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Record W3121319590 · doi:10.11575/sppp.v6i0.42452

How a Guaranteed Annual Income Could Put Food Banks Out of Business

2017· article· en· W3121319590 on OpenAlexaffabout
J.C. Herbert Emery, Valerie C. Fleisch, Lynn McIntyre

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPovertySafety netGovernment (linguistics)BusinessSocial securityDemographic economicsEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

The federal Conservative government recently began phasing in a plan to raise the age of eligibility for Old Age Security from 65 to 67. But a more sensible move for improving the effectiveness of Canada’s social safety-net system may be to actually lower the age below 65 and rely strictly on an income test instead, regardless of age. The government could go a lot further toward the reduction of poverty in Canada by building on the success of its income supports for seniors, and making them available to poor Canadians of all ages. Canada can boast of having one of the lowest rates for poverty among seniors in the world, largely due to its guaranteed income programs for those 65 years and older. When low-income Canadians turn 65 years old and leave behind low-paying, often unstable jobs, their poverty levels drop substantially. What a guaranteed income provides, that their vulnerable job situation did not, is a form of protection against budget shocks — a sudden volatility in income or expenses without the access to savings or credit to smooth things out until stability returns. A guaranteed income provides a kind of “disaster insurance” that can protect someone in a crisis situation from going without necessities such as food or even shelter. Statistics show that the rate of Canadians experiencing “food insecurity” — that is, lack of access to food because of financial constraints — is half that among Canadians aged 65 to 69 years than it is among those aged 60 to 64. Self-reported rates of physical and mental health improve markedly as well after lowincome Canadians move from low-wage, insecure employment to a guaranteed income at the age of 65. That dramatic shift in physical and mental health indicates that expanding guaranteed income programs to younger Canadians is more than a simple cost calculation: there are potential savings to be found as poorer Canadians, given a guaranteed income, become healthier and therefore reduce the burden on the public health-care system. Canadian governments already spend billions of dollars on the downstream effects of poverty, but scant emphasis is put on programs targeting poverty’s roots. There is no evidence, where smaller-scale experiments have been tried, to show that a guaranteed income program creates a serious problem with negative incentives and discourages people from working who otherwise might. But because this is a common worry with working-age guaranteed income eligibility, phasing in the program gradually, by lowering eligibility a few years at a time, will allow ongoing investigation and analysis of the effects, before the program is rolled out on a large scale. The tremendous impact that guaranteed incomes have had on reducing poverty and improving health among seniors is something for which Canadians can be rightly proud. So much so that it is incumbent upon us to investigate whether Canada could use the same policy tools to drastically reduce poverty and improve health among Canadians of all ages.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0110.008
Open science0.0010.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0550.016

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 head0.044
GPT teacher head0.339
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations39
Published2017
Admission routes2
Has abstractyes

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