Interpreting trader networks as value chains: experiencewith Business Development Services in smallholder dairy inTanzania...
Outline1.   Overview of the research to date2.   BDS as a development intervention3.   Networks in development, and an ove...
Research overview (so far)  Representations of the Value Chain in pro-poor development:  • have a poor theoretical basis u...
Intro on BDS in pro-poor dairy development in EA    Linkages in milk quality assurance in informal markets                ...
BDS in pro-poor dairy development in EA:      Linkages in inputs and services provision                          Milk     ...
Networks as an approach to Value Chain AnalysisValue chains entail:• parallel/convergent/divergent paths• multiple and var...
Approach and methods - 1Hypotheses formulationPerformance of BDS programme:• improved milk handling• higher production/pro...
Approach and methods - 2Approach1. Focus Group Discussions with traders, producers, and BDS providers2. Formulation + test...
Approach and methods - 3Sampling1. Start with BDS providers:       i. select ALL “programme” BDS providers (11 in Mwanza) ...
Approach and methods - 3                          Mwanza    ArushaBDS ProvidersProgramme                      11       9No...
Pajek – General introductionWhat is Pajek?Preparation of data.• Social network analysis software (SNA software)• Open sour...
Pajek – ExampleSomali clans5 Levels only
Results in BDS study - Uganda milk supply          Blue triangle : Trader          Red cirle: Producer          Thickness ...
Results – milk supply in Mwanza
Results - Uganda Milk sales, input supply                                       Blue triangle : Trader                    ...
Results - Uganda Milk sales, input supply (detail)                                         Blue triangle : Trader         ...
Results - Uganda milk sales and training services                                        Blue triangle : Trader           ...
Results - Uganda milk sales and all BDS                                    Blue triangle : Trader                         ...
Results - Uganda milk sales and all BDS (detail)                                     Blue triangle : Trader               ...
Results - Degree centrality for producers                                Number of connections for producers in Uganda on ...
Results - Degree centrality for traders                          Milk. Number of connections for Traders in Uganda        ...
Results - Network characteristics for BDS provision - 1                                     PRODUCERS                     ...
Results - Network characteristics for BDS provision - 2       Connection of BDS. Producers.             Number services pr...
Results - maps of production and procurement
Results - maps of network connections
Results - nature of data                                                  ... Variables....                           .......
Future analysis – a logical progression of hypothesesConventional view: H01: Actors’ characteristics/performance = f(exoge...
Conclusions1. Impressions from the work so far    I. Hypotheses difficult at first    II. Sampling is complex, numbers can...
Next steps1.   Further simple network statistics2.   Improved compilation of PAJEK + conventional databases3.   Impact ass...
Contact: Derek Baker d.baker@cgiar.orgInternational Livestock Research Institute www.ilri.org
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Interpreting trader networks as value chains: Experience with Business Development Services in smallholder dairy in Tanzania and Uganda

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Presented by Derek Baker, Amos Omore, David Guillemois, Eunice Kariuki and Alice Njehu at an ILRI Seminar, 25 June 2012

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Interpreting trader networks as value chains: Experience with Business Development Services in smallholder dairy in Tanzania and Uganda

  1. 1. Interpreting trader networks as value chains: experiencewith Business Development Services in smallholder dairy inTanzania and Uganda Derek Baker, Amos Omore, David Guillemois, Eunice Kariuki and Alice Njehu ILRI Seminar, Nairobi, 25 June 2012
  2. 2. Outline1. Overview of the research to date2. BDS as a development intervention3. Networks in development, and an overview of software and data handling4. Intro to networks as an approach to value chain analysis5. Approach taken, results so far6. Discussion: handling network data alongside other data7. Discussion: experience gained8. Conclusions: 1. Impressions from the work so far 2. Potential uses for other ILRI research 3. Interface with other work by partners and other organisations9. Next steps
  3. 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 methodsStory 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. 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. 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 & ServiceMilk Traders $$ Providers Payment agreement (BDS)
  6. 6. Networks as an approach to Value Chain AnalysisValue chains entail:• parallel/convergent/divergent paths• multiple and varied flows and relationships• “horizontal” and well as vertical linkagesi.e. Value chains are in the nature ofnetworks or “net chains”The equivalence of market theory with network theory has steadily emerged• efficiency• marginality• equilibriumSome applied aspects of economics (e.g. market structure, economies ofscale, logistic efficiency ) have been studied in terms of networksNetworks, like VCs, are unique/idiosyncratic: well-suited to micro-level analysisand surveys.Connections between/amongst actors, and the nature of those connections, addsa new analytical dimension, with many possibilities.
  7. 7. Approach and methods - 1Hypotheses formulationPerformance of BDS programme:• improved milk handling• higher production/productivity• shifted seasonal pattern• more sales/greater sales as % of production• higher profits• improved dairy market structuresNetwork-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. 8. Approach and methods - 2Approach1. Focus Group Discussions with traders, producers, and BDS providers2. Formulation + testing of a questionnaire3. Questionnaire: listings of linkages within the network4. Sampling5. Data processing: mixing Pajek with other data analysis6. Analytical targets
  9. 9. Approach and methods - 3Sampling1. 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” BDSproviders 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 lists3. Randomly select 2 “programme-linked” TRADERS and 5 “programme” BDSproviders 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. 10. Approach and methods - 3 Mwanza ArushaBDS ProvidersProgramme 11 9Non-programme 11 9Traders-linked 20 16Traders-non-linked 20 16Producers-linked 18 15Producers-non-linked 18 15BDS providers 22 18 40Traders 40 33 73Producers 36 29 65Total interviews 98 80 178
  11. 11. Pajek – General introductionWhat 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. 12. Pajek – ExampleSomali clans5 Levels only
  13. 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. 14. Results – milk supply in Mwanza
  15. 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. 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. 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. 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. 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. 20. Results - Degree centrality for producers Number of connections for producers in Uganda on Milk 160Number 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. 21. Results - Degree centrality for traders Milk. Number of connections for Traders in Uganda 40 36 traders buy from just 1 producerNumber 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. 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 isNo. 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. 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 40.0010 9 35.00 88 30.00 7 6 25.006 5 20.004 4 15.00 3 10.002 2 1 5.000 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 21 231. Note variation in network intensities: numbers of BDS connections per BDS provider2. Question: are these connections better if “bundled” (i.e. >1 service per client, to a few cor “non-bundled” (i.e. =1 service per client, to many clients)?
  24. 24. Results - maps of production and procurement
  25. 25. Results - maps of network connections
  26. 26. Results - nature of data ... Variables.... ....Agents… A B... Observations.... C ... connections … A to B A& B ....network C to D ...
  27. 27. Future analysis – a logical progression of hypothesesConventional 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. 28. Conclusions1. Impressions from the work so far I. Hypotheses difficult at first II. Sampling is complex, numbers can become overwhelming III. Data handling is demanding2. 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 panels3. 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. 29. Next steps1. Further simple network statistics2. Improved compilation of PAJEK + conventional databases3. Impact assessment of BDS programme4. Econometric assessment of agents’ performance, related to networks5. Econometric assessment of networks’ performance, related to networks6. Econometric assessment of bundling vs non-bundling (BDS, hubs, IPs)7. Question: What is in this for your research?
  30. 30. Contact: Derek Baker d.baker@cgiar.orgInternational Livestock Research Institute www.ilri.org

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