Protection and the determinants of household income in Tanzania 1991-2007
Bibliographic record
Abstract
This paper analyses the association between household characteristics' in particular size and location, and for the household head age, sector of employment (and the tariff applicable to that sector) and education - and household income using data from the Tanzania Household Budget Survey for the years 1991/92, 2000/01 and 2007. The static analysis of the determinants of household income is based on the full sample and is complemented by a dynamic analysis using a pseudo-panel (representative households). Larger households have lower income; living in urban areas is associated with income around one quarter higher than rural households; and location in the Coastal zone, which includes Dar es Salaam, increases household income by about 15% compared to the poorest region (Central). Years of education of the household head is associated with higher income: each additional year of education adds about 4.5%. Average incomes of agriculture households are lower than for manufacturing households, but within each broad sector incomes appear to be higher in sub-sectors with higher tariffs. Household income tends to increase in both tariffs and education, but the effect of tariffs diminishes or becomes negative for household heads with secondary education and alters over time. Observing that tariffs offer less protection to the incomes of more educated workers compared to less educated (less skilled) workers is consistent with better educated workers being more productive and therefore in firms, or sectors, better able to compete with imports. Given data limitations it would be incorrect to infer a causal effect of tariffs on household incomes. Nevertheless, the analysis is informative about the effect of the cross-sector pattern of tariff protection on household incomes allowing for other determinants.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".