TY - JOUR
AU - Schofer,, Evan
AB - Abstract This paper extends the comparative literature on divorce by theorizing how international institutions and norms influence societal divorce rates. Drawing on developmental idealism and world society theory, the paper argues that global institutions legitimize cultural principles such as individualism, human rights, and gender equality, which reshape “modern” understandings of marriage and family relations. Formal international institutions and treaties do not explicitly proclaim or codify the right to divorce, but we suggest that new norms regarding divorce emerge from the “penumbras” of world culture and diffuse globally. Panel regression models covering 84 countries between 1970 and 2008 find a strong association between global cultural influence and divorce rates, controlling for other factors. Results highlight the effects of world society on the private lives of individuals, and suggest that world society affects a wider set of outcomes than the conventional literature would predict. Introduction Divorce rates have increased dramatically in countries around the world, representing one of the major societal shifts of the late twentieth century (Coontz 2004; Goode 1993; Härkönen 2014). While many studies examine individual-level predictors of divorce in affluent countries, only a handful of studies explore macro-level explanations for cross-national and historical variations in societal divorce rates (Cole and Powers 1973; Greenstein and Davis 2006a; Hendrix and Pearson 1995; Kalmijn 2007; Trent and South 1989). Cross-national studies mainly attribute rising divorce rates to socio-economic modernization and consequent shifts in values (Beck and Beck-Gernsheim 2002; Goode 1993; Lesthaeghe 1983). This paper draws on insights from developmental idealism (Thornton 2005; Thornton, Binstock, and Ghimire 2008) and world society theory (Meyer, Boli, et al. 1997) to argue that global culture and norms shape patterns of divorce. Cultural principles of individualism and human rights are increasingly embedded in international organizations, discourses, and treaties, driving changes in law, policy, and individual behavior (Boyle, Smith, and Guenther 2006; Frank, Camp, and Boutcher 2010; Meyer 2010; Meyer and Jepperson 2000). A particular “modern” family form has increasingly come to be seen as normative and legitimate, shaping state policy and individual behavior (Jayakody, Thornton, and Axinn 2008; Thornton et al. 2012). The case of divorce differs from conventional world society analyses of global diffusion in that there are not strong international norms regarding divorce. Treaties and international discourses rarely address the issue explicitly. We extend world society theory by arguing that new understandings of divorce emerge from the “penumbras” of global culture, potentially generating social change even in the absence of clearly codified norms. In addition, this paper advances the comparative literature on divorce with improved data and methods. Prior comparative studies have relatively small samples, and most are cross-sectional. This paper draws on a new panel dataset, which covers a larger span of time and many more countries than prior studies. In addition, this study measures divorce as a proportion of the married population, which improves over prior studies of “crude divorce rates” (divorces per 1,000 population). Background: Cross-National Divorce Trends and Variations Divorce has become more common throughout the world in the late twentieth century (Amato 2010; Asia Research Institute 2014; Härkönen 2014; Kiernan 2001; Kreider and Ellis 2011; Hill and Kopp 2015). The United States and Europe experienced large increases in divorce rates during the 1960s and 1970s. East Asian countries have seen rising divorce rates since the 1970s (Chen and Li 2014). After divorce was legalized in many countries of Latin America, divorce rates rose there as well (Arriagada 2014). Exceptions to this trend are rare, and mainly follow periods of rapid growth.1 Explaining the Rise of Divorce Individual-Level Explanations The bulk of the literature addresses individual-level predictors of divorce, typically focusing on affluent countries such as the United States. Key risk factors for divorce include low levels of education and income, early marriage, and unemployment (for reviews, see Amato 2010; Härkönen 2014; and Wagner and Weiß 2006). While these risk factors are reasonable for individuals in affluent societies (Becker, Landes, and Michael 1977), some of these arguments are not well suited to explain macro-level country differences or patterns in developing countries. For instance, while income is negatively associated with divorce at the individual level, one can observe a very different relationship at the macro level: divorce increases in tandem with country-level economic development (Greenstein and Davis 2006b; Hendrix and Pearson 1995).2 Thus, comparative scholars have explored a broader set of arguments. Macro-Level Explanations: Structural and Cultural Changes Early comparative scholarship attributes rising divorce rates to structural changes, such as economic development and industrialization, rising incomes, growth of the service sector, and urbanization, which establish institutions that may serve as a substitute for the economic and social functions of marriage and family (Cole and Powers 1973; Goode 1993; Nimkoff 1965; Ogburn and Nimkoff 1955). Moreover, the growing status of women, and resulting increases in women’s educational and occupational attainment, makes it easier for women to sustain themselves outside traditional marriage (Clark 1990; Cooke et al. 2013; Greenstein and Davis 2006a; Hendrix and Pearson 1995). Related to this, economic development is thought to drive shifts in societal values (e.g., Inkeles and Smith 1974; Inglehart 1989), which in turn propel changes in family behaviors (e.g., Jeng and McKenry 1999). While the specific arguments vary, scholars in this tradition point to the “higher needs” of individual freedom and self-development (Lesthaeghe and Surkyn 1988), post-materialist values and changing views on gender equality (Inglehart and Norris 2003), secularization of marital and reproductive behaviors (Lesthaeghe 1983), and individualism (Beck and Beck-Gernsheim 2002). In sum, structural and cultural changes associated with modernization are thought to drive increases in divorce. Education is seen as an important dimension of modernization in its own right, and also as a proxy for attitudinal shifts that are otherwise difficult to measure directly. The following hypotheses follow: Hypothesis 1a: The level of economic development is positively associated with a country’s divorce rate. Hypothesis 1b: Women’s labor force participation is positively associated with a country’s divorce rate. Hypothesis 1c: Mass education enrollment is positively associated with a country’s divorce rate. Religion Prior research highlights religion as a potent set of cultural orientations that affects family formation and divorce (Castles and Flood 1991; Coudert 1893; Nichols 2012). Indeed, religion is often seen as the basis for categorizing comparative differences in family systems (Therborn 2004). Scholars point to Catholicism, in particular, as a religious tradition that frowns upon divorce. Indeed, many predominantly Catholic societies have historically prohibited divorce (e.g., Chile until 2004). In the case of Islam, scholars have made arguments in both directions. Conservative variants of Islam may inhibit divorce by establishing patriarchal rules and norms (Htun and Weldon 2011; Mashhour 2005). On the other hand, some scholars have argued that Islam considers marriage to be a secular tie and dissolvable by men, making divorce easier and less morally stigmatized. Prior cross-national studies have yielded mixed findings on these points (Clark 1990; Fu 1992; Greenstein and Davis 2006a; Trent and South 1989), but the arguments are sufficiently common to warrant examination: Hypothesis 2a: Catholicism is negatively associated with a country’s divorce rate. Hypothesis 2b: Islam may be negatively or positively associated with a country’s divorce rate. Global Culture, Developmental Idealism, and Diffusion While most research focuses on domestic processes, some scholars call attention to cultural and institutional forces beyond national borders that drive local change (Freedman 1979; Giddens 1992; Meyer, Boli, et al. 1997). Arland Thornton’s (2005) concept of “developmental idealism” is a sweeping argument that explains social change in terms of the global spread of ideas about what an ideal “developed” society should look like. Specifically, the societal arrangements of Western Europe—typified by individualism, expanded education, urbanization, and technological sophistication—increasingly come to define the aspirations of societies throughout the global South. The modernization of family, as Thornton argues, is regarded by reformers as the foundation of a developed society. Thornton (2005) argues that family practices of Northwest European societies, including the idea of the nuclear family, courtship before marriage, delaying first marriage, higher status of women, and so on, and the underlying values, including individual freedom, equality, and consent, have come to define the ideal family form, the epitome of modernization (Thornton 2001, 2005). Studies show that as new ideas regarding development spread across the global South, people’s beliefs about familial practices change as well (Jayakody, Thornton, and Axinn 2008). In a similar vein, world society theory argues that the expansion of international organizations and activity in the post-WWII era institutionalizes and propagates cultural models that stimulate isomorphic global changes (Barnett and Finnemore 2004; Boli and Thomas 1997; Hughes et al. 2009; Meyer, Frank, et al. 1997). International institutions extoll and legitimize cultural principles, such as individualism and equality, and proffer specific policy models and scripts for nations to follow—including issues related to marriage and family (Barrett 1995; Meyer 2010; Meyer and Jepperson 2000). These cultural scripts diffuse through organizational ties between global and national or local actors and/or through individual or organizational actors (e.g., see Frank, Hironaka, and Schofer 2000). World society theory and developmental idealism are complementary, offering a similar critical take on modernization ideology of the late twentieth century. Both stress the importance of cultural frames, global diffusion, and the central role of experts and international organizations in defining and propagating Western cultural ideals. Developmental idealism, however, brings much-needed substantive attention to issues of family structure and demography, which have been overlooked by world society scholarship. At the same time, world society scholarship brings useful theoretical ideas and research designs for describing dynamics of global institutionalization and diffusion, for instance, elaborating some of the diffusion channels in Thornton’s argument. A number of world society studies document globally driven patterns of policy diffusion, in domains such as education, women’s rights, human rights, and population policy (Barrett 1995; Berkovitch 1999; Ramirez, Soysal, and Shanahan 1997; Risse-Kappen, Ropp, and Sikkink 1999; Tsutsui 2006). Moreover, diffusion can affect policy outcomes, such as reducing child labor, lowering adolescence fertility, and ameliorating environmental degradation (Boyle and Kim 2009; Hironaka 2014; Kim et al. 2013; Schofer and Hironaka 2005). In addition, connections to world society unlock political opportunities for social movements (Hafner-Burton and Tsutsui 2005; Keck and Sikkink 1998), which bolster local advocates seeking to make demands for policy implementation and change. Finally, scholars have extended the world society literature by showing that world cultural norms affect individuals, not just state policy (Boyle, McMorris, and Gomez 2002; Givens and Jorgenson 2013; Hadler and Haller 2011; Pierotti 2013). World Cultural Principles, Penumbras, and Divorce Following these studies, we argue that global norms, culture, and institutions are reshaping individual behavior regarding divorce. Rising divorce rates around the globe may not only reflect local economic or political changes, but may be the result of a general set of cultural shifts that render divorce more appropriate or palatable. Yet, divorce is an interesting case to test the boundaries of world society theory because divorce is not the core focus of international treaties or discourses. This paper extends world society theory by exploring whether and how world society may have effects when relevant global cultural norms are not explicitly codified and articulated, but might be supported within the “penumbras” of existing treaties and discourses. Scholars have mainly looked at domains where international norms are formally codified, for instance, in international treaties, INGO mission statements, and the like. By contrast, world society effects are fragile or absent when the cultural scripts are sharply contested, such as abortion policy (Boyle, Kim, and Longhofer 2015). However, many topics fall somewhere in the middle: they fall under major international themes such as individual rights or gender equality, but they are neither explicitly singled out nor vociferously contested. The question is whether general international norms and institutions have “spillover” effects into issue areas that are not explicitly addressed. The legal term penumbra provides a useful analogy for thinking about how global culture may have influence beyond formal codified principles. The penumbras of the law refer to areas in which legal rights and duties are not explicitly codified, but may be inferred from existing law. For instance, the US Constitution does not guarantee a right to privacy, but judges have argued that such rights are implicit in the constitution (Dixon 1965; Henly 1987). The analogy is loose in that there are no judges in world society who can formally invoke the penumbras of global culture. Nevertheless, norms in areas such as human rights provide a basis for individual and organizational actors to develop new scripts for novel issues that were not directly addressed in existing treaties or institutionalized discourses, similar to the social movement concept of frame extension (Snow et al. 1986). We expect that this dynamic may apply to many issue areas, producing effects on societal outcomes that are substantially broader than the codified rules and discourses of international organizations and treaties. Divorce has received little attention in the international sphere, and is hardly addressed in major treaties. However, international treaties and discourses enshrine general norms of individual rights, gender equality, and consent, which characterize people as autonomous decision-makers who ought to have the freedom to choose their own destinies. As Developmental Idealism scholars note, these principles have strong implications for understandings of marriage and divorce (Thornton 2005). In the rare instances where divorce is addressed in global institutions and treaties, it is addressed obliquely, as an aspect of individual rights or gender equality. The Universal Declaration of Human Rights of 1948, for instance, asserts that both men and women should have “equal rights to marriage, during marriage, and at its dissolution,” and that marriage should be established only with the “free and full consent of the intending spouses.” These discourses regarding marriage are rooted in modern egalitarian individualism, as opposed to the historical alternatives such as patriarchal familism (Frank, Camp, and Boutcher 2010). In line with developmental idealism arguments, this voluntary (and implicitly nuclear) understanding of the family is privileged in global discourses, as “the natural and fundamental group unit of society… entitled to protection by society and the State” (United Nations 1948). Marriage is characterized as an individual voluntary choice among consenting adults, as opposed to occurring at the discretion of parents, families, or clans, as has been common in many societies. The corporatist functions of marriage, including collective resource pooling or reproduction for continuation of lineages, should not overshadow an individual’s will in marital decisions. Spouses are characterized as equal actors, stripped of their gender, social status, or other characteristics. Subsequent human rights treaties, such as the International Covenant on Civil and Political Rights (ICCPR) and the International Covenant on Economic, Social, and Cultural Rights (ICESCR), reiterated this language almost verbatim (United Nations 1966a, 1966b). The Convention to Eliminate All Forms of Discrimination Against Women (CEDAW) built on this framework and clarified what gender equality should mean, entailing rights and responsibilities regarding reproduction, children, personal life choices, and property. In addition, the treaty reiterates that spouses should enjoy the same rights and responsibilities both “during marriage and at its dissolution” (United Nations 1979, Article 16[c]). The UN Declaration and subsequent treaties do not explicitly enshrine the right to divorce. However, the fact that the issue of marital “dissolution” comes up in the context of gender equality in marital and familial decision-making implies that the cultural principles of individual freedom and consent are taken as given. By invoking the possibility of marital dissolution, international treaties (and key organizations like the UN) adopt an individualist and voluntaristic understanding of the family in which divorce may be a legitimate option. This sharply contrasts with many traditional family systems, where divorce is unequivocally proscribed (e.g., traditional Catholicism) or is a familial decision. Despite the absence of an explicit “right to divorce” in international treaties, women’s rights groups and social movement organizations were galvanized by international treaties and conferences in pursing progressive reforms around the world. The World Conference on Women in Nairobi, which followed CEDAW, yielded the Nairobi Forward-Looking Strategies that brought attention to women’s rights issues and provided powerful new language for local activists (Pietilä and Vickers 1996). The conference also witnessed the much greater mobilization and interaction among women’s rights activists around the world. While the international women’s movement did not explicitly address divorce as a core part of its agenda, the cross-national exchange of advocacy experiences among organizations helped crystallize new views on divorce rooted in increasingly taken-for-granted global women’s rights discourses (Levitt and Merry 2009). Groups concerned with women’s rights focused on the fundamental principle of individualism and the explicit “equality” frame that were enshrined in the international treaties and discourses. For example, monitoring documents of international women’s organizations began to include divorce policies and statistics as one indicator of a country’s status of women (Morgan 1996). CEDAW signatories began to mention divorce law amendments in their periodic reports as a sign of treaty compliance (e.g., Albania 2002, Cyprus 2004, and India 2005). And, national women’s movements increasingly pointed to inequalities in divorce laws as one of their concerns (e.g., see Dontopoulos [1982] for Greece; Agnes [1994]; Agnihotri and Mazumdar [1995] for India; Chew [1994] for Singapore). Scholars have argued that such demands followed directly from the codification of women’s rights at the international level, and often used similar framing (Agnes 1994; Jones and Ramdas 2004). In sum, actors—ranging from women’s groups to national governmental bodies—distilled new norms about divorce from the penumbras of fundamental world cultural principles, despite the absence of specific international rules or proclamations on the issue. This discussion gives rise to the following hypothesis: Hypothesis 3: Country ties to world society—via international organizations, women’s groups, and treaties related to gender equality and rights—will be positively associated with country divorce rates. Data and Methods This paper analyzes cross-national data on divorce rates for 84 countries from 1970 to 2008.3 Countries with fewer than 300,000 people—mainly tiny Pacific and Caribbean islands—were excluded because the divorce rates fluctuate rapidly due to small denominators.4 Countries included in the analysis are listed in table 2. The sample includes countries from all major global regions, although data coverage is better for affluent Western countries, compared to sub-Saharan Africa, South Asia, and the Middle East. Descriptive statistics and correlations are presented in table 1. Table 1. Descriptive Statistics and Correlations of Variables A) B) C) D) E) F) G) H) I) J) K) L) M) Mean S.D. A) Divorce rate 1.00 3.88 2.86 B) Youth dependency −0.30 1.00 45.16 21.16 C) GNI p/cap (log) 0.17 −0.72 1.00 8.88 1.31 D) School enrollment 0.34 −0.82 0.70 1.00 79.07 27.15 E) Female employment 0.38 −0.80 0.53 0.73 1.00 31.29 12.44 F) Catholic pop % −0.19 0.19 0.02 −0.26 −0.32 1.00 42.60 38.93 G) Muslim pop% −0.10 0.38 −0.44 −0.23 −0.38 −0.43 1.00 12.94 29.64 H) INGO membership (log) 0.16 −0.70 0.78 0.70 0.53 0.02 −0.31 1.00 6.78 0.84 I) WINGO membership 0.15 −0.52 0.65 0.63 0.47 −0.13 −0.21 0.84 1.00 6.17 4.19 J) CEDAW ratification 0.21 −0.36 0.09 0.40 0.45 −0.04 −0.09 0.35 0.29 1.00 0.69 0.46 K) World society index 0.18 −0.62 0.75 0.68 0.48 −0.03 −0.29 0.95 0.97 0.27 1.00 0.64 0.80 L) Divorce law equality 0.22 −0.48 0.48 0.34 0.48 0.30 −0.70 0.34 0.19 0.25 0.26 1.00 2.70 0.75 M) State capacity 0.05 0.16 −0.39 −0.08 −0.04 −0.19 0.28 −0.25 −0.22 0.03 −0.30 −0.22 1.00 0.19 0.08 A) B) C) D) E) F) G) H) I) J) K) L) M) Mean S.D. A) Divorce rate 1.00 3.88 2.86 B) Youth dependency −0.30 1.00 45.16 21.16 C) GNI p/cap (log) 0.17 −0.72 1.00 8.88 1.31 D) School enrollment 0.34 −0.82 0.70 1.00 79.07 27.15 E) Female employment 0.38 −0.80 0.53 0.73 1.00 31.29 12.44 F) Catholic pop % −0.19 0.19 0.02 −0.26 −0.32 1.00 42.60 38.93 G) Muslim pop% −0.10 0.38 −0.44 −0.23 −0.38 −0.43 1.00 12.94 29.64 H) INGO membership (log) 0.16 −0.70 0.78 0.70 0.53 0.02 −0.31 1.00 6.78 0.84 I) WINGO membership 0.15 −0.52 0.65 0.63 0.47 −0.13 −0.21 0.84 1.00 6.17 4.19 J) CEDAW ratification 0.21 −0.36 0.09 0.40 0.45 −0.04 −0.09 0.35 0.29 1.00 0.69 0.46 K) World society index 0.18 −0.62 0.75 0.68 0.48 −0.03 −0.29 0.95 0.97 0.27 1.00 0.64 0.80 L) Divorce law equality 0.22 −0.48 0.48 0.34 0.48 0.30 −0.70 0.34 0.19 0.25 0.26 1.00 2.70 0.75 M) State capacity 0.05 0.16 −0.39 −0.08 −0.04 −0.19 0.28 −0.25 −0.22 0.03 −0.30 −0.22 1.00 0.19 0.08 Table 1. Descriptive Statistics and Correlations of Variables A) B) C) D) E) F) G) H) I) J) K) L) M) Mean S.D. A) Divorce rate 1.00 3.88 2.86 B) Youth dependency −0.30 1.00 45.16 21.16 C) GNI p/cap (log) 0.17 −0.72 1.00 8.88 1.31 D) School enrollment 0.34 −0.82 0.70 1.00 79.07 27.15 E) Female employment 0.38 −0.80 0.53 0.73 1.00 31.29 12.44 F) Catholic pop % −0.19 0.19 0.02 −0.26 −0.32 1.00 42.60 38.93 G) Muslim pop% −0.10 0.38 −0.44 −0.23 −0.38 −0.43 1.00 12.94 29.64 H) INGO membership (log) 0.16 −0.70 0.78 0.70 0.53 0.02 −0.31 1.00 6.78 0.84 I) WINGO membership 0.15 −0.52 0.65 0.63 0.47 −0.13 −0.21 0.84 1.00 6.17 4.19 J) CEDAW ratification 0.21 −0.36 0.09 0.40 0.45 −0.04 −0.09 0.35 0.29 1.00 0.69 0.46 K) World society index 0.18 −0.62 0.75 0.68 0.48 −0.03 −0.29 0.95 0.97 0.27 1.00 0.64 0.80 L) Divorce law equality 0.22 −0.48 0.48 0.34 0.48 0.30 −0.70 0.34 0.19 0.25 0.26 1.00 2.70 0.75 M) State capacity 0.05 0.16 −0.39 −0.08 −0.04 −0.19 0.28 −0.25 −0.22 0.03 −0.30 −0.22 1.00 0.19 0.08 A) B) C) D) E) F) G) H) I) J) K) L) M) Mean S.D. A) Divorce rate 1.00 3.88 2.86 B) Youth dependency −0.30 1.00 45.16 21.16 C) GNI p/cap (log) 0.17 −0.72 1.00 8.88 1.31 D) School enrollment 0.34 −0.82 0.70 1.00 79.07 27.15 E) Female employment 0.38 −0.80 0.53 0.73 1.00 31.29 12.44 F) Catholic pop % −0.19 0.19 0.02 −0.26 −0.32 1.00 42.60 38.93 G) Muslim pop% −0.10 0.38 −0.44 −0.23 −0.38 −0.43 1.00 12.94 29.64 H) INGO membership (log) 0.16 −0.70 0.78 0.70 0.53 0.02 −0.31 1.00 6.78 0.84 I) WINGO membership 0.15 −0.52 0.65 0.63 0.47 −0.13 −0.21 0.84 1.00 6.17 4.19 J) CEDAW ratification 0.21 −0.36 0.09 0.40 0.45 −0.04 −0.09 0.35 0.29 1.00 0.69 0.46 K) World society index 0.18 −0.62 0.75 0.68 0.48 −0.03 −0.29 0.95 0.97 0.27 1.00 0.64 0.80 L) Divorce law equality 0.22 −0.48 0.48 0.34 0.48 0.30 −0.70 0.34 0.19 0.25 0.26 1.00 2.70 0.75 M) State capacity 0.05 0.16 −0.39 −0.08 −0.04 −0.19 0.28 −0.25 −0.22 0.03 −0.30 −0.22 1.00 0.19 0.08 Dependent Variable This paper improves upon previous comparative studies by using newly collected data to calculate national divorce rates as the number of registered divorces per 1,000 married population, sometimes referred to as the gross divorce rate (Anser 2014) or the relative divorce rate. Measurement has long been an issue for comparative studies of divorce. Prior comparative studies generally employed the crude divorce rate, divorces per 1,000 total population, or variations of that measure.5 However, the crude rate does not take into account the age structure and proportion of married population of the society. The gross divorce rate is arguably more meaningful, given that marriage rates have declined in some countries. Other options—closely correlated with our measure—are the number of divorces per married couples or per married women, but fewer countries provide data to construct these measures. Balancing measurement precision and data availability, we measure divorce rates as follows:6 Divorcerate=numberoflegallyregistereddivorcesmarriedpopulation (1) This study uses a new dataset compiled from many historical editions of the UN Demographic Yearbooks and statistics databank, which include data on the proportion of married population, total population, and crude divorce rate, reported by individual UN member countries. When multiple editions overlap and report inconsistent data, we use data from the more recent edition, assuming revised estimates were superior. Small gaps between the measurements were filled by interpolation.7 When colonies/disputed areas gained independence, we included the data under the names of the newly independent countries. As is generally the case for cross-national datasets, important limitations and caveats must be acknowledged. The data rely on government infrastructure to accurately register divorces and calculate vital statistics, which may be limited in poor countries and those that lack rationalized legal systems (thus, for instance, we include controls for GDP and state capacity). In addition, national divorce laws and their implementation may affect the reporting. Stringent divorce laws make it more difficult to obtain a legal divorce, and may result in couples divorcing without legal registration. One strategy to address this is to include a control for features of a country’s divorce law as a robustness check. The actual stringency of the divorce law is not available, but a proxy—the extent to which laws are “gender equal,” which often correlates with stringency—exists for roughly 50 countries. To our knowledge, this dataset is the most comprehensive and cross-nationally comparable dataset on divorce rates (per married population) that is currently available. Independent Variables Independent measures are described below. If not otherwise specified, the data source is the World Development Indicators (World Bank 2012). Level of Economic Development Level of economic development is measured by the natural log of real Gross National Income (GNI) per capita in constant 2005 US dollars. Women’s Labor Force Participation Women’s labor force participation is measured by the percentage of women above age 15 who were active in the labor force, taken from the International Labour Organization online database. Mass Education Enrollment We use the gross secondary education enrollment ratio to indicate the overall expansion of education in a country.8 Religion: Catholic and Muslim Population The World Christian Encyclopedia (Barrett, Kurian, and Johnson 2011) provides information on the size of each country’s Catholic and Muslim population at four time points (1970, 1995, 2000, and 2010). We divide these by total population to measure the proportion of the population that is Catholic or Muslim. Gaps in the data are filled by linear interpolation. World Society: Institutionalization and Diffusion This study employs several measures of global culture and institutions to address developmental idealism and world society arguments: INGO and WINGO Memberships World society scholars have argued that international non-governmental organizations (INGOs) are important embodiments and carriers of global culture (Boli and Thomas 1997). INGOs represent a concrete mechanism for propagating global norms and ideas (e.g., operating as part of transnational advocacy networks), and are a useful proxy for other forms of global cultural influence that are harder to measure directly (Schofer and Longhofer 2011). Following prior studies, we use a standard measure, a country’s total count of memberships in all INGOs (logged to reduce skewness).9 The paper also includes a specific measure of membership in INGOs devoted to women’s issues (WINGOs). We focus on WINGOs because the women’s movement appears to have been an important source of normative and social movement pressure that may have brought global norms to bear on countries. Data on memberships for specific types of organizations across time must be hand-coded. We lack the resources to code memberships in all WINGOs, and instead construct our measure based on a random sample of 25 women’s INGOs. The INGO and WINGO measures are taken from the Yearbook of International Organizations (UIA 1948–2015). The Yearbook has an elaborate coding system to distinguish INGOs from other types of organizations, but in a nutshell, INGOs conform to the conventional understandings of an international “non-profit” organization: formal, durable organizations (as opposed to, e.g., a conference series) that are independent from governments, IGOs (such as the UN or EU), and private for-profit firms, and with members in more than two countries. CEDAW Ratification Treaty ratification is commonly used to measure the influence of global culture and institutions on nations (e.g., Frank, Hironaka, and Schofer 2000). We employ a time-varying dichotomous variable reflecting a country’s ratification of CEDAW, the central treaty addressing gender issues, as an indicator of the state’s commitment to the cultural principles of individualism and gender equality. World Society Index We create an index of global influences based on the measures described above (INGOs, WINGOs, CEDAW) by using factor analysis and computing country factor scores. Other methods of constructing an index, such as summing z-scored variables, produced similar results. Additional Control Variables Youth Dependency Ratio We control for the youth dependency ratio because the heavy economic demands of multiple children (as well as elders) can discourage divorce (Cherlin 1977; Mott and Moore 1979). The youth dependency ratio is measured as the ratio of the population under 15 to the population between 15 and 64. We also examined the population sex ratio, as a shortage of females can render men less willing to “give up” their wives (Greenstein and Davis 2006b; Trent and South 1989). However, we ultimately dropped this from our analyses because the variable is never significant. Other demographic factors, including the marriage rate and the population age structure, are addressed in that they are incorporated into the denominator of the gross divorce rate. Methods Panel regression models are used to analyze the effects of covariates on national divorce rates from 1970 to 2008. The unit of analysis is the country-year. The dataset is unbalanced due to missing data and because countries come into existence (or cease to exist) over time. Panel regression models improve upon previous cross-sectional studies of divorce rates by including longitudinal information and supporting models that address unobserved heterogeneity. Panel regression datasets involve non-random within-country errors that violate the assumption of ordinary least squares regression. This is commonly addressed by incorporating country random or fixed effects into the model. Based on Hausman tests, we opt to use fixed-effects models (Halaby 2004; Wooldridge 2002). Fixed-effects models focus on longitudinal change within each country, effectively controlling for (time-invariant) unobserved heterogeneity among countries. Our models, thus, take into account country differences in region, geography, colonial history, durable features of national history and culture, and all other time-invariant factors affecting national divorce rates. Sample Selection Models and Other Robustness Checks We conducted a series of robustness checks. First, although the dataset compiled here is the best data available, one can imagine several sources of selection bias. For example, the infrastructure needed to collect vital statistics may be limited in poor and/or unstable countries, leading to non-random missing data. Also, countries that do not legally allow divorce (or did not until recently) might not gather divorce statistics—which may correlate with variables such as religion. We address the issue by using a Heckman sample selection model (Wooldridge 2002). Logged GNI per capita, percent Catholic, percent Muslim, government consumption as percentage of GDP (a measure of state capacity),10 and gender equality in national divorce laws are included as potential sources of selection bias. We obtain the index of equality level of divorce law from the family law dataset developed by Htun and Weldon (2012).11 Inclusion of the divorce law measure shrinks the sample to roughly 50 countries. The results of the Heckman model are generally consistent with the results of the panel regression models (see Appendix A), though one of the four global culture variables (INGOs) loses significance (corollary analyses suggest that this is due to the much smaller sample size). Results suggest that economic development and government capacity affect reporting of divorce data, but the overall results do not change a great deal. Additional robustness checks, presented in table 4, include controls for state capacity and divorce laws, models with lagged independent variables, models for first-order autoregression, and dynamic panel models that include the lagged dependent variable. We discuss the results of the robustness checks below, but the main findings are generally stable. We also conducted conventional diagnostics for multicollinearity and influential cases. In particular, the United States turns out to be a rather extreme outlier in our analyses (based on Cook’s D and partial regression plots), and we exclude it from the main analyses. However, we reintroduce outliers in table 4 to show that our main findings hold up with all countries in the sample. We also replicated our main findings with alternative measures of divorce rates, such as the crude divorce rate (not presented; available upon request). Results We begin by describing regional and global variations and trends in divorce rates. Table 2 shows the minimum, maximum, and average divorce rates (per 1,000 married population) for each country in the sample. Divorce rates are highest in Northern/Western Europe and European settler colonies in North America and Oceania (United States, Canada, Australia, and New Zealand). The divorce rates of these countries can be as high as 9.73 and 7.88 (Australia 1976 and Belgium 2008, respectively). More typically, the average divorce rates of these countries fall around 4 to 6 divorces per 1,000 married people. By contrast, Southern Europe and Latin America have the lowest divorce rates, which may reflect the influence of Catholicism. Peru has the lowest average divorce rate, at 0.46. With the exceptions of Cuba, the Dominican Republic, and Puerto Rico, the average divorce rates of the Southern European and Latin American countries generally fall between 1 and 3 divorces per 1,000 married population. Divorce rates in Eastern Europe and Central Asia vary a lot, but are often quite high, reflecting the extremely egalitarian and lenient divorce laws of many Communist-era societies (Htun and Weldon 2011; Therborn 2014).12 Table 2. Descriptive Statistics of National Divorce Rates Country Mean Min Max Country Mean Min Max Country Mean Min Max Sub-Saharan Africa Syria 1.98 1.50 2.40 Tajikistan 1.21 0.99 1.77 Mauritius 1.97 1.57 2.84 Tunisia 3.10 2.41 4.65 Turkey 1.66 0.77 2.97 South Africa 2.69 2.69 2.69 Eastern Asia and Pacific Ukraine 8.02 7.38 8.72 China 1.13 0.95 1.44 Western Europe North America Hong Kong 3.97 3.97 3.97 Austria 4.22 2.94 5.87 Canada 5.25 3.03 6.42 Indonesia 3.00 1.97 4.08 Belgium 4.28 1.29 7.88 Latin America and Caribbean Japan 3.01 1.96 4.48 France 4.03 1.74 6.18 Brazil 1.27 0.66 2.01 Korea,Rep. 2.99 1.06 7.46 Germany 5.05 4.31 5.78 Chile 0.91 0.34 1.23 Macao 2.16 1.71 2.58 Ireland 1.94 1.67 2.16 Costa Rica 3.97 0.51 8.18 Mongolia 1.87 1.52 2.29 Luxembourg 3.36 1.27 5.21 Cuba 11.03 7.89 19.44 Thailand 1.35 0.80 2.03 Netherlands 4.07 1.41 5.24 Dominican Rep. 9.33 1.74 15.23 South Asia Switzerland 4.57 3.09 6.24 Ecuador 1.09 0.68 2.23 Sri Lanka 0.45 0.40 0.50 United Kingdom 6.55 6.17 7.07 El Salvador 2.76 2.08 3.59 Nepal 1.99 1.99 1.99 Northern Europe Guatemala 0.92 0.63 1.16 Eastern Europe and Central Asia Denmark 6.22 3.95 7.32 Honduras 1.55 1.17 2.35 Armenia 1.54 1.12 2.04 Finland 5.62 2.98 7.05 Jamaica 3.79 3.79 3.79 Azerbaijan 1.81 1.47 2.32 Iceland 5.01 4.88 5.19 Mexico 1.48 0.81 2.21 Belarus 8.31 6.50 10.06 Norway 4.54 1.84 6.29 Nicaragua 1.83 1.15 3.08 Bulgaria 2.60 1.62 3.55 Sweden 6.02 3.36 7.27 Panama 3.38 2.35 4.04 Czech Rep. 6.45 4.89 7.13 Southern Europe Paraguay 3.95 3.38 4.82 Cyprus 2.08 0.41 4.52 Albania 1.73 1.02 2.63 Peru 0.46 0.33 0.61 Estonia 8.43 7.90 8.78 Croatia 1.83 1.57 2.17 Puerto Rico 9.43 8.62 11.04 Hungary 5.17 4.21 6.29 Greece 1.31 0.78 2.03 Trinidad and Tobago 3.58 2.90 4.63 Kazakhstan 19.01 14.37 25.35 Italy 0.93 0.34 1.82 Uruguay 4.07 2.51 8.25 Kyrgyz Rep. 3.52 2.82 4.68 Macedonia 0.99 0.61 1.38 Venezuela 3.60 1.53 5.92 Latvia 7.13 5.20 12.27 Portugal 2.47 0.13 6.97 Middle East and North Africa Lithuania 7.64 7.64 7.64 Slovenia 2.57 1.79 3.24 Egypt 4.27 2.21 6.70 Moldova 6.81 5.16 9.83 Spain 1.49 1.23 1.98 Iran 1.80 1.08 2.92 Poland 2.26 1.50 3.67 Oceania Israel 4.03 3.33 4.69 Romania 3.07 2.51 3.46 Australia 5.61 2.08 9.73 Jordan 3.09 2.83 3.46 Russian 11.49 9.96 13.93 New Zealand 6.29 3.71 8.63 Kuwait 4.68 4.68 4.68 Slovak Rep. 4.22 3.26 5.29 Total 4.08 0.13 25.35 Country Mean Min Max Country Mean Min Max Country Mean Min Max Sub-Saharan Africa Syria 1.98 1.50 2.40 Tajikistan 1.21 0.99 1.77 Mauritius 1.97 1.57 2.84 Tunisia 3.10 2.41 4.65 Turkey 1.66 0.77 2.97 South Africa 2.69 2.69 2.69 Eastern Asia and Pacific Ukraine 8.02 7.38 8.72 China 1.13 0.95 1.44 Western Europe North America Hong Kong 3.97 3.97 3.97 Austria 4.22 2.94 5.87 Canada 5.25 3.03 6.42 Indonesia 3.00 1.97 4.08 Belgium 4.28 1.29 7.88 Latin America and Caribbean Japan 3.01 1.96 4.48 France 4.03 1.74 6.18 Brazil 1.27 0.66 2.01 Korea,Rep. 2.99 1.06 7.46 Germany 5.05 4.31 5.78 Chile 0.91 0.34 1.23 Macao 2.16 1.71 2.58 Ireland 1.94 1.67 2.16 Costa Rica 3.97 0.51 8.18 Mongolia 1.87 1.52 2.29 Luxembourg 3.36 1.27 5.21 Cuba 11.03 7.89 19.44 Thailand 1.35 0.80 2.03 Netherlands 4.07 1.41 5.24 Dominican Rep. 9.33 1.74 15.23 South Asia Switzerland 4.57 3.09 6.24 Ecuador 1.09 0.68 2.23 Sri Lanka 0.45 0.40 0.50 United Kingdom 6.55 6.17 7.07 El Salvador 2.76 2.08 3.59 Nepal 1.99 1.99 1.99 Northern Europe Guatemala 0.92 0.63 1.16 Eastern Europe and Central Asia Denmark 6.22 3.95 7.32 Honduras 1.55 1.17 2.35 Armenia 1.54 1.12 2.04 Finland 5.62 2.98 7.05 Jamaica 3.79 3.79 3.79 Azerbaijan 1.81 1.47 2.32 Iceland 5.01 4.88 5.19 Mexico 1.48 0.81 2.21 Belarus 8.31 6.50 10.06 Norway 4.54 1.84 6.29 Nicaragua 1.83 1.15 3.08 Bulgaria 2.60 1.62 3.55 Sweden 6.02 3.36 7.27 Panama 3.38 2.35 4.04 Czech Rep. 6.45 4.89 7.13 Southern Europe Paraguay 3.95 3.38 4.82 Cyprus 2.08 0.41 4.52 Albania 1.73 1.02 2.63 Peru 0.46 0.33 0.61 Estonia 8.43 7.90 8.78 Croatia 1.83 1.57 2.17 Puerto Rico 9.43 8.62 11.04 Hungary 5.17 4.21 6.29 Greece 1.31 0.78 2.03 Trinidad and Tobago 3.58 2.90 4.63 Kazakhstan 19.01 14.37 25.35 Italy 0.93 0.34 1.82 Uruguay 4.07 2.51 8.25 Kyrgyz Rep. 3.52 2.82 4.68 Macedonia 0.99 0.61 1.38 Venezuela 3.60 1.53 5.92 Latvia 7.13 5.20 12.27 Portugal 2.47 0.13 6.97 Middle East and North Africa Lithuania 7.64 7.64 7.64 Slovenia 2.57 1.79 3.24 Egypt 4.27 2.21 6.70 Moldova 6.81 5.16 9.83 Spain 1.49 1.23 1.98 Iran 1.80 1.08 2.92 Poland 2.26 1.50 3.67 Oceania Israel 4.03 3.33 4.69 Romania 3.07 2.51 3.46 Australia 5.61 2.08 9.73 Jordan 3.09 2.83 3.46 Russian 11.49 9.96 13.93 New Zealand 6.29 3.71 8.63 Kuwait 4.68 4.68 4.68 Slovak Rep. 4.22 3.26 5.29 Total 4.08 0.13 25.35 Table 2. Descriptive Statistics of National Divorce Rates Country Mean Min Max Country Mean Min Max Country Mean Min Max Sub-Saharan Africa Syria 1.98 1.50 2.40 Tajikistan 1.21 0.99 1.77 Mauritius 1.97 1.57 2.84 Tunisia 3.10 2.41 4.65 Turkey 1.66 0.77 2.97 South Africa 2.69 2.69 2.69 Eastern Asia and Pacific Ukraine 8.02 7.38 8.72 China 1.13 0.95 1.44 Western Europe North America Hong Kong 3.97 3.97 3.97 Austria 4.22 2.94 5.87 Canada 5.25 3.03 6.42 Indonesia 3.00 1.97 4.08 Belgium 4.28 1.29 7.88 Latin America and Caribbean Japan 3.01 1.96 4.48 France 4.03 1.74 6.18 Brazil 1.27 0.66 2.01 Korea,Rep. 2.99 1.06 7.46 Germany 5.05 4.31 5.78 Chile 0.91 0.34 1.23 Macao 2.16 1.71 2.58 Ireland 1.94 1.67 2.16 Costa Rica 3.97 0.51 8.18 Mongolia 1.87 1.52 2.29 Luxembourg 3.36 1.27 5.21 Cuba 11.03 7.89 19.44 Thailand 1.35 0.80 2.03 Netherlands 4.07 1.41 5.24 Dominican Rep. 9.33 1.74 15.23 South Asia Switzerland 4.57 3.09 6.24 Ecuador 1.09 0.68 2.23 Sri Lanka 0.45 0.40 0.50 United Kingdom 6.55 6.17 7.07 El Salvador 2.76 2.08 3.59 Nepal 1.99 1.99 1.99 Northern Europe Guatemala 0.92 0.63 1.16 Eastern Europe and Central Asia Denmark 6.22 3.95 7.32 Honduras 1.55 1.17 2.35 Armenia 1.54 1.12 2.04 Finland 5.62 2.98 7.05 Jamaica 3.79 3.79 3.79 Azerbaijan 1.81 1.47 2.32 Iceland 5.01 4.88 5.19 Mexico 1.48 0.81 2.21 Belarus 8.31 6.50 10.06 Norway 4.54 1.84 6.29 Nicaragua 1.83 1.15 3.08 Bulgaria 2.60 1.62 3.55 Sweden 6.02 3.36 7.27 Panama 3.38 2.35 4.04 Czech Rep. 6.45 4.89 7.13 Southern Europe Paraguay 3.95 3.38 4.82 Cyprus 2.08 0.41 4.52 Albania 1.73 1.02 2.63 Peru 0.46 0.33 0.61 Estonia 8.43 7.90 8.78 Croatia 1.83 1.57 2.17 Puerto Rico 9.43 8.62 11.04 Hungary 5.17 4.21 6.29 Greece 1.31 0.78 2.03 Trinidad and Tobago 3.58 2.90 4.63 Kazakhstan 19.01 14.37 25.35 Italy 0.93 0.34 1.82 Uruguay 4.07 2.51 8.25 Kyrgyz Rep. 3.52 2.82 4.68 Macedonia 0.99 0.61 1.38 Venezuela 3.60 1.53 5.92 Latvia 7.13 5.20 12.27 Portugal 2.47 0.13 6.97 Middle East and North Africa Lithuania 7.64 7.64 7.64 Slovenia 2.57 1.79 3.24 Egypt 4.27 2.21 6.70 Moldova 6.81 5.16 9.83 Spain 1.49 1.23 1.98 Iran 1.80 1.08 2.92 Poland 2.26 1.50 3.67 Oceania Israel 4.03 3.33 4.69 Romania 3.07 2.51 3.46 Australia 5.61 2.08 9.73 Jordan 3.09 2.83 3.46 Russian 11.49 9.96 13.93 New Zealand 6.29 3.71 8.63 Kuwait 4.68 4.68 4.68 Slovak Rep. 4.22 3.26 5.29 Total 4.08 0.13 25.35 Country Mean Min Max Country Mean Min Max Country Mean Min Max Sub-Saharan Africa Syria 1.98 1.50 2.40 Tajikistan 1.21 0.99 1.77 Mauritius 1.97 1.57 2.84 Tunisia 3.10 2.41 4.65 Turkey 1.66 0.77 2.97 South Africa 2.69 2.69 2.69 Eastern Asia and Pacific Ukraine 8.02 7.38 8.72 China 1.13 0.95 1.44 Western Europe North America Hong Kong 3.97 3.97 3.97 Austria 4.22 2.94 5.87 Canada 5.25 3.03 6.42 Indonesia 3.00 1.97 4.08 Belgium 4.28 1.29 7.88 Latin America and Caribbean Japan 3.01 1.96 4.48 France 4.03 1.74 6.18 Brazil 1.27 0.66 2.01 Korea,Rep. 2.99 1.06 7.46 Germany 5.05 4.31 5.78 Chile 0.91 0.34 1.23 Macao 2.16 1.71 2.58 Ireland 1.94 1.67 2.16 Costa Rica 3.97 0.51 8.18 Mongolia 1.87 1.52 2.29 Luxembourg 3.36 1.27 5.21 Cuba 11.03 7.89 19.44 Thailand 1.35 0.80 2.03 Netherlands 4.07 1.41 5.24 Dominican Rep. 9.33 1.74 15.23 South Asia Switzerland 4.57 3.09 6.24 Ecuador 1.09 0.68 2.23 Sri Lanka 0.45 0.40 0.50 United Kingdom 6.55 6.17 7.07 El Salvador 2.76 2.08 3.59 Nepal 1.99 1.99 1.99 Northern Europe Guatemala 0.92 0.63 1.16 Eastern Europe and Central Asia Denmark 6.22 3.95 7.32 Honduras 1.55 1.17 2.35 Armenia 1.54 1.12 2.04 Finland 5.62 2.98 7.05 Jamaica 3.79 3.79 3.79 Azerbaijan 1.81 1.47 2.32 Iceland 5.01 4.88 5.19 Mexico 1.48 0.81 2.21 Belarus 8.31 6.50 10.06 Norway 4.54 1.84 6.29 Nicaragua 1.83 1.15 3.08 Bulgaria 2.60 1.62 3.55 Sweden 6.02 3.36 7.27 Panama 3.38 2.35 4.04 Czech Rep. 6.45 4.89 7.13 Southern Europe Paraguay 3.95 3.38 4.82 Cyprus 2.08 0.41 4.52 Albania 1.73 1.02 2.63 Peru 0.46 0.33 0.61 Estonia 8.43 7.90 8.78 Croatia 1.83 1.57 2.17 Puerto Rico 9.43 8.62 11.04 Hungary 5.17 4.21 6.29 Greece 1.31 0.78 2.03 Trinidad and Tobago 3.58 2.90 4.63 Kazakhstan 19.01 14.37 25.35 Italy 0.93 0.34 1.82 Uruguay 4.07 2.51 8.25 Kyrgyz Rep. 3.52 2.82 4.68 Macedonia 0.99 0.61 1.38 Venezuela 3.60 1.53 5.92 Latvia 7.13 5.20 12.27 Portugal 2.47 0.13 6.97 Middle East and North Africa Lithuania 7.64 7.64 7.64 Slovenia 2.57 1.79 3.24 Egypt 4.27 2.21 6.70 Moldova 6.81 5.16 9.83 Spain 1.49 1.23 1.98 Iran 1.80 1.08 2.92 Poland 2.26 1.50 3.67 Oceania Israel 4.03 3.33 4.69 Romania 3.07 2.51 3.46 Australia 5.61 2.08 9.73 Jordan 3.09 2.83 3.46 Russian 11.49 9.96 13.93 New Zealand 6.29 3.71 8.63 Kuwait 4.68 4.68 4.68 Slovak Rep. 4.22 3.26 5.29 Total 4.08 0.13 25.35 Figure 1 presents trends in the average divorce rate of major global regions as well as the global average. The global average divorce rate (the solid line with square data markers) shows a historical increase from 2.643 to 5.476 divorces per 1,000 married population, essentially doubling over the course of four decades. The pattern mainly reflects the trends in Latin America, Europe and Central Asia, and the East Asia and Pacific region. The average divorce rate in Eastern Europe and Central Asia spikes in 1989, due to the addition of new post-Soviet cases to the dataset (e.g., Kazakhstan), which often have exceptionally high divorce rates. The recent divorce decline in the East Asian and Pacific region results from a plateau in most countries combined with a sharp decline of the divorce rate in Indonesia. Other regions, including the Middle East and North Africa and sub-Saharan Africa, demonstrate a long-term increase in the divorce rate. Figure 1. View largeDownload slide Annual average divorce rate trends, by region Note: This category also includes settler colonies such as Australia and New Zealand. Figure 1. View largeDownload slide Annual average divorce rate trends, by region Note: This category also includes settler colonies such as Australia and New Zealand. Table 3 turns to panel regression models examining the relationship between national and global factors and a country’s divorce rate. National-level indicators have effects consistent with the prior literature. Economic development, female labor force participation, and secondary education enrollment are positively associated with a country’s divorce rate. Economic development yields the largest raw coefficient. In model 1, every one-unit increase in logged GNI per capita is associated with 0.47 more divorces per 1,000 married population (0.616 more divorces with the increase of one standard deviation). In comparison, every one standard deviation in the female labor force participation rate adds 0.815 to the gross divorce rate. The effect of secondary education enrollment is also stable and significant (the coefficients range from 0.01 to 0.02). Table 3. Panel Regression Models with Country Fixed Effects Examining Cross-National Variation in Divorce Rates, 1970–2008 Variables (1) INGO membership (2) WINGO membership (3) CEDAW ratification (4) World society index Youth dependency 0.01* 0.00 0.00 0.00 (0.006) (0.005) (0.005) (0.005) GNI p/cap (log) 0.47** 0.46** 0.75*** 0.41* (0.168) (0.170) (0.156) (0.171) School enrollment 0.01*** 0.02*** 0.02*** 0.01*** (0.003) (0.003) (0.003) (0.003) Female employment 0.03*** 0.04*** 0.03*** 0.03*** (0.008) (0.008) (0.008) (0.008) Catholic pop % −0.07*** −0.06*** −0.07*** −0.06*** (0.013) (0.013) (0.013) (0.013) Muslim pop % −0.02+ −0.05*** −0.04*** −0.02 (0.012) (0.011) (0.011) (0.012) INGO membership 0.74*** (0.137) WINGO membership 0.07*** (0.016) CEDAW ratification 0.22* (0.088) World society index 0.58*** (0.100) Constant −4.57** −0.05 −2.04 0.76 (1.546) (1.597) (1.528) (1.635) Observations 1,740 1,772 1,779 1,733 R-squared 0.260 0.239 0.231 0.262 # of countries 84 83 84 83 Variables (1) INGO membership (2) WINGO membership (3) CEDAW ratification (4) World society index Youth dependency 0.01* 0.00 0.00 0.00 (0.006) (0.005) (0.005) (0.005) GNI p/cap (log) 0.47** 0.46** 0.75*** 0.41* (0.168) (0.170) (0.156) (0.171) School enrollment 0.01*** 0.02*** 0.02*** 0.01*** (0.003) (0.003) (0.003) (0.003) Female employment 0.03*** 0.04*** 0.03*** 0.03*** (0.008) (0.008) (0.008) (0.008) Catholic pop % −0.07*** −0.06*** −0.07*** −0.06*** (0.013) (0.013) (0.013) (0.013) Muslim pop % −0.02+ −0.05*** −0.04*** −0.02 (0.012) (0.011) (0.011) (0.012) INGO membership 0.74*** (0.137) WINGO membership 0.07*** (0.016) CEDAW ratification 0.22* (0.088) World society index 0.58*** (0.100) Constant −4.57** −0.05 −2.04 0.76 (1.546) (1.597) (1.528) (1.635) Observations 1,740 1,772 1,779 1,733 R-squared 0.260 0.239 0.231 0.262 # of countries 84 83 84 83 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 Table 3. Panel Regression Models with Country Fixed Effects Examining Cross-National Variation in Divorce Rates, 1970–2008 Variables (1) INGO membership (2) WINGO membership (3) CEDAW ratification (4) World society index Youth dependency 0.01* 0.00 0.00 0.00 (0.006) (0.005) (0.005) (0.005) GNI p/cap (log) 0.47** 0.46** 0.75*** 0.41* (0.168) (0.170) (0.156) (0.171) School enrollment 0.01*** 0.02*** 0.02*** 0.01*** (0.003) (0.003) (0.003) (0.003) Female employment 0.03*** 0.04*** 0.03*** 0.03*** (0.008) (0.008) (0.008) (0.008) Catholic pop % −0.07*** −0.06*** −0.07*** −0.06*** (0.013) (0.013) (0.013) (0.013) Muslim pop % −0.02+ −0.05*** −0.04*** −0.02 (0.012) (0.011) (0.011) (0.012) INGO membership 0.74*** (0.137) WINGO membership 0.07*** (0.016) CEDAW ratification 0.22* (0.088) World society index 0.58*** (0.100) Constant −4.57** −0.05 −2.04 0.76 (1.546) (1.597) (1.528) (1.635) Observations 1,740 1,772 1,779 1,733 R-squared 0.260 0.239 0.231 0.262 # of countries 84 83 84 83 Variables (1) INGO membership (2) WINGO membership (3) CEDAW ratification (4) World society index Youth dependency 0.01* 0.00 0.00 0.00 (0.006) (0.005) (0.005) (0.005) GNI p/cap (log) 0.47** 0.46** 0.75*** 0.41* (0.168) (0.170) (0.156) (0.171) School enrollment 0.01*** 0.02*** 0.02*** 0.01*** (0.003) (0.003) (0.003) (0.003) Female employment 0.03*** 0.04*** 0.03*** 0.03*** (0.008) (0.008) (0.008) (0.008) Catholic pop % −0.07*** −0.06*** −0.07*** −0.06*** (0.013) (0.013) (0.013) (0.013) Muslim pop % −0.02+ −0.05*** −0.04*** −0.02 (0.012) (0.011) (0.011) (0.012) INGO membership 0.74*** (0.137) WINGO membership 0.07*** (0.016) CEDAW ratification 0.22* (0.088) World society index 0.58*** (0.100) Constant −4.57** −0.05 −2.04 0.76 (1.546) (1.597) (1.528) (1.635) Observations 1,740 1,772 1,779 1,733 R-squared 0.260 0.239 0.231 0.262 # of countries 84 83 84 83 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 The proportion of a country’s population that is Catholic is negatively associated with the gross divorce rate, as expected. Each one-unit increase in the Catholic population percentage is associated with 0.07 fewer divorces per 1,000 married population. The substantive effect size is very large: a one-standard-deviation increase in the proportion of a country’s Catholic population is associated with a 2.725 decrease in the divorce rate. The Muslim population is negatively associated with the gross divorce rate, but the coefficient is not significant in some analyses. The age dependency ratio control is generally not significant, except in model 1. Results in table 3 highlight the influence of global culture over and above national-level factors. All three cultural diffusion indicators—INGO memberships, WINGO memberships, and CEDAW ratification—and the combined index have a significant positive association with the gross divorce rate. Net of all national factors, a one-point increase in the diffusion indicators adds 0.75, 0.07, and 0.22 to the divorce rate, respectively (which translates into 0.63, 0.30, and 0.10 increase with every standard deviation). Each additional unit of increase in the index (model 4) is associated with an increase of 0.59 in the divorce rate (0.50 increase with each additional standard deviation). Consistent with prior studies of sex law reform (Frank, Camp, and Boutcher 2010), these results suggest that the legitimation of world cultural principles at the global level can propel local change. Table 4 presents a series of robustness checks. For brevity, models include the overall world society index, but not each component measure. Model 5 replicates results when outliers are not excluded. Results are quite similar to model 4 in table 3, demonstrating that removal of outliers (which is a matter of judgment) does not alter the findings. In model 6, we add two additional control variables—gender equality of a nation’s divorce law and government capacity (discussed above)—which capture legal limitations on divorce and the state’s administrative capacity to record them. Due to missing data, the sample is reduced to 51 countries. Both measures have a positive effect on the number of registered divorces. State capacity presumably allows more accurate recording of legal divorces. National laws may reflect better state recording of divorce (compared to countries with stringent traditional divorce laws, where some may not be recorded) as well as the direct effect of law equity on the divorce rate.13 Yet another possibility, following developmental idealism and world society theory, is that gender-equal divorce laws may be adopted in response to global norms and pressures. If so, the law variable may, in part, mediate the effect of global forces. Future research is needed to explore these possibilities. Table 4. Robustness Checks: Panel Regression Models with Country Fixed Effects Examining Cross-National Variation in Divorce Rates Variables (5) Outliers included (6) Additional controls (7) Lagged IVs (8) Auto- regressive (9) Dynamic panel Youth dependency −0.00 0.02*** −0.00 −0.02* −0.02** (0.005) (0.006) (0.005) (0.012) (0.007) GNI p/cap (log) 0.34+ 1.49*** 0.41* 0.53* 1.42*** (0.176) (0.256) (0.170) (0.247) (0.213) School enrollment 0.01*** 0.01*** 0.01*** 0.01 −0.01+ (0.003) (0.003) (0.003) (0.004) (0.003) Female employment 0.03*** 0.01 0.03*** −0.01 0.01 (0.008) (0.009) (0.008) (0.009) (0.009) Catholic pop % −0.07*** −0.13*** −0.07*** −0.06* −0.06*** (0.013) (0.015) (0.013) (0.026) (0.018) Muslim pop % −0.01 −0.21*** −0.00 0.04 −0.02 (0.013) (0.025) (0.012) (0.029) (0.016) World society index 0.39*** 0.50*** 0.44*** 0.30+ 0.34** (0.101) (0.112) (0.100) (0.153) (0.116) State capacity 5.10*** (0.710) Divorce law equality 0.37*** (0.112) Lagged DV 0.15*** (0.027) Constant 1.80 −6.72** 1.18 2.01*** −5.10** (1.677) (2.339) (1.622) (0.405) (1.925) Observations 1,772 1,237 1,708 1,650 1,111 R-squared 0.233 0.354 0.265 0.291 # of countries 84 51 80 76 66 Variables (5) Outliers included (6) Additional controls (7) Lagged IVs (8) Auto- regressive (9) Dynamic panel Youth dependency −0.00 0.02*** −0.00 −0.02* −0.02** (0.005) (0.006) (0.005) (0.012) (0.007) GNI p/cap (log) 0.34+ 1.49*** 0.41* 0.53* 1.42*** (0.176) (0.256) (0.170) (0.247) (0.213) School enrollment 0.01*** 0.01*** 0.01*** 0.01 −0.01+ (0.003) (0.003) (0.003) (0.004) (0.003) Female employment 0.03*** 0.01 0.03*** −0.01 0.01 (0.008) (0.009) (0.008) (0.009) (0.009) Catholic pop % −0.07*** −0.13*** −0.07*** −0.06* −0.06*** (0.013) (0.015) (0.013) (0.026) (0.018) Muslim pop % −0.01 −0.21*** −0.00 0.04 −0.02 (0.013) (0.025) (0.012) (0.029) (0.016) World society index 0.39*** 0.50*** 0.44*** 0.30+ 0.34** (0.101) (0.112) (0.100) (0.153) (0.116) State capacity 5.10*** (0.710) Divorce law equality 0.37*** (0.112) Lagged DV 0.15*** (0.027) Constant 1.80 −6.72** 1.18 2.01*** −5.10** (1.677) (2.339) (1.622) (0.405) (1.925) Observations 1,772 1,237 1,708 1,650 1,111 R-squared 0.233 0.354 0.265 0.291 # of countries 84 51 80 76 66 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 Table 4. Robustness Checks: Panel Regression Models with Country Fixed Effects Examining Cross-National Variation in Divorce Rates Variables (5) Outliers included (6) Additional controls (7) Lagged IVs (8) Auto- regressive (9) Dynamic panel Youth dependency −0.00 0.02*** −0.00 −0.02* −0.02** (0.005) (0.006) (0.005) (0.012) (0.007) GNI p/cap (log) 0.34+ 1.49*** 0.41* 0.53* 1.42*** (0.176) (0.256) (0.170) (0.247) (0.213) School enrollment 0.01*** 0.01*** 0.01*** 0.01 −0.01+ (0.003) (0.003) (0.003) (0.004) (0.003) Female employment 0.03*** 0.01 0.03*** −0.01 0.01 (0.008) (0.009) (0.008) (0.009) (0.009) Catholic pop % −0.07*** −0.13*** −0.07*** −0.06* −0.06*** (0.013) (0.015) (0.013) (0.026) (0.018) Muslim pop % −0.01 −0.21*** −0.00 0.04 −0.02 (0.013) (0.025) (0.012) (0.029) (0.016) World society index 0.39*** 0.50*** 0.44*** 0.30+ 0.34** (0.101) (0.112) (0.100) (0.153) (0.116) State capacity 5.10*** (0.710) Divorce law equality 0.37*** (0.112) Lagged DV 0.15*** (0.027) Constant 1.80 −6.72** 1.18 2.01*** −5.10** (1.677) (2.339) (1.622) (0.405) (1.925) Observations 1,772 1,237 1,708 1,650 1,111 R-squared 0.233 0.354 0.265 0.291 # of countries 84 51 80 76 66 Variables (5) Outliers included (6) Additional controls (7) Lagged IVs (8) Auto- regressive (9) Dynamic panel Youth dependency −0.00 0.02*** −0.00 −0.02* −0.02** (0.005) (0.006) (0.005) (0.012) (0.007) GNI p/cap (log) 0.34+ 1.49*** 0.41* 0.53* 1.42*** (0.176) (0.256) (0.170) (0.247) (0.213) School enrollment 0.01*** 0.01*** 0.01*** 0.01 −0.01+ (0.003) (0.003) (0.003) (0.004) (0.003) Female employment 0.03*** 0.01 0.03*** −0.01 0.01 (0.008) (0.009) (0.008) (0.009) (0.009) Catholic pop % −0.07*** −0.13*** −0.07*** −0.06* −0.06*** (0.013) (0.015) (0.013) (0.026) (0.018) Muslim pop % −0.01 −0.21*** −0.00 0.04 −0.02 (0.013) (0.025) (0.012) (0.029) (0.016) World society index 0.39*** 0.50*** 0.44*** 0.30+ 0.34** (0.101) (0.112) (0.100) (0.153) (0.116) State capacity 5.10*** (0.710) Divorce law equality 0.37*** (0.112) Lagged DV 0.15*** (0.027) Constant 1.80 −6.72** 1.18 2.01*** −5.10** (1.677) (2.339) (1.622) (0.405) (1.925) Observations 1,772 1,237 1,708 1,650 1,111 R-squared 0.233 0.354 0.265 0.291 # of countries 84 51 80 76 66 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 In model 7, we lag the independent variables by one year to account for possibility that it might take some time for the independent variables to affect the divorce rate (longer lags are considered below in our dynamic model). Results are again similar. Model 8 presents results of a model assuming an AR(1) disturbance to address potential serial correlation within countries. In model 8, the coefficient for secondary education loses significance and some other coefficients shrink, but the key findings are robust. Finally, model 9 presents a dynamic panel model that includes the lagged dependent variable and lags all independent variables by 10 years. The effect of logged GNI is much bigger in the dynamic panel model, while the effect of female employment and school enrollment either loses significance or becomes negative. The effect of the world society index remains positive and significant. Discussion Results presented in table 3 provide evidence consistent with conventional arguments about structural change and religion, as well as evidence supporting hypotheses derived from developmental idealism and world society theory. Economic Development Results show a strong positive association between the national income and rising divorce rates, consistent with prior cross-sectional studies. In addition to creating institutional substitutes for marriage, economic change can also trigger ideational transformations conducive to divorce, such as new norms of gender equality. Women’s Employment Panel analyses presented here reaffirm the importance of female labor force participation. Engagement in employment brings women independence and also the added stress of the so-called “second shift.” This affords women the economic ability to divorce, and may also place increasing stress on marriages. Expansion of Mass Education We also observe a strong effect of mass education on divorce, which is generally robust (but see models 8 and 9). The growth of education can influence the divorce rate by promoting “modern” cultural values, and creating conditions for divorce by augmenting the human capital of women for the labor market. Also, education may be another mechanism for world society and developmental idealism dynamics: modern school curricular content tends to reflect global cultural norms of individualism and gender equality (Bromley, Meyer, and Ramirez 2011). Religion Results suggest that Catholicism and Islam both discourage the incidence of divorce. While Catholicism clearly frowns on divorce, the implications of Islam for divorce are more complex and multifaceted. Our results fall in line with scholars who have suggested that Islam may discourage women from opting out of their marriages, for instance by undercutting the status of women (e.g., Esposito and DeLong-Bas 2001). Developmental Idealism, World Culture, and Diffusion Results suggest that global factors are important for changes in divorce. We argue that cultural principles of freedom, consent, and gender equality, which are recognized and codified in world society, diffuse into national contexts through global-local organizational ties and national treaty ratification and reshape the understandings of marriage and family. We observe that measures of national ties to international organizations and treaties are associated with higher rates of divorce. The consistently significant coefficients of WINGO membership counts and CEDAW ratification across models suggest diffusion through global organizational actors concerned with gender issues and through the state commitment to world cultural principles. The fact that national ratification of CEDAW has significant effects even when controlling for national divorce laws further suggests that the influence of world society may not only operate via state policy, but may directly reshape local marriage practices. Conclusion This paper extends the comparative literature on divorce by addressing theories of global cultural diffusion: developmental idealism and world society theory. Findings suggest that world culture—which carries ideas of individual freedom, consent, and gender equality—may influence country divorce rates. Moreover, the study improves on prior empirical scholarship with a more precise measure of divorce, a much larger sample of longitudinal data, and more sophisticated panel models. This study extends globalization scholarship and world society theory in two ways. First, this research explores the effect of world society on family behaviors. Classic world society research looked mainly at diffusion of government policies, but the impacts of world society penetrate much more deeply into societies, organizations, and individuals. This study suggests that global cultural scripts can produce substantive changes in individuals’ private lives (also see Kim et al. 2013; Pierotti 2013). Second, this paper develops the concept of penumbras to explain how global culture and norms may have important “spill-over” effects that extend well beyond the codified rules and discourses of international treaties and organizations. Conventional world society studies examine issues that are squarely in the global spotlight, such as education and human rights. By contrast, divorce is only briefly discussed in international treaties and documents. We argue that the principles of individual rights and gender equality—which are strongly articulated in global treaties and discourse—shape how people think about divorce. Empirically, we find that countries more strongly influenced by global culture tend to have higher divorce rates. The penumbra argument suggests that world society and global culture may affect a broader set of global changes that emerge from the shadows, rather than just issues in the global spotlight. A task for future research would be to identify additional examples of penumbra effects, both to assess the generalizability of this phenomenon and to explore the conditions that bring it about. For instance, global environmental treaties, which trigger a “bee swarm” of pressures on nations, might affect environmental outcomes well beyond formal treaty goals and requirements (Hironaka 2014). Likewise, human rights treaties and discourses might have unexpected diffuse effects on civil society and political participation (e.g., Tsutsui 2006) or on acceptance of homosexuality (Roberts 2017). This project also speaks to research on historical trends toward individualism. Scholars generally agree that the rise of divorce was accompanied by the individuation of marriage (Beck and Beck-Gernsheim 2002; Cherlin 2009; Giddens 1992; Inglehart and Norris 2003). While most theorists view the change as a natural result of societal modernization, this research adds support to a growing body of work arguing that international discourses shape the private lives of local people (Frank, Camp, and Boutcher 2010; Frank and McEneaney 1999). Global diffusion arguments may help explain the puzzle of why societies with vastly different customs and levels of development are all experiencing similar shifts in marital patterns. However, the claim that world society affects individual behaviors requires some qualification. This research uses aggregate statistics, and so the findings cannot directly evaluate claims about individual decision-making. Current individual-level datasets do not support longitudinal comparison among a large set of countries, though harmonization of national surveys may allow such analyses in the future.14 Replication of our findings with individual-level data, when they become available, would be a valuable next step in the cross-national study of divorce. Footnotes 1 Divorce rates in the United States, parts of Europe, and a few East Asian countries have flattened or occasionally declined following episodes of rapid expansion (Lappegård 2014; Manning and Brown 2014). Also, some scholars point to declines among a handful of Arab countries, though the issue is debated (Anser 2014; El-Saadani 2006). 2 Micro-level studies differ from cross-national scholarship for many reasons, including non-linearity, measurement issues at different levels, theoretical complexity, and sampling issues. Micro-level analysis typically focuses on relatively educated and affluent countries. For instance, education tends to be negatively associated with divorce in affluent countries, but may have positive effects in societies where levels of schooling are extremely low (e.g., Takyi 2001). This sort of non-linearity creates the potential for pooled analyses of countries to produce results that depart from within-country studies of affluent democracies. Also, national-level characteristics identify in what types of societies divorce is more prevalent, whereas micro-level analysis focuses on identifying the relative risk to divorce of various subpopulations within a society. It is therefore entirely possible that a country that is more industrialized and more educated has a higher gross divorce rate (higher number of divorces) due to the pursuit of post-materialist or individualist values (Beck-Gernsheim 2002; Inglehart and Norris 2003), but its citizens who are more educated and richer can better prevent or handle martial stresses. In addition, the effect of income and educational level has more nuanced effects depending on gender, stages/duration of marriage, and time period (Chen 2012; Kalmijn and Poortman 2006; South and Spitze 1986), as well as the sample countries. 3 The analysis begins in 1970 because data on key variables are sparse in prior years. This period captures the intensification of the human rights and women’s rights regimes in the international sphere (e.g., Berkovitch 1999) and the rapid growth of divorce rates around the world. 4 Also, a number of small islands appear to be extreme (high) outliers in terms of divorce rates. 5 For example, other research tries to improve the measurement by presenting the number of divorce per 1,000 population of age 15 and older (e.g., El-Saadani 2006). While that improves upon on the crude divorce rate, it still does not take into consideration the size of the married population. 6 With the limitation of available data, our divorce rate data is calculated as follows: numberoflegallyregistereddivorcemarriedpopulation=crudedivorcerateproportionofmarriedpopulation (2) 7 Interpolation is used to fill out 8.8 percent of our divorce rate data. Models with the non-interpolated data yield similar results, though the effect of economic development is stronger with interpolated data. 8 We examined several other education measures, including net enrollments, female enrollments, and the average years of schooling among the adult population. These alternatives yielded similar results but reduced sample size, so we present results with gross enrollments. 9 The measure does not count how many individuals in a country are members of each INGO; rather, it is a count of the total number of INGOs in which a country’s citizens hold membership. The former would be ideal, but is simply not available. 10 Penn World Tables provide the data for government consumption (CICUP 2012). 11 The dataset includes indicators of family laws of 75 countries at four time points (1975, 1985, 1995, and 2005). We combined three binary variables indicating gender equality of divorce laws: equal right to initiate divorce, to child custody, and equal assignment of property upon divorce. While gender equity is not the same as having a more individualistic and permissive law, as a practical matter the two generally coincide. 12 It is an interesting exception to our argument that the Communist countries pursued some dimensions of gender equality more aggressively than countries that are typically seen as the “core” of world society. The Communist countries warrant closer study in the future. 13 Some scholarship hints toward the latter. Research on the relation between divorce law and divorce practices in Western Europe suggests that the enactment of lax divorce laws (e.g., no-fault divorce) does not necessarily lead to more divorces, but the implementation of such laws makes divorces more likely to be counted (Kneip and Bauer 2009). 14 The Minnesota Population Center is currently incorporating the Demographic and Health Surveys, which contain records for marital status. However, these datasets mostly focus on developing countries, making comparison between developing and developed countries difficult. Appendix A. Results of Heckman Correction Models Table A1. Heckman Correction Models Examining Potential Selection Bias from Government Capacity and Divorce Law Variables Model1 Model 2 Model 3 Model 4 Control Divorce law 0.423*** 0.477*** 0.424*** 0.467*** (0.115) (0.114) (0.113) (0.115) State capacity 5.612*** 5.139*** 5.570*** 5.317*** (0.769) (0.770) (0.760) (0.773) Population sex ratio 0.021 0.040 0.035 0.031 (0.037) (0.037) (0.037) (0.037) Youth dependency 0.015* 0.013* 0.017** 0.015* (0.007) (0.006) (0.006) (0.006) National factors Economic development 1.485*** 1.208*** 1.584*** 1.169*** (0.258) (0.245) (0.217) (0.262) Female employment 0.009 0.011 0.006 0.007 (0.009) (0.009) (0.009) (0.009) Expansion of mass education 0.020*** 0.018*** 0.020*** 0.018*** (0.003) (0.003) (0.003) (0.003) Catholic population −0.143*** −0.140*** −0.146*** −0.139*** (0.016) (0.016) (0.016) (0.016) Islamic population −0.159*** −0.227*** −0.213*** −0.181*** (0.030) (0.020) (0.020) (0.031) INGO membership 0.060 (0.136) Women’s INGO membership 0.061*** (0.017) CEDAW ratification 0.198* (0.096) World society index 0.312** (0.112) Constant −6.674 −5.804 −8.131+ −4.608 (4.195) (4.197) (4.156) (4.242) SELECTION MODEL Divorce law 0.033 0.056 0.056 0.033 (0.054) (0.053) (0.053) State capacity 5.448*** 5.742*** 5.742*** 5.448*** (0.495) (0.487) (0.487) (0.495) Economic development 0.623*** 0.615*** 0.615*** 0.623*** (0.031) (0.031) (0.031) (0.031) Catholic population 0.003** 0.003** 0.003** 0.003** (0.001) (0.001) (0.001) (0.001) Islamic population 0.003* 0.003* 0.003* 0.003* (0.002) (0.002) (0.002) (0.002) Constant −6.104*** −6.130*** −6.130*** −6.103*** (0.304) (0.304) (0.304) (0.304) Inverse H-Rho 0.015 0.009 0.015 0.009 (0.082) (0.081) (0.080) (0.082) Ln(sigma) −0.039+ −0.035+ −0.032 −0.042* (0.020) (0.020) (0.020) (0.020) Wald’s Chi-square 9,112.45*** 11,697.34*** 11,608.30*** 9,173.81*** Observations 1,939 1,965 1,965 1,939 Censored Observation 675 675 675 675 Uncensored Observation 1,264 1,290 1,290 1,264 Variables Model1 Model 2 Model 3 Model 4 Control Divorce law 0.423*** 0.477*** 0.424*** 0.467*** (0.115) (0.114) (0.113) (0.115) State capacity 5.612*** 5.139*** 5.570*** 5.317*** (0.769) (0.770) (0.760) (0.773) Population sex ratio 0.021 0.040 0.035 0.031 (0.037) (0.037) (0.037) (0.037) Youth dependency 0.015* 0.013* 0.017** 0.015* (0.007) (0.006) (0.006) (0.006) National factors Economic development 1.485*** 1.208*** 1.584*** 1.169*** (0.258) (0.245) (0.217) (0.262) Female employment 0.009 0.011 0.006 0.007 (0.009) (0.009) (0.009) (0.009) Expansion of mass education 0.020*** 0.018*** 0.020*** 0.018*** (0.003) (0.003) (0.003) (0.003) Catholic population −0.143*** −0.140*** −0.146*** −0.139*** (0.016) (0.016) (0.016) (0.016) Islamic population −0.159*** −0.227*** −0.213*** −0.181*** (0.030) (0.020) (0.020) (0.031) INGO membership 0.060 (0.136) Women’s INGO membership 0.061*** (0.017) CEDAW ratification 0.198* (0.096) World society index 0.312** (0.112) Constant −6.674 −5.804 −8.131+ −4.608 (4.195) (4.197) (4.156) (4.242) SELECTION MODEL Divorce law 0.033 0.056 0.056 0.033 (0.054) (0.053) (0.053) State capacity 5.448*** 5.742*** 5.742*** 5.448*** (0.495) (0.487) (0.487) (0.495) Economic development 0.623*** 0.615*** 0.615*** 0.623*** (0.031) (0.031) (0.031) (0.031) Catholic population 0.003** 0.003** 0.003** 0.003** (0.001) (0.001) (0.001) (0.001) Islamic population 0.003* 0.003* 0.003* 0.003* (0.002) (0.002) (0.002) (0.002) Constant −6.104*** −6.130*** −6.130*** −6.103*** (0.304) (0.304) (0.304) (0.304) Inverse H-Rho 0.015 0.009 0.015 0.009 (0.082) (0.081) (0.080) (0.082) Ln(sigma) −0.039+ −0.035+ −0.032 −0.042* (0.020) (0.020) (0.020) (0.020) Wald’s Chi-square 9,112.45*** 11,697.34*** 11,608.30*** 9,173.81*** Observations 1,939 1,965 1,965 1,939 Censored Observation 675 675 675 675 Uncensored Observation 1,264 1,290 1,290 1,264 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 Table A1. Heckman Correction Models Examining Potential Selection Bias from Government Capacity and Divorce Law Variables Model1 Model 2 Model 3 Model 4 Control Divorce law 0.423*** 0.477*** 0.424*** 0.467*** (0.115) (0.114) (0.113) (0.115) State capacity 5.612*** 5.139*** 5.570*** 5.317*** (0.769) (0.770) (0.760) (0.773) Population sex ratio 0.021 0.040 0.035 0.031 (0.037) (0.037) (0.037) (0.037) Youth dependency 0.015* 0.013* 0.017** 0.015* (0.007) (0.006) (0.006) (0.006) National factors Economic development 1.485*** 1.208*** 1.584*** 1.169*** (0.258) (0.245) (0.217) (0.262) Female employment 0.009 0.011 0.006 0.007 (0.009) (0.009) (0.009) (0.009) Expansion of mass education 0.020*** 0.018*** 0.020*** 0.018*** (0.003) (0.003) (0.003) (0.003) Catholic population −0.143*** −0.140*** −0.146*** −0.139*** (0.016) (0.016) (0.016) (0.016) Islamic population −0.159*** −0.227*** −0.213*** −0.181*** (0.030) (0.020) (0.020) (0.031) INGO membership 0.060 (0.136) Women’s INGO membership 0.061*** (0.017) CEDAW ratification 0.198* (0.096) World society index 0.312** (0.112) Constant −6.674 −5.804 −8.131+ −4.608 (4.195) (4.197) (4.156) (4.242) SELECTION MODEL Divorce law 0.033 0.056 0.056 0.033 (0.054) (0.053) (0.053) State capacity 5.448*** 5.742*** 5.742*** 5.448*** (0.495) (0.487) (0.487) (0.495) Economic development 0.623*** 0.615*** 0.615*** 0.623*** (0.031) (0.031) (0.031) (0.031) Catholic population 0.003** 0.003** 0.003** 0.003** (0.001) (0.001) (0.001) (0.001) Islamic population 0.003* 0.003* 0.003* 0.003* (0.002) (0.002) (0.002) (0.002) Constant −6.104*** −6.130*** −6.130*** −6.103*** (0.304) (0.304) (0.304) (0.304) Inverse H-Rho 0.015 0.009 0.015 0.009 (0.082) (0.081) (0.080) (0.082) Ln(sigma) −0.039+ −0.035+ −0.032 −0.042* (0.020) (0.020) (0.020) (0.020) Wald’s Chi-square 9,112.45*** 11,697.34*** 11,608.30*** 9,173.81*** Observations 1,939 1,965 1,965 1,939 Censored Observation 675 675 675 675 Uncensored Observation 1,264 1,290 1,290 1,264 Variables Model1 Model 2 Model 3 Model 4 Control Divorce law 0.423*** 0.477*** 0.424*** 0.467*** (0.115) (0.114) (0.113) (0.115) State capacity 5.612*** 5.139*** 5.570*** 5.317*** (0.769) (0.770) (0.760) (0.773) Population sex ratio 0.021 0.040 0.035 0.031 (0.037) (0.037) (0.037) (0.037) Youth dependency 0.015* 0.013* 0.017** 0.015* (0.007) (0.006) (0.006) (0.006) National factors Economic development 1.485*** 1.208*** 1.584*** 1.169*** (0.258) (0.245) (0.217) (0.262) Female employment 0.009 0.011 0.006 0.007 (0.009) (0.009) (0.009) (0.009) Expansion of mass education 0.020*** 0.018*** 0.020*** 0.018*** (0.003) (0.003) (0.003) (0.003) Catholic population −0.143*** −0.140*** −0.146*** −0.139*** (0.016) (0.016) (0.016) (0.016) Islamic population −0.159*** −0.227*** −0.213*** −0.181*** (0.030) (0.020) (0.020) (0.031) INGO membership 0.060 (0.136) Women’s INGO membership 0.061*** (0.017) CEDAW ratification 0.198* (0.096) World society index 0.312** (0.112) Constant −6.674 −5.804 −8.131+ −4.608 (4.195) (4.197) (4.156) (4.242) SELECTION MODEL Divorce law 0.033 0.056 0.056 0.033 (0.054) (0.053) (0.053) State capacity 5.448*** 5.742*** 5.742*** 5.448*** (0.495) (0.487) (0.487) (0.495) Economic development 0.623*** 0.615*** 0.615*** 0.623*** (0.031) (0.031) (0.031) (0.031) Catholic population 0.003** 0.003** 0.003** 0.003** (0.001) (0.001) (0.001) (0.001) Islamic population 0.003* 0.003* 0.003* 0.003* (0.002) (0.002) (0.002) (0.002) Constant −6.104*** −6.130*** −6.130*** −6.103*** (0.304) (0.304) (0.304) (0.304) Inverse H-Rho 0.015 0.009 0.015 0.009 (0.082) (0.081) (0.080) (0.082) Ln(sigma) −0.039+ −0.035+ −0.032 −0.042* (0.020) (0.020) (0.020) (0.020) Wald’s Chi-square 9,112.45*** 11,697.34*** 11,608.30*** 9,173.81*** Observations 1,939 1,965 1,965 1,939 Censored Observation 675 675 675 675 Uncensored Observation 1,264 1,290 1,290 1,264 Note: Standard errors in parentheses. ***p < 0.001 **p < 0.01 *p < 0.05 +p < 0.1 About the Authors Cheng-Tong Lir Wang received her PhD in sociology from University of California–Irvine and currently works as a research specialist at the department. She is interested in the interaction among globalization, social movements, and law, and how the interaction shapes organizational practices and individual behaviors. Evan Schofer is Professor of Sociology at the University of California–Irvine. His research examines global expansions of NGOs, environmentalism, and education, as well as comparative differences in political participation. Much of his work seeks to develop and extend world society theory, to better understand global patterns of social change. He received his PhD in sociology from Stanford University. References Agnes , Flavia . 1994 . “ Women’s Movement within a Secular Framework: Redefining the Agenda .” Economic and Political Weekly 29 ( 19 ): 1123 – 28 . Agnihotri , Indu , and Vina Mazumdar . 1995 . “ Changing Terms of Political Discourse: Women’s Movement in India, 1970s–1990s .” Economic and Political Weekly 30 ( 29 ): 1869 – 78 . 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They also wish to express their sincere gratitude to scholars who generously shared their data, including David John Frank, Mala Htun, Laurel Weldon, and Rachael Russell. © The Author(s) 2018. Published by Oxford University Press on behalf of the University of North Carolina at Chapel Hill. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Coming Out of the Penumbras: World Culture and Cross-National Variation in Divorce Rates
JO - Social Forces
DO - 10.1093/sf/soy070
DA - 2018-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/coming-out-of-the-penumbras-world-culture-and-cross-national-variation-0bl5igWIRJ
SP - 675
VL - 97
IS - 2
DP - DeepDyve
ER -