Targeting and coverage of the Bolsa Família Programme: Why knowing what you measure is important in choosing the numbers
Bibliographic record
Abstract
[Introduction] The trade-off between targeting and coverage has always been something of a quandary for progressive cash transfers, particularly those that are not entitlements. Undue inclusion errors mean that families or individuals whose need is not so great are being paid at the expense of either taxpayers or other budgetary priorities. Undue exclusion errors mean that those who are in need, sometimes in desperate need, are not being helped by the state. This trade-off is somewhat less extreme for entitlements. If the law says that families whose income is less than a quarter of a minimum wage are entitled to a given cash allowance, then all those whose income falls under that line should receive the allowance. There is still a trade-off because measurement error still occurs, but the discussion centres only on the inclusion criteria. Most conditional cash transfers (CCTs), however, are not entitlements. When cash transfers of any kind are not entitlements, the targeting-coverage trade-off becomes more dramatic. In addition to inclusion criteria, coverage targets must be set and met. These coverage targets are usually set ex-ante and estimated before administrative records have given a clear idea of what is happening at the micro household level. Since these cash transfer programmes have suffered (or benefitted) from very high political visibility, it becomes difficult to change coverage targets once these are announced. Our objective in this paper is to illustrate these quandaries using Latin America’s largest CCT scheme, Brazil’s Bolsa Família programme (PBF). To do this, we first describe briefly how the programme came to be and its targeting mechanisms. Section 3 discusses the size of the programme according to different criteria (this is important in deciding whether it is too small or just right). The following section evaluates how good Bolsa Família is at reaching the poor and only the poor. Section 5 considers the concept of income volatility and why the poor are especially hard-hit by uncertainty about tomorrow’s income. Section 6 examines cross-sectional coverage. Section 7 discusses Bolsa Família’s marginal targeting and explains why this is the right concept to use in estimating how big it should be to cover all the poor.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".