Recent Theories of the Relationship between Education and Development
The Relationship Between Education and Adult Mortality in the United States This paper examines whether education has a causal impact on health. Cutler and Lleras-Muney, w Education and Health: Evaluating Theories and. (), found a linear relationship between years of education and mortality among adults in For other health outcomes, the education effect may not be linear on education and health It is important to measure the causal effects of. The relationship between education and health - the ”education gradient” - is widely egy generates causal estimates that are internally valid for individuals affected ating theories and evidence, Working Paper , National Bureau of.
For example, Singh-Manoux and Marmot 18 followed this practice, concluding that, The effect of blood pressure on cognition was stronger among women, and was stronger for some measures of cognitive ability than others … Confounding factors of age, educational level, occupational position, smoking, alcohol consumption, use of antihypertensive medication, diagnoses of diabetes, and cardiovascular disease were controlled in the analyses … p.
Cognition and education were assessed as described above. Sometimes, the assumption is that education represents childhood social position. For example, in an important study that compared education and adult occupational social class, Davey Smith et al.
They noted, however, that this way of assessing early life socio-economic circumstances was far from universally used. Other times, it is at least recognized that education might lie between mental ability and health outcomes on a causal path.
For example, Osler et al. Here, education is cast as the causal factor, though it might be acting as a surrogate for other aspects of the earlier-measured cognitive ability. Some economists, too, have examined education as a variable related to health, without considering the role of intelligence in the creation of educational variance.
For example, the large study of the US censuses ofand found that education was related to mortality. People with less education had higher mortality rates. Some possible mediating variables were mentioned, including stress, depression and hostility, but the place of intelligence as a possible influence on educational outcomes was not mentioned.The Role of Theory in Research
On the other hand, other economists have been nuanced in looking at the contributions of intelligence and education to health. In an analysis of the National Longitudinal Survey of Youththere was an interaction between them: They argued that education should not be statistically controlled at all in examining the association between adolescent cognitive ability and later life outcomes, because intelligence is a determinant of education.
Their argument was that there is movement of people into higher levels of education based upon prior intelligence differences that are in part caused by genetic variation. This is actually consistent with current teachings of statistical practice in epidemiology, 24 but both interpretations and statistical approaches rely on causal models of the processes involved that should be tested rather than assumed.
Rather than conceptualize education as an indicator of childhood social position—a confounding factor—they explained how it also might be a mediating factor or a proxy indicator of intelligence. Higher IQ test scores may lead to educational success, and entry into well remunerated, high-status employment with a concomitantly high salary. An alternative, but often ignored, explanation is that educational attainment may represent a proxy for IQ, rather than the converse.
That is, people with higher IQs stay longer within education, gaining more and higher qualifications. Such a treatment, though perhaps also arbitrary, at least makes the alternative causal accounts explicit. The IQ tests in this study were two of the Moray House Test series, which mainly contain items requiring verbal reasoning, but not material that is explicitly taught in school. Education was assessed using qualifications, which were classified into six categories, from none to postgraduate qualifications.
We have now shown that intelligence and education are correlated, and given illustrations of how education is sometimes assumed to be causal in epidemiology without considering that it might be in part an outcome of intelligence, and might even share genetic as well as environmental influences with it. Next, we examine the extent to which this is found. One way to resolve some of the confusion over the causes of the association between intelligence and education is to examine the transactions among the genetic and environmental influences contributing to them.
As we have already noted, the presence of genetic influences on intelligence is well established. Non-shared environment contributes a sizeable minority of the influence through most of life, though this term also contains error of measurement. Multivariate variance decompositions can take this exploration further. They can estimate the environmental and genetic contributions to the correlation between two measured variables such as intelligence and education, and the extent to which the two variables share common genetic and environmental influences.
For example, the national test of educational achievement used in The Netherlands at age 12 years the Cito test 29 correlated between 0. In fact, the causes of the association between intelligence and education might be more complex.
The relationship between women’s education and fertility | World Economic Forum
A similar set of analyses was conducted in Sweden, with importantly similar and different results. At higher levels of intelligence, as was found in the Minnesota twin sample, 33 genetic variance in educational outcomes were greater than at low levels of intelligence. For shared environment variance, however, the two countries had opposing results: Genetic influences common to educational attainment and cognitive ability are also found among older people.
At present, clinicians are taught to discern cognitive loss when a diagnosis of dementia is considered, and final diagnostic criteria specify that a decline in ability must have occurred before a definite diagnosis of dementia is made. Because in most situations no data on premorbid level of function are available, the general practice is to use education and occupational attainment as substitute measures of premorbid levels of function.
In this regard, education adjustment seems useful and necessary, and the present finding of a common genetic factor supports this practice p. This provides a marked contrast to the quotation from Richards and Sacker, 14 above.
Whereas Richards and Sacker viewed education as an environmental contributor to peak cognitive ability, these researchers 34 viewed education as a proxy for peak prior cognitive ability precisely because it captured at least some of the genetic influences on intelligence. Importantly, although the statistical approach these researchers 34 recommended is the opposite of that recommended by Hernnstein and Murray, 23 their conceptions of the role of education in cognitive function are the same.
This emphasizes that the appropriateness of statistical approaches are dependent not only on the accuracy of the causal conceptualizations underlying their use, but also on the specific timing of measurement of the variables involved. Conclusions and recommendations These examples illustrate the diversity of assumptions that underlie approaches to study design involving education and intelligence among epidemiologists and other health and social scientists.
At the same time, they highlight the impact that such assumptions can have on study design, results and interpretation of results. Because these assumptions are often unstated and unacknowledged, these examples also demonstrate that part of the difficulty in disentangling the possible causal associations linking these two variables can be traced to less-than-objective examination of all of the causal possibilities during study design and interpretation.
Some of these difficulties can be remedied by greater attention to, awareness and statement of, underlying assumptions, and the consideration of reasonable alternatives by all researchers making use of education and intelligence and other closely related variables. This is important if we are to understand how cognitive function is involved in the development, maintenance, improvement and deterioration of physical health.
We are far from being the first to state that one must be suspicious about inferences after statistical tests to assess confounding, or mediation. We concentrated narrowly on this matter with respect to how education and intelligence are treated in epidemiology because these closely related variables are critical to understanding the role of cognitive function in epidemiology.
We also tried to argue that knowledge about causal background enhances analytical decisions and interpretations. These have different correlations with intelligence test scores, because all result from somewhat different personal traits and circumstances, and they are measured with varying degrees of accuracy.
It is clear that not everyone derives the same benefit from any given educational opportunity and that the same educational opportunities are not available to everyone. Distinguishing between the processes involved in education and intelligence is difficult because it requires measurement that can simultaneously establish causal attributions through precise timing and identify both genetic and environmental influences and their relations to the timing of measurement.
The data necessary to do this with respect to education and intelligence are not often available. There are clear implications of the above points for study design.
First, the temporal cascade between intelligence and education will be clearer when repeated measures of each are available. This would allow longitudinal models to examine the direction and strengths of the mutual causal influences.
Secondly, genetically informative designs—such as twin studies—can help to uncover the environmental and genetic aetiologies of the correlations between intelligence and education, and the other life outcomes with which both are associated. It will be especially interesting when specific genetic variants are found that are associated with intelligence differences, as these can also be examined to discover whether they are associated with educational differences.
Thirdly, it should be kept in mind that, even though intelligence and education are correlated, one can still act as a moderator of the other with respect to life outcomes, such as health. Fourthly, where it is possible to do so, multiple assessments of intelligence and educational outcomes at a single time point will alleviate the problems of measurement error through the construction of latent variables.
Finally, we should not be blinkered by considering only intelligence and education. It should be kept in mind that there might be other variables that contribute to the association between intelligence and education.
Possible candidates could be personality traits and their influences on coping styles and motivations. Therefore researchers should consider measuring such constructs. We show how this influences approaches to analyses and the interpretations of results. Both require an investment to create and, once created, both have economic value.
Physical capital earns a return because people are willing to pay to use a piece of physical capital in work as it allows them to produce more output.
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To measure the productive value of physical capital, we can simply measure how much of a return it commands in the market. In the case of human capital calculating returns is more complicated — after all, we cannot separate education from the person to see how much it rents for.
To get around this problem, the returns to human capital are generally inferred from differences in wages among people with different levels of education.
Hall and Jones have calculated from international data that on average that the returns on education are Predicted versus actual GDP per worker.
The relationship between women’s education and fertility
A strong correlation between GDP and education is clearly visible among the countries of the world, as is shown by the upper left figure. It is less clear, however, how much of a high GDP is explained by education.
After all, it is also possible that rich countries can simply afford more education. This was based on the above-mentioned calculations of Hall and Jones on the returns on education. Finally, the matter of externalities should be considered. Usually when speaking of externalities one thinks of the negative effects of economic activities that are not included in market prices, such as pollution. These are negative externalities. However, there are also positive externalities — that is, positive effects of which someone can benefit without having to pay for it.
Education bears with it major positive externalities: Educated workers can bring new technologies, methods and information to the consideration of others. They can teach things to others and act as an example. The positive externalities of education include the effects of personal networks and the roles educated workers play in them. If people were left on their own, they would not take into account the full social benefit of education — in other words the rise in the output and wages of others — so the amount they would choose to obtain would be lower than the social optimum.
The central idea is that undertaking education is investment in the acquisition of skills and knowledge which will increase earningsor provide long-term benefits such as an appreciation of literature sometimes referred to as cultural capital. Studies from attempted to calculate the returns from additional schooling the percent increase in income acquired through an additional year of schooling. Later results attempted to allow for different returns across persons or by level of education.