THE IMPACT OF EDUCATION ON ECONOMIC AND SOCIAL OUTCOMES: AN OVERVIEW OF RECENT ADVANCES IN ECONOMICS
CHAPTER NO 1: INTRODUCTION
CHAPTER NO 2: REVIEW OF LITERATURE
2.1. IMPACT OF EDUCATION ON ECONOMIC GROWTH
2.1.1.RELATIONSHIP BETWEEN EDUCATION AND ECONOMIC
2.1.2. RELATIONSHIP BETWEEN HUMAN CAPITAL AND
2.2. IMPACT OF EDUCATION ON SOCIAL OUTCOME:
SOCIAL OUTCOMES OF LEARNING PROJECT
RELATIONSHIP BETWEEN EDUCATION AND SOCIAL ANGAGEMENT
CHAPTER NO 3: METHODOLOGY
3.1. MODEL OF TESTING:
3.2. DIMENSIONS OF ENGAGEMENT MEASURED IN THE SOCIAL
3.2.1. POLITICAL ENGAGEMENT: COMPETITIVE AND
3.2.2. VOLUNTARY ASSOCIATIONS : 3.2.3. VOTING
3.2.4. TRUST: INTERPERSONAL AND INSTITUTIONAL:
CHAPTER NO 4:RESULTS/DATA INTERPRETATION:
4.1. EXPRESSIVE POLITICAL ACTIVITY:
4.3. VOLUNTARY ASSOCIATION
4.4. INTERPERSONEL TRUST
4.5. INSTITUTIONAL TRUST
CHAPTER NO 5:CONCLUSION:
CHAPTER NO.6: REFERENCES:
It has widely been acknowledged that education is a major source of economic prosperity and social well-being. Education is not only an important factor in the productivity and innovative capacity of an economy, but is also a prerequisite for social and cultural changes in patterns of consumption and leisure behaviour to achieve a sustainable lifestyle. It puts people in a position to take well-informed decisions about the future, to assume responsibility for these decisions and to judge how their personal behaviour will affect future generations. Thus, we are then well aware that education gives access to knowledge that helps individuals and society to be more stable and resilient in times of change. These social returns can take the form of “market outcomes” such as productivity or earnings and “non-market outcomes” such as health, civic participation and more generally social capital. Deeper understanding of the contribution of education to the provision of these social outcomes is a desirable goal.
While the educational system is the primary agent for the acquisition of such knowledge, learning may also take place in the family, the workplace and among our social acquaintances all throughout our live. Nowadays, constant changes taking place in society encourage individuals that besides grasping occupation-specific skills they must also stock some other various information processing skills to help them cope with this rapid changing environment, especially in the labor market.
The Survey of Adult Skills (PIAAC) was designed to gather information on some of these key skills in society. It directly measures proficiency in several information-processing skills –namely literacy, numeracy and problem solving in technology-rich environments. Simultaneously, it provides insights on key social outcomes such as the level of trust in others, participation in associative, religious, political or charity activities (volunteering), political efficacy or the sense of influence on the political process, and self-assessed health status. The main findings on the relationship between education in its different forms (i.e. years of attainment, skills and adult lifelong learning) and the different social outcomes considered are reported below. Gathering information on the impact of knowledge acquired beyond formal education becomes crucial since individuals’ abilities to successfully meet complex demands in the current context of globalization (measured as their proficiency in numeracy, literacy and problem solving tests or positive attendance to any type of adult lifelong learning in the past 12 months) play a key role in the effective and fruitful participation of citizens in the social and economic life of advanced economies.
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Education is fundamental to sustainable development, it is a powerful driver of development and one of the strongest instruments for reducing poverty and improving health; it enables people to be more productive, to earn a better living and enjoy a better quality of life, while also contributing to a country’s overall economic growth. Education is critical for breaking the poverty cycle and its importance is reflected in the commitments of the Millennium Development Goals (MDGs) and Education for All(EFA).?
In every budget the government announces various developments to be done in the education sector with high government expenditures diverted towards it. The motivation for such an increase lies in the belief that the education of children in developing countries is crucial for future economic growth and lasting democracy, thereby leading to a greater stability and improved standards of living.
Becker (1964) and Mincer (1974) have argued the importance to characterise the benefits of education by means of the notion of investment in human capital. This idea captures the fact that investment in human beings, like investment in tangible forms of capital such as buildings and industrial equipment, generates a stream of future benefits. Human capital is one of the big unknowns of research on the determinants of economic development. The majority of empirical and theoretical literature suggests the existence of a relationship between social indicators and economic growth. Education is regarded as an investment in human capital, since benefits accrue to an educated individual over a lifetime of activities.
There are various debates over whether changes in educational attainment do affect the long-run growth rate of an economy. The macroeconomic evidence on level effect is consistent with microeconomic estimates of private rates of return to schooling, it appears, however, that there are also significant long-term growth effects that is the more educated is the workforce, the better it is able to implement technological advances. Many growth models include education and offer predictions as to the implications of education policy changes on macroeconomic performance. Some empirical analysis of the growth rate of real per capita GDP in the U.S. suggest that years of secondary and higher schooling contribute positively towards economic growth. Such research is of particular importance as developed nations continue in taking a more active role in the development of third-world nations, as growth models offer important predictions that are useful in aiding policy decisions.
The educational systems of OECD economies continue to grow and with this the total
amount of resources dedicated to the total learning effort is reaching unprecedented
levels. Are the resources organised and used in a way that fulfills what society intends
educational systems to achieve? Do the educational systems provide the right forms and
types of learning opportunities? Are the learning opportunities offered at the right timeand distributed over the lifespan in the best possible way? Answers are necessary for both
public and private officials to effectively guide and manage education and training
systems, including the design and implementation of effective and well-informed
The effects of education extend beyond the economic sphere. Most agree that the total
benefits to society from education are greater than the sum of what individuals earn as a
result of their educational attainment. Besides providing the knowledge and skills
necessary for economic participation, the schooling system is the primary agent of
socialisation in modern societies. Education at all ages plays an equally important role in
sustaining economic, social and personal well-being. Accordingly there is now a growing
consensus that the links between personal, social and economic well-being and education
need to be understood better and communicated to policy makers and the wider public
(OECD, 2001).Policy concerns such as mental and physical health, active citizenship and social
cohesion have assumed greater prominence on the political agenda, including as potential
benefits of education. But this interest precedes theoretical development and a good
information base to make sound policy decisions. While human capital theory links
education to economic outcomes and offers a robust framework for scientific
investigation and policy analysis, there is to date no widely accepted theory linking
education to social outcomes. We need coherent models for understanding better these
relationships; for gathering and synthesising what we know and what we want to know;
and for drawing out their implications for policy (Behrman and Stacey, 1997; McMahon,
1998, 2000; Schuller et al., 2004).
IMPACT OF EDUCATION ON ECONOMIC GROWTH:
2.1.1.RELATIONSHIP BETWEEN EDUCATION AND ECONOMIC GROWTH:
Bils and Klenow (2000) found that greater schooling enrolment in 1960 consistent with one more year of attainment is associated with a faster annual growth over 1960-90. According to them, this conclusion is robust in allowing a positive external benefit from human capital to technology. Their results are consistent with Barro (1995) in which transitional differences in human capital growth rates explain temporary differences in country growth rates.
Mankiw et al (1992) and Barro (1991) investigated the link between education and economic growth. They examined variations in school enrolment rates, using a single cross-section of both the industrialised and the less-developed countries. Both studies concluded that schooling has a significantly positive impact on the rate of growth of real GDP. Barro and Sala-i-Martin (1995) also investigated the impact of educational expenditures by governments. Their findings showed a strong positive impact. Using instrumental variable techniques to control for simultaneous causation, their regressions suggest that the annual rate of return on public education is of the order of twenty percent.
Pritchett (2001) has argued that poor policies and institutions have hampered growth in many of the least developed economies, directing skilled labour into relatively unproductive activities, hence disrupting the statistical relationship between education and growth in samples that include less-developed economies.
Krueger and Lindahl (2001) suggest that the problem of unobserved variation in educational quality is exacerbated in panel data. Taking data quality into account, they show that increases in the stock of schooling do improve short-run economic growth.
Hanushek and Kimko (2000) confirm that direct measures of labour-force quality, from international mathematics and science test scores, are strongly related to growth. Temple (2001) finds that growth effects are positive, but non-linear. These non-linear effects may be missed by studies that impose linearity.
A series of subsequent studies made use of panel data, examining changes over time in both education and growth. Several of these panel studies failed to detect any significant relationship between the rate of increase of educational capital and the rate of economic growth. They suggested that the positive findings of the earlier cross-section studies were due to omitted variable bias, failing to control for country specific effects.
Benhabib and Spiegel (1994) compared models that treat human capital as a direct input into production with models treating human capital as an intermediate input into the acquisition of skills and/or knowledge. The former implies a relationship between output growth and educational growth, whereas the latter implies a relationship between output growth and the average stock of human capital per worker. Their econometric evidence favours the latter model. A more educated workforce can more readily identify, adapt and implement new ideas whether the ideas are generated domestically or overseas.
Teixeira and Fortuna (2003) studied human capital effects on economic growth of Portugal from 1960 to 2001. By using VAR and co integration analyses, they confirm that human capital and indigenous innovation efforts are enormously important to the process of Portuguese economic growth during the period 1960-2001, though the relevance of the former overpasses that involving the creation of an internal basis of R&D. In addition, the indirect effect of human capital, through innovation, emerges as critical, showing that a reasonably higher stock of human capital is important to enable a country to reap the benefits of its innovation indigenous efforts.
2.1.2. RELATIONSHIP BETWEEN HUMAN CAPITAL AND ECONOMIC PERFORMANCE:
One of the most prominent and influential contributions is that of Lucas (1988), which is in turn related to previous work by Uzawa (1965). In these models, the level of output is a function of the stock of human capital. In the long run sustained growth is only possible if human capital can grow without bound. That makes it difficult to interpret the Uzawa-Lucas conception of human capital in terms of the variables traditionally used to measure educational attainment, such as years of schooling. Their use of the term ‘human capital’ seems more closely related to knowledge, rather than to skills acquired through education.
Bils and Klenow (2000) argued that one way to relate the Uzawa-Lucas model is that the quality of education could be increasing over time. In this view, the knowledge imparted to school children in the year 2000 is superior to the knowledge that would have been imparted in 1950 or 1990 and will make a greater difference to their productivity in later employment. Even if average educational attainment is constant over time, the stock of human capital could be increasing in a way that drives rising levels of output. Yet this argument runs into difficulties, even at the level of university education. There may be some degree courses in which the knowledge imparted currently has a greater effect on productivity than before (medicine and computer science) but there is other, less vocational qualifications for which this argument is less convincing.
An alternative class of models places more emphasis on modelling the incentives that firms have to generate new ideas. Endogenous growth models based on the analysis of research and development, notably the International Business & Economics Research Journal – August 2010 Volume 9, Number 8 143
landmark contribution of Romer (1990), yield the result that the growth rate partly depends on the level of human capital. The underlying assumption is that human capital is a key input in the production of new ideas. In contrast with the Uzawa-Lucas framework, this opens up the possibility that even a one-off increase in the stock of human capital will raise the growth rate indefinitely.
In practice, the generality of these results, and the contrast with the Uzawa-Lucas model, should not be overdrawn. The Uzawa-Lucas framework can be seen as a model of knowledge accumulation in a similar spirit to that of Romer, but easier to analyze and restrictive assumptions are needed to yield the Romer result that the long-run growth rate depends on the level of human capital Jones (1995). But even under more general assumptions, a rise in the level of human capital is likely to be associated with a potentially substantial rise in the level of output, brought about through a transitional increase in growth rates.
Another interesting aspect of growth models as argued by Rustichini and Schrnitz (1991) is that individuals may under-invest in education. They presented a model in which individuals divide their time between production, original research, and the acquisition of knowledge. Each individual knows that acquiring knowledge through education will raise their productivity in subsequent research, but since they do not fully capture the benefits of research, they will tend to spend less time in acquiring knowledge relative to the socially optimal outcome. They found that although policy intervention has only small effects on the allocation of time to education, it can have a substantial effect on the growth rate. Romer (2000) maintained that models of growth driven by Research and Development (R&D) are determined by the quantity of inputs and not simply the expenditure upon it. Incentives like tax credits to encourage R&D may be ineffective unless they encourage a greater number of scientists and engineers to work towards developing new ideas.
In most endogenous growth models based on research and development, the stock of human capital is taken to be exogenously determined. Acemoglu (1997) and Redding (1996), have relaxed this assumption, and considered what happens when individuals can choose to make investments in education or training, while firms make investments in R&D. For some parameter values multiple equilibrium are possible, since the incentives of workers to invest in human capital, and those of firms to invest in R&D are interdependent.
IMPACT OF EDUCATION ON SOCIAL OUTCOME:
SOCIAL OUTCOMES OF LEARNING PROJECT:
In 2005 the OECD’s Centre for Educational Research and Innovation (CERI) in
cooperation with the OECD INES Network B (responsible for devising indicators on the
outcomes of education) launched a project entitled “Measuring the Social Outcomes of
Learning” (SOL). The SOL project is designed to inform economic and social policy that
relates to education and lifelong learning. It involves in depth investigations into the
nature of the link between learning and well-being, and how such linkages, if warranted,
could be used as policy levers to improve well-being through education, and to achieve
greater equity in the distribution of well-being. Thirteen countries have so far taken active
part in the SOL project.1
The project seeks to:
• Develop a framework to investigate these various links.
• Improve the knowledge base for policy decisions on private and public benefits.
• Contribute to more integrated policies across education and other policy domains.
• Foster the gathering and application of evidence on SOL.
• Enable thinking about interactions between economic and social outcomes.
The project is initially focusing on two domain areas: Health (physical and mental)
outcomes of learning; and civic and social engagement outcomes of learning. Two
cross-cutting themes are also considered: intergenerational effects of learning via the
family and home environment; and distributional effects of learning: how different social
groups benefit from education.
The work to date (summer 2006) has achieved a number of things:
• It has been a substantial ground-clearing exercise, ranging over a wide array of
existing quantitative studies at national and international level.
• It has explored the issues involved in developing an understanding of the causal
relationships in this field; in other words, how to go beyond simple associations
between education and social outcomes in order to understand how education
directly or indirectly affects them.
• It has developed models for understanding the data better.
• It has begun the work of developing robust indicators which will help us lay the
basis for better empirical data and understanding.
• It has begun to sketch out policy implications.
There are a number of general issues which remain to be addressed:
• The material gathered to date does not include qualitative studies which may give
important insights into the causal processes. An equally rigorous overview of this
material is important.
• The analyses to date have concentrated on schooling, primarily because this is
where data are most readily available. Serious attention needs to be paid to
learning later in life, and to informal and non-formal modes of learning.
• We need to build on the current work by differentiating more between types and
modes of learning, so as to understand the range of educational effects (including
where there is no impact).
• These steps will enable a more developed set of policy implications to be drawn
RELATIONSHIP BETWEEN EDUCATION AND SOCIAL ANGAGEMENT:
The overview by David Campbell draws together much of the evidence, focussingespecially on schooling. It confirms the strong association between education and CSE, and begins to unpack the multiple relationships by means of a framework which
distinguishes between absolute, relative and cumulative effects (see below). Campbell’s analysis shows how different aspects of the education-CSE relationship are explained by one or other of these models. This framework, applied here to CSE, could be a powerful one for analysing the effects of education in general. For example the education-earnings relationship is subject to the very same alternative mechanisms encompassed within this framework.
As with all the responses, the paper by Tom Healy considers some of the gaps and
further questions that arise from Campbell’s work. Among other points, he elaborates on why we should be interested in CSE outcomes of learning, summarises what we know so far and considers what it is policy makers could do with such information. A major point that he draws our attention toward is that many CSE outcomes of learning are not easily observed or quantified.
John Andersen and Jørgen Elm Larsen make a link between Campbell’s paper and
the social capital literature. They point out the importance of taking into account the
wider socio-political context of a nation because this can imply important differences in the quality and purpose of social capital in different national contexts and hence the CSE outcomes that societies are interested in. They also offer a series of reflections regarding a possible multi- and mixed-method approach to further research, including possible ways of measuring school ethos.
Christine Mainguet and Ariane Baye elaborate on some of the key elements that are
necessary to take into account for developing a framework of indicators relating to CSE. This work is now being carried forward by the OECD INES Network B.
Pascaline Descy contributes a small paper that presents select research results on the
macro social outcomes of education and training. The results derive from work
commissioned by the European Centre for the Development of Vocational Training
(CEDEFOP). It illustrates relationships at the macro-social level between educational and income inequality and social outcomes such as general trust, crime and feeling of
3.1. MODEL OF TESTING:
From the literature on how CSE is affected by one’s educational environment, we can distill three potential causal mechanisms:
• Absolute education model:
This has been the standard view of how education affects the many dimensions of CSE: individuals with more education are more engaged, without regard for their educational environment.
• Sorting model:
Engagement is a function of one’s educational environment. In this model, engagement is driven by an individual’s level of formal education relative to her social environment – more education drives engagement only to the extent that educational attainment results in a higher position within the social hierarchy.
Again, educational environment matters, but in the opposite way than predicted by the sorting model. Living in an environment with a higher average level of education increases an individual’s level of engagement.
TABLE: Three causal mechanisms linking education and engagement
What leads to more engagement?
Absolute education model The more education you have
Sorting model The more education you have vs. the average education your peers have
Cumulative model The more education your peers have
The dataset employed to test these three models must meet two criteria. First, it must include a wide range of nations, to ensure sufficient variation in educational environments. Second, it must include measures of multiple dimensions of CSE. Fortunately, the European Social Survey (ESS) meets both requirements. The ESS was conducted in multiple European nations, from all parts of the continent.4 Also, its questionnaire includes numerous items pertaining to a wide array of civic and social engagement. While it does not cover every dimension discussed earlier, it does include most of them. No other publicly-available source of cross-national data includes as many. The sheer variety of nations within the ESS is a double-edged sword for the analyst. On the one hand, the array of countries included in the sample makes it possible to test hypotheses in widely varying environments – to look for consistency amidst the variety. But on the other hand, that same variety only raises questions about the idiosyncrasies of the individual nations. Regrettably, space constraints mean that for the purposes at hand the analysis will be limited to cross-national analysis only and not a detailed discussion of results for each country. Therefore, this analysis should be considered preliminary at best, as there is much more to be learned about the nation-specific results.
As we have seen, the critical issue in determining the impact of education is the measurement of the educational environment. Far from an abstruse question to be relegated to technical appendices, the question of who is being compared to whom is central to the debate over the claims made by NJS-B. Helliwell and Putnam criticise NJSB for relying on a measure of the educational environment that was (a) too large in scope; (b) backward-looking (individuals’ educational attainment compared to the mean education level of people who were 25-50, at the time the respondent was 25). In response to these criticisms, this analysis uses a measure of the educational environment that varies by both nation and cohort. In each nation, the mean educational level was calculated for the following four cohorts: 25 to 39 years of age; 40 to 54; 55 to 69; 70 and up. Thus, in addition to her/his own level of education, each respondent has a corresponding variable reflecting the mean level of education for people of the same birth cohort (both older and younger) within the same nation. Note that respondents under the age of 25 have been omitted from the model, since the early twenties is generally the period of life when young people are most likely to be in the process of acquiring a postsecondary educational education. Owing to the varying educational systems across the nations included in the ESS, there is no uniform measure of educational attainment by, say, degree or diploma earned. Instead, the most comparable measure of educational attainment is simply the number of years of formal schooling the respondent has completed. Each model thus includes two measures of education: the number of years of education completed by the individual respondent (education level), and the mean level of education completed by members of the same age cohort within that nation (education environment). Understanding the relationship between educational attainment, educational environment, and the various dimensions of CSE requires not only attention to how education is operationalised, but also the measurement of civic and social engagement. We thus turn next to the dimensions of CSE that can be tested using the ESS: competitive political activity, expressive political activity, voluntary associations, voting, institutional trust, and interpersonal trust. Below is a description of each dimension, how it is operationalised, and the a priori hypothesis of whether it is better explained by the absolute education, sorting, or cumulative models. Note that while the ESS includes most dimensions of CSE in which we are interested, there are two notable omissions: tolerance and knowledge. While it would be preferred to have measures of these dimensions in addition to those that are included, this is a case where the best (or ideal) ought not to be the enemy of the good. The positive relationship between absolute educational attainment and both tolerance and political knowledge is well established, although future research could profitably examine the precise nature of education’s relationship to both.
3.2. DIMENSIONS OF ENGAGEMENT MEASURED IN THE SOCIAL SURVEY
3.2.1. POLITICAL ENGAGEMENT: COMPETITIVE AND EXPRESSIVE
The sorting model rests on conceptualising political engagement as inherently zerosum, with winners and losers. The more likely that a form of engagement is constrained by its competitive, finite nature, the more likely it is to be explained by the sorting model. A good test of the sorting model, therefore, is to compare two types of engagement that are both political, namely with the objective of influencing public policy, but do and do not involve activities that are inherently zero-sum in their nature: “The ESS is ideal for this purpose, as it includes questions about a wide array of activities. Accordingly, the myriad forms of political engagement included in the ESS have been divided into those activities that are most likely to be zero-sum in nature, namely contacting political leaders and working for a political party or ‘action group'”. (Competitive Political Activity). These two activities are examples of where, at least according to NJS-B, the zero-sum logic applies best. The more people who contact a political leader, the less the impact made by each individual contact; the more people who volunteer for a party, the less the relative value of each individual volunteer. This is the sort of activity where we should have the strongest expectation for the sorting model. In contrast to the set of competitive political activity, the same battery also includes a set of expressive activities, where participation is more likely to be cooperative than competitive. In contrast to contacting political leaders and working for a political party, these activities do not have an obviously instrumental motivation. Such activities include boycotting consumer products, marching in demonstrations, and signing petitions (Expressive Political Activity). Rather than inherently zero-sum activities, with multiple participants scrambling to have their individual influence felt or voice heard, these are activities whose effectiveness rests on mass involvement. I gain more from a boycott, petition, or demonstration when others join me – the more, the better. In this case, the hypothesis is clearly that the sorting model does not apply, since these are not inherently competitive activities, but that the absolute education model does. These are activities identified with social movement-oriented politics, which in turn are often spurred by postmaterialist motivations – and post-materialism is largely the province of the highlyeducated (Abramson and Inglehart, 1995). It is also possible that participation in these expressive activities becomes more likely as the average level of education within the environment increases, or what Helliwell and Putnam have labeled the cumulative model. Because their effectiveness requires a cascade of participation, we might expect a “contagion effect”, whereby living amongst people with a higher level of education legitimises such activity. Since the cumulative model has not been discussed as thoroughly as the sorting and absolute education models in the extant literature, it is more difficult to generate expectations for it. Therefore, it is mentioned here as a plausible, though tenuous, possibility only.
3.2.2. VOLUNTARY ASSOCIATIONS : Above, I argued that it is not clear why we should expect participation in voluntary associations – to many, the quintessential example of a civically-oriented activity – to have a zero-sum, inherently competitive nature. Unlike political engagement, people do not generally get involved in voluntary organisations in order to advance or protect their interests. Instead, they presumably have an intrinsic interest in the activities of the group, and enjoy the camaraderie of their fellow group-members. If this is an accurate characterisation of what we might call associationalism, then there is no reason to expect the sorting model to explain why people get involved in groups, clubs, and associations. Instead, we should hypothesise that the absolute education model pertains, simply on the grounds of the almost universal relationship between educational attainment and CSE generally. Notwithstanding my objections to NJS-B’s reasoning, their belief that the sorting model applies to participation in voluntary associations is not totally unwarranted. It is a reasonable possibility that relative social status is a factor explaining engagement in membership organisations, in which case relative education would be relevant. Supporting this perspective, NJS-B do, in fact, find empirical evidence that the sorting model – at least as they operationalise it – explains organisational involvement (recall, however, that Helliwell and Putnam find by shifting the measure of educational environment, it does not). In the ESS, involvement in a voluntary association is measured with an item that asks whether respondents have worked for an organisation or association. Unfortunately, the placement of this item may prime the respondent to think of political organisations, rather than a wider array of groups, since it immediately follows the competitive political activities, and immediately precedes the expressive activities. As a robustness check, therefore, a parallel analysis has been conducted with the European Values Survey.
3.2.3. VOTING : As discussed above, voting has been placed into a category all its own. Just as light has properties of both a wave and a particle, voting has the properties of both civic and political engagement. Therefore, it is difficult to predict a priori whether the sorting model applies to voting or not. We might expect that, just as contacting political leaders is a zero-sum activity, so is voting. Conversely, however, voting is clearly not driven entirely by the advancement of one’s self-interested political objectives, but instead has an expressive component to it. People vote, at least in part, because they receive civic gratification from doing so. In the ESS, voter turnout is measured in reference to the most recent national election, with a lead-in to the question designed to minimise the social desirability bias associated with the measurement of voter turnout (whereby more people claim to vote in surveys than indicated by the actual turnout rate as tabulated by election officials). To the extent that voting has a political motivation, the sorting model is hypothesised to apply as an explanation for voter turnout; to the degree that it is grounded in civicallyoriented sensibilities, the absolute education model gets the nod. Indeed, it is even conceivable that the cumulative model applies, as the expressive aspect of voting may be greater in environments where people have a higher level of education and thus a stronger sense that voting is a civic obligation or duty.
3.2.4. TRUST: INTERPERSONAL AND INSTITUTIONAL:
To this point, the forms of engagement under consideration consist of activities, things one does. Trust, however, consists of an attitude or a mindset – what one thinks – albeit with likely behavioral consequences. For interpersonal trust, these consequences are comparable to what we observe for educational attainment. If education is the “universal solvent”, interpersonal trust’s universality ranks a strong second, as trusting people are more engaged in a whole host of activities than their less-trusting counterparts. While the behavioral implications of trust in government institutions are not as clear-cut, this form of trust has long been theorised to be an important ingredient for political stability (Easton, 1965; Hetherington, 2005). The ESS measures interpersonal trust with three related questions: whether most people can be trusted, whether most people would try to take advantage of you, and whether most of the time people try to be helpful. The index of institutional trust includes seven institutions: your country’s Parliament, the legal system, the police, politicians, political parties, the European Parliament, and the United Nations. For both interpersonal and institutional trust, an index has been constructed by simply adding the individual responses together.6 There are competing expectations regarding the relationship of education to trust, both interpersonal and institutional. One perspective is that trust has largely social origins, and is thus driven by socioeconomic status. If so, the sorting model would apply. The nearer you are to the top of the social hierarchy, the more reason you have to be trusting. Conversely, if trust is primarily a psychological predisposition immune to one’s position on the social ladder, then an individual’s absolute level of education is most likely to matter. A third perspective, which seems most compelling, is that trust is driven by both individual attainment and the educational environment (and, by implication, has both a sociological and a psychological flavor). Rather than the sorting model, though, the environment affects trust through a cumulative mechanism – trust begets trust. Under this scenario, a higher educational level within the environment triggers a positive feedback process, leading to a higher level of both interpersonal and institutional trust.
RESULTS/ DATA INTERPRETATION:
Correctly testing the impact of education not only requires attention to the measurement of educational environment, but also the method of estimation. Because these data are cross-national, a standard regression model would be flawed. A key assumption of linear regression is that the units of analysis are independent of one another – information about one does not provide information about another. Data that are clustered by nation, however, clearly violate this assumption, as intra-national variation is going to be smaller than the variation between nations. In more intuitive terms, this means that two respondents from, say, Spain are likely to have more in common with one another than a respondent from Spain and one from Sweden. This problem is likely to be especially acute in a study of a nation’s educational environment, where we would expect wide variation in the relationships between education, educational environment, and CSE. There are a number of econometric strategies of handling such a violation of this fundamental assumption underpinning linear regression. One is to run separate models for each nation, but with 17 nations (32 in the European Values Survey, discussed below) this can quickly become cumbersome, and makes generalisations across nations difficult. Instead, an alternative estimator is employed, namely a random coefficient (mixed-effects model) in which the slopes for the relationships in which we are interested are allowed to vary for each nation. Specifically, the relationships between the dependent variable and both education level and educational environment are permitted to vary cross-nationally.7 In order to keep the focus on the education variables, the models only include a small number of controls. Since education is often taken to be a proxy for socioeconomic status, the model includes household income. By including both, we can be sure that we are not conflating the impact of education and income. The model also controls for gender, given that there are gender-related differences in civic and social engagement (Burns, Schlozman and Verba, 2001; Christy, 1987; Norris, Lovenduski and Campbell, 2004). And, because educational environment is measured in relation to a respondent’s age cohort, the models also account for a respondent’s age (specifically, generational cohort.).To facilitate comparisons across the different forms of engagement, each continuous dependent variable has been standardised to have both a mean and standard deviation of 1.0. Since voting and voluntary association are both dichotomous measures, they have not been standardised in this way. In interpreting the models, it is important to keep in mind that education can have multiple effects. Thus, rather than declaring an hypothesis supported or not, I instead characterise the evidence favoring an hypothesis as strong or weak. More specifically, the interpretation of the models is as follows: • A positive, significant coefficient for education level and a non-significant coefficient for educational environment is strong evidence for the absolute education model. • A negative coefficient for educational environment is evidence for the sorting model. If it is greater in magnitude than education level, that is strong evidence favoring the sorting model. If it is smaller in magnitude, then the evidence can only be characterised as weak, and the absolute education model can also be said to have received support. • A positive coefficient for educational environment is evidence for the cumulative model. As with the evaluation of the sorting model, a coefficient greater than education level is strong evidence, and one smaller than education level is weak evidence. Table: Testing the absolute education, sorting, and cumulative models
Results from mixed-effects maximum likelihood regression
political activity Expressive
political activity voting Voluntary
trust Institutional trust
Education level 0.038 0.052 0.013 0.013 0.013 0.026
environment -0.043 -0.040 -0.020 -0.011 0.042 0.010
cohort 0.002 -0.077 0.056 0.009 0.109 0.056
Gender -0.148 0.081 -0.008 -0.043 0.074 -0.009
Household income 0.028 0.018 0.021 0.012 0.038 0.031
constant 1.166 0.801 0.622 0.123 -0.413 0.275
Nations 17 17 17 17 17 17
Observations 22,428 22,294 21,562 22,432 22,241 18,701
Prob;chi 2 0.000 0.000 0.000 0.000 0.000 0.000
Table: The absolute education, sorting, and cumulative models as applied to
voluntary organisations Results from mixed-effects maximum likelihood regression
Organizational memberships Voluntary activity
Education level 0.079 0.062
Educational environment -0.056 -0.037
Cohort 0.002 0.018
Gender -0.017 -0.036
Household income 0.032 0.020
Constant 0.808 0.834
Nations 32 31
Observations 29,698 29,136
Prob;chi 2 0.000 0.000
4.1. EXPRESSIVE POLITICAL ACTIVITY:
weak evidence for sorting, strong evidence for absolute education The fact that there is only weak evidence for the sorting mechanism when applied to expressive forms of political engagement suggests that relative education as an indicator of social status is most suitable as an explanation for those forms of engagement that best approximate a zero-sum competition.
weak evidence for sorting, strong evidence for absolute education Interpreting the evidence regarding voting is a little tricky. The coefficient for educational environment is negative and greater in magnitude than the positive coefficient for education level, which would suggest strong evidence for the sorting model (as with the political index). However, the coefficient for education level falls just short of statistical significance at a conventional level (p=0.11). Because the coefficient misses the usual cut-off for significance (in a dataset with 22 000 cases, where achieving significance is not difficult) I have classified the evidence as weak in favor of the sorting model. Perhaps a more accurate characterisation would be that it straddles the line between weak and strong which, given the Janus-faced nature of the motivations underpinning voting, is perhaps not surprising.
4.3. VOLUNTARY ASSOCIATIONS:
weak evidence for sorting, strong evidence for absolute education There is weak evidence that involvement in a voluntary association is driven by the sorting model, suggesting that social status may play a role in spurring involvement in such organisations. Note, however, that this measure of organisational involvement is less than ideal for teasing out any differences between civically- and politically-oriented engagement, since it is included in a battery that likely primes the respondent to think of organisations that have a political side to them. Recall that the question about involvement in a group is embedded amidst other items that ask whether the respondent has worked for a political party, marched in a demonstration, participated in a boycott, etc. Further evidence regarding organisational involvement and membership is provided by the European Values Survey (EVS), which includes a wider array of nations (31 instead of 17) and a more extensive set of questions about the respondent’s involvement in voluntary associations.8 The models using data from the EVS use an identical method of estimation, including a random coefficient model, and educational environment is again coded in relation to each respondent’s age cohort. In this case, however, educational environment must be calculated using educational level rather than the number of years spent in formal education. The two dependent variables are organisational memberships and volunteering. Respondents were first asked whether they belong to any in a long list of association types, including everything from social welfare groups to religious organisations to sports groups. Then they were asked whether they do any unpaid volunteer work for each type of association. The EVS results are comparable to those derived from the ESS.9 For both organisational memberships and volunteering, the coefficient for education level is positive (and significant), while the coefficient for educational environment is negative. However, in both cases the magnitude of educational environment is less than education level, leading again to the conclusion that there is only weak evidence for the sorting model when applied to organisational involvement. It is remarkable that these two sources of data produce consistent results, notwithstanding that they cover different nations and use different measures of organisational involvement.
4.4. INTERPERSONAL TRUST:
strong evidence for cumulative As hypothesised, interpersonal trust is driven by the cumulative model. The higher the average level of education in one’s environment, the higher is that individual’s trust in others. The evidence in favor of the cumulative model can be characterised as strong, as the magnitude for educational environment exceeds that for education level.
4.5. INSTITUTIONAL TRUST:
is driven only by absolute education, as the educational environment has neither a negative nor a positive effect.
CONCLUSIONS AND POLICY IMPLICATIONS:
We entered into this comparison of absolute education versus the educational environment in response to NJS-B’s provocative claim that educational attainment is correlated with numerous dimensions of CSE simply because education serves as a marker of social status. If this is true, then any efforts to increase civic and social engagement through encouraging more education would be futile. Higher levels of education for everyone would not change the underlying distribution of engagement, as those with more education relative to their environment would still be expected to be more engaged. By testing the impact of the educational environment on multiple forms of engagement across European nations, we see that the sorting model proposed by NJS-B does hold up for the most clearly instrumental forms of political engagement. Therefore, these data suggest that efforts to boost political engagement (narrowly defined) by simply increasing the education level of the population would likely not succeed. This evidence for the sorting model also sheds partial light on the paradox of participation, as it explains why rising levels of education do not automatically translate into rising levels of political engagement. Indeed, if rising education levels produce an inequitable distribution of the opportunities for educational advancement – thus boosting education levels for some groups within a population but not others – it could actually produce a growing engagement gap. These results offer only partial illumination on the paradox of participation, however, because the sorting model cannot explain why political engagement has fallen in the wake of a more educated populace. A drop in engagement must be explained by factors other than education. Ceteris paribus, what forms of engagement would be expected to increase as education levels rise? NJS-B have already argued, persuasively, that political tolerance increases across the board in the wake of increased educational attainment. The above analysis also indicates that interpersonal trust increases as education levels climb. In fact, trust accelerates as the overall level of education within one’s environment rises – rather than sorting, the cumulative model applies. Institutional trust also increases along with an individual’s level of educational attainment, although without the educational environment as an accelerator. Expressive activities, voting, and involvement in a voluntary association are all forms of engagement that have been shown to rise with increasing individual-level education, but with the educational environment serving as a decelerator. That is, a higher average level of education within one’s age cohort pulls engagement down, but not enough to outweigh the impact of an individual’s own level of educational attainment. Perhaps a concrete example clarifies. Imagine two people, each with a college degree. Both will have a higher level of engagement than someone with a high school diploma. But the “engagement gap” between a college and high school education will be greater for the person whose age cohort has a lower average level of college education.
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