

80

120

160

2815

26

73

140

172

92

100

Efficiency scores (Mean 0.56)

Firm

s

Efficiency scores

26% less than 0.5 ef-ficiency score

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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.

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• Policies to improve farm profit should be directed atEnhancing 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 andEncouraging farmers to engage in crop farming,

particularly in feed production.

Conclusion and policy implications

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• 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

Conclusion and policy implications

Thank you!