3
Education, Fertility, and Heritability: Explaining a Paradox

Hans-Peter Kohler and Joseph L. Rodgers

Wilson (1998:8) promoted the importance of “consilience” in science. Consilience is the “‘jumping together’ of knowledge by the linking of facts and fact-based theories across disciplines to create a common groundwork of explanation.” Such a process—in which disciplinary boundaries break down and then disappear (and then, perhaps, are reconstituted)—is occurring in research on human fertility. Our particular interest is in the interplay between demography and biology, as these two very different disciplines begin to blend in the long-standing effort to develop models and theories to explain human fertility. In fact, in this chapter our focus will be even sharper: We treat the interplay between genetics and fertility.

The signals that such a consilience between geneticists and demographers has been developing can be seen in the work of Adams et al. (1990) and Wood (1994). Udry (1995, 1996) added important impetus. There are two directions in which this disciplinary boundary can be crossed. The early work cited above represented (mostly) research in which demographers crossed the boundary from demography into biology and back again. Udry’s (1995) article, “Sociology and biology: What biology do sociologists need to know?,” is illustrative. A number of more recent publications show that the boundary is also being crossed in the other direction, as those trained in molecular and behavioral genetics present research using genetic methods but applied to topics in the traditional purview of demographers (e.g., Kohler et al., 1999; MacMurray et al., 2000; Miller et al., 1999, 2000; Rodgers et al., 2001a). Two recent papers in the journal Demography apply behavioral genetics



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Offspring: Human Fertility Behavior in Biodemographic Perspective 3 Education, Fertility, and Heritability: Explaining a Paradox Hans-Peter Kohler and Joseph L. Rodgers Wilson (1998:8) promoted the importance of “consilience” in science. Consilience is the “‘jumping together’ of knowledge by the linking of facts and fact-based theories across disciplines to create a common groundwork of explanation.” Such a process—in which disciplinary boundaries break down and then disappear (and then, perhaps, are reconstituted)—is occurring in research on human fertility. Our particular interest is in the interplay between demography and biology, as these two very different disciplines begin to blend in the long-standing effort to develop models and theories to explain human fertility. In fact, in this chapter our focus will be even sharper: We treat the interplay between genetics and fertility. The signals that such a consilience between geneticists and demographers has been developing can be seen in the work of Adams et al. (1990) and Wood (1994). Udry (1995, 1996) added important impetus. There are two directions in which this disciplinary boundary can be crossed. The early work cited above represented (mostly) research in which demographers crossed the boundary from demography into biology and back again. Udry’s (1995) article, “Sociology and biology: What biology do sociologists need to know?,” is illustrative. A number of more recent publications show that the boundary is also being crossed in the other direction, as those trained in molecular and behavioral genetics present research using genetic methods but applied to topics in the traditional purview of demographers (e.g., Kohler et al., 1999; MacMurray et al., 2000; Miller et al., 1999, 2000; Rodgers et al., 2001a). Two recent papers in the journal Demography apply behavioral genetics

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Offspring: Human Fertility Behavior in Biodemographic Perspective methods to human fertility (Rodgers et al., 2001b) and race differences in birth weight (van den Oord and Rowe, 2000). The controversy that can occur when disciplinary boundaries are crossed in the consilience process is illustrated by the responses to the van den Oord and Rowe paper in a later issue of Demography (Frank, 2001; Zuberi, 2001).1 Similarly, a paper by Morgan and King (2001) that reviews the biological predispositions, social coercion, and individual incentives for having children in contemporary below-replacement fertility contexts resulted in diverging opinions about the usefulness of biodemographic or behavioral genetics approaches in understanding contemporary fertility behavior (Kohler, 2001; Capron and Vetta, 2001). The idea that the disciplinary boundary can be crossed in either direction suggests two very different questions: What does genetics have to contribute to demographic research on fertility? What can demography contribute to a geneticist’s thinking about fertility? These questions cannot easily or naturally be addressed together because the specificity of the models and theories in the two disciplines are at such completely different levels. Further, Wilson (1998:198) implied that an asymmetry exists in how the goals of consilience will be received by the two disciplines. In comparing medical science to social science, he stated, “The crucial difference between the two domains is consilience: The medical sciences have it and the social sciences do not.” If he is correct, we can infer that genetics, as emergent from medical science, would be more naturally disposed to such an integrative and cross-disciplinary effort than demography, a social science. We take the position that consilience is a positive development, that cross-disciplinary research has the potential to generate methods and models that are far beyond the sum of the separate contributions. Those more strongly wedded to a focal disciplinary perspective will undoubtedly be uncomfortable with our specific and also with broader efforts toward consilience. In this paper we are concerned primarily with the issue of how designs and methods that emerge from genetics research (more specifically from behavioral genetics) can contribute to demographic thinking about fertility. In particular, in this specific study we apply behavioral genetics methods to study the relationship of education, fertility, and heritability of fertility. Before embarking on these specific analyses, we briefly consider the second question: Can demographic methods inform genetics research? In a sense the answer to that question is embedded in population genetics, and that interplay has been occurring for quite some time. Population geneticists are, fundamentally, demographers at heart. Accounting for the distri- 1   We should note that much of the controversy arising in this exchange was over the use of a race variable rather than behavioral genetics per se.

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Offspring: Human Fertility Behavior in Biodemographic Perspective bution of genes in the population, and studying how gene frequencies change over time through adaptive processes, is similar to the study of various demographic phenomena. The ways that demographers deal with selection (their own type of selection, as opposed to the meaning of the term in genetic/evolutionary contexts) might be one example of a domain in which demography could inform genetics. That consideration, however, is for a different paper at a different time. How can genetic thinking inform demographic research on fertility? We list several potential answers to this question: Behaviors are always constrained by the limits of biological/genetic potential. Humans cannot run at 70 mph, though cheetahs can; each organism’s genotype helps define the “reaction norm” involved in performance. In the domain of fertility, humans are limited in the number of offspring they can produce in a given time period (e.g., limited by the menstrual cycle and the gestational period as well as social norms). Those constraints are defined by genetic influences on physiology (which, e.g., limit the human reproductive process to few children at a time) and by genetic influences on behavior (more on that later). Individuals always need to take these restrictions as given in their own decision making and behavior, and even on the population level these biological and genetic limits are fixed within time horizons that may not allow evolutionary adaptation. Genes/biology limit not only the theoretical potential at the extremes but also the practical and achievable outcomes. Social behavior has genetic origins. In a distal sense, fertility motivation is influenced genetically (e.g., Miller et al., 2000). Social theories of fertility, which have long ignored the potential for genetic influence, may in fact be much closer to explaining the available variance than is commonly believed (see Rodgers, et al., 2001b). In other words, there may be less social variance available to be modeled than is widely appreciated. Genetic influences interact with environmental influences in fascinating and subtle ways. For example, Neiss et al. (2002) showed that, while “education partially mediated the negative association between IQ and age of first birth” in a purely social model, this mediating effect virtually disappeared when genetic influences were entered into the model. The assumed existence of preferences that guide behavior is a clear limitation in rational choice or the economic theories often invoked to explain fertility behavior and its change over time. Besides a set of regularity and consistency conditions, however, remarkably little can be said about the preferences for children themselves. Morgan and King (2001), for instance, realized the opportunity that evolutionary theories and behavioral genetics provide for improving our understanding of human preferences for children, and their arguments are closely related to other work that has

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Offspring: Human Fertility Behavior in Biodemographic Perspective tried to interpret the preferences for children and related behaviors, like sexual intercourse, pair bonding, and changing fertility rates in an evolutionary perspective (e.g., Carey and Lopreato, 1995; Foster, 2000; Kaplan et al., 1995; Kohler et al., 1999; Miller and Rodgers, 2001; Potts, 1997; Udry, 1996). Hence, starting from genetic dispositions on the desire to have sex and the “joy” of nurturing, evolutionary reasoning suggests that important aspects of human reproductive behavior are shaped by genetic dispositions (which clearly must be distinguished from genetically “hard-wired” behavior). With proper designs, behavioral genetics models can therefore help to understand human preferences for children and motivations for fertility-related demographic behaviors, such as union formation or parental investments in children, and precursors of fertility such as menarche and sexual initiation. Multivariate behavioral genetics modeling has the potential to identify overlapping sources of variance in both genetic and environmental domains. Topics of potential interest to demographers include the following: Are the genetic sources of influence on human fertility of the same nature for early fertility, general fertility, and later fertility? Are genetic influences on fertility similar when considered across generations as those occurring within generations? How do the precursors to fertility—puberty, sexual initiation, marriage, fertility planning—overlap with one another? Further, how do these processes overlap genetically, and environmentally, and how do genetic/environmental processes interact with one another? Rodgers et al. (2001b) and Kirk et al. (2001) illustrate how multivariate analysis can inform our understanding of the overlap between different fertility-related behaviors. Many of the above issues are of considerable relevance for researchers interested in contemporary patterns of fertility. In the next section we provide an outline of a conceptual framework that facilitates the integration of behavioral genetics modeling and thinking into more standard socioeconomic approaches to fertility and related behaviors. Subsequently, we provide a brief review of the methodology on which behavioral genetics is based. In our empirical analyses, we then apply behavioral genetics design and models to study a question of interest to demographers concerning the role of education in fertility. INTERPRETING BEHAVIORAL GENETICS IN RESEARCH ON FERTILITY AND RELATED BEHAVIORS Behavioral genetics is both a way of thinking about causal influence and a set of methods developed to support that thinking. As a way of thinking about causality, it is motivated by the idea that genetic and envi-

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Offspring: Human Fertility Behavior in Biodemographic Perspective ronmental influences both compete and interact with one another to influence behavior. Gottlieb (2000) criticized the “central dogma” of molecular biology that causality flows in one direction from the genes that activate DNA through proteins that they produce and ultimately to behavior. Rather, he reviewed evidence in support of probabilistic epigenesis, in which the environment also has causal influences on genes and the activation of DNA. Behavioral genetics, however, does not have a particular focus on genetic determinism. As Plomin and Rende (1991:162) noted, “The power of behavioral genetics lies in its ability to consider nurture as well as nature—that is, environmental as well as genetic sources of individual differences in behavior.” In fact, one of the coauthors of this paper came into the behavioral genetics arena because it provided mechanisms to control for genetic variations in the study of environmental (i.e., social and cultural) influences (e.g., Rodgers, Rowe, and Li, 1994a). For example, Rodgers, Rowe, and May (1994b) showed the influence of taking trips to museums on the mathematical ability in children and the influence of owning books on reading ability in children; each of these influences was a social/environmental influence observed after controlling for genetic processes that make children naturally similar to and dissimilar from one another. In the application to fertility and related behaviors such as marriage, these interactions of genetic disposition with environmental contexts and individual characteristics can be naturally explored. Traditionally, demographers and related social scientists have emphasized the demand for children as a key factor in explaining contemporary fertility changes. The explanations of a shifting demand for children often focus on changes in education, income, labor market opportunities, female wages, child care arrangements, and so forth, that have been associated with the socioeconomic transformations in developed countries in recent years. These shifts in socioeconomic conditions have also led to changes in many fertility-related behaviors, such as marriage/union formation and female labor force participation, which are also closely related to changing demands for children. It should be noted that such socioeconomic considerations of fertility change are rarely inconsistent with biological theories. To the contrary, recent evolutionary approaches to demographic change frequently incorporate transformations in the context of fertility decisions through socioeconomic changes or technical innovations. In particular, socioeconomic changes and technological innovations lead to adjustments in the optimal fertility strategies because they alter incentives for allocating scarce resources, such as time and energy, to reproductive efforts, child quality versus quantity, somatic investments, and several other competing uses (e.g., see Hill and Kaplan, 1999; Kaplan et al., 2000; Lam, this volume). While evolutionary and socioeconomic theories are therefore quite compatible in their general approaches toward explaining levels of fertility, the

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Offspring: Human Fertility Behavior in Biodemographic Perspective challenge for incorporating biological dispositions with sociological theories is in the explanation of within-population variations in behavior. In particular, differential biological dispositions of individuals—resulting, for instance, from genetic variations or hormonal influences—can be important determinants of individual behavioral differences in addition to socioeconomic incentives or structural influences. In order to see these potential interactions, we sketch a simplistic, but for our purposes sufficient, framework for fertility decisions in contemporary developed societies (see also Lam, this volume). In particular, the number of children of an individual in these contexts is strongly influenced by the age at marriage/union-formation and the number of reproductive years spent in stable unions, the level of education of the individual and his/her spouse, the abilities for and extent of labor force participation, and similar factors (Becker, 1981; Marini, 1981; Morgan and Rindfuss, 1999; Willis, 1973). While there is some divergent assessment between economically and sociologically oriented scholars about the extent to which individuals rationally account for the potentially complex interactions between the above behaviors in their life course planning about fertility, it is probably not controversial to assert that individuals conduct conscious life course planning, including plans for marriage and fertility. These life course plans take into account an individual’s (potentially incomplete) knowledge about his/ her educational opportunities and returns to education, attractiveness in the marriage market (both in terms of physical and socioeconomic characteristics), assessments about opportunities in the labor market, preferences for children, and several other “goods” and/or goals. In addition, these plans are subject to important random elements, for instance, with respect to finding a partner in the marriage market, receiving positive or negative income ”shocks” upon entering the labor market, or shocks in the conception and gestation processes leading to the birth of a child. Individuals are likely to update their life course plans as they learn more about their own relevant characteristics and as they experience different random shocks that affect fertility-related aspects of their life courses. Variations in individuals’ life courses—including variations with respect to important fertility outcomes—therefore arise for two reasons: First, individuals’ desired life courses differ because of different socioeconomic opportunities or constraints and because individuals have different abilities, preferences, physical characteristics, and so forth, that they take into account in making life cycle decisions and plans. Second, the realized life courses of individuals differ from their initial plans, as well as among individuals, due to shocks in fertility and fertility-related behaviors/processes, such as an unexpectedly long waiting time to conception or the death of a spouse. This complex embeddedness of fertility outcome into life course deci-

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Offspring: Human Fertility Behavior in Biodemographic Perspective sions and processes points to a broad framework of genetically mediated influences. In particular, differential genetic dispositions can exert influences on fertility and related behaviors through at least three distinct pathways. On the one hand, biological dispositions affect fertility relatively directly through genetically mediated variations in physiological characteristics affecting fertility outcomes. Genetic influences on fecundity are an obvious example (e.g., see Christensen et al., 2003), but there are also other possibilities, including, for instance, the fact that physical characteristics might render a person especially attractive in the marriage market, which increases the probability of an early marriage because of an unexpected high frequency of attractive marriage offers in early adulthood. On the other hand, and potentially more interesting in the context of this paper, biological dispositions affect fertility through deliberate fertility decisions and a broad range of fertility-related behaviors that are subject to substantial volitional control. Within this category of influences, we can further distinguish between two different pathways. First, some biological dispositions exert their effect on behavior through conscious decision making and life course planning. Second, biological dispositions may also operate subconsciously on decision processes if individuals are not aware of their background influences on aspects such as emotions, preferences, or cognitive abilities. Examples for the former are individuals’ knowledge about their fecundity (e.g., see Rosenzweig and Schultz, 1985) or knowledge about their returns to schooling and delaying fertility (see, for instance, Behrman et al., 1994, 1996). In addition, Halpern et al. (2000) found that “smart teens don’t have sex or kiss much either,” which is consistent with higher cognitive abilities and an awareness about the high costs of early pregnancies due to foregone opportunities. Further examples of subconscious influences are variations in evolved preferences for nurturing (Foster, 2000; Miller and Rodgers, 2001). Moreover, early sexual activity, which is a predictor of early fertility, has also been related to nonvolitional factors such as hormone levels and body fat (e.g., Halpern et al., 1997, 1999), both of which are subject to strong genetic variations. In summary, the above interpretations view genetic dispositions as part of individuals’ endowments that affect their life course patterns, including those pertaining to fertility and related behaviors, through their effect on (1) conscious decision making and deliberate life cycle planning, (2) nonvolitional processes affecting life course outcomes, and (3) physical characteristics or cognitive abilities that partially determine opportunities in the labor market and marriage market. Instead of focusing on these specific pathways of how genetic dispositions affect fertility outcomes, most current behavioral genetics designs try to identify and estimate the net contribution of a broad range of genetically mediated biological factors on the variations in fertility behavior within a population or within cohorts.

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Offspring: Human Fertility Behavior in Biodemographic Perspective The advantage of this approach is that it provides an estimate of the overall relevance of genetically mediated biological effects on variations in fertility behavior. This information about the overall relevance of genetically mediated variations is interesting in itself. Moreover, this information will guide future research with respect to the scope of investigating specific mechanisms and pathways of biological influences: If the net overall variation attributed to genetic factors is high, the search for specific pathways (or possibly even specific gene factors) is likely to be more promising compared to a situation in which the overall influence is found to be low. In addition, studies of overall genetic variations in fertility outcomes can suggest specific socioeconomic contexts of cohorts that seem to facilitate genetically-mediated variation in fertility behavior, and these genetic-socioeconomic interactions will provide considerable scope for integrating sociological and biological theories about reproductive behavior. Although most behavioral genetics work, including that applied to fertility, still focuses on partitioning variances into genetic and environmental components, broader applications are emerging (see Rutter, this volume). On the one hand, more sophisticated modeling and theorizing are supported by an increased availability of data, including large-scale twin and family data that are rich in socioeconomic life course information pertaining to education, marriage/union or labor market history, and genetic relatedness. Such data can be used to estimate multivariate behavioral genetics models that disentangle the pathways of genetic influences, such as the processes affecting marriage/union formation, age at first birth, educational attainment, and so forth. For an example of such an application, see the study by Rodgers et al. (2003), which uses age at first pregnancy attempt as an indicator of (volitional) fertility motivation and lag to pregnancy as a measure of (nonvolitional) fecundity. The study implements a methodology that allows competition between these two domains—the psychological and the biological—in accounting for the genetic variance underlying fertility outcomes. In addition to suggesting the joint investigation of genetic dispositions and life course processes/decisions, the framework outlined above implies that the relevance of genetic factors for variations in fertility outcomes within cohorts is likely to be strongly conditioned by the socioeconomic context of the cohorts. A possibly surprising—but robust—finding in some of our earlier analyses (Kohler et al., 1999, 2002b), for instance, is a systematic relationship between fertility transitions and patterns in both heritabilities and shared environmental variance in data on female Danish twins: Increased opportunities for education and labor market participation and the emergence of relaxed and flexible reproductive norms in recent decades seem to have strengthened the genetic component in fertility outcomes. Finding these varying influences is consistent with, even predicted by, our

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Offspring: Human Fertility Behavior in Biodemographic Perspective understanding of how genetic factors affect fertility decisions that are embedded in broader-context life cycle decision making. For instance, changes in patterns of female labor force participation may heighten the extent to which genetically mediated influences on ability and hence wages affect fertility, or higher mobility may increase the size of the marriage market and therefore strengthen the implications of variation in endowments on the timing and probability of marriage. We have argued (Kohler et al., 1999) that reduced social constraints on fertility and related behaviors, increased opportunities, and more egalitarian societies have increased the relevance of genetically mediated variation in preferences for children on fertility outcomes. Few of these pathways have been explored in detail, but future studies with twin (or kinship) data that contain extensive socioeconomic information and life course histories can potentially overcome this limitation. Future analyses therefore not only need to estimate sophisticated behavioral genetics models for fertility and related behaviors but also need to allow for interactions between the socioeconomic context, individual characteristics and life histories, and patterns of heritability. In the empirical part of this chapter we apply behavioral genetics designs and models to study one such interaction between genetic dispositions and socioeconomic environments, specifically education, and show how the effect of genetic dispositions on fertility differs between individuals with different education levels. Before we embark on these empirical analyses, we provide a more general introduction to behavioral genetics designs and methods. BEHAVIORAL GENETICS DESIGNS AND METHODS The methodology of behavioral genetics begins with research design and then moves to a set of analytic models that can be used to estimate the parameters of the biometrical model. The classic behavioral genetics design is the twin design in which the similarity between identical and fraternal twins is compared on some trait of interest (e.g., completed fertility). Other designs include the family design, the adoption design, and the identical-twins-raised-apart design. As in any research arena, each design has logical weaknesses that leave threats to the validity of conclusions based on those designs. Also, each design rests on a set of assumptions. For example, assumptions of the twin design include no influence of assortative mating and equal environments (e.g., Plomin, 1990). A complete statement of the behavioral genetics design logic and the basic quantitative genetics model is beyond the scope of this chapter, although we can summarize some of the basic theory and original references. Much of the original design work was done by Fisher (1930). The original and most complete statement of the quantitative genetics model on which

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Offspring: Human Fertility Behavior in Biodemographic Perspective behavioral genetics research comes from Falconer (1981). A cogent and comprehensive textbook that reviews the field is that by Plomin et al. (1990), and a more accessible review is contained in Plomin (1990). A common feature of the analytic models used in behavioral genetics work is that the analyses estimate parameters related to genetic variability (usually referred to as heritability, or h2), shared environmental variability (or c2), and nonshared environmental variability (or e2). These parameters represent, respectively, the fraction of variance in a trait or outcome that is due to genetic factors, shared environmental influences like common family backgrounds, and finally individual-specific environmental factors. This interpretation of behavioral genetics results in terms of c2, h2 and e2, however, is correct only in a particular model that specifies how the different factors interact to jointly influence the outcome, quite similar to the fact that the structural interpretation of regression parameters in socioeconomic studies of fertility depends critically on the correctness of the underlying behavioral model and the appropriateness of the estimation strategy. Common—but not necessarily required—assumptions of behavioral genetics models include the additivity of the elements in the model (e.g., additive genetic and environmental influences). In principle, both the design and analytic assumptions are often violated. Extensive study has been given to the nature and effect of violating these assumptions, and these effects are well documented (e.g., Plomin, 1990). In addition, there have been substantial developments in methods and data innovations that increase the value of twin and family designs. On the one hand, studies of twins are increasingly based on large-scale twin data, sometimes representing the complete population of twins of a country and including longitudinal follow-up (Kyvik et al., 1996, 1995; Pedersen et al., 1991), with low measurement error on key biological and socioeconomic variables and potentially extensive information about nonshared environments in childhood and adulthood. On the other hand, the limitations of the textbook behavioral genetics model are increasingly being overcome, including also in the application of behavioral genetics models to demography. For instance, Kohler and Rodgers (1999) have developed models for binary and ordered models, which are especially suited to the dependent variables such as “having at least one child” or “number of children,” and Yashin and Iachine (1997) describe behavioral genetics models suitable for the analysis of mortality or other duration data such as the timing of children. Major advances in disentangling gene-environment interactions are possible with large twin datasets that encompass cohorts that experience substantially different socioeconomic and demographic contexts. For instance, using local regression techniques and cohort interactions, we have shown that genetic influences on fertility have been subject to variations over time (Kohler et al., 1999, 2002b). These local regression techniques

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Offspring: Human Fertility Behavior in Biodemographic Perspective therefore account for major gene-environment interactions that occur across cohorts. An alternative possibility is to test gene-environment interactions by incorporating additional socioeconomic conditions and information about the parental household and spouses of the twins. Moreover, these twin models sometimes allow for explicit tests of alternative genetic models, such as dominance effects. An additional development of particular value to demographers is the identification of kinship structure in large national datasets like the National Longitudinal Survey of Youth (NLSY). Using information from the survey, Rodgers et al. (1994) developed an algorithm that specified kinship structure in the children of the NLSY youth data and in another study Rodgers et al. (1999) reported a similar linking algorithm that specified kinship links for the NLSY youth respondents. These links open up the potential to do behavioral genetics analyses on national probability samples (a common criticism of the twins study is its low external validity) using the rich longitudinal and multivariate structure of such data sources. Behavioral genetics methods have long been criticized by those outside the field (e.g., Lewontin et al., 1984, have defined a popular set of criticisms of the basic behavioral genetics design, including in particular the confounded nature of the genotype of monozygotic twins genotype and similar treatment by their families and peers).2 Interestingly, the behavioral genetic community itself is filled with internal critics who have carefully scrutinized and criticized these methods, and (arguably) the strongest and most cogent criticisms arise from behavioral geneticists themselves. For example, 2   In fact, reviewers of the empirical results presented later in this chapter raised the issue of whether the results could have been caused by the greater similarity in appearance/attractiveness of MZ twins compared to dizygotic (DZ) twins. For example, could MZ twin correlations in fertility that are higher than DZ twin correlations in fertility be caused by the greater similarity in appearance/attractiveness for MZ twins? This concern is a form of the well-studied equal environments assumption of behavioral genetics modeling. This assumption has been studied in several ways. “Mislabeling studies” have studied persons (especially twins) misdiagnosed for zygosity; typically, their correlational patterns are similar to their biological zygosity, rather than their presumed (incorrect) zygosity (Scarr and Carter-Saltzman, 1979). Other studies have directly addressed similarity of appearance (e.g., Loehlin and Nichols, 1976). Plomin et al. (1990:319) reviewed these studies and concluded that the data “strongly support the reasonableness of the equal environments assumption.” More specifically related to the current research are a number of past studies of fertility based on family designs that come to similar conclusions as our studies of twins (see Rodgers et al., 2001a, for a review). Those family studies include cousins, half siblings, and full siblings (in addition to a few twins). Though siblings are more genetically related than half siblings (for example), they are not likely to be much more similar in appearance or attractiveness. Though the concern over the appearance confound is well founded, evidence has not yet emerged that either behavioral traits in general or fertility in particular are especially influenced by violations of the equal environments assumption. Nevertheless, behavioral genetics texts recommend continued caution.

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Offspring: Human Fertility Behavior in Biodemographic Perspective TABLE 3-6 Males—Bivariate Ordered Probit Estimation for Number of Children   Number of Children Males Model 1 Model 2 Model 3 Model 4 Model 5 Variables influencing mean level   Cohort (reference category: cohort, 1953-1954)   1955-1956 0.013 (0.079) 0.032 (0.079) 0.034 (0.078) 0.028 (0.079) 0.030 (0.078) 1957-1958 0.053 0.081) 0.077 (0.081) 0.077 (0.079) 0.073 (0.081) 0.073 (0.079) 1959-1960 –0.083 (0.077) –0.049 (0.077) –0.050 (0.076) –0.054 (0.077) –0.055 (0.076) 1961-1962 –0.228 (0.082)** –0.197 (0.082)* –0.198 (0.081)* –0.201 (0.082)* –0.201 (0.081)* 1963-1964 –0.269 (0.081)** –0.236 (0.081)** –0.235 (0.080)** –0.244 (0.081)** –0.243 (0.080)** 1965-1966 –0.626 (0.079)** –0.584 (0.080)** –0.584 (0.079)** –0.592 (0.080)** –0.592 (0.079)** 1967-1968 –0.947 (0.085)** –0.892 (0.086)** –0.893 (0.086)** –0.897 (0.086)** –0.899 (0.086)** 1969-1970 –1.327 (0.090)** –1.279 (0.090)** –1.276 (0.090)** –1.281 (0.090)** –1.280 (0.090)**

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Offspring: Human Fertility Behavior in Biodemographic Perspective Years of elementary and secondary education   –0.055 (0.010)** –0.054 (0.010)** –0.055 (0.010)** –0.054 (0.010)** Constant 0.830 (0.061)** 1.387 (0.120)** 1.375 (0.119)** 1.398 (0.120)** 1.386 (0.119)** Correlation within twin pairs   DZ twins 0.208 (0.032)** 0.202 (0.032)** 0.209 (0.033)** 0.185 (0.035)** 0.192 (0.036)** DZ x (birth year - 1961.5)     0.006 (0.007)   0.006 (0.007) DZ x (years of elementary + secondary Education - 11.04)       –0.032 (0.022) –0.032 (0.023) MZ twins 0.324 (0.036)** 0.317 (0.036)** 0.327 (0.036)** 0.332 (0.036)** 0.337 (0.036)** MZ x (birth year - 1961.5)     0.017 (0.008)*   0.015 (0.008)+ MZ x (years of elementary + secondary Education - 11.04)       0.034 (0.021)+ 0.027 (0.021) N (twin pairs) 2073 2073 2073 2073 2073 NOTES: p values: + < .1; * < .05; ** < .01. Cut points of the ordered probit model are not reported.

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Offspring: Human Fertility Behavior in Biodemographic Perspective DISCUSSION Changes in the levels of and returns to education, especially female education, are central to many theories of demographic and related social changes. For instance, education features prominently in theories about fertility decline, changing female labor force participation, and household allocation modes. Here we investigate whether education, and changes in education, are also an important aspect in biodemographic approaches to fertility. The analyses presented in this chapter investigate these questions from a twofold perspective and provide a clear indication that education constitutes an important aspect related to the biodemography of fertility. In our first analyses, we applied a multivariate behavioral genetics model to investigate genetic and shared environmental contributions to the variance and covariance in completed education and (almost) completed fertility. These analyses showed that both education and fertility are subject to genetic and shared environmental influences, but the overlapping sources of genetic influences are relatively small. Variation in fertility for both males and females is therefore primarily related to residual genetic variance that is independent of genetic influences on completed education. Our second analyses focused on the question of whether changes in educational attainment across cohorts provide an explanation for the increased heritabilities in fertility observed in our earlier studies. In particular, our analyses estimated the correlations in the latent propensity to have children in MZ and DZ twins and included tests for interactions of those correlations with birth year and levels of education. For females these analyses revealed that education and cohorts are two key factors that interact with MZ twin correlations, while DZ twin correlations are almost not affected by including these interactions. This implies that it is primarily the genetic factors consistent with variation in fertility outcomes that are affected by education and cohorts. In particular, our analyses suggested that genetic influences tend to become stronger in twin pairs with a higher level of education and that genetic influences tend to become stronger in more recent cohorts. However, this secular trend across cohorts was substantially reduced once the interaction with education was included, suggesting that increased levels of education constitute an important factor contributing to the increased heritabilities in younger cohorts found in our earlier studies. For males the interaction with education was present but seemed to be weaker in terms of both statistical significance and the magnitude of the effect. Again, this is consistent with our earlier findings that cohort trends in heritabilities are much weaker for males than for females. An interesting aspect of the above analyses is the fact that years of primary and secondary education, not years of tertiary education, resulted in important interactions with the within-twin-pair correlations in the pro-

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Offspring: Human Fertility Behavior in Biodemographic Perspective pensity to have children. This may be due to the fact that years of primary and secondary education are an important determinant of the overall “options” available to young adults in Denmark. Hence, in many ways the primary and secondary years of education are a key determinant of the “life course options” available to young adults, and this finding is consistent with our arguments in Kohler et al. (1999) and Rodgers et al. (2001b): Genetic variation in fertility outcome may become most relevant in societies and contexts where there is a large set of life course options that affect fertility and related demographic outcomes. Udry (1996) developed this argument on purely theoretical grounds, anticipating these empirical findings. These options increase the set of potential pathways through which genetic influences affect fertility outcome, and increased opportunities—for instance in the labor market—are likely to heighten the implications of endowments for labor market outcomes and therefore indirectly also on fertility. The theoretical framework of fertility behavior that is embedded in a broad context of life cycle decisions and processes provides a basis for analyzing and understanding these changing contributions of genetic factors to variations in fertility outcomes, and future analyses that combine detailed socioeconomic information and multivariate behavioral genetics models can investigate these different pathways in greater detail. Another interesting aspect of our analyses is that for females the standard additive genetic model does not seem to hold. In particular, the MZ correlations are more than twice as high as the DZ correlations, which indicates that more complicated gene interactions—for instance, due to dominance effects or epistasis—are present. There are several potential interpretations for those types of nonadditive patterns. Genetic interactions are implied by dominance (interaction within alleles) and epistasis (interaction across alleles). Or complex polygenic patterns can be caused by a particular configuration of genes (Lykken et al., 1992), a process called emergenesis. These effects are implied by sizable MZ twin correlations and very small—approximately zero—correlations for all other relatives (because only MZ twins will share a genetic configuration). The presence of such gene interactions is consistent with the evolutionary theory of life history traits, where Fisher’s (1930) fundamental theorem of natural selection suggests that additive variance tends to be diminished over time and is reintroduced again only through “perturbing forces,” such as mutations, changes in socioeconomic or normative contexts, or contraceptive (or proceptive) technologies (see Rodgers et al., 2001a). In our analyses we avoided the specification of a specific genetic model by reporting within-pair correlations instead of heritabilities and shared environmental influences. Nevertheless, the presence of such gene interactions is an important question for future research, and we have argued in

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Offspring: Human Fertility Behavior in Biodemographic Perspective related research (Christensen et al., 2003) that genetic factors contributing to fecundity, as measured by the waiting time to pregnancy, occur primarily through relatively complicated gene interactions across different loci. The main conclusion of our analyses that the patterns of genetic variance, whether measured as heritabilities or by comparing MZ and DZ twin correlations, are strongly socially conditioned and that contemporary societies might lead to a strengthening instead of a weakening role of genetic favors for variation of fertility outcomes is supported by recent investigations of the intergenerational transmission of fertility. Studies in a number of countries and time periods (e.g., Anderton et al., 1987; Berent, 1953; Johnson and Stokes, 1976; Pullum and Wolf, 1991) have shown that there is usually a positive correlation between the number of children of parents and their offspring, while there is also the possibility of a negative relation due to cohort size effects (e.g., see Easterlin, 1980). Studies of intergenerational correlation without indicators of genetic relatedness can obviously not identify the contribution of genetic and social factors to this intergenerational transmission of fertility. Nevertheless, because (additive) genetic influences on fertility tend to cause a positive intergenerational correlation (though not necessarily if genetic factors operate through epistatis), findings that intergenerational correlations are also not weakened in posttransitional societies and more recent cohorts is supportive of our behavioral genetics analyses. Murphy and Wang (2001), for instance, estimated the correlations between number of siblings and children for different contemporary developed countries, including Italy, Great Britain, Australia, Norway, and Germany, and found that the positive intergenerational relationship in fertility is not only substantial and present in all countries investigated but also that this relationship has been increasing in younger cohorts and persists even after controlling for socioeconomic characteristics. Similarly, in a study using Danish register data, Murphy and Knudsen (2002) did not find that the intergenerational fertility transmission weakened in younger cohorts, despite the fact that the socioeconomic and ideational changes experienced by these cohorts during the second demographic transmission would tend to attenuate parental influences and intergenerational transmission. In summary, our empirical work falls into the category of research encouraged by Rutter and Silberg (2002) of gene-environment interplay. The specification of models in which behavioral genetics design/analysis is complemented by environmental measures is a natural way to formalize the goals of developing consilience between biodemographic and demographic approaches to studying fertility. Further, our empirical finding that the genetic variance implied by analysis of the twin design is strongly conditioned on educational level is an example of how the ultimate result of such efforts toward consilience can be greater than the sum of the parts.

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Offspring: Human Fertility Behavior in Biodemographic Perspective ACKNOWLEDGMENTS The authors contributed equally to this paper. Much appreciation is extended to Kaare Christiansen, Kirstin Kyvik, and others involved in collecting and managing the Danish Twin Registry in Odense, Denmark. REFERENCES Adams, J., A. Hermalin, D. Lam, and P. Smouse 1990 Convergent Issues in Genetics and Demography. New York: Oxford University Press. Anderton, D.L., N.O. Tsuya, L.L. Bean, and G.P. Mineau 1987 Intergenerational transmission of relative fertility and life course patterns. Demography 24:467-480. Becker, G.S. 1981 A Treatise on the Family. Cambridge, MA: Harvard University Press. Behrman, J.R., M.R. Rosenzweig, and P. Taubman 1994 Endowments and the allocation of schooling in the family and in the marriage market: The twins experiment. Journal of Political Economy 102(6):1131-1173. 1996 College choice and wages: Estimates using data on female twins. Review of Economics and Statistics 73(4):672-685. Berent, J. 1953 Relationship between family sizes of the successive generations. Milbank Memorial Fund Quarterly Bulletin 31:39-50. Bongaarts, J., and S.C. Watkins 1996 Social interactions and contemporary fertility transition. Population and Development Review 22:639-682. Capron, C., and A. Vetta 2001 Comments on “Why have children in the 21st century?” European Journal of Population 17(1):23-30. Carey, A.D., and J. Lopreato 1995 The evolutionary demography of the fertility-mortality quasi-equilibrium. Population and Development Review 21(3):613-630. Christensen, K., O. Basso, K.O. Kyvik, S. Juul, J. Boldsen, J.W. Vaupel, and J. Olsen 1998 Fecundability of female twins. Epidemiology 9(2):189-192. Christensen, K., H.-P. Kohler, O. Basso, J. Olsen, J.W. Vaupel, and J.L. Rodgers 2003 The correlation of fecundability among twins: Evidence of a genetic effect on fertility? Epidemiology 14(1):60-64. Cleland, J. 2001 Potatoes and pills: An overview of innovation-diffusion contributions to explanations of fertility decline. Pp. 39-65 in Diffusion Processes and Fertility Transition. Committee on Population, J. Casterline, ed. Division of Behaviorial and Social Sciences and Education. Washington, DC: National Academy Press. DeFries, J.C., and D.W. Fulker 1985 Multiple regression analysis of twin data. Behavior Genetics 15(5):467-473. Easterlin, R.A. 1980 Birth and Fortune: The Impact of Numbers on Personal Welfare. Chicago: University of Chicago Press.

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