American Journal of Sociology -. 1968. Statistical Abstract of the United States. Washington, D.C.: Government Printing Office, -. 1973. Statistical Abstract of the United States. Washington, D.C.: Government Printing Office. -. 1975. Historical Statistics of the United States, Part I. Washington, D.C.-Government Printing Office. Walker, Jack L. 1969. "The Diffusion of Innovations among the American States'* American Political Science Review 63:880-99. Warren, Earl. 194S. Letter from Walter A. Gordon to Earl Warren, November 2£: Papers of Earl Warren, series 395, folder F3640:84S2, Legislative Files: Special: Session Legislation—Race Relations, 1945. California State Archives, Sacramento; -. 1946. Letter from F. A. Ferguson to Earl Warren, October 14. Papers of Ear! Warren, series 402, folder F3640:8854, Legislative Files: Governor's Files—Racial Matters, 1946^t7. California State Archives, Sacramento. Wright, Gerald C, Robert S. Erikson, and John P. Mclver. 1985. "Measuring State Partisanship and Ideology with Survey Data." Journal of Politics 47:469-89. Yamaguchi, Kazuo. 1991. Event History Analysis. Newbury Park, Calif.: Sage....... Zeitlin, Maurice, and L. Frank Weyher. 2001. '"Black and White, Unite and Fight': Interracial Working-Class Solidarity and Racial Employment Equality." American Journal of Sociology 107:430-67. Zylan, Yvonne, and Sarah A. Soule. 2000. "Ending Welfare as Wc Know It (Again): Welfare State Retrenchment, 1989-1995." Social Forces 79:623-52. Explaining Change in Social Fluidity: Educational Equalization and Educational Expansion in Twentieth-Century Sweden1 Richard Breen Yale University Jan O. Jonsson Stockholm University The authors analyze social fluidity among Swedish men and women using a series of 24 annual surveys, 1976-99 (N = 63,280). A theoretical model suggests that changes in fluidity are normally driven by cohort rather than period effects. The results support this argument: changes in fluidity between the mid-1970s and late 1990s were due to the successive replacement of older and less fluid, by younger and more fluid, cohorts. Cohorts differed in their fluidity because the effect of class origins on educational attainment declined (an equalization effect) and because greater shares of each cohort had higher levels of educational attainment, which placed them in labor markets that operate more meritocratically (a compositional effect). The article discusses the relevance of these results for other countries and for policy. INTRODUCTION In studies of social mobility, Sweden has long been recognized as holding a distinctive place: class origins (the social class in which a person is 1 Versions of this article were presented at the meeting of International Sociological Association Research Committee 28, Mannheim, April 2001, and at seminars at Yale University in October 2005 and the University of Cambridge in January 2006. We are grateful to the participants on these occasions for their comments and suggestions. We extend particular thanks to Robert Erikson, David Firth, Ruud Luijkx, Louis-Andre Vallet, and the AJS reviewers for helpful comments on earlier drafts. Jonsson acknowledges financial support from the Swedish Council for Working Life and Social Research (FAS D2001-2881 and D2002-2893). Direct correspondence to Richard Breen, Yale University, Department of Sociology, New Haven, Connecticut 06529-8265, E-mail: richard.breen@Yale.edu, janne.jonsson@sofi,su,se © 2007 by The University of Chicago. All rights reserved. 0002-9602/2007/11206-0004$10.00 1774 AJS Volume 112 Number 6 (May 2007): 1775-1810 1775 American Journal of Sociology brought up) appear to have a smaller influence on class destinations (the class which the person comes to occupy as an adult) than in most other countries (Erikson and Goldthorpe 1992; Breen 2004), and there has been a fairly lengthy period during which the impact of origins on destinations has steadily weakened. In this article we reaffirm this trend, but we go beyond most research in social mobility by developing a theoretical model of how class origins are linked to class destinations and in providing an explanation, which we test empirically, of the trends that we find. It is no secret to say that contemporary research in social mobility is, for the most part, technically sophisticated but theoretically weak. In this article, we work to strike a better balance by providing a firm theoretical footing for the empirical study of social mobility. The term "social fluidity" is often used to refer to the degree to which class destinations depend on class origins: the weaker the statistical association between origins and destinations, the greater social fluidity is said to be.2 Accordingly, social fluidity is often interpreted as an index of equality in the chances of access to more or less advantageous social positions between people coming from different social origins, and studies of whether societies are moving toward greater "openness" use social fluidity as a key indicator. Whether countries differ substantively in their degree of social fluidity and whether fluidity changes over time are questions that have been much debated in the stratification literature. Research in the 1980s and early 1990s (e.g., Grusky and Hauser 1984; Erikson and Goldthorpe 1992) found support for the "FJH hypothesis," or variants of it, which claims that social fluidity is similar in all industrial societies "with a market economy and a nuclear family system" (Featherman, Jones, and Hauser 1975, p. 340). Some of these studies also argued for "a high degree of temporal stability" (Erikson and Goldthorpe 1992, p. 367; also Goldthorpe 2000, chap. 11) in social fluidity. But particularly in more recent research, greater emphasis has been placed on change and variation. Ganzeboom, Luijkx, and Treiman (1989, p. 47) claimed that "there are substantial cross-national and cross-temporal differences in the extenl of mobility," and Breen (2004; also Breen and Luijkx 2004a) documents significant variation in social fluidity among the countries of Europe and a fairly widespread temporal trend toward greater fluidity in the closing, decades of the 20th century (see also the review in our earlier work [Breen and Jonsson 2005]).3 Our findings in this article support the latter position insofar as we 2 In the log-linear modeling context in which this article is situated, the association between origins and destinations is measured using odds ratios. 3 And some former proponents of the FJH hypothesis now reject it (see Hauser 1995, pp. 176-77). Educational Equalization report change in Swedish social fluidity over the course of the 20th century, but we also provide a theoretical framework for understanding how social fluidity may change, and we use this framework to account for the change that we observe. Hitherto, the task of explaining temporal change within a country has often been an ad hoc affair and, although some studies (esp. Hout 1988) have provided valuable insights into the mechanisms that might underlie change, it has not been possible wholly to account for temporal trends by the addition of variables which represent plausible processes by which fluidity might change. But this is what we do in the present study: we show that variables capturing two simple processes can indeed account for trends in social fluidity in 20th-century Sweden. Previous research shows that the association between class origins and destinations in Sweden was relatively stable during the first decades of the 20th century (Carlsson 1958) but weakened during the postwar period and up to the beginning of the 1980s (Erikson 1983, 1987).4 This trend toward increased openness continued to 1991, particularly for women (Jonsson and Mills 1993; Jonsson and Erikson 1997). In this article we first examine changes in social fluidity among Swedish men and women by comparing successive birth cohorts. This allows us to cover almost the entire 20th century, because the oldest cohorts in our data were born around 1912 and the youngest in the early 1970s. On the other hand, and in contrast to this cohort perspective, we can also compare social fluidity across the different surveys in which our data were collected. These were carried out. between 1976 and 1999, and so this period perspective allows us to focus on changes that took place in the final quarter of the 20th century. But the period and cohort perspectives are different ways of examining the same data, and so the second goal of the article is to relate the two. As we explain below, we believe that there are good reasons for supposing that changes in fluidity are normally and mainly—though not exclusively—driven by cohort-related, rather than period-related, factors. If this is true we should expect that most, if not all, of any period change that we detect will prove to be a consequence of changes occurring between cohorts. The third, and major, goal of the article is to account for the pattern of change that we observe. .We look a.t the role of education, given the widespread recognition that it is one of the major channels through which intergenerational class reproduction occurs (Ishida, Miiller, and Ridge 1995), and we identify two processes through which education might cause 'Ganzeboom et at. (1989), using five Swedish data sets collected between 1950 and 1983, claim that Sweden shared in the general worldwide trend to increasing fluidity that they identify. Wong (1994), in his reanalysis of their data, found that this trend was evident only for Hungary and Sweden. American Journal of Sociology social fluidity to change. The first, which we call "equalization," is a decline in the association between class origins and educational attainment: in other words, class origins come to exercise a weaker effect on educational attainment. In several European countries, including Sweden (as we document below), reforms of the educational system have been undertaken with exactly this goal. Equalization affects social fluidity because, for a given association between education and class destinations, a lesser impact of class origins on educational attainment will weaken the overall association between origins and destinations. The second process we call "compositional": if there is an association between origins, education, and destinations such that the origin-destination association is weaker at higher levels of education, and if educational expansion places increasing shares of each cohort in those educational levels where the association is weakest, then this compositional change can be expected to lead to an overall reduction in the gross association between origins and destinations.5 A three-way interaction between class origins, educational qualifications, and class destinations may be present when, for example, higher qualifications are a powerful signal for employers that leaves little leeway for social network effects, or when the job markets in which degreeholders operate are particularly meritocratic. A weaker origin-destination association at higher levels of education is in fact reported from the United States (Hout 1988), France (Vallet 2004), and Sweden (Erikson and Jons-son 1998), making it possible that an expansion of higher education across cohorts in these countries led to increasing fluidity. In our analyses we document the existence of both equalization and compositional effects, and we also seek to quantify the importance of each in accounting for cohort changes in social fluidity. In the next section of the article we present a theoretical model of the processes that underlie change and stability in social fluidity, and we explain why these processes are more likely to manifest themselves as cohort than as period effects. Successive sections present our data and s A stronger result cars be shown in the case of linear systems. Let X, Z and Y be continuous measures of, respectively, class origins, educational attainment, and class destinations. We then have a two-equation stochastic system: (i) Z = a0 + atX + and (ii) Y = b0 + bxX + b,Z + b3XZ + it2. By substituting (i) into (ii) and arranging terms we see that the derivative, SYldX (i.e., the unconditional effect of X on Y), is given by b^ + b1al 4- b,a0 + 2b3a,X, and the derivative of this with respect to a change in the overall level of educational attainment (i.e., a0) is given by bs. So if there is no interaction between origins and education (b3 = 0), changes in the mean level of education will not change the gross association between origins and destinations. But if, as in the case under discussion, £>, <0, such a change will reduce this association (the compositional effect). But the association will always change for a change in the origin-education relationship (captured by a„ the equalization effect). Educational Equalization our analyses, and a final section summarizes our results and draws conclusions. A THEORETICAL MODEL OF SOCIAL FLUIDITY The association between origins and destinations (parents' class and respondent's class) depends on the degree to which factors associated with class position in the parental generation can influence which classes their children come to occupy.6 Figure 1 provides a simplified depiction of this process. In the parental and filial generations occupation of a particular class position depends on assets (such as educational qualifications) and gives rise to consequences (such as income). In the parental generation the impact of a given asset on class position is labeled a, and in the filial generation, y, while the "return" to class in terms of consequences is given by X and by

diag 330 315.85 .703 -3,120 350.76 .207 -3,051 4 ... ODkfic+dieLg 343 331.12 .667 -3,240 358.79 .268 -3,177 5 ... ODkf 349 398.65 .033 -3,235 379.01 .130 -3,219 Note.—AH models include the terms OC DC. O = origin; D = destination; C = cohort; diag= 6 parameters fitted to cells on main diagonal of the O-D table; k ~ I, . . . ,15. difference in their |8 parameters, and thus declining values of this parameter over cohorts correspond to increasing social fluidity. This model uses 14 degrees of freedom (i.e., number of cohorts minus one) more than the independence model, but reduces the deviance by only eight for men. For women, however, there is evidently more change as captured by the unidiff model—the reduction in deviance is around 57, which is clearly a significant improvement in fit. The third model introduces parameters for each of the cells on the main diagonal of the mobility table. This imposes the constraint that the sum, over all cohorts, of the frequencies in each cell of the main diagonal of the origin by destination table is fitted exactly. The log-odds ratios under this model are given by ln6mk = &in0;i + 5;i - 5ijV - + bil}„ fis = 0, if i * j. (3) Here 6^ denotes the diagonal parameters. Because these parameters are constant over cohorts, the difference between cohorts in their log-odds ratios is still proportional to the size of their (3 coefficients. As equation (3) makes clear, the diagonal effects operate over and above the evolution of the pattern of local odds ratios and so, even though the diagonal parameters do not vary over cohorts, this does not mean that propensities for individuals to be found in their class of origin remain constant over cohorts: rather, this propensity changes according to the common log-multiplicative evolution of the whole pattern of local association. What does remain constant is the tendency for class inheritance that exists over and above that which is implied by this pattern.19 Adding these diagonal " We tested models in which the diagonal parameters were allowed to vary over cohorts, but this never yielded a significant improvement in fit. i ct n American Journal of Sociology Educational Equalization parameters makes a substantial difference to the fit of the men's tables though rather less difference for women. This is not surprising, since it is well known that men are more likely than women to be found in the same class as their father (e.g., see Jonsson and Mills 1993 for the Swedish case). In model 4 we impose a linear constraint on the evolution of the 0 parameters: so now we write ln9m = k(3ln61} + 5, - Sw - bi6 + 5,jV, 0, if i *j,k= 1, .,15. (4) For both men and women model 4 is preferred to model 3 and to model 1. Last, model 5 removes the diagonal effects but retains the linear constraint. This considerably worsens the goodness of fit of the model for men, but has less impact on women, once again illustrating the lesser importance of inheritance effects among women. Figure 2 shows the parameter values for the diagonal cells generated by model 3. As is evident, there is strong inheritance in the upper service class—for women, in particular—and in the petty bourgeoisie; and among men inheritance effects are very strong in the farming class.20 Of more interest to us, however, are the /3 coefficient estimates from model 3 and also the linear 0 estimated from model 4, both of which are shown in figure 3. They display a trend toward increased social fluidity over cohorts, with an estimated slope for the association between origins and destinations of - .03 6 for men and -.043 for women, though the cohort-specific j3s suggest that, from the 1948-54 cohort onwards, the trend toward more fluidity disappears.21 It should be borne in mind that for the oldest and youngest cohorts we have only one observation, two for the second oldest and second youngest, and so on, so the end points on this and the other figures depicting cohort change should be regarded as particularly subject to uncertainty (and they also exercise less influence on the estimate of the slope). If we compare the /3<, coefficients for the 1964-70 cohorts, we see that they are two-thirds or less of the value for those for the 1916-22 cohorts, indicating a substantial increase in fluidity over the 20th century. Age Effects Having established that fluidity changed over cohorts in 20th-century Sweden, we now turn to period change. The first question we address is 20 There is also some "disinheritance" among men in class II and women in class Ilia (see also figs. 7 and 8, below), suggesting that these are origin classes which are disproportionately likely to be vacated by those born into them. 21 We included a quadratic term in addition to the linear term for the evolution of the log-odds ratios, but this was not a significant improvement for either sex, 5 4 3 c j 1 m Women BMen Social class Fig, 2.—Parameter values for the diagonal cells generated by model 3 whether there is any indication of substantive changes in fluidity within cohorts as they progress in their career: in other words, an age effect." We cannot address this issue for our very oldest and youngest cohorts, for each of which we have only one table. Table 5 shows the result of fitting, to each of the remaining 13 cohorts, a model that assumes constancy over periods in the OD association {PO PD OD) and a model of uniform change in fluidity across periods (PO PD OD f3r). In every case the model of common fluidity fits the data and the model of uniform change never yields a significant improvement in fit. The result is overwhelmingly comforting for the cohort view: there seems to be no substantive change in fluidity across periods for people in a given cohort.23 This conclusion can be checked by using the panel element in our data, though with a smaller sample.24 For some respondents we have information on their class position at two points, eight years apart, which we call Dl and D2. Information on Dl was collected at the initial ULF interview between 1979 and 1991 and on Dl between 1986 and 1999. " An age effect cannot explain change over periods, since, by definition, an age effect is specific to an age group but constant over periods and cohorts. Thus, to explain change, age effects must change—as they would, for example, in an age by period interaction. " Note that this does not mean that members of a cohort do not change occupations or classes across their careers (they most certainly do, at least up to the age of 30-40), only that these changes are unrelated to their social origins. " We thank an AJS reviewer for suggesting this analysis. American Journal of Sociology 12- 18- 20- 24» 28- 32- 36- 40- 44- 48- 52- 56- 60- 64- 68-18 22 26 30 34 38 42 46 50 54 58 62 66 70 74 Birth Cohort -Women-linear -Women — — Men-linear -Men Fig. 3.-/3 coefficient estimates from model 3; linear 0 estimated from model. Respondents born before 1921 were too old, and those born after 1966 too young, to have a value on D2: furthermore, respondents born between these dates may also not have a value for D2 depending on how old they were when they were first interviewed. Finally, only half of each year's sample was reinterviewed eight years later. As a result we have panel data on 9,906 respondents (compared with the 63,280 used elsewhere in our analyses). Because our goal is to check whether the origin-destination association is the same over the life course we form the table of origins by Dl by D2 for each sex, and distinguish two broadly defined cohorts. In the older cohort both Dl and D2 occurred after the age of 34: it is made up of those born 1921-44 together with those born 1945-48 who were 35 years old or older at Dl. In the younger cohort Dl occurred before 35. These respondents were born 1949-66 or were born 1945-48 and were 34 years old or younger at Dl. Forming two cohorts in this way allows us to distinguish those whose life-course mobility may have occurred before the age of occupational maturity (which we take to be 3S) and those whose mobility, if any, occurred after this point. The small number of cases forced us to move to a four-class categorization for origins, first destination (Dl), and second destination (D2). We distinguish class I, clas- Educational Equalization TABLE 5 Social Fluidity Models Fitted to Each Cohort Deviances Men Women Cohort Table PO PD OD PO PD ODff' PO PD OD PO PD ODp' 2 ....... 1916-22 2 15.42 15.32 25.19 19.02 3 ....... 1920-26 3 44.82 41.17 31.75 30.73 4 ....... 1924-30 4 64.57 64.24 66.41 65.89 j ....... 1928-34 5 72.81 70.65 102.92 100.36 6 ....... 1932-38 6 129.65 112.28 131.04 125.34 I ....... 1936-42 6 105.41 103.97 104.79 99.73 g ....... 1940-46 6 109.11 108.18 137.88 134.43 o ....... 1944-50 6 103.28 98.53 85.02 79.10 10 ...... 1948-54 6 119.55 113.17 136.71 129.14 II ...... 1952-58 5 112.66 107.43 108.62 98.80 12 ...... 1956-62 4 61.24 61.07 70.57 68.34 U ...... 1960-66 3 50.62 49.69 46.08 45.97 14 ...... 1964-70 2 22.27 22.17 22.78 22.20 NOTE.—Degrees of freedom for PO PD OD mode] — (no. of tables — 1) x 25, and degrees of freedom for PO PD ODfi1' model = (no. of tables - 1) x 24; df for the comparison of the two. ses II and Ilia, classes IVa, IVb, and IVc, and classes Illb, VI, and VIII." The test is simple: we fit the model ODl OD2 D\D2 and we compare its goodness of fit with the same model in which the ODl and OD2 associations are fixed to be the same (which forces the association between origins and destinations to be the same for both destinations). The test has nine degrees of freedom and in no case is the constrained model a significantly poorer fit: the deviances are 13.25, 13.86, 10.37, and 14.87 for the older and younger cohorts of men and the older and younger cohorts of women, respectively.26 Period Change The absence of change within cohorts as they age does not mean, of course, that social fluidity does not change across periods, and so table 6 uses the same set of five models as table 4, this time applied to test for period change, now ignoring cohorts. Evidence for period change is much weaker than for cohort change, but adding parameters for the main diagonal once " The sample sizes in each of the origin by Dl by D2 tables are 3,493, 1,503, 3,375, and 1,535 for older and younger men and older and younger women, respectively. " We carried out the same test using the model ODl ODl (i.e., omitting the DID2 term capturing association in life course mobility) with the same results (details available on request from the authors). 1793 American Journal of Sociology TABLE 6 Goodness of Fit of Models for the Origin x Destination x Period Table for Men and Women No. Model df (n Men = 33,281) (n Women = 29,999) Deviance P BIC Deviance P BIC 1 ... OD 125 1S0.3 .001 -1,121 184.6 .001 -1,104 2 ... OD$r 120 167.5 .003 -1,082 167.4 .003 -1,070 3 ... OD/SVdiag 114 133.3 .105 -1,054 160.0 .003 -1,015 4 ... ODl^'+diag 118 142.8 .060 -1,086 167.8 .002 -1,049 S ... 124 174.4 .002 -1,117 175.1 .002 -1,103 NOTE.—All models include the terms OP DP: O = origin; D = destination; P = period; diag = ft parameters fitted to celis on main diagonal of the O-D table; 1=1,. . . ,6. again leads to a large improvement in fit for men (compare models 2 and 3). On the other hand, models 3 and 4 again fit the data and are a significant improvement over both models 1 and 5." Comparing models 3 and 4, the latter is more parsimonious, Among women, although none of the models fits the data according to the deviance, model 5 would be preferred according to this criterion, once again indicating the lesser importance of inheritance. If, however, we take model 4 as the preferred model for both sexes, we find that the slope of j3 is -.060 for men and — .050 for women.28 Period and Cohort Change Having established a trend toward increasing social fluidity over both cohorts and periods, we turn, in table 7, to models that allow for both types of change. All the models reported in table 7 fit the OPC and DPC margins: thus we allow the origin distribution and the destination distribution to vary over both cohorts and periods. Our interest is in the OD association, and we test whether, given change over periods, cohort change persists, and vice versa. We take as the point of departure two models of change in social fluidity incorporating both period and cohort change, as before using the unidiff model in its unconstrained (models 2-4) and linear (models 5-7) forms to model change (including time constant parameters " There is no evidence that the diagonal parameter values vary over periods. 28 If period fluidity were simply a weighted sum of the fluidity in each cohort represented in that period's table, and if all cohorts were the same size, then the period and cohort slopes in our data would be identical (within sampling error). This is because, given a cohort slope — b, the association in an entering cohort in any period is equal to (—b x 10 x the association in the exiting cohort), and as there are 10 cohorts in every period survey, the difference between periods is then — b. f Educational Equalization ■■■f" TABLE 7 \ goodness of Fit of Models for the Origin x Destination x Cohort x Period : Table for Men and Women fllllRSf. Men Women (n = 33,281) (n = 29,999) No. Model df Deviance BIC Deviance BIC 1 ... OD 1,475 1,404.28 -13,955 1,493.23 -13,712 2 ... OD^+diag 1,464 1,361.65 -13,883 1,472.42 -13,620 3 ... OD|3ct+diag 1,455 1,317.37 -13,883 1,420.52 -13,579 4 ... OD^/SVdiag 1,450 1,311.85 -13,787 1,412.11 -13,536 5 ... ODlj3p + diag 1,469 1,370.84 -13,925 1,481.12 -13,663 6 ... ODkI0c+diag 1,469 1,332.54 -13,964 1,428.55 -13,715 7 ... ODk/3c10p+diag 1,468 1,332.18 -13,954 1,427.77 -13,706 Note.—All models include the terms OPC DPC. P-values are not reported: they are all greater than 0.4. 0 = origin; D = destination; P = period; C = cohort; diag = 6 parameters fitted to cells on main diagonal of the O-D table; k = i,. . . , 15; ( = 1,. . ., 6. for the main diagonal), These models allow a common pattern of fluidity to vary over either or both the C and P margins simultaneously: in other words, the pattern of social fluidity is multiplied by a cohort-specific and a period-specific p1 parameter. Ignoring, for ease of notation, the effects of the diagonal parameters, in the case of model 4 we have = ft^flnO,, (5) where k indexes cohorts and 1 periods, while for model 7 we have for k - 1, . . . , 15, and I = 1, ... ,6. (6) The results show that, given a model which includes cohort change, the addition of change over periods (model 3 compared with model 4, and model 6 compared with model 7) does not significantly improve the fit of the model, but the reverse is not the case (model 2 compared with 4 and model 5 compared with 7). That is to say, when we control for cohort effects, the period effects vanish, Reflecting this, in model 7, which is the direct counterpart to model 4 in the earlier cohort and period analyses, the partial period slope is now estimated as being not significantly different from zero (—0.008 for men and +0.015 for women), whereas the partial cohort slope is almost unchanged from its unconditional value (-.034 for men, compared with -.036 and -.045 for women, compared with -.043). These results, together with the finding, reported in table 5, that fluidity does not vary within a given cohort, and the results of our analysis of the panel element in our data, lead us to conclude that period American Journal of Sociology Educational Equalization change in fluidity in the last quarter of the 20th century was the consequence of the replacement of older, less fluid cohorts, with younger, more fluid ones. Understanding Cohort Change How are we now to understand the change in social fluidity across cohorts? Perhaps the most plausible mechanism to explain cohort change concerns the transmission of assets during childhood, pointing to the important role of education for social mobility. Increasing social fluidity may. then come about in two different ways, which we earlier labeled equalization and compositional effects. We will look at each of these in turn" A common explanation of increasing social fluidity is that it is driven by a weakening association between social origin and educational qualifications. Such a weakening did occur in Sweden among cohorts born approximately between the early 1920s and the 1950s (Jonsson and Erikson 2000), and this weakening is likely to have brought about changes: in the gross association between origins and destinations of the kind that we have shown above. In our data we find the same result. Table 8 reports three models fitted to the origin x education x cohort table, the first of which is constant association between origins and education (OE), the second of which allows for uniform change in the OE association over-cohorts, and the third of which constrains this change to be linear. As': with the OD association, the model of linear change provides a good account of the data. In figure 4, the j3 coefficients from the second of these models show the decline in the association flattening out after the 1950s: this is very similar to the trend shown in figure 3, which provides some prima facie evidence that the change in social fluidity was driven, in some part, by changes in educational inequality. Our estimates, which we presented below, suggest that around half of the total association between. TABLE 8 Goodness of Fit of Models foe the Origin x Education x Cohort Table for Men and Women Men (« = 33,281) Women (n = 29,999) No. Model df Deviance P BIC Deviance P BIC 1 ... OE 350 442.66 .001 -3,203 406.32 .020 ~3,202 2 ... OE$ct 336 386.19 .031 -3,112 365.61 .128 -3,098 3 ... OEkj3c 349 405.01 .021 -3,229 385.43 .087 -3,212 Note. k = l, . — All models include the terms DC EC. 0 = ori . ,15. ;in; D = destination; c = cohort; £ = education, -i-1-1-1-1-!-1-1-1-1-1-r 12- 16- 20- 24- 28- 32- 36- 40- 44- 48- 52- 56- 60- 64- 68-18 22 26 30 34 38 42 46 50 54 58 62 66 70 74 Birth cohort Fig. 4.—j3 coefficients for uniform change in the OE association over cohorts origins and destinations is mediated via education, showing that a weakening of this indirect path through a decline in the origin-education association can indeed lead to increases in social fluidity. Table 9 uses the origin x destination x education x cohort table to address the compositional question. Here there are two sets of models: 2-4 fit completely unrestricted log-linear models, and 5-7 fit unrestricted log-multiplicative uniform difference models. In models 5 and 7 we denote the log-multiplicative evolution of the OD association over educational levels as OD(3^, where m indexes educational levels from 1 to 6. In both sets of models we reach the same conclusion: once we allow for variation in the OD association between levels of education, there is no significant change in fluidity over cohorts. So, model 2 is not a poorer fit to the data than model 4, but model 3 is, indicating that the omission of the ODC term is not statistically significant once the ODE term is included in the model, whereas the ODE term cannot be omitted even when ODC is included. Likewise, model 5 is not a poorer fit than model 7, but model 6 is, indicating that the OD$t term is required but that the ODftf term is not. This result does not mean that it is the compositional effect that is wholly responsible for partialling out cohort change, because the model of necessity includes the OEC and EDC terms: thus the trend in fluidity over cohorts may depend on both the equalization effect—which is already included in the models reported in table 9—and on the compositional effect. The basis of this compositional effect is shown in figure 5, which reports the /3 coefficients for each educational level, taken from model 5 American Journal of Sociology Educational Equalization TABLE 9 Goodness of Fit of Models for the Origin x Destination x Education Cohort Table for Men and Women No. Model df Men (k = 33,281) Women (h = Deviance BIC Deviance 1 ... OD 2,225 1,928.98 -21,239 1,918.85 2 ... ODE 2,100 1,677.76 -20,189 1,689.20 3 ... ODC 1,875 1,571.07 -17,953 1,545.22 4 ... ODE ODC 1,750 1,333.32 -16,887 1,326.36 5 ... OD/3Km+diag 2,214 1,841.16 -21,213 1,875.45 6 ... OD^ck+diag 2,205 1,877.20 -21,083 1,874.66 7 ... OD/3EmJ3ck+diag 2,200 1,834.34 -21,074 1,859.45 Model Comparisons Term Tested df Deviance P Deviance ODC 350 342.44 .603 362.84 3 versus 4 .......... ODE 125 235.75 .001 218.86 OD^y 14 6.82 .941 16.00 6 versus 7 ..... OD@K„, 5 42.S6 .001 15.21 .307 .001 .313 .010 Note.—All models include the terms OCE DCE. O = origin; D = destination; P = period; C cohort; E = education; diag = 6 parameters fitted to ceils on main diagonal of the O-D table- k = . . ., 15; m = 1, . . . , 6. of table 9. This shows a clear pattern, though not a linear one, of weaker association between origins and destinations at higher educational levels. Thus, as successive cohorts have come to have higher levels of education (see table 2) so the gross association between origins and destinations has weakened. This effect is illustrated in figure 6, which shows the weighted sum of.: the estimated (3 values for each educational level in every cohort (where the weights are the relative sizes of the educational categories). In other words, this would be the social fluidity in each cohort if that were simply the weighted sum of the fluidities in each educational category. The picture shown in figure 6 is similar to that in figures 3 and 4: a decline, parallel for both sexes, until the cohorts born in the 1950s, then stability. The temporal coincidence of the equalization and compositional effects derives from the fact that the equalization that affected cohorts born in the first half of the century had the consequence of expanding the middle and upper levels of the educational system; this expansion translated into a compositional effect because labor markets were more meritocratic the higher the level of educational qualifications. Although the weighted averages of the educational level /3s in each cohort point to similar trends to those shown by the cohort 0s themselves, the relationship between the unconditional cohort trends and the corresponding trends across educational levels is more complicated than this. 1c 2ab 2c 3a Educational levels (low to high) Fig. 5.—0 coefficients for each educational ievel Drawing on results by Goodman (1972, pp. 1070-75), we know that we can find an unconditional cohort effect, (that is, ODC) even when the partial ODC terms are zero, provided that the partial ODE and EC associations are nonzero. From this it follows that the three-way ODC term when ODE is not in the model depends not only on the educational effect but also on the distribution of educational categories across periods. It is therefore not a simple matter to infer what pattern of unconditional cohort effects is implied by a given set of education effects, and figure 6 should be taken only as illustrative. Education and Social Fluidity In the final part of our analysis we try to make a more formal assessment of the effects of educational equalization and compositional change on the trend over cohorts in social fluidity. Essentially this involves determining how much of the association between origins and destinations is mediated via educational attainment and, following from this, how much of the change in fluidity over cohorts comes about through, on the one hand, changes in the effects of origins on educational attainment and of educational attainment on class destinations, and, on the other, the shift of the population into those educational categories in which origins have a weaker effect. Given continuous measures of social position we could do this using path analysis, but with categorical variables this is not possible. We therefore use an approximation, following Breen and Luijkx (2004a). American Journal of Sociology 1,2 12- 16- 20- 24- 26-18 22 26 30 34 32- 36- 40- 44- 48- 52- 56- 60- 64» 38 42 46 50 54 58 62 66 70 Birth cohort 74 Fig. 6.—Weighted sum of estimated (3 values for each educational level, every cohort We must begin with a measure of the evolution of the gross OD association over cohorts, and so we could fit the log-multiplicative ODfic model to the three-way origin by destination by cohort table (as reported in the models of table 4). The obvious next step might then be to use the four-way table of origins by destinations by cohorts by education to fit a model which includes the partial effects of education on destination controlling for origins, and the partial effects of origin on destination controlling for education. This would, in fact, be one of the models reported in table 9 and, if the partial OD association were fitted using a log-multiplicative specification, it might seem that we could compare the /3s from this model (the partial (3s) and compare them with the j3s from one of the models of table 4 (the gross /3s). But such a comparison would be invalid because the pattern of local OD association will be different in the two cases: that is, the pattern of OD association that evolves log multiplicatively over cohorts depends on whether we control for the effect of education on destinations or not.2'J We attempt to overcome this difficulty by constraining the pattern (though not the strength) of the OD association in the partial model to be the same as the estimated gross OD association. This allows us to make a comparison of the 0 parameters from the two models and so measure the relative strength of the association with and without controlling for the effects of education. M This is because the local OD association and the (5 parameters are estimated together in the log-multiplicative model. In the unconditional model they depend on the gross association between origins and destinations, whereas in the conditional model they depend on the association between origins and destinations holding education constant. Educational Equalization But things are not so simple because we can reasonably suppose that the pattern (and not just the strength) of the OD association will differ significantly depending on whether education is in the model or not. Educational attainment is a more important asset for mobility to some class destinations rather than others: in particular, entry into self-employment or farming among children born into these classes is a question of inheritance, rather than of educational attainment (Ishida, Mtiller, and Ridge 1995). With this in mind we use, as our baseline model, model 4 of table 4, which includes the set of parameters applied to the main diagonal of the table (but whose effects are held constant over cohorts and which we henceforth referred to as "diag"). Our model for the unconditional OD association can be written OC DC ODkpc + diag: this allows the OD association to evolve linearly over cohorts. The first of our models for the conditional or partial OD association is OEC EDC X°Dk$c + diag. X0D here represents the OD association which is fixed to be equal to that estimated from the gross model, and this is, once again, constrained to evolve linearly over cohorts. We allow the diagonal parameters to differ between the partial and gross models and we fit the OEC and EDC margins exactly in order to focus on the difference between the j3s from the gross and partial models. Comparing the /3C estimates tells us the extent to which the OD association, and its trend over cohorts, weakens once we take into account the association between origins and education and that between education and destination. However, we can go further than this and fit a second partial model in which we allow the association to vary over both cohorts and educational levels as follows: OEC EDC XODP*kPc + diag. Now the /3C parameter tells us the slope of the OD association over cohorts when we also allow that association to vary over educational levels. Here the constrained OD association, XOD, evolves linearly over cohorts, as before, but varies freely over educational levels. Table 10 contains the results of these analyses. Model 1 repeats model 4 of table 4. Model 2 is a poorer fit to the data than the counterpart model, which preserves the linear trend but estimates the OD association freely (the difference in deviance is 60.4 for men and 88.3 for women; 25df), but nevertheless still provides an adequate fit to the data,30 as does model 3. Once we control for education (model 2), the strength of the OD association—as measured by the value of the log-odds ratios in model 2 30 The large increase in the deviance for mode! 2 compared with model 6 of table 8 suggests that the pattern of the OD association in the off-diagonal cells also changes when education is taken into account—but this is something which our model cannot capture. a o m ^ a) w .3 a -0 0 o CJ a CJ o S3. X3 o **■ Q CJ W Q O w W CJ o w o ^ tfl rr: c*j o o o o ^ ° ^ 2 « « 3t <1 <^ « N *o a. ^ & o bo m .2 "a .2 10 + 'a j. u O Q O « « a o o w o W CJ o w o ES fa -~ f s 2 * Educational Equalization ■ Model 1 | m Mode! 2 i-Q Model 3 j in to i I! Ilia IVab IVcd UlbVVI Social class Fig. 7.—Diagonal parameter estimates from models 1-3: men compared with model 1—-is reduced by just less than half. That is, once we control for the path that links origins to destination via education, the direct effect of origins on destination is about halved. The slope of the log-odds ratios over cohorts, expressed as a proportionate decline in the association for that model, strengthens in model 2 compared with model 1; however, if we express the slope as the change in the absolute value of the log-odds ratios then it is only slightly weaker in model 2 than in model l.31 So, although controlling for class inequality in educational attainment accounts for a good deal of the association between origins and destinations, it does not explain much of the trend of change in this association over cohorts. In model 3 we also allow the OD association to vary over educational levels, and we report the strength of the origin-destination association at each educational level and also the absolute slope within each educational level. In both cases the figures run from the lowest to the highest educational level, and they should be read by row. Not only does the association vary in strength quite noticeably over educational levels (as we already saw in fig. 5), but, among men, the absolute slopes are quite close to zero. Among women they are close to zero at the higher educational levels but somewhat further from zero at the lower levels. These results suggest that the declining trend in the association between origins and destinations is mainly due to the com- The proportional slope reports the proportional decline per birth cohort: so, in model 2, for men this is just less than 6%. But this is relative to the initial association, which, in model 2, is weaker than that in model 1. If we then ask, What would this decline be as a share of the original association? (i.e., that in model 1), we find that this 6% proportional reduction equates to a 3% absolute decline. iso.! American Journal of Sociology Educational Equalization 5 4 E 2 3 0} q. c 2 1 0 -1 B Model 1 B Model 2 □ Model 3 IVcd 1Mb VVI Social class Fig. 8.—Diagonal parameter estimates from models 1-3: women positional effect of educational expansion rather than to the process coeducational equalization. Finally, the diagonal parameter estimates from the three models reported in table 10 are shown in figures 7 and 8. The first bar of the histogram for each class repeats that shown in figure 2. Overall the parameters show remarkably little change, indicating that the tendency for classes to be self-recruiting (where this tendency exists) operates largely independently of the educational mechanism. CONCLUSION Previous research on social fluidity has not been successful in accounting for temporal change; nor, indeed, have researchers agreed on the extent to which modern societies are characterized by change at all. Our analysis documents trends toward greater openness in Sweden, and we extend previous research by addressing the question of how this change came about. On the basis of a theoretical model of the intergenerational transmission of class position we argue that change in social fluidity will, under .. normal circumstances, be driven by cohort replacement rather than by period effects. Following from this, we pay special attention to the way in which changes in children's educational attainment can account for changing fluidity. The Swedish data are particularly suitable for addressing the question of temporal change: we have access to 24 annual surveys covering the period 1976-99, with cohorts born between 1912 and 1974, and which have comparable classifications of social class origins, educational qual- ifications, and class destinations (derived from current job). Our first conclusion is that social fluidity increased in Sweden, particularly among those cohorts of men and women born during the first half of the 20th century. We also observed a trend toward greater fluidity in the working-age population during the last quarter of the 20th century, but this period change disappears when we control for differences between birth cohorts. Moreover, we show that social fluidity does not vary across periods within a cohort, and, using the panel element in the data, we find that fluidity does not change across the life course. These results led us to conclude that period change is, in fact, driven by a process of cohort replacement. To the extent that change in social fluidity generally comes about through cohort replacement, this may explain why many sociologists have failed to discern any period change when analyzing two or more surveys that are not so many years apart and that therefore mainly comprise samples from the same cohorts. It may well be the case that true period changes are likely to occur only in specific, and perhaps rather dramatic, circumstances, but historical changes that equalize the opportunities for successive birth cohorts may have a substantial impact that is only visible in a longer time perspective. Our second conclusion is that the evolution of fluidity over cohorts in Sweden has been driven by educational equalization and by a compositional effect based in the changing educational distributions of successive birth cohorts. Hout and Dohan (1996) have portrayed the U.S. and Swedish "strategy of educational equality" as very different: the first seeks to expand educational opportunities while the second focuses on equality of condition as a means of improving the relative chances of children from disadvantaged origins. While there is little evidence that educational expansion in the 20th century led to a decreasing association between class origins and educational attainment in Sweden (Jonsson and Erikson, in press), our results show that expanding the educational system nevertheless helped to reduce the association between class origins and class destinations. It allowed more children to reach educational levels that led them to labor market segments where meritocratic selection was more prevalent and origin characteristics counted for less. The equalizing of educational chances in Sweden also led to increasing social fluidity. Moreover, although one can have compositional effects without equalization, equalization almost certainly implies expansion of the middle and/or higher levels of education, given that equalization is unlikely to occur through a reduction in educational participation by the middle classes. This means that promoting such equalization is likely to be a very effective strategy because it also advances compositional change. Our results provide perhaps the clearest example, within the social mobility literature, of how educational equalization can drive social fluidity: equalization American Journal of Sociology implies expansion of the middle and/or higher levels of the educational system, and, if social origin has less impact in the labor market for those with such qualifications, compositional change accelerates the trend toward increasing social fluidity. It is of course possible that expansion also might lead to countervailing tendencies, with social origin reasserting itself in the labor market for graduates as their number increases (something that Vallet [2004, p. 142] reports for France), though we did not find any such development in our data. Our results indicate that the trend toward educational equalization in Sweden—and thus also the trends in the compositional effect and in overall social fluidity—ground to a halt in those cohorts born around midcentury. If the situation had persisted among cohorts born after 1974 (and assuming the continued absence of any independent period influences on fluidity) the trend of equalization in period fluidity in Sweden would have been expected to come to a halt by 2020, as the Swedish workforce came to consist only of cohorts born after the middle of the 20th century. However, recent studies show that the impact of social origin on the transition to upper secondary education diminished further during the 1990s (Gustafsson, Andersson, and Hansen 2001),32 which may, along with the rapid expansion in the provision of higher education in the same decade, allow the trend toward equalization in Sweden to continue. Finally, we turn to the relevance of our results for other countries and for questions of policy. Sweden is well known in the mobility literature for its high level of social fluidity (see Breen and Luijkx 2004&, pp. 59, 72) and for its long period of gradual equalization. Nevertheless, the educational reforms introduced in Sweden during the 20th century, to which we referred earlier, are rather typical of many developed countries, especially in Europe. This raises two questions: Has the same trend of increasing social fluidity occurred in other countries? and Why is fluidity higher in Sweden than elsewhere? One difficulty in answering the first question is the dearth of analyses that adopt a cohort, rather than a period, perspective: even so, there is now a large body of evidence from period-based studies to suggest a widespread trend toward increasing social fluidity (Breen and Luijkx 2004a). Whether this can be attributed to the same causes as the Swedish case is not known, but recent research has demonstrated an equally widespread trend toward a weaker association between class origins and educational attainment (for a comparative analysis see Breen et al. [2005]; various single country studies are referred to in Breen and Jonsson [2005, p. 226]). Sweden's high level of fluidity might be attributed to two main factors. 3' We cannot observe this in our data because the individuals affected by it are too young to have been included in our samples, Educational Equalization Qn the one hand, equality of condition (especially with respect to income and the risk of prolonged unemployment) has been attained to a much greater level there than elsewhere, and this has played an important role in weakening the transmissibility of mobility assets between generations. On the other hand, Swedish employers appear to consider formal merits rather than characteristics related to the family of origin when employing those with higher education, particularly graduates (see fig. 5). This, in turn, may be because the tertiary educational system is relatively homogeneous, being free of fees and not displaying any marked differences in prestige between institutions (at least not for the cohorts we analyze). 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Adolescent First Sex and Subsequent Mental Health1 Ann M. Meier University of Minnesota The 1996 Welfare Reform Legislation and its reauthorization in 2002 included financial provisions for programs promoting sexual abstinence until marriage. Under this legislation, programs are encouraged to teach that nonmarital sex is likely to have harmful psychological effects. Life course concepts and identity theory suggest that sex may be consequential for the mental health of some adolescents. Using the National Longitudinal Study of Adolescent Health, this article investigates mental health consequences of adolescent sex. The analyses reveal important contingencies of the effect of first sex. Timing relative to age norms, romantic relationship factors, and gender interact to condition the effect of first sex on mental health. While some adolescents experience mental health decrements, the majority of those who had first sex did not. This finding highlights the importance of considering contingencies when investigating the effects of life events on mental health. Until the mid-1990s, the average age at which young people began having sex had steadily decreased. Now, almost half of American adolescents report that they have had sex by the time they graduate from high school 1 This research was supported by the National Institute of Child Health and Human Development and the Office of Behavioral and Social Sciences (K01-HD49S71), the Adolescent and Youth Dissertation Award from the Henry A. Murray Research Center at Harvard University and the Radcliffe Institute for Advanced Study, and the Center for Demography and Ecology at the University of Wisconsin—Madison, which received core support for population research from the National Institute for Child . J Health and Human Development (P30-HD0.5876), This research uses data from Add Health, funded by the National Institute of Child Health and Human Development IP01-HD31921) with cooperative funding from 17 other agencies, Thanks to Gary Sandeftir, Jeylan Mortimer, Jeffrey Smith, Ross Macmillan, Elizabeth Thomson, Larry Bumpass, John Delamater, Janet Sibley Hyde, Charles Halaby, Mike Shanahan, Evan Schofer, and Nan Astone for comments on this article. Direct correspondence to Ann Meier, Department of Sociology, University of Minnesota, 267 19th Avenue South, Minneapolis, Minnesota 55455. E-mail: meierann@umn.edu © 2007 by The University of Chicago. Al! rights reserved. 0002-9602/2007/11206-0005$ 10.00