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
Bibliographic record
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 characteristicsamong 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".