This document summarizes research using network analysis to evaluate business development services (BDS) programs for smallholder dairy farmers in Tanzania and Uganda. The research collected data on linkages between producers, traders, and BDS providers. Preliminary network analysis using software identified different configurations between regions. Future analysis will assess the impact of BDS programs and relationships between network structure and agents' performance and value chain outcomes using additional statistics and econometrics. The research aims to advance understanding of development interventions and interactions within value chains.

1. Interpreting trader networks as value chains: experience
with Business Development Services in smallholder dairy in
Tanzania and Uganda
Derek Baker, Amos Omore, David Guillemois, Eunice Kariuki and Alice Njehu
ILRI Seminar, Nairobi, 25 June 2012

2. Outline
1. Overview of the research to date
2. BDS as a development intervention
3. Networks in development, and an overview of software and data handling
4. Intro to networks as an approach to value chain analysis
5. Approach taken, results so far
6. Discussion: handling network data alongside other data
7. Discussion: experience gained
8. Conclusions:
1. Impressions from the work so far
2. Potential uses for other ILRI research
3. Interface with other work by partners and other organisations
9. Next steps

3. Research overview (so far)
Representations of the Value Chain in pro-poor development:
• have a poor theoretical basis upon which to base research hypotheses
• lack quantitative intuition
• fail to capture inter-agent interactions
• cannot adequately address analysis of interventions
The research for which this is a preliminary presentation has sought to address
these weaknesses. Its goals:
1. Evaluate BDS programme for dairy in Uganda and Tanzania
2. Advance knowledge of trader-producer-service linkages and development
orientation
3. Test new empirical methods
Story so far
Theories of networks, applied to value chain analysis, used to formulate hypotheses
Measures of performance of BDS interventions formulated
Measures of VC-related network characteristics formulated
Data collected
Data processed using network-dedicated software (Pajek)
Preliminary analysis done

4. Intro on BDS in pro-poor dairy development in EA
Linkages in milk quality assurance in informal markets
Milk Trader
Training Hygienic
guides cans
Training
Accreditation & monitoring Service
Regulatory
Providers
Authority Reporting
(BDS)
(Trialled in Tanzania and Uganda – now being evaluated)

5. BDS in pro-poor dairy development in EA:
Linkages in inputs and services provision
Milk
Producer
Milk Market Hub
(Emphasis on traditional milk
market hubs to grow them) Inputs &
Service
Milk Traders $$ Providers
Payment agreement (BDS)

6. Networks as an approach to Value Chain Analysis
Value chains entail:
• parallel/convergent/divergent paths
• multiple and varied flows and relationships
• “horizontal” and well as vertical linkages
i.e. Value chains are in the nature of
networks or “net chains”
The equivalence of market theory with network theory has steadily emerged
• efficiency
• marginality
• equilibrium
Some applied aspects of economics (e.g. market structure, economies of
scale, logistic efficiency ) have been studied in terms of networks
Networks, like VCs, are unique/idiosyncratic: well-suited to micro-level analysis
and surveys.
Connections between/amongst actors, and the nature of those connections, adds
a new analytical dimension, with many possibilities.

7. Approach and methods - 1
Hypotheses formulation
Performance of BDS programme:
• improved milk handling
• higher production/productivity
• shifted seasonal pattern
• more sales/greater sales as % of production
• higher profits
• improved dairy market structures
Network-related evidence
• contact via a network enhances BDS programme performance
• contact varies in intensity and form, and for a variety of reasons
• variety in network configurations exists for a reason
• network configuration has implications for many interventions
 form of BDS provision
 applicability of Hubs, Innovation Platforms, and other collective action
 forms and entry points for intervention
 tracking of action/reaction amongst actors

8. Approach and methods - 2
Approach
1. Focus Group Discussions with traders, producers, and BDS providers
2. Formulation + testing of a questionnaire
3. Questionnaire: listings of linkages within the network
4. Sampling
5. Data processing: mixing Pajek with other data analysis
6. Analytical targets

9. Approach and methods - 3
Sampling
1. Start with BDS providers:
i. select ALL “programme” BDS providers (11 in Mwanza)
ii. mirror with an equal number (11) of “non-programme” BDS providers
iii. Ask each BDS provider for a COMPLETE list of clients (traders and
producers)
2. Randomly select 5 “programme” BDS providers, and 5 “non-programme” BDS
providers from above
i. Randomly select 4 TRADERS from client list of each (i.e. 2*20 = 40)
ii. mirror with an equal number (20) of TRADERS not linked to the programme
iii. Ask ALL actors for contact lists
3. Randomly select 2 “programme-linked” TRADERS and 5 “programme” BDS
providers
i. Randomly select 2 PRODUCERS from each contact list (2*5 + 2*4 = 18)
ii. Mirror with an equal number (18) of PRODUCERS not linked to the
programme
iii. Ask ALL actors for contact lists

10. Approach and methods - 3
Mwanza Arusha
BDS Providers
Programme 11 9
Non-programme 11 9
Traders-linked 20 16
Traders-non-linked 20 16
Producers-linked 18 15
Producers-non-linked 18 15
BDS providers 22 18 40
Traders 40 33 73
Producers 36 29 65
Total interviews 98 80 178

11. Pajek – General introduction
What is Pajek?
Preparation of data.
• Social network analysis software (SNA software)
• Open source software
• Facilitates quantitative or qualitative analysis of social
networks, by describing features of a network, either
through numerical or visual representation.

12. Pajek – Example
Somali clans
5 Levels only

13. Results in BDS study - Uganda milk supply
Blue triangle : Trader
Red cirle: Producer
Thickness line: Quantity of milk traded between producers and traders.
Number: Quantity of milk traded per connection.

14. Results – milk supply in Mwanza

15. Results - Uganda Milk sales, input supply
Blue triangle : Trader
Red circle: Producer
Yellow box: BDS
Dot line: Milk traded
Blue line: BDS service

16. Results - Uganda Milk sales, input supply (detail)
Blue triangle : Trader
Red circle: Producer
Yellow box: BDS
Dot line: Milk traded
Blue line: BDS service

17. Results - Uganda milk sales and training services
Blue triangle : Trader
Red circle: Producer
Yellow box: BDS
Dot line: BDS service
Blue line: Milk delivered

18. Results - Uganda milk sales and all BDS
Blue triangle : Trader
Red circle: Producer
Yellow box: BDS
Thickness of the line: Number of exhanges/services

19. Results - Uganda milk sales and all BDS (detail)
Blue triangle : Trader
Red circle: Producer
Yellow box: BDS
Thickness of the line: Number of exhanges/services

20. Results - Degree centrality for producers
Number of connections for producers in Uganda on Milk
160
Number of producers
140 140 producers have just 1 buyer
120 38 producers have 2 buyers
10 producers have 3 buyers
100
8 producers have 4 buyers
80 ….
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12
Number of connections between producers and traders

21. Results - Degree centrality for traders
Milk. Number of connections for Traders in Uganda
40
36 traders buy from just 1 producer
Number of traders
35
30
18 traders buy from 2 producers
25 ….
20
15
Note small peak (10 traders) buying
10
5
from 5 producers
0
1 2 3 4 5 6
Number of connections between producers and traders
Number of connections for Traders in Mwanza on
Number of connections for Traders in Arusha on Milk
Milk
25
16
14 20
12
10 15
8
10
6
4 5
2
0 0
1 2 3 4 5 1 2 3 4 5 6
Note different configuration between Arusha and Mwanza

22. Results - Network characteristics for BDS provision - 1
PRODUCERS TRADERS BDS
Connection of BDS. Traders. Uganda Number connections per BDS. Uganda
Connection of BDS. Producers.
One service received by one BDS is counted as One service to one entity is counted as
Uganda
"one" "one
One service received by one BDS is
No. of producers
counted as "one" 12 40
20 10
30
8
15
6 20
10
4
5 10
2
0 0 0
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 1 4 7 10 13 16 19 22 25 28 31 34 37 40
No. of connections producer to BDS
Connection of BDS. Producers. Arusha Connection of BDS. Traders. Arusha Number connections per BDS. Arusha
One service received by one BDS is One service received by one BDS is counted as One service received by one BDS is
counted as "one" "one" counted as "one"
4.5 7 40
4 6 35
3.5 30
5
3
25
2.5 4
20
2 3
15
1.5
2 10
1
0.5 1 5
0 0 0
1 3 5 7 9 11 13 15 17 19 1 3 5 7 9 11 13 15 17 19 21 1 2 3 4 5 6 7 8 9 10 11

23. Results - Network characteristics for BDS provision - 2
Connection of BDS. Producers. Number services provided per BDS. Mwanza Number connections per BDS.
Mwanza One service received by one BDS is counted as Mwanza
One service received by one BDS is "one" One service to one entity is counted
counted as "one" as "one"
10 40.00
10 9 35.00
8
8 30.00
7
6 25.00
6
5 20.00
4 4 15.00
3
10.00
2 2
1 5.00
0 0 0.00
1 3 5 7 9 11 13 15 17 19 21 23 25 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 3 5 7 9 11 13 15 17 19 21 23
1. Note variation in network intensities: numbers of BDS connections per BDS provider
2. Question: are these connections better if “bundled” (i.e. >1 service per client, to a few c
or “non-bundled” (i.e. =1 service per client, to many clients)?

24. Results - maps of production and procurement

25. Results - maps of network connections

26. Results - nature of data
... Variables....
....Agents…
A
B
... Observations....
C
...
connections …
A to B
A& B
....network
C to D
...

27. Future analysis – a logical progression of hypotheses
Conventional view:
H01: Actors’ characteristics/performance = f(exogenous data collected)
Progression… (nested models?)
H02: Actors’ characteristics/performance = f(exogenous data collected,
number and form of network links)
H03: Number and form of links = f(exogenous data collected,
factors affecting linkages)
H04: Actors’ value chain behaviour = f(exogenous data collected,
factors affecting linkages)
H05: Value chain performance = f(exogenous data collected,
actors’ value chain choices)
H06: Development outcomes = f(exogenous data collected,
factors affecting network structure)

28. Conclusions
1. Impressions from the work so far
I. Hypotheses difficult at first
II. Sampling is complex, numbers can become overwhelming
III. Data handling is demanding
2. Potential uses for other ILRI research
I. Analysis of VC performance
II. Aspects of transactions (incl. input delivery)
III. Analysis of collective action potential/ex ante/ex post
IV. Spatial analysis, suited to panels
3. Interface with other work by partners and other organisations
I. Identifying entry points for interventions
II. Identifying best strategies for interventions
III. Mapping of impact pathways

29. Next steps
1. Further simple network statistics
2. Improved compilation of PAJEK + conventional databases
3. Impact assessment of BDS programme
4. Econometric assessment of agents’ performance, related to networks
5. Econometric assessment of networks’ performance, related to networks
6. Econometric assessment of bundling vs non-bundling (BDS, hubs, IPs)
7. Question: What is in this for your research?

30. Contact: Derek Baker d.baker@cgiar.org
International Livestock Research Institute www.ilri.org