On the mediating effect of education, Kuha and Goldthorpe (2010) examine how educational attainment of an individual affects the effect of the relationship between parental social class (mainly father’s) and the individual’s social class, using British data for men and women. The authors classify social class into three groups, namely; salariat and employees, intermediate class, and working class. They find, firstly, a positive correlation between parental class and the individual’s class. For the direct and indirect effects estimated using multinomial logit, the authors found that the direct effect of parental class on women’s social class is considerably lower compared with men. As regards the indirect effect (via education) of parental class, this was generally larger for women than for men. Although an implication of their findings is that the mediating effect of education on the effect of class mobility could differ from one social class to another, their finding also suggest that parental background helps women more via parent’s investment in the education of women. Men’s socioeconomic status is found to be influenced more by the direct effects of their parent’s socioeconomic status.
Other studies, notably Raitano and Vona (2015) on eight European countries14,
find that the influence of parental background on the earnings of children in Nordic countries (Finland, Denmark) and Germany is largely indirect (via the effects of education). Although the authors find no direct parental background effect on the earnings of children, they argue that this may be due to the strong welfare system,
14
The eight countries considered in the study were: Germany, France, Spain, Italy, UK, Ireland, Denmark, and Finland
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regulated wage setting, and an effective education system that offers equal chances to all in these countries. For the remaining countries, the authors find positive parental background effects, and they suggest that the positive effects may reflect the unobservable quality of the education system and family networks.
Similarly, Mazzonna (2014) investigate the relationship between socio- economic background and later outcomes such as health and income of adults in Europe. Their study shows considerable differences in the outcomes of adults who had different socio-economic backgrounds during childhood. They note that although education largely mediates how family background and later adulthood outcomes are related, differences in institutional and cultural environment may explain some of these differences across countries.
In contrast to evidence of the indirect effects of family background on the socioeconomic outcomes of children, Hudson and Sessions (2011) find evidence of direct parental influence on children’s earnings. They argue that the direct effect of parental background is mainly because educated parents tend to have better connections and are therefore able to assist their children in finding good jobs. Pellizzari (2010) also note that in labour markets with intermediaries, the wage effects of finding jobs through personal contacts tend be higher. For Italy, Raitano and Vona (2015b), find evidence that parental background exerts a significant influence on sons’ earnings even after conditioning on observable and unobservable characteristics of sons. The authors also find a large effect of parental background on the earnings of children even after controlling for the education of sons after twenty years of work experience. This implies that in some areas, the effects of family background on labour market success is still important.
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The above evidence show mixed results of the effects of family background on earnings. These differences may be due to the nature of the labour market, and the education system of these countries. In labour markets with frictions and intermediaries, it is possible that personal contacts and social networks will be a key factor in finding good jobs. This possibility is examined for the case of Ghana by looking at the links between education of the individual, family background, and labour market success (earnings).
3.4 Methodological issues: estimating intergenerational correlations
If the economic success of children transmits from the economic status of their parents, then the association between the incomes of parents, 𝑦𝑝 and the adult child’s income, 𝑦𝑐 is important in studies of intergenerational mobility. This relationship can be summarised as below;
𝑙𝑛𝑦𝑐𝑖 = 𝛼 + 𝛽𝑙𝑛𝑦𝑝𝑖+ 𝑢𝑖 (5)
where 𝛽 measures the elasticity of an individual’s income given the income of the individual’s parent. Should 𝛽 be zero, then this means the incomes of the adult child’s parents do not matter and there is complete mobility. In the case where 𝛽 is 1, then there is evidence that the earnings of the adult child are reflected by the earnings of his/her parent.
A major concern of estimating intergenerational mobility using this approach, and more relevant for developing countries is that information on parent’s income are hardly available in most surveys. For developed countries where this is less of an issue, economic mobility is captured by the elasticity of the adult child’s (mostly sons’) earnings given the father’s earnings. Another issue associated with the use of earnings
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is that, since most data covers only a very short period, earnings measures might be prone to error, which could lead to bias and inconsistent estimates of the 𝛽 parameter (Checchi, 2006). Attempts have however been made to overcome this caveat. For instance, Mazumder (2005), argues that even averaging the earnings of the father over a period will still not be adequate to fully address any potential measurement errors. The author associate this to the fact that the closeness of observations to each other is not representative of the father’s lifetime income. A possible reason as argued by Haider and Solon (2006) is how the differences in permanent income and current income are related in the course of one’s life. For instance, at younger ages, incomes are generally low for most individuals, but this increases at older ages. The authors argue further that the direction of bias due to measurement error is linked to the age at which earnings of the individual is observed. A proposed solution to this problem has been to use the method of instrumental variable. However, this technique has its own challenges since most variables correlated with parent’s income may have independent effect on the status of the child.
Another common but more traditional approach of measuring mobility across generations is to use the occupational or employment status of parents and children (Blanden, 2013; Marks, 2011). Sociologists mostly adopt this approach and it works by generating a socio-economic index that ranks occupations of both parents and adult children. The index is then matched to the occupations of fathers and sons, and correlated across generations. Contrary to the approach that uses income of parents, the requirements for this intergenerational mobility approach are less demanding. Data on parents’ occupation is easy to come by and is readily available in most surveys. In addition, since occupation varies less over an individual’s life-cycle, age-related biases
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associated with the use of income/earnings is less of a problem (Blanden, 2013). This makes the use of occupational status or social class a more desirable and reliable measure. The only difficulty that arises with this measure is the quality of information, although this seems more relevant for cross-country studies.
The last measure of intergenerational mobility considers the correlation between the education of parents and children’s education, which is similar to the approach used in chapter 2. Intergenerational education mobility can then be estimated from a regression of the form;
𝐸𝑑𝑢𝑐𝑐𝑖 = 𝛼 + 𝛽𝐸𝑑𝑢𝑐𝑝𝑖 + 𝑢𝑖 (6)
where 𝐸𝑑𝑢𝑐𝑐𝑖 is the education of the individual, and 𝐸𝑑𝑢𝑐𝑝𝑖 is parent’s education. A major shortfall in using this approach, however, is that, only one parents’ characteristic (usually fathers’) is mostly used in estimating intergenerational educational mobility. However, family background effect on children’s education cannot be determined by only one single trait, usually fathers’ (Björklund and Jäntti, 2012). Though fathers generally tend to be household heads, and often have stronger bargaining power in the house, mother’s role in child development is equally important as it has benefits that go beyond just economic success of the individual (Chen and Li, 2009; Miller, 2008). In addition to using only one single variable as a measure of family background, there is also the problem of how education is measured. A common approach has been the use of years of schooling or educational attainment. This becomes a problem of major concern particularly for cross-country studies, because of the differences in education system and the differences across age cohorts. Nevertheless, an advantage of this approach similar to the approach that uses social class or occupations, and income/earnings is that, information on parental education and children’s education
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are easy to acquire and available in most surveys. Similarly, educational attainment compared with occupation, and income, remain fairly unchanged over the lifecycle (Marks, 2011). To overcome this problem, this study considers the education of both parents, by using the average years of parents’ education, measured in schooling years completed. The study also generates an index as an alternative measure of family background based on parents’ education and occupation.
The alternative measures of intergenerational correlation discussed so far suggests that regardless of how intergenerational correlation is measured, there is the tendency to be confronted with estimation and measurement issues. These issues are however aggravated depending on the nature of the study. Cross-country studies will be compounded with more difficulties because of the differences in measurement and definition (Corak, 2006; Blanden, 2013) of variables. As the data does not provide information on the earnings of the individual’s parents (it is not possible to match data on parents and children in the sample used for this study. These adults have completed schooling and are active in the labour market, and most not living at home with parents), parental earnings are not used as a measure of family background. Instead, the average years of parents’ education is used, since the data provides information on parents’ highest level of education completed. As an alternative measure of family background, the study generates an index for family background using parents’ education and occupation. The results from these regressions are comparable with my preferred family background measure, that is, average years of parents education.
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3.5 Data
The data used is the 2006 Ghana Living Standards Survey. The survey provides rich information on demographic characteristics such as place of birth, educational attainment and marital status. The survey has detailed information on labour market characteristics such as employment status, employment conditions, hours of work, earnings, and other characteristics. The sample is restricted to individuals aged 25 to 60 years in rural and urban areas. Individuals in this age group will be in the prime of their working lives (OECD, 2016), and not likely to be participating in any schooling activities, making them suitable for this study which is focussed on people who are strongly engaged with the labour market. The age range is also limited to 60 years because the official retirement age in Ghana is 60 years.