IntergenerationalPersistence andEthnic Disparitiesin Education

P E R E NG Z E L L

C© Per Engzell, Stockholm 2016

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Introduction

WHAT should we make of the influence that children’s family of originexerts on their educational achievement and attainment? And, how

do we assess the educational success of children from immigrant minoritiesrelative to children of ethnic majority origin? These two questions and thenexus between them form the motivating concern of this thesis.

The reproduction of social (dis)advantage from one generation to thenext is a topic of longstanding sociological interest and intimately bound upwith liberal ideals of equal opportunity. Parents with greater skills and re-sources are able to invest in their children in numerous ways, and counter-acting this imbalance is a key objective for education policy in many coun-tries. Since education is an important determinant of outcomes throughoutthe life course, inequalities that emerge at this stage will have major reper-cussions not only for equality of opportunity, but also for more instrumen-tal objectives such as social cohesion or economic e�ciency.

Similar concerns underlie the study of ethnic di�erentials in education.In Sweden and other countries, increased diversity following a period ofsustained net immigration has brought new challenges to the study of edu-cational disadvantage. The groups of most acute concern in the educationsystem are not the same as they were thirty or forty years ago. Here, thepicture is complex in that the story is not uniformly one of minority disad-vantage, and conclusions might di�er depending on the outcome studiedor the strategy of analysis chosen. Our understanding is also hampered bythe fact that we often have a remarkably poor grasp of the home conditionsthat these children come from.

In studying how educational trajectories di�er by social background,a common approach has been to distinguish a number of discrete classesbased on the occupation and employment status of parents in the house-hold (Erikson 1984). This is not the only index of a family’s social standingimaginable; others include parents’ education, income, wealth, various con-

This introduction benefited from extensive comments and discussion by the SOFILevel-of-Living group (as did, indeed, the whole thesis). I want to thank youall but in particular Robert Erikson, Peter Fallesen, Jenny Torssander, and, asalways, Jan O. Jonsson and Carina Mood. Remaining errors and omissions aremy own and not to be duplicated without credit to the author.

1

2 INTRODUCTION

tinuous indices of occupational status, or composite measures drawing onthese and other items such as home possessions.1 A popular if controversialmeasure has been the number of books in the home, and STUDY II o�ers acritical examination of this proxy on conceptual and empirical grounds.

In principle, these various measures come with di�erent bundles ofresources that may assist children in di�erent ways throughout the lifecourse.2 In practice, such processes are hard to disentangle due to the factthat status measures are correlated both internally and with personal at-tributes of parents that remain unobserved. Consequently, it is often rea-sonable to treat them as manifestations of a common underlying dimensionof advantage. The first half of the thesis takes this as a given and asks whichsuch measures are most empirically reliable. STUDY I makes a qualified casefor relying on information about parents’ occupation, especially when chil-dren are surveyed. STUDY II goes on to dissect the classical measurementmodel underlying most e�orts to undo the impact of observational errors,in the context of the widely used books-at-home proxy for social back-ground.

Further challenges arise when conventional statusmeasures are broughtto bear on immigrant populations, where they threaten to conceal as muchas they reveal. These concerns are laid out in the second half of the thesis.Current occupation could be a weak indicator of class culture insofar asimmigrants are likely to have su�ered downward social mobility, or maybe out of the labour force altogether. STUDY III asks whether educationalattainment can be relied on to provide more valid information about theclass origins of minority parents. It distinguishes an absolute and relativedimension of education and estimates ethnic achievement gaps adjustingfor each. The findings from this study are then followed up in theoretical

1 Certain models also presuppose a given scale of measurement. Continuous mea-sures are required by the influential path analytical approach pioneered by Dun-can (1966, cf. Wright 1934) and by the research program on status attainmentthat grew from it (Blau and Duncan 1967, Sewell, Haller, and Portes 1969,Sewell and Hauser 1975, Jencks, Crouse, and Mueser 1983).

2 A canonical treatment of social stratification as multidimensional is that of We-ber ([1922] 1978). Subsequent studies addressing the topic from various anglesinclude Chan and Goldthorpe (2004, 2007), Jæger (2007), Weeden and Grusky(2012), Bukodi and Goldthorpe (2013), Torssander (2013), Breen, Mood, andJonsson (2016), and Erikson (2016).

INTRODUCTION 3

detail in STUDY IV, which examines immigrants’ own perceptions of theirstanding in society.

COMPARATIVE EDUCATIONAL INEQUALITY

Within the Swedish context, early contributions to the study of social re-production in education were made by Gunnar Boalt (1947). In his dis-sertation, Boalt studied how social selection played out at the transitionbetween di�erent stages in the schooling system, and found a lingeringinfluence of social class background that diminished at higher transitions(cf. Mare 1980). Political interest in the issue fueled further investigationsinto this area, in the form of Härnqvist’s (1958) governmental report onthe untapped ‘pool of talent’ (begåvningsreserven). In the United States, acommissioned report by James Coleman and associates (1966) became aclassic in the field, together with works by Jencks and coauthors (1972,1979). Similar motivations lay behind Erikson and Jonsson’s (1993) semi-nal investigation which later led to an influential book, Can Education BeEqualized? (1996a), and numerous other publications (Erikson and Jonsson1994a, 1998, 2002, Jonsson and Erikson 2000, 2007).

One key interest of studies in this area is comparative: we want to knowwhether the strength of intergenerational persistence in education andother stratification outcomes is particularly pronounced in certain kindsof societies over others (Breen and Jonsson 2005). A presumption is thatequalisation of living conditions and expanded opportunities for furthereducation should cause the influence of social origin on education to di-minish over time. This pattern has at times proven di�cult to detect, to theextent that claims of little trend or variation have been recurrent (Shavit andBlossfeld 1993). A trend toward equalisation is, however, well documentedfor Sweden (Erikson and Jonsson 1996a, Rudolphi 2013) and has more re-cently been established for several other countries (Breen et al. 2009, 2010).

Still, it remains an issue of contention whether widening access to ed-ucation represents a real change in the underlying transmission patterns ormerely a shift to more subtle forms of stratification (Lucas 2001, Gerberand Cheung 2008). Hällsten (2011) documents an important ‘horizontal’dimension of inequality within higher education in Sweden: students fromunderprivileged homes on average opt for programmes of shorter duration,less prestige, and lower return in the labour market (cf. Erikson and Jon-

4 INTRODUCTION

sson 1994b). Only studying the progression to a given level of educationthen, could mask substantial heterogeneity within that level. In France,Ichou and Vallet (2011) show that equalisation of access to the baccalauréatappears to have been o�set precisely by such horizontal stratification.

Seeking to contrast di�erent stratification regimes, sociologists rou-tinely estimate an equation where some measure of academic success is re-gressed on one or a set of proxies for the family’s socio-economic status (seeSTUDY I, II, and III). If family background thus measured turns out to havegreater predictive power in one context than in others, this is typically re-ferred to with the blanket term ‘inequality of opportunity’ (e.g., Breen andJonsson 2005). But the relationship between parameters estimated in in-tergenerational models and the political ideals that motivate them is hardlyobvious. The moral status of intergenerational persistence depends less onits strength than on why children of academically successful parents tend toenjoy a higher probability of success (Swift 2004, Jencks and Tach 2006).

One crucial distinction noted already by Boalt (1947) is that betweensocial di�erences generated by school performance on the one hand, andthose generated by the inclination to remain in education conditional onprevious performance, on the other. Following a terminology that Boudon(1974) popularised drawing on Girard and Bastide (1963), these respectiveprocesses are sometimes labelled ‘primary’ and ‘secondary’ e�ects of socialorigin (Erikson et al. 2005, Jackson 2013). The distinction is illuminatingbecause partly di�erent processes are likely to underlie each. In the Swedishcontext, it is estimated that close to two thirds of social class di�erences ineducational careers are accounted for by di�erential school performance(Erikson and Rudolphi 2010). Given the power of achievement di�er-ences in generating educational inequalities, understanding their sourcesis of some importance.

ORIGINS OF ACHIEVEMENT DIFFERENTIALS

The Coleman report on Equality of Educational Opportunity famously con-cluded that “schools bring little influence to bear on a child’s achievementthat is independent of his [sic] background and general social context”(Coleman et al. 1966: 325). The backdrop of this reasoning is that mostvariance in academic performance occurs within schools rather than be-tween them; in Sweden at the turn of the millennium this figure was well

INTRODUCTION 5

above 90%, although it has dropped slightly in the last decade (Erikson1994, Östh, Andersson, and Malmberg 2013). While part of this within-school variability reflects purely idiosyncratic di�erences between students,or classroom-specific influences such as that of a particular teacher, much ofit traces its origin to the family. The shared family background of Swedishsiblings explains about 50% of the variance in school grades and this hasbeen stable over time (Björklund, Lindahl, and Sund 2003).3

The relatively low proportion of variability between schools does notby itself imply that they have little import in generating social inequali-ties. To make this argument, we must also assume that on average schoolsworks to reduce, rather than magnify, social di�erences between students.At the time of Coleman’s writing this was not a given; see Bowles andGintis (1976) for a dissenting view. By now, there is considerable evidenceto substantiate this belief (Raudenbush and Eschmann 2015).4 The find-ing that achievement di�erences by social background emerge well beforeschool entry suggests as much (Feinstein 2003, Bradbury et al. 2015). So,too, does the fact that social di�erences grow faster during summer holi-days when children are not in school (Heyns 1978).

Traditionally, sociologists have been wont to attribute family resem-blance to early childhood socialisation while acknowledging (often in pass-ing) that parents also contribute to the genetic makeup of their children.Oneway to assess the weight of the social environment is to study the influ-ence that adoptive parents have on children to which they are not biologi-cally related (Björklund, Lindahl, and Plug 2006, Holmlund, Lindahl, andPlug 2011). Typically, associations between parental education and chil-dren’s outcomes shrink to about half their size when this is done. Anothersource of information is the correlation between biologically unrelatedchildren reared together, reflecting the proportion of variance explainedby all environmental factors that step-siblings share. Whereas the biologi-cal sibling correlation in IQ and school grades is around 0.50, estimates for

3 For earlier applications of this approach, see Corcoran, Jencks, and Olneck(1976), Hauser and Mossel (1985), and, on Swedish data, Erikson (1987).

4 On the contrary, the amount of variability observed between schools mightoverstate their causal importance if students are not randomly allocated acrossthem. Brännström (2008) reports that a limited number of individual-level co-variates su�ce to explain more than 70% of between-school variance in present-day Sweden.

6 INTRODUCTION

non-related siblings tend to range between 0.25 and 0.35 (Bouchard 1996,Plomin and Spinath 2004).

These figures are lower than for biologically related family members,yet large enough to suggest that the environment matters. What does thisinfluence consist of ? That which first springs to mind is perhaps variousforms of cognitive and verbal stimulation in the home (Hart and Risley1995, Lareau 2003, Jæger and Breen 2016). Existing evidence of the rel-evant socialisation processes tends to be rather anecdotal, so this is an areathat cries out for further research.5

A separate type of explanation that has gained increasing prominenceconsists of aspects of ‘nurture’ that cannot readily be characterised as so-cialisation: childhood nutrition, experience of stressful life events, and ex-posure to various toxins fall in this category (Evans 2006, Evans and Kim2013, Takeuchi et al. 2016). Recent research has attempted to locate suchprocesses to the childbearing period, through maternal stress or inferiornutrient intake and its impact on foetal development. For example, Aizerand coauthors (2016) compare siblings exposed to di�erent levels of cor-tisol (a stress hormone) while in utero and find di�erences in IQ, health,and attained education.6 It remains an open question whether biological-environmental factors such as these can account for a meaningful part ofthe intergenerational transmission of advantage.

The sociological investedness in environmental causation can be con-trasted with findings from twin and other family studies in behaviouralgenetics. In its canonical form, this research calculates correlations in atrait separately for fraternal and genetically identical twin pairs. Assum-ing, among other things, no assortative mating and that the environmentsshared by one twin type is no more alike than that of the other, twice thedi�erence between the two correlations is an estimate of the population

5 As an aside, sibling correlations also include a number of influences not directlyattributable to parents, including that which siblings exert on each other. Thefinding that siblings who are closer in age resemble each other more is sug-gestive of such influence but might also be the result of time-varying parentalresources, exposure tomore similar school settings, etc. (see Sundet, Eriksen, andTambs 2008, and, for contrary evidence, Conley and Glauber 2008, Björklund,Hederos, and Jäntti 2010).

6 Other studies have examined e�ects of family deaths (e.g., Class et al. 2011) ornatural disasters (e.g., Torche 2011), arguing that unborn children exposed tothese events experienced inferior outcomes as a result of maternal stress.

INTRODUCTION 7

variance accounted for by genetic likeness, also called heritability (Gold-berger 1979). Reviews of this research have repeatedly claimed to find thatfamily environment explains only a negligible part of important outcomessuch as IQ in adult age (Turkheimer 2000, Polderman et al. 2015). Howcan these views be reconciled?

It is important to recognise that a larger environmental componentis usually found when traits are measured at younger ages (Plomin et al.1997, Bouchard 2013), for more specific competencies such as verbal flu-ency (Bouchard 1998, Nielsen 2006), and for complex social outcomes suchas years of schooling (Branigan, McCallum, and Freese 2013). It is also pos-sible to criticise these studies based on their assumptions. For example, ifgenetically identical twins are treated more similarly, heritability estimateswill conceal a component of environmental origin. On the other hand, ifassortative mating takes place – meaning, spouses overlap genetically morethan the average random pair of individuals – the influence of genotypewill be underestimated by this design.

Attempts to test these simplifying assumptions tend to find that the stan-dard results are surprisingly robust (e.g., Björklund, Jäntti, and Solon 2005),so dismissing this research out of hand will not do. A more serious limita-tion of heritability estimates lies in their interpretation. Heritability beinga purely descriptive concept, it is open to the standard concern of omittedexplanatory factors. That is to say, heritability estimates include not onlythe direct influence of genes (if, indeed, such a thing exists), but also pas-sive, evocative, and active gene–environment correlations.7 It is importantto see that this is not a shortcoming of the empirical studies, but a caveat totheir interpretation.

Sociologists could arguably do much more to untangle the complexweb of causation at the interface of biological and social inheritance, andthis is an area that is likely to see considerable development in the future(Freese 2008, Freese and Shostak 2009, Conley, Fletcher, and Dawes 2014,Conley 2016, Nielsen 2016). An often overlooked point is that while genesmay be highly predictive for individual outcomes, they will explain a largefraction of inherited educational advantage only if the same genetic vari-ants predict educational success across two or more generations. Yet theevidence uncovered by Flynn (1987, 2012) of historical gains in some cog-

7 For more detailed expositions of these problems, see Goldberger (1979), Jencks(1980), Bowles and Gintis (2002), Manski (2011), and Stenberg (2013).

8 INTRODUCTION

nitive domains coupled with stagnation in others suggests that society de-mands di�erent skills of each generation and molds them thereafter, whichwould lessen the role of genes in the intergenerational transmission of sta-tus.

Incidentally, Flynn’s discovery is also highly suggestive of the potencyof long-term environmental exposure as a cause of group di�erences in intel-lectual achievement, by demonstrating the immense gains that some pop-ulations have experienced in recent history. Consider the remarkable factthat in the three decades between 1952 and 1982, Dutch cohorts gained 113of a standard deviation on Raven’s Progressive Matrices, an intelligence testnot unlike that used in STUDY I and III of this volume (Flynn 1987: 172). IQis, by construction, a normally distributed trait so this would imply that theDutch cohort of 1982 scored on average at the 90th percentile of the 1952distribution. Dickens and Flynn (2001) present a model that reconciles anenvironmental explanation for these secular changes with high estimates ofindividual heritability.

WHICH TRAITS DO SCHOOLS REWARD?

While most human traits are passed on from parent to child to some degree,not all of them can be expected equally relevant for explaining stratificationin education. A crucial question is therefore which specific attributes arerewarded by teachers and by the institutional structure of schooling. Thetypes of abilities that are believed to be rewarded in school range fromintelligence, through behavioural skills and traits, to the ability to masteraspects of a highbrow or majority culture conducive to teacher approval.

Intelligence is often claimed to be the most important predictor ofachievement and, indeed, IQ tests were first developed by Alfred Binet forthe purpose of predicting academic success (Neisser et al. 1996). Despitean extensive and at times heated debate on the cohesion and utility of in-telligence as a concept, there is little doubt that whatever it is that com-mon IQ tests measure, it has substantive predictive power for a range of lifeoutcomes (JJJJJJJJJJJJJJJJJencks et al. 1979, Sternberg, Grigorenko, and Bundy 2001).A recent meta-analysis puts the average correlation with school grades at0.54, corrected for sampling andmeasurement error (Roth et al. 2015). Ar-guments about causality aside, this dependence still leaves more than two

INTRODUCTION 9

thirds (1 ´ 0.542 “ 0.71) of the variance unexplained, so it is clear thatintelligence alone cannot explain how well a child will perform in school.8

Skills that might explain the remainder are often grouped together un-der the slight misnomer ‘noncognitive’, but are perhaps better described asaspects of character or personality. Bowles and Gintis (1976) were earlyto argue that traits such as “agreeableness, extroversion, work orientation,emotionality, and helpfulness” would be relevant to consider in the socialstratification process (p. 135, cf. Jencks et al. 1979). A relatively large lit-erature has sprung up on this topic since (see Farkas 2003, Bowles, Gintis,and Groves 2006). In spite of some fervent advocates (Cunha et al. 2006,Heckman, Stixrud, and Urzua 2006), it is reasonable to say that for the mostpart, this literature has failed to deliver fully on its initial promise. Proba-bly the main exception is conscientiousness – the disposition to plan aheadand exert sustained e�ort – associated with academic grades to an extentapproaching that of intelligence in some studies (Poropat 2009).9

Mood, Jonsson and Bihagen (2012) use one of the more complete mea-sures of personality employed in any large-scale study to decompose thecorrelation between fathers’ years of schooling and that of their sons. Theirinventory is from the Swedishmilitary enlistment and draws on assessmentsby professional psychologists in four di�erent dimensions: sociability, per-severance, engagement, and emotional stability. These character traits to-gether account for about 5% of the father-to-son education correlation, tobe compared with a corresponding 37% for cognitive ability in their analy-sis. There are some caveats here. Noncognitive traits are arguably harder tomeasure which could lead to underestimation.10 Another relevant consid-

8 Moreover, IQ is far from perfectly transmitted across generations. Bouchard andMcGue (1981, in Bowles and Gintis 2002) report intergenerational correlationsin the range 0.42 to 0.72, where the higher figure refers to estimates averagingover more than one parent or sibling. Björklund, Hederos, and Jäntti (2010)report a father-to-son correlation of 0.35 in Swedish enlistment data.

9 To what extent e�ort is a stable or context-dependent characteristic is an inter-esting question that appears not to have been fully resolved. Some theories ofeducational stratification tend to treat it as a matter of autonomous choice (e.g.,Breen 1999), and if so, the question of motivation that has received extensivecoverage in psychological literature arguably becomes pivotal (see Ryan 2012).

10A crucial omission of this study and others drawing on conscription records isof course any data on women. Apart from the fact that girls mature earlier thereis little to suggest that psychological processes of intellectual achievement di�ersubstantially for men and women (cf. Hyde 2005, Halpern 2013). The supply of

10 INTRODUCTION

eration is that they might be more malleable than intelligence and thereforehold more promise as a target for policy interventions (cf. Holmlund andSilva 2014).

The ‘cultural capital’ hypothesis of educational inequality is most closelyassociated with French sociologist Pierre Bourdieu (1977), but has, as atestament to the evocative nature of his theorising, taken on many di�er-ent interpretations in the literature (Lamont and Lareau 1988, Lareau andWeininger 2003). The core argument is that familiarity with attributes ofa dominant culture will allow children of privileged origin to master thecurriculum with relative ease.

A strong form of this hypothesis would posit that such cultural famil-iarity is irrelevant for the actual tasks that schooling is supposed to preparefor. To my knowledge, there is no compelling evidence to back up thisnotion, at least not beyond the French case that Bourdieu describes (seeKingston 2001 for a review). In fact, in the Swedish system, teachers appearto grade students from academically weak backgrounds more leniently, notless (Klapp Lekholm and Cli�ordson 2009), discrediting the notion of cul-tural bias. A weaker form of the cultural capital thesis (cf. Swidler 1986)allows substantial overlap with the same types of skills sought after in thelabour market. Arguably, this brings us into the territory of the varioustraits discussed earlier and strips the theory of much of its distinctive flavour.

Needless to say, there are various ways that parents can help their chil-dren to achieve in school over and above any endowed ability: by encour-aging them, setting goals and norms, monitoring their progress, assistingin school work, advising them to strategically distribute e�ort, etc. (Eriksonand Jonsson 1996b). Much of what parents do to set norms andmonitor be-haviour may also occur in concert with the wider community, somethingoccasionally referred to as ‘social closure’ (Coleman 1988).11 Togetherwiththe influence that students exert on each other, these community e�ects aresometimes captured under the umbrella term ‘social capital’ (Mouw 2006,cf. Coleman 1988).

noncognitive traits like attention and work ethic seems to di�er considerably bygender, however, and is a leading explanation of women’s outperforming malesin school grades and educational attainment across the industrialised world (seeBuchmann, DiPrete, and McDaniel 2008, Bertrand and Pan 2013).

11Not to be confused with the more common usage of this term for attempts of asocial or economic clique to secure advantages by engaging in exclusionary ormonopolistic behaviour (Weber [1922] 1978, Weeden 2002).

INTRODUCTION 11

Because of these and other processes, it is clear that school grades reflectmore than raw ‘ability’ – even if some literature tends to treat grades asmoreor less interchangeable with scores on cognitive tests designed to stand freeof the curriculum. As an ability measure, grades are both broader andnarrower than cognitive tests. Broader because they reflect a wider paletteof skills, including discipline and the exertion of sustained e�ort over time;narrower because they capture only a subset of students’ potential that hasbeen more or less consciously directed at school success. At the same time,it is di�cult to draw a sharp line between the two, as even the purportedlymost ‘culture-free’ test contains a component of familiarity with contextand habitual learning (cf. Resnick 1987, Saxe 1988).

THE ANATOMY OF EDUCATIONAL CHOICE

Regardless of what underlies social class di�erences in educational perfor-mance, they are likely to persist as long as the family remains the unit ofprimary socialisation. This makes it interesting to focus on the part of ed-ucational success not accounted for by performance, namely secondary ef-fects. Here, explanations of a slightly di�erent kind are called for. Someearly influential studies in sociology pointed to cultural di�erences trans-mitted across generations, or class-specific values or orientations (Hyman1953, Kohn 1959, Sugarman 1966). This is certainly in line with recentsuggestive evidence that parents may encourage their children to pursueactual occupational destinations qualitatively similar to their own (JJJJJJJJJJJJJJJJJonssonet al. 2009, for a critique see Erikson, Goldthorpe, and Hällsten 2012).

In contrast, quantitative sociology of more recent vintage has been atpains to point out that observed attainment di�erentials could result fromrational decision making under di�erences in actual or perceived costs andbenefits of academic education (Boudon 1974, Jencks, Crouse, and Mueser1983, Gambetta 1987, Erikson and Jonsson 1996b, Goldthorpe 1996, Breenand Goldthorpe 1997, Morgan 2005). For example, the model of Eriksonand Jonsson (1996b) stipulates that the student’s evaluation of a given edu-cational track will be subject to an assessment where the derived utility isthe product of the subjective probability and the expected benefits of suc-cessful completion of that track, discounting associated study costs. Thereare a number of mechanisms that could lead expected utilities to di�er bysocial background.

12 INTRODUCTION

First, the economic costs of prolonged education will di�er dependingon the extent to which parents are able to provide support during the stud-ies. Although direct costs of education are fairly minimal in the Swedishcontext, indirect costs associated with living expenses and foregone earn-ings during studies may be considerable. Second, psychological costs interms of e�ort as well as the probability of successful completion mightdi�er depending on whether parents are able provide qualified help withschool work. Third, the benefits of education could di�er if social connec-tions provided by parents are instrumental in translating an attained degreeinto outcomes in the labour market (e.g., finding a job). Fourth, studentsfrom privileged backgrounds may have more accurate information aboutthe costs or benefits of academic education, or more detailed knowledgeabout the education required to reach a given destination, leading them tomake more informed choices.

A fifth and final mechanism occupies something of a middle groundbetween cultural and rationalist explanations, and refers to parents’ socialposition as an important anchor point in evaluating benefits of status attain-ment. Specifically, individuals are expected to be more concerned aboutavoiding downward social mobility than they are about pursuing upwardmobility and hence their main concern should be to attain a position atleast that of their parents. Although a pivotal assumption in much soci-ological research on class attainment (Keller and Zavalloni 1964, Boudon1974, Erikson and Jonsson 1996b, Goldthorpe 1996, Breen andGoldthorpe1997), this explanation also has a�nities in economic prospect theory andthe psychological concept of loss aversion (Kahneman and Tversky 1979),as Erikson and Jonsson note (1996b: 29). Subjective processes of statusevaluation are the subject of STUDY IV, and implicit in STUDY III.

Although many studies have purported to test rational choice explana-tions against somemore or less coherently specified alternative,12 consider-able care must be taken if such explanations are not to lapse into tautology.This is a point elaborated by Mood (2009) who argues that the dilemmafaced by rational choice theorists is that of choosing between “an unrealis-tic but testable theory and a realistic but untestable theory” (p. 271). Thegreater the number of purposes that are subsumed as rationally legitimate

12 For a comprehensive review, see Kroneberg and Kalter (2012). More recentwork includes Jæger and Holm (2012), Breen, van de Werfhorst, and Jæger(2014), and Holm and Breen (2016).

INTRODUCTION 13

‘ends’ by the theory, the more realistic it becomes but at the expense of giv-ing it an increasingly ad hoc flavour. Mood suggests that a credible theorywould have to proceed by making ends endogenous to the social context,but it is not entirely clear to what extent this preserves the rationalist credoor marks a departure from it.

The idea that class di�erences in educational choice are easier to levelthan di�erences in performance depends largely on the assumption thatthese choices are the outcome of rational, conscious thought. There mightbe good grounds to question this assumption. STUDY III shows that in theimmigrant population under study, parents’ years of schooling predict chil-dren’s achievement whereas children’s aspirations are better predicted byparents’ relative place in the origin distribution of education. If this findingcan be given a causal interpretation and extrapolated to the population atlarge (two big ifs), the upshot would seem to be that raising the average ed-ucation level in one generation will raise the performance level of the next(cf. Dickson, Gregg, and Robinson 2016), but keep the class patterning ofaspirations largely intact.13

SCHOOL CONTEXTS AND PEER INFLUENCE

If students of di�erent origin are systematically channeled into separateschools or tracks, interactions between them could serve to magnify socialor ethnic di�erences. That students in the same schools, classrooms, andsocial circles influence each other is intuitively reasonable, and has beenvariously referred to as social interactions, network externalities, or peere�ects (Sacerdote 2011). Empirically, such processes are notoriously hardto pin down due to a number of inferential obstacles (Angrist 2014). Theseinclude selection (are outcomes similar just because like associates with like?),the reflection problem (if influence occurred, who influenced whom?), andcommon shocks (is similarity due to something else entirely, such as a sharedteacher?). Yet, there is a strong intuition that they should matter, as evi-denced by the lengths some parents go to in order to secure ‘good’ neigh-bourhoods or schools for their children.

The social context is perhaps especially likely to matter for aspira-tions and educational choice, although surprisingly this is an area that hasreceived less attention than corresponding influences on school achieve-

13 Sara Roman helped draw my attention to this interpretation.

14 INTRODUCTION

ment (JJJJJJJJJJJJJJJJJonsson and Mood 2008, Sacerdote 2011). A considerable challengeadding to the inferential obstacles listed above is that the sign of peer e�ectsin choice is not theoretically unambiguous.

On the one hand, being surrounded by a high-aspiring environmentcould serve to raise one’s aspirations, as peers’ aspirations might ‘rub o�’much like those of parents or other significant others. In progressingthrough formal education, students might also choose to apply to the verysame institutions as their current schoolmates because they value their com-pany or that of others like them. On the other hand, peers’ achievementcould inform self-assessments by contrast so that, all else equal, being sur-rounded by high achieving peers makes one’s own achievements pale incomparison, putting a damper on aspirations (Erikson 1994). Social psy-chological literature refers to this mechanism (or rather, its inverse) as the‘big fish, little pond’ e�ect (see Marsh 1987, Marsh and Seaton 2015).

Jonsson andMood (2008) test this ideawith Swedish register data on thetransition to upper secondary school, comparing adjacent cohorts withinthe same school to account for unobserved self-selection (cf. Hoxby 2000).While their analysis indicates the presence of such a mechanism, it alsosuggests that it is probably too trivial in size to be of much real-worldconsequence: in their model, a full standard deviation in peers’ averageachievement at the time of completing comprehensive education swingsthe probability of choosing an academic track in secondary school by ameagre 3 percentage points. Like all results in this area, however, it is onethat is heavily dependent on the chosen strategy of empirical identification.

CONTEMPORARY MIGRATION AND DIVERSITY

In just a few decades, the landscape of educational inequality has changedprofoundly with the advent of large-scale migration to Sweden and otherdeveloped economies. In scope, Swedish immigration rivals that of severalof its neighbours: among the cohorts who completed compulsory school-ing in recent years, around one in ten are born abroad. A further tenth areborn in Sweden to two foreign-born parents, and somewhat fewer haveone Swedish-born and one foreign-born parent (JJJJJJJJJJJJJJJJJonsson and Rudolphi2011). In other words, about a fourth of each cohort have either immi-grated themselves or have at least one parent who did so. These figuresare broadly comparable to those of more longstanding immigration coun-

INTRODUCTION 15

tries including the US, Canada, France, Germany, and the UK (see Lessard-Phillips, Fleischmann, and van Elsas 2014).

However, Swedish immigrants stand out in two regards: a dispropor-tionately large share have come as refugees, and they have done so froma remarkably diverse set of sending countries (OECD 2012). This has itsexplanations. While systematic recruitment of labour migrants from theNordic countries and Southern Europe was widespread during the 1960s,the 1973 recession prompted policy changes that brought this practice toa halt (JJJJJJJJJJJJJJJJJonsson 2007, Hammarstedt and Palme 2012). Since Sweden hasno post-colonial ties and shares no border with a developing country, inthe absence of active labour recruitment there remained few links to anyparticular sending country.

Immigration did not stall when labour recruitment did, and refugeemigration soon came to the fore. Among the first groups to arrive post-1973 were political refugees who fled Chile following the coup d’état thatyear. Common origins thereafter include the Middle East including Iran,Iraq, Lebanon, Syria, and Turkey; the former Yugoslavia; as well as anumber of East African countries including Ethiopia, Eritrea, and Somalia(Statistics Sweden 2014). The eastward EU expansion has led to a slight up-turn in labour immigration, but this has not eclipsed migration for asylumand subsequent family reunification reasons which reached a new spikewith the Syrian refugee crisis beginning in 2011 and hitting Europe inearnest from 2015.

Some of these newcomer ethnic minorities have arrived from sendingcountries that di�er substantially from the receiving society in social anddemographic makeup. As a consequence of these di�erences – and otherdi�culties, including language barriers or discrimination in the labourmarket – they tend to face considerable hardships and are often dispro-portionally concentrated toward the bottom rungs of the socioeconomicladder. An important question is to what extent this disadvantage will per-sist beyond the first generation, or conversely, to what extent the childrenof immigrants can expect to rise above the initially disadvantaged positionof their parents in the new country.

16 INTRODUCTION

A PUZZLE ABOUT MINORITY ASPIRATIONS

Like children of native-born parents in disadvantaged economic circum-stances, the children of low-status immigrants tend on average to achievelower test scores and grades in school (Schnepf 2007, Jonsson and Rudol-phi 2011, Heath and Brinbaum 2014). This is perhaps not unexpected,however objectionable. Even for the second generation who were bornand grew up in the host country, parents continue to play an immenserole and whatever factors may have impeded their integration will to someextent transmit to their children (Hällsten and Szulkin 2009). Limited lan-guage proficiency, lack of institutional knowledge, or inferior social andeconomic resources could limit the degree of support that they are ableto provide. Moreover, immigrants may live in areas of concentrated dis-advantage, with poorer school quality and more disruptive environments(Szulkin and Jonsson 2007).14

However, the parallel seems to stop there: studying the residual partof educational careers – secondary e�ects – the decisions of minority youthfail to conform to preconceived notions. In contrast to the pattern for socialclass di�erentials, we find that their poorer performance is often compen-sated for by a higher likelihood to pursue academic education net of achievedgrades (JJJJJJJJJJJJJJJJJonsson and Rudolphi 2011, Heath and Brinbaum 2014). This re-mains something of a puzzle, and among the explanations that have beenproposed for it are: the strength of parents’ social networks in ethnic com-munities; avoidance of blue-collar ethnic discrimination; underestimationof the obstacles to academic success; or some latent ‘ambition’ or ‘drive’ de-termining both parents’ migration decision and their children’s aspirations.

Prevailing explanations of this ‘immigrant optimism’ (Kao and Tienda1995) refer mostly to experiences that are minority-specific. But beforeassuming that the motivational forces acting on these groups fundamen-tally di�er from those of the majority, we should preferably appeal to so-cial mechanisms of a broader kind. One possibility is that the pattern arisesfrom school segregation and the ‘small pond’ mechanism described ear-

14 It is also possible that minority students receive lower grades because of discrim-ination. Previous studies from the Swedish context if anything seem to suggestthe opposite (Lindahl 2008, Klapp Lekholm and Cli�ordson 2009), although re-cent evidence is somewhat mixed (Hinnerich, Höglin, and Johannesson 2015,Hinnerich and Vlachos 2016).

INTRODUCTION 17

lier.15 Precisely because they are concentrated to schools where aggre-gate achievement is lower, the averageminority studentmay see themselvesas having relatively higher ability than if surrounded by more privilegedpeers. If we are to believe Jonsson and Mood (2008), however, this mech-anism is probably not weighty enough to explain much.

Another possibility is that students act not on their current gradesbut on a knowledge of their own potential, and grades might underesti-mate that potential among minority children. Suppose that achievement ishampered by factors such as language di�culties, potential family trauma,poor school environments, or stress due to acculturation and economicmarginalisation. If these students expect to perform better when placed inmore favourable circumstances, ‘overshooting’ in relation to their currentgrades is only an appropriate response. While this explanation is appealing,evidence o�ered in STUDY III actually seems to run counter to it. Researchalso shows that minority children report higher average levels of psycho-logical well-being than their majority peers (Mood, Jonsson, and Låftman2016), contradicting the notion of underperformance attributable to stress.

A more promising explanation refers to parents’ social standing be-fore migration, which we typically cannot observe (Feliciano 2005, Ichou2014). If parents have su�ered downward social mobility in relocating, it isnatural to expect that their children will aspire for positions commensurateto those that their parents have abandoned. Unfortunately, existing large-scale survey data do not shed much light on this issue, but the two closingstudies attempt to test this conjecture in a roundabout fashion. Under theassumption that countries di�er more in terms of average education levelthan in the underlying system of class stratification, immigrants’ relativeplace in the education distribution at origin will act as a rough proxy fortheir prior social status. STUDY III and IV confirm that this aspect of educa-tion is indeed of consequence for immigrants’ perceptions of their place inthe social status order and the level of aspiration that they transmit to theirchildren.

Regardless of the source of these high aims, they are of first-order im-portance for educational policies; as Jackson (2012) notes, they serve tosuppress ethnic di�erences in educational careers which would have beenmuch larger had minority students made the same decisions at a given levelof achievement as their majority peers. This poses an interesting dilemma

15 I thank Helena Holmlund for this suggestion.

18 INTRODUCTION

for policy-makers who might seek to curtail social class di�erentials by re-ducing the influence of aspirations on educational choice. Imposing poli-cies that select more heavily on proven aptitude might achieve this aim, butonly at the expense of increasing ethnic di�erentials which are suppressedby the very same aspirations. In a recent study, Dollman (2016) ingeniouslyillustrates this drawing a German policy experiment.

TROUBLES WITH PROXY VARIABLES

As is widely recognised, estimated associations in education between onegeneration and their o�spring are, for the most part, not causal but ratherreflect the transmission of unobserved characteristics that are in turn pre-dictive of educational success (Holmlund, Lindahl, and Plug 2011). Con-ceptions about equality of opportunity di�er, but reasonable ones hold thatat least some of the traits that parents transmit to their children should le-gitimately be allowed to exert an influence on academic success (JJJJJJJJJJJJJJJJJencksand Tach 2006). Because it is di�cult to imagine a world where children’soutcomes bear no resemblance to their parents’ status, it is equally hard tosay what would constitute an ideal degree of persistence or fluidity in thisrespect.

The comparative perspective o�ers a way around this awkward moralambiguity, by studying relative di�erences across countries or historicaltime and attempting to unravel the mechanisms behind them. Unfortu-nately, there are a number of reasons why model parameters might di�erthat have little to do with the extent to which educational institutions sys-tematically work to the benefit of one group over another. As is set outin detail in STUDY II, when making inferences from estimated parent-childassociations to the relative degree of opportunity, we are forced to assumethat the parental background proxies tap into the same unobserved traits,and do so equally well across the spectrum of geographical or historicalcontexts. Prima facie there are no good grounds to believe that this is thecase. This is a problem that is underappreciated, but nevertheless one whichpermeates the field.

It is probably uncontroversial to say that, as a rule, contemporaryWest-ern societies di�er less in occupational composition than they do in the con-figuration of educational institutions and system of academic degrees. It istherefore notable that, in STUDY I, when the more reliable (and arguably,

INTRODUCTION 19

more comparable) proxy of occupational status is used, socio-economicgradients in student achievement appear considerably more similar acrosscountries than when parental education is substituted. STUDY II o�ers sim-ilar caution with regard to the common books-at-home proxy. Becauselow-achievers tend to underestimate the amount of books in the home, as-sociations will tend to be larger in countries where most families have ahome library if only because there is more for students to underestimate.

There is a long line of thought in social stratification research claim-ing that most variation in intergenerational associations may actually beattributable to such perturbations and methodological artifacts. In scholar-ship on class mobility, this notion goes back at least to Featherman, Jones,and Hauser’s (1975: 340) conjecture that once marginal distributions instatus measures have been netted out, the underlying mobility processesshould be similar across “industrial societies with a market economy anda nuclear family system”.16 More recently, Grätz and coauthors (2016)have compiled estimates of sibling correlations in cognitive outcomes fromseveral countries, detecting little in the way of meaningful cross-countryvariation (but see Branigan, McCallum, and Freese 2013).

ERRORS-IN-VARIABLES AND BEYOND

Because the so-called classical measurement error model is a recurrent con-cern in several of the essays, it might be helpful to outline it here. Supposethat a researcher is interested in estimating a population relationship of thetype:

yi “ α` βxi ` εi (1)

Here yi is the regression outcome (e.g., student achievement), xi is aproxy for social origin, α and β are unknown parameters to be estimated,and εi is a well-behaved error term. The ordinary least squares (OLS) esti-mator for β in Equation 1 above is:17

16According to Richard Breen (2007: 2369-70), since its formulation “many pub-lications have been devoted to testing this hypothesis, the majority of which havefound support for it”. One notable example is Erikson and Goldthorpe (1992).

17The equivalent expression in matrix form is bOLS “ pX1Xq´1X1y, where bOLS ”` ab˘

is a parameter vector containing the estimated intercept and slope, and X ”p1, xq includes a vector of 1’s to allow for the former. As explored in STUDY II,

20 INTRODUCTION

bOLS “covpxi, yiqvarpxiq

“pn´ 1q´1

∑ipxi ´ xqpyi ´ yq

pn´ 1q´1∑

ipxi ´ xq2(2)

Unable to observe xi we instead observe an error ridden measure zi “xi ` ui. Ignoring mismeasurement and naively substituting zi for xi inEquation 1 above, the consequence is that we are led to estimate:

yi “ α` βzi ` pεi ´ βuiq (3)

It is well known that under the assumptions that xi, ui, εi are joint nor-mal with mean and covariance matrices pµx, 0, 0q and diagpσ2x,σ2u,σ2ε q, theresult is one of attenuation: the estimate will be downwardly biased inmag-nitude, the more so the larger the error (e.g., Greene 2003: 84-86). Onefamiliar expression for this bias is:18

plim bOLS “ β

«

1´σ2u

σ2x ` σ2u

ff

(4)

where σ2x is the variance of the unobserved true regressor and σ2u is thevariance of the observation error, as in STUDY I. This classical errors-in-variables model is usually the starting point for discussions of measurementerror. But how plausible are its assumptions? In many sociological appli-cations, one of them cannot hold for the simple reason that the regressoris categorical (or more generally, bounded). For instance, we may wantto compare educational outcomes between children whose parents di�erin social class or educational level, or from immigrant and non-immigrantorigin, and these are variables that are categorical or bounded in nature.

If so, there will be a nonzero covariance of the measurement error withthe true values if only because individuals in the top category can onlyreport accurately or underreport whereas the reverse holds for those in thebottom category. Even so, the net e�ect is still typically one of attenuationif we are willing to retain the assumption of zero covariance between the

Sections 2.3 and 2.5, and Appendix 3.A of STUDY III, this framework o�ers veryflexible opportunities to move beyond the classical bivariate case.

18Another extreme, as Wooldridge (2001: 73-74) notes, is that the observationerror ui is correlated with xi but uncorrelated with zi. This type of error occursnaturally when using group averages of an unobserved regressor, and will causelittle or no systematic bias (Berkson 1950, Hyslop and Imbens 2001).

INTRODUCTION 21

error and the regression residuals (Aigner 1973). For the case of a binaryregressor, denote themisclassification probabilities ω10 ” Ppxi “ 1

ˇ

ˇzi “ 0qand ω01 ” Ppxi “ 0

ˇ

ˇzi “ 1q. In this case:

plim bOLS “ β“

1´ω10 ´ω01‰

(5)

Since ω10 and ω01 are probabilities, they are either positive or zero andthe bias is again downwards in magnitude, save the case where more thanhalf of the sample is misclassified. In this event, the two probabilities sum toa number greater than one, the total attenuation factor becomes negative,and the sign on the estimated coe�cient will be opposite that of the truecoe�cient (Bollinger 1996).

While useful for building intuition, at the end of the day these are pe-culiar special cases that are unlikely to hold exactly in any given empiricalapplication. In STUDY I this type ofmodel is accepted as a reasonable approx-imation,19 albeit with an extension to the multivariate case, whereas STUDYII and III more explicitly examine departures from it. In particular, STUDYII questions the notion that the error ui is unrelated to the unexplained partof the outcome εi, necessary for all analytical methods of bias correctionin linear and nonlinear models alike (Chen, Hong, and Nekipelov 2011).STUDY III assumes a similar scenario but focuses instead on the bias thatwill result in conditional estimates for a categorical third variable, immi-grant background, when a control is used that may systematically under-or overestimate origin status in certain groups.

THIS VOLUME

The four essays in this volume are self-standing and numbered roughly inthe order theywere conceived, but nevertheless they loosely follow a logicalsequence that can be described as follows.

STUDY I takes its departure from a canonical regression in the liter-ature, of child test scores on two di�erent social background proxies –parental occupation and education – and compares estimates by data col-

19 STUDY II speculates that low achievers may exaggerate their parents’ educa-tion, which would lead to further downward bias. Applying the decompositionmethod from this study to the data of STUDY I actually confirms this, and revealsthat systematic error accounts for almost a third of the total bias in regressionestimates using student reports on parental education.

22 INTRODUCTION

lected from children, parents, and registers. It focuses on misreporting andnon-response in survey reported data and the impact they have on the sizeof estimated associations. Under conventional assumptions, both problemsare expected to bias associations downwards. A contrasting analysis usingregister data with high reliability and near-complete coverage confirmsthese expectations. The study also shows how bias can be mitigated bycarefully choosing which sources and variables to draw on. Because non-response is most serious in parent-reported data, and misreporting so inchild reported data, relying on occupation which is relatively reliably re-ported even by children limits the extent of bias. Another important resultis that estimates of the cognitive gap between children of native-born andimmigrant parents appears surprisingly robust to these problems.

STUDY I showed that noisily measured background proxies will typicallybias associations toward the null. A similar argument applies to validity:if the variables measured are poorly related to the constructs they proxyfor, associations are likely to be attenuated. This suggests that proxies thatshow large associations with the outcome are to be preferred, because ofsupposed higher reliability and/or validity. STUDY II shows how this rea-soning breaks down when the conventional assumptions are not met. Thecase under study is the number of books at home, a proxy that has grownin popularity precisely because of its large associations, especially whenreported by young children. I show that much of these associations areexplained by systematic errors; not only do students from bookish homesachieve higher, but high achievers bringmore books into the home, and arebetter informed about their home libraries. This inflates estimates relativeto other predictors, and makes findings prone to distortions and spuriousresults.

STUDY III revisits the social and ethnic disparities from STUDY I, and askswhat use we have of imperfect social background proxies in comparing ed-ucational outcomes along ethnic lines. Often, we are interested in holdingconstant aspects of the home environment and assess net (dis)advantages as-sociated with immigrant origin or belonging to a particular ethnic group.Such studies usually control for immigrant parents’ education in absoluteterms, ignoring a key aspect: how this education compares relative to thatof the population at origin. Here I demonstrate that relative education isa better predictor of attitudes to education, occupational aspirations, andtransition to academic tracks, while it does little to explain students’ cog-

INTRODUCTION 23

nitive or language achievement. Contrary to the results on misreportingand non-response, then, the choice of which proxies to include and how toscale them may matter for the conclusions reached.

STUDY III illustrated the importance of considering immigrants’ place inthe sending society when studying their host country integration. STUDYIV develops this argument in theoretical detail and takes issue with domi-nant theories of immigrant self-selection. Instead of attributing the oftenremarkable achievements of these populations to a di�use ‘drive’ particularto immigrants, it speculates that successful social and economic assimila-tion often results from a desire to make up for social status that has beenlost in resettling. To test this argument, we analyse the subjective economicwell-being of immigrants in twenty countries of destination. In line withtheoretical expectations, those higher educated by origin country than hostcountry standards turn out to make more dismal assessments of their cur-rent social status and financial situation which could be one explanation fortheir apparent determination to overcome disadvantage.

While each study draws on insights from the preceding one(s), they alsoaddress separate problems and speak to partly di�erent audiences. STUDY Iseeks to reestablish a concern with measurement that was a prominent fea-ture of status attainment research in the 1970s and early 1980s but thatseems to have fallen, momentarily at least, by the wayside. STUDY II is writ-ten primarily with applied education researchers in mind, but also speaks toa wider debate on proxy based inference that leans heavily on the classicalassumptions. STUDY III and IV both address the literature on intergenera-tional status transmission at the heart of this thesis, but they also tap intoa separate debate on immigrant ‘selectivity’ – how migrants di�er fromnon-migrants – and how this might impact their host country assimilation.STUDY IV also draws on a rich sociological tradition of research on socialcomparison processes and relative deprivation.

REFERENCES

Aigner, D. J. (1973). Regression with a Bi-nary Independent Variable Subject to Er-rors of Observation. Journal of Economet-rics, 1(1), 49-59.

Aizer, A., Stroud, L., and Buka, S. (2016).Maternal Stress andChildOutcomes: Ev-idence from siblings. Journal of HumanResources, 51(3), 523-555.

Angrist, J. D. (2014) The Perils of Peer Ef-fects. Labour Economics, 30, 98-108.

Berkson, J. (1950). Are There Two Regres-sions? Journal of the American StatisticalAssociation, 45(250), 164-180.

Bertrand, M., and Pan, J. (2013). The Trou-ble with Boys: Social influences and thegender gap in disruptive behavior. Amer-ican Economic Journal: Applied Economics,5(1), 32-64.

Björklund, A., Hederos Eriksson, K., andJäntti, M. (2010). IQ and Family Back-ground: Are associations strong or weak?The BE Journal of Economic Analysis & Pol-icy, 10(1):2.

Björklund, A., Jäntti, M., and Solon, G.(2005). Influences of Nature and Nur-ture on Earnings Variation: A report on astudy of various sibling types in Sweden.Ch. 4 in Bowles, S., Gintis, H., Groves,M. O. (eds.), Unequal Chances: Familybackground and economic success. PrincetonUniversity Press.

Björklund, A., Lindahl, M., and Plug,E. (2006). The Origins of Inter-generational Associations: Lessons fromSwedish adoption data. Quarterly Journalof Economics, 121(3), 999-1028.

Björklund, A., Lindahl M., and Sund, K.(2003). Family Background and SchoolPerformance during a Turbulent Era ofSchool Reforms. Swedish Economic PolicyReview, 10, 111-136.

Blau, P. M., and Duncan, O. D. (1967).The American Occupational Structure. NewYork: The Free Press.

Boalt, G. (1947). Skolutbildning och skolresul-tat för barn ur olika samhällsgrupper i Stock-holm. Stockholm: Norstedts.

Bollinger, C. R. (1996). Bounding MeanRegressions when a Binary Regressor isMismeasured. Journal of Econometrics,73(2), 387-399.

Bouchard, T. J. (1996). Galton Lecture: Be-haviour Genetic Studies of Intelligence,Yesterday and Today: The long jour-ney from plausibility to proof. Journal ofBiosocial Science, 28(4), 527-555.

Bouchard, T. J. (1998). Genetic and En-vironmental Influences on Adult Intelli-gence and Special Mental Abilities. Hu-man Biology, 70(2), 257-279.

Bouchard, T. J. (2013) The Wilson E�ect:The increase in heritability of IQ withage. Twin Research and Human Genetics,16(5), 923-930.

Bouchard, T. J., and McGue, M. (1981).Familial Studies of Intelligence. Science212(4498), 1055-1059.

Boudon, R. (1974). Education, Opportunity,and Social Inequality: Changing prospects inWestern society. New York: Wiley.

Bourdieu, P. (1977). Cultural Reproductionand Social Reproduction, pp. 487-511 inKarabel, J., Halsey, A. H. (eds.), Power andIdeology in Education. Oxford UniversityPress.

Bowles, S., and Gintis, H. (1976). Schoolingin Capitalist America. New York: BasicBooks.

Bowles, S., and Gintis, H. (2002). The In-heritance of Inequality. Journal of Eco-nomic Perspectives, 16(3), 3-30.

Bowles, S., Gintis, H., and Groves, M. O.(eds., 2006). Unequal Chances: Familybackground and economic success. PrincetonUniversity Press.

Bradbury, B., Corak, M., Waldfogel, J., andWashbrook, E. (2015). Too Many Chil-dren Left Behind: The US achievement gap incomparative perspective. Russell Sage Foun-dation.

Branigan, A. R., McCallum, K. J., andFreese, J. (2013). Variation in the Her-itability of Educational Attainment: Aninternational meta-analysis. Social Forces,92(1), 109-140.

Brännström, L. (2008). Making theirMark:The e�ects of neighbourhood and uppersecondary school on educational achieve-ment. European Sociological Review, 24(4),463-478.

24

Breen, R. (1999). Beliefs, Rational Choiceand Bayesian Learning. Rationality andSociety, 11(4), 463-479.

Breen, Richard. (2007). IntergenerationalMobility: Core model of social fluidity,pp. 2369-2370 in Ritzer, G. (ed.), Black-well Encyclopedia of Sociology. Malden:Blackwell.

Breen, R., and Goldthorpe, J. H. (1997). Ex-plaining Educational Di�erentials: To-wards a formal rational action theory.Rationality and Society, 9(3), 275-305.

Breen, R., and Jonsson, J. O. (2005). In-equality of Opportunity in Compara-tive Perspective: Recent research on ed-ucational attainment and social mobility.Annual Review of Sociology, 31, 223-243.

Breen, R., Luijkx, R., Müller, W., and Pol-lak, R. (2009). Nonpersistent Inequal-ity in Educational Attainment: Evidencefrom eight European countries. AmericanJournal of Sociology, 114(5), 1475-1521.

Breen, R., Luijkx, R., Müller, W., and Pol-lak, R. (2010). Long-term Trends in Ed-ucational Inequality in Europe: Class in-equalities and gender di�erences. Euro-pean Sociological Review, 26(1), 31-48.

Breen, R., Mood C., and Jonsson, J. O.(2016). How Much Scope for a MobilityParadox? The relationship between socialand income mobility in Sweden. Socio-logical Science, 3, 39-60.

Breen, R., van de Werfhorst, H. G., andJæger, M. M. (2014). Deciding UnderDoubt: A theory of risk aversion, timediscounting preferences, and educationaldecision-making. European SociologicalReview, 30(2), 258-270.

Buchmann, C., DiPrete, T. A., and Mc-Daniel, A. (2008). Gender Inequalities inEducation. Annual Review of Sociology,34, 319-337.

Bukodi, E., and Goldthorpe, J. H. (2013).Decomposing Social Origins: The e�ectsof parents’ class, status and education onthe educational attainment of their chil-dren. European Sociological Review, 29(5),1024-1039.

Chan, T. W., and Goldthorpe, J. H. (2004).Is There a Status Order in ContemporaryBritish Society? Evidence from the occu-

pational structure of friendship. EuropeanSociological Review, 20(5), 383-401.

Chan, T. W., and Goldthorpe, J. H. (2007).Class and Status: The conceptual distinc-tion and its empirical relevance. AmericanSociological Review, 72(4), 512-532.

Chen, X., Hong, H., and Nekipelov, D.(2011). Nonlinear Models of Measure-ment Errors. Journal of Economic Litera-ture, 49(4), 901-937.

Class, Q. A., Lichtenstein, P., Långström,N., and D’Onofrio, B. M. (2011). Tim-ing of Prenatal Maternal Exposure to Se-vere Life Events and Adverse PregnancyOutcomes: A population study of 2.6mil-lion pregnancies. Psychosomatic Medicine,73(3), 234.

Coleman, J. S. (1988). Social Capital in theCreation of Human Capital. AmericanJournal of Sociology, 94, S95-S120.

Coleman, J. S., Campbell, E. Q., Hobson, C.J., McPartland, J., Mood, A. M., Wein-feld, F. D., and York, R. L. (1966). Equal-ity of Educational Opportunity. Washing-ton: USDepartment of Health, Education,and Welfare, O�ce of Education.

Conley, D. (2016). Socio-Genomic Re-search Using Genome-Wide MolecularData. Annual Review of Sociology, 42,275-299.

Conley, D., Fletcher, J., and Dawes, C.(2014). The Emergence of Socio-Genomics. Contemporary Sociology: AJournal of Reviews, 43(4), 458-467.

Conley, D., and Glauber, R. (2008). Allin the Family? Family composition, re-sources, and sibling similarity in socioe-conomic status. Research in Social Stratifi-cation and Mobility, 26, 297-306.

Corcoran, M., Jencks, C., and Olneck, M.(1976). The E�ects of Family Back-ground on Earnings. American EconomicReview, 66(2), 430-435.

Cunha, F., Heckman, J. J., Lochner, L.,and Masterov, D. V. (2006). Interpret-ing the Evidence on Life Cycle Skill For-mation. In Hanushek, E. A., Welch, F.(eds.), Handbook of the Economics of Ed-ucation, Vol. 1, 697-812. Amsterdam:North-Holland.

25

Dickens, W. T., and Flynn, J. R. (2001).Heritability Estimates Versus Large En-vironmental E�ects: The IQ paradox re-solved. Psychological Review, 108(2), 346-369.

Dickson, M., Gregg, P., and Robinson, H.(2016). Early, Late or Never? Whendoes parental education impact child out-comes? Economic Journal, 126(596), F184-F231.

Dollmann, J. (2016). Less Choice, Less In-equality? A natural experiment on so-cial and ethnic di�erences in educationaldecision-making. European SociologicalReview, 32(2), 203-215.

Duncan, O. D. (1966). Path Analysis: So-ciological examples. American Journal ofSociology, 72(1), 1-16.

Erikson, R. (1984). Social Class of Men,Women and Families. Sociology, 18(4),500-514.

Erikson, R. (1987). The Long Arm of theOrigin: The e�ects of family backgroundon occupational and educational achieve-ment, in Bergryd, U., Janson C. G. (eds.),Sociological Miscellany: Essays in honour ofGunnar Boalt. Department of Sociology,Stockholm University.

Erikson, R. (1994). Spelar valet av skolanågon roll?, pp. 132-71 in Erikson, R.,Jonsson, J. O. (eds). Sorteringen i skolan:studier av snedrekrytering och utbildningenskonsekvenser. Stockholm: Carlssons.

Erikson, R. (2016). Is it Enough to beBright? Parental background, cognitiveability and educational attainment. Euro-pean Societies, 18(2), 117-135

Erikson, R., and Goldthorpe, J. H. (1992).The Constant Flux: A study of class mobilityin industrial societies. Oxford: Clarendon.

Erikson, R., Goldthorpe, J. H., and Häll-sten, M. (2012). No Way Back Up FromRatcheting Down? A critique of the ‘mi-croclass’ approach to the analysis of socialmobility. Acta Sociologica, 55(3), 211-229.

Erikson, R., Goldthorpe, J. H., Jackson, M.,Yaish, M., and Cox, D. R. (2005). OnClass Di�erentials in Educational Attain-ment. Proceedings of the National Academyof Sciences of the United States of America,102(27), 9730-9733.

Erikson, R., and Jonsson, J. O. (1993). Ur-sprung och utbildning: social snedrekryteringtill högre studier. SOU 1993:85. Stockholm:Fritzes.

Erikson, R., and Jonsson, J. O. (1994a). Sor-teringen i skolan: studier av snedrekryteringoch utbildningens konsekvenser. Stockholm:Carlssons.

Erikson, R., and Jonsson, J. O. (1994b),Ökade löneskillnader: ett sätt att ta till-vara begåvningsreserven? Ekonomisk De-batt, 22(6), 581-94.

Erikson, R., and Jonsson, J. O. (eds., 1996a).Can Education Be Equalized? The Swedishcase in comparative perspective. WestviewPress.

Erikson, R., and Jonsson, J. O. (1996b). Ex-plaining Class Inequality in Education:The Swedish test case, pp. 1-64 in Erik-son, R., Jonsson, J. O. (eds.), Can Educa-tion Be Equalized? Westview Press.

Erikson, R., and Jonsson, J. O. (1998). So-cial Origin as an Interest-Bearing Asset:Family background and labour-marketrewards among employees in Sweden.Acta Sociologica, 41(1), 19-36.

Erikson, R., and Jonsson, J. O. (2002). Var-för består den sociala snedrekryteringen?Pedagogisk forskning i Sverige, 7(3), 210-217.

Erikson, R., and Rudolphi, F. (2010).Change in Social Selection to Upper Sec-ondary School: Primary and secondarye�ects in Sweden. European SociologicalReview, 26(3), 291-305.

Evans, G. W. (2004) The Environment ofChildhood Poverty. American Psycholo-gist, 59(2), 77-92.

Evans, G. W., and Kim, P. (2013). Child-hood Poverty, Chronic Stress, Self-Regulation, and Coping. Child Develop-ment Perspectives, 7(1), 43-48.

Farkas, G. (2003). Cognitive Skills andNoncognitive Traits and Behaviors inStratification Processes. Annual Review ofSociology, 29, 541-562.

Featherman, D. F., Jones, L., and Hauser, R.M. (1975). Assumptions of Social Mobil-ity Research in the US: The case of oc-cupational status. Social Science Research,4(4), 329-60.

26

Feinstein, L. (2003). Inequality in the EarlyCognitive Development of British Chil-dren in the 1970 Cohort. Economica,70(277), 73-97.

Feliciano, C. (2005). Does Selective Migra-tion Matter? Explaining ethnic dispari-ties in educational attainment among im-migrants’ children. International MigrationReview, 39(4), 841-871.

Flynn, J. R. (1987). Massive IQ Gains in14 Nations: What IQ tests really measure.Psychological Bulletin, 101(2), 171-191.

Flynn, J. R. (2012). Are We Getting Smarter?Rising IQ in the twenty-first century. Cam-bridge University Press.

Freese, J. (2008). Genetics and the SocialScience Explanation of Individual Out-comes. American Journal of Sociology,114(S1), S1-S35.

Freese, J., and Shostak, S. (2009). Geneticsand Social Inquiry. Annual Review of So-ciology, 35, 107-128.

Gambetta, D. (1987). Were They Pushed orDid They Jump? Individual decision mecha-nisms in education. Cambridge UniversityPress.

Gerber, T. P., and Cheung, S. Y. (2008).Horizontal Stratification in Postsec-ondary Education: Forms, explanations,and implications. Annual Review of Soci-ology, 34, 299-318.

Girard, A., and Bastide, H. (1963). La strat-ification sociale et la démocratisation del’enseignement. Population [French edi-tion], 18, 435-472.

Goldberger, A. S. (1979). Heritability. Eco-nomica, 46(184), 327-347.

Goldthorpe, J. H. (1996). Class Analysis andthe Reorientation of Class Theory: Thecase of persisting di�erentials in educa-tion attainment. British Journal of Sociol-ogy, 47(3), 481-505.

Grätz, M., Barclay, K., Wiborg, Ø., Lyn-gstad, T., Karhula, A., Erola, J., Präg,P., Laidley, T., and Conley, D. (2016).Comparing Sibling Similarity in EducationWithin and Between Countries. Popula-tion Association of America (PAA) AnnualMeeting, Washington, DC, March 2016.

Greene,W. (2003). Econometric Analysis, 5thedn. Prentice-Hall.

Hällsten, M. (2011) The Structure of Ed-ucational Decision Making and Conse-quences for Inequality: A Swedish testcase. American Journal of Sociology, 116(3),806-54.

Hällsten, M., and Szulkin, R. (2009). Fam-ilies, Neighborhoods, and the Future: Thetransition to adulthood of children of nativeand immigrant origin in Sweden. SULCISWorking Paper 2009:9. Stockholm Uni-versity Linnaeus Center for IntegrationStudies.

Halpern, D. F. (2013). Sex Di�erences in Cog-nitive Abilities, 4th edn. Psychology Press.

Hammarstedt, M., and Palme, M. (2012).Human Capital Transmission and theEarnings of Second-Generation Immi-grants in Sweden. IZA Journal of Migra-tion, 1:4. doi: 10.1186/2193-9039-1-4

Härnqvist, K. (1958). Reserverna för högre ut-bildning. Beräkningar och metoddiskussion.1955 års universitetsutredning III. Stock-holm: SOU 1958:11. Stockholm: IdunsTryckeriaktiebolag Esselte.

Hart, B., and Risley, T. R. (1995). Meaning-ful Di�erences in the Everyday Experiencesof Young American Children. Baltimore:Brookes.

Hauser, R. M., and Mossel, P. A. (1985).Fraternal Resemblance in EducationalAttainment and Occupational Status.American Journal of Sociology, 91(3), 650-673.

Heath, A., and Brinbaum, Y. (2014). Un-equal Attainments: Ethnic educational in-equalities in ten Western countries. OxfordUniversity Press for the British Academy.

Heckman, J. J., Stixrud, J., and Urzua, S.(2006). The E�ects of Cognitive andNoncognitive Abilities on Labor MarketOutcomes and Social Behavior. Journal ofLabor Economics, 24(3), 411-482.

Heyns, B. (1978). Summer Learning and theE�ects of Schooling. New York: AcademicPress.

Hinnerich, B. T., Höglin, E., and Johannes-son, M. (2015). Discrimination AgainstStudents with Foreign Backgrounds: Ev-idence from grading in Swedish publichigh schools. Education Economics, 23(6),660-676.

27

Hinnerich, T. B., and Vlachos, J. (2016).The Impact of Upper-secondary VoucherSchool Attendance on Student Achievement:Swedish evidence using external and inter-nal evaluations. Working Paper 2016:2,Stockholm University Department ofEconomics.

Holm, A., and Breen, R. (2016). Behav-ioral and Statistical Models of EducationalInequality. Rationality and Society, 28(3),270-298

Holmlund, H., Lindahl, M., and Plug,E. (2011) The Causal E�ect of Parents’Schooling on Children’s Schooling: Acomparison of estimation methods. Jour-nal of Economic Literature, 49(3), 615-651.

Holmlund, H., and Silva, O. (2014). Tar-geting Non-Cognitive Skills to ImproveCognitive Outcomes: Evidence from aremedial education intervention. Journalof Human Capital, 8(2), 126-160.

Hoxby, C. (2000). Peer E�ects in the Class-room: Learning from gender and race varia-tion. Working Paper no. 7867, NationalBureau of Economic Research.

Hyde, J. S. (2005) The Gender SimilaritiesHypothesis. American Psychologist, 60(6),581-592.

Hyman, H. H. (1953). The Value Systems ofDi�erent Classes, pp. 426-442 in Bendix,R., Lipset, S. M. (eds.), Class, Status, andPower. Glencoe: The Free Press.

Hyslop, D. R., and Imbens, G. W. (2001).Bias From Classical and Other Forms ofMeasurement Error. Journal of Business &Economic Statistics, 19(4), 475-481.

Ichou, M. (2014). Who They Were There:Immigrants’ educational selectivity andtheir children’s educational attainment.European Sociological Review, 30(6), 750-765.

Ichou, M., and Vallet, L. A. (2011). DoAll Roads Lead To Inequality? Trendsin French upper secondary school anal-ysed with four longitudinal surveys. Ox-ford Review of Education, 37(2), 167-194.

Jackson, M. (2012). Bold Choices: How eth-nic inequalities in educational attainmentare suppressed. Oxford Review of Educa-tion, 38(2), 189-208.

Jackson, M. (ed., 2013). Determined To Suc-ceed? Performance versus choice in edu-cational attainment. Stanford UniversityPress.

Jæger, M. M. (2007). Educational MobilityAcross Three Generations: The changingimpact of parental social class, economic,cultural and social capital. European Soci-eties, 9(4), 527-550.

Jæger, M. M., and Breen, R. (2016). ADynamic Model of Cultural Reproduc-tion. American Journal of Sociology, 121(4),1079-1115.

Jæger, M. M., and Holm, A. (2012). Con-formists or Rebels? Relative risk aversion,educational decisions and social class re-production. Rationality and Society, 24(2),221-253.

Jencks, C. (1980). Heredity, Environment,and Public Policy Reconsidered. Ameri-can Sociological Review, 45(5), 723-36.

Jencks, C. et al. (1972). Inequality: A Re-assessment of the e�ect of family and schoolingin America. New York: Basic Books.

Jencks, C. et al. (1979). Who Gets Ahead?The determinants of economic success inAmerica. New York: Basic Books.

Jencks, C., Crouse, J., andMueser, P. (1983).The Wisconsin Model of Status Attain-ment: A national replication with im-proved measures of ability and aspiration.Sociology of Education, 56(1), 3-19.

Jencks, C., and Tach, L. (2006). WouldEqual Opportunity Mean More Mobil-ity?, pp. 23-58 in Morgan, S. L., GruskyD. B., and Fields, G. S. (eds.),Mobility andEquality: Frontiers of research from sociologyand economics. Stanford University Press.

Jonsson, J. O. (2007). The Farther TheyCome, the Harder They Fall? First-and second-generation immigrants in theSwedish labour market, pp. 451–505 inHeath, A. F., Cheung, S. Y. (eds.), Un-equal Chances: Ethnic minorities in Westernlabour markets. Oxford University Press.

Jonsson, J. O., and Erikson, R. (2000). Un-derstanding Educational Inequality: TheSwedish experience. L’année sociologique,50, 345-382.

Jonsson, J. O., and Erikson, R. (2007). Swe-den: Why educational expansion is not

28

such a great strategy for equality. The-ory and Evidence, pp. 113-139 in Shavit,Y., Arum, R., Gamoran, A. (eds.), Strati-fication in Higher Education: A comparativestudy. Stanford University Press.

Jonsson, J. O., Grusky, D. B., Di Carlo, M.,Pollak, R., and Brinton, M. C. (2009).Microclass Mobility: Social reproductionin four countries. American Journal of So-ciology, 114(4), 977-1036.

Jonsson, J. O., and Mood, C. (2008). Choiceby Contrast: How peers’ achievement af-fects educational choice in Sweden. SocialForces, 87, 741-765.

Jonsson, J. O., and Rudolphi, F. (2011).Weak Performance—Strong Determina-tion: School achievement and educationalchoice among children of immigrants inSweden. European Sociological Review,27(4), 487-508.

Kahneman, D., and Tversky, A. (1979).Prospect Theory: An analysis of decisionunder risk. Econometrica, 47(2), 263-291.

Kao, G., and Tienda, M. (1995). Optimismand Achievement: The educational per-formance of immigrant youth. Social Sci-ence Quarterly, 76, 1-19.

Keller, S., and Zavalloni, M. (1964). Am-bition and Social Class: A respecification.Social Forces, 43(1), 58-70.

Kingston, P. W. (2001) The UnfulfilledPromise of Cultural Capital Theory. So-ciology of Education, 74, 88-99.

Klapp Lekholm, A., and Cli�ordson, C.(2009). E�ects of Student Characteris-tics and Gender on Grades in Compul-sory School. Educational Research & Eval-uation, 15(1), 1-23.

Kohn, M. L. (1959). Social Class andParental Values. American Journal of So-ciology, 64(4), 337-351.

Kroneberg, C., and Kalter, F. (2012). Ra-tional Choice Theory and Empirical Re-search: Methodological and theoreticalcontributions in Europe. Annual Reviewof Sociology, 38, 73-92.

Lamont, M., and Lareau, A. (1988). CulturalCapital: Allusions, gaps and glissandos inrecent theoretical developments. Socio-logical Theory, 6(2),153-168.

Lareau, A. (2003). Unequal Childhoods: Class,race, and family life. University of Califor-nia Press.

Lareau, A., and Weininger, E. B. (2003).Cultural Capital in Educational Research:A critical assessment. Theory and Society,32(5), 567-606.

Lessard-Phillips, L., Fleischmann, F., andvan Elsas, E. (2014). Ethnic Minorities inTenWesternCountries: Migration flows,policies and institutional di�erences. Ch.2 in Heath, A. F., Brinbaum, Y. (eds.),Unequal Attainments. Oxford UniversityPress.

Lindahl, E. (2008). Comparing Teachers’Assessments and National Test Results:Evidence from Sweden, pp. 56-73 inEm-pirical Studies of Public Policies within thePrimary School and the Sickness Insurance.Uppsala University Department of Eco-nomics Dissertation series 2009:1.

Lucas, S. R. (2001). E�ectively Main-tained Inequality: Education transitions,track mobility, and social backgrounde�ects. American Journal of Sociology,106(6), 1642-1690.

Manski, C. F. (2011). Genes, Eyeglasses, andSocial Policy. Journal of Economic Perspec-tives, 25(4), 83-94.

Mare, R. D. (1980). Social Background andSchool Continuation Decisions. Jour-nal of the American Statistical Association,75(370), 295-305.

Marsh, H. W. (1987). The Big-Fish-Little-Pond E�ect on Academic Self-Concept.Journal of Educational Psychology, 79(3),280.

Marsh, H. W., and Seaton, M. (2015).The Big-Fish–Little-Pond E�ect, Com-petence Self-perceptions, and Relativity:Substantive advances and methodologicalinnovation. Advances in Motivation Science,2, 127-184.

Mood, C. (2009). Problems WithoutEnds: How rational choice theory es-capes its explanatory task, pp. 271-287 inCherkaoui, M., Hamilton, P. (eds.), Ray-mond Boudon: A life in sociology. Oxford:The Bardwell Press.

Mood, C., Jonsson, J. O., and Bihagen, E.(2012). Socioeconomic Persistence across

29

Generations: Cognitive and noncogni-tive processes. Ch. 3 in Ermisch, J., Jäntti,M., Smeeding, T.M. (eds.), From Parentsto Children: The intergenerational transmis-sion of advantage. Russell Sage Founda-tion.

Mood, C., Jonsson, J. O., and Låftman, S. B.(2016). Immigrant Integration andYouthMental Health in Four European Coun-tries. European Sociological Review, earlyaccess. doi: 10.1093/esr/jcw027

Morgan, S. L. (2005). On the Edge of Com-mitment: Educational attainment and racein the United States. Stanford UniversityPress.

Mouw, T. (2006). Estimating the Causal Ef-fect of Social Capital: A review of recentresearch. Annual Review of Sociology, 32,79-102.

Neisser, U., Boodoo, G., Bouchard Jr, T.J., Boykin, A. W., Brody, N., Ceci,S. J., Halpern, D. F., Loehlin, J. C.,Perlo�, R., Sternberg, R. J., and Urbina,S. (1996). Intelligence: Knowns and un-knowns. American Psychologist, 51(2), 77-101.

Nielsen, F. (2006). Achievement and As-cription in Educational Attainment: Ge-netic and environmental influences onadolescent schooling. Social Forces, 85(1),193-216.

Nielsen, F. (2016). The Status-AchievementProcess: Insights from genetics. Frontiersin Sociology, 1:9. doi: 10.3389/fsoc.2016.00009.

OECD (2012). International Migration Outlook:Sweden. Paris: OECD Publishing. doi:10.1787/migr_outlook-2012-41-en

Östh, J., Andersson, E., and Malmberg, B.(2013). School Choice and IncreasingPerformance Di�erence: A counterfac-tual approach. Urban Studies, 50(2), 407-425.

Plomin, R., Fulker, D. W., Corley, R., andDefries, J. C. (1997). Nature, Nurture,and Cognitive Development from 1 to16 Years: A parent-o�spring adoptionstudy. Psychological Science, 8(6), 442-447.

Plomin, R., and Spinath, F. M. (2004). Intel-ligence: Genetics, genes, and genomics.

Journal of Personality and Social Psychology,86(1), 112-129.

Polderman, T. J., Benyamin, B., De Leeuw,C. A., Sullivan, P. F., Van Bochoven, A.,Visscher, P.M., and Posthuma, D. (2015).Meta-Analysis of the Heritability of Hu-man Traits Based on Fifty Years of TwinStudies. Nature Genetics, 47(7), 702-709.

Poropat, A. E. (2009). A Meta-Analysisof the Five-Factor Model of Personalityand Academic Performance. PsychologicalBulletin, 135(2), 322-338.

Raudenbush, S. W., and Eschmann, R. D.(2015). Does Schooling Increase or Re-duce Social Inequality? Annual Review ofSociology, 41, 443-470.

Resnick, L. B. (1987). Learning in Schooland Out. Educational Researcher, 16(9),13-54.

Roth, B., Becker, N., Romeyke, S., Schäfer,S., Domnick, F., and Spinath, F. M.(2015). Intelligence and School Grades:A meta-analysis. Intelligence, 53, 118-137.

Rudolphi, F. (2013). Ever-Declining In-equalities?, pp. 185-227 in Jackson, M.(ed.). Determined To Succeed? Performanceversus Choice in Educational Attainment.Stanford University Press.

Ryan, R. M. (ed., 2012). The Oxford Hand-book of Human Motivation. Oxford Uni-versity Press.

Sacerdote, B. (2011). Peer E�ects in Edu-cation: How might they work, how bigare they and how much do we knowthus far?, pp. 249-277 in Hanushek, E.,Machin, S., Woessmann, L. (eds.), Hand-book of the Economics of Education, Vol 3.Amsterdam: North-Holland.

Saxe, G. B. (1988). Candy Selling and MathLearning. Educational Researcher, 17(6),14-21.

Schnepf, S. V. (2007). Immigrants’ Edu-cational Disadvantage: An examinationacross ten countries and three surveys.Journal of Population Economics, 20(3),527-545.

Sewell, W. H., Haller, A. O., and Portes, A.(1969). The Educational and Early Oc-cupational Attainment Process. AmericanSociological Review, 34, 82-92.

30

Sewell, W. H., and Hauser, R. M. (1975).Education, Occupation, and Earnings:Achievement in the early career. New York:Academic Press.

Shavit, Y., and Blossfeld, H. P. (1993). Per-sistent Inequality: Changing educational at-tainment in thirteen countries. WestviewPress.

Statistics Sweden. (2014). Statistisk Års-bok för Sverige. Vol 4: Befolkning. [Sta-tistical Yearbook of Sweden. Vol 4:Population.] www.scb.se/Statistik/_Publikationer/OV0904_2014A01_BR_06_A01BR1401.pdf

Stenberg, A. (2013). Interpreting Estimatesof Heritability: A note on the twin de-composition. Economics & Human Biol-ogy, 11(2), 201-205.

Sternberg, R. J., Grigorenko, E. L., andBundy, D. A. (2001). The PredictiveValue of IQ. Merrill-Palmer Quarterly,47(1), 1-41.

Sugarman, B. N. (1966). Social Class andValues As Related to Achievement andConduct in School. The Sociological Re-view, 14(3), 287-301.

Sundet, J. M., Eriksen, W., and Tambs,K. (2008). Intelligence Correlations Be-tween Brothers Decrease with IncreasingAge Di�erence: Evidence for shared en-vironmental e�ects in young adults. Psy-chological Science, 19(9), 843-847.

Swidler, A. (1986). Culture in Action: Sym-bols and strategies. American SociologicalReview, 51(2), 273-286.

Swift, A. (2004). Would Perfect Mobilitybe Perfect? European Sociological Review,20(1), 1-11.

Szulkin, R., and Jonsson, J. O. (2007). Eth-nic Segregation and Educational Outcomes

in Swedish Comprehensive Schools. SULCISWorking Paper 2007:2. Stockholm Uni-versity Linnaeus Center for IntegrationStudies.

Takeuchi, D. T., Park, L. S. H., Thomas, Y.F., and Teixeira, S. (eds., 2016). Specialissue on Race and Environmental Equity.The Du Bois Review, 13(2).

Torche, F. (2011). The E�ect of MaternalStress on Birth Outcomes: Exploiting anatural experiment. Demography, 48(4),1473-1491.

Torssander, J. (2013). Equality in Death?How the social positions of individuals andfamilies are linked to mortality. SwedishInstitute for Social Research DissertationSeries no. 89.

Turkheimer, E. (2000). Three Laws of Be-havior Genetics and What They Mean.Current Directions in Psychological Science,9(5), 160-164.

Weber, M. ([1922] 1978). Economy and So-ciety: An outline of interpretive sociology.University of California Press.

Weeden, K. A. (2002). Why Do Some Oc-cupations Pay More Than Others? So-cial closure and earnings inequality in theUnited States. American Journal of Sociol-ogy, 108(1), 55-101.

Weeden, K. A., and Grusky, D. B. (2012).The Three Worlds of Inequality. Amer-ican Journal of Sociology, 117(6), 1723-1785.

Wooldridge, J. M. (2001) Econometric Anal-ysis of Cross Section and Panel Data. MITPress.

Wright, S. (1934). The Method of Path Co-e�cients. Annals of Mathematical Statistics,5(3), 161-215.

31