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Record W4399751740 · doi:10.1002/cl2.1414

Effects of guaranteed basic income interventions on poverty‐related outcomes in high‐income countries: A systematic review and meta‐analysis

2024· review· en· W4399751740 on OpenAlexaff
Anita Rizvi, Madeleine Kearns, Michael Dignam, Alison Coates, Melissa K. Sharp, Olivia Magwood, Patrick Labelle, Nour Elmestekawy, Sydney Rossiter, Ali A. A. Al‐Zubaidi, Omar Dewidar, Leanne Idzerda, Jean Marc P. Aguilera, Harshita Seal, Julian Little, Alba Antequera, Jennifer Petkovic, Janet Jull, Lucas Gergyek, Elizabeth Tanjong Ghogomu, Beverley Shea, Cristina M. Atance, Holly Ellingwood, Christina Pollard, Lawrence Mbuagbaw, George A. Wells, Vivian Welch, Elizabeth Kristjansson

Bibliographic record

VenueCampbell Systematic Reviews · 2024
Typereview
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsMcMaster UniversityCarleton UniversityQueen's UniversityImpactUniversity of OttawaMcGill UniversityCentre for Global Health ResearchUniversity of TorontoVineland Research and Innovation CentreLibrary and Archives CanadaWilfrid Laurier UniversityBruyère
FundersEconomic Research ServiceU.S. Department of Agriculture
KeywordsPovertyPsychological interventionSubsidyPublic economicsGovernment (linguistics)Actuarial scienceBusinessEconomicsEconomic growthMedicineNursing

Abstract

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Background: High-income countries offer social assistance (welfare) programs to help alleviate poverty for people with little or no income. These programs have become increasingly conditional and stringent in recent decades based on the premise that transitioning people from government support to paid work will improve their circumstances. However, many people end up with low-paying and precarious jobs that may cause more poverty because they lose benefits such as housing subsidies and health and dental insurance, while incurring job-related expenses. Conditional assistance programs are also expensive to administer and cause stigma. A guaranteed basic income (GBI) has been proposed as a more effective approach for alleviating poverty, and several experiments have been conducted in high-income countries to investigate whether GBI leads to improved outcomes compared to existing social programs. Objectives: The aim of this review was to conduct a synthesis of quantitative evidence on GBI interventions in high-income countries, to compare the effectiveness of various types of GBI versus "usual care" (including existing social assistance programs) in improving poverty-related outcomes. Search Methods: Searches of 16 academic databases were conducted in May 2022, using both keywords and database-specific controlled vocabulary, without limits or restrictions on language or date. Sources of gray literature (conference, governmental, and institutional websites) were searched in September 2022. We also searched reference lists of review articles, citations of included articles, and tables of contents of relevant journals in September 2022. Hand searching for recent publications was conducted until December 2022. Selection Criteria: We included all quantitative study designs except cross-sectional (at one timepoint), with or without control groups. We included studies in high income countries with any population and with interventions meeting our criteria for GBI: unconditional, with regular payments in cash (not in-kind) that were fixed or predictable in amount. Although two primary outcomes of interest were selected a priori (food insecurity, and poverty level assessed using official, national, or international measures), we did not screen studies on the basis of reported outcomes because it was not possible to define all potentially relevant poverty-related outcomes in advance. Data Collection and Analysis: We followed the Campbell Collaboration conduct and reporting guidelines to ensure a rigorous methodology. The risk of bias was assessed across seven domains: confounding, selection, attrition, motivation, implementation, measurement, and analysis/reporting. We conducted meta-analyses where results could be combined; otherwise, we presented the results in tables. We reported effect estimates as standard mean differences (SMDs) if the included studies reported them or provided sufficient data for us to calculate them. To compare the effects of different types of interventions, we developed a GBI typology based on the characteristics of experimental interventions as well as theoretical conceptualizations of GBI. Eligible poverty-related outcomes were classified into categories and sub-categories, to facilitate the synthesis of the individual findings. Because most of the included studies analyzed experiments conducted by other researchers, it was necessary to divide our analysis according to the "experiment" stage (i.e., design, recruitment, intervention, data collection) and the "study" stage (data analysis and reporting of results). Main Results: Our searches yielded 24,476 records from databases and 80 from other sources. After screening by title and abstract, the full texts of 294 potentially eligible articles were retrieved and screened, resulting in 27 included studies on 10 experiments. Eight of the experiments were RCTs, one included both an RCT site and a "saturation" site, and one used a repeated cross-sectional design. The duration ranged from one to 5 years. The control groups in all 10 experiments received "usual care" (i.e., no GBI intervention). The total number of participants was unknown because some of the studies did not report exact sample sizes. Of the studies that did, the smallest had 138 participants and the largest had 8019. The risk of bias assessments found "some concerns" for at least one domain in all 27 studies and "high risk" for at least one domain in 25 studies. The risk of bias was assessed as high in 21 studies due to attrition and in 22 studies due to analysis and reporting bias. To compare the interventions, we developed a classification framework of five GBI types, four of which were implemented in the experiments, and one that is used in new experiments now underway. The included studies reported 176 poverty-related outcomes, including one pre-defined primary outcome: food insecurity. The second primary outcome (poverty level assessed using official, national, or international measures) was not reported in any of the included studies. We classified the reported outcomes into seven categories: food insecurity (as a category), economic/material, physical health, psychological/mental health, social, educational, and individual choice/agency. Food insecurity was reported in two studies, both showing improvements (SMD = -0.57, 95% CI: -0.65 to -0.49, and SMD = -0.41, 95% CI: -0.57 to -0.26) which were not pooled because of different study designs. We conducted meta-analyses on four secondary outcomes that were reported in more than one study: subjective financial well-being, self-rated overall physical health, self-rated life satisfaction, and self-rated mental distress. Improvements were reported, except for overall physical health or if the intervention was similar to existing social assistance. The results for the remaining 170 outcomes, each reported in only one study, were summarized in tables by category and subcategory. Adverse effects were reported in some studies, but only for specific subgroups of participants, and not consistently, so these results may have been due to chance. Authors' Conclusions: The results of the included studies were difficult to synthesize because of the heterogeneity in the reported outcomes. This was due in part to poverty being multidimensional, so outcomes covered various aspects of life (economic, social, psychological, educational, agency, mental and physical health). Evidence from future studies would be easier to assess if outcomes were measured using more common, validated instruments. Based on our analysis of the included studies, a supplemental type of GBI (provided along with existing programs) may be effective in alleviating poverty-related outcomes. This approach may also be safer than a wholesale reform of existing social assistance approaches, which could have unintended consequences.

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)0.012
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0200.026
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.042
GPT teacher head0.367
Teacher spread0.325 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations29
Published2024
Admission routes1
Has abstractyes

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