The study analyzed pig traders in Kampala, Uganda to better understand their functions and constraints. Researchers used three sampling frames - lists from local producers, retailers, and authorities - to survey 33 traders. Younger traders sourced from producers and retailers traded more piglets, purchased from groups, and faced constraints of capital and transport. Older traders sourced from authorities traded fewer piglets, purchased less from groups, and faced constraints of customers, competition, and prices. The study aims to improve methods for sampling informal traders.

1. The analysis of traders in a developing country
value chain: Pig traders in Uganda
Nadhem Mtimet and Derek Baker
23rd annual International Food and Agribusiness Management Association (IFAMA) forum and symposium
17-19 June 2013, Atlanta, GA

2. Outline
1. Study of value chains, and trader functions
2. Sampling approaches
3. Experience and results from Kampala, Uganda
4. Next steps, including Symposium on trader sampling at African Association of
Agricultural Economists’ Conference, September 23-25, 2013, Hammamet, Tunisia

3. Background to research
Traders perform valuable value chain functions that are:
• often not recognised
• rarely quantified
• constrained by unknown factors
• conducted in isolation from
• officialdom
• large scale agribusiness
• collective actions
• the aid community
Studying traders requires that:
• they can/want to be found
• they co-operate in divulging information
• they see a benefit in participation in research
• variation amongst traders is reflected by sampling
The research for which this is a preliminary presentation has sought to:
1. Better understand and quantify traders and their actions
2. Test approaches to sampling
3. Identify possible future sampling strata

4. Trader sampling
Traders are often surveyed
Literature review: 3 common sample situations:
• No information at all about the sampling strategy and how respondents
have been selected (Ajala and Adesehinwa 2007, Jabbar et al. 2008, Loc
et al. 2010, Hap et al. 2012, MacFayden et al. 2012)
• Researchers report random trader selection but without much
explanation and detail (Bista and Webb 2006, Abdulai and Birachi 2009,
Kocho et al. 2011, Minten et al. 2013).
• Detailed information is provided about sample selection (Rab et al. 2006,
Wanyoike et al. 2010, Aoudji et al. 2012, Lagerkvist et al. 2013)

5. Sampling frames
Source of list for
sampling frame
1
Local producers
2
Local retailers
3
Local
processors/focal
processing facility
4
Local authorities
Advantage
Disadvantage
Relies on another sample
Targets correct
commodities/products
Targets correct
commodities/products
Can rely on population responses
Simplicity
Favours buyers
Relies on another sample
Favours sellers
Open to strategic response
May not correctly target products
May exclude non-locals
Open to strategic response
Unlikely to correctly target
products
Excludes non-registered traders
Excludes non-locals
5
6
Word of mouth:
other traders
Word of mouth:
experts
Simplicity
Allows “snowball” tracking
Simplicity
Links to geography, infrastructure,
commercial interests
Limits to knowledge
Open to strategic response

6. Ugandan study
Situation:
• an unknown number traders around Kampala, apparently including
Mukono location
• active trading in both grown pigs and piglets
• some observed vertical integration of traders
• no information on transaction mechanisms, seasonality, margins,
price-quality incentives, services used, food safety and hygiene
practices, future plans,…, nor constraints faced
Action:
• workshop-type survey activity
Sampling frames used: lists of traders
• from a sample of local producers
• from a sample of local retailers
• from local authorities.

7. Detail of traders’ sampling, by sampling source*
Sampling source
Number of traders
contacted
Percentage
Farmers/producers
22
Number of
traders who
participated to
the workshop
16
Retailers
28
11
39%
Local authority
18
6
33%
*
14 traders belong to 2 different sampling sources.
None belong to all three lists
73%

8. Summary statistics
Variables
Age (years)
Experience (years)
Piglets trading (%)
Purchase from group of producers (%)
Taxes payment (%)
Groups
Group 1
Group 2
Sourced from Sourced from
retailers or
local
producers
government
(n1=16)
(n2=6)
“Young”
“Experienced”
28.19
42.17
5.31
13.00
75%
17%
81%
33%
50%
100%
a t-test; b Z-test
***, ** :
statistically significant respectively at 1% and 5% levels
Statistical
tests
10.691a***
10.045a***
2.478b**
2.149b**
2.171b**

9. Constraints reported by traders
In day-to-day buying operations
In day-to-day selling operations

10. Conclusions
Different approaches to sampling frame yielded different samples of traders
Few traders appeared on more than one sampling-frame-basis list
The samples generated exhibited:
• different characteristics, able to be assigned and named
• different functions
• different statements about constraints faced
Main constraints reported:
Concerning buying activities
Lack of working capital
Transport cost or quality
Seasonality
Low productivity
Poor animal health
Inappropriate animal feeds
Concerning selling activities
Lack of customers
Competition between traders
Unpredictable market conditions
Bad debts
Animal disease
Low prices
Young



Young





Experienced



Experienced




11. Symposium: September 2013
Sampling people that don’t stand still:
Targeting traders as key elements of value
chain function and performance, and how
they can be sampled
African Association of Agricultural Economists’ Conference
September 23-25, 2013, Hammamet, Tunisia
Sponsored by PIM

12. Contact:
Nadhem Mtimet n.mtimet@cgiar.org
Derek Baker d.baker@cgiar.org
International Livestock Research Institute www.ilri.org