A behavioral economics analysis of the participation in early childhood development social programs promoted by civil societies in Mexico
Bibliographic record
Abstract
Based on a behavioral economics (BE) approach, we analyzed the decision to participate in an early childhood development (ECD) program implemented in Mexico by a non-governmental organization. We conducted a literature review and a qualitative study of four localities participating in the ECD program. Situated in the state of Oaxaca, these communities are characterized by high and very high levels of social marginalization. From May 20 to 30, 2019, we collected primary data through semi-structured interviews (n = 30) and focus groups (n = 7) with a total of 61 informants (51 women and 10 men). We then performed an inductive systematic analysis of the data to identify documented cognitive bias associated with the decisions of individuals to participate and remain in or abandon social programs. The interviewees were living in conditions of poverty, facing difficulties in meeting even their most basic needs including food. Program participants attached far greater weight to incentives such as the basic food basket than to the other benefits offered by the program. The four localities visited maintained traditional views of domestic roles and practices, particularly regarding child-rearing, where women were in charge of childcare, home care and food preparation. Problems linked to child malnutrition were a decisive factor in the decision of residents to participate and remain in the program. Testimonials gathered during the study demonstrated that the longer the mothers remained in the program, the more they understood and adopted the concepts promoted by the interventions. In contexts marked by economic vulnerability, it is essential that ECD programs create the necessary conditions for maximizing the benefits they offer. Our analysis suggests that cognitive load and present bias were the biases that most severely affected the decision-making capacity of beneficiaries. Therefore, considering loss aversion and improving the management of incentives can help policymakers design actions that "nudge" people into making the kinds of decisions that contribute to their well-being.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".