Regional Economic Favoritism and Redistributive Politics as a Public Good: The Case of Tigray Region in Northern Ethiopia
Bibliographic record
Abstract
The study investigated whether or not misdirection of public resources to a favored region brings material improvements in the lives of the population that is alleged to be receiving the resources.  In this study, the region in question is Tigray province in northern Ethiopia.  Economic data from the 2016 Demographic and Health Survey (DHS) are examined with a focus on Tigray Region.  The neighboring Amhara Region is used as control. Sample data on 1734 households from Tigray and 1902 households from Amhara Region were analyzed without weighting using the statistical software SAS 9.4 and the Geographic Information System software ArcGIS 10.4.1. We found evidence of a statistically significant advantage for Tigray Region in ownership of four modern amenities – radio, mobile phones, refrigerator, and access to electricity by individual households (p< 0.001). However, we did not find evidence of greater wealth in Tigray for the general population when the analysis was rerun based on DHS’ wealth index. On the contrary, the data for sampling clusters in Tigray appeared to show the region as being poorer than Amhara when viewed through the lens of DHS’ wealth index which is a more comprehensive measure of economic wellbeing than owning a radio or possessing a mobile phone.  A one-tailed Wilcoxon Man-Whitney U statistic of DHS’ wealth index for Tigray and Amhara Regions showed a statistically significant difference (p < 0.001) with a higher mean score for Amhara Region (1870.3) than for Tigray Region (1761.6) suggesting a better economic standing for the population of Amhara Region than Tigray Region. We also found Amhara Region to be more egalitarian and Tigray Region less so on the scale of livelihoods captured by DHS’ economic indicators. Evidence for this comes from a Geographic Information System (GIS) Kernel Density analysis of DHS’ wealth index which showed what appear to be significant geographic concentrations of both poverty and wealth in Tigray Region.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".