Barriers to pension affiliation in Bolivia: Evidence on accessibility, affordability, and acceptability
Bibliographic record
Abstract
This paper uses a multidimensional framework that incorporates constraints of accessibility, affordability, and acceptability to examine the determinants of effective access to the contributory component of Bolivia's pension system. Using nationally representative household surveys from 2005 and 2019, we evaluate the influence of labor-market segmentation, financial capacity, informational barriers, and sociocultural factors on workers' likelihood of affiliation. To address key empirical challenges, including nonlinearity, non-random selection into employment, and perfect or near-perfect prediction, we estimate Probit, Heckprobit, and Firth-Logit models and compute gender-specific average marginal effects. The results indicate persistent structural barriers across periods and settings. Self-employment, unpaid work, and low or unstable earnings consistently reduce affiliation. Informational constraints and distrust were decisive in 2005, while digital access became a critical determinant by 2019. Sociocultural factors, particularly Indigenous identity, also emerged as significant acceptability constraints in the later period. Gender differences in affiliation mainly reflect disparities in employment status, income, and access to information, rather than heterogeneous behavioral responses. Overall, the findings underscore the need for integrated policies that address informational gaps, financial constraints, and labor-market segmentation to bolster access to the contributory component of Bolivia's pension system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".