Time Budgets, Life Histories and Social Position

Quality & Quantity 33: 277–289, 1999.© 1999Kluwer Academic Publishers. Printed in the Netherlands.

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Time Budgets, Life Histories and Social Position

JONATHAN GERSHUNYInstitute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, U.K.tel.: +44 (0)1206 872734; fax: +44 (0)1206 873151; e-mail: [email protected]

Abstract. This paper falls into two parts. It has an initial brief theoretical discussion of the use ofwell structured short- and long-term narrative accounts of individuals’ life experiences (respectivelytime-diary and work/life histories) in establishing the extent of their personal resources, and hence,ultimately, their social position. The second section discusses the development of a new social-positional indicator (currently called the Interim Essex Score or IES), and illustrates its use in aninvestigation of the consequences of women’s career breaks on their subsequent levels of access toeconomic resources.

Key words: social structure, narrative data, women’s career attainment.

1. Narrative Data

There are two sorts of narrative accounts of of our past behaviour, long-term andshort-term, which have a number of characteristics in common. They both consistof sequences of events, which are placed in order and can be placed approximatelyin time, by narrators who, having experienced the events themselves are competentto answer such questions. We all become to some degree skilled narrators of ourown stories: so these narratives are important sociological resources.

Social statisticians now often collect quite large and nationally representativesamples of such narratives. The longer-term ones are called variously work his-tories (if they deal with employment issues), life histories if they deal with othersorts of events (marital or fertility for example). The shorter term narratives aresimply the diaries used in time budget analysis. Both the event histories and thetime diaries are organised in the same way, either as a ‘repeating structure’ ofevents each with a start date/time, one or more activity characteristics, and theneither a duration or a finish time (though this third may be redundant if the startof the next event marks the termination of the present one) or as a fixed interval‘calendar’ of states, conditions or activities. And both sorts of narrative can beanalysed using the same sorts of descriptive and modelling procedures.

I will argue, that narratives are a very important source of sociological informa-tion, which will take a key place in the development of a new sort of account ofchange in social structure. Of course longitudinal data are already quite widely usedin studies of social mobility – but what I have in mind is an altogether much more

278 JONATHAN GERSHUNY

general application. Sociologists have a long-standing problem in understandingthe relationship between social structure and individual behaviour. People’s ac-tions are both constrained and enabled by social structures. But social structuresare themselves constituted by aggregations of individual behaviour. How then canthere be social change? How can social beingsact so as toalter that very system ofconstraints and opportunitieswithin which they act? This is what goes in our so-ciological jargon as “the problem of agency”, and the solution I am going to outlineis in essence hardly different from the one set out by Giddens in hisConstitutionof Society. The central point of what follows is that the new sorts of narrative dataprovide the essentialempirical basis for understanding the relationship betweenindividuals’ behaviour and social change.

2. A Recursive Model of Action

The narratives in effectconstitutepeople’s individual characteristics. We have donevarious things in the past, and some of these serve to establish precisely ‘who weare’. And we interact with the social and material circumstances which surround usin particular ways which are determined in part by ‘who we are’ and in part by thecharacteristics of the actors and institutions in our environment, so as to producenew events for the narrative. And these new events in turn may perhaps change‘who we are’. In short: we are what we have done; what we are determines whatwe do next; what we do next determines what we become. And so on. This is amodel ofrecursive determination.

2.1. THE MICRO-SOCIOLOGICAL NARRATIVE SEQUENCE

The little narratives of our marital, employment, fertility and other experiences allslot together into a single, very long and detailed, grand life-narrative. We mightthink of this as a set of nested cycles, a sort of coiled coil, in which the events ofeach day slot into weeks, and the weeks into years. Each person lies at the endof an enormously extended sequence of events, say 25 or 50 distinct activities ina day which means a sequence of hundreds of thousands of events for an adultlife. Almost all of these events are simply inconsequential. Of course, nothing thathappens can be made to unhappen, but the inconsequential events happen and areforgotten, like shouts in the street. Some events, however, have specific causal sig-nificance for subsequent events. Such events, or sequences or repetitions of events,are distinct in thatthey change thestatusor characteristicsof the person whoexperiences them.

There are two different ways in which the past events in the personal narrativecan have or acquire salience for the future life.1. A singular event may directly affect status(e.g., conceiving a child, winning

a lottery, losing a limb, perhaps falling in love or getting married). These eventshappen in a ‘one-off’ fashion, and though their consequences may under some

TIME BUDGETS, LIFE HISTORIES AND SOCIAL POSITION 279

circumstances be mitigated they tend in general to have a continuing effect onsubsequent behaviour

2. Repeated activities may cumulatively lead to the acquisition of certainsorts of functional skills or capacities or other characteristics(includingnorms, expectations and values). This may for example be childhood social-isation producing consumption skills or preferences. In this category also arethe ‘events’ of school-based or other formal education, in which we cumulatecapacities or qualifications.

The consequential events and processes are those which serve to place theindividual in the social structure. They establishsalient social-structuralcharacteristics – those attributes which determine the range of possible futureactions.

Notice that one of the traditionally important ways of acquiring salient socialcharacteristics,inheritance, falls across these two categories. In the special caseof the inheritance of financial resources the ‘event’ vocabulary may seem a littleforced – we have the particularly singular event of being born to rich parents. Butin general, inheritance, whether of skills or social connections, is achieved throughchildhood socialisation, and this does fall naturally into the theoretical language.

We tend to think of a clear separation between, on one hand, an individual’sstructural characteristics or position in the social structure, and on the other, thatperson’s behaviour. In the approach I am suggesting, of course, there can be nosuch separation.Structure has consequences for behaviour, and behaviour inturn crystallises out into structural characteristics, in an endless recursivesequence.

The narratives are quintessentially micro-social data. To understand the signif-icance or otherwise of particular events or sequences, we need to consider themin the context of more macro-sociological conditions. People live in a social envi-ronment which includes both other people, and also other sorts of institutions andactors such as families or firms or governments. And it is the interaction with thisenvironment which produces new behaviour, new events.

Things can happen to change the salience of events or sequences over historicaltime. These affect the relationship between accumulated personal characteristicand the institutional environment with which it interacts. Some examples:

• There may bea change in the distribution of personal characteristics acrossthe population e.g., skills may become less scarce).

• These may bea change in the circumstances of institutions(e.g., technologychanges, skills become less salient).

• Or there may bechange in the actions or practices of institutionsin the waythat they intercat with personal characteristics (e.g., firms alter their formaleducational requirements for employment in particular jobs).


2.2. HABIT AND ACTION

The behavioural model I am suggesting is an extremely simple one: it consistsmostly of habit, modified at particular rare but important points by choices whichmight be considered to be in varying degrees rational.

Most people have a personal moment of inertia, which maintains them on awell established trajectory, a given and for the most part unconsidered sequenceof activities through the day and the year – we get up, wash, eat, go to work, playgames, visit friends, and so on. This repetition is the outcome of the process ofrecursive determination.

But at particular strategic junctures, we take some form of action to modify thehabitual sequence. Sometimes this is forced on us (e.g., we are offered a new jobor lose one), sometimes we actively choose to consider a consequential decision(we search for a new job), sometimes we make such a choice by accident (weimpulsively accept a job offer without thought for its consequences for our familyor long term career). The action we take is a conscious modification of routine withthe aim of achieving some relatively distant object – which is a sufficient definitionof rational choice for present purposes.

At a minimum, we have the example of the impulsive job change with no moreprecise intent than to ‘do something different’. At a maximum we have a full ratio-nal analysis of the time-discounted expected future flow of benefits from the offeredjob as compared to the present one. Most actions of most people lie somewherebetween these two extremes. But the outcomes of the more considered sorts of ac-tions will tend to correspond in systematic ways to salient structural characteristics,while the less considered actions will be relatively random (though still constrainedby these same characteristics). In aggregate terms we should expect to find thestatistical associations between structural characteristics and behaviour which arecharacteristic of ‘rational choice’, but accompanied by substantial random ‘error’components.

So to set out the full model in brief, the historical sequence of events determinesindividuals’ social structural characteristics, and establishes habitual activity pat-terns or ‘routines’, as well as values and expectations. The values and expectationsinteract with the individual social-structural characteristics and the characteristicsof the institutional and other environment, to produce occasional modifications tothe routine through some sort of (limited) rational action, which in turn over timemodify the individual’s structural characteristics, values and expectations.

3. Individual Location Within the Social Structure

Broadly speaking there are three standard approaches to the empirical under-standing of location within the social structure. Social position may be viewed(depending, though this dependency is not always expressed with sufficient clarity)on the particular objectives of the analysis in hand, in terms of (i) social class, or(ii) of social status, esteem, ‘standing’ or prestige, or (iii) of personal or household


resources. The first of these approaches is distinct insofar as it allows, in additionto the enabling/disabling of participation in particular consumption or productionactivities at the individual level, for the possibility of mass mutual identificationof individuals in similar positions, and hence for sentiments of solidarity and forthe intention to engage in collective action. But such sentiments and intentions arenot necessary attributes of social positions; we may be clearly and unambiguouslylocated within the social structure without having any such class identification. Andin any case it might be argued that the basis for class identification may be no morethan the assemblages of the particular characteristics that constitute the resourcesstudied in third approach.

Clearly the model of action set out above would lead us to give preference to thethird of these approaches, and to distinguish the resources view clearly as more thana mere indicator of hierarchical ranking (as suggested in Erikson and Goldthorpe,1993: 29).

The remainder of this paper is devoted to the classification of social positionthrough the possession of salient social resources, in an approach somewhat anal-ogous to economists’ conceptions of human capital. In this section I discuss thedevelopment of a new indicator of social position, based on individuals possessionof economically salient (i.e., job-related) resources which I will call the InterimEssex Score (the IES: ‘interim’ pending further testing and development).

In the following section I show, through an application of this approach tothe investigation of the effect of domestic organisation on women’s economicposition, that the IES approach can reveal aspects of mobility between social po-sitions that are invisible when social location is viewed through the conventionalcurrent-occupation-derived class approach.

3.1. ECONOMIC RESOURCES AND‘ EARNINGS POTENTIAL’

The employment-related characteristics, constitute just one category of personalresource among other structural characteristics, in production and in consumption,and their impact on life-chances is only to be seen in interaction with other per-sonal and historical characteristics and conditions (e.g., current family position ormacro-economics circumstances). But in many respects these personal economicresources may bekeycharacteristics insofar as they can be exchanged in variousways for other sorts of production or consumption.

Theoretical expectations of which characteristics are ‘salient’ to employmentoppoprtunities include:• social and cultural capital from household of origin, particularly ‘economic

socialisation’;• educational and occupational attainment;• and employment and unemployment records.

These sorts of characteristics determine the accessibility of future earnings fromemployment. They collectively constitute the economic aspect of the individual’s


current social position; each of these characteristics has a distinct effect, and formany purposes we might wish to consider them separately. But in the present case,we wish to find a simple way of summarising the net effect of these disparatecharaceristics each given a monetary value.

The value of these economically salient characteristics might be derived as wellfrom labour market outcomes (i.e., wages) as from any. In this case, the socio-logical category of ‘salient economic resources’ corresponds reasonably closelyto the economists’ concept of ‘human capital’, and indeed both Bourdieu andColeman use this term in this context in a quite unselfconscious way (Bourdieu,1985; Coleman, 1990). We must however bear in mind that ‘human capital’ is justa metaphor, and that these accumulating human characteristics behave over timein some respects differently to physical capital; in particular, and unlike physicalcapital goods, human capital is not depleted but enhanced by use). Current wagesreflect an economic return on individuals’ deployment of their own human capitalto do a particular paid job. So we can infer the value of individuals’ current stock ofjob-related human capital straightforwardly from their current earnings and hoursof work.

A particular problem emerges at this point: everyone has the potential to earn awage, but at any given time, some are not wage-earners. Those currently not in em-ployment also have economically salient characteristics, so in estimating economicresources, we need to consider more than just current earnings.

3.2. ESTIMATING ‘ EARNINGS POTENTIAL’

Earnings potential (the IES), operationalised as hourly wage that eitheris be-ing earned in employment, or thatcould beearned if that respondent were inemployment, is clearly a consequence of various continuously accumulating char-acteristics: different quantities and combinations of education and work experienceand other personal circumstances, all have particular implications for potentialearnings. And there is a clear empirical procedure for estimating their impact:simply regress longitudinal evidence on accumulated characteristics on hourlyearnings at some particular point in time. In what follows we use data from waves1–4 of the British Household Panel Study, covering the period from 1991–1994, asthe basis for the calibration of the IES.

The procedure for estimating the economic value of accumulated job-relatedcharacteristics that follows makes use of a particular attribute of the panel approachto data collection: it gives in later ‘waves’ of data collection, information about thesubsequent earnings of those nonemployed in previous ‘waves’ of data collection.The procedure I have used here takes one or other of two different routes dependingon current employment status:• for those currently in employment: establishing the ‘structural’ effects of eco-

nomic characteristics by relating evidence of wage levels at a particular pointin time to previous labour market and other experiences.


Table I. Earnings potential

Variables in the equation: Interaction terms in the equation:

• Sex age • Hope–Goldthorpe Score with sex

• Current/most recent occupational status• Grouped HGS with age

(Hope–Goldthorpe Score) • Sex with child status

• Employment statuses over last 4 years • Employment status with sex

• Highest educational qualification

• Number of age of children Quadratic terms in the equation:

• Parents’ occupation • HGS

• Age

• Grouped HGS with age

• for those currently not in employment: using the panel evidence of structuraleffects on earned income (if any) in the following year as a basis for estimatingpotential earnings.

This approach will produce biased results, somewhat overestimating the scoresfor the currently unemployed. And there are econometric procedures which mightperhaps improve the results. But for the moment this issue is side-stepped, since itis entirely tangential to the main thrust of an article arguing for the usefulness of an‘economic resources’ approach to the understanding of social position. The current‘interim’ version of the estimation procedure seems to work effectively enough forthe purposes of the present argument.

The IES, as used in what follows, has been estimated on the basis of OLSregressions of 1994 wage rates, using the predictor variables listed in Table I.

4. A Work History Application: Work-Breaks and Women’s EconomicPotential

It is well established that work breaks for family care have negative effects onwomen’s career prospects (e.g., Dex, 1982; Jacobs, 1995). There is, to put the pointdramatically, the evidence that allows us to generalise from the well known storyof Allerednic (i.e., Cinderella-in-reverse, in which the prince marries a princessand turns her into a scullery maid); we find that the career attainments of wiveswho take substantial periods out of employment to care for their families, divergemarkedly from those of their husbands, and from those of wives who do not takesuch breaks (Gershuny, 1998).

But the Allerednic evidence, which of necessity relies on the occupational sta-tuses only of those wives who are in employment, gives us information about aparticular, and potentially unrepresentative, part of the population. What about thepositions of women, at various stages into their marriages who donot at present


Table II. Wives’ months in ft family care, in 15th year aftermarriage, by birth cohort

<7 months 7–36 >36 months N

Born 1917–21 14.1 3.8 82.2 185

Born 1922–26 17.5 10.1 72.4 308

Born 1927–31 16.6 7.5 75.9 253

Born 1932–36 9.2 9.6 81.2 261

Born 1937–41 9.5 9.9 80.5 262

Born 1942–46 14.5 12.2 73.3 344

Born 1947–51 13.4 14.2 72.3 372

Born 1952–56 14.3 17.9 67.7 279

have jobs? These may perhaps be relatively well-educated, with intermittent em-ployment but in high end jobs, who could get good jobs but choose to be outside thelabour force for the present because their husbands have sufficient incomes to meettheir needs. Or they could be in a much less satisfactory position, now effectivelyexcluded from all but the most menial of jobs because of their lack of accumulatedhuman capital.

It should be remembered that relatively high rates of women’s participationin the workforce at any one point in time are still compatible with relatively lowrates of continuous women’s employment. Table II, using a combination of BHPSmarital and work history data, shows that the proportion of women with short work-breaks (i.e., high levels of attachment to the workforce) has remained pretty muchunchanged through much of this century. And even for the most recent cohort forwhich we can make sensible estimates, still more than two-thirds of women havetaken more than 36 months out of the workforce in full-time family care after 15years of marriage. So there are clearly still important groups which are excludedwhen we concentrate on employment-related positional measures to indicate theconsequences of work breaks – this is why we should use the IES, which can bederived for all the members of the sample, irrespective of their current employmentposition.

There are certainly problems in using the IES in this way. The coefficients ofthe IES that relate the different components of education, skills and experienceto earnings power have been calibrated for the 1990s: these may not apply toearlier periods. And indeed there are reasons for suspecting that there may in facthave been systematic changes in these coefficients over time. But the problemshere are in principle no different to those, for example of applying scores derivedfrom empirical prestige rankings from the mid 1970s (as in the Hope–GoldthorpeScale) or categorical classifications associated with employment relations in theU.K. in the early 1980s (as with the Goldthorpe class schema) to the same range


Figure 1. Gap between husbands with no breaks and wives with 3+ years break.

of periods. (And with the IES approach there are other experimental possibilities,involving alternative simulations of economic resources, progressively adjustingthe coefficients according to the theoretical expectations, or in accordance withcalibrations derived from earlier data if this is available.)

But we can certainly apply the IES to earlier periods as long as we rememberthat the results are an interpretation of previous social positions from a fixed 1990sperspective. And doing so we arrive at a rather dramatic result. Figures 1 and 2show for various (wives’) birth cohorts, the proportional gap between the wivesand husbands earnings at yearly intervals through the first 20 years of marriage(these figures include only current BHPS marriages as at 1992). Figure 1 showscomparisons for those couples in which the wives took three years or more out ofthe labour force in the first 20 years of marriage. We can see no particular consistenttrend of change in successive cohorts, but if anything, the gender gap is larger forthe more recent cohorts. Figure 2, by contrast, includes those couples in which thewife took only short work breaks, or none at all. Here there is an unmistakeabletrend: the gap reduces over time.

These two pictures indicate what looks like a historically growing polarisationbetween wives who maintain their positions in the workplace and those who donot.

Table III gives the size of the husbands’ extra earnings potential 11 years afterthe first recorded marriage, for each ten-year birth cohort within the sample. Forwives who take time out of the workforce, this premium has remained pretty muchunchanged over the approximately 50 year period covered by the table. By contrast,the premium has halved from 31% to 16% over those wives who stay continuouslyin employment. Wives committed to the labour force have got progressively to


Figure 2. Gap between husbands and wives with no work breaks.

Table III. Husbands’ IES Premium relative towives with and without work breaks, after

No breaks Breaks>3 years

Born 1952–61 16% 30%

Born 1942–51 24% 34%

Born 1932–41 28% 34%

Born 1922–31 27% 25%

Born 1912–21 31% 28%

look, in economic terms, rather more like their husbands, but still lag behind them;wives out of the workforce are in overall terms in approximately the same positionsat the beginning and at the end of the period.

There is of course always the possibility that the result is driven entirely by thechanging distributions of the underlying characteristics. It could be, for example,that the convergence of the no-breaks wives with the no-breaks husbands reflectsthe growth of women’s education levels relative to men’s, or to changes in fertilitypatterns (i.e., smaller/later families providing a lesser inhibition of women’s abilityto amass human capital).

To cope with such possibilities we must turn to a more formal modelling ofthe process of determination of the IES. Table IV sets out successive stages inthe modelling process. Model 1 shows the relationships between educational level(with no qualifications as the default category and number of children with IES


Table IV. Modelling IES after 15 years of marriage (women only)

Model 1 Model 2 Model 3 Model 4

WOMAN NOTE

−1.476∗∗Higher degree 3.500∗∗ 3.520∗∗ 3.369∗∗ 3.411∗∗ 5.116∗∗1st degree 3.560∗∗ 3.590∗∗ 3.462∗∗ 3.527∗∗ 4.201∗∗HND, HNC etc. 2.683∗∗ 2.692∗∗ 2.625∗∗ 2.677∗∗ 2.980∗∗A Level 0.889∗∗ 0.917∗∗ 0.841∗∗ 0.892∗∗ 1.451∗∗O Level 0.352∗∗ 0.379∗∗ 0.296∗∗ 0.361∗∗ 0.691

CSE −0.206 −0.116 0.075 −0.093 0.261

N kids, age 35 −0.186∗∗ −0.185∗∗ −0.118∗ 0.004

. . ∗∗2 0.090∗∗ 0.090∗∗ 0.082∗∗ 0.043∗∗

< 6 months break −0.130 −0.187 −0.006

> 36 months break −0.044 −0.079∗ −0.027

Born 1917–26 0.200∗ 0.345∗∗ 0.328∗∗ 0.667∗∗Born 1927–36 0.129 0.254∗ 0.165 0.364∗∗Born 1937–46 0.118 0.078 0.061 0.176

Born 1947–56 0.136 0.034 0.068 0.089

17–26∗ <6 months −0.540∗∗ −0.462∗∗ −0.101

27–36∗ <6 months −0.230 −0.107 0.070

37–46∗ <6 months 0.455∗ 0.581∗∗ 0.530∗∗47–56∗ <6 months 0.562∗∗ 0.651∗∗ 0.488∗∗

(Constant) 4.665∗∗ 4.517 4.791∗∗ 4.622∗∗ 5.622∗∗Adj. R Sq 0.382 0.382 0.309 0.393 0.563

∗Significant at .05∗∗Significant at .005

for women only). Unsurprisingly, given that educational level and numbers of chil-dren form part of the procedure for estimating the IES, they emerge as stronglyassociated; this is not a finding, but is a necessary control for discovering any realeffects. Model 2 adds dummy variables for the birth cohorts (with 1957–1966 asthe default category); there is little significant effect – implying no significantnethistorical change in women’soverall economic position.

However the lack ofoverall change in women’s economic position hides asubstantial change amongdifferent groupsof women. In Model 3 we add in thework-break dummy variables (7–35 months being the default category) as well asa set of interactive dummy categories, which multiply the birth-cohort indicatorsby an indicator of whether the respondent has taken less than 7 months out of theworkforce during the first 15 years of marriage. Now we can clearly see the growth


in the IES score associated with short work-breaks over successive cohorts (thedefault category does not fit the trend – but this reflects a sample selection bias: therelatively few born after 1957 who already have 16pplus years of marriage are bydefinition early marriers, and early marriage is strongly correlated with low levelsof human capital). In Model 4 we add in also the numbers of children.

As we move to the full model, a clear picture emerges: we see a (small but)significant overall historical decline associated with the cohort indicators, whichmore than compensated for by the historical increase specific to those women whohave only short work-breaks. This is the polarisation effect: women with high levelsof attachment to the workforce doing better (in terms of IES) over time, womenwith lower levels of attachment doing slightly worse.

This finding of course poses more questions that answers. What are the mecha-nisms of this polarisation? Is it in fact that previously highly educated women (withrich husbands) stayed voluntarily out of the labour market, while subsequently lesswell educated women (with poorer husband are involuntarily excluded? There is asubstantial programme of work here, which I do not propose to take further inthis article. My point for the moment is simply this: understanding life coursemobility now requires that we study more than just the social positions of thosein employment at any particular point in time. We need some indicator of socialposition that, like the IES, reveals the social structural characteristics of the entirepopulation, not just the working population.

5. Conclusion

The foregoing discussion of human capital is just one example of the generalprocess through which social structural characteristics are accumulated. Just asour past employment experiences build up into capacities which fit us for new andbetter jobs (or our lack of them render us unfit), so, for example, our past activ-ities in organisations in our local communities establish us as the sorts of peoplewho can engage in these and other activities in the future, or so the last years’investments in sporting or cultural activity accrue to determine what leisure wewant and expect to enjoy, and just so do our past patterns of emotional relationshipwith our families and friends form the basis on which our future relationships arebased. This is a simple, general and plausible model, and one which is reasonablystraightforward to research in a systematic quantitative manner: we seek indicatorsof these various behavioural attributes in the form of questionnaire items (which weare probably best-advised to devise through serious and well structured qualitativeinvestigations); and we ask these questions repeatedly, of the same individuals, tosee how the succession of earlier states affect the later ones. Then we build modelsof the recursive determination of our personal characteristics and our behaviourand beliefs and aspirations.

And it is here that we see the real promise of the narrative data for sociology. Itis precisely these long-term ‘life history’ and the short-term ‘time-diary’ datasets,


now increasingly available to sociological researchers, that are the necessary rawmaterials for the construction of these recursive determination models.

References

Bourdieu, P. (1985).Distinction, London: RKP.Coleman, J. S. (1990).Foundations of Social Theory.Boston: Belknap.Dex, S. (1984). Women’s work histories: An analysis of the women and employment survey,

Department of Employment Research Papers 44.Erikson, J. & Goldthorpe, J. H. (1991).The Constant Flux.Oxford: Clarendon Press.Gershuny, J. (1998). Thinking dynamically: Sociology and narrative data. In: L. Leisering & R.

Walker (eds)The Dynamics of Modern Society.Bristol: The Policy Press, pp. 34–48.Giddens, A. (1984).The Constitution of Society. Cambridge: Polity Press.Jacobs, S. (1996). Routes from origins to destinations: A longitudinal study of women’s social

mobility, occupational and marital using retrospective data, Doctoral Thesis, Oxford University.Jacobs, S. (1999). Trends in women’s career patterns and in gender occupational mobility in Britain.

Gender, Work and Organisation6(1).

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Time Budgets, Life Histories and Social Position