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Education, income inequality, and mortality

A multiple regression analysis

Bibliographic Data

ID23364258
AuthorsAndréas Müller (0000-0003-1375-6459, University of Arkansas at Little Rock, corresponding author)
Year2002
Volume324
Issue7328
Pages23-23
Publication date2002-01-05
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMJ (JOURNAL)
Journal identifiersISSN: 0007-1447 • E-ISSN: 0267-0623
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/bmj.324.7328.23
PMID11777800
OpenAlexW2143803495
LanguageEN
Citations received89
References cited14

Objective: To test whether the relation between income inequality and mortality found in US states is because of different levels of formal education. Design: Cross sectional, multiple regression analysis. Setting: All US states and the District of Columbia (n=51). Data sources: US census statistics and vital statistics for the years 1989 and 1990. Main outcome measure: Multiple regression analysis with age adjusted mortality from all causes as the dependent variable and 3 independent variables—the Gini coefficient, per capita income, and percentage of people aged ≥18 years without a high school diploma. Results: The income inequality effect disappeared when percentage of people without a high school diploma was added to the regression models. The fit of the regression significantly improved when education was added to the model. Conclusions: Lack of high school education accounts for the income inequality effect and is a powerful predictor of mortality variation among US states. What is already known on this topic Aggregate studies have shown a positive relation between income inequality and mortality and three possible explanations have been suggested (relative deprivation, absolute deprivation, and aggregation artefact) Income inequality may reflect the effects of other socioeconomic variables that are also related to mortality What this study adds Multiple regression analysis of the 50 US states and District of Columbia for 1989-90 indicates that the relation between income inequality and age adjusted mortality is due to differences in high school educational attainment: education absorbs the income inequality effect and is a more powerful predictor of variation in mortality among US states Lack of high school education seems to affect mortality by economic resource deprivation, risk of occupational injury, and learnt risk behaviour. It may also measure the lifetime, cumulative effect of adverse socioeconomic conditions

Cross-sectional regression · Econometrics · Economic inequality · Economics · Gini coefficient · Inequality · Linear regression · Per capita · Per capita income · Polynomial regression · Population · Regression · Regression analysis · Sociology · Statistics · Variables · Demography · Employment and Welfare Studies · Health disparities and outcomes · Intergenerational and Educational Inequality Studies · Mathematics · Medicine

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