The Long Term Effects of Rural Postings on Health Workers' Careers in Ethiopia: Results of a Natural Experiment
Bibliographic record
Abstract
International attention has recently been drawn to the problems of attracting, retaining, and motivating health workers in developing countries, particularly to more remote areas - the so-call human resources in health (HRH) crisis. Ethiopia, with less than one doctor per 100,000 citizens, and virtually no physicians outside urban areas, provides an acute manifestation of this crisis. Rural postings are viewed negatively for both flow and stock reasons: current living standards may be reduced if the quality of infrastructure, children's education, and general consumption possibilities is low in rural areas; but the medium- to long-term impact of a rural posting on a health worker's future career may represent an important additional cost. Current costs can be offset by higher wages, better housing conditions, etc. However, estimating the size of the longer-term impact - which is necessary if suitable incentives are to be designed - is fraught with selection-induced identification problems. In this paper, we use a unique feature of the Ethiopian health worker allocation mechanism to resolve this problem. Until recently, new medical and nursing school graduates in Ethiopia were assigned to their first posting via a national lottery. We use this natural experiment to assess the long term effects of rural postings on the careers of doctors and nurses, using data from a new survey of about 1,000 health workers in Addis Ababa and two other regions of Ethiopia. We also investigate the impact of the end of the lottery system on the ability of the government to attract workers to remote areas, and on health worker absenteeism.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".