The impact of overweight on employment outcomes: a review of the evidence
Bibliographic record
Abstract
Abstract Background Overweight has been recognised as a significant public health issue. Several hypotheses have been posed to explain the impact of overweight on employment outcomes, including the role of productivity, labour supply, and discrimination. In this review, we provide a systematic synthesis of the evidence on the causal impact of overweight on labour market outcomes worldwide. Methods We searched Econ Lit, Medline, and Web of Science databases for relevant studies from 1st Jan 2010 to 20th Jan 2023. Studies were included if they were either longitudinal analysis, pooled cross-sectional or cross-sectional studies if they used Mendelian Randomisation methodology and have been published since 2010. Only those studies with measures of overweight and employment outcomes were included. Results The number of potentially relevant studies constituted 4,321 hits. A total of 59 studies met the inclusion criteria and were qualitatively reviewed by the authors. Most of the included studies were carried out in the USA (N = 18), followed by the UK (N = 9), Germany (N = 6), Finland (N = 4), and non-EU countries (N = 22). Evidence from the included studies suggests that the overweight effect differs by gender, ethnicity, country, and time period. White women with obesity in the USA, the UK, Germany, Canada, and in the EU (multi-country analyses) are more likely to experience overweight/obesity related labour market penalties such as reduced likelihood of employment, and when employed they also face lower wages compared to non-overweight counterparts. For men there is no evidence of an effect of overweight on employment outcomes or the magnitude of the effect is much smaller or even positive in some cases. Conclusions This review has shown that despite ample research on the impact of overweight on employment status and wages, robust causal evidence on the effects of weight status on employment outcomes remains scarce and relies significantly on strong statistical and theoretical assumptions. Key messages • The first study to evaluate the evidence base and the methodology of studies that assessed the economic consequences of overweight. • There is a need for better identification approaches and more recent datasets, especially from low-income countries.
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 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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".