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Agent-Based Modeling of Family Formation and Dissolution

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Analytical Family Demography

Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 47))

Abstract

Patterns of family formation and dissolution are typically assumed to derive from an interplay between the structure of the marriage market and people’s partner preferences. One challenge that family researchers often face is that they have no direct measures of the preferences that have guided the partnering decisions that underlie observed family patterns. This is particularly the case when the interest is in long-term changes that have started before survey data about such preferences became available. This chapter introduces agent-based computational (ABC) modeling as a way to deal with this challenge. In ABC modeling, researchers make explicit assumptions about the constraints and preferences that guide people’s partnering decisions. These assumptions are then translated into formal models that are submitted to computational simulations of familial behavior in potentially large and heterogeneous populations, along with relevant and available empirical information. The results of these simulations make it possible to (1) assess whether a given set of preferences may have plausibly been involved in generating observed familial behavior given the constraints that people face and (2) to assess whether very different sets of preferences may generate similar patterns. We illustrate these capabilities of ABC modeling with three examples from our own research in the areas of assortative mating and divorce.

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Notes

  1. 1.

    Here we describe only the most important aspects of the model. Additional details are provided in Grow and Van Bavel (2015).

  2. 2.

    We chose this simulation period based on the empirical input data that was available for initializing the model; see details below.

  3. 3.

    Technically, a i increases by one in every simulation step. Considering that each simulation step represents a 10th of a year, a i ranges from 0 to 800 and the value 800 represents the age 80 years.

  4. 4.

    The average age difference to the disadvantage of wives reported by England and McClintock (2009) increased with the age-at-marriage of husbands, but this increase leveled off for husbands who married after their 30s. As suggested by England and McClintock (2009), this can happen when women are aversive of marrying men who are much older than they are, which makes it more difficult for older men to actually find a young partner who is willing to marry them.

  5. 5.

    In Grow and Van Bavel (2015) u i is denoted α i ; we changed this here to avoid confusing this variable with a i .

  6. 6.

    Technically, agents are selected one after the other (without replacement) to seek out somebody else in a given simulation step. Hence, it can happen that a given agent encounters multiple others in one simulation step.

  7. 7.

    European Social Survey Rounds 5 and 6 Data (2010–2012). Norwegian Social Science Data Services, Norway—Data Archive and distributor of ESS data for ESS ERIC.

  8. 8.

    The exact parameterization for female agents (f) was w s f =.385, w y f =1.201, and w a f =10.833; for male agents (m), the parameter values were w s m =.934, w y m =1.025, and w a m =5.009. The larger value of w a f compared to w a m is congruent with the observation that women tend to marry men who are on average 2–3 years older, regardless of their own age (indicating less lenient age preferences), whereas men tend to marry women who are increasingly younger than themselves, but also increasingly older than the ideal of 24 years, as they grow older (indicating more lenient age preferences). This greater leniency among men may be partly owed to the fact that as they grow older themselves, it becomes more and more difficult for them to find women who are in their mid-20s and who are willing to form unions with them.

  9. 9.

    As we show in Grow and Van Bavel (2015), these results are robust to changes in the preference structures that the model assumes to underlie partner search. More specifically, in a separate sensitivity analysis, we ‘switched off’ agents’ preferences for each of the different partner characteristics one at a time, by setting the corresponding exponents to 0. For example, to assess we effect that the models’ assumptions related to agents’ age preferences have on model outcomes net of all other assumptions, we set w a m = w a f =0 (which implies that agents would not care at all about the age of their partners), while leaving all other parameters unchanged. The fact that our main results were qualitatively not affected implies that model outcomes are not contingent on one specific assumption related to agents’ partner preferences.

  10. 10.

    See Grow and Van Bavel (2017) for details on the sample selection. Given that in Europe unmarried cohabitation is becoming increasingly prevalent and in some countries even has attained a meaning similar to marriage (Hiekel et al. 2014), some scholars have combined both union types in their analysis of relative household incomes across Europe (Klesment and Van Bavel 2017; Van Bavel and Klesment 2017). For comparability, we also used this approach here.

  11. 11.

    If everybody strives for partners with high-quality characteristics, people with the highest-quality characteristics will be in the best position to attract partners with high-quality characteristics. This renders it likely that men and women with the highest-quality characteristics will form unions with each other first. Once these individuals are removed from the partner market, those who occupy the highest ranks in the new quality distribution will form unions with each other next, and so on.

  12. 12.

    Members of the proposing sex can exhaust their list of alternatives before being matched if the sex-ratio is imbalanced so that there is a shortage of opposite-sex members.

  13. 13.

    For example, in linear regression models, the explained variance in the outcome tends to increase with the number of variables that are included in the model, even if none of these variables are causally related to the outcome.

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Acknowledgments

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 312290 for the GENDERBALL project. Eurostat, the European Commission, and the national statistical offices collecting the data have no responsibility for the results and conclusions which were drawn in this paper on the basis of the European Union Statistics on Income and Living Conditions. This paper is partly based on data from Eurostat, European Community Household Panel 1994–2001.The responsibility for all conclusions drawn from the data lies entirely with the authors.

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    André Grow & Jan Van Bavel

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Grow, A., Van Bavel, J. (2019). Agent-Based Modeling of Family Formation and Dissolution. In: Schoen, R. (eds) Analytical Family Demography. The Springer Series on Demographic Methods and Population Analysis, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-319-93227-9_6

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