How does parental social mobility during childhood affect socioeconomic

status over the life course?

Adrian Byrne; Research Fellow, University of Nottingham, UK

Division of Rehabilitation and Ageing

School of Medicine

Queen's Medical Centre

University of Nottingham

Nottingham

NG7 2UH

07540160209

adrian.byrne@nottingham.ac.uk

ORCID iD: 0000-0003-4887-2572

Tarani Chandola

Social Statistics Department

School of Social Sciences

Humanities Bridgeford Street Building

University of Manchester

Manchester

M13 9PL

Natalie Shlomo

Social Statistics Department

School of Social Sciences

Humanities Bridgeford Street Building

University of Manchester

Manchester

M13 9PL

Funding

Dr Adrian Byrne, School of Social Sciences PhD Studentship, University of Manchester

Acknowledgements

NCDS data were accessed via the UK Data Service plus Annual Survey of Hours and Earnings

data and Consumer Price Index data were accessed via the Office for National Statistics. The

authors have no conflicts of interest to declare.

Abstract

The gap in occupational earnings between children from parents of higher versus lower

socioeconomic background widens over the life course. This gap can be at least partly

explained by family background characteristics in childhood but the extent to which

parental social mobility in childhood can provide additional explanatory power is less well

researched. This study compares socially mobile and socially stable families in childhood

with static measures of parental socioeconomic status with respect to the children’s life

course occupational earnings using data from the 1958 National Child Development Study.

In doing so, this study investigates the extent to which the children’s life course

occupational earnings are constrained by their parents’ (lack of) social mobility during

childhood. Furthermore, the strength of intergenerational socioeconomic transmission may

depend on specific socioeconomic characteristics of the parents. Therefore, this study

explores the influence of changes in childhood socioeconomic characteristics, namely

father’s social class and housing ownership status, both separately and combined for

assessing independent effects, on adult life course occupational earnings whilst also

controlling for maternal education, sex and region of residence. Using life course step

function multilevel models, the results indicate that parental social mobility predicts life

course occupational earnings over and above socioeconomic variables measured when the

child was 16. Moreover, the father’s social class mobility measure showcases greater

intergenerational accumulation of social (dis)advantage. We also demonstrate social

mobility during childhood appears to have long lasting and independent effects on life

course occupational earnings.

Keywords: life course; multilevel; occupational earnings; gradient constraint; social mobility

1 IntroductionThe occupational earnings gap between children from parents with higher versus lower

socioeconomic status (SES) widens as they get older (Byrne, Shlomo, & Chandola, 2017).

This gap can be at least partly explained by family background at one point in time but the

extent to which parental social mobility can provide additional explanatory power is less

well researched. Previous research on intergenerational social mobility assumed that

parental SES does not change during the child’s early years, thereby disregarding the

potential life course effects on the children of socially mobile parents (Blanden, Goodman,

Gregg, & Machin, 2004; Goldthorpe & Jackson, 2007; Li & Devine, 2011). Parental social

mobility is not taken into account when determining adult socioeconomic outcomes (such

as a person’s adult life course occupational earnings) and previous analyses make no

distinction between children from socially mobile and socially stable family backgrounds. By

taking account of parental social mobility, later life SES inequalities may actually be less than

those observed in cross-sectional studies (Plewis & Bartley, 2014). This study explores the

extent to which parental social mobility matters, by comparing people from socially mobile

and socially stable families during childhood and contrasting the results with analyses using

static measures of parental SES, using longitudinal birth cohort survey data.

A key concept in studies about the impact of social mobility on later life inequalities is the

“gradient constraint”. Gradient constraint is a term that has been used in many studies on

intra-generational social mobility and health inequalities that describes the tendency for

attained health among the socially mobile to lie between the average levels found in their

social classes of origin and destination (Bartley & Plewis, 1997, 2007; Blane, Harding, &

Rosato, 1999; Langenberg, Hardy, Kuh, Brunner, & Wadsworth, 2003; Power, Manor, & Li,

2002). Unlike studies of social mobility which analyse changes in the same measure of SES

over a period of time, studies on gradient constraint analyse how social mobility affects life

course outcomes such as health or behaviours (Lahtinen, Wass, & Hiilamo, 2017). Previous

research on the gradient constraint associated with social mobility has shown a moderating

effect on later life outcomes thereby suggesting the social mobility gradient constraint effect

is to constrain/reduce later life inequality rather than widen/increase it (Deary et al., 2005;

Fry, Al-Hamad, & White, 2012). With respect to later life health outcomes, Bartley and

Plewis (2007) argue the term gradient constraint refers to the inequality in health being

diluted rather than increased by social mobility. Therefore, in a society with no mobility,

there would be more, rather than less, health inequality. However, the authors stress that

social mobility does not inevitably result in a gradient constraint as that would rule out the

possibility of those moving from more to less favourable conditions experiencing worse

health outcomes than existing members of the less advantaged social class (and vice versa).

As such, Deary et al. (2005) state that social process relationships are tendencies rather than

laws so whether or not a gradient constraint has occurred is an empirical issue. Hence we

employ an empirical approach in this life course investigation of childhood social mobility

gradient constraint on attained occupational earnings throughout adulthood.

To date, there have been very few studies, such as Plewis and Bartley (2014), that address

the role of childhood social mobility in explaining intergenerational mobility and to our

knowledge none that compare different parental socioeconomic characteristics over the

individual’s life course. Moreover, the strength of intergenerational socioeconomic

transmission may depend on the type of parental characteristic observed. For example,

using Finnish register data Erola, Jalonen, and Lehti (2016) showed that parental education

explained their children’s attained occupations more so than parental income. Therefore,

this study explores the variation in influence of different parental characteristics on adult

child life course occupational earnings; namely father’s social class and housing ownership

status controlling for mother’s education. The justification for using these measures and

their interconnections is provided in the motivation section. Furthermore, the difference

between parental SES measured at a single time point during childhood (or “static parental

SES”) and childhood social mobility measures using father’s social class and housing

ownership status will be explored to learn if such mobility measures provide inference

beyond that of static measures by examining model fit information as well as investigating

the existence of a gradient constraint which will be formally defined after the motivation

section. This study compares the influence of mobility measures and static variations on the

life course occupational earnings of the adult children. We also check for significant

independent effects by controlling for all parental characteristics in the same model as

previous studies have found this to be the case (Bukodi & Goldthorpe, 2012; Gugushvili,

Bukodi, & Goldthorpe, 2017).

Consequently, our research questions for this study are outlined as follows:

1. Does parental social mobility add to the explanatory power of static parental social

position measures in relation to understanding occupational earnings differences

during adulthood?

2. Does parental social mobility during childhood attenuate differences in occupational

earnings between children from advantaged/disadvantaged backgrounds when they

become adults?

3. Do different measures of parental social position (paternal social class, housing

tenure, maternal education) produce different and independent effects on adult life

course occupational earnings?

To address these research questions life course occupational earnings is measured in a

continuous and repeated fashion within a multilevel model structure that focuses on

variation between individuals (Byrne et al., 2017). We begin by motivating the analysis

within this study in conjunction with defining the gradient constraint, introducing the data,

variables and life course statistical model of interest. We next introduce our static versus

mobility analysis in terms of parental socioeconomic predictors and present the results of

our investigations using longitudinal birth cohort survey data. Our discussion concludes the

paper.

2 MotivationThe inheritance of SES as defined by Bowles and Gintis (2001) is the transmission of SES

from parents to offspring. The OECD have determined that parental SES influences

descendants’ educational, earnings and wage outcomes in virtually all countries for which

they have available evidence (Padoan, 2010). A stronger influence of parental SES indicates

a more socially immobile society. Erola and Jalovaara (2017) reported research on social

status inheritance that consistently shows children benefit from greater parental resources,

which are measured in terms of education, occupational status and income. This research

follows on from Erola et al. (2016) who argued family background plays an important role in

the inheritance of social class, education and income. This study adds to the existing

literature by comparing dynamic parental predictors with static versions on the children’s

life course occupational earnings.

The commonly applied indicators of SES, namely social class, education and income, are

correlated but using a single indicator of parental SES is likely to underestimate the total

effect of family socioeconomic background and overestimate the influence of the specific

characteristic we are controlling for (Bukodi & Goldthorpe, 2012; Gugushvili et al., 2017).

Similarly, if both mothers and fathers matter to children, including an indicator relating to

only one of them in the model will also lead to an underestimation (Mood, 2017).

Moreover, given the cohort study used in this paper, the effects are likely to be different for

fathers and mothers, i.e. while fathers are more likely to be working full time and to have

higher earnings than mothers, thereby contributing more to intergenerational achievement

through material assets (Beller, 2009), mothers are often found to contribute through

factors more closely associated with their educational attainment (Korupp, Ganzeboom, &

Van Der Lippe, 2002). This paper examines the extent to which different parental measures

produce different life course occupational earning outcomes for the children.

Furthermore, while maternal education can have a direct or independent effect on the adult

status of the children, for instance as a proxy for the skills and traits that can be inherited

genetically or by learning, mother’s education may also play a role in homogamy. Paternal

social class in turn plays a role in one’s ability to buy a house, an indirect indicator for the

material resources available in the childhood family and an important predictor of life

course socioeconomic position as it is the most common form of wealth accumulation in the

UK (Vanhoutte, Wahrendorf, & Nazroo, 2017). Also, father’s social class may be a signal of

social status or prestige that may be helpful for the children in the form of advantageous

social networks (Erola et al., 2016). This evidence suggests parental measures of

socioeconomic position are connected but can also influence different outcomes for the

children. As such, this study assesses the extent to which these different parental measures

produce independent effects in relation to life course occupational earnings.

Finally, Bukodi and Goldthorpe (2009) found that educational attainment does not fully

mitigate the connection between social origins and destination. In addition, Connelly,

Sullivan, and Jerrim (2014) also argued that socioeconomic differences in educational

attainment trump both race and gender and suggest tackling these differences should be

given the highest priority. More recent research has also found that the family background

effect on children’s outcomes is due in large part to mechanisms other than education

(Gregg, Jonsson, Macmillan, & Mood, 2017; Gugushvili et al., 2017; Mendolia & Siminski,

2017). This inference is supported by Hartas (2015) who documents that parental practices

do not contribute substantively to children’s school outcomes whereas socioeconomic

factors do. The author suggests differences in the educational outcomes between poor and

wealthier children are due to their parents’ social class and not due to different methods of

child rearing. This evidence underlines the importance of appreciating the relationship

between socioeconomic background and life course position. Moreover, this study

investigates the extent to which life course occupational earnings are constrained

depending on whether the parents were socially stable or mobile in childhood.

3 Gradient constraintPreviously, Plewis and Bartley (2014) found that the educational attainment of the children

of socially mobile parents were in between the attainment of those whose parents

remained socially stable in either a more or a less advantaged social position. This finding

indicated a hierarchical process whereby educational outcomes are influenced by the

change (or not) in the social position of the parents. This hierarchical process illustrated the

gradient constraint hypothesis which suggested that social mobility tends to attenuate

social and health inequalities. Moreover, this process can be thought of as an accumulation

of conditions whereby those who experience a mixture of advantage and disadvantage over

the life course would be expected to attain social and health outcomes more favourable

than those who only experienced disadvantage but less favourable than those who never

experienced disadvantage over the life course (Bartley & Plewis, 2007).

In the context of this study using life course occupational earnings as the outcome rather

than educational attainment, we expect to find a similar hierarchical process based on

previous research investigations into gradient constraint. This is illustrated with the help of

Figure 1. Read from left to right, Figure 1 visualises this concept by taking the social gradient

idea between childhood and adulthood advantage and integrating it with mobility during

childhood to produce an example of life course gradient constraint. The social gradient plot

on the left represents the idea that childhood SES is positively correlated with adulthood

SES. Byrne et al. (2017) provide evidence of a life course social gradient in occupational

earnings- higher childhood advantage is associated with higher adulthood advantage in

terms of occupational earnings. The middle plot on childhood mobility separates parental

SES into a binary grouping of high and low over two time points during childhood, leading to

four possible transitions as indicated by the arrows. These transitions help us distinguish

between the socially stable (low/high) and the socially mobile (downward/upward) families.

The gradient constraint plot on the right combines the ideas presented in the other two

plots to suggest the strength of positive correlation between childhood and adulthood SES

depends on the parental SES transition during the child’s upbringing, with the correlation

strengthening when the child’s parental SES is stable high. Another way of framing it would

be to say that the children of socially mobile (downward/upward) families attain later life

SES positions in between the children of stable high and stable low families and each of the

three transition patterns are clearly differentiated from each other. A lack of social mobility

during childhood could mean much lower adult life course SES attainment. This study

examines to what extent social mobility (or the lack of social mobility) during childhood

reduces (or increases) inequalities in occupational earnings in later life.

Figure 1. Visual derivation of gradient constraint in a life course contextSocial gradient Childhood mobility Gradient constraint

4 Data, variables and modelling method4.1 National Child Development Study life course dataBuilding upon the framework set out by Schuller, Wadsworth, Bynner, and Goldstein (2011),

this paper conducts a secondary analysis of existing longitudinal birth cohort survey data to

enhance our understanding of the effect of intra-generational social mobility on

intergenerational social mobility. Our chosen birth cohort longitudinal dataset is the 1958

National Child Development Study (NCDS) (University of London. Institute of Education.

Centre for Longitudinal Studies., 2008a, 2008b, 2008c, 2008d, 2012, 2014, 2015). The NCDS

follows the lives of all people born in England, Scotland and Wales in one particular week of

March 1958. Since the birth survey in 1958, there have been nine further ‘sweeps’ of all

cohort members at ages 7, 11, 16, 23, 33, 42, 46, 50 and 55. In the first three sweeps (at

ages 7, 11 and 16), the target sample was augmented to include immigrants born outside of

Great Britain in the same week. The survey NCDS cohort members remain part of the target

sample until they either die or permanently emigrate from Great Britain. The survey data is

collected at individual level across Great Britain making it a nationally representative

longitudinal cohort study. Data from the parents was collected from 1958 to 1974 with the

children becoming the main survey responders from 1981 onwards.

The total eligible sample, including those not resident in Great Britain up to the age of 16 in

1974, is 18558. There are currently no survey weights to compensate for attrition. Our

target population is based on those with known occupation data over the life course as

defined by the adulthood period between the ages of 23 and 55 (6 time points). Therefore,

the total eligible NCDS sample size of 18558 reduces to 14268 individuals as 4290 (23.1%)

NCDS cohort members have no occupation data over the adulthood period. For the cohort

members included in this study, we obtained their parents’ data from early life (birth, age 7)

and adolescence (age 11, age 16) and we also obtained the cohort members’ occupational

earnings from age 23 (evolving career stage) through to age 55 (mature career stage).

Therefore, from birth to age 55 represents the range of this life course study. Our target

population has a 51:49 men to women ratio whereas the ratio is 54:46 for those NCDS

cohort members omitted from our target population with no occupation data. As there are

a total of 14268 person-level (defined as Level 2 units “L2” hereafter) available cases in this

analysis, that allows for a possible total of 85608 (14268*6 time points) occasion-level

(defined as Level 1 units “L1” hereafter) observations over the life course. However, with

missing data in our response variable and model covariates reduces the number of L1 units

to 37478 and L2 units to 9622. Based on previous work by (Byrne et al., 2017) using similar

statistical models and underlying survey data, we have shown that the missing data adheres

to the “missing at random” mechanism thereby allowing us to employ full information

maximum likelihood (FIML) estimation methods in this paper.

4.2 Life course occupational earningsWe derive our life course outcome measure of SES from the Occupational Earnings Scale

(Bukodi, Dex, & Goldthorpe, 2011; Nickell, 1982). This measure injects a form of hierarchy

by classifying occupations into Standard Occupational Classes and then by ordering each of

these classes according to their mean hourly wage rate using ONS Annual Survey of Hours

and Earnings data (1997-2013). By linking published earnings data to routinely collected

occupation data the problem of survey responders not declaring their income is avoided.

Therefore, we choose a hierarchical occupation variable that is more observed than income

and more fine-grained than other variables associated with SES such as level of education

and social class. These mean hourly wages have been adjusted for inflation and wages are

deflated to 1997 prices using ONS Consumer Price Index data. The 1997 prices are imposed

on pre-1997 occupation wage data and post-1997 occupation wage data have been deflated

to keep prices constant at 1997 to aid comparability across the life course. These hourly

wage data have also been transformed onto the natural logarithmic scale to correct for

positive skewness.

We use this Occupational Earnings Scale to develop NCDS occupation data as a continuous,

repeated outcome measure of SES over the life course (age range 23-55 over 6 time

points).The scale was originally developed in the course of research into the determinants of

‘occupational success’ and is based on a well-defined attribute of occupations, namely

earnings (Nickell, 1982). The construct validity of the scale is easier to appreciate than more

complicated, composite measures of SES over the life course with respect to longitudinal

measurement invariance issues. Bukodi et al. (2011) argue that the Occupational Earnings

Scale can be used as both an explanatory variable and as dependent variable in relation to a

range of individuals’ life-chances and life-choices and as a basis for assessing occupational

mobility and success. Bukodi and Dex (2009) demonstrated that the Occupational Earnings

Scale is largely gender-neutral and converted the scale into scores ranging from 1 to 100 by

way of standardising over the life course rather than adjusting for inflation as we have done

in this paper thereby introducing floor and ceiling effects which are absent in this study. We

denote our outcome variable as “Mean Hourly Occupational Earnings” (MHOE) and refer to

it in the text as “occupational earnings”. By way of a sensitivity analysis, we investigated

correlations over the life course between MHOE, take-home pay and social class (NS-SEC)

using our NCDS dataset. Both MHOE and NS-SEC had average correlations of 0.6 with take-

home pay and had an average correlation of 0.8 with each other. Therefore, NS-SEC is

similar to MHOE in terms of its approximation for income with the difference being the

granularity of each measure.

Figure 2 displays the box plots for occupational earnings at each age throughout the life

course. The sample life course trend shows an upward trajectory between the ages of 23

and 50 (1981 – 2008) and trends downward between the ages of 50 and 55 (2008 – 2013).

This decline might be explained by the Great Recession which began in 2008 with the

financial crisis when the survey NCDS cohort members where aged 50 and/or by the effects

of early retirement. However, there is substantial individual variation around this sample life

course trend and multilevel modelling provides a suitable way of accounting for this

variation when modelling the average life course trend.

Figure 2. Life course Mean Hourly Occupational Earnings on natural log scale at 1997 prices

4.3 Static versus mobility analysis: Father’s social classThroughout this paper, father’s social class is dichotomised into non-manual and manual

classes. The static measure is a binary variable recorded when the NCDS cohort members

were aged 16 in 1974. In terms of counts and proportions for this variable, manual

represents 6,827 observations (65%) and non-manual has 3,673 observations (35%). The

mobility measure utilises information from the first four NCDS survey sweeps from birth in

1958 to age 16 in 1974, i.e. father’s social class recorded at each survey sweep so the static

measure information is nested within the mobility measure. This sixteen year period is

separated into two periods based on the survey sweeps, i.e. from birth to age 7 and from

age 11 to age 16. The outline of how we classify the mobility measure is presented in Table

1. Classification was conducted in this way to minimise the loss of information due to

missing data. The sample size has increased for this measure compared to the static version

at age 16 due to its construction but the per-group proportions have reduced.

Table 1. Classification of Father’s Social Class Mobility measure from birth (1958) to age 16 (1974)Birth to age 7 Age 11 to 16 Mobility

status

n %

Non-manual at least

once

Non-manual at least

once

Stable high 4,07

2

28.5

5

Always manual Non-manual at least

once

Upward 1,21

4

8.51

Non-manual at least

once

Always manual Downward 839 5.88

Always manual Always manual Stable low 8,14

0

57.0

6

Table 2 presents a series of mean and 95% confidence interval statistics across the life

course for occupational earnings on the natural log scale at 1997 prices. The mean range

between stable low and high NCDS cohort member occupational earnings is wider for our

mobility measure (FSCM) compared to the static version (FSC1974) indicating a stronger

effect of accumulation with respect to stable (dis)advantage during childhood. Also, there is

preliminary evidence of the gradient constraint with confidence interval overlapping

occurring between the upward and downward mobility groups across the life course while

there is no overlapping with the socially stable groups. In preliminary analysis not presented

here, t-tests for the static measure and f-tests for the mobility measure conclusively showed

statistically significant mean differences at each age over the life course. Another distinction

between the static and mobility measures relates to the amount of missing data. Using the

1974 static measure instead of our mobility version leads to a 23% average increase in

missing data in relation to our life course occupational earnings outcome variable. We refer

the reader to Table A1 in Appendix A of the supplementary material for the number of

observations for each group over the life course.

Table 2. Life course log Mean Hourly Occupational Earnings mean (95% confidence intervals) statistics with Father’s Social Class in 1974 (FSC1974) and Father’s Social Class Mobility (FSCM) from birth (1958) to age 16 (1974)

Age

Overall

sample

mean

FSC1974

Manual

mean

FSC1974

Non-

manual

mean

FSCM

Stable low

mean

FSCM

Downward

mean

FSCM

Upward

mean

FSCM

Stable

high

mean

23 1.94 1.90 2.04 1.89 1.92 1.97 2.05

(1.93-1.94) (1.89-1.90) (2.02-2.05) (1.88-1.89) (1.90-1.95) (1.94-1.99) (2.04-2.07)

33 2.07 1.99 2.21 1.98 2.04 2.07 2.25

(2.06-2.08) (1.98-2) (2.19-2.23) (1.97-1.99) (2.00-2.07) (2.04-2.10) (2.23-2.26)

42 2.20 2.12 2.34 2.11 2.16 2.19 2.37

(2.20-2.21) (2.11-2.14) (2.32-2.35) (2.10-2.12) (2.13-2.2) (2.16-2.22) (2.36-2.39)

46 2.34 2.26 2.46 2.25 2.32 2.35 2.48

(2.33-2.35) (2.25-2.28) (2.44-2.48) (2.24-2.26) (2.28-2.36) (2.32-2.38) (2.47-2.50)

50 2.37 2.31 2.48 2.30 2.36 2.36 2.51

(2.37-2.38) (2.29-2.32) (2.46-2.49) (2.29-2.31) (2.32-2.40) (2.33-2.38) (2.49-2.52)

55 2.31 2.24 2.41 2.23 2.30 2.30 2.43

(2.30-2.32) (2.22-2.25) (2.39-2.43) (2.21-2.24) (2.25-2.34) (2.27-2.33) (2.42-2.45)

Average 2.21 2.14 2.32 2.12 2.18 2.21 2.35

(2.20-2.21) (2.12-2.15) (2.30-2.34) (2.11-2.14) (2.15-2.22) (2.18-2.24) (2.33-2.37)

4.4 Static versus mobility analysis: Housing ownershipSimilarly to father’s social class, housing ownership is dichotomised into owner occupier and

other categories. Other here refers to council rented, private rented, tied/rent free and

other. The static measure is a binary variable recorded when the NCDS cohort members

were aged 16 in 1974. In terms of sample size and proportions for this variable, other

represents 5,834 observations (50%) and owner occupier has 5,819 observations (50%). The

mobility measure utilises information from three NCDS survey sweeps from age 7 in 1965 to

age 16 in 1974 so again the static measure information is nested within the mobility

measure. No information on housing tenure is available for the year of birth in 1958.

However, this 9 year period is still separated into two periods similar to father’s social class,

i.e. age 7 alone and from age 11 to age 16. The outline of how we classify the mobility

measure is presented in Table 3. The classification was conducted in this way to minimise

the loss of information due to missing data. The sample size has increased for this measure

compared to the static version at age 16 due to its construction but the per-group

proportions have reduced with never being an owner-occupier during childhood, i.e. stable

low, the most prevalent group.

Table 3. Classification of Housing Ownership Mobility measure from age 7 (1965) to age 16 (1974)Age 7 Age 11 to 16 Mobility

status

n %

Owner

occupier

Owner occupier at least

once

Stable high 5,315 39.74

Other Owner occupier at least

once

Upward 1,419 10.61

Owner

occupier

Always other Downward 295 2.21

Other Always other Stable low 6,345 47.44

Similar to Table 2, Table 4 presents a series of mean and 95% confidence interval statistics

across the life course for occupational earnings on the natural log scale at 1997 prices. The

sample average is contrasted with housing ownership at age 16 on life course occupational

earnings with housing ownership mobility. As with father’s social class, the mean range

between stable low and high NCDS cohort member occupational earnings is wider for our

mobility measure (HOM) compared to the static version (HO1974) indicating a stronger

effect of accumulation with respect to stable (dis)advantage during childhood. Also, the

upwardly mobile group have mean occupational earnings that are always greater than the

sample average across the life course suggesting that the mobility measure may be less

skewed upwards by the stable high group than the father’s social class version. However,

the evidence of gradient constraint is less clear for housing ownership as there exists some

overlap in the confidence intervals of the socially mobile and socially stable groups. Similar

to father’s social class, preliminary analysis not presented here, t-tests for the static

measure and f-tests for the mobility measure conclusively showed statistically significant

mean differences at each age over the life course. Regarding the amount of missing data,

using the 1974 static measure instead of our mobility version produces a 10% average

increase in missing data in relation to our life course occupational earnings outcome

variable. The amount of missingness here is less than that of father’s social class. We refer

the reader to Table A2 in Appendix A of the supplementary material for the number of

observations for each group over the life course.

4.5 Mother’s education and control variablesMother’s education was recorded at birth in 1958 and again in 1974 at age 16. We coded

the variable as binary where if a mother reported staying in school beyond the minimum

age at either time point a value of one was assigned (4,868 observations; 27%), otherwise a

zero was assigned (13,153 observations; 73%). It is treated as a static measure. Table 5

presents the series of mean and 95% confidence interval statistics across the life course for

occupational earnings on the natural log scale at 1997 prices. We refer the reader to Table

A3 in Appendix A of the supplementary material for the number of observations for each

group over the life course. The mean range between children from more educated and less

educated mothers is greater than the mean range for home ownership status at age 16 but

less than the mean range for father’s social class at age 16. In preliminary analysis not

presented here, t-tests conclusively showed statistically significant mean differences at each

age over the life course.

Table 4. Life course log Mean Hourly Occupational Earnings mean (95% confidence intervals) statistics with Housing Ownership in 1974 (HO1974) and Housing Ownership Mobility (HOM) from age 7 (1965) to age 16 (1974)

Age

Overall

sample

mean

HO1974

No mean

HO1974

Yes

mean

HOM

Stable

low

mean

HOM

Downward

mean

HOM

Upward

mean

HOM

Stable

high

mean

23 1.94 1.88 2.00 1.88 1.90 1.95 2.02

(1.93-1.94) (1.87-1.89) (1.99-2.01) (1.87-1.89) (1.85-1.95) (1.93-1.97) (2.01-2.03)

33 2.07 1.97 2.16 1.97 2.05 2.10 2.18

(2.06-2.08) (1.95-1.98) (2.15-2.17) (1.95-1.98) (1.99-2.11) (2.07-2.12) (2.17-2.20)

42 2.20 2.11 2.29 2.10 2.19 2.23 2.30

(2.20-2.21) (2.09-2.12) (2.28-2.30) (2.09-2.12) (2.12-2.25) (2.20-2.26) (2.29-2.32)

46 2.34 2.25 2.41 2.24 2.29 2.39 2.42

(2.33-2.35) (2.24-2.27) (2.40-2.43) (2.23-2.26) (2.22-2.37) (2.36-2.42) (2.41-2.44)

50 2.37 2.30 2.44 2.29 2.32 2.40 2.45

(2.37-2.38) (2.28-2.31) (2.43-2.45) (2.28-2.31) (2.25-2.39) (2.37-2.43) (2.44-2.47)

55 2.31 2.23 2.37 2.22 2.26 2.32 2.39

(2.30-2.32) (2.21-2.24) (2.36-2.39) (2.20-2.23) (2.19-2.33) (2.29-2.35) (2.37-2.40)

Average 2.21 2.12 2.28 2.12 2.17 2.23 2.29

(2.20-2.21) (2.11-2.13) (2.27-2.29) (2.10-2.13) (2.10-2.24) (2.20-2.26) (2.28-2.31)

Table 5. Life course log Mean Hourly Occupational Earnings mean (95% confidence intervals) statistics with Mother’s Education by 1974 (Age 16)

Age Overall sample mean

Mother did not stay

beyond school leaving

minimum age mean

Mother stayed in

education beyond

minimum age mean

23 1.94 1.91 2.04

(1.93-1.94) (1.90-1.91) (2.02-2.05)

33 2.07 2.01 2.22

(2.06-2.08) (2.00-2.02) (2.21-2.24)

42 2.20 2.14 2.34

(2.20-2.21) (2.13-2.15) (2.33-2.36)

46 2.34 2.29 2.45

(2.33-2.35) (2.28-2.30) (2.44-2.47)

50 2.37 2.32 2.49

(2.37-2.38) (2.31-2.33) (2.47-2.50)

55 2.31 2.26 2.41

(2.30-2.32) (2.25-2.27) (2.39-2.43)

Average 2.21 2.15 2.33

(2.20-2.21) (2.14-2.16) (2.31-2.34)

We adjust the relationship between the parental predictors and life course occupational

earnings by controlling for key socio-demographic variables namely sex and region of

residence as advocated by (American Psychological Association, 2007).

4.6 Modelling life course occupational earnings using multilevel modelling

The advantages of treating our outcome variable as a finely-grained continuous and

repeated measure of life course SES are twofold:

1. A better opportunity of appreciating the variance in ‘hierarchical’ living conditions

(Gregg et al., 2017) between individuals obscured by the aggregate floor and ceiling

effects generated by previous social mobility studies of ‘origin’ and ‘destination’ who

have used conventional broad categorical measures (Goldthorpe & Jackson, 2007; Li

& Devine, 2011; Sturgis & Sullivan, 2008).

2. The opportunity to estimate the changing effect of childhood SES on adult SES over

the life course by interacting childhood SES with the time points of the repeated

measurements. This examination could not be undertaken with just two time points

between childhood and adulthood as with conventional ‘origin’ and ‘destination’

social mobility research (Blanden et al., 2004; Goldthorpe & Jackson, 2007; Li &

Devine, 2011).

Furthermore, a life course approach enables a more dynamic view on socioeconomic

position which can challenge the common assumption that the circumstances at time of

measurement reflect one’s position over a lifetime (Vanhoutte & Nazroo, 2016). Therefore,

analysing life course occupational earnings provides a better opportunity to evaluate the life

course influence of parental social mobility and parental socioeconomic position at age 16

and this is important with respect to our first research question. Moreover, examining

parental social mobility in this fashion allows for a life course evaluation of the gradient

constraint as set out in the introduction.

Multilevel modelling provides a method for analysing change over time whereby the

repeated measures are viewed as outcomes that are dependent on some metric of time and

predictors of interest at either level (occasion/person) and may include cross-level

interactions (Steele, 2008). Repeated observations over time, which need not be equally

spaced out, constitute level one units nested within individuals at level two and the

multilevel framework accounts for correlations of observations across time. Multilevel

modelling summarises the change in the outcome variable for each individual over the

observation period and each individual’s summarised change can be allowed to vary in

relation to the overall sample average summarised change. This individual variability can be

summarised via the random effects employed within the multilevel model (MLM) set-up.

We choose a step function MLM for our life course analysis with the time function treated

as a categorical variable as it allows us to focus on between-individual occupational earnings

development over the life course. By doing so, this MLM multivariate formulation provides

more accurate inference given the amount of variation that exists within the data compared

to more parsimonious life course models and yet is simpler to interpret than complex

polynomial growth curve models (Steele, 2014). Furthermore, using the step function MLM

strengthens the FIML approach using all available cases (Byrne et al., 2017). The statistical

analysis was carried out using MLwiN v2.32 with the default FIML method of estimation,

Iterative Generalised Least Squares (IGLS). MLwiN only listwise deletes model predictors

with missing data and not the outcome variable thereby establishing a complete-case

analysis with respect to the predictors.

Our model of interest is set up with six age dummy variables and no model intercept to aid

interpretation. Each age dummy variable has a random term attached to it allowing for

individual variation at each time point in relation to the MHOE response variable. We

employ cross-level interactions between each of the age dummies (L1) and our L2

explanatory variables, namely the Parental Socio-Economic Predictors (PSEPs) and Female,

to explore life course legacy effects of these person-level predictors in relation to the MHOE

response variable. The PSEPs are father’s social class and housing ownership status, both as

static and mobility measures, as well as mother’s education. To ensure model identification,

the age 23 cross-level interaction effects are omitted and act as reference categories. The

reference category for our nominal categorical L1 region of residence predictor is North &

Midlands. Therefore, our life course step function MLM of interest is specified in Equation 1

with subscripts t for L1 time points and j for L2 individuals. The βs represent the model’s

fixed effects and the us represent the model’s random effects allowing each cohort member

to deviate from the sample average at each time point across the life course. These random

effects are assumed to be normally distributed with a mean of zero and constant variance at

each time point across the life course and Ωu represents the variance-covariance matrix of

all these random effects consisting of 21 quantities to be estimated by our model of

interest.

Equation 1. Life course step function multilevel model of interest

logMeanHourly Occupational Earningst j=β1age23tj+β2age33tj+ β3age 42tj+β4age 46tj+β5age50tj+ β6age55 tj+β7PSEP j+β8 Female j+β9age33tj∗PSEP j+β10 age42tj∗PSEP j+β11age 46tj∗PSEP j+β12age50tj∗PSEP j+β13age55 tj∗PSEP j+β14 age33tj∗Female j+β15age 42tj∗Female j+ β16age 46tj∗Female j+β17age50 tj∗Female j+β18age55tj∗Female j+ β19South∧East t j+β20Walest j+β21Scotlandt j+u1 jage23 tj+u2 j age33tj+u3 jage42tj+u4 jage46 tj+u5 j age50tj+u6 jage55tj

u N (0 ,Ωu )

Ωu=V [u1 j⋮u6 j]=[ σu12 ⋯ σ u1u6

⋮ ⋱ ⋮σ u1u6 ⋯ σu6

2 ]t=23 ,33 ,42 ,46 ,50,55; j=1 ,….,≤14268

By setting the life course model up in this way, we can evaluate the role played in

intergenerational mobility by childhood social mobility. Model comparisons can be made

between mobility and age 16 measures as well as between different measures of childhood

advantage. It is also possible to include different measures of childhood advantage within

the same model to check for significant independent effects. We acknowledge that this

approach does not allow us to differentiate between the mobility effect and the position

effect as is possible with a Diagonal Reference Model (Zhao, Li, Heath, & Shryane, 2017) but

we believe our approach is sufficient for investigating the gradient constraint over the life

course using panel data.

5 Results5.1 Static versus mobility analysis: Father’s social classIn this section, we investigate the difference between static (age 16) and mobility measures

for father’s social class and housing ownership status separately and then combined using

the MLM of interest defined in Equation 1. In every model, we control for mother’s

education, sex and region of residence with age interactions included for mother’s

education and sex as they are L2 covariates. Table 6 displays the mean and 95% confidence

interval MHOE life course subject specific model predictions in terms of father’s social class

at age 16 contrasted with our mobility measure. To ensure we compare the same people,

we restrict the number of observations to be the same for both models. We refer the reader

to Table B1 in Appendix B of the supplementary material for the model coefficients and fits

underpinning the results displayed in Table 6. Stable high NCDS cohort members are

expected to experience higher occupational earnings throughout the life course than NCDS

cohort members who had non-manual fathers in 1974 at age 16. In terms of effect size

(which are calculated by taking the exponential of the model coefficients in Table B1,

Appendix B), belonging to the stable high group equates to a life course average boost in

occupational earnings of 19.6% compared to belonging to the stable low group. The

corresponding effect size for those hailing from the non-manual fathers at age 16 group

compared to the manual fathers group is 15.9%. This finding suggests that the age 16

measure underestimates the accumulated benefits accruing to NCDS cohort members from

stable high fathers.

Based on the concept of gradient constraint as presented in Figure 1, Table 6 offers

evidence of the expected gradational pattern over the life course. The occupational earnings

gap between upwardly mobile NCDS cohort members and stable high NCDS cohort

members widens after the age of 23 whereas the gap between the upwardly mobile group

and the downwardly mobile group narrows from age 42 onwards. Moreover, relative to the

stable low group at age 23, only those from the stable high group experienced significant,

positive age interactions translating into meaningful increments to their occupational

earnings over the life course. Again this highlights the life course benefits in terms of

occupational earnings for NCDS cohort members from stable high social class childhood

backgrounds compared to NCDS cohort members with some experience of social class

disadvantage sometime during childhood.

Table 6. Life course exponentiated Mean Hourly Occupational Earnings multilevel model

subject specific mean (95% confidence intervals) predictions grouped by Father’s Social Class; Static at Age 16 versus Mobility

Age

FSC1974

Manual

mean

FSC1974

Non-

manual

mean

FSCM

Stable low

mean

FSCM

Downward

mean

FSCM

Upward

mean

FSCM

Stable high

mean

23 6.91 8.17 6.86 7.11 7.53 8.27

(6.86-6.96) (8.07-8.26) (6.80-6.91) (6.92-7.30) (7.35-7.71) (8.17-8.37)

33 7.80 9.90 7.72 8.01 8.54 10.19

(7.71-7.90) (9.74-10.06) (7.62-7.82) (7.68-8.34) (8.29-8.79) (10.01-10.36)

42 8.84 11.10 8.71 9.20 9.68 11.42

(8.72-8.95) (10.91-11.29) (8.59-8.83) (8.74-9.67) (9.34-10.02) (11.21-11.63)

46 10.15 12.44 10.03 10.70 11.09 12.72

(10.02-10.28) (12.24-12.64) (9.89-10.16) (10.21-11.19) (10.75-11.43) (12.50-12.94)

50 10.61 12.71 10.50 11.07 11.23 13.01

(10.47-10.74) (12.51-12.91) (10.35-10.64) (10.53-11.61) (10.89-11.57) (12.79-13.24)

55 9.81 11.75 9.69 10.34 10.69 11.94

(9.69-9.94) (11.56-11.94) (9.56-9.82) (9.89-10.79) (10.34-11.04) (11.74-12.15)

Average 9.02 11.01 8.92 9.41 9.79 11.26

(8.91-9.13) (10.84-11.18) (8.80-9.03) (9.00-9.82) (9.49-10.09) (11.07-11.45)

5.2 Static versus mobility analysis: Housing ownershipTable 7 displays the mean and 95% confidence interval MHOE life course subject specific

model predictions in terms of age 16 housing ownership status contrasted with our mobility

measure. We restrict the number of observations to be the same for both models to ensure

a fair comparison. We refer the reader to Table B2 in Appendix B of the supplementary

material for the model coefficients and fits underpinning the results displayed in Table 7.

The results suggest that the age 16 measure underestimates the expected life course

occupational earnings for NCDS cohort members hailing from stable high owner-occupier

homes to a lesser extent than the father’s social class results with a reduction in the effect

size difference. By taking the exponential of the model coefficients in Table B2 (Appendix B),

we calculate the average boost to life course occupational earnings for those belonging to

the stable high group compared to belonging to the stable low group is 15.5%. The

corresponding effect size for those hailing from the home owning parents group at age 16

compared to non-home owning group is 14.3%.

Table 7 displays weaker evidence of the expected gradient constraint over the life course in

relation to occupational earnings compared with father’s social class. Relative to the stable

low group at age 23, NCDS cohort members from both the upwardly mobile and stable high

groups experienced significant positive boosts to their occupational earnings at that stage of

the life course. This finding was similar for both housing ownership and father’s social class

mobility. However, unlike father’s social class mobility, significant positive age interactions

were found for both the upwardly mobile and stable high housing ownership groups over

the life course indicating meaningful increments to their life course occupational earnings.

Those from the housing ownership upwardly mobile group experienced an occupational

earnings life course average boost of 10.6% compared to the stable low group whereas the

corresponding boost for the upwardly mobile father’s social class group was 8%. Comparing

the trajectories of the upwardly mobile groups in Tables 6 and 7, it appears that

transitioning from not owning to owning your house in childhood provides a significant

benefit with respect to life course occupational earnings compared to having a father who

transitioned from manual to non-manual work during childhood. Furthermore, there is

stronger evidence of an overlap between the stable low and downward mobility groups

from age 46 onwards with respect to housing ownership compared to father’s social class.

Table 7. Life course exponentiated Mean Hourly Occupational Earnings multilevel model

subject specific mean (95% confidence intervals) predictions grouped by Housing Ownership; Static at Age 16 versus Mobility

Age

HO1974

No mean

HO1974

Yes mean

HOM

Stable

low

mean

HOM

Downward

mean

HOM

Upward

mean

HOM

Stable

high

mean

23 6.78 7.87 6.76 7.16 7.33 8.00

(6.72-6.84) (7.79-7.94) (6.70-6.82) (6.75-7.57) (7.19-7.46) (7.91-8.09)

33 7.62 9.45 7.58 8.19 8.80 9.60

(7.51-7.73) (9.32-9.58) (7.47-7.69) (7.63-8.76) (8.54-9.06) (9.45-9.74)

42 8.64 10.58 8.61 9.40 9.89 10.70

(8.51-8.77) (10.42-10.73) (8.48-8.74) (8.72-10.07) (9.61-10.17) (10.52-10.88)

46 9.99 11.90 9.93 10.80 11.65 11.93

(9.84-10.13) (11.74-12.07) (9.78-10.08) (9.88-11.72) (11.30-11.99) (11.75-12.11)

50 10.42 12.21 10.38 10.93 11.63 12.33

(10.27-10.57) (12.05-12.38) (10.23-10.54) (10.11-11.75) (11.29-11.97) (12.15-12.52)

55 9.61 11.33 9.59 9.75 10.82 11.44

(9.47-9.76) (11.18-11.49) (9.44-9.73) (9.03-10.46) (10.48-11.15) (11.27-11.62)

Average 8.84 10.56 8.81 9.37 10.02 10.67

(8.72-8.96) (10.42-10.70) (8.68-8.93) (8.69-10.05) (9.74-10.30) (10.51-10.83)

5.3 Static versus mobility analysis: Father’s social class and housing ownership combined

In the previous sections, we have demonstrated that father’s social class and housing

ownership mobility may be unearthing different characteristics about life course

occupational earnings for the NCDS cohort members. In this section, we demonstrate that

these significant mobility measures are more powerful predictors than their static

counterparts both separately and collectively. To do so, we once again restricted the

number of observations to be the same for all models and we removed the age interactions

to enable easier interpretation of the model coefficients. We refer the reader to Table C1 for

father’s social class, Table C2 for housing ownership and Table C3 for both measures

combined in Appendix C of the supplementary material for the model coefficients

underpinning the results presented in Table 8. With the age 16 information nested within

the mobility measures and the base model nested within both the age 16 and mobility

models, we conducted likelihood ratio tests to show that mobility version of each model

presented in Table 8 is a significant improvement on the other two model versions in terms

of model fit. The AIC values also lead us to arrive at the same conclusion. This is perhaps

unsurprising given the additional information utilised by this measure compared to the age

16 version.

Moreover, this section reaffirms what previous studies have found in relation to the

significant independent effects of separate parental SES predictors when controlled for in

the same model. Whether examining age 16 or mobility measures and including/excluding

age interactions over the life course, father’s social class, housing ownership status and

mother’s education by age 16 all produce significant independent effects in relation to life

course occupational earnings when all adjusted for in the same model. We refer the reader

to Table C3 in Appendix C of the supplementary material for model results that do not

include any age interactions.

5.4 “Max” mobility measure: sensitivity analysisWe designed our measure of father’s social class mobility as set out in Table 1 and our

measure of housing ownership mobility in Table 3 to minimise missing data and maximise

use of available data hence we refer to these measures as “max”. However, in doing so we

assumed there is no difference between a father being (non-)manual at birth and/or age 7

and again between age 11 and/or 16. Similarly, we assumed no difference between being an

owner occupier at age 11 and/or 16. Therefore, we tested the validity of these assumptions

Table 8. Life course log Mean Hourly Occupational Earnings multilevel model fit statistics Base Age 16 Mobility

Father ’ sSocialClass ∫

Deviance 14953 14524 14446Parameters 32 33 35LR χ2 429 *** 78 ***AIC 15017 14590 14516

HousingOwnership∬

Deviance 15137 14738 14723Parameters 32 33 35LR χ2 399 *** 15 ***AIC 15201 14804 14793

Father ’ sSocialClass∧HousingOwnership∭Deviance 13784 13211 13167Parameters 32 34 38LR χ2 573 *** 44 ***AIC 13848 13279 13243p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *∫Occasions = 35694; Individuals = 9112; Occasions per Individual: min=1; mean=3.9; max=6∬Occasions = 35930; Individuals = 9182; Occasions per Individual: min=1; mean=3.9; max=6∭Occasions = 32764; Individuals = 8327; Occasions per Individual: min=1; mean=3.9; max=6

by rerunning our analyses using direct mobility measures between the ages of birth and 7

plus the ages of 11 and 16 and compared the results with our “max” versions. Inference

derived from the father’s social class “max” mobility measure proved to be similar when

contrasted with direct mobility measures between the ages of birth and 7 plus the ages of

11 and 16. We refer the reader to Table D1 in Appendix D of the supplementary material for

this sensitivity analysis justifying the use of our max mobility measure between birth and

age 16. Similarly, inference derived from the housing ownership “max” mobility measure

proved to be similar when contrasted with direct mobility measures between the ages of 7

and 11 as well as the ages of 11 and 16. We refer the reader to Table D2 in Appendix D of

the supplementary material for this sensitivity analysis justifying the use of our max mobility

measure between the ages of 7 and 16.

Furthermore, in another sensitivity analysis not presented here, we compared the results

presented in this paper with a restricted version of father’s social class mobility that was

constructed in the same way as housing ownership mobility using only data from age 7 to 16

and excluding data from the birth year. We found the inference remained the same for both

versions of father’s social class mobility thereby removing the possibility that discernible

differences between father’s social class and housing ownership mobility may be due to

using unequal amounts of data in their construction.

6 DiscussionThis research extends the findings on the effects of intergenerational social mobility on life

course opportunities by examining how intra-generational social mobility during childhood

affects the life course occupational earnings of adults. We have shown marked differences

in the life course occupational earnings of adults from socially stable and socially mobile

families. Our findings concur with the few previous studies that have considered the life

course variation of parental status which suggest that changes in parental socioeconomic

status matter for adult life course outcomes (for parental income in the US, see Akee,

Copeland, Keeler, Angold, and Costello (2010); for parental social class in the UK, see Plewis

and Bartley (2014)).

By employing a step function multilevel life course model, we were able to investigate the

legacy effect of parental socioeconomic predictors on achieved occupational earnings in

adulthood by introducing cross-level interaction dummy variables between the parental

predictors and age over the life course. In doing so, we examined the gradient constraint

hypothesis throughout the life course which would indicate that NCDS cohort members’

achieved occupational earnings development would be constrained differentially according

to the social mobility status of their parents during childhood. With respect to our second

research question, this study showed that such a gradient constraint is more evident with

respect to father’s social class than housing ownership status in childhood and the gap in life

course occupational earnings between those with more compared to those with less

advantaged parents would have been greater if there had been no parental social mobility

during childhood.

Specifically, the father’s social class mobility results highlight the lifelong disadvantage of

children growing up in disadvantaged socioeconomic backgrounds characterised by a lack of

mobility. This finding indicates an intergenerational accumulation effect with those exposed

to low/high SES for longer in childhood, i.e. the stable groups, have lower/higher SES in

adulthood. Indicating evidence of gradient constraint, the adult occupational earnings of

children from upwardly mobile families converge towards children from downwardly mobile

families later in the life course. In contrast, the housing ownership mobility analysis

produced evidence of the upwardly mobile being closer to the average occupational

earnings of the stable high group while the stable low group were closer to the mean

occupational earnings of the downwardly mobile group. This evidence suggests there is less

of a gradient constraint occurring with housing ownership mobility in childhood compared

to father’s social class mobility. With respect to the first part of our third research question,

these findings suggest that the two socioeconomic background measures have different

relationships with our life course outcome variable and perhaps this is because father’s

social class may play a bigger role in helping their offspring acquire employment later in life

through job exposure and social networks. Furthermore, these findings could not have been

established with a static analysis of socioeconomic background using only one point in

childhood thereby addressing our first research question. The greater use of parental

information provides for more powerful parental predictors as we have demonstrated in

this study.

Erola et al. (2016) found the bias caused, in an intergenerational socioeconomic attainment

investigation, by ignoring the life course variation when measuring parental status only at a

certain point during childhood is likely to be small implying it does not matter at what age

parental education, class or income is observed. However, our study has revealed that a

richer analysis of intergenerational social mobility can be achieved by incorporating parental

information from more than one time point during childhood. Moreover, this study has

shown the effects from parental predictors can extend into the later life course of the

children. Gugushvili et al. (2017) found that the direct effect of social origin can be stronger

when individuals’ destinations are considered later in the working lives rather than at entry

into the labour market, i.e. social origins tend to count for more over the course individuals’

employment histories. By examining more of the life course, the authors posit there is a

greater possibility of observing ‘opportunity hoarding’ by families in the higher reaches of

the income distribution who engage in exploiting their advantaged social positions in

various ways to safeguard their children’s labour market chances thereby countering

downward mobility. The evidence we present in this paper supports the idea of children

from stable high families having a greater opportunity to obtain higher life course

occupational earnings compared to NCDS cohort members who experienced some form of

disadvantage during their childhood. Moreover and in reference to our first research

question, studying the social positions of parents at multiple time points during childhood

revealed that an analysis of parental social position at one time point may underestimate

the accumulated (dis)advantage in terms of attained occupational earnings experienced by

children born into socially stable (dis)advantaged groups.

This study also demonstrated significant independent effects on NCDS cohort members’

occupational earnings for all the parental socioeconomic predictors when they are included

in the same life course model thereby addressing the second part of our third research

question. Bukodi and Goldthorpe (2012) also found independent effects when treating

parents’ social class and their level of education as predictors for the educational attainment

of their children. The authors concluded, as we do, that these variables cannot be taken as

interchangeable indices of social origins and the sole use of one variable will likely

overestimate its effects while also underestimating the full extent to which later life

inequalities are associated with social origins. Moreover and specifically in relation to the

1958 NCDS cohort, Elliott and Lawrence (2014) advocated using multiple parental measures

of socioeconomic background rather than the sole reliance on one measure. Our results in

this study support this recommendation.

While father’s social class is a widely used measure when analysing intergenerational social

mobility (Goldthorpe & Jackson, 2007; Li & Devine, 2011; Sturgis & Sullivan, 2008), our use

of the measure in this study, which was done deliberately to tease out the gradient

constraint analysis, collapsed the underlying categorical data into two categories at each

time point during childhood. By doing so, a more granular level of detail was sacrificed and it

remains to be studied whether this loss of information would reveal additional inference not

uncovered by this research. However, we believe housing ownership status is an important

additional measure of socioeconomic background given the results presented in this study.

In recent research, Vanhoutte et al. (2017) found that a longer duration of renting and

owning accommodation is related to respectively worse and better later life outcomes. Also,

downward housing trajectories are associated with significantly worse outcomes for the

children. This concurs with our findings. Haurin, Parcel, and Haurin (2002) demonstrated the

positive impact of homeownership on child outcomes compared to renting and Blanden and

Machin (2017) showed that parental home ownership increases the likelihood of the

children owning their own home by age 42. Treating home ownership as a form of wealth,

this finding exacerbates SES inequality over the life course. Moreover, social housing can

also be a later life indicator of disadvantage with children more likely to come from

disadvantaged families surrounded by disadvantaged neighbours with homes of poorer

build quality and these characteristics do not produce positive ‘value added’ effects on adult

outcomes (Lupton et al., 2009). That said, Harkness and Newman (2003) found that

homeownership is beneficial to children’s outcomes in almost any neighbourhood so there

appears to be enhanced life course SES benefits for children raised by home owning parents

which are not available for children raised by non-home owning parents.

While this study presented evidence of a life course gradient constraint, and hence an

attenuation of later life occupational earnings inequality especially in relation to father’s

social class mobility, we still acknowledge the relative privilege and lifelong benefits in terms

of higher occupational earnings accruing to children from stable high families regardless of

the measure used. In that regard, we agree with the sentiment expressed by Connelly et al.

(2014) when they suggested increased opportunities for students from poorer backgrounds

must be evaluated alongside the increased opportunities for students from richer

backgrounds. Moreover, Hartas (2015) argued that a wider narrative of social mobility

should acknowledge the existence of social class differentials in social advancement in an

effort to move away from a culture whereby individual parents are invested with the power

to overcome structural constraints and reverse inequality. In this study we find quantitative

evidence from the 1958 NCDS cohort to support the claim that social mobility during

childhood appears to have long lasting and independent effects on life course occupational

earnings.

References

Akee, R. K., Copeland, W. E., Keeler, G., Angold, A., & Costello, E. J. (2010). Parents' incomes and children's outcomes: a quasi-experiment using transfer payments from casino profits. American Economic Journal: Applied Economics, 2(1), 86-115.

American Psychological Association. (2007). Task Force on Socioeconomic Status. Retrieved from Bartley, M., & Plewis, I. (1997). Does health-selective mobility account for socioeconomic differences

in health? Evidence from England and Wales, 1971 to 1991. Journal of health and social behavior, 376-386.

Bartley, M., & Plewis, I. (2007). Increasing social mobility: an effective policy to reduce health inequalities. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 469-481.

Beller, E. (2009). Bringing intergenerational social mobility research into the twenty-first century: Why mothers matter. American Sociological Review, 74(4), 507-528.

Blanden, J., Goodman, A., Gregg, P., & Machin, S. (2004). Changes in intergenerational mobility in Britain. Generational income mobility in North America and Europe, 122-146.

Blanden, J., & Machin, S. (2017). Home Ownership and Social Mobility. Retrieved from Blane, D., Harding, S., & Rosato, M. (1999). Does social mobility affect the size of the socioeconomic

mortality differential?: evidence from the Office for National Statistics Longitudinal Study. Journal of the Royal Statistical Society: Series A (Statistics in Society), 162(1), 59-70.

Bowles, S., & Gintis, H. (2001). The inheritance of economic status: Education, class and genetics. International encyclopedia of the social and behavioral sciences: Genetics, behavior and society, 6, 4132-4141.

Bukodi, E., & Dex, S. (2009). Bad start: Is there a way up? Gender differences in the effect of initial occupation on early career mobility in Britain. European Sociological Review, jcp030.

Bukodi, E., Dex, S., & Goldthorpe, J. H. (2011). The conceptualisation and measurement of occupational hierarchies: a review, a proposal and some illustrative analyses. Quality & Quantity, 45(3), 623-639.

Bukodi, E., & Goldthorpe, J. H. (2009). Class origins, education and occupational attainment: cross-cohort changes among men in Britain: Centre for Longitudinal Studies, Institute of Education, University of London.

Bukodi, E., & Goldthorpe, J. H. (2012). Decomposing ‘social origins’: The effects of parents’ class, status, and education on the educational attainment of their children. European Sociological Review, 29(5), 1024-1039.

Byrne, A., Shlomo, N., & Chandola, T. (2017). Multilevel modelling approach to analysing life course socioeconomic status and compensating for missingness.

Connelly, R., Sullivan, A., & Jerrim, J. (2014). Primary and secondary education and poverty review. York, Joseph Rowntree Foundation.

Deary, I. J., Taylor, M. D., Hart, C. L., Wilson, V., Smith, G. D., Blane, D., & Starr, J. M. (2005). Intergenerational social mobility and mid-life status attainment: Influences of childhood intelligence, childhood social factors, and education. Intelligence, 33(5), 455-472.

Elliott, J., & Lawrence, J. (2014). Refining childhood social class measures in the 1958 British cohort study. Retrieved from

Erola, J., Jalonen, S., & Lehti, H. (2016). Parental education, class and income over early life course and children's achievement. Research in Social Stratification and Mobility, 44, 33-43.

Erola, J., & Jalovaara, M. (2017). The replaceable: The inheritance of paternal and maternal socioeconomic statuses in non-standard families. Social Forces, 95(3), 971-995.

Fry, S., Al-Hamad, A., & White, C. (2012). Patterns of Social Mobility by NS-SEC: England and Wales 1981-2001.

Goldthorpe, J. H., & Jackson, M. (2007). Intergenerational class mobility in contemporary Britain: political concerns and empirical findings1. Br J Sociol, 58(4), 525-546.

Gregg, P., Jonsson, J. O., Macmillan, L., & Mood, C. (2017). The role of education for intergenerational income mobility: A comparison of the United States, Great Britain, and Sweden. Social Forces, 1-31.

Gugushvili, A., Bukodi, E., & Goldthorpe, J. H. (2017). The Direct Effect of Social Origins on Social Mobility Chances:‘Glass Floors’ and ‘Glass Ceilings’ in Britain. European Sociological Review, 33(2), 305-316.

Harkness, J., & Newman, S. J. (2003). Effects of homeownership on children: The role of neighborhood characteristics and family income.

Hartas, D. (2015). Parenting for social mobility? Home learning, parental warmth, class and educational outcomes. Journal of Education Policy, 30(1), 21-38.

Haurin, D. R., Parcel, T. L., & Haurin, R. J. (2002). Does homeownership affect child outcomes? Real Estate Economics, 30(4), 635-666.

Korupp, S. E., Ganzeboom, H. B., & Van Der Lippe, T. (2002). Do mothers matter? A comparison of models of the influence of mothers' and fathers' educational and occupational status on children's educational attainment. Quality & Quantity, 36(1), 17-42.

Lahtinen, H., Wass, H., & Hiilamo, H. (2017). Gradient constraint in voting: The effect of intra-generational social class and income mobility on turnout. Electoral Studies, 45, 14-23.

Langenberg, C., Hardy, R., Kuh, D., Brunner, E., & Wadsworth, M. (2003). Central and total obesity in middle aged men and women in relation to lifetime socioeconomic status: evidence from a national birth cohort. Journal of Epidemiology & Community Health, 57(10), 816-822.

Li, Y., & Devine, F. (2011). Is social mobility really declining? Intergenerational class mobility in Britain in the 1990s and the 2000s. Sociological Research Online, 16(3), 4.

Lupton, R., Tunstall, R., Sigle-Rushton, W., Sabates, R., Meschi, E., Salter, E., . . . Kneale, D. (2009). Growing up in social housing in Britain: A profile of four generations from 1946 to the present day.

Mendolia, S., & Siminski, P. (2017). Is Education the Mechanism Through Which Family Background Affects Economic Outcomes? A Generalised Approach to Mediation Analysis. Economics of Education Review.

Mood, C. (2017). More than Money: Social Class, Income, and the Intergenerational Persistence of Advantage. Sociological Science, 4, 263-287.

Nickell, S. (1982). The determinants of occupational success in Britain. The Review of Economic Studies, 49(1), 43-53.

Padoan, P. (2010). A family affair: Intergenerational social mobility across OECD countries (pp. 181-198): OECD Paris.

Plewis, I., & Bartley, M. (2014). Intra-generational social mobility and educational qualifications. Research in Social Stratification and Mobility, 36, 1-11.

Power, C., Manor, O., & Li, L. (2002). Are inequalities in height underestimated by adult social position? Effects of changing social structure and height selection in a cohort study. Bmj, 325(7356), 131-134.

Schuller, T., Wadsworth, M., Bynner, J., & Goldstein, H. (2011). The Measurement of Well-being: the Contribution of Longitudinal Studies.

Steele, F. (2008). Multilevel models for longitudinal data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 5-19.

Steele, F. (2014). Multilevel Modelling of Repeated Measures Data. LEMMA VLE Module 15, 1-62. (http://www.bristol.ac.uk/cmm/learning/course.html).

Sturgis, P., & Sullivan, L. (2008). Exploring social mobility with latent trajectory groups. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 65-88.

University of London. Institute of Education. Centre for Longitudinal Studies. (2008a). National Child Development Study: Sweep 4, 1981, and Public Examination Results, 1978. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-5566-1

University of London. Institute of Education. Centre for Longitudinal Studies. (2008b). National Child Development Study: Sweep 5, 1991. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-5567-1

University of London. Institute of Education. Centre for Longitudinal Studies. (2008c). National Child Development Study: Sweep 6, 1999-2000. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-5578-1

University of London. Institute of Education. Centre for Longitudinal Studies. (2008d). National Child Development Study: Sweep 7, 2004-2005. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-5579-1

University of London. Institute of Education. Centre for Longitudinal Studies. (2012). National Child Development Study: Sweep 8, 2008-2009. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-6137-2

University of London. Institute of Education. Centre for Longitudinal Studies. (2014). National Child Development Study: Childhood Data, Sweeps 0-3, 1958-1974. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-5565-2

University of London. Institute of Education. Centre for Longitudinal Studies. (2015). National Child Development Study: Sweep 9, 2013. Retrieved from: http://dx.doi.org/10.5255/UKDA-SN-7669-1

Vanhoutte, B., & Nazroo, J. (2016). Life course pathways to later life wellbeing: A comparative study of the role of socio-economic position in England and the US. Journal of population ageing, 9(1-2), 157-177.

Vanhoutte, B., Wahrendorf, M., & Nazroo, J. (2017). Duration, timing and order: How housing histories relate to later life wellbeing. Longitudinal and Life Course Studies, 8(3), 227-244.

Zhao, Y., Li, Y., Heath, A., & Shryane, N. (2017). Inter-and intra-generational social mobility effects on subjective well-being–Evidence from mainland China. Research in Social Stratification and Mobility, 48, 54-66.

Supplemental Material: How does parental social mobility during childhood affect

socioeconomic status over the life course?

Appendix ATable A1. Life course Mean Hourly Occupational Earnings sample size (n) with Father’s Social Class in 1974 (FSC1974) and Father’s Social Class Mobility (FSCM) from 1958 to 1974

AgeOverall sample

FSC1974 Manual

FSC1974 Non-

manual

FSCM Stable

lowFSCM

DownwardFSCM

Upward

FSCM Stable high

23 10,049 4,514 2,333 5,170 524 769 2,45233 10,677 4,522 2,706 5,174 534 835 2,91742 9,612 4,054 2,508 4,565 497 760 2,69646 8,284 3,473 2,260 3,846 423 675 2,41550 8,236 3,403 2,225 3,816 415 659 2,40455 7,100 2,889 1,993 3,202 375 573 2,163Average % 62 38 54 6 9 31

Table A2. Life course Mean Hourly Occupational Earnings sample size (n) with Housing Ownership in 1974 (HO1974) and Housing Ownership Mobility (HOM) from 1965 to 1974

AgeOverall sample

HO1974 No

HO1974 Yes

HOM Stable low

HOM Downward

HOM Upward

HOM Stable high

23 10,049 3,793 3,762 4,027 181 924 3,26733 10,677 3,755 4,172 4,010 180 954 3,74742 9,612 3,411 3,764 3,612 160 898 3,35346 8,284 2,892 3,385 3,015 129 765 3,02950 8,236 2,837 3,338 2,994 129 766 2,98955 7,100 2,382 2,974 2,496 106 658 2,689Average % 47 53 45 2 11 42

Table A3. Life course Mean Hourly Occupational Earnings sample size (n) with Mother’s Education by 1974 (Age 16)

AgeOverall sample

Mother did not stay beyond school leaving minimum age

Mother stayed in education beyond minimum age

23 10,049 7,335 2,52733 10,677 7,476 3,00242 9,612 6,699 2,73046 8,284 5,688 2,44850 8,236 5,638 2,43555 7,100 4,795 2,167Average % 71 29

Appendix B overleafTable B1. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Father’s Social Class and interactions

Base Age 16 MobilityAge Coeff. (se) Coeff. (se) Coeff. (se)23 1.976 (0.006) *** 1.950 (0.006) *** 1.946 (0.007) ***33 2.105 (0.007) *** 2.064 (0.008) *** 2.059 (0.008) ***42 2.223 (0.008) *** 2.183 (0.008) *** 2.175 (0.009) ***46 2.335 (0.008) *** 2.298 (0.008) *** 2.290 (0.009) ***50 2.374 (0.008) *** 2.344 (0.008) *** 2.339 (0.009) ***55 2.290 (0.008) *** 2.259 (0.008) *** 2.251 (0.009) ***Mother’s education by 16 0.137 (0.008) *** 0.096 (0.008) *** 0.088 (0.008) ***Age*Mother’s education by 16 (Ref: Age 23*Mother’s education)33 * Mother’s education 0.071 (0.010) *** 0.050 (0.011) *** 0.044 (0.011) ***42 * Mother’s education 0.056 (0.011) *** 0.036 (0.012) ** 0.028 (0.012) *46 * Mother’s education 0.033 (0.011) ** 0.017 (0.012) 0.011 (0.012)50 * Mother’s education 0.031 (0.011) ** 0.024 (0.012) * 0.020 (0.012)55 * Mother’s education 0.026 (0.012) * 0.018 (0.013) 0.015 (0.013)Female -0.147 (0.007) *** -0.147 (0.007) *** -0.147 (0.007) ***Age*Female (Ref: Age 23*Female)33 * Female -0.078 (0.009) *** -0.078 (0.009) *** -0.077 (0.009) ***42 * Female -0.083 (0.010) *** -0.083 (0.010) *** -0.082 (0.010) ***46 * Female -0.034 (0.010) ** -0.034 (0.010) ** -0.034 (0.010) **50 * Female -0.025 (0.010) * -0.025 (0.010) * -0.025 (0.010) *55 * Female -0.013 (0.011) -0.013 (0.011) -0.013 (0.011)Region of residence (Ref: North & Midlands)South & East 0.034 (0.006) *** 0.023 (0.006) *** 0.021 (0.006) ***Wales -0.039 (0.013) ** -0.037 (0.012) ** -0.038 (0.012) **Scotland -0.010 (0.010) -0.005 (0.009) -0.006 (0.009)Father’s non-manual at 16 0.119 (0.008) ***Age*Father’s non-manual at 16 (Ref: Age 23*Father’s manual)33 * Father’s non-manual 0.053 (0.010) ***42 * Father’s non-manual 0.049 (0.011) ***46 * Father’s non-manual 0.039 (0.011) ***50 * Father’s non-manual 0.014 (0.011)55 * Father’s non-manual 0.015 (0.012)Father’s social class mobility (Ref: Stable low)Downward 0.028 (0.016)Upward 0.070 (0.012) ***Stable high 0.138 (0.009) ***Age*Father’s social class (Ref: Age 23*Stable low)33 * Downward -0.006 (0.021)42 * Downward 0.010 (0.023)46 * Downward 0.024 (0.023)50 * Downward 0.003 (0.024)55 * Downward 0.023 (0.025)33 * Upward 0.011 (0.016)42 * Upward 0.014 (0.018)46 * Upward 0.016 (0.018)50 * Upward -0.014 (0.018)55 * Upward 0.013 (0.019)33 * Stable high 0.070 (0.011) ***42 * Stable high 0.070 (0.012) ***46 * Stable high 0.054 (0.012) ***50 * Stable high 0.026 (0.013) *55 * Stable high 0.024 (0.013)

Deviance 14778 14308 14203Parameters 42 48 60

LR χ2 470 *** 105 ***AIC 14862 14404 14323

p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *Occasions = 35694; Individuals = 9112; Occasions per Individual: min=1; mean=3.9; max=6

Table B2. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Housing Ownership and interactions

Base Age 16 MobilityAge Coeff. (se) Coeff. (se) Coeff. (se)23 1.979 (0.006) *** 1.931 (0.007) *** 1.930 (0.007) ***33 2.110 (0.008) *** 2.042 (0.008) *** 2.038 (0.009) ***42 2.224 (0.008) *** 2.159 (0.009) *** 2.157 (0.009) ***46 2.337 (0.008) *** 2.275 (0.009) *** 2.272 (0.009) ***50 2.377 (0.008) *** 2.324 (0.009) *** 2.332 (0.009) ***55 2.295 (0.008) *** 2.237 (0.009) *** 2.235 (0.010) ***Mother’s education by 16 0.136 (0.008) *** 0.104 (0.008) *** 0.100 (0.008) ***Age*Mother’s education by 16 (Ref: Age 23*Mother’s education)33 * Mother’s education 0.067 (0.010) *** 0.053 (0.011) *** 0.052 (0.011) ***42 * Mother’s education 0.059 (0.011) *** 0.047 (0.012) *** 0.048 (0.012) ***46 * Mother’s education 0.032 (0.011) ** 0.022 (0.012) 0.025 (0.012) *50 * Mother’s education 0.032 (0.011) ** 0.028 (0.012) * 0.029 (0.012) *55 * Mother’s education 0.020 (0.012) 0.013 (0.012) 0.014 (0.012)Female -0.150 (0.007) *** -0.149 (0.007) *** -0.150 (0.007) ***Age*Female (Ref: Age 23*Female)33 * Female -0.078 (0.009) *** -0.077 (0.009) *** -0.076 (0.009) ***42 * Female -0.082 (0.010) *** -0.081 (0.010) *** -0.081 (0.010) ***46 * Female -0.033 (0.010) ** -0.032 (0.010) ** -0.032 (0.010) **50 * Female -0.028 (0.010) ** -0.028 (0.010) ** -0.027 (0.010) **55 * Female -0.017 (0.011) -0.017 (0.011) -0.016 (0.011)Region of residence (Ref: North & Midlands)South & East 0.030 (0.006) *** 0.026 (0.006) *** 0.026 (0.006) ***Wales -0.037 (0.012) ** -0.041 (0.012) ** -0.040 (0.012) **Scotland -0.012 (0.010) 0.017 (0.010) 0.019 (0.010) *House owner at 16 0.109 (0.007) ***Age*House owner at 16 (Ref: Age 23*House non-owner)33 * House owner 0.045 (0.010) ***42 * House owner 0.037 (0.010) ***46 * House owner 0.032 (0.011) **50 * House owner 0.009 (0.011)55 * House owner 0.022 (0.011) *House owner mobility (Ref: Stable low)Downward 0.037 (0.025)Upward 0.062 (0.011) ***Stable high 0.123 (0.008) ***Age*House ownership mobility (Ref: Age 23*Stable low)33 * Downward 0.031 (0.033)42 * Downward 0.036 (0.037)46 * Downward 0.007 (0.038)50 * Downward -0.010 (0.038)55 * Downward -0.028 (0.040)33 * Upward 0.051 (0.015) **42 * Upward 0.044 (0.016) **46 * Upward 0.067 (0.017) ***50 * Upward 0.027 (0.017)55 * Upward 0.040 (0.018) *33 * Stable high 0.048 (0.010) ***42 * Stable high 0.033 (0.011) **

46 * Stable high 0.022 (0.011)50 * Stable high 0.005 (0.012)55 * Stable high 0.016 (0.012)

Deviance 14968 14538 14509Parameters 42 48 60LR χ2 430 *** 29 **AIC 15052 14634 14629

p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *Occasions = 35930; Individuals = 9182; Occasions per Individual: min=1; mean=3.9; max=6

Appendix CTable C1. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Father’s Social Class

Base Age 16 MobilityAge Coeff. (se) Coeff. (se) Coeff. (se)23 1.982 (0.006) *** 1.952 (0.006) *** 1.947 (0.006) ***33 2.092 (0.006) *** 2.061 (0.006) *** 2.056 (0.007) ***42 2.204 (0.007) *** 2.173 (0.007) *** 2.167 (0.007) ***46 2.333 (0.007) *** 2.301 (0.007) *** 2.296 (0.007) ***50 2.376 (0.007) *** 2.343 (0.007) *** 2.338 (0.007) ***55 2.296 (0.007) *** 2.263 (0.007) *** 2.258 (0.007) ***Mother’s education by 16 0.161 (0.007) *** 0.113 (0.007) *** 0.103 (0.007) ***Female -0.171 (0.006) *** -0.172 (0.006) *** -0.171 (0.006) ***Region of residence (Ref: North & Midlands)South & East 0.033 (0.006) *** 0.022 (0.006) *** 0.020 (0.006) **Wales -0.039 (0.013) ** -0.037 (0.012) ** -0.037 (0.012) **Scotland -0.010 (0.010) -0.005 (0.009) -0.006 (0.009)Father’s non-manual at 16 0.137 (0.007) ***Father’s social class mobility (Ref: Stable low)Downward 0.033 (0.013) *Upward 0.074 (0.010) ***Stable high 0.164 (0.007) ***

Deviance 14953 14524 14446Parameters 32 33 35LR χ2 429 *** 78 ***AIC 15017 14590 14516p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *Occasions = 35694; Individuals = 9112; Occasions per Individual: min=1; mean=3.9; max=6

Table C2. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Housing Ownership

Base Age 16 MobilityAge Coeff. (se) Coeff. (se) Coeff. (se)23 1.985 (0.006) *** 1.931 (0.006) *** 1.929 (0.006) ***33 2.097 (0.006) *** 2.042 (0.007) *** 2.040 (0.007) ***

42 2.207 (0.007) *** 2.152 (0.007) *** 2.150 (0.007) ***46 2.336 (0.007) *** 2.281 (0.007) *** 2.279 (0.007) ***50 2.378 (0.007) *** 2.322 (0.007) *** 2.320 (0.007) ***55 2.298 (0.007) *** 2.242 (0.007) *** 2.240 (0.007) ***Mother’s education by 16 0.158 (0.007) *** 0.123 (0.007) *** 0.118 (0.007) ***Female -0.174 (0.006) *** -0.174 (0.006) *** -0.174 (0.006) ***Region of residence (Ref: North & Midlands)South & East 0.029 (0.006) *** 0.026 (0.006) *** 0.026 (0.006) ***Wales -0.037 (0.013) ** -0.040 (0.012) ** -0.039 (0.012) **Scotland -0.012 (0.010) 0.018 (0.010) 0.020 (0.010) *House owner at 16 0.124 (0.006) ***House ownership mobility (Ref: Stable low)Downward 0.042 (0.021) *Upward 0.083 (0.009) ***Stable high 0.137 (0.007) ***

Deviance 15137 14738 14723Parameters 32 33 35LR χ2 399 *** 15 ***AIC 15201 14804 14793p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *Occasions = 35930; Individuals = 9182; Occasions per Individual: min=1; mean=3.9; max=6

Table C3. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Father’s Social Class and Housing Ownership

Base Age 16 MobilityAge Coeff. (se) Coeff. (se) Coeff. (se)23 1.986 (0.006) *** 1.921 (0.007) *** 1.920 (0.007) ***33 2.098 (0.007) *** 2.031 (0.007) *** 2.030 (0.007) ***42 2.208 (0.007) *** 2.141 (0.007) *** 2.140 (0.008) ***46 2.336 (0.007) *** 2.269 (0.007) *** 2.267 (0.008) ***50 2.378 (0.007) *** 2.310 (0.007) *** 2.309 (0.008) ***55 2.298 (0.007) *** 2.229 (0.007) *** 2.228 (0.008) ***Mother’s education by 16 0.159 (0.007) *** 0.095 (0.007) *** 0.086 (0.007) ***Female -0.172 (0.006) *** -0.173 (0.006) *** -0.173 (0.006) ***Region of residence (Ref: North & Midlands)South & East 0.029 (0.006) *** 0.017 (0.006) ** 0.016 (0.006) **Wales -0.039 (0.013) ** -0.043 (0.013) ** -0.043 (0.013) **Scotland -0.007 (0.010) 0.018 (0.010) 0.018 (0.010)Father’s non-manual at 16 0.107 (0.007) ***House owner at 16 0.094 (0.007) ***Father’s social class mobility (Ref: Stable low)Downward 0.018 (0.014)Upward 0.056 (0.011) ***Stable high 0.123 (0.008) ***House ownership mobility (Ref: Stable low)

Downward 0.019 (0.022)Upward 0.055 (0.010) ***Stable high 0.098 (0.007) ***

Deviance 13784 13211 13167Parameters 32 34 38LR χ2 573 *** 44 ***AIC 13848 13279 13243p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *Occasions = 32764; Individuals = 8327; Occasions per Individual: min=1; mean=3.9; max=6

Appendix DTable D1. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Father’s Social Class mobility measures

Max Mobility 0-7 Mobility 11-16 Mobility 0-7 & 11-16 Mobility

Age Coeff. (se) Coeff. (se) Coeff. (se) Coeff. (se)23 1.941 (0.005) *** 1.956 (0.005) *** 1.954 (0.006) *** 1.957 (0.007) ***33 2.049 (0.006) *** 2.064 (0.006) *** 2.066 (0.007) *** 2.067 (0.008) ***42 2.162 (0.006) *** 2.175 (0.006) *** 2.174 (0.007) *** 2.176 (0.008) ***46 2.290 (0.006) *** 2.301 (0.006) *** 2.303 (0.007) *** 2.303 (0.008) ***50 2.333 (0.006) *** 2.343 (0.006) *** 2.346 (0.007) *** 2.346 (0.008) ***55 2.254 (0.006) *** 2.266 (0.006) *** 2.267 (0.007) *** 2.266 (0.008) ***Mother’s education by 16 0.107 (0.006) *** 0.109 (0.007) *** 0.107 (0.007) *** 0.106 (0.008) ***Female -0.171 (0.005) *** -0.172 (0.005) *** -0.173 (0.006) *** -0.174 (0.007) ***Region of residence (Ref: North & Midlands)South & East 0.023 (0.005) *** 0.022 (0.005) *** 0.017 (0.006) ** 0.016 (0.007) **Wales -0.029 (0.011) ** -0.029 (0.011) * -0.042 (0.013) ** -0.042 (0.014) **Scotland -0.002 (0.008) -0.002 (0.009) -0.002 (0.010) -0.002 (0.011)Father’s social class max mobility (Ref: Stable low)Downward 0.038 (0.011) **Upward 0.075 (0.009) ***Stable high 0.169 (0.006) ***Father’s social class 0-7 mobility (Ref: Stable low)Downward 0.028 (0.012) * 0.003 (0.015)Upward 0.116 (0.010) *** 0.059 (0.015) ***Stable high 0.164 (0.007) *** 0.082 (0.014) ***Father’s social class 11-16 mobility (Ref: Stable low)Downward 0.068 (0.016) *** 0.039 (0.018) *Upward 0.079 (0.013) *** 0.068 (0.014) ***Stable high 0.155 (0.008) *** 0.088 (0.013) ***

# occasions 47316 42704 32141 27777# individuals 12321 11084 8161 6973Occasions per individual (min)

1 1 1 1

Occasions per individual (Mean)

3.8 3.9 3.9 4.0

Occasions per individual (Max)

6 6 6 6

Deviance 19368 17686 13019 11085p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *

Table D2. Life course log Mean Hourly Occupational Earnings multilevel model fixed effects comparisons of coefficients (standard errors) with Housing Ownership mobility measures

Max Mobility 7-11 Mobility 11-16 Mobility

Age Coeff. (se) Coeff. (se) Coeff. (se)23 1.925 (0.006) *** 1.932 (0.006) *** 1.932 (0.006) ***33 2.035 (0.006) *** 2.045 (0.006) *** 2.042 (0.007) ***42 2.147 (0.006) *** 2.154 (0.007) *** 2.152 (0.007) ***46 2.273 (0.006) *** 2.282 (0.007) *** 2.283 (0.007) ***50 2.316 (0.006) *** 2.326 (0.007) *** 2.327 (0.007) ***55 2.238 (0.007) *** 2.248 (0.007) *** 2.247 (0.007) ***Mother’s education by 16 0.122 (0.006) *** 0.123 (0.006) *** 0.120 (0.007) ***Female -0.172 (0.005) *** -0.171 (0.005) *** -0.173 (0.006) ***Region of residence (Ref: North & Midlands)

South & East 0.029 (0.005) *** 0.029 (0.005) *** 0.027 (0.006) ***Wales -0.032 (0.011) ** -0.032 (0.011) ** -0.041 (0.012) **Scotland 0.024 (0.009) ** 0.025 (0.009) ** 0.022 (0.010) *Housing ownership max mobility (Ref: Stable low)Downward 0.040 (0.018) *Upward 0.087 (0.009) ***Stable high 0.138 (0.006) ***Housing ownership 0-7 mobility (Ref: Stable low)Downward 0.048 (0.018) **Upward 0.094 (0.012) ***Stable high 0.129 (0.006) ***Housing ownership 11-16 mobility (Ref: Stable low)Downward 0.017 (0.022)Upward 0.064 (0.012) ***Stable high 0.131 (0.007) ***

# occasions 44507 41281 35612# individuals 11568 10684 9077Occasions per individual (min) 1 1 1

Occasions per individual (Mean) 3.8 3.9 3.9

Occasions per individual (Max) 6 6 6

Deviance 18373 16939 14528p-value < 0.001 ***, p-value < 0.01 **, p-value < 0.05 *