2  Calculating and comparing intergenerational mobility

Researchers have often studied intergenerational mobility by examining the extent to which a person's childhood economic circumstances predict their adult economic circumstances. For economists, the aim of this research has often been to identify obstacles to people improving their economic position (Blanden, Gregg and Macmillan, 2007). This model is often used to calculate intergenerational income mobility:

where:

Ιn (Y_i,t) = a natural log of an individual's permanent income[3] (or a proxy) when they have grown up

i = the family to which children and parents belong

t = an index of generations

α = the constant

β = the generational income elasticity (the marginal effect of a 1% relative difference in parental income on a person's own income as an adult)

Ιn (Y_i,t-1) = a natural log of parents' permanent income (usually just of fathers, and usually a proxy) when their children were growing up

Z_i = control variables (usually just ages of parents and their ages squared, but sometimes also age variables for their children)

ε_i,t = a random error term.

In this equation, the natural log of a person's income (Ιn (Y_i,t)) is a function of the intercept (α), plus a fraction (β) of the natural log of their parents' incomes Ιn (Y_i,t-1) plus a variety of other factors (Z_i). The intergenerational income elasticity (β value) quantifies intergenerational mobility by estimating the average percentage effect that a small relative difference in the income of a person's parents has on their own income as an adult.[4] Assuming that everything else is constant, a high intergenerational income elasticity implies parents' incomes have a large effect on the incomes of their children, and that there is low intergenerational mobility. A positive intergenerational income elasticity implies that children from higher income families on average grow up to earn more than children from lower income families.[5]

To control for life-cycle income effects, variables for parents' ages are “standard” in intergenerational mobility equations, while age controls for their children are usually included when there is significant variation in this variable (Blanden, 2007, p. 5; Couch and Lillard, 2004, p. 196; Grawe, 2006, p. 551). The best and most internationally comparable estimates of total intergenerational economic mobility for a country usually omit other variables, such as children’s educational qualifications, that are associated with or influenced by parental income (Bowles and Gintis, 2002, pp. 5-10, 22). When researchers do include such controls they are usually testing the extent to which these variables mediate the effects of parental income (Blanden, Goodman, Gregg and Machin, 2004, p. 139; Ng, 2007, p. 17).

2.1  Methodological obstacles to accurately measuring intergenerational mobility

Demanding data requirements make accurately calculating intergenerational income mobility difficult. Ideally researchers would have comprehensive data on both the incomes of children's parents and on the incomes of these children when they are adults. Very large datasets containing intergenerational individual-level income data have been developed from taxation and census data in the Nordic countries and in Canada. However, in most countries such data is not available (Corak, 2006, p. 7; Jäntti, Bratsberg, et al., 2006, pp. 28-30). In Germany and the United States, intergenerational income mobility research has usually used panel data on the incomes of parents and on the incomes of their children when they are grown up and left home. However, relatively few studies have collected data on people’s economic circumstances for prolonged periods (Corak, 2006, p. 6).

Since relying on people's recollection of their parents' incomes is fraught with difficulties, sometimes survey data on the occupations, qualifications and housing tenure of respondents' parents has been used to predict their incomes.[6] Income mobility estimates for France, Italy, Spain, Switzerland, and Japan are only available using this method. However, calculating incomes using these instrumental variables seems to inflate estimates compared to using actual income data, especially when only one or two years of data on the actual incomes of parents is available (Grawe, 2006, pp. 551, 555). This is partly because the characteristics used to predict income, such as parents’ educational levels, have an independent effect on children’s outcomes (Blanden, 2008, p. 6; Blanden and Machin, 2007, p. 4; Grawe, 2004, p. 69). Just using finely grained occupation data to predict income (Andrews and Leigh, 2008, 2009) has produced estimates that are often considerably different from results based on detailed national income datasets (Blanden, 2008, p. 14). Using socio-economic status (SES) data, based on the average economic return for different occupations, often seems to generate similar intergenerational mobility results to self-reported income (Blanden, 2008, p. 16; Ermisch, Francesconi and Siedler, 2006), although sometimes estimates are lower (Ermisch and Francesconi, 2004, p. 182). For some people SES may better capture their long-term economic position than their current income. However, there are obvious limitations to imputing a person’s economic situation on the basis of their occupation (Corak, 2004, p. 23; Zimmerman, 1992, p. 419).

Even when income data is available, life-cycle effects on people's incomes make accurately measuring their economic situation difficult. This is because some types of workers, and particularly those with high life-time earnings, tend to reach their peak earning years later than other workers (Bohlmark and Lindquist, 2006, pp. 882, 885; Grawe, 2006, p. 552). While the entry of highly educated workers into the labour force is often delayed by the time they spend training, on average they usually subsequently enjoy higher earnings growth than those who have fewer educational qualifications (Vogel, 2006, pp. 9, 29). Measuring incomes when people are in their fifties and sixties is also likely to inaccurately measure their permanent income because people’s real wages are often declining at this age (Corak, 2006, p. 10). As a result of life-cycle bias, estimates of intergenerational mobility in some countries are “highly sensitive” to the age at which earnings are observed (Jäntti, et al., 2006, p. 3; Vogel, 2006, p. 14). The incomes of parents and of their children should ideally normally be measured between their thirties and mid-forties, when their income is more likely to accurately reflect their permanent income and their life-time earnings (Haider and Solon, 2006, pp. 1316-1319).

In addition, because people's incomes often vary from year to year in response to transitory short-term factors, measures of incomes from just a few time points tend to produce “snapshots” that poorly capture people's life-time or permanent income (Jenkins, 1987, p. 1149). In other words, while researchers would like long-term data on the incomes of families, usually only a few measurements are available (Corak, 2006, p. 6). This “errors in variables” bias depresses measures of intergenerational mobility because each income observation contains a random component, and only having a small number of income observations can mask the relationship between parental income and the income of their children (Solon, 1992, pp. 396, 401). Higher and more accurate intergenerational mobility results occur in most countries when a large number of income measurements from peak-earning years are available. However, in Norway additional years of income data had very little effect (Corak, 2006, pp. 9, 52; Corak and Heisz, 1999, p. 512; Haider and Solon, 2006, p. 1309; Jäntti, et al., 2006, p. 20; Mazumder, 2005, pp. 248-249).

Sample selection rules and the accuracy of the dataset are also important. For instance, intergenerational mobility results are affected by the inclusion or exclusion of unemployed and part-time workers (Couch and Lillard, 1998, p. 328; Minicozzi, 2003, p. 291). Using total income slightly inflates estimates of immobility in the United States and Canada compared to using just labour market earnings, but data on this effect is unavailable for most countries (Corak and Heisz, 1999, pp. 504, 512-513; Mazumder, 2005, p. 250; Peters, 1992, p. 466). Sometimes datasets on incomes omit those who were dependent on benefits. Intergenerational income mobility results using these datasets may not apply to those who grew up with unemployed parents. For instance, estimates of intergenerational earnings mobility in Canada effectively exclude families from the lowest decile of family income because this group contains few workers who are included in Canadian tax return data (Fortin and Lefebvre, 1998, p. 17; Gorard, 2008, p. 320). There are also obvious incentives for people to under-report their taxable income (Corak and Heisz, 1999, p. 515). Furthermore, using self-reported income can result in errors resulting from inaccurate recall, while using bands to collect income data results in further imprecision (Atkinson, 1980, p. 207). What people earn is also a very private matter, and non-response rates to survey questions about income tend to be relatively high (Dearden, Machin and Reed, 1997, p. 53). Rates of intergenerational mobility in a country can also gradually change over time (Blanden and Machin, 2008, p. 106; Fortin and Lefebvre, 1998, pp. 20, 26).

In addition, research into intergenerational mobility has often covered just men and their fathers, when to be representative and comprehensive, research should include both men and women (Chadwick and Solon, 2002, p. 335). Although fathers’ incomes are likely to be more stable, with women frequently leaving the paid workforce or reducing their working hours to have and look after children, ideally researchers should test the effect of total family income as well as the incomes of fathers (Corak, 2006, pp. 6, 9). For similar reasons, using total family income as a dependent variable is sometimes desirable (Chadwick and Solon, 2002, pp. 335, 342-343; Raaum, Bratsberg, et al., 2007, pp. 3, 31). In some countries the number of children in a family also seems to affect the results (Björklund, Eriksson, et al., 2004).

Researchers using large datasets have also shown that the rate of intergenerational mobility sometimes varies across the income distribution, and mobility is usually lowest near the extremes of the income distribution (Bratsberg, Roed, et al., 2007). As a result, modelling intergenerational mobility as a linear relationship may sometimes produce imprecise results (Corak and Heisz, 1999). The causal mechanisms by which the long-run economic conditions of families affect the subsequent incomes of their children are also unclear (Raaum, et al., 2007, p. 6). In addition, sometimes only a very small proportion of variation in people’s incomes is explained by what their parents earned, suggesting that other variables are more important (Gorard, 2008, p. 319).

Indeed, New Zealand research shows that individual specific factors, such as child poverty and coming from a dysfunctional home environment, tend to have a modest effect on subsequent outcomes for people (Ferguson and Horwood, 2003, p. 22; Melchior, Moffitt, et al., 2007, p. 972). Multiple disadvantages are associated more strongly with negative outcomes, but many people are still able to overcome them (Ferguson and Horwood, 2003, p. 130; Welch and Wilson, 2009a). Protective factors include individual characteristics, family cohesion and warmth, good parenting and external support systems (Mackay, 2003, p. 118; Ward, 2008, pp. 31-32).