Technology, value chains, and development: Evidence from Ethiopia.
Bibliographic record
Abstract
The four chapters in this dissertation rely on unique data from a large-scale survey of the different value chain actors in Ethiopia for teff and coffee-arguably the most important crops in Ethiopia. While teff is the most widely grown cereal -accounting for about 24% of total crop area by itself (CSA, 2017/18), coffee is by far the most important single export item - accounting for about 30% of total export over last decade (NBE, 20087-2017). A large number of people (estimated to be more than 50 million) directly or indirectly rely on these two sectors for their livelihood. The first two chapters focus on teff. The first chapter entitled 'The rapid expansion of herbicide use in smallholder agriculture in Ethiopia: patterns, drivers, and implications' looks at adoption of agro-chemicals (particularly of herbicide) in Ethiopia. Our study indicates that adoption of herbicides by Ethiopian smallholders has grown rapidly, with application on cereals doubling to more than a quarter of the area under cereals between 2004 and 2014. The ever increasing price of labor coupled with the relatively cheaper (labor saving) herbicides are behind this rapid herbicide application. The second chapter entitled 'Feeding Africa's cities: The case of the supply chain of teff to Addis Ababa' explores the value chain structure, price formation, and marketing behavior in rural-urban staple food value chains in Ethiopia. The chapter documents that contrary to the general perceptions, developing countries' value chains might be efficient in that: i) there are very few middlemen between farmers and end users, ii) farmers can get up to 86% of the final retail price in urban areas, and iii) there is smooth release of stocks by the farmers -i.e., little distress sale. The third and fourth chapters relate to the coffee sector in Ethiopia. Chapter three, entitled 'Investing in wet mills and washed coffee in Ethiopia: Benefits and constraints', looks at constraints of value addition and how the added value can be transmitted through the value chain. The study indicates that washed coffee from Ethiopia is being sold internationally with a substantial premium, ceteris paribus, and that this premium is largely transmitted to producers. Nonetheless, we also find that only a minor share of Ethiopia's coffee is exported as washed and that this share is not increasing over time, implying that Ethiopia is losing out on much needed foreign exchange earnings. Even if coffee farmers have access to a wet mill, they often do not sell all their coffee cherries to them, and labor costs and labor productivity are identified as the important constraints to the adoption of washed coffee production. The last chapter, entitled 'Exchange rates and trade incentives: Evidence from coffee in Ethiopia', explores the link between exchange rate policies and exchange rate pass-through, and export supply responses after exchange rate adjustments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".