Essay On Education Inequality Google

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  • Abstract

    It is widely believed that countries with greater levels of income inequality also have lower levels of intergenerational mobility. This relationship, known as the Great Gatsby Curve (GGC), has been prominently cited by high-ranking public policymakers, bestselling authors, and Nobel Prize–winning academics. Yet, relatively little cross-national work has empirically examined the mechanisms thought to underpin the GGC—particularly with regard to the role of educational attainment. This paper uses the cross-nationally comparable Programme for International Assessment of Adult Competencies (PIAAC) data set to shed new light on this issue. We find that income inequality is associated with several key components of the intergenerational transmission process—including access to higher education, the financial returns on education, and the residual effect of parental education upon labor-market earnings. Thus, consistent with theoretical models, we find that educational attainment is an important driver of the relationship between intergenerational mobility and income inequality. We hence conclude that unequal access to financial resources plays a central role in the intergenerational transmission of advantage.

    Introduction

    Income inequality is high and rising in a number of developed countries (OECD 2012). There is widespread concern that this may lead to lower levels of intergenerational mobility. For instance, Ermisch, Jäntti, and Smeeding (2012, 3) stated that:

    Of all the potential consequences of rising economic inequality, none is more worrisome than the possibility that rising inequality will have the long-term effect of reducing equality of opportunity and intergenerational mobility.

    This statement was supported by Duncan and Murnane (2011, 20):

    Only if our country [the United States] faces the consequences of growing income inequality will it be able to maintain its rich heritage of upward social mobility.

    A key reason why many believe income inequality and intergenerational mobility are linked is that this can be observed cross-nationally; economically unequal countries are the least socially mobile. This is often demonstrated via a graph that plots the Gini coefficient (income inequality) against the intergenerational income elasticity (a measure of social mobility). An upward-sloping line, then, demonstrates what has become known as the “Great Gatsby Curve (GGC)”; greater income inequality is associated with less social mobility.

    This finding has been subject to much attention. It has been cited by Nobel Prize–winning economists (Heckman 2013), high-ranking policymakers (White House 2013; Obama 2013), bestselling authors (Wilkinson and Pickett 2009), and the popular science press (Economist 2013). Indeed, Professor Alan Krueger (2012) even used the curve to predict that, due to recent increases in income inequality, income mobility will further decline in the United States over the next 25 years.

    Yet, despite widespread interest in the GGC, relatively little work has examined the mechanisms thought to underpin it. This includes the role of educational attainment, a factor many sociologists and economists deem critical in the transmission of (dis)advantage across generations (Duncan and Hodge 1963; Blau and Duncan 1967; Becker and Tomes 1986; Hout 1988; Ishida, Müller, and Ridge 1995; Breen and Goldthorpe 2001; Breen and Jonsson 2007; Duncan and Murnane 2011). Although recognition of the importance of education dates back to Blau and Duncan (Blau and Duncan 1967; Duncan and Hodge 1963), few empirically examined the role of education in social mobility until the 1990s (Ganzeboom, Treiman, and Ultee 1991; Breen and Jonsson 2005). Since then, numerous studies have considered how education mediates the link between social origin and destination, including how this compares across national settings (see Breen and Jonsson [2005] for a review). However, there is little evidence as to how this is then linked to income inequality. This paper fills this gap by examining cross-national variation in the relationship between parental education, educational attainment of offspring, and labor-market outcomes, and whether stronger associations are found in societies with more income inequality. We address the following research questions.

    First, we ask whether there is indeed a strong link between income inequality and intergenerational mobility. As noted by Saunders (2012), Jäntti and Jenkins (2013), and Blanden (2013), different methods have been used across countries to produce the income mobility estimates usually plotted on the GGC, with substantial differences across countries in terms of data quality. Indeed, two experts recently emphasized how, despite the prominence of the GGC, relatively little is actually known about the link between income inequality and intergenerational mobility (Jäntti and Jenkins 2013, 188). They have therefore highlighted the need for further work in this area—particularly greater use of cross-nationally comparable data.

    Our first aim is to provide new evidence closely related to what the aforementioned academics are calling for. Specifically, the cross-nationally comparable Programme for International Assessment of Adult Competencies (PIAAC) data set is used to investigate the link between comparable measures of parental education and offspring's earnings. We thus investigate whether the GGC can be replicated using an alternative definition of intergenerational mobility and data designed to facilitate such international comparisons.

    Research Question 1. How does the link between parental education and offspring's earnings vary across countries? Is this association stronger in more unequal countries?

    Our second contribution is to consider the role of offspring's educational attainment in forming our version of the GGC. Despite the prominence of education in theoretical models of intergenerational persistence (Blau and Duncan 1967; Sewell and Ohlendorf 1970; Boudon 1974; Breen 2004), relatively little empirical work has examined whether this may be driving the link between income inequality and mobility. (Gregg et al. [2013] is an exception, who investigate the role of education in explaining income mobility across Sweden, the UK, and the United States. They find that Sweden has the most income mobility, due to smaller “residual” effects of parental income on offspring's earnings, and lower returns to education). We further the research of Gregg et al. (2013) by investigating whether educational attainment mediates the intergenerational transmission process, how this varies across several countries, and whether this is independently associated with income inequality.

    Research Question 2. Does educational attainment mediate the relationship between parental education and offspring's earnings? Is this “through education” effect stronger in more unequal countries? Is there also an association between income inequality and “residual” family background effects?

    Finally, our decomposition shows that the mediating effect of education operates through two channels:

    • The socio-economic gradient in offspring's educational attainment (“access to education”); and

    • The labor-market value of qualifications (“returns to education”).

    Both are likely to vary across countries. For instance, returns to education will depend upon the structure of a country's economy (e.g., the main industries and whether they require an educated workforce) and the supply and demand for skills. Likewise, returns will also be influenced by structural factors, such as young people's willingness to migrate to find employment (i.e., whether they can “match” their skills to an appropriate job) and the strength of labor unions. However, we argue that both channel (i) and (ii) will also be stronger in more unequal countries (Solon 2004; Breen and Jonsson 2005; Mayer 2010). Our final aim is to bring data to bear on these issues by investigating whether (a) income inequality is linked to differences in university completion rates by parental education group; (b) the returns to education are indeed higher in more unequal countries; and (c) if either stands out as a particularly important driver of our version of the GGC.

    Research Question 3. Is the relationship between parental education and access to higher education stronger in more unequal countries? Are the economic returns to education greater in more unequal countries?

    Note that our objective is to establish whether strong associations between income inequality and intergenerational opportunities exist at the cross-country level, and the extent to which educational attainment is an important mediating factor. Although establishing causality is clearly an important long-term goal, it is beyond the scope of this paper and the data currently available.

    Theoretical Framework and Empirical Methodology

    Becker and Tomes (1986), Breen and Jonsson (2005), Corak (2013), Duncan and Murnane (2011), and Solon (2004) argue that in societies with greater inequality, there are larger disparities in the resources invested in children between rich and poor. This begins in utero (e.g., quality of prenatal care), and continues throughout early childhood via educational inputs (including parental time). Consequently, large socio-economic differences in cognitive functioning emerge before compulsory schooling has begun (Becker 2011; Cunha et al. 2006). Income inequality then leads to greater school and neighborhood segregation (Harding et al. 2011), with disadvantaged children attending lower-quality schools than their more affluent peers (Garner and Raudenbush 1991; Mayer 2002). This, along with continuing disparities in educational investments, reinforces the skill gap between socio-economic groups. Thus, by the end of secondary school, there will be substantial differences in academic abilities (Marks 2014), future aspirations (Sikora and Saha 2007), and a range of other social (“noncognitive”) skills (Erikson and Jonsson 1996).

    This will influence whether children obtain a bachelor's degree (Jackson et al. 2007). Income inequality may also directly influence college access if low-income families cannot afford tertiary education (Jackson and Jonsson 2013) or the increasingly necessary extracurricular experiences (Lehmann 2012). Yet, college graduates earn substantially more than other groups (Hout 2012)—with these returns greater in more unequal labor markets. Moreover, family resources continue to matter, as the wealthy support their offspring during their job search (Lin 1999). Successful labor-market transitions are therefore harder for those from poor backgrounds, even conditional on educational attainment (Goldthorpe 2013)—particularly when labor markets are very unequal. The Great Gatsby Curve presents “a summary of all these underlying gradients, reflecting the outcome of a whole host of ways that inequality of incomes affects children” (Corak 2013, 7).

    This argument is formalized in figure 1, which links parental education to offspring's earnings. The raw association between parental education and offspring earnings is the measure of intergenerational mobility used in this paper, henceforth labeled βK (where K equals country). We estimate βK across 24 countries using the following OLS regression model:1

    (1)

    where = Offspring earnings, = Highest level of parental education, C = A vector of control variables (quadratic age, immigrant status), ϵ = Error term, i = Individual i, j = Cluster j (referring to the sample design, with respondents clustered within geographic units), and ∇ K = Refers to the same model being estimated separately in each country (K).

    Parental education is the measure of social stratification used in our estimations of intergenerational mobility. This is in contrast to previous work on the GGC, where parental income has been preferred. We depart from this convention for both theoretical and data reasons. Regarding the latter, there is no cross-nationally comparable data set containing high-quality information on parental income across a large number of countries. This includes PIAAC, with parental education being the best available proxy. However, we argue that parental education may be preferable anyway, as it is likely to capture a broader array of parental inputs into children's development (Leibowitz 1977).2 For instance, educated parents not only earn more (“financial resources”), but also have greater social and cultural capital (“nonfinancial resources”); see Bukodi and Goldthorpe (2012). Figure 1 illustrates how both influence children's educational attainment and labor-market outcomes, and that measures of social stratification should incorporate both. We maintain that parental education probably performs this function better than parental income.3

    Figure 1 illustrates that the link between parental education and offspring's earnings can be separated into two components: the part working through offspring's educational attainment (dashed arrows) and the part that is not (solid gray arrows). (One may view this as an extension of the Origin – Education – Destination (OED) triangle that has a long tradition in social stratification research. See Breen [2004] and Goldthorpe [2013].) Formally, following Gregg et al. (2013), the intergenerational association (β) will be divided into the following parts: where β = Total association between parental education and offspring's earnings; γ = Labor-market value of qualifications; λ = Relationship between parent and offspring's educational attainment; and δ = The (unexplained) residual influence of parental education on offspring's earnings.

    γ.λ represents the “through education” effect of parental education upon offspring's earnings; it is the part that can be accounted for by differences in educational attainment across offspring. The second component, the intergenerational correlation of education (λ), is itself determined by two factors (see Becker [1964]):

    • Parental capacity to invest in their offspring's education. (This will be influenced by the dispersion of financial and nonfinancial resources in the country in which the parents live.)

    • Parental incentives to invest in their offspring's education. (This will be influenced by the parents' perception of the future returns to education, γ, when their offspring are adults.)

    Unfortunately, it is not possible to estimate the separate effects of (i) and (ii) in our analysis, as both are likely to be influenced by income inequality at the same point in time.4 However, to provide indicative evidence on this matter, we will discuss results from additional analysis investigating the link between family background and subject choice. (We argue that, while families' capacity to invest is unlikely to differ by subject, their incentives may due to large differences in economic returns.)

    In contrast, δ is the unexplained (residual) effect; it is the association between parental education and offspring's earnings that remains after controlling educational attainment. We estimate the magnitude of each component across countries, and examine whether they are larger in more unequal societies. These components are further discussed below.

    The Intergenerational Correlation of Education (λ)

    λ represents the intergenerational correlation of education; the association between the educational attainment of parents () and offspring (). We investigate how λ varies across countries, and whether it is linked to income inequality (this hypothesised correlation with income inequality is denoted ρλ).

    Figure 1 illustrates that three factors drive λ:

    • Heredity (H) = The genetic transfer of skills across generations

    • Nonfinancial resources (NF) = Nonfinancial inputs into children's development (e.g., reading stories, helping with homework).

    • Financial resources (F) = Monetary inputs into children's development (e.g., private tuition, school quality, tuition fees).

    If λ does vary across countries, this is unlikely to be due to channel H; heredity transfers will not lead to stronger intergenerational associations in Britain than Australia (for example). Conversely, the distribution of NF resources may vary across countries; cultural and scholarly capital could be more evenly spread among the population in Sweden than the United States (for example). Although this would lead to cross-national variation in λ, it is not then clear why λ would be strongly associated with income inequality (though one cannot rule this possibility out).5 Consequently, if an association between λ and income inequality does exist (i.e., ρλ > 0), then it is likely to work mainly through channel F (where a strong plausible mechanism is clear). Specifically, greater income inequality leads to greater disparity in financial resources between high and low parental education groups, which generates bigger differences in offspring's educational attainment.

    Appendix C presents empirical evidence on this matter by exploring the relationship between income inequality and family background differences in financial and nonfinancial investments using the Programme for International Student Assessment (PISA). The cross-national correlation between income inequality and financial investments is strong (Spearman's rank = 0.73), while for nonfinancial investments it is relatively weak (Spearman's rank = 0.20). This is consistent with channel F driving any association between income inequality and the intergenerational correlation of education.

    λ is estimated using the following OLS regression model, before being plotted against income inequality:

    (3)

    The stronger the association (ρλ), the greater the evidence that access to financial resources (and, to a lesser extent, nonfinancial resources) matters in the intergenerational transmission of advantage.

    The Returns on Education (γ)

    There is likely to be a strong association between parent and offspring education (λ) due to financial, nonfinancial, and heredity factors. The impact upon offspring's earnings will depend, however, upon the value of qualifications in the labor market; that is, returns on education (γ). The product of λ* γ hence determines the impact of offspring's education on intergenerational persistence (β). For instance, there may be strong parent-child education links within a country, but this may have little impact upon β if economic rewards to schooling are low.

    Figure 1 illustrates our hypothesis that γ will be greater in more unequal countries. (We denote this correlation as ργ) This is because financial rewards to more schooling are likely to be greater in societies where the income distribution is more dispersed. For example, university graduates will earn more, on average, than high school graduates in every country. But, with more inequality in the earnings distribution, the wage differential between graduates and non-graduates will be considerably larger. Similarly, wages are likely to be taxed and redistributed more in low-income-inequality countries, further reducing the private returns on education (relative to high-income-inequality countries).

    Consequently, income inequality will have a double influence upon the “through education” component of the intergenerational transmission process; it will affect both the intergenerational correlation of education (λ) and the economic rewards of holding higher qualifications (γ). Becker (1964) suggests that this creates the perfect storm—more advantaged families have greater resources to invest in their children's education and greater incentives to do so in more unequal countries. This then leads to a pronounced relationship between income inequality and the “through education” component of β. We test this hypothesis in our analysis.

    γ is estimated via model (4), capturing the link between offspring's education and their earnings, conditional upon parental education:

    (4)

    Moreover, by rearranging equation (2) one can see that the combined “through education” effect (γ.λ) is the difference between the unconditional () and conditional () parameter estimates given by (1) and (4):

    (5)

    In our empirical analysis, we investigate whether (γ.λ) is linked to income inequality, before considering each subcomponent in turn.

    School Systems and Public versus Private Investment in Education

    There are a number of other important factors, beyond family-specific investments and behaviors, that could vary across countries and be associated with income inequality. Examples include residential income segregation and public expenditure on education. We argue that parental financial resources will be particularly important where such factors are not well aligned to the interests of disadvantaged groups (i.e., where segregation is high and public expenditure on education is low).

    Focusing on public expenditure on schooling, for financial resources to be important in the intergenerational transmission process, parents must be able to gain an advantage for their offspring from using them (Lucas 2001

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