The authors argue that cultural fragmentation models predict that cultural change is driven primarily by period effects, whereas acquired dispositions models predict that cultural change is driven by cohort effects. To ascertain which model is on the right track, the authors develop a novel method to measure “cultural durability,” namely, the share of over-time variance that is due to either period or cohort effects for 164 variables from the 1972–2014 General Social Surveys. The authors find fairly strong levels of cultural durability across most items, especially those connected to values and morality, but less so for attitudes toward legal and political institutions.

Cultural sociologists have focused a great deal on the issue of “culture in action,” that is, on the question of how culture influences people’s conduct. One (admittedly simplified) way to frame the question is to ask whether people act on the culture they internalize during their formative years (Parsons and Shils 1951) or whether they strategically use cultural “tools” available at any one moment to navigate and make sense of social interactions and institutions independently of whether this cultural has been deeply internalized or not (Swidler 1986, 2001a, 2008). This is essentially a question of the relation between the timing of cultural exposure and the timing cultural influence: can the past make itself felt in the present, or do present contingencies override past cultural influences?

Recently, we have seen convergence around the idea that both of these models of the culture-action linkage carry some validity (Lizardo and Strand 2010; Patterson 2014). More specifically, researchers have proposed that both accounts may be profitably combined under some version of a psychological dual-process theory (Hoffmann 2014; Miles 2015; Srivastava and Banaji 2011; Vaisey and Lizardo 2010). People do internalize beliefs, values, and other cultural constructs that influence their conduct at subsequent time points, but they often do so in an implicit way that cannot readily be articulated. At the same time, people also learn a wide variety of cultural scripts that can be used to make sense of the world to themselves and to others and to navigate the requirements of social institutions at a given point in time (Chan 2012:174–78; Vaisey 2009).

To this point, however, cultural sociologists have been focused on showing how cultural schemes matter (or do not matter) for action at the personal level, but they have not seriously considered the different implications of these debates for understanding cultural change at the aggregate level. Our argument is that considerations about the culture-action linkage do have such implications and that analysts interested in the nature, pacing, and direction of cultural change at the macrosocial level (Caren, Ghoshal, and Ribas 2011; Inglehart and Baker 2000) should be attuned to the implications of these debates. To that end, in this article, we outline two ideal-typical models of cultural internalization informing most research in the field, the cultural fragmentation model and the acquired dispositions model, and consider the predictions each makes with regard to process of cultural change.

The key to our argument is that there are important differences in how these two models portray the relationship between the timing of exposure to cultural influences and its effect on individual behavioral or attitudinal outcomes. In a nutshell, the cultural fragmentation model implies that the contemporaneous effects of the external environment are preponderant in shaping behavioral and attitudinal dispositions, whereas the acquired dispositions model implies a sort of “imprinting” effect (cf. Marquis and Tilcsik 2013) such that contemporaneous responses are primarily a function of previous exposure as historically encoded in persons (cf. Marquis and Tilcsik 2013). We can thus draw a theoretically productive theoretical analogy (Vaughan 2014) between the key empirical implications of the two dominant models of internalization in cultural sociology and how demographers and attitude researchers have traditionally conceptualized the difference between “period” and “cohort” effects in relation to processes of social and cultural change (Ryder 1965).

To concretize the argument, we show that analytic tools for the disambiguation of these mechanisms can be used as a window to test the global empirical validity of the two most prevalent theoretical models in cultural sociology. In a nutshell, cultural fragmentation models predict that social change processes are driven primarily by period effects, and acquired dispositions models predict that social change processes are driven primarily by cohort effects. To evaluate this hypothesis, we analyze 164 time-series variables from the 1972–2014 General Social Surveys (GSS) using a novel, yet simple, technique to deal with the age, period, and cohort identification problem. We find that in the majority of the cases (but not all), the predictions of the acquired dispositions model are better supported, suggesting that when it comes to certain attitudinal and behavioral dispositions, cultural change happens via the slow enculturation of persons early in life and not via contemporaneous exposure to external cultural influences.

Mechanisms of Social and Cultural Change Social change can come about through a combination of three processes: age effects, period effects, and cohort effects (Ryder 1965). Age effects are individual changes that are the result of developmental processes resulting from social and biological maturation (e.g., people get sicker as they get older). Period effects are influences caused by contemporaneous changes (e.g., the effect of an ongoing foreign conflict on political views). Cohort effects are population changes that result because older cohorts in a population are dying and being replaced by younger cohorts that are different in some way as a result of having made an important social transition (e.g. being born, coming of age, entering the labor force, forming a family) at a given historical time (e.g., those who grew up during the Great Depression were consistently different in many ways from surrounding cohorts over time). Here we are primarily concerned with the relative contribution of period and cohort effects. Does culture change because of a changing public zeitgeist? Or does it change because people are socialized under different conditions and take acquired dispositions with them over the rest of their lives? Put even more simply, if we want to predict (say) a person’s opinion on an issue, or their behavior, would we do better to know what year it is when we ask the question, or what year the person was born? Even though there is a tight conceptual fit between demographic models of social change and theoretical debates in cultural sociology, we cannot apply a simple statistical technique and “solve” the issue. Although it is possible to conceptualize separate age, period, and cohort effects on changes in some outcome variable, it is impossible to simply directly estimate these effects in a regression model. This is because of the “identification problem”: whenever we know any two of age, period, or cohort, we automatically know the third. For example, if we know that a person is 29 years old and that it is 2015, we also automatically know that she was born in 1986 (perfect collinearity). This makes it impossible to estimate a regression model that simply includes age, period, and cohort as predictors, because one of the variables will be perfectly collinear with the other two. We refer to this as the age-period-cohort (APC) problem. Researchers have used many different approaches to address the APC problem (see, e.g., Glenn 2005). This is not the place for a comprehensive review of these approaches; the main observation we wish to make is that all must rely on some identifying restriction. For example, assuming that age can be treated as a quadratic function or that cohorts can be grouped into five-year intervals breaks the perfect collinearity between the variables, thus allowing estimation (Yang and Land 2006). Unfortunately, all such restrictions are have an arbitrary component and may affect the resulting estimates (Bell and Jones 2014). Let us set the APC problem aside for a moment, however, and consider what we would actually want to know if we could. If we could get separate effects for each age, each year, and each cohort, we could compare the variances of these effects to get a sense of the relative importance of each. Given the motivating question of this study—comparing the cultural fragmentation and acquired dispositions models of cultural change—we may want to pose the question in terms of cultural durability. That is, once a person acquires his or her dispositions (a cohort-based process), how robust or durable are those dispositions in the face of changes in the contemporaneous social environment (a period-based process)? Consider the following: let V ( C ) be the variance in some outcome attributable to cohort effects, and let V ( P ) be the variance in some outcome attributable to period effects. Then V ( C ) V ( C ) + V ( P ) is the proportion of the total change in that outcome that is attributable to cohort effects as opposed to period effects, with 1 meaning all cohort effects and 0 meaning all period effects net of age.6 We can use this quotient as an index of cultural durability, which we designate δ. To the extent that δ approaches 1 in a particular case, the predictions of the acquired dispositions model are better supported, because it means that we only need to know (other than age) about the historical “conditions of production” to predict a person’s position. To the extent that δ approaches 0, the predictions of the cultural fragmentation model are better supported because the contemporaneous zeitgeist fully predicts a person’s response.

Acknowledgements We would like to thank Jason Beckfield, Kyle Longest, Andy Perrin, and Matt Desmond for very helpful comments and suggestions on previous drafts and the Socius reviewers and editors for incisive comments and suggestions that helped improve the article.

Authors’ Note

This work was presented at seminars in the departments of sociology at Harvard University and Yale University.

1

Strauss and Quinn (1997) referred to this as the “fax model” of culture. We have updated the metaphor, but the idea is the same. 2

Zaller’s (1992) definition of “predispositions” in the political opinion research context is consistent with this conceptualization of dispositions. Zaller defined predispositions as “stable, individual-level traits that regulate the acceptance or non-acceptance of the political communication the person receives” (p. 22). 3

The imprinting hypothesis was first developed for the case of organizations by Stinchcombe (1965) in his classic essay. However, Marquis and Tilcsik (2013:217–20) showed that the general mechanism can be shown (theoretically and empirically) to also be applicable to individuals. Although not all imprinting effects necessarily lead to cohort effects, the sort of early socialization effects that lead to durable dispositions that we discuss do. 4

Some forms of pragmatism (e.g., Gross 2009) also make arguments compatible with an acquired dispositions imagery. 5

For a strong statement of this view, see Quinn (2016). 6

Age effects are not of substantive interest in this article. But these effects must of course be properly estimated to obtain unbiased estimates of the other effects. 7

To assess the SMS thesis, Caren et al. fitted Yang and Land’s (2006) APC decomposition models to a pooled sample of every major national survey that has ever collected data on protest event participation. Their results are fully consistent with an acquired dispositions interpretation: the rise in protest participation is largely a result of generational (cohort) effects (δ = 0.92; our calculation from model 3 in Table 2 in Caren et al. 2011:140) and is only minimally affected by period effects. Most important, this pattern of results is different when it comes to low-risk, highly institutionalized forms of movement participation (such as signing a petition). In this case, period effects dominate over cohort effects (δ = 0.18; our calculation from model 9 in Table 3 in Caren et al. 2011:142). 8

This assumes for the sake of illustration that we limit the sample to those between the ages of 18 and 85 years. 9

This is made slightly more complicated by the fact that the GSS eventually became a biennial survey. But this does not affect the logic of the reasoning here. 10

The same logic applies to more complex periodic functions (e.g., the sine function).