Dyadic fertility decisions in a life course perspective.

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Dyadic fertility decisions in a life course perspective Gerrit Bauer a,*, Thorsten Kneip b,1 a b

University of Munich (LMU), Department of Sociology, Konradstr. 6, 80801 Munich, Germany Max-Planck-Institute for Social Law and Social Policy, Munich Center for the Economics of Aging, Amalienstr. 33, 80799 Munich, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 15 May 2013 Received in revised form 24 September 2013

This paper focuses on how couples arrive at joint decisions with regard to fertility behaviour. We build upon previous work on decision rules that couples might apply as heuristics in order to arrive at joint action in cases in which partners’ fertility preferences differ. Previous research found either stronger effects of women’s desires or symmetrical effects of both spouses’ desires and net benefits associated with (further) children on proceptive behaviour. The latter finding is in line with the notion of household utility maximisation, in which both partners’ preferences enter into a joint utility function with equal weight. On the other hand, some evidence indicates that one partner can exercise a ‘veto’ if he or she anticipates individual utility losses from a further child (due to opportunity costs arising in other life domains). We now enhance previous research by applying a life-course perspective. Our analysis makes use of variation in initial conditions due to previous births: couples decide on fertility in different situations as they find themselves in different life course stages and have had certain experiences. Parity-specific differences affect not only fertility outcomes but also the decision-making process itself. Our findings show that the decision to have a first child is made jointly, and each partner may exercise a veto. On the other hand, women appear to dominate decisions on higher parity births, not per se, but because they are (still) the ones more affected by the concomitant housework. ß 2013 Elsevier Ltd. All rights reserved.

Accepted 13 November 2013 Keywords: Childbearing Decision-making Decision rules Partnership perspective Utility of children Pairfam

1. Introduction Life course approaches in the social sciences highlight interdependencies. Transitions and the underlying decisions, as for example founding or enlarging a family, depend on previous transitions and contribute to the situational context, which is relevant for future decisions (Heinz, Huinink, & Weymann, 2009). Analysing fertility decisions within the framework of life course sciences draws researchers’ attention to interdependencies in joint action. Oftentimes, women and men do not control all

* Corresponding author. Tel.: +49 8921803965. E-mail addresses: [email protected] (G. Bauer), [email protected] (T. Kneip). 1 Tel.: +49 8938602303.

relevant resources and therefore interact, exchange goods, bargain and jointly aim to find solutions. Life course research refers to the impact of the opinions, careers and behaviours of such significant others with the concept of linked lives (Elder, 1994). Further, women’s fertility is limited by biological constraints, i.e. the fertile phase, and requires the – at least temporary – presence of a male partner. From a life course perspective, the decisions to found (i.e. to have a first child) and to enlarge (i.e. to have a higher parity child) a family differ substantially. With the first birth, which we term ‘family formation’, certain biographical decisions, investments, and experiences have already occurred. These may constitute a substantially different situational context at the time of deciding whether to enlarge the family or to continue with just one child. Women and men learn after the birth of a first child how childcare affects their daily life, partners gather

1040-2608/$ – see front matter ß 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.alcr.2013.11.003

Please cite this article in press as: Bauer, G., & Kneip, T. Dyadic fertility decisions in a life course perspective. Advances in Life Course Research (2013), http://dx.doi.org/10.1016/j.alcr.2013.11.003

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information on how childcare-related work is actually divided between the spouses, and women acquire knowledge of the biological consequences of pregnancy and birth, and, not of least importance, stable parenting couples constitute a selective sample of all couples. Thus, from parity to parity, couples seem to accumulate knowledge and to reduce uncertainty. This already illustrates that life courses are complex and dynamic. Analysing life course transitions thus requires careful considerations of how the context – previous decisions, investments, experiences as well as linked lives – influences life course trajectories. In this article, we focus on fertility (pregnancy and/or birth) of women. Because fertility requires a male partner, an appropriate analysis demands taking into account how a woman’s life is linked to a man’s life. Thus, we will adopt a dyadic perspective and analyse empirically how female and male partners’ fertility desires or preferences affect the behavioural outcomes. Joint decision-making with regard to fertility has received much attention in sociology, demography and behavioural economics but, to our knowledge and our literature review, no one until now has applied a strictly dyadic perspective systematically within a life course framework. In this article, we contribute to such a dyadic life course perspective and combine Elder’s paradigm of ‘‘linked lives’’ with the idea that social context is not static, relevant situational factors varying with (a number of selected) previous life course transitions within the family. This article aims to contribute to the following research questions: How do couples decide on fertility, and how do previous births (including the consequences of having children for a couple’s daily life) affect the decision-making process? More precisely: What are the ‘‘decision rules’’ which partners apply when deciding on family formation (i.e. on first births)? And: Do those rules differ when women and men make decisions on family enlargement? Different life course stages and dyadic fertility preferences allow us to analyse interdependencies among life pathways on an intra- and inter-individual level (Elder, 1994; Hagestad & Call, 2007). We will argue that fertility depends on both – life course stages (here: parity, previous investments and experiences) and the ways in which a couple’s lives are linked (e.g. degree of agreement in family planning). Because we focus on these two aspects, neither the theoretical approach nor the empirical analyses covers the whole time-complexity discussed in the life course paradigm. Historical changes in economic and social conditions have taken place over recent decades and welfare state regulations and provisions vary across time and space. Changes both in historical and in institutional time (Abbott, 2001) have certainly had effects on fertility behaviour. Authors argue, for example, that shifts in educational attainment, in labour force participation and in the (in) compatibility of combining family formation and occupational careers have effects on the timing of family formation and on the spacing of births (Hank & Kreyenfeld, 2003; Keizer, Dykstra, & Jansen, 2008; Kru¨ger & Baldus, 1999; Liefbroer & Corijn, 1999). Besides those ‘‘institutional’’ effects, the (historical) increase in life expectancies has led to greater complexity in (women’s) life courses as paid work and active parenting now occupy

smaller portions of adulthood (Hagestad, 1990). This article neglects all such changes in the historical and institutional setting and focuses instead on two selected aspects of variation in individual life courses: (1) variation between partners with regard to agreement and disagreement in fertility desires and (2) variation between couples with regard to parity. The next section, theory, explicates ‘‘decision rules’’ that couples might apply as heuristics in order to arrive at joint action in cases where partners’ fertility preferences differ. Before hypotheses are derived, the section explains why and how decision heuristics vary over the life course. 2. Theory So far, a number of studies have analysed fertility from a dyadic perspective and have elaborated on the theoretical framework. One research strand focuses on socio-economic characteristics of women and men, as for example partners’ occupations and labour market participation (e.g. Gebel & Giesecke, 2009; Kurz, 2005), education and the educational constellation (Bauer & Jacob, 2009, 2010; Kreyenfeld, 2002; Kreyenfeld & Konietzka, 2008; Wirth, 2007) or religiosity (Corijn, Liefbroer, & Gierveld, 1996). Another strand of research states that surveys should be used for measuring fertility desires, intentions and preferences of both partners directly. Decision rules then inform the fertility outcome. Depending on the rule, childbirth could require consensus in preference, or it could require only the woman’s or only the man’s positive evaluation. It is the latter research strand that this article enhances by building on the work of Jansen and Liefbroer (2006) and our own previous considerations (Bauer & Kneip, 2013). Note that this research relies on the assumption that fertility preferences precede and determine the observable behavioural outcomes. Jansen and Liefbroer (2006) have summarised (most of) the decision rules mentioned in Thomson, McDonald, and Bumpass (1990) and Corijn et al. (1996): golden-mean-, sphere-of-interest-, social-drift- and power-rule form discriminating heuristics couples may apply in situations in which the fertility preferences of the two spouses diverge. The ‘‘golden-mean-rule’’ implies that women and men have an equal weight in the decision-making process. If, for example, one partner desires one child, the other partner three children, the couple would end up with two children. Jansen and Liefbroer then argue that fertility decisions might fall in the interest-sphere of women, basically because women are more affected by pregnancy and birth as well as by caretaking. Were this heuristic used, women would dominate fertility decisions. Note that this argument relates to two very different potential mechanisms: Whereas women are always more affected by pregnancy and giving birth for biological reasons, they are more affected by care giving only on average and for sociocultural reasons, which are not universal. The social-drift rule states that changing behaviour requires consensus. Without consensus, couples would continue their previous habits, which could either be proceptive behaviour or (likely more often in Western societies) contraceptive use.


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Finally, bargaining over fertility might depend on power. If economic resources (income, occupational positions) determine power, men should be able to make the ultimate decisions whether to found or to enlarge families simply because men would have (on average) access to more relevant resources and thus more bargaining power. Again, the proclaimed mechanism is not universal but depends on the specific social context. The described pattern should, accordingly, diminish if one controls effectively for socioeconomic resources. Although the above rules can indeed be seen as discriminating, we have previously argued they do not allow researchers to identify fully the mechanisms at work (Bauer & Kneip, 2013). Table 1 depicts the decision rules formulated in Jansen/Liefbroer in the first column and the one tested in Bauer/Kneip in the second. The main differences compared to the heuristics in Jansen and Liefbroer (2006) appear with regard to the sphere-of-interest- and the power-rule. Instead of assuming that women are more affected by having children and thus have more interest in preventing or fostering births, interest (in economic terms: expected net utility, because interest derives from utility and costs) should be measured. In a joint utility model, the influence of both partners on fertility would be proportional to their interest. The model is consistent with an ‘‘egalitarian rule’’ (Corijn et al., 1996; Thomson, McDonald, & Bumpass, 1990) or a ‘‘golden-mean-rule’’ (Jansen & Liefbroer, 2006), but it also incorporates the possibility of the partners being differently affected (so one could say it is about equity rather than on equality). The economic perspective behind it states that women and men negotiate their fertility behaviour in order to come to a Pareto efficient compromise. Applying an egalitarian rule can thus be modelled as a cooperative Nash bargaining game (e.g. Manser & Brown, 1980). The joint utility increases with the expected utility for each partner. If a child increases the couple’s joint utility, classical family economic models predict a positive fertility decision, simply because partners could (by assumption) transfer additional collective utility between themselves so that both would be better off individually

(for a discussion of the assumptions of Coasian bargaining within the family see Kneip & Bauer, 2009; Reinhold, Kneip, & Bauer, 2013). Thus, the joint utility model combines the ideas of the sphere-of-interest and the golden-mean-rule and predicts that the probability of giving birth to a child rises with each partner’s expected utility. The joint utility model requires actors’ awareness of the expected collective utility of a child and of their partners’ preferences. How would couples decide if individual outcomes were of uncertain size – either because they did not know how a child would affect the family’s benefit as a whole or because they were uncertain whether a utility redistribution between spouses would be successful and thus benefit each partner individually? The socialdrift-rule and the veto-player-model provide possible rational solutions. If one partner will possibly not profit from family formation or enlargement, he or she can exercise a veto (which is gender-neutral and egalitarian). Following this rule, fertility behaviour requires consensus. In regression models, this requirement should result in a positive interaction effect of partners’ expected utilities: One partner’s desire should have no effect if the partner does not want to have a child, and the effect should increase with his/her partner’s utility expectations. Finally, a power rule determines a partner’s assertiveness based on his or her relative bargaining power. The power rule thus assumes that the bargaining power mediates the effect of the expected utility of a child. Instead of assuming that men’s bargaining power exceeds women’s, we suggested that both partners’ dependencies from the current partnerships should be measured and used as decision-weights. The power-rule model is not explicitly tested here. See instead Bauer and Kneip (2013) for an extensive discussion and empirical tests using partner market opportunities (sex ratios) as power indicators and Bauer and Kneip (2011) for alternative concepts of how to measure dependencies from the current partnership. In summary, the main advantages of the decision rules used in Bauer and Kneip (2013) compared to those discussed and tested in Jansen and

Table 1 Decision rules in dyadic fertility research. Heuristics applied in Jansen and Liefbroer (2006)

Corresponding heuristic applied in Bauer and Kneip (2013)

Corresponding heuristic applied in this paper

Golden mean rule: partners have equal influence in negotiations.

Joint utility model: interest derives from expected utility and costs; influence is proportional to interest.

Joint utility model: interest derives from expected utility and costs; influence is proportional to interest. Sphere of interest rule: the main child-carer dominates the decision

Matriarchal rule: the female partner decides concerning fertility, irrespective of interest or power Veto player model: joint action requires consensus Power rule: dependencies from current partnership determine decision weights

Matriarchal rule: the female partner decides concerning fertility, irrespective of interest or power Veto player model: joint action requires consensus

Sphere of interest rule: as childbirth affects women more than men, such decisions are in the woman’s ‘‘sphere of interest’’

Social drift rule: disagreement leads to continuation of the status quo Power rule: access to social and economic resources (usually greater for the male) determines influence

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Patriarchal rule: the male partner decides concerning fertility, irrespective of interest or power

Power rule: dependencies from current partnership determine decision weights (not explicitly tested here) Patriarchal rule: the male partner decides concerning fertility, irrespective of interest or power


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Liefbroer (2006) result from measuring instead of assuming each partner’s interest and power. Having separated interest and power from gender, two additional decision heuristics appear: In a world characterised by matriarchal decisions, women would make the ultimate decisions if the partners’ preferences were not identical, irrespective of interest and power. In a patriarchal society, men would enforce their preferences, again irrespective of power and interest as they are conceptualised. A domination of fertility decisions by either the female or the male partner would then likely be attributable to gender norms or, in the case of the woman, to biological reasons. Bringing together the decision heuristics and a life course perspective, we refer to the sphere-of-interest-idea as discussed by Corijn et al. (1996) and Jansen and Liefbroer (2006). Although it may seem reasonable to assume that it should ultimately be the woman who decides whether to beget a child or not for biological reasons, social mechanisms are also at work. These may either reinforce or undermine the woman’s autonomy. Given that men and women are not perfectly informed about future benefits and costs associated to childrearing and how the parents will divide them, partners make fertility decisions under uncertain conditions. As far as the woman is in need of a partner providing material security for the family and sharing the costs of childrearing, this gives her partner some leverage concerning fertility decisions. Particularly when deciding on a first child, the partner’s reliability is hardly predictable for the woman. However, after the partnership has evolved, children have been born, and the partnership has been proven stable, the situation changes as the partners are able to refer to previous experiences. Most notably, the woman will have learned whether her partner will remain trueto ex ante agreements on the division of childrearing costs. If the partners ex post come to a division of labour in which the women is predominately concerned with child-care and related housework, the male partner’s position will be weakened in the future: On one hand, there is little credible threat for the woman of being abandoned as the man has already made considerable investments in the family and, on the other hand, the man cannot credibly promise to extend or cut his share of household chores. The woman might then make fertility decisions more or less autonomously. If, however, the man is considerably involved in child-related housework, he should also be able to influence fertility decisions. In summary, previous experiences enable couples to collect information on the ways in which utility and costs associated with children are shared. If interest derives from actual utility and costs, this suggests that couples learn whose interest is more affected by another child and a child-related interest sphere will only emerge over the course of the relationship and particularly after the birth of the first child. The above perspective points to a possible shift in dominant decision rules over the course of a relationship, in particular after the first child is born. As the decision rules under consideration differ with respect to partners’ relative decision weights, a possible overarching framework incorporating all these rules should acknowledge ‘‘flexible’’ relative decision weights and relate them to

features of the couple which may themselves vary over the course of the relationship. 3. Hypothesis The development of the theoretical model that we have outlined above can be summarised and simplified: We have extended a simple joint (or additive) utility model by introducing ‘‘flexible’’ decision weights. The basic joint utility model leads to the hypothesis that (1) The probability of pregnancy/birth increases with both partners’ strength of desire/expected net utility of a child. More specifically, decision weights are expected to be symmetric and both partners’ preferences should have the same impact on actualised fertility. This reflects the notion of cooperative bargaining between partners. The flexibility assumption leads to the following hypotheses: (2) The woman’s (man’s) decision weight may depend on her (his) partner’s expected utilities of having a (further) child. In case of a veto-decision-rule, the effect of the woman’s (man’s) expected utility will increase with her (his) partner’s expected utility. (3) The man’s (woman’s) relative decision weights may decline from parity to parity if the woman (man) bears, for the most part, the costs of childrearing. Hypothesis 2 points to some need to assure the partner’s commitment to the fertility decision. This will result in each partner’s decision weight contingency on the other partner’s preferences. Hypothesis 3 states that a partner’s decision weight will decline with the need to get his or her buy-in to the decision. Presumably, this will not happen before a ‘‘sphereof-interest’’ has been established and expected costs of a further child have been internalised into child preferences. If parity progression indeed leads to insights as to how the costs due to a child are divided between the partners, we can also expect that (4) the decision weight should vary with the housekeeping status, giving the partner more involved in (childrelated) housework a higher decision weight. Further, the decision weight could depend on gender only. We would then expect to find: (5) If decisions are based solely on a matriarchal (patriarchal) decision rule, the male (female) partner’s strength of desire will have no effect on the probability of a pregnancy or birth. Finally, we could hypothesise that relative independence from the current partnership leads to higher influences in the decision-making process (power-rulehypothesis). However, the latter hypothesis is not tested here explicitly. 4. Previous research Most research related to fertility desires and fertility intentions has so far discussed and analysed how fertility


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desires and expectations emerge over the life course (e.g. Hayford, 2009), under which conditions actors (mostly women) realise their desires for or against children (e.g. Quesnel-Valleé & Morgan, 2003), and how timing preferences affect fertility (Miller & Pasta, 1994). In our analysis, we will not consider how desires emerge and change within the partnership’s context but instead take both partners’ desires as essential and given aspects of the situation within which partners bargain with regard to fertility behaviour. Some papers have already assessed how contradiction or agreement in desires influences fertility. These studies contrast the fertility behaviour of couples whose goals differ with that of couples with identical fertility preferences (Jansen & Liefbroer, 2006; Thomson, 1997; Thomson & Hoem, 1998; Thomson et al., 1990). Results with respect to which partner’s desires have stronger effects and/or whether couples apply a veto heuristic are mixed. The more recent findings suggest that both partners’ desires have equally strong effects on fertility and that the partner (irrespective of whether male or female) not desiring a child can exercise a veto. Results supporting a joint utility model (hypothesis 1) and a veto heuristic (hypothesis 2) contradict older studies which conclude that women are the predominant decision-makers and that women, but not men, can exercise a veto (matriarchal rule, hypothesis 5). A higher decision weight of women, which we expect if either a matriarchal- or a sphere-of-interest rule is in operation, is reported for American couples in the studies by Townes, Beach and Campbell (1980). Beckman, Aizenber, Forsythe, and Day (1983) find that men’s fertility intentions have no direct effect on birth control use, and Beckman reports that women’s desires have stronger effects on pregnancy and birth than men’s desires, especially if a woman does not want to have a child (i.e. the veto is not gender-neutral). A decade later, Berrington (2004) obtains similar results, exploiting survey data from the UK. Here, couples consisting of two spouses intending to have a child do not show significantly higher transition rates to a first child compared to couples in which a woman desires a child and a male partner does not desire fatherhood. Couples in which women do not wish to have a child have significantly lower birth probabilities. Besides the literature reporting a higher decision weight of women, a number of (more recent) studies find that both partners’ expectations influence the probability of births, with effects approximately equal in strength. Those results rely upon survey data from the US (Thomson, 1997), Sweden (Thomson & Hoem, 1998), the Netherlands (Jansen & Liefbroer, 2006), and Germany (Bauer & Kneip, 2013; Stein & Pavetic, 2013). Although the measures used for capturing fertility intentions, the composition of the analysis sample, and the methods applied differ considerably from study to study, desires of both partners are found to codetermine fertility transitions with equal weights. In most studies both partners appear to hold veto positions. Most comparable to the analysis presented in this paper is our previous work, which focussed on proceptive behaviour (i.e. the non-use of contraception), exploiting the same data (although only the first wave of the German family panel study ‘‘pairfam’’). Here, the effects of both partners’ expected net utilities due

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to a child do not differ significantly in size. In addition, each partner can exercise a veto in case of strongly opposing aims in other life domains. In sum, previous research provides mixed results, but the more recent studies generally find equally strong effects of desires reported by women and men, and genderneutral veto options (supporting the joint-utility-hypothesis (1) and the veto-hypothesis (2), contradicting the matriarchal/matriarchal-hypothesis (5)). Our literature review reveals that an explicit life course perspective has not until now been applied systematically in analyses based on dyadic fertility preferences. With respect to parity differences, some studies controlled for parity (e.g. Bauer & Kneip, 2013), while others restricted the sample to childless couples (e.g. Berrington, 2004). Controlling for parity or restricting the sample does not, however, allow one to test the hypotheses that interest spheres evolve over the life course and that interest and information alter the use of certain decision heuristics. A recent working paper by Testa, Cavalli and Rosina (2012) matches most closely a life course perspective, although the paper lacks a theoretical argument for parity-specific differences in decisions over fertility. Using Italian data, the authors develop separate models for couples that are either childless, have one child, or have two or more children. They find that at parities zero and one, the effect of disagreement between partners results in lower fertility as compared to couples in which two partners desire another child. For couples that already have two or more children, the effect of the female partner’s desire significantly exceeds the effect of the male partner’s desire. Further, previous studies did not take into account how couples divide their housework after a first child was born and how this affects the bargaining process. Testa et al. (2012) formulate an ad-hoc hypothesis stating that two partners have the same influence on childbearing if they share housework and childcare duties equally. This hypothesis, however, does not relate to the relevant theoretical constructs (experiences and information from previous births) and was not empirically tested, as the regression model does not include an interaction effect between the division of housework and fertility desires. Hypotheses 3 (parity-interaction hypothesis) and 4 (housework-interaction hypothesis) have, to our knowledge, not sufficiently been tested in previous work. 5. Data and analysis sample Our analyses rely on recent data from the first three waves available so far of the pairfam (Panel Analysis of Intimate Relationships and Family Dynamic) survey, release 3.1 (Nauck, Bru¨derl, Huinink, & Walper, 2013). Huinink et al. (2011) provide a detailed description of the survey and its methodology. The data were collected between autumn 2008 and spring 2009 (wave 1), 2009/10 (wave 2) and 2010/ 11 (wave 3). The survey relies on a cohort stratified random sample (birth cohorts ‘71–‘73, ‘81–‘83, ‘91–‘93) of individual respondents. Current partners are asked to participate as well and to answer a short questionnaire. The data contain information on both partners’ current fertility preferences and on fertile behaviour, i.e. pregnancies and births. We


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excluded the youngest cohort (‘91-’93) as one could argue that planned fertility is rather unlikely before adulthood. Moreover, these respondents usually do not cohabit with a partner and partners more often do not take part in the survey. We further restricted the sample to heterosexual fertile couples not known to be pregnant at the time of wave 1. The sample thus consists of 1617 couples with nonmissing dyadic information. In the analyses all explanatory variables are measured one year before the dependent variable. This leads to 2440 observations of the dependent variable in waves 2 and 3. The number of observed pregnancies or births is 203. 6. Measures The following measures and control variables enter in the analyses. Pregnancy/birth (yes/no). The dichotomous depended variable takes the value 1 if a woman is currently pregnant or if she has given birth to a child in the time-span between 2 interviews. Alternatively, the variable takes the value 0. Strength of desire for children. The pairfam study provides a measurement based on ‘‘points of importance’’. It has been developed for the German Family Panel Study and undergone a validation study (Maul, 2008). Respondents were asked to allocate fifteen tokens on five areas of life, expressing thereby the relative importance of these domains: leisure time activities, social contacts, relationship, education/career and family formation/enlargement. This measurement accounts for the fact that fertility decisions depend on aims in other life domains (Blossfeld & Huinink, 1991). Its use as an independent variable constitutes an alternative to approaches which rely on additional control variables capturing career ambitions and other opportunity costs (e.g. Stein & Pavetic, 2013). Note that an advantage of this approach is to avoid the obvious endogeneity of current employment status with respect to fertility (intentions). We consider the relative part of ‘‘points of importance’’ a respondent allocated to the area of family formation/enlargement. The same operationalization has already been employed in Bauer and Kneip (2013). The variable is continuous on the range of values from 0 to 1. After centring the variable, it ranges from 0.1 to +0.9. About 90% of the observations show values in the interval [ 0.1; +0.2]. The variable constitutes an attempt to measure parsimoniously the expected net utility of a child. It is measured individually for both partners and assumed to precede and to determine fertility behaviour. Parity. Parity indicates a couple’s number of common children currently alive. We constructed three dummy variables indicating whether a couple is thus far childless (reference), has one child, or has two or more children. Couple’s division of housework. Both partners were asked to indicate on a five-point-scale whether housework is always done by the respondent, mostly by the respondent, equally divided, mostly done by the partner or always done by the partner. The variable used in this article combines the responses of women and men. It was recoded in order to measure women’s housework involvement and then we calculated the mean value from both partners’ responses. After centring the variable, the value 1 indicates that both

partners agree that the male partner always does housework; the value 1 shows that the woman always does housework. The mean value, 0, refers not to situations where spouses divide housework equally but refers to the empirical mean: here, women are mostly, but not always, responsible for housework (the mean is about 3.9 on the 5point-scale before centring the variable). The models include a set of further control variables: each partner’s age in linear and squared form; each partner’s religiosity/church attendance frequency (more than once a week; once a week; 1–3 times per month; several times a year; less; never); each partner’s educational level and enrolment (enrolled; low; medium; high secondary; tertiary); partnership duration in years; a dichotomous variable indicating whether a couple cohabits or is living apart together (LAT); a wave-dummy capturing period effects (dependent variable measured in wave 2 (reference) or 3). Like all substantial explanatory variables, control variables are also lagged and thus measured one year (panel wave) before the fertility outcome. 7. Methods The following analyses use logistic regression models and linear probability models, calculated with pooled and lagged data. We report clustered standard errors. Unfortunately, logistic regression models do not permit interpretation of interaction effects, neither such effects’ strengths nor their statistical significances (Ai & Norton, 2003). Nonetheless, we intend to test interaction hypotheses. Hypothesis 2, the veto-player-rule, requires modelling an interaction effect between the strengths of both partners’ desires. Hypotheses 3 (parity) and 4 (housework involvement) state that parity and housework moderate the effects of desires. Our model thus contains both partners’ desires, an interaction effect between these two variables, two-way interactions between each spouse’s strength of desire and parity-dummies, three-way interactions between both spouses’-desires-interaction and parity. We also include women’s age in a nonlinear way, as well as interactions of desires and age. This allows the association of child preferences and actualised births to vary over the life course (e.g. due to biological factors) and for a separation of age and parity effects. We then present a similar model for couples that already have a child, including two-way interactions between desires and housework and a three-way-interaction between both spouses’ desires-interaction and housework. To permit a straightforward interpretation of our results, we make use of graphical solutions offered by the software package Stata and show predicted probabilities and average marginal effects (see Bauer, 2014 for a discussion of how regression graphics guide the interpretation of complex models). Average marginal effects (AME) are calculated by assessing the effect of a certain variable for each observation with its specific covariate characteristic. In non-linear models and in models with interaction effects, the effect sizes differ between observations and are averaged to a single coefficient (Best & Wolf, 2014 provide a detailed description of how Stata calculates AMEs). We additionally report


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all regressions as linear probability models (LPM) which provide a simple interpretation of the direction and significance of the (average) interaction effect (Wooldridge, 2002). Note that our model exploits longitudinal information but does not focus on the within-information as fixedeffect-models do. The latter identify effects based on within-variation only. Preferences, however, hardly change over short time-spans, and the identification of

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effects thus necessarily relies on differences between couples. 8. Results This section is structured as follows: We briefly present selected descriptive statistics and then discuss the model that permits testing most of our hypotheses (Table 2 and Figs. 1–5). In the last part, the analyses focus on the

Table 2 Logistic regression and linear probability models – the moderating effect of number of children.

Strength of desire for a child Desire/man Desire/woman Desire/M desire/W Parity-interactions Parity 1 desire/M Parity 2 desire/M Parity 1 desire/W Parity 2 desire/W Parity 1 desire/M desire/W Parity 2 desire/M desire/W Woman’s age interactions Desire/M woman’s age Desire/M woman’s age2 Desire/W woman’s age Desire/W woman’s age2 Desire/M desire/W woman’s age Desire/M desire/W woman’s age2 Parity (reference: 0) Parity 1 (already 1 child) Parity 2 (already 2+ children) Woman’s age Age (linear) Age (squared) Constant

Observations: couples Observations: dependent variable Observations: pregnancy or birth

Logit

LPM1

LPM2

LPM3

7.527*** (0.000) 6.572*** (0.000) 19.071* (0.022)

0.194*** (0.000) 0.295*** (0.000) 1.146** (0.006)

0.295*** (0.000) 0.392*** (0.000) 1.173 (0.102)

0.401*** (0.000) 0.429*** (0.000) 0.792 (0.344)

0.098 (0.428) 0.260* (0.020) 0.157 (0.208) 0.204 (0.101) 0.166 (0.873) 1.206 (0.175)

0.204 (0.136) 0.402** (0.003) 0.110 (0.414) 0.116 (0.428) 0.356 (0.754) 1.083 (0.339)

6.082*** (0.001) 7.534*** (0.000) 4.203** (0.008) 3.287+ (0.067) 24.648* (0.012) 15.311 (0.166) 0.694*** (0.001) 0.043 (0.173) 0.122 (0.454) 0.004 (0.862) 1.915 (0.106) 0.193 (0.277)

0.033* (0.022) 0.003+ (0.065) 0.003 (0.831) 0.002 (0.214) 0.050 (0.693) 0.010 (0.554)

1.035*** (0.000) 0.675* (0.037)

0.038* (0.021) 0.016 (0.336)

0.045** (0.006) 0.018 (0.311)

0.045** (0.007) 0.020 (0.290)

0.056+ (0.078) 0.007+ (0.086) 3.026*** (0.001)

0.003 (0.261) 0.000 (0.163) 0.144* (0.033)

0.003 (0.215) 0.000 (0.214) 0.152* (0.024)

0.003 (0.258) 0.000 (0.133) 0.145* (0.033)

1617 2440

1617 2440

1617 2440

1617 2440

203

203

203

203

Note: Further control variables include the man’s age, partnership duration, cohabitation, both partners’ education and church attendance. See Table A1 in the appendix for effects of control variables. Table A1 continues Table 2. p-Values in parentheses. + p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001


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8

mediating effect of how spouses divide their housework. The latter model refers to a subsample of couples, selecting partners that already have at least one common child and have thus experienced how a child affects their daily life (Table 3 and Figs. 6 and 7). 8.1. Descriptive findings

we further interpret with a number of visual displays. The LPMs include, besides parity and control variables, only both spouses’ desires (LPM1) and parity interactions (LMP2). The full model in accordance with the LOGIT also includes interactions between desires and the woman’s age (LPM3). We model those interaction effects in order to allow for a straightforward interpretation of parityspecific effects, as decisions on higher parities are made at higher ages.

The strength of the desire for a child is measured separately for women and men. Before centring the variable, the measure takes a mean value of 0.106 (sd = 0.119) for men and the value of 0.107 (sd = 0.126) for women. On average, respondents have thus allocated approximately 10% of the 15 tokens to family formation or family enlargement, respectively, and 90% to the four competing fields of life. The difference between women and men is not significant on a 10-percent level. Although, on average, the strength of desire (expected net utility of a child) does not differ significantly between women and men, the correlation between spouses’ expectations is rather moderate (Person’s r = 0.42). This indicates that the measure captures individual traits rather than agreed, previously bargained couple-preferences. For both women and men, the strength is highest for a second child and lowest for a third (or higher parity) child. Further, the correlation between spouses’ desires deceases by parity (childless couples: r = 0.46; already one child: r = 0.37; two or more children: r = 0.25).

Regression coefficients of a man’s and a woman’s strength of desire are positive and highly significant, irrespective of the model specification. Predicting fertility probabilities based on the logistic regression model and plotting these values against the woman’s and the man’s utility expectations reveals symmetrical effects (Fig. 1). Fixing the partner’s fertility desire to the mean value (here: 0) and averaging over the other covariates, the model predicts that about 5% of the couples will become pregnant or beget a child between two waves if the female partner’s fertility desire (left graph) or the male partner’s (right) take the minimum value (i.e. no point of importance was allocated to fertility). If one partner strongly desires a child, fertility occurs in 12–13%. This finding supports the hypothesis derived from a joint utility model (hypothesis 1).

8.2. Logistic regression models and linear probability models

8.4. Veto-player-model

The central regression models in Table 2 show effects of both spouses’ desires on fertility (pregnancy or birth). The table continues with further control variables as shown in Appendix-table 1. In the first column, we present the LOGIT coefficients from the binary regression model that

If the partners applied a veto-player heuristic, the effect of a fertility desire should increase with the fertility desire of the spouse. Technically, one expects a positive interaction effect between both partner’s preferences. In Table 2, the interaction effect is indeed positive in the simple linear

8.3. Joint-utility-model

Fig. 1. Joint-utility model


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9

Fig. 2. Veto-player model.

model (LMP1). Fig. 2 illustrates the veto-player-model by predicting fertility probabilities, conditional on both partners’ desires. In Fig. 1, the partners’ fertility desire was fixed at the mean value 0. In Fig. 2, the partners’ strength of desire is fixed at both the lowest ( 0.1; very low) and the highest (+0.2; very high) values. Women’s strength of desire (left) and men’s desire (right) varies on the horizontal axis. Fertility occurs most probably – in about 20% of cases – if partners share a strong desire. By trend, couples who disagree become parents more often if

only the woman strongly desires a child compared to couples where the man strongly wants to have a child but his partner does not (10% compared to 8%). The slopes appear more pronounced in the scenarios with a partner having a strong desire (the dashed line on the left and the dashed-dotted line on the right) compared to those where the partner disapproves of fertility. This, however, may not necessarily support the veto hypothesis, as this visual impression could emerge simply due to the model’s functional requisites.

Fig. 3. Conditional effect of both partners’ desires.


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In order to test the veto-player-hypothesis, we assess the conditional average marginal effects of fertility preferences and depict those in Fig. 3. The average marginal effect indicates how a one-unit change in x affects the probability of y being 1 on average. Thus, the AME can be interpreted in analogy to an OLS regression coefficient; averaging becomes necessary, however, as it is non-constant over the range of y. It is computed for each observation with its given covariate structure and then averaged to a single coefficient. The conditional average marginal effect also averages the effects for each observation, but does so at a number of defined values of another variable (here: the partner’s strength of desire). Fig. 3 shows the conditional AMEs of the woman’s desire on the left side and the man’s on the right. The ordinate now displays effect size, not a predicted probability. As the interpretation appears rather complex, we provide an example. The left graph shows at the x-value of 0.1 how an increase in the woman’s fertility desire affects fertility in a situation in which her partner has allocated zero tokens to the importance of having (further) children on the child preference measure employed. The effect size of 0.2 means that the likelihood of fertility within the next year increases by 20% if a woman ‘‘changes’’ her preference from minimum to maximum (0 versus 15 tokens) and if her partner allocates no points of importance to family formation or enlargement. If, however, the male partner also desires a child, the effect of her fertility preference increases continuously from 20% to about 32%. Likewise, the effect of the man’s strength of desire is conditional on her desire (right side of the figure). The figure indeed reveals a positive interaction effect, similar to the

Fig. 4. Partners’ AMEs on fertility conditional on parity.

effect estimated in the LMP1. This finding supports hypothesis 2 (and it also shows how misleading the interpretation of interaction-effect coefficients in logit models can be, c.f. the negative coefficient [ 19.071] of the interaction term in the logit model in Table 2). 8.5. Parity-specific-differences: emerging spheres-ofinterest? Having outlined the approach of how to test the vetohypothesis, this section now refers to flexibility in decision weights induced by the emergence of interest spheres. We have argued that information and experiences – both

Fig. 5. AMEs on fertility conditional on parity and partner’s desire.


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varying with parity – may alter the decision mode. The hypothesis stated that women (or more generally: partners primarily concerned with homemaking) have a higher weight in family-enlargement decisions compared to decisions on family formation (i.e. first births). Fig. 4 again depicts average marginal effects and bases on the LOGIT model from Table 2. Effects have been estimated conditional on parity, i.e. on the number of children already born to a couple. Fixing the partner’s strength of desire to the mean value, a oneunit increase in the expected utility of a child raises the probability of a pregnancy or birth by 20% – under the condition that the couple did not have a common child before. With increasing parity, the effect of the female partner’s desire remains stable, whereas the effect of the male partner’s desire decreases. The 95% confidence interval (not shown) includes the null value already in the situation with one previous child. The depicted conditional AMEs are consistent with the negative coefficients of interaction effects between the man’s strength of desire and parity in the linear models LPM2 and LPM3. The regression model also includes a triple interaction, modelling not only that parity may alter the effects of both partners’ desires, but also that partners’ relative veto positions may change when couples have already experienced the birth of at least one common child. Fig. 5, in analogy to Fig. 3 (veto-model, how effect sizes depend on the expected utilities of the partner), shows the respective conditional effects, grouped by parity. Whereas the lines take positive slopes for couples deciding on a first or on a second child, decisions on a third (parity 2) child seem not to require consensus. Here, the female partner’s desire has a positive effect that does not depend on her partner’s preferences at all. The latter may want to have an additional child or not; his strength of desire appears to have no effect on the likelihood of a positive fertility outcome. However, according to the LPM (triple interactions in models 2 and 3), parity does not significantly moderate the veto. We thus cannot reject the null hypothesis that both partners’ ability to exercise a veto remains stable over parity. Our findings are still in line with hypothesis 3, though, as the effect of a man’s desire significantly declines over parity whereas the effect of a woman’s desire remains unchanged. Interpreting the results, however, depends on the assumption that – on average – women take responsibility of the greater part of child-related housework. The next analysis aims at an alternative, more direct test of the proclaimed mechanism.

11

Table 3 Logistic regression and linear probability models – the moderating effect of housework involvement in parenting couples.

Strength of desire for a child Desire/man Desire/woman Desire/M Desire/W Woman’s housework-involvement-interactions Desire/M woman’s housework Desire/W woman’s housework Desire/M desire/W woman’s housework Woman’s housework involvement Constant

Observations: couples Observations: dependent variable Observations: pregnancy or birth

Logit

LPM

1.772 (0.219) 3.279** (0.007) 11.708 (0.162)

0.206+ (0.068) 0.316** (0.002) 2.109* (0.020)

1.121 (0.272) 1.209 (0.245) 8.973 (0.151) 0.047 (0.777) 0.791 (0.556)

0.127+ (0.067) 0.030 (0.717) 1.129* (0.041) 0.001 (0.941) 0.285* (0.050)

985 1496 121

985 1496 121

Note: Further control variables include all variables listed in Appendix Table A1 and all interactions listed in Table 2. Effects of these control variables are not shown here but are available from the authors. p-Values in parentheses. + p < 0.10. * p < 0.05. ** p < 0.01. ***p < 0.001

Because the model incorporates interaction effects, the main effects of strength of desires refer to the reference value 0 – a situation where women do most of the housework (before centring, the mean is almost 4 on the five-point-scale). The LPM also shows two significant interactions: the effect of the male partner’s desire decreases with a woman’s housework involvement while the effect of her desire is not moderated. Further, the positive veto-interaction (desire desire) also decreases with the woman’s dominance in housekeeping. Figs. 6 and 7, showing AMEs conditional on the housekeeping status, have been estimated based on the

8.6. Supplementary analysis exploiting a subsample with parity > 0 For the following analysis, the sample is restricted to couples that already have at least one child and are thus to make decisions on family enlargement. Table 3 shows the regression models estimated as LOGIT and LPM. We control for all variables as in the previous analysis (besides parity). In the LPM, we find that the effect of the female desire on fertility is stronger than that of the male desire.

Fig. 6. Partners’ AMEs on fertility conditional on housework involvement.


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Fig. 7. AMEs on fertility conditional on housework involvement and partner’s desire.

LOGIT model in Table 3 and help in interpreting the findings. In Fig. 6, partner’s strength of desire is again fixed at the mean value. The graph shows effect sizes on the vertical and her housework involvement on the horizontal axis. At the value 1, both spouses responded that the man does all the housework; at the value +1, the woman does it all. Due to the asymmetric distribution, at the mean (0) housework is still predominantly carried out by women. The graph clearly shows that housekeeping moderates the effect of both partners’ desires. The more housework a partner does relative to her or his spouse, the higher is her or his say with regard to family enlargement decisions. Men who leave the field completely to their partners are virtually unable to participate actively in the decision-making. Finally, the model allows us to assess how housekeeping affects the ability to veto one’s partner. Fig. 7 illustrates the triple-interaction, separating the graph by three selected housekeeping statuses (the continuous housework variable is fixed to 1, 0 and +1). When the man does all the housework ( 1) or takes some housework responsibility (0), the effect of the woman’s strength of desire depends on his preferences (upper part of Fig. 7). Likewise, the effect of his desire is stronger if it is shared with the female partner (lower part). If a couple leaves all housework to the female spouse, only her desire has an effect on the probability of a pregnancy or birth. Interestingly, the same is not true if the man is the partner most involved in housework duties. This finding may possibly reflect an ultimate decision power of the female as the partner carrying the child. The findings from the housework-specific analysis support hypothesis 4, stating that the partner who is more involved in (child-related) housework receives a higher decision weight. The findings mirror the parityspecific effects depicted in Figs. 4 and 5. We thus find support for the assumption that interest-spheres are likely

to evolve over the life course, in particular after the birth of a first child (i.e. family formation). 9. Summary & discussion Previous research employing a dyadic approach to fertility has elaborated on a number of decision rules couples could apply when deciding upon family formation (parity 0) or enlargement (parity 1+). We have previously argued that these rules allow for competing hypotheses as to how couples might make decisions (Bauer & Kneip, 2013), but a number of studies failed in actually testing the proclaimed mechanisms. In this paper, we have addressed a significant shortcoming in the theoretical framework previously applied: When couples decide on fertility without having yet experienced the associated demand in childcare and additional housework, they necessarily make the decision under uncertain conditions. How, in such situations, can they internalise expected costs into their preferences? We have employed a life course perspective – previously ignored or at least not sufficiently accounted for – to relate some selected situational changes to uncertainty, experiences, and the trustworthiness of bargaining partners. Although research has referred to a set of different mechanisms, an overarching framework incorporating the entire set of possible rules can be formulated. The general mechanism appears to be that partners jointly decide over fertility with ‘‘flexible’’ relative decision weights. These are linked to different life course stages (here: parity) and to their social consequences (here: emerging housework arrangements). Empirically, we find that the likelihood of family formation increases with both partner’s utility expectations (supporting a joint utility model, hypothesis 1). In making decisions on a first child, both partners can exercise a (gender-neutral) veto. This result supports hypothesis 2, stating that the decision


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weight of a woman or a man depends on her or his partner’s desires. These findings are in accordance with a number of previous studies from the US (Thomson, 1997), Sweden (Thomson & Hoem, 1998), the Netherlands (Jansen & Liefbroer, 2006), Italy (Testa et al., 2012), and Germany (Bauer & Kneip, 2013). The results of applying a life course perspective and testing hypotheses to our knowledge not previously considered show how men’s decision weights erode over parity. This suggests the emergence of a (woman’s) sphere of interest as the partner more affected by birth and childrelated housework internalises actual costs into preferences. Women appear to dominate decisions on higher parity births (hypothesis 3). However, they do so not per se, but because they are, on average, (still) the ones more affected by the concomitant housework caused by children. The declining decision weight of men thus appears to be due to a persistent gender inequality in the division of housework (hypothesis 4). Our finding that under some conditions partners decide rather equitably, whereas otherwise the women dominates the decision, might also shed some light on the heterogeneity in previous findings. Interpreting the results on the basis of an overarching theoretical framework, we conclude that decision weights vary over the life course. In a broader sense, one might formulate that decision weights depend on ‘‘power.’’ The latter increases with the share of costs borne ex post, and decreases with the need to buy into the partner’s agreement to found or to enlarge a family. Particularly the former aspect has been overlooked in previous research. Finally, we must discuss some possible shortcomings. Although the analyses have exploited longitudinal data, identification of effects relies primarily on variation among couples, as we can hardly observe multiple births within the same partnerships. While a within-approach would be favourable, it requires a rather long panel to provide sufficient variation in preferences and parity within couples. Such data would then allow testing an additional mechanism which could potentially introduce gender inequality: If, for instance, women are more successful in influencing their partners’ desires (Stein & Pavetic, 2013), women’s desire could in the final event have a stronger effect on fertility, although coefficients of both partners’ desires might still be of equal size. Future research is clearly needed once the respective data is available. We have provided arguments in favour of our interpretation of results based on information and interest, but we cannot exclude possible selection effects. For example, couples observed at higher parities might be a selective sample with regard to the dominantly applied decision rule. Furthermore, couples who do not reach agreement with regard to family formation or enlargement may be more affected by separation (and thus attrition) than couples who come successfully to joint and agreedupon decisions. The latter point, however, would need to be considered also in a ‘real’ panel analysis.

Appendix A. Appendix Table A1

13

Table A1 Table 2 continued – additional control variables.

Man’s age Age (linear) Age (squared) Partnership duration (years) Cohabitation (reference: LAT) Man’s education Enrolled (reference) Low Medium High secondary Tertiary Woman’s education Enrolled (reference) Low Medium High secondary Tertiary

Logit

LPM1

LPM2

LPM3

0.012 (0.594) 0.001 (0.651) 0.054* (0.013) 0.301 (0.347)

0.001 (0.483) 0.000 (0.483) 0.002* (0.048) 0.021 (0.279)

0.001 (0.535) 0.000 (0.535) 0.002* (0.037) 0.015 (0.417)

0.001 (0.525) 0.000 (0.525) 0.003* (0.031) 0.018 (0.350)

0.675 (0.287) 0.644 (0.301) 0.721 (0.260) 0.407 (0.516)

0.039 (0.172) 0.038 (0.171) 0.044 (0.136) 0.023 (0.391)

0.039 (0.175) 0.035 (0.202) 0.041 (0.169) 0.018 (0.501)

0.043 (0.150) 0.039 (0.169) 0.045 (0.140) 0.022 (0.432)

0.615 (0.209) 0.216 (0.619) 0.643 (0.140) 0.973* (0.025)

0.039 (0.199) 0.014 (0.572) 0.033 (0.181) 0.059* (0.018)

0.035 (0.237) 0.010 (0.683) 0.031 (0.198) 0.058* (0.018)

0.037 (0.215) 0.009 (0.719) 0.032 (0.192) 0.056* (0.022)

0.102+ (0.073) 0.028 (0.509) 0.094* (0.023) 0.056 (0.163) 0.053 (0.195)

0.098+ (0.085) 0.024 (0.571) 0.091* (0.029) 0.052 (0.193) 0.049 (0.227)

0.180** (0.005) 0.219*** (0.001) 0.202*** (0.001) 0.187** (0.002) 0.202** (0.001)

0.173** (0.007) 0.213*** (0.001) 0.195** (0.002) 0.180** (0.003) 0.196** (0.002)

0.033** (0.004) 0.152* (0.024)

0.035** (0.003) 0.145* (0.033)

Man’s church attendance More than 1/week (reference) 1/week 1.096* 0.096+ (0.029) (0.087) 1–3/month 0.174 0.026 (0.829) (0.537) Several times/year 1.539* 0.096* (0.014) (0.019) Less 0.983 0.056 (0.105) (0.159) Never 0.920 0.055 (0.130) (0.176) Woman’s church attendance More than 1/week (reference) 1/week 1.684** 0.174** (0.002) (0.006) 1–3/month 2.753*** 0.216*** (0.000) (0.000) Several times/year 2.237*** 0.200*** (0.000) (0.001) Less 1.997*** 0.184** (0.001) (0.002) Never 2.264*** 0.201*** (0.000) (0.001) Dependent variable from wave 2 (reference) Wave 3 0.543** 0.031** (0.004) (0.007) Constant 3.026*** 0.144* (0.001) (0.033)

Observations: couples 1617 1617 1617 1617 Observations: dependent 2440 2440 2440 2440 variable 203 203 203 Observations: pregnancy 203 or birth This table continues Table 2. Effects shown here are net of both partners’ desires, parity and woman’s age. p-Values in parentheses. + p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001.


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Fertility analysis from a life course perspective.

Educational differences in fertility desires, intentions and behaviour: A life course perspective.

Aging in place: a life course perspective.

Abortion: a dyadic perspective.

Developmental influences on fertility decisions by women: an evolutionary perspective.

Unintended pregnancy in the life-course perspective.

Agency in Fertility Decisions in Western Europe During the Demographic Transition: A Comparative Perspective.

Bringing the MCH Life Course Perspective to life.

Teaching age and discrimination: a life course perspective.

Juvenile Sex Offending Through a Developmental Life Course Criminology Perspective.

Religiosity and quality of life: a dyadic perspective of individuals with dementia and their caregivers.

Adverse outcomes of critical illness from a dyadic perspective.

Spousal involvement and CPAP adherence: a dyadic perspective.

A life course perspective on working beyond retirement-results from a longitudinal study in the Netherlands.

A life course perspective on socioeconomic inequalities in health: a critical review of conceptual frameworks.

Female fertility preservation: a clinical perspective.

Acceptance and decision making in amyotrophic lateral sclerosis from a life-course perspective.

A life-course perspective on stigma-handling: resilience in persons of restricted growth narrated in life histories.

Can socioeconomic trajectories during the life influence periodontal disease occurrence in adulthood? Hypotheses from a life-course perspective.

The influence of negative life events on hippocampal and amygdala volumes in old age: a life-course perspective.

Ethnicity, Obesity, and Related Cardio-Metabolic Risk Factors: A Life-Course Perspective.

The life course of psychological resilience: A phenomenological perspective on deflecting life's slings and arrows.

Life-course fertility patterns associated with childhood externalizing and internalizing behaviors.

Independent decisions are fictional from a psychological perspective.