coefficient estimate for is interpreted as a summary measure of the degree of intergenerational β persistence. Its complement, is a measure of intergenerational mobility. The correlation 1 – β, coefficient is instead interpreted as a measure of intergenerational Corr(,) “standardized” persistence. Typically, equation (1) is estimated using measures of status in logarithmic form (most notably log earnings), hence is generally referred to as the intergenerational elasticity β (IGE). Estimates of the intergenerational earnings elasticity in contemporary advanced economies have shown significant variation across countries and over time. The IGE for the United States is around 0.5, while the Scandinavian countries are typically at the lower end of international IGE estimates at about 0.2 (see review in Black and Devereux, 2011). Hertz et al. (2007) provide a cross-country comparison in educational persistence across generations in 42 countries over the second half of 20th century and report a global average value of 0.4 for the intergenerational correlation. Several other indicators of individual status have been used in the economic and sociology literatures (e.g. occupation, social class, BMI, height). While performing similar descriptive functions, different variables will quantify the intergenerational association between conceptually distinct measures of long-run status. Longevity has the advantage of being a long-run (or lifetime) individual outcome. This is not the case in most applications where income or earnings and to a lesser extent, occupation and social class are – – used as outcome variables. In fact, much of the earlier literature in intergenerational income mobility has focused on issues of measurement of or income. lifetime permanent Several studies amongst others, Solon et al. (2000), Bjorklund et al. (2002), and – Mazumder (2008) have used the correlation between siblings to measure the proportion of the – variation in outcomes that can be attributed to the family of origin. The intuition is individuals’ that if family effects are important, the correlation in the outcome of interest among siblings will be significantly higher than among randomly selected individuals. This can be expressed formally following the notation in Solon (1999): let be the outcome of interest for the yij jth sibling in family and let us characterize it as follows i, (2)
coefficient estimate for β is interpreted as a summary measure of the degree of intergenerational persistence. Its complement, 1 – β, is a measure of intergenerational mobility. The correlation coefficient Corr(,) is instead interpreted as a measure of “standardized” intergenerational persistence. Typically, equation (1) is estimated using measures of status in logarithmic form (most notably log earnings), hence β is generally referred to as the intergenerational elasticity (IGE). Estimates of the intergenerational earnings elasticity in contemporary advanced economies have shown significant variation across countries and over time. The IGE for the United States is around 0.5, while the Scandinavian countries are typically at the lower end of international IGE estimates at about 0.2 (see review in Black and Devereux, 2011). Hertz et al. (2007) provide a cross-country comparison in educational persistence across generations in 42 countries over the second half of 20th century and report a global average value of 0.4 for the intergenerational correlation. Several other indicators of individual status have been used in the economic and sociology literatures (e.g. occupation, social class, BMI, height). While performing similar descriptive functions, different variables will quantify the intergenerational association between conceptually distinct measures of long-run status. Longevity has the advantage of being a long-run (or lifetime) individual outcome. This is not the case in most applications where income or earnings – and to a lesser extent, occupation and social class – are used as outcome variables. In fact, much of the earlier literature in intergenerational income mobility has focused on issues of measurement of lifetime or permanent income. Several studies – amongst others, Solon et al. (2000), Bjorklund et al. (2002), and Mazumder (2008) – have used the correlation between siblings to measure the proportion of the variation in individuals’ outcomes that can be attributed to the family of origin. The intuition is that if family effects are important, the correlation in the outcome of interest among siblings will be significantly higher than among randomly selected individuals. This can be expressed formally following the notation in Solon (1999): let yij be the outcome of interest for the jth sibling in family i, and let us characterize it as follows (2)
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