Food Insecurity & Trading Networks in Northeastern Uganda: Finding Opportunities for Market Interventions

Eric Jospe (I will be taking the second module)

Overview

Food insecurity in rural regions of developing countries is characterized by high prices of staple commodities, high price variation over small distances, and market dependence. Karamoja, the poorest and least developed region of Uganda, is a case in point. The population experiences chronic food insecurity, with high rates of global acute malnutrition across the region, and large differences in market prices within and between districts. Mercy Corps Uganda recently conducted a network mapping of the staple crop food trading system in three districts of Karamoja. Their preliminary analysis shows the most fragmented trading network in the poorest of the three districts, and the most connected trading network in the wealthiest of the three. In order to design an effective livelihoods program, they would like further analyses conducted, particularly breaking down the networks by the type of crop traded.

Research Questions

Some of the crops are not as important as others. Sorghum is the most important, with beans and maize among the other important ones for market-dependent households. If the trading networks are broken down by commodity traded, focusing on these three crops, is it possible to find other features that would give Mercy Corps Uganda leads to make the food trading system more efficient? Are bottlenecks causing price discrimination for one or more crops? What characteristics of traders are the most important in determining who they trade with, and how frequently they trade? Are there individual traders within and between markets that can be connected to reduce price variation?

Hypothesis

Social network analysis of the staple crop food trading system in Karamoja, Uganda will reveal the specific traders that can be connected to make the market more efficient. By analyzing the network by each commodity traded, bottlenecks will emerge, along with potential bridges. Attribute data showing the characteristics of traders that make them more likely to trade can be used to identify potential trade partners across markets and districts.

Data

Fortunately, the data has already been gathered for this project. The survey was conducted over a two-month period in 2014. Local staff of Mercy Corps Uganda used a snowballing technique to interview every trader they could find in three districts of the Karamoja region in northeastern Uganda. Overall, 283 traders were surveyed, with 543 trading relationships identified. Respondent information includes name, district, gender, age, ethnicity, religion, birth parish, ownership status, decision-maker status, residence, whether they trade with other traders, if so how many, and whether they sell to consumers of the same parish or other parishes. Data on trading partners includes which individuals/markets they trade with most frequently (1-6), the number of commodities traded (1-4), commodity type, frequency of trade, and quantity traded.

I will transform the data from its current state in an Excel spreadsheet into a one-mode, directed network showing how traders are connected to each other, as well as an attribute dataset. Other possibilities are to create a 2-mode undirected network showing traders by marketplace and a 2-mode undirected network showing traders by commodity. Centrality measures that have been highlighted in the preliminary analysis were clustering coefficient and betweenness centrality. The former did not prove to be an effective measure in this type of network, but the latter did. Additional measures that may highlight important traders are eigenvector and closeness. As the survey asked about frequency and quantity of trade, this can be viewed as stronger ties.

Conclusion

This is a new frontier in the analysis of rural trading networks in the developing world. Social network analysis has the potential to identify structural explanations for inefficient market characteristics that keep the rural poor market-dependent. By identifying potential bridges within and between markets and districts, SNA could help Mercy Corps develop an effective market intervention to reduce prices of primary commodities and improve food security.