interpreting trader networks as value chains: experience with business development services in...
DESCRIPTION
Presented by Derek Baker, Amos Omore, David Guillemois, Eunice Kariuki and Alice Njehu at an ILRI Seminar, 25 June 2012TRANSCRIPT
ILRI Seminar, Nairobi, 25 June 2012
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
Outline
1. 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 far2. Potential uses for other ILRI research3. Interface with other work by partners and other organisations
9. Next steps
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 Tanzania2. Advance knowledge of trader-producer-service linkages and development
orientation3. Test new empirical methods
Theories of networks, applied to value chain analysis, used to formulate hypothesesMeasures of performance of BDS interventions formulatedMeasures of VC-related network characteristics formulatedData collectedData processed using network-dedicated software (Pajek)Preliminary analysis done
Story so far
Milk Trader
Training Service Providers
(BDS)
Regulatory Authority
Certific
ation/Lice
nsing
Training & certification of
competence
Accreditation & monitoring
Reporting
Cess f
ee
Training guides
Intro on BDS in pro-poor dairy development in EA Linkages in milk quality assurance in
informal markets
Hygieniccans
Fee
(Trialled in Tanzania and Uganda – now being evaluated)
Milk Market Hub(Emphasis on traditional milk
market hubs to grow them)
Milk Producer
Inputs,
$$
Inputs & services
$$Payment agreement
Milk
BDS in pro-poor dairy development in EA: Linkages in inputs and services provision
Check-off agreement
Inputs & Service Providers (BDS)
Milk Traders
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 linkagesi.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.
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
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
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 providersiii. 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
iv. Randomly select 4 TRADERS from client list of each (i.e. 2*20 = 40)v. mirror with an equal number (20) of TRADERS not linked to the programme vi. Ask ALL actors for contact lists
3. Randomly select 2 “programme-linked” TRADERS and 5 “programme” BDS providers
vii. Randomly select 2 PRODUCERS from each contact list (2*5 + 2*4 = 18)viii. Mirror with an equal number (18) of PRODUCERS not linked to the
programmeix. Ask ALL actors for contact lists
Approach and methods - 3
Mwanza Arusha BDS ProvidersProgramme 11 9Non-programme 11 9
Traders-linked 20 16Traders-non-linked 20 16
Producers-linked 18 15Producers-non-linked 18 15 BDS providers 22 18 40Traders 40 33 73Producers 36 29 65Total interviews 98 80 178
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.
Pajek – Example
Somali clans5 Levels only
Results in BDS study - Uganda milk supply
Blue triangle : TraderRed cirle: ProducerThickness line: Quantity of milk traded between producers and traders.Number: Quantity of milk traded per connection.
Results – milk supply in Mwanza
Results - Uganda Milk sales, input supply
Blue triangle : TraderRed circle: ProducerYellow box: BDSDot line: Milk tradedBlue line: BDS service
Results - Uganda Milk sales, input supply (detail)
Blue triangle : TraderRed circle: ProducerYellow box: BDSDot line: Milk tradedBlue line: BDS service
Results - Uganda milk sales and training services
Blue triangle : TraderRed circle: ProducerYellow box: BDSDot line: BDS service Blue line: Milk delivered
Results - Uganda milk sales and all BDS
Blue triangle : TraderRed circle: ProducerYellow box: BDSThickness of the line: Number of exhanges/services
Results - Uganda milk sales and all BDS (detail)
Blue triangle : TraderRed circle: ProducerYellow box: BDSThickness of the line: Number of exhanges/services
Results - Degree centrality for producers
1 2 3 4 5 6 7 8 9 10 11 120
20
40
60
80
100
120
140
160
Number of connections for producers in Uganda on Milk
140 producers have just 1 buyer38 producers have 2 buyers10 producers have 3 buyers8 producers have 4 buyers….
Num
ber
of p
rodu
cers
Number of connections between producers and traders
Results - Degree centrality for traders
1 2 3 4 5 60
5
10
15
20
25
30
35
40
Milk. Number of connections for Traders in Uganda
1 2 3 4 50
2
4
6
8
10
12
14
16
Number of connections for Traders in Arusha on Milk
1 2 3 4 5 60
5
10
15
20
25
Number of connections for Traders in Mwanza on Milk
36 traders buy from just 1 producer 18 traders buy from 2 producers….
Note small peak (10 traders) buying from 5 producers
Note different configuration between Arusha and Mwanza
Num
ber
of
trad
ers
Number of connections between producers and traders
Results - Network characteristics for BDS provision - 1
PRODUCERS TRADERS BDS
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12
Connection of BDS. Producers. Uganda
One service received by one BDS is counted as "one"
02
468
1012
1 2 3 4 5 6 7 8 9 10 11
Connection of BDS. Traders. Uganda One service received by one BDS is counted as
"one"
0
10
20
30
40
1 4 7 10 13 16 19 22 25 28 31 34 37 40
Number connections per BDS. Uganda One service to one entity is counted as
"one
00.5
11.5
22.5
33.5
44.5
1 3 5 7 9 11 13 15 17 19
Connection of BDS. Producers. Arusha One service received by one BDS is
counted as "one"
0
1
2
3
4
5
6
7
1 3 5 7 9 11 13 15 17 19 21
Connection of BDS. Traders. Arusha One service received by one BDS is counted as
"one"
0
5
10
15
20
25
30
35
40
1 2 3 4 5 6 7 8 9 10 11
Number connections per BDS. Arusha One service received by one BDS is
counted as "one"
No.
of
prod
uce
rs
No. of connections producer to BDS
0
2
4
6
8
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27
Connection of BDS. Producers. Mwanza
One service received by one BDS is counted as "one"
0123456789
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Number services provided per BDS. Mwanza One service received by one BDS is counted as
"one"
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
1 3 5 7 9 11 13 15 17 19 21 23
Number connections per BDS. Mwanza
One service to one entity is counted as "one"
Results - Network characteristics for BDS provision - 2
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 clients)or “non-bundled” (i.e. =1 service per client, to many clients)?
Results - maps of production and procurement
Results - maps of network connections
Results - nature of data
... Variables....
ABC...
A to BA & BC to D...
...
Obs
erva
tions
....
....
Age
nts…
....
netw
ork
conn
ectio
ns …
Future analysis – a logical progression of hypotheses
H01: Actors’ characteristics/performance = f(exogenous data collected)
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)
Conventional view:
Progression… (nested models?)
Conclusions
1. Impressions from the work so farI. Hypotheses difficult at firstII. Sampling is complex, numbers can become overwhelmingIII. Data handling is demanding
2. Potential uses for other ILRI researchI. Analysis of VC performance II. Aspects of transactions (incl. input delivery)III. Analysis of collective action potential/ex ante/ex postIV. Spatial analysis, suited to panels
3. Interface with other work by partners and other organisationsI. Identifying entry points for interventionsII. Identifying best strategies for interventionsIII. Mapping of impact pathways
Next steps
1. 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?
