New Market Information Applications in East Africa: Three Essays on Mapping Trade Flows, Identifying Vulnerability in Food Commodity Networks, and Nowcasting Agricultural Prices in East Africa

Abstract

Agricultural commodity market systems in East Africa are characterized by significant spatial heterogeneity. Many competing actors engage in small-volume transfers through marketplaces with complex and overlapping feedback mechanisms. For development policy practitioners working in economic measurement at the subnational level, these characteristics - among others - frustrate the accuracy and relevance of typical market systems analyses. Recently however, sources of local market information are rapidly proliferating throughout the region. This new trend raises new opportunities for researchers to develop practitioner-oriented monitoring and assessment tools for East Africa's agricultural market systems. The first two papers in this dissertation focus on generating data-driven economic measures to assist policy targeting: The first paper presents a novel combination of network analysis and price transmission analysis to open the 'black-box' of subnational trade. By decomposing cointegration measures correlated with market access and trade efficiency, this paper presents an empirical method for identifying, mapping, and evaluating subregional trade corridors in East Africa. While the first paper applies network analysis to explore the dynamics of trade between marketplaces, the second paper extends the network approach to explore how individual marketplaces transmit or react to the price shocks. This paper proposes a measure of economic vulnerability for maize marketplaces in Uganda to identify which marketplaces along the supply chain are vulnerable to price shocks from connected marketplaces. Despite the proliferation of local market information, however, analysts still face challenges when contexts are limited by data quality, timeliness, and precision. The third paper proposes a novel empirical method based in machine-learning and big-data feature management, which algorithmically adjusts the feature selection to the available data. By optimizing the feature selection with known market structures and the available data, analysts can meaningfully overcome the data challenges for estimating near-real-time food prices. Moreover, the proposed method can facilitate more timely assessments of current food security in contexts challenged by inconsistent data collection and poor data coverage. Ultimately, this thesis demonstrates the analytical benefits of combining aspects of network analysis, price transmission theory, and big data management strategies to support new tools and promote more effective and inclusive development across East Africa.