Three essays on agriculture and economic development in Tanzania
Bibliographic record
Abstract
One cannot study poverty in Tanzania without understanding the agricultural sector, which employs more than two-thirds of the population and accounts for nearly a quarter of national GDP. This thesis examines three themes that focus on the difficulties that rural Tanzanians face in achieving a reasonable livelihood: the adverse legacy of a failed historical policy, a difficult climate, and market failures. \n \nThe first empirical chapter examines the legacy of the villagization program that attempted to transform the predominantly agricultural and rural Tanzania. Between 1971 and 1973, the majority of rural residents were moved to villages planned by the government. This essay examines if the programs e↵ects are persistent and have had a long-run legacy. It analyzes the impact of exposure to the program on various outcome measures from recent household surveys. The primary finding of this study is that households living in districts heavily exposed to the program have worse measures of various current outcomes. \n \nThe second empirical chapter examines the role of reliability of rainfall, which is important in Tanzania as agriculture is predominantly rain-fed and a small fraction of plots are irrigated. This chapter investigates if households cope with this major risk to income by re-allocating their labor supply between agriculture, wage labor, and self-employment activities. This chapter combines data on labor allocation of households within and outside of agriculture from the National Panel Survey with high-resolution satellite-based rainfall data not previously used in this literature. The primary finding of this study is that households allocate more family labor to agriculture in years of good rainfall and more labor to self-employment activities in years of poor rainfall. \n \nMarket failures are often cited as a rationale for policy recommendations and government interventions. The third chapter implements four tests of market failures suggested in the literature, all of which rely on the agricultural household model but di↵er in how market failures are manifested. The common finding of these tests is that market failures exist in agricultural factor markets in Tanzania, although significant heterogeneity exists. Markets are more likely to fail in rural areas, remote locations, and are more likely to affect female-headed households. Households are also more likely to face market failure when they try to supply labor to the market than when they try to hire labor from the market.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".