Analysing transitions in intimate relationships with panel data

Feldhaus, Michael; Preetz, Richard Article Analysing transitions in intimate relationships with panel data Comparative Population Studies (CPoS) Provided in Cooperation with: Bundesinstitut für Bevölkerungsforschung (BiB), Wiesbaden Suggested Citation: Feldhaus, Michael; Preetz, Richard (2021) : Analysing transitions in intimate relationships with panel data, Comparative Population Studies (CPoS), ISSN 1869-8999, Bundesinstitut für Bevölkerungsforschung (BiB), Wiesbaden, Vol. 46, pp. 331-362, https://doi.org/10.12765/CPoS-2021-12 This Version is available at: https://hdl.handle.net/10419/309232 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/ Analysing Transitions in Intimate Relationships with Panel Data* Michael Feldhaus, Richard Preetz Abstract: Panel data on intimate relationships are becoming increasingly available, enabling a closer examination and deeper understanding of why and how they develop over time. The aim of this review is to illustrate to what extent demographic research has made progress in understanding the dynamics of intimate relationships by examining panel data. We focus on hypotheses about key transitions throughout the progression of intimate relationships, ranging from union formation up to cohabitation, marriage, divorce and repartnering. For every hypothesis, we will present fi ndings from cross-sectional data and illustrate whether the use of panel data and longitudinal methods modifi ed the previous understandings of transitions in intimate relationships. Keywords: Intimate relationships · Panel data · Life course transitions · Cohabitation · Marriage · Divorce 1 Introduction Over recent decades, tremendous societal changes have taken place regarding the development of intimate relationships over the life course. These include changes in union formation processes, increasing rates of living-apart-together relationships, cohabitation, separation or divorce, repartnering, and same-sex relationships, alongside the postponement and overall decreasing rates of marriage. Thus, trajectories of intimate relationships have become more complex and divergent in many countries (Elzinga/Liefbroer 2007; Billari/Liefbroer 2010; Perelli-Harris/Amos 2015). A large body of research in Demography, Sociology, and related disciplines focus on these ongoing changes in order to uncover the mechanisms of partnership-related Comparative Population Studies Vol. 46 (2021): 331-362 (Date of release: 23.08.2021) Federal Institute for Population Research 2021 URL: www.comparativepopulationstudies.de DOI: https://doi.org/10.12765/CPoS-2021-12 URN: urn:nbn:de:bib-cpos-2021-12en9 * This article belongs to a special issue on "Identifi cation of causal mechanisms in demographic research: The contribution of panel data". • Michael Feldhaus, Richard Preetz 332 dynamics. In this regard, the objective of collecting longitudinal data is to improve the analytical potential for investigating ongoing decision-making processes and dynamics for a better understanding of the underlying mechanism. In this review, we focus on the research question, to what extent has demographic research made progress in understanding the dynamics of intimate relationships through applying longitudinal data? To address this question, we limit the topics and focus on key demographic outcomes, such as the transition to union formation, cohabitation, marriage, separation or divorce, and repartnering. We start with a brief overview of demographic facts and theoretical advancements for investigating intimate relationships. We then review major fi ndings and achievements with regard to demographic transitions in the context of using panel data. For this large fi eld of research, not all fi ndings from the past decades can be reported. We must explicitly admit that the presented results are in some way selective. Rather, the aim is to concentrate on a few key hypotheses that cover the full range of the progress of intimate relationships. Here, we focus on assumptions and research questions that have been extensively internationally studied over the past decades and have been affected by the rise of longitudinal data and modern causal statistical methods. We conclude with a critical summary. 2 Theoretical approaches for changing trajectories in intimate relationships In recent decades, the occurrence, trajectories, and dynamics of partnership-related living arrangements have changed in many European countries. Terms such as “the retreat from marriage” or the “de-institutionalisation of marriage” (Cherlin 2004; Sassler/Lichter 2020) summarise these demographic trends as a result of decreasing marriage rates, increasing divorce rates and higher shares of extramarital births. The dissemination of cohabitation is another key indicator of ongoing demographic change in Europe and Western countries (Sassler/Lichter 2020; see also Andersson et al. 2017). Furthermore, the rates of consensual unions and repartnering after a separation or divorce has increased in European countries over the past decades (Gałęzewska et al. 2017). Taken together, these fi ndings document tremendous changes in union formation and partnership trajectories in Europe, and we fi nd similar developments, for example, in North America and Australia. Looking at these demographic developments, questions as to the factors that explain these changes arise. Various theoretical explanations are discussed and used for framing ongoing changes. A very popular approach is known as the Second Demographic Transition (SDT, van de Kaa 1987; Lestheaghe 1995, 2010). SDT emphasises the ideational changes that have occurred from the 1950s and 1960s onwards. It describes a preference drift from material needs (subsistence, shelter, physical and economic security) to non-material needs (freedom of expression, participation and emancipation, self-realisation, and autonomy). This shift in needs occurred alongside a shift in values and attitudes, accompanied by greater tolerance for diversity and respect for individual choices. This led to a decline of Analysing Transitions in Intimate Relationships with Panel Data • 333 traditional values, less social group adherence and cohesion, and more liberal laws and attitudes (e.g., toward divorce, non-marital births, sexuality, and pre-marital cohabitation) (Lesthaghe 2010). These developments were accompanied by a more rational “utility evaluation” of cohabitation and marriage in terms of the welfare of both adult partners, an aspect which was accentuated in the New Home Economics approach (Becker et al. 1977; Becker 1991; Browning et al. 2014). New Home Economics and other related costbenefi t or utility maximizing models were highly infl uential in explaining partnershiprelated behaviour and decision-making processes. These models particularly focused on men’s and women’s economic status (earnings, education, social status), fi nancial independence, partnership-related investments (homeownership, children), gender differentiations, direct or indirect costs and benefi ts (e.g., Becker et al. 1977; Oppenheimer 1988; 1997; Kalmijn 2011, 2013). This approach arose in conjunction with the increase in female labour force participation, which has in turn been linked to trends of delayed or postponed marriage, low fertility, and increased union dissolution. These changes are also often discussed in the theoretical framework of changing gender relationships in the public and private spheres, described as the “gender revolution” (Goldscheider et al. 2015). The strong focus of cost-benefi t approaches on a rational evaluation of partnership-related transitions has been criticised at times by pointing out that cohabitation or marriage are rather heart-over-head matters (Basu 2006). Billari and Liefbroer argue that more attention should be paid to emotions and other related indicators rather than focusing purely on utility evaluation (Billari/Liefbroer 2016). As an extension of microsociological approaches, cross-national comparative studies surged in recent decades. These approaches recognise that partnershiprelated behaviour and decisions are embedded, shaped, or constrained by larger social, economic, and cultural conditions, including laws, local or national opportunity structures, economic conditions, and demographic variables. Related studies analyse country-specifi c or contextual indicators with regard to union formation, cohabitation, marriage, divorce, and repartnering (Lundberg et al. 2016; Cohen/Pepin 2018). Similarly, the life course approach places greater emphasis on the microfoundation of individual behaviour (Mayer 2009; Bernardi et al 2019). As a heuristic model, the life course approach helps modulate the occurrence, timing, spacing, and stopping of biographical transitions (such as union formation, marriage, fertility behaviour, and separation) with an emphasis on the interdependencies of life domains and their contextual infl uences. Another strand of theories relevant to partnership behaviour and decisionmaking processes stems from Psychology. In many studies, the Theory of Planned Behaviour (TPB) (Ajzen 1991) is used for modelling biographical transitions (Billari/ Liefbroer 2007). Behaviour is infl uenced by proximal (behavioural intentions and actual behaviour control) and other determinants (attitudes or beliefs, subjective norms, perceived behaviour control). There are also a number of exchange theory approaches that attribute the stability and satisfaction of intimate relationships to exchange processes and (pre-) marital investment. Other psychological approaches often focus on stress theoretical arguments, coping strategies, vulnerabilities, and • Michael Feldhaus, Richard Preetz 334 the roles of personality traits. More recent developments emphasise that fi ndings from cognitive and neurological research as well as from evolutionary biology must be taken into account, especially for partnership decisions (Bachrach/Morgan 2013; Lieberwirth/Wang 2014; Billari/Liefbroer 2016). Taken together, we can say that a bundle of theoretical approaches from various disciplines have been developed over the past 40 years and have been applied for analysing dynamics and ongoing biographical transitions on intimate relationships. These theoretical approaches are worth mentioning because they outline a broad range of explanations often used for partnership-related transitions. 3 Methods, data innovations, and causality Regarding data and methods, available data sets for research on partnership dynamics have changed considerably. In many countries, panel studies have been implemented alongside offi cial statistics, register data, and cross-sectional studies, or at least studies that explicitly take a life course perspective by collecting partnerrelated events and partnership-related trajectories. The collection of partnership histories since the 1970s has contributed considerably to broadening the focus of analysis. It is now possible to analyse partnership trajectories and episodes which mark different transitions (e.g. cohabitation, marriage, separation/divorce, repartnering, remarriage, etc.). Simultaneously, advances in statistical methods, such as Event History Analysis and various types of Panel Regression Models, have made considerable contributions to a better understanding of underlying causal relations and offering new perspectives on the progress of intimate relationships. Another important improvement was the collection of dyadic data. In many surveys, both partners are asked about their partnership, their intentions, emotions, dynamics – also from a longitudinal perspective. Here too, the existence of Longitudinal Dyadic Models has considerably expanded the scope of analysis (Kenny et al. 2006). More generally, there is an extensive literature on causality from different academic disciplines, implying different answers or at least different emphases regarding the concept of causality (Bunge 1979; Spirtes et al. 1993; Pearl 2000; Woodward 2003; Opp 2010; Pearl et al. 2019). In his book about “Making Things Happen” Woodward (2003) argued that it is heuristically useful to think of explanatory and causal relationships as relations that are exploitable for manipulation and control. The guiding idea of the manipulability approach as an approach for a working defi nition of causation has the advantage of fi tting a wide range of the social and behavioural science, which is well-documented and formally elaborated by Pearl et al. (2019; Woodward 2003; Pearl 2000). Many statistics textbooks have pointed out that correlation does not imply causation and that no statistical method can determine the causal story from the data alone (Pearl et al. 2019). Pearl et al. (2019) elaborate upon four items to get a clear picture of causality: (1) a working defi nition of causation, (2) a method to formally articulate causal assumptions, (3) a method to link the structure of a causal model to the data, and (4) statistical methods to draw conclusions from the combination of causal assumptions embedded in a model and Analysing Transitions in Intimate Relationships with Panel Data • 335 the data. Put simply, causation is defi ned as follows: “X is a cause of Y if Y listens to X and decides its value in response to what it hears” (Pearl et al. 2019: 5). Having no deterministic laws in social science, we must refer to indeterministic causation, which means that a manipulation of X changes the probability distribution of Y. In the deterministic case, changes of X always lead to the same changes of Y. However, if causal relationships are indeterministic, a relevant equation has to specify the causal assumptions and, therefore, the direct causes of X variables on a probability distribution of Y. In the indeterministic context, this approach comes along with a bundle of criteria that must be considered in order to estimate the unbiased causal effect of a set of variables (Woodward 2003: 43; Pearl 2000): One widely accepted indicator that X is a cause of Y is based on time: The change of X must occur before Y, which can be ensured by an intervention in experimental research or by collecting longitudinal data in a non-experimental setting. The research question determines how temporally close the measurements of X and Y must be. Another point relates to how to measure the change in X. There is broad literature in social statistics regarding how changes over time can be measured within persons on the individual level and/or between persons (Brüderl 2010; Andreß et al. 2013). For example, while the fi xed effects approach focuses on within-variation, random effect models include both withinand between-variation. Measuring changes in X and related changes in Y on the individual level is closer to causality and an experimental design than an overall correlation of cross-sectional data is. Furthermore, measuring causal relationships also depends on the set of included variables. We often do not know all determinants of Y, or we do not have appropriate measurements in the data. Therefore, it could be that we have omitted explanatory variables and therefore unobserved heterogeneity or a specifi c kind of selection, which leads to biased estimates. In this case, our conclusions on causality claims of X among Y may be wrong, as X is related to important unknown or not included variables. These criteria can be readily implemented in experimental designs in particular. Here, the sample is randomly divided into two or more groups, one of which is assigned an intervention while another is not. Alternatively, this procedure can be approximated with longitudinal data, when the change of X proceeds the measurement of Y and where the assumption is that both variables are randomly distributed. Compared to these approaches, the use of cross-sectional data does not seem to be an appropriate design for assessing causality due to omitted variable bias and the lack of the possibility of modelling changes in X and Y (Andreß et al. 2013). Keeping these general criteria of causality in mind, we next describe the progress of causality-based research for selected hypotheses in partnership research. Here, we will focus on key hypotheses about the progress of intimate relationships ranging from union formation to further transitions such as cohabitation, marriage, divorce, and repartnering. For each hypothesis, we will present previous fi ndings from cross-sectional studies and discuss whether these initial fi ndings are supported by the more recent the application of longitudinal data and causal methods. • Michael Feldhaus, Richard Preetz 336 4 Findings from Longitudinal Data 4.1 Union Formation and Living-Apart-Together Today, individuals may have a choice between numerous romantic options, including entering into casual short-term sexual relationships, living as a non-cohabiting couple in separate households, entering into stable cohabitation without marriage, or being a married couple with a joint household (Sassler 2010). Contrary to the increasing postponement of marriage, there is no general trend in postponing union formation. For Germany, Konietzka/Tatjes (2014) show that men born in the early 1980s started their fi rst partnership about one year earlier in life than those born in the early 1970s. However, for women, no differences by birth cohort are observed. For the US, Manning et al. (2014) found no delay in median age at fi rst union for men and women in the same timeframe. Research about union formation often focuses on the infl uence of socio-economic resources, resulting in fi ndings that higher outcomes in education and employment are associated with higher chances of entry into a partnership (Klein 1990; Bracher/Santow 1998; Oppenheimer 2003; Jalovaara 2012; Rapp 2018). However, in the following, we will discuss two main hypotheses in the fi eld of union formation and living apart together as the main type of partnership after individuals start their unions. Do imbalanced partner markets affect union formation? The composition of partner markets, usually measured as the relative number of men and women (Sex Ratio), has been emphasised as a key theoretical factor for starting a new relationship (Becker 1991; Blau 1994; South et al. 2001; Stauder 2008). Chances and opportunities of forming cross-sex associations are hypothesised to be determined by the numerical distribution of men and women in the population. Thus, the partner who is in scarce has higher chances and opportunities of fi nding a partner. Studying these partner market effects has a long tradition going back to the 1920s (Groves/Ogburn 1928; Cox 1940). Several studies show signifi cant effects of being the scarcer sex and higher rates of marriage (Trovato 1988; Fossett/Kiecolt 1991; South/Lloyd 1992; Angrist 2002; Schacht/Kramer 2016). All of these studies analyse marriage, rather than being in a relationship of any type, as the outcome. Only the study by Warner et al. (2011) investigates young adults’ chances of being in a relationship depending on partner market imbalances with combined US census and cross-sectional survey data. They fi nd no correlations between relationship status and sex ratios for men and women. These previous studies theoretically assume a causal link between partner market imbalances and union formation through a time-ordered mechanism, in that imbalances result in different subsequent partnership outcomes. However, these studies do not use longitudinal data and methods that take this time-dependent relationship into account. Rather, they use cross-sectional data correlating the relative number of men and women with marriage or union status, and are thereby not able to uncover to what extent partner market conditions indeed affect Analysing Transitions in Intimate Relationships with Panel Data • 337 subsequent outcomes. A recent study by Eckhard/Stauder (2019) combines register data and longitudinal survey data from the German Socio-Economic Panel (GSOEP) to test how different partner market measures like sex ratios, availability ratios, and partner market density affect the transition into a partnership. They start with a sample of single persons and follow them over time to investigate how the different partner market measures affect later transition rates into a new partnership. Their piecewise-constant models show signifi cant effects of the availability ratio for men and partner market density for women, but no effects for sex ratios like those used in previous cross-sectional studies. Another recent study by Filser/Preetz (2021) uses combined register and longitudinal survey data from the German Family Panel (pairfam) to investigate whether regional sex ratios and subjective perceptions of meeting more men or women are associated with, and result in, different transition rates into partnership. They model the assumed link between partner market and partnership outcomes with a sample of people who were initially not in a partnership and follow them over time. Here, partner market imbalances predict later partnership outcomes. While there is no general association between regional sex ratios and subjective perceptions of meeting more men or women, results from the longitudinal event history analysis show only weak effects of being the scarcer sex, measured with age-specifi c sex ratios, and having higher transition rates into a partnership for women. No signifi cant effects are found for men. On the contrary, the use of the subjective partner market measure shows strong effects of perceiving to meet more people of one’s own sex on rates of union formation, in that rates of transition into partnership decrease if someone meets more people of their own sex. In sum, longitudinal data and longitudinal methods suggest that strong partner market effects on union formation identifi ed in previous studies are based on crosssectional fi ndings and therefore may not relate to causality. Recent longitudinal approaches show only a weak infl uence of changing partner markets on partnership formation and may relativise the strong assumed relationship between imbalanced partner markets and union formation. As recent fi ndings have shown, one explanation for this weak association may be the missing link between the regional measurement of partner market imbalances and how individuals perceive and experience these imbalances subjectively in their daily life contacts. Is living apart together a more stable or more transitory type of partnership? Research on living apart together (LAT) relationships is relatively new, with a growing number of studies since the early 2000s due to the availability of new datasets and more detailed questionnaires. While many of these studies are based on different concepts, defi nitions, and meanings of living apart together, such as committed or long-distance relationships, they all focus on the same basic concept of intimate partners living in separate households. Several studies put the prevalence of LAT relationships between around 6 percent to 10 percent in Australia (Reimondos et al. 2011), Canada (Milan/Peters 2003), the US (Strohm et al. 2009), the UK (Haskey 2005), • Michael Feldhaus, Richard Preetz 338 France (Régnier-Loilier et al. 2009), Germany (Asendorpf 2008), Sweden (Olàh et al. 2020) and Eastern Europe (Liefbroer et al. 2015). They show that living in an intimate relationship with separate households occurs throughout all stages of the life course and is not characteristic for a specifi c stage and phase in someone’s partnership biography. The number of LAT couples is highest in early adulthood and decreases until the age of 40, but becomes stable later in life when living apart together is seen as an alternative stable type of relationship, e.g. after a divorce in later life. Further studies use descriptive methods such as cluster-analysis to investigate different types of LAT relationships, identifying clusters from independence-seeking young adults up to older adults in post-family life stages (Lois/Lois 2012; Régnier-Loilier et al. 2009; Coulter/Hu 2017; Pasteels et al. 2017). Liefbroer et al. (2015) and Rhoades et al. (2009) analyse the reasons for living apart together and fi nd that the majority of couples live in separate households because of practical constraints such as the partners’ employment or fi nancial situations, or the housing market. Only around 20 percent of LAT couples live apart for reasons of independence or not feeling ready yet. These fi ndings from cross-sectional studies have resulted in a debate and the key assumption that living apart together may be a “new” alternative partnership form to cohabitation and marriage. The results suggest the existence of a signifi cant group of couples who live in a stable LAT partnership over time. More recent studies have used longitudinal data to observe the progress of LAT couples and investigate the assumption of living apart together as a stable alternative relationship form. Several further factors that infl uence the development and transition of living apart together relationships have been identifi ed. The studies mostly use longitudinal methods of time-discrete event history analysis, fulfi lling the time assumption that the measurements of X must occur before Y. For Germany, Dorbritz/Naderi (2012) and Lois/Lois (2012) analyse the progression of LAT couples in their mid-20s and mid-30s over one and two years with longitudinal data from the German Family Panel (pairfam). Their fi ndings show that around half of LAT couples experience a transition to cohabitation or separation within two years. Results from France with data from the Generation and Gender Survey (GGS) confi rm the transitory character of living apart together relationships, with only 22 percent of couples still living in the same LAT relationship after three years, and 12 percent after six years (Régnier-Loilier 2016). Further results show differences in relationship progression by age. While the share of stable LAT relationships is lowest for young adults aged 22-27 with 6 percent survival after three years, for older adults, LAT may indeed be a form of coupledom in its own right, with around one-third of couples still in their LAT relationship after six years (Régnier-Loilier 2016). Schnor (2015) uses retrospective data for partnership biographies from Germany, showing the highly transitory character of LAT relationships, with 98 percent of LAT relationships experiencing either a transition into cohabitation or separation within 10 years. A recent study by Bastin (2019) examines single mothers’ partnerships and shows a share of 20 percent in the same living apart together relationship after three years. Besides investigating the number of couples still in LAT relationships within different time periods, all of these studies also examine several factors that infl uence Analysing Transitions in Intimate Relationships with Panel Data • 345 family and friends. When union dissolutions were excluded from the analysis, there were no statistically signifi cant differences between the married and cohabiting for depression, relationships with parents, contact with parents, or time spent with friends (Musick/Bumpass 2012). Vespa/Painter (2011) extend research on the relationship between wealth accumulation and union experiences, such as marriage and cohabitation. Using longitudinal data, their fi ndings indicate that marriage is positively correlated with wealth accumulation: Individuals who marry their one and only cohabiting partner experience a wealth premium that is twice as large as that for married individuals who never cohabited prior to marrying. Furthermore, some studies using longitudinal data indicate that relationship quality declines with union duration (e.g., Brown 2004; Umberson et al. 2005; Zimmermann/Easterlin 2006; Soons et al. 2009), showing a “honeymoon effect” in subjective well-being following marriage. Such analyses, which consider couples’ well-being immediately before and after a transition or change, are only possible with appropriate panel data (see the contribution by Gattig/Minkus 2021 in this SI). These are very important results, but we must note that research regarding the specifi c impacts of a biographical transition on future outcomes in many cases does not go behind the given associations over time. It is not the status of cohabitation or marriage itself, but rather the underlying partnership dynamics arising from or amplifi ed by that status (e.g., stress and daily hassles, communication, common activities). Some papers discuss these aspects in their theoretical framework, but the variables are then not specifi ed later in the model. For causal explanation, the link between a causal model, related variables, and available data is sometimes not suffi cient or could be improved by using richer data. 3.3 Divorce, Repartnering and Remarriage As divorce rates have increased, the investigation of reasons and consequences of separation and divorce has become a central topic in family research, fi rst in the USA and also in European countries (Goode 1956). After a period of sharp increases in Europe since the 1970s, rates of divorce have begun to stabilise or even decline in nations with some of the highest levels, a phenomenon that suggests the possibility of a widespread stabilisation of divorce at a moderately high plateau (Cherlin 2017; Härkönen 2014). Are there changing impacts of education and employment on divorce over time? In initial studies, a number of socio-demographic characteristics (such as age at marriage, education, pregnancy, religion, parental divorce, children, employment, income, ethnic differences, etc.) hypothesised to make divorce more likely were fi rst examined with cross-sectional data (e.g., Udry 1966; Bumpass/Sweet 1972; Becker et al. 1977). Many of the topics dealt with at the time remain relevant today (Raley/ Sweeny 2020). In his review, Amato (2010) identifi ed nine consistent predictors of divorce: teenage marriage, poverty, unemployment, low educational attainment, • Michael Feldhaus, Richard Preetz 346 pre-marital cohabitation, pre-marital fertility, interracial marriage, previous divorce, and parental divorce. In recent years, the analysis of the causes and outcomes of divorce has become much more differentiated: Teachman (2010), using the panel data of the NLSY-79, found that women’s higher incomes and higher income ratios act to destabilise marriages, whereas cumulative labour market participation acts to stabilise marriages. Sayer et al. (2011) use longitudinal data to assess distinct predictors of wives and husbands leaving marriages. They fi nd that when men are not employed, either spouse is more likely to leave. When wives report betterthan-average marital satisfaction, their employment affects neither spouse’s exit. However, when wives report below-average marital satisfaction, their employment makes it more likely they will leave. The authors’ fi ndings suggest that theories of divorce require a “gendering” to refl ect asymmetric gender dynamics. With regard to the uncertainty hypothesis and using Finnish register data, Jalovaara (2013) fi nds that lower levels of education, unemployment (of the man in particular), and the male partner’s (or the couple’s) low income increased dissolution rates of cohabitation and marriage. The stabilizing effects of each partner’s high educational level as well as the male partner’s employment and high income were stronger in marriage than in cohabitation. With the availability of long-term panel data, such as the PSID or the SOEP, it is also possible to examine changes over longer periods with panel analysis. In this regard, Schwartz/Gonalons-Pons (2016) fi nd that wives’ relative earnings were positively associated with the risk of divorce among couples married in the late 1960s and the 1970s. Using discrete-time event-history techniques on data on fi rst marriages from the Fertility and Family Surveys (FFS), Härkönen/Dronkers (2006) fi nd that women with higher education had a higher risk of divorce in France, Greece, Italy, Poland, and Spain, but not in Estonia, Finland, West Germany, Hungary, Latvia, Sweden, and Switzerland. In Austria, Lithuania, and the United States, the educational gradient of divorce is negative. They fi nd that the de-institutionalisation of marriage and unconventional family practices are associated with a negative educational gradient of divorce, while welfare state expenditure is associated with a more positive gradient (see also Matysiak et al. 2014). Therefore, with regard to the educational infl uence on divorce, Raley/Sweeny (2020) conclude that the argument that increased educational attainment reduces divorce risk by reducing fi nancial hardship and stress, and by increasing marital quality, stands on weak empirical ground. More research on this potential link is needed. Does pre-marital cohabitation increase divorce risk? Previous fi ndings indicate that pre-marital cohabitation was associated with a higher risk of martial dissolution (Bumpass/Sweet 1989), though it was unclear whether this was a selection effect or whether the cohabitation experience itself indeed destabilises marriage. New panel data and statistical tools can better disentangle this cohabitation–divorce association. Pre-marital cohabitation may increase the risk of divorce by truncating the marital search: couples may begin cohabiting without giving much consideration to long-term compatibility and then marry out of inertia or Analysing Transitions in Intimate Relationships with Panel Data • 347 in response to a pregnancy (Stanley et al. 2006). Furthermore, there is also be an age effect: the younger a couple is when they begin living together, the more likely they are to eventually divorce (Kuperberg 2014). A growing body of evidence suggests that self-selection (rather than causal processes) is driving this statistical association (Brüderl/Kalter 2001; Impicciatore/Billari 2012). Findings by Kulu & Boyle indicate that those who cohabit prior to marriage have a higher risk of marital dissolution. However, once observed and unobserved characteristics are controlled for, the risks of marital dissolution for those who cohabit prior to marriage are signifi cantly lower than those who marry directly, which supports the “trial marriage” theory (Kulu/Boyle 2010). Some recent studies conclude that the association between premarital cohabitation and elevated risk of divorce may have weakened over time (Manning/Cohen 2012; Raley/Sweeny 2020). Looking at the US, since the mid-1990s, pre-marital cohabitation has not been associated with marital instability (Manning/ Cohen 2012). A meta-analysis found that pre-marital cohabitation was more strongly associated with marital stability as long as one married the fi rst cohabiting partner (Jose et al. 2010). Once again, we must keep in mind that it is often not the status itself, but rather the underlying mechanism within a specifi c status, that is inducing these results. It seems that more research is needed regarding this question as well. Repartnering after a divorce Another example of the increasing use of panel data is the question of the occurrence and factors infl uencing repartnering and remarriage. Due to the increasing number of divorces, numerous studies have dealt with questions of remarriage in the US from the 1970s onwards (e.g., Sweet 1973; Becker et al. 1977). For example, Teachman/Heckert (1985) use marital histories, covered by the National Survey of Family Growth (1973), and compute life tables and proportional hazard models. Similar methods and estimates are used by Bumpass et al. (1990): They fi nd that the presence and number of children, ethnic groups, women’s age at divorce, age at fi rst marriage, presence of children (in some studies), higher socio-economic position of men, and lower socio-economic position of women, as well as regional differences, infl uence the probability of remarriage. In the meantime, many of the earlier results have been supported and expanded upon by more recent studies (de Graaf/Kalmijn 2003; Gałęzewska et al. 2017). De Graaf/Kalmijn (2003) use life history data and calculate competing-risk models. They fi nd less support for economic theories and related variables. With regard to cultural aspects, they fi nd that women with more individualistic orientations are less likely to repartner. This effect is primarily a rejection of marriage after divorce, not a rejection of cohabitation. Stronger support is obtained for the role of meeting and mating opportunities. Findings show that men and women who are more integrated into society are more likely to repartner. On the other hand, dependent children are also likely to restrict women’s meeting opportunities as they increase the cost of time women spend searching for a new partner (de Graaf/Kalmijn 2003; Ivanova et al. 2013). Poortman (2007) computes frailty models and fi nds that chances of repartnering are smaller than chances of fi rst union formation. Formerly married • Michael Feldhaus, Richard Preetz 348 persons are less likely to enter a new union than former cohabiters are. Findings also indicate that “the fi rst cut is the deepest”: Union formation probabilities drop substantially after the fi rst union dissolves but remain constant after subsequent break-ups. Using data from the Divorce in Flanders Study, Ivanova et al. (2013) analyse the impact of parenthood on repartnering. They fi nd that full-time residential parents are the least likely to start a new union following separation and that parents are more likely to start a union with another parent than with a childless partner. Schnor et al. (2017) demonstrated that Flemish mothers with sole custody were less likely to repartner than those with shared custody, indicated that parenthood may not be a particularly attractive status on the partner market (e.g., Gałęzewska et al. 2017; Ivanova et al. 2013). A further special topic is remarriage or repartnering after a “gray divorce”, a divorce at age 50 or older (Brown et al. 2019): There is an increasing rate of gray divorces over the past few decades, but little is known about the mechanisms undergirding decisions to repartner after gray divorce. Brown et al. (2019) examined women’s and men’s likelihood of forming a marriage or cohabiting union following gray divorce by estimating competing risk multinomial logistic regression models using discrete-time event history data. About 22 percent of women and 37 percent of men repartnered within 10 years after a gray divorce. Repartnering more often occurred through cohabitation than remarriage, particularly for men. Resources such as economic factors, health, and social ties were linked to repartnering (Brown et al. 2019). The chances of men repartnering increase in higher income quintiles, whereas women in lower income quintiles are more likely to repartner, in opposite to women in higher income groups, who are less likely to do so (Pasteels/Mortelmann 2017) Another important infl uencing factor receiving increasing attention is existing contextual factors. Contextual factors – such as social norms towards divorce and remarriage, economic conditions, legal policies, welfare state regulations, the partner market, and the attractiveness of the separated women and men – are important because they may infl uence the interplay between individual-level characteristics and repartnering behaviour, resulting in different associations across countries (Billari 2015). Current fi ndings with longitudinal data confi rm that there are many country-specifi c differences in this respect. Findings show that economic conditions, legal policies, and welfare state regulations infl uence women’s need to repartner (de Graaf/Kalmijn 2003; Ivanova et al. 2013; Vanassche et al. 2015). 6 Conclusions In this review, we have reviewed selected articles in order to analyse to what extent demographic research have made progress in understanding the dynamics of intimate relationships by applying longitudinal data. Given the wide range of studies and topics on intimate relationships, we focus on key demographic transitions: the entry into cohabitation, marriage, separation or divorce, and repartnering. Therefore, many studies and topics on partnership dynamics, such as the effects of Analysing Transitions in Intimate Relationships with Panel Data • 349 various socio-cultural factors on partnership stability or the role of personal traits or personal constructs on partnership-related dynamics, are not considered here. We want to conclude our refl ections on partnership relationships and their related transitions with the benefi ts that have emerged with the introduction of longitudinal data in regard to causal claims and the challenges that still remain. Looking at studies on partnership-related transitions, we can conclude that signifi cant initial studies have been carried out, particularly in the North American context. For example, the world’s longest running household panel, the Panel Study of Income Dynamics (PSID), was launched in the USA in 1968. It is therefore not surprising that pioneering research is coming from North America. However, we can also emphasise that the foundations of longitudinal analyses have also improved considerably in European countries in recent decades. Concerning partnership transitions (the entry into cohabitation, marriage, divorce, repartnering), life history data has introduced major improvements regarding the collection of complete partnership trajectories and related demographic events. Given complete partnership histories, it is now also possible to analyse different episodes, their duration, the timing or postponing, and resulting sequences over the life course, often differentiated by various cohorts. The simultaneous development of event data analysis, with its various possibilities, has led to a signifi cant improvement of the time-based analysis of associations between variables over time. This was an essential step towards identifying causal relationships. In subsequent years, event history data were more strongly supplemented by panel data, often based on annual surveys and rich data sets. These advances allowed for more accurate analyses over time and a much more precise detection of time-varying dependent and independent variables. Additional prospective information for analysing transition-related decision-making processes was also included with the newly-launched panel studies, such as intentions, attitudes, expectations, personality traits, etc. This has again considerably increased the potential of causal analysis because measurements of changes of dependent and independent variables are temporally closer and measured on the individual level over time. In this case, various panel regressions models (fi xed-effects, randomeffects, dynamic panel models, latent growth curve modelling, etc.) are more likely to be used. Overall, these approaches are in line with the mentioned criteria about causality, and by applying these methods, our knowledge about causal claims within intimate relationships increases considerably. Furthermore, the problems of omitted variables and unobserved heterogeneity as well as questions about selectivity bias are at least reduced given rich individual-level databases over long time periods. Therefore, we can conclude that the availability of longitudinal data, together with the improvement of statistical techniques, has led to a more precise analysis of causal claims regarding partnership dynamics and related transitions in recent decades. However, some challenges remain: Careful attention must be paid to the link between the causal model and used data. In particular, this includes whether the measurement of a change in variable X and the outcome Y really represents the proposed and often theoretically justifi ed underlying causal mechanism. Findings • Michael Feldhaus, Richard Preetz 350 signifi cant effects of, for example, gender, race, or partnership status, are in many cases not convincing or comparable with a manipulability approach of causal explanation. If we refer to causal relationships as relations that are exploitable for manipulation and control, strict causal analysis is essentially out of the question. Therefore, in many cases, the underlying mechanisms behind observed effects are of greater interest. In this regard, a rich database from different disciplines increase the options of examining assumed underlying mechanisms more directly. Furthermore, longitudinal data are not a suffi cient alternative for experimental designs. But with regard to the analysis of biographical transitions, conducting experimental studies is neither practically feasible nor ethically desirable. In this respect, questions of causality are perhaps diffi cult to answer conclusively. In their recent review, Sharon Sassler and Daniel Lichter conclude that “the processes of union formation will become increasingly diverse, fragmented, and complicated. Family scholarship will be challenged as never before by these developments. International migration, racial and ethnic diversity, challenges to traditional gender relations and sexual identities, population aging, economic inequality, and new technologies, such as the internet and social media will increasingly reshape the mateselection process” (Sassler/Lichter 2020: 48). Of course, there is still enough to do, but if we expect that our fi ndings offer more well-founded results for policy markers and practitioners, it is necessary to place greater emphasis on causal relations and related effect sizes: “It arguably has never been a better time to study the changing demographic, economic, or policy contexts of marriage.” (Sassler/Richter 2020: 41) References Ajzen, Icek 1991: The Theory of Planned Behavior. In: Organizational Behavior and Human Decision Processes, Theories of Cognitive Self-Regulation 50,2: 179-211. https://doi.org/10.1016/0749-5978(91)90020-T Amato, Paul R. 2010: Research on Divorce: Continuing Trends and New Developments. In: Journal of Marriage and Family 72,3: 650-666. https://doi.org/10.1111/j.1741-3737.2010.00723.x Andersson, Gunnar; Thomson, Elizabeth; Duntava, Aija 2017: Life-Table Representations of Family Dynamics in the 21st Century. In: Demographic Research 37,35: 1081-1230. https://doi.org/10.4054/DemRes.2017.37.35 Andreß, Hans-Jürgen; Golsch, Katrin; Schmidt, Alexander W. 2013: Applied Panel Data Analysis for Economic and Social Surveys. Berlin/Heidelberg: Springer-Verlag. https://doi.org/10.1007/978-3-642-32914-2 Angrist, Josh 2002: How Do Sex Ratios Affect Marriage and Labor Markets? Evidence from America’s Second Generation. In: The Quarterly Journal of Economics 117,3: 997-1038. https://doi.org/10.1162/003355302760193940 Asendorpf, Jens B. 2008: Living Apart Together: Altersund Kohortenabhängigkeit einer heterogenen Lebensform. In: KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie 60,4: 749-764. https://doi.org/10.1007/s11577-008-0035-4 Analysing Transitions in Intimate Relationships with Panel Data • 351 Axinn, William G.; Barber, Jennifer S. 1997: Living Arrangements and Family Formation Attitudes in Early Adulthood. In: Journal of Marriage and Family 59,3: 595-611. https://doi.org/10.2307/353948 Axinn, William G.; Thornton, Arland 1992: The Relationship between Cohabitation and Divorce: Selectivity or Causal Infl uence? In: Demography 29,3: 357-374. https://doi.org/10.2307/2061823 Bachrach, Christine A.; Morgan, S. Philip 2013: A Cognitive–Social Model of Fertility Intentions. In: Population and Development Review 39,3: 459-485. https://doi.org/10.1111/j.1728-4457.2013.00612.x Bastin, Sonja 2019: Single Mothers’ New Partners: Partnership and Household Formation in Germany. In: Journal of Marriage and Family 81,4: 991-1003. https://doi.org/10.1111/jomf.12575 Basu, Alaka Malwade 2006: The Emotions and Reproductive Health. In: Population and Development Review 32,1: 107-121. https://doi.org/10.1111/j.1728-4457.2006.00107.x Becker, Gary S.; Landes, Elisabeth M.; Michael, Robert T. 1977: An Economic Analysis of Marital Instability. In: Journal of Political Economy 85,6: 1141-1187. Becker, Gary S. 1991: A Treatise on the Family. Harvard University Press. Bernardi, Laura; Huinink, Johannes; Settersten, Richard A. 2019: The Life Course Cube: A Tool for Studying Lives. In: Advances in Life Course Research, Theoretical and Methodological Frontiers in Life Course Research 41: 100258. https://doi.org/10.1016/j.alcr.2018.11.004 Billari, Francesco C. 2015: Integrating Macroand Micro-Level Approaches in the Explanation of Population Change. In: Population Studies 69,sup1: S11-S20. https://doi.org/10.1080/00324728.2015.1009712 Billari, Francesco C.; Liefbroer, Aart C. 2016: Why Still Marry? The Role of Feelings in the Persistence of Marriage as an Institution. In: The British Journal of Sociology 67,3: 516-540. https://doi.org/10.1111/1468-4446.12202 Billari, Francesco C.; Liefbroer, Aart C. 2010: Towards a New Pattern of Transition to Adulthood? In: Advances in Life Course Research, Demographic Perspectives on the Transition to Adulthood 15,2-3: 59-75. https://doi.org/10.1016/j.alcr.2010.10.003 Billari, Francesco C.; Liefbroer, Aart C. 2007: Should i Stay or Should i Go? The Impact of Age Norms on Leaving Home. In: Demography 44,1:181-198. https://doi.org/10.1353/dem.2007.0000 Blau, Peter M. 1994: Structural Contexts of Opportunities. University of Chicago Press. Boertien, Diederik; Härkönen, Juho 2018: Why Does Women’s Education Stabilize Marriages? The Role of Marital Attraction and Barriers to Divorce. In: Demographic Research 38,41: 1241-1276. https://doi.org/10.4054/DemRes.2018.38.41 Bracher, Michael; Santow, Gigi 1998: Economic Independence and Union Formation in Sweden. In: Population Studies 52,3: 275-294. https://doi.org/10.1080/0032472031000150466 Brown, Susan L. 2004: Moving from Cohabitation to Marriage: Effects on Relationship Quality. In: Social Science Research 33,1: 1-19. https://doi.org/10.1016/S0049-089X(03)00036-X Brown, Susan L.; Booth, Alan 1996: Cohabitation versus Marriage: A Comparison of Relationship Quality. In: Journal of Marriage and Family 58,3: 668-678. https://doi.org/10.2307/353727 • Michael Feldhaus, Richard Preetz 352 Brown, Susan L. et al. 2019: Repartnering Following Gray Divorce: The Roles of Resources and Constraints for Women and Men. In: Demography 56,2: 503-523. https://doi.org/10.1007/s13524-018-0752-x Brown, Susan L. et al. 2006: Links between Premarital Cohabitation and Subsequent Marital Quality, Stability, and Divorce: A Comparison of Covenant versus Standard Marriages. In: Social Science Research 35,2: 454-470. https://doi.org/10.1016/j.ssresearch.2006.03.001 Browning, Martin; Chiappori, Pierre-André; Weiss, Yoram 2014: Economics of the Family. Cambridge Surveys of Economic Literature. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139015882 Brüderl, Josef 2010: Kausalanalyse Mit Paneldaten. In: Wolf, Christof; Best, Henning (Eds.): Handbuch der sozialwissenschaftlichen Datenanalyse. Wiesbaden: VS Verlag für Sozialwissenschaften: 963-994. https://doi.org/10.1007/978-3-531-92038-2_36 Brüderl, Josef; Kalter, Frank 2001: The Dissolution of Marriages: The Role of Information and Marital-specifi c Capital. In: The Journal of Mathematical Sociology 25,4: 403-421. https://doi.org/10.1080/0022250X.2001.9990262 Bumpass, Larry L.; Sweet, James A. 1989: National Estimates of Cohabitation. In: Demography 26,4: 615-625. https://doi.org/10.2307/2061261 Bumpass, Larry L.; Sweet, James A. 1972: Differentials in Marital Instability: 1970. In: American Sociological Review, 37,6: 754-766. https://doi.org/10.2307/2093585 Bumpass, Larry; Sweet, James; Martin, Teresa Castro 1990: Changing Patterns of Remarriage. In: Journal of Marriage and Family 52,3: 747-756. https://doi.org/10.2307/352939 Bunge, Mario 1979: Causality and Modern Science. Courier Corporation. Casper, Lynne; Bianchi, Suzanne 2002: Continuity & Change in the American Family. Thousand Oaks, California: SAGE Publications, Inc. https://doi.org/10.4135/9781452233871 Cherlin, Andrew 2017: Introduction to the Special Collection on Separation, Divorce, Repartnering, and Remarriage around the World. In: Demographic Research S21,38: 1275-1296. https://doi.org/10.4054/DemRes.2017.37.38 Cherlin, Andrew J. 2004: The Deinstitutionalization of American Marriage. In: Journal of Marriage and Family 66,4: 848-861. https://doi.org/10.1111/j.0022-2445.2004.00058.x Clarkberg, Marin 1999: The Price of Partnering: The Role of Economic Well-Being in Young Adults’ First Union Experiences. In: Social Forces 77,3: 945-968. https://doi.org/10.2307/3005967 Clarkberg, Marin; Stolzenberg, Ross M.; Waite, Linda J. 1995: Attitudes, Values, and Entrance into Cohabitational versus Martital Unions. In: Social Forces, 74, 2: 609-632. https://doi.org/10.2307/2580494 Cohan, Catherine L.; Kleinbaum, Stacey 2002: Toward a Greater Understanding of the Cohabitation Effect: Premarital Cohabitation and Marital Communication. In: Journal of Marriage and Family 64,1: 180-192. https://doi.org/10.1111/j.1741-3737.2002.00180.x Cohen, Philip N.; Pepin, Joanna R. 2018: Unequal Marriage Markets: Sex Ratios and First Marriage among Black and White Women. In: Socius 4: 2378023118791084. https://doi.org/10.1177/2378023118791084 Coombs, Robert H. 1991: Marital Status and Personal Well-Being: A Literature Review. In: Family Relations 40,1: 97-102. https://doi.org/10.2307/585665 Analysing Transitions in Intimate Relationships with Panel Data • 353 Coulter, Rory; Hu, Yang 2017: Living Apart Together and Cohabitation Intentions in Great Britain. In: Journal of Family Issues 38,12: 1701-1729. https://doi.org/10.1177/0192513X15619461 Cox, Oliver C. 1940: Sex Ratio and Marital Status Among Negroes. In: American Sociological Review 5,6: 937-947. https://doi.org/10.2307/2084528 de Graaf, Paul M.; Kalmijn, Matthijs 2003: Alternative Routes in the Remarriage Market: Competing-Risk Analyses of Union Formation after Divorce. In: Social Forces 81,4: 1459-1498. https://doi.org/10.1353/sof.2003.0052 Di Giulio, Paola; Impicciatore, Roberto; Sironi, Maria 2019: The Changing Pattern of Cohabitation: A Sequence Analysis Approach. In: Demographic Research 40,42: 12111248. https://doi.org/10.4054/DemRes.2019.40.42 Diener, Ed et al. 2000: Similarity of the Relations between Marital Status and Subjective Well-Being across Cultures. In: Journal of Cross-Cultural Psychology 31,4: 419-436. https://doi.org/10.1177/0022022100031004001 Dorbritz, Jürgen; Naderi, Robert 2012: Stability of Bilocal Relationships – Conditions and Development Paths. An Analysis of the First and Second Wave of Pairfam. In: Comparative Population Studies 37,3-4: 429-462. https://doi.org/10.12765/CPoS-2012-08 Dush, Claire M. Kamp; Cohan, Catherine L.; Amato, Paul R. 2003: The Relationship between Cohabitation and Marital Quality and Stability: Change across Cohorts? In: Journal of Marriage and Family 65,3: 539-549. https://doi.org/10.1111/j.1741-3737.2003.00539.x Duvander, Ann-Zofi e; Kridahl, Linda 2020: Decisions on Marriage? Couples’ Decisions on Union Transition in Sweden. In: Genus 76,22. https://doi.org/10.1186/s41118-020-00092-5 Dykstra, Pearl A.; Poortman, Anne-Rigt 2010: Economic Resources and Remaining Single: Trends Over Time. In: European Sociological Review 26,3: 277-290. https://doi.org/10.1093/esr/jcp021 Eckhard, Jan; Stauder, Johannes 2019: Partner Market Opportunities and Union Formation over the Life Course – A Comparison of Different Measures. In: Population, Space and Place 25,4: e2178. https://doi.org/10.1002/psp.2178 Elzinga, Cees H.; Liefbroer, Aart C. 2007: De-Standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis: DéStandardisation Des Trajectoires de Vie Familiale Des Jeunes Adultes: Comparaison Entre Pays Par Analyse Séquentielle. In: European Journal of Population / Revue Européenne de Démographie 23,3-4: 225-250. https://doi.org/10.1007/s10680-007-9133-7 Esping-Andersen, Gøsta; Billari, Francesco C. 2015: Re-Theorizing Family Demographics. In: Population and Development Review 41,1: 1-31. https://doi.org/10.1111/j.1728-4457.2015.00024.x Filser, Andreas; Preetz, Richard 2021: Do Local Sex Ratios Approximate Subjective Partner Markets? In: Human Nature 32: 406-433. https://doi.org/10.1007/s12110-021-09397-6 Finn, Christine; Johnson, Matthew D.; Neyer, Franz J. 2020: Happily (n)Ever after? Codevelopment of Romantic Partners in Continuing and Dissolving Unions. In: Developmental Psychology 56,5: 1022-1028. https://doi.org/10.1037/dev0000897 • Michael Feldhaus, Richard Preetz 354 Fossett, Mark A.; Kiecolt, K. Jill 1991: A Methodological Review of the Sex Ratio: Alternatives for Comparative Research. In: Journal of Marriage and Family 53,4: 941957. https://doi.org/10.2307/352999 Fulda, Barbara Elisabeth 2016: The Diversity in Longitudinal Partnership Trajectories during the Transition to Adulthood: How is it Related to Individual Characteristics and Regional Living Conditions? In: Demographic Research 35,37: 1101-1134. https://doi.org/10.4054/DemRes.2016.35.37 Gałęzewska, Paulina; Perelli-Harris, Brienna; Berrington, Ann 2017: Cross-National Differences in Women’s Repartnering Behaviour in Europe: The Role of Individual Demographic Characteristics. In: Demographic Research 37,8: 189-228. https://doi.org/10.4054/DemRes.2017.37.8 Gattig, Alexander; Minkus, Lara 2021: Does Marriage Increase Couples’ Life Satisfaction? In: Comparative Population Studies 46: 123-148. https://doi.org/10.12765/CPoS-2021-05 Goldscheider, Frances; Bernhardt, Eva; Lappegård, Trude 2015: The Gender Revolution: A Framework for Understanding Changing Family and Demographic Behavior. In: Population and Development Review 41,2: 207-239. https://doi.org/10.1111/j.1728-4457.2015.00045.x Gonalons-Pons, Pilar; Schwartz, Christine R. 2017: Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage? In: Demography 54,3: 985-1005. https://doi.org/10.1007/s13524-017-0576-0 Goode, William Josiah 1956: After Divorce. New York: Free Press. Groves, Ernest Rutherford; Ogburn, William Fielding 1928: American Marriage and Family Relationships. H. Holt. Härkönen, Juho 2014: Divorce: Trends, Pattern, Causes, and Consequences. In: Treas, Judith; Scott, Jacqueline; Richards, Martin (Eds.): The Wiley Blackwell Companion to the Sociology of Families: 303-322. Härkönen, Juho; Dronkers, Jaap 2006: Stability and Change in the Educational Gradient of Divorce. A Comparison of Seventeen Countries. In: European Sociological Review 22,5: 501-517. https://doi.org/10.1093/esr/jcl011 Haskey, John 2005: Living arrangements in contemporary Britain: Having a partner who usually lives elsewhere and Living Apart Together (LAT). In: Population Trends 122: 35-45. Heuveline, Patrick; Timberlake, Jeffrey M.; Furstenberg, Frank F. 2003: Shifting Childrearing to Single Mothers: Results from 17 Western Countries. In: Population and Development Review 29,1: 47-71. https://doi.org/10.1111/j.1728-4457.2003.00047.x Hiekel, Nicole; Liefbroer, Aart C.; Poortman, Anne-Rigt 2014: Understanding Diversity in the Meaning of Cohabitation Across Europe. In: European Journal of Population 30,4: 391-410. https://doi.org/10.1007/s10680-014-9321-1 Impicciatore, Roberto; Billari, Francesco C. 2012: Secularization, Union Formation Practices, and Marital Stability: Evidence from Italy. In: European Journal of Population / Revue Européenne de Démographie 28,2: 119-138. https://doi.org/10.1007/s10680-012-9255-4 Ishizuka, Patrick 2018: The Economic Foundations of Cohabiting Couples’ Union Transitions. In: Demography 55,2: 535-557. https://doi.org/10.1007/s13524-018-0651-1 Analysing Transitions in Intimate Relationships with Panel Data • 361 Date of submission: 09.11.2020 Date of acceptance: 23.06.2021 Prof. Dr. Michael Feldhaus (). Carl von Ossietzky Universität Oldenburg, Institute for Social Sciences. Oldenburg, Germany. E-mail: [email protected] URL: https://uol.de/en/school1/institute-for-social-sciences/sociology-of-the-lifecourse-and-social-inequality/team/michael-feldhaus Richard Preetz. University of Bremen, SOCIUM Research Center on Inequality and Social Policy. Bremen, Germany. E-mail: [email protected] URL: https://www.socium.uni-bremen.de/about-the-socium/members/richard-preetz/ Carl von Ossietzky Universität Oldenburg, Institute for Social Sciences. Oldenburg, Germany. E-mail: [email protected] URL: https://uol.de/en/school1/institute-for-social-sciences/sociology-of-the-lifecourse-and-social-inequality/team/richard-preetz Published by Prof. Dr. Norbert F. 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