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Record W2886312974 · doi:10.5539/ijef.v10n9p26

Social Security Reform and Personal Saving: Evidence from Brazil

2018· article· en· W2886312974 on OpenAlexvenueno aff
Pedro Tonon Zuanazzi, Adelar Fochezatto, Marcos Vinício Wink

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSocial securityConstitutionEconomicsPublic economicsFace (sociological concept)PopulationDevelopment economicsDemographic economicsEconomic growthPolitical scienceSociologyDemographyMarket economy

Abstract

fetched live from OpenAlex

The population aging process has caused a financial imbalance in the social security systems of countries based on pay as you go system, as is the case in Brazil. To face this challenge, the Brazilian governments have undertaken several reforms since the 1988 Constitution. Confronting the life cycle hypothesis, the aim of this paper is to estimate the causal effects of Social Security Reforms on the Likelihood of Saving in Brazil by exploring two exogenous events, the 41th (of 2003) and 47th (of 2005) Constitutional Amendments, that reduced the expectations of benefits only for public servants. Using data from the House Budget Surveys, the results of differences-in-differences models show that the reform increased in a range of 2.1 to 2.9 percentage points in the probability of saving of the treated group. The results are in line with the recent literature indicating that reforms contribute to an increase in personal savings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.258
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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