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The analysis of traders in a developing country value chain: Pig traders in Uganda
 

The analysis of traders in a developing country value chain: Pig traders in Uganda

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Presented by Nadhem Mtimet and Derek Baker at the 23rd annual academic symposium of the International Food and Agribusiness Management Association (IFAMA) held at Atlanta, Georgia, 17-18 June 2013.

Presented by Nadhem Mtimet and Derek Baker at the 23rd annual academic symposium of the International Food and Agribusiness Management Association (IFAMA) held at Atlanta, Georgia, 17-18 June 2013.

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    The analysis of traders in a developing country value chain: Pig traders in Uganda The analysis of traders in a developing country value chain: Pig traders in Uganda Presentation Transcript

    • 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
    • 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
    • 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
    • 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)
    • 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
    • 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.
    • 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%
    • 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**
    • Constraints reported by traders In day-to-day buying operations In day-to-day selling operations
    • 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   
    • 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
    • Contact: Nadhem Mtimet n.mtimet@cgiar.org Derek Baker d.baker@cgiar.org International Livestock Research Institute www.ilri.org