Bibliographic record
Abstract
African leaders are widely presumed to favor members of their own ethnic groups with patronage resources. We assess the empirical validity of this claim by studying ethnic favoritism in the education sector in Kenya. We use data on the educational attainment of more than fifty thousand Kenyans dating back to the colonial era, as well as information about the ethnic identities of Kenyan presidents, cabinet members, and high-level education bureaucrats since the 1960s. We find that having a coethnic as president during one’s primary school-age years is associated with about a one-quarter of a year increase in years of primary schooling and with substantial increases in the probability of attending and completing both primary and secondary school. Coethnics of the minister of education also acquire more schooling than children from other ethnic groups. In contrast to recent studies, multiparty political competition has no impact on the degree of ethnic favoritism by presidents and ministers of education. We find that these patterns stem from both targeting by elites and expectations of such targeting by parents who make decisions about investments in their children’s schooling. * The authors thank members of the Working Group in African Political Economy (WGAPE) and seminar participants at Dartmouth, Georgetown, MIT, NBER, Oxford, Penn, Princeton, Rochester, Stanford, Virginia, the World Bank, and the Juan March Institute for their helpful comments on earlier drafts. We also thank Antony Munene for his research assistance. Kramon acknowledges support by the Air Force Office of Scientific Research (AFOSR) under Award No. FA9550-09-1-0314. Posner thanks the Center for Advanced Study in the Behavioral Sciences for its support during the paper’s initial stages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".