We argue that polygyny creates a social imbalance where few, economically well-off men marry many wives and many poor men marry late or never. By definition, polygyny produces what we refer to as “excess men.” In order to gain material wealth, excess men are likely to raid, plunder, and rob neighboring ethnic groups. We test this hypothesis with georeferenced data on polygyny and intergroup conflict in rural Africa and find strong support. Drawing on Afrobarometer survey data, we explore the underlying mechanisms and find that young men who belong to polygynous groups feel that they are treated more unequally and are readier to use violence in comparison to those belonging to monogamous groups. Our article makes an important contribution to the peace, conflict, and development literature by emphasizing a fundamental aspect of human life: marriage and family.

Social institutions have long been a focal point in the analysis and explanation of intrastate peace and conflict. While state institutions have been studied to a large extent in this context, the internal norms, traditional institutions, and customary laws of ethnic groups have not yet received much attention. Key institutions that influence the social order of ethnic groups are marriage and family. In most societies, the family is the smallest social entity that shapes the everyday life of people (Weber [1922] 1980, xvii). The family typically fulfills reproductive, social, economic, and prestige functions (Becker 1993; Murdock 1949; Hudson and Matfess 2017).

We argue in this article that the type of marriage institution practiced by an ethnic group, monogamy or polygyny, affects the likelihood of members of that group attacking neighboring groups. By definition, polygyny creates a social imbalance: while some men marry several wives, rear many children, and have large families, other men marry late in life or not at all. A common pattern is that marriage is confined to economically well-off men in the highest tiers of society, leaving economically deprived men unwed (Irons 1983; Mesquida and Wiener 1999; McDermott 2018). We refer to the latter as excess men. In traditional rural societies where social norms make a man’s reputation dependent on, among other things, the size of his family, excess men fail to meet basic criteria for attaining social prestige (Henrich, Boyd, and Richerson 2012, 657; Hudson and Matfess 2017, 12).

However, in our understanding, excess men will not accept the fate of remaining bachelors. According to Hans Morgenthau, propagation is one of the main drivers of any political action ([1948] 1985, 39). Since economic resources are key to getting married and starting a family, excess men have incentives to acquire these resources. When legitimate sources of income are unavailable or insufficient, excess men become “risk-takers” (Barash 2016, 30): crime, theft, violence, and raids become viable options. Excess men in rural areas who strive to conform to the social norms that derive from marriage and family therefore have two basic choices: to steal from, plunder, and raid one’s own group or to do the same to another group.

Since ethnic groups often function as extended families and have established mechanisms to monitor and sanction misbehavior (cf. Fearon and Laitin 1996), excess men will be more likely to raid other groups than their own. Following this reasoning, we expect that polygyny does not necessarily increase intragroup violence but rather heightens the risk of violence for neighboring ethnic groups.

In our analysis, we examine whether the extent of borders shared with polygynous ethnic groups increases a group’s risk of experiencing intergroup violence. Specifically, we create a risk profile for each ethnic group that measures the percentage of total border shared with polygynous neighbors. Building on the growing literature that analyses the long-term effect of historical institutions and politics (e.g., De Juan and Koos 2019; Wig 2016; Michalopoulos and Papaioannou 2016; Nunn and Wantchekon 2011; Nunn 2008), we rely on precolonial data on ethnic groups’ mode of marriage—which has been shown to correlate with current polygyny rates (Dalton and Leung 2014)—to predict contemporary violent conflict events between ethnic groups in rural Africa. Using a set of pretreatment exogenous geographical and historical variables that could have affected both the prevalence of polygyny and intergroup conflict (e.g., ancient wars, slave trade, and malaria prevalence), we show robust evidence that for groups with higher percentages of shared boundaries with polygynous groups, the number of violent events increases substantively, a finding which supports our hypothesis.

In a second step, we employ a pooled sample of Afrobarometer survey data to better understand the underlying mechanisms of this relationship. We are able to demonstrate that childless young men who belong to polygynous ethnic groups feel that they are treated more unequally and regard violence more frequently as a justified means to achieve their goals in comparison to their peers in monogamous groups. This lends support for our proposed mechanism, which suggests that excess men are the linkage between polygyny and intergroup violence.

In addition to our contribution to the literature on the long-term effects of historical institutions, we provide a substantially refined theoretical argument and improved empirical test to the literature on family institutions and violent conflict, an aspect that has not received adequate attention. Additionally, we complement the literature on local-level and communal conflicts (e.g., Eck 2014; Fjelde and von Uexkull 2012; Varshney 2003; Tajima 2013; Fearon and Laitin 2000).

Analysis Our outcome variable counts the number of intergroup conflict events per total ethnic group territory (Hypothesis 1a) and those within a twenty-five kilometer buffer along the group’s border to its neighboring group (Hypothesis 1b). As Figure 4 has shown, these count variables are overdispersed. Therefore, we use a negative binomial model with robust standard errors clustered at the country level and we include country fixed effects to account for unobserved heterogeneity. In Table 1, we test Hypothesis 1a using both ACLED and UCDP-GED. We focus first on the discussion of the main variables and then highlight some control variables. Columns 1 and 3 show the parsimonious models including our main explanatory variable polygynous neighbors, the polygyny status of the observed group, and land area and population size of the ethnic group.13 As expected by Hypothesis 1a, a higher share of common borders with polygynous neighbors has a positive and statistically significant effect on the likelihood of intergroup conflict. Columns 2 and 4 show our main specification, in which we control for pretreatment exogenous geographic and historical variables. Essentially, the effect of polygynous neighbors remains robust to the parsimonious model. These results lend support to Hypothesis 1a, which suggests that a higher percentage of shared borders with polygynous groups increase an observed group’s conflict risk. Table 1. Polygynous Neighboring Groups and Intergroup Conflict Events. View larger version To assess Hypothesis 1b, Table 2 shows the results when we limit our outcome variable to conflicts within a twenty-five kilometer distance from group boundaries. We argue that excess men on raids have a tactical advantage in villages in the outer regions of neighboring groups’ territories, which lie closer to their own homelands. As in Table 1, columns 1 and 3 show the parsimonious models and columns 3 and 4 our main specification. We see an essentially similar pattern at work: sharing more borders with polygynous neighboring groups increases the number of conflicts for an observed group. While the results are supportive of our general theoretical argument, the regression coefficients of polygynous neighbors are similar to Table 1. We examine the substantive effects below. Table 2. Polygynous Neighboring Groups and Intergroup Conflict Events in 50 km Buffer Zone. View larger version Apart from our main variable, a few other variables are noteworthy. For instance, in all models using the ACLED sample, we see that when an observed group is polygynous, the effect is negative and statistically significant. Monogamous groups are therefore more likely than polygynous groups to experience violent events on their territories. We did not formulate prior expectations on the effect of an observed group’s mode of marriage and can therefore only propose a tentative explanation. As an addition to our main theoretical argument, an observed group’s mode of marriage may proxy a target selection mechanism. Excess men that result from polygynous groups are easily mobilized for offensive acts but should also increase defensive capabilities of their group. However, monogamous groups should not produce excess men and thereby the pool of mobilized defenders should be smaller. These fighting capabilities may matter greatly for excess men’s strategic considerations of whom to attack. When the pool of defenders in monogamous groups is smaller than in polygynous ones, attacking excess men should rationally choose the easier target: monogamous groups. Consequently, monogamous groups should be attacked more often and thus experience higher levels of violence. Nevertheless, we believe that this aspect requires more in-depth research to be understood more comprehensively. The other control variables have the expected effect direction and significance. Land area and population size increase the number of conflict events. The indicator for slave exports has a consistent negative effect in the ACLED sample, which reflects previous findings arguing that areas affected by the slave trade and the resulting reduction of men in these societies reduced the pressure on the marriage market (Dalton and Leung 2014). Apart from the statistical significance, the results of our main models 2 and 4 in Tables 1 and 2 are substantively meaningful. We use these four models to compute the predicted number of events by varying the values of our explanatory variable polygynous neighbors from its minimum 0 to its maximum 1. Each of the panels in Figure 5 shows that an increase in shared borders with polygynous groups increases the predicted number of intergroup conflict events significantly. Moving from 0 percent to 100 percent shared border with polygynous groups increases the predicted number of intergroup conflict events by about 300% from less than two events to almost eight in panel (A). The pattern is similar in the other three panels, albeit the effect strength is somewhat smaller, in particular when using the UCDP-GED data. While the pattern holds for conflicts in border regions (Hypothesis 1b), we do not find a stronger effect and thus do not find support for Hypothesis 1b. Although the absolute number of predicted events appears small, the effects are massive in magnitude for both hypotheses. Remember that violent-event data particularly for rural areas—not so much for cities—typically suffer from underreporting bias (cf. Eck 2012; Weidmann 2016). We therefore assume to underestimate the effect. It is furthermore important to note that the substantive effect occurs not only when we compare the extremes of having no (0) to only (1) polygynous neighbors but also with more moderate in-between ranges. Download Open in new tab Download in PowerPoint In sum, we find robust support for Hypothesis 1a, which suggests that a larger share of polygynous neighbors increases the number of conflict events between ethnic groups. The effect of polygynous neighbors on conflicts in the border regions (Hypothesis 1b) is of comparable magnitude, but not stronger. Bringing this result back to our research question, we can say that polygyny is associated with higher levels of intergroup conflict.

Robustness Tests Next, we perform a number of robustness checks to minimize the risk of model and specification dependence. First, we exchange country-level fixed effects against region-level fixed effects and add spatial lags of the outcome variable on the left-hand side of the model. This model reflects our main intuition that states (i.e., country fixed effects) can be conceptualized as posttreatment variables (see King, Keohane, and Verba 1994, 182). Table A2 shows that the results remain almost identical. Importantly, our explanatory variable polygynous neighbors retains its positive effect and is highly significant both in the UCDP-GED and in the ACLED sample. Second, we take a closer look at our outcome variable and exclude events that could potentially reflect intragroup fighting and not intergroup conflict. To do so, we manually exclude UCDP-GED events in which ethnic subgroups are mentioned to fight against each other. The number of UCDP-GED events decreases by 111 from 3,085 to 2,974. In the ACLED sample, we excluded 378 events in which both conflict actors have the same name. This reduced the number of events from 4,985 to 4,607 events. We employ the same specification as in the main results presented above. The coefficient of the polygynous neighbors variable remains robust in both samples UCDP-GED and ACLED (Table A3). Third, we use different specifications for our explanatory variable. In a first step, we rely on the EA’s initial coding of the polygyny variable, which includes an intermediate category for “limited or occasional polygyny.” Table A4 in the Online Appendix shows the results when using the ACLED data (model 1) and the UCDP-GED data (model 2). There are now two border variables. The variable polygynous neighbors: limited expresses the percentage of border shared with groups that practice “limited or occasional” polygyny. The variable polygynous neighbors: general is identical to the one used in the main analysis. Model 1 shows that while the limited polygyny variable is positive and insignificant, the general polygyny variable has a stronger effect on the number of conflict events than in the main model. Model 2 uses the UCDP-GED data and shows a similar pattern. This result supports our suspicion that it is general and widely practiced polygyny that results in intergroup conflict. Fourth, in Table A5, we use state-based conflict events on the group’s territory as the outcome variable. We include these two models to demonstrate that polygyny is not related to state-based conflict as argued in our theory. We exclude the polygynous neighbors variable since this measure has no theoretical relationship or conceptual relevance to violence between an ethnic group and the state. If anything, in the spirit of Kanazawa (2009) and Gleditsch et al.’s (2011) work, we believe that polygyny can additionally create youth bulges that serve as a recruitment pool for rebel groups or government forces in civil wars. However, this does not seem to be the case since the variable observed group: polygynous is not significant. Fifth, we preprocess the data with the coarsened exact matching (CEM) algorithm to remove observations without common empirical support. Blackwell et al. (2009) have shown that CEM reduces model and specification dependence and thereby improves causal inference. The detailed approach of applying the CEM procedure is described in the Online Appendix before the presentation of the results in Table A6. The results confirm our main findings, although the CEM procedure significantly reduces our sample size. The effect of our explanatory variable polygynous neighbors is statistically significant at 1 percent. Sixth, we additionally test political (polity level and change) and economic variables (GDP growth) used in standard civil war models as well as measures for legal polygamy, polygamy provisions in customary law, and a women’s rights indicator from the data used by Gleditsch et al. (2011) on the basis of the WomanStats Project (Caprioli et al. 2009). In Table A7, we present the results which show that our explanatory variable polygynous neighbors remains statistically significant. Note that our sample is reduced from 805 to 761 observations due to missing data. These tests demonstrate that even with the inclusion of posttreatment political, economic, and legal variables, our hypothesis holds. Finally, we use the number of polygynous neighboring groups instead of the percentage of shared border with polygynous neighboring groups as explanatory variable. The number of polygynous neighbors ranges from 0 to 19 with a mean value of 4.2 and a standard deviation of 2.5. Table A8 reports the results in the same regression setup as in the main table. The results strongly support our previous findings. The number of polygynous neighbors is statistically significant at 1 percent and 5 percent (models 1 and 2) and 1 percent (models 3 and 4), respectively. The other variables retain their effect direction and statistical significance.

Mechanism: Frustration and Aggression among Excess Men In the previous section, we have performed an analysis at the ethnic group level and established a link between polygyny and intergroup conflict. Next, we provide evidence on the suggested mechanism linking polygyny and conflict between ethnic groups: the role of excess men. Specifically, we rely on individual-level evidence from a pooled sample of Afrobarometer surveys, which we matched with our polygyny measure. We have argued that polygyny produces excess men who are disadvantaged in competing as viable partners on the marriage “market,” largely because they lack the financial means to compete with better-off men. We further argued that this inability leads to frustration because the expectations and social pressure on men to start a family and produce offspring are supposedly particularly high in such traditional polygynous social environments. We should therefore observe that young men without family in polygynous ethnic groups perceive more inequality than their peers in monogamous ethnic groups where the competition on the marriage market is by definition much less fierce. Since gaining resources through stealing, raiding, and plundering in neighboring ethnic groups allows excess men to increase the financial competitiveness on the marriage “market,” we should also observe that men in polygynous societies are somewhat more inclined to accept violence as a legitimate means. We test the mechanism using data from the 2005 Afrobarometer. In this version of the Afrobarometer, Nunn and Wantchekon (2011) have matched the ethnic group names of Murdock’s EA with those respondents reported in the Afrobarometer. We match our polygyny dummy variable to the ethnic groups to distinguish between respondents who belong to monogamous or polygynous ethnic groups. The fully merged sample of the Afrobarometer includes 25,397 respondents from eighteen countries. These countries reflect a bias in the sense that these are rather stable countries (e.g., Benin, Botswana, Ghana, Kenya, Mozambique) and exclude African conflict hotspots such as the DR Congo, Sudan, South Sudan, or Chad. However, we believe that traces of our mechanisms should be independent of the political context. In order to test our assumptions, we limit the Afrobarometer sample to those we believe are most severely affected, young men below 40 years without children (excess men).14 Furthermore, we employed placebo tests on two further samples where we would not expect a similar effect: (a) for men above 40 years and (b) for women. We have identified two questions in the Afrobarometer that tap into aspects that we believe are indicative of a frustration–aggression mechanism, which we propose to link to polygyny and conflict. These questions reflect perceptions of inequality and a justification to use violence. In Figure 6, we show the predicted values for the two questions depending on whether a respondent belongs to a polygynous group (1) or not (0).15 The underlying regression models are presented in Table A9 in the Online Appendix.16 The left panels show the results for men below 40 years without children (excess men), the central panels for men above 40 years, and the right panels for women. The upper row shows the predicted values for the degree to which people feel treated unequally under the law (Q53D).17 Higher values indicate higher perceptions of inequality. Young childless men in polygynous societies are significantly more likely to report higher perceived inequality than their peers belonging to monogamous groups. For men above 40 years, we do not find this effect. Women in polygynous groups also report perceptions of inequality, which we believe resonates with the notion of gender inequality associated with polygyny (cf. McDermott and Cowden 2018; Hudson et al. 2010). Download Open in new tab Download in PowerPoint The lower row shows the results for the question on whether people see the use of violence as a justified means for their cause (Q51), where higher values indicate agreement. While young men below 40 years without children (excess men) are significantly more likely to view violence as justified, our placebo groups of men above 40 years and women do not share this perspective. This result supports our initial argument that excess men are an important link between polygyny and conflict and not a broader societal disposition toward violence. For males above the age of 40, we do not find any statistically significant difference in the two survey items between polygynous and monogamous groups. Next to other possible explanations, such as age, this could be argued to add to the self-sustainability of polygyny as a sticky institution. Thereby, one could speculate that polygynous males will likely be more supportive of transmitting the marriage institution of polygyny to the next generation (McDermott and Cowden 2018; Hudson 2018). In sum, the results provide individual-level evidence that excess men hold views compatible with a disposition to theft, crime, and raids. These results provide suggestive support for our mechanisms that young, childless men in polygynous societies are under social pressure to perform. They perceive this pressure as unequal and unfair and that, more broadly, they evince a greater readiness to exert to violence than their peers in monogamous groups. We acknowledge that these questions are very general and do not really point toward violence against neighboring ethnic groups. However, given the data constraints and the usual noise in survey data, we believe these findings lend additional powerful evidence to our theoretical argument that polygyny produces excess men which in turn contribute to intergroup violence experienced between neighboring ethnic groups.

Conclusion We have argued that, by definition, polygyny creates a social imbalance where a few, usually well-off, men marry many wives and many, usually poor, men marry late or never. Polygyny therefore systematically creates a surplus of young, poor, unmarried men: excess men. Since marriage, family, and offspring are often the social metrics according to which the value of a man is assessed in traditional societies, excess men seek alternatives to become viable mates. In traditional and particularly rural settings, the ethnic group is perceived as the extended family, which leads excess men to abstain from turning against their kin; however, they have an incentive to pursue violent economic ventures (e.g., theft, crime, raids) in neighboring groups. From a security perspective, polygyny is not a problem for the polygynous group itself but rather for its neighbors. Being surrounded by many polygynous groups increases the risk of intergroup violence. To examine this theory, we have applied georeferenced data on polygyny for more than 800 African ethnic groups and combined these with violent-event data. We have used a set of exogenous geographic and precolonial controls and report a strong effect of polygyny on conflict—that is, we find robust support for this theory. By exploiting Afrobarometer survey data, we find additional individual-level evidence for our proposed underlying mechanism that respondents who belong to polygynous ethnic groups hold more problematic views on perceived fairness, the obedience to the rule of law, and the readiness to use violence. Our article contributes to several research strands and should therefore be of interest to a wide audience of scholars. First of all, it emphasizes the importance of the institutions of marriage and family and their role in social order, peace, and conflict. Our study substantially improves the only two existing quantitative studies with conflicting findings on polygyny and conflict in several ways. We view polygyny as a local dynamic that affects local violence. We also use more coherent, expansive, and reliable data and engage in elaborate reliability tests using alternative data sources. We provide a more sensible model specification, taking into account the geographic and historical determinants that have affected the emergence of polygyny in the first place. Second, our article contributes to the small but growing literature on low-intensity community-level conflict (e.g., Eck 2014; Varshney 2003; Fjelde and von Uexkull 2012; Raleigh 2010). This research strand has significantly increased our understanding of conflict processes worldwide, also because local violent events frequently spur larger conflicts (Brass 1997). Due to this, we stress that the type of conflict analyzed should be carefully chosen on the basis of theoretical considerations. We disagree, for instance, that large-scale civil wars are driven by polygyny as a “first law” (Kanazawa 2009). Third, this study contributes to the literature on the long-term effects of historical institutions. As we have theorized and demonstrated, polygyny can be understood as a “sticky” institution that is still at work in today’s societies in Africa (Dalton and Leung 2014). In this sense, our article adheres to the spirit of recent seminal studies examining the long-term effects of traditional institutions (Wig 2016; Michalopoulos and Papaioannou 2013), precolonial nation states (De Juan and Koos 2019), the slave trade (Nunn 2008; Nunn and Wantchekon 2011), the Berlin Conference (Michalopoulos and Papaioannou 2016), and ancient wars (Besley and Reynal-Querol 2014). With regard to its practical relevance, our article provides insights for humanitarian and development agencies. We are aware that polygyny does not explain all conflicts, but we believe that our study provides systematic support for anecdotal examples where polygyny and conflict are widespread—for instance, in South Sudan, the DR Congo (Verweijen 2017), Nigeria, and even Western countries (Rauch 2006). While our analysis focuses on Africa, we believe that the operating principles and societal implications of polygyny are—with few exceptions—universally problematic as they create a cohort of society that has always been associated with trouble around the world: excess men.

Acknowledgments We would like to thank Kristian Skrede Gleditsch, Katharina Holzinger, Valerie M. Hudson, Tobias Ide, Alexander de Juan, and Stephen Nemeth, four anonymous reviewers, as well as participants at the annual meetings of American Political Science Association (APSA 2017), the European Political Science Association, the German Political Science Association (DVPW 2017), the International Studies Association (ISA 2018), and a research colloquium at the University of Essex for their helpful comments. All errors are our own.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support from the German Research Foundation (DFG Grants HO 1811/10-1 and KO 5170/1-1) is gratefully acknowledged. ORCID iD

Carlo Koos https://orcid.org/0000-0002-1259-7495 Supplemental Material

Supplemental material for this article is available online.

Notes 1.

Gleditsch et al. (2011) furthermore substantiate the nonreplicability by arguing that misogyny, rather than polygyny, is the mechanism driving political violence, which they test at the state level. Although a broader set of gender-based discrimination may be a source of political violence (see, e.g., Melander 2005; Hudson et al. 2010), we do not believe that polygyny—which can be regarded as part of misogynistic practices—and a general concept of misogyny adhere to the same underlying mechanism with regard to local conflict. In other words, misogyny and polygyny cannot be seen as competing hypotheses when studying intergroup conflicts at the local level. 2.

Another explanation of the emergence of polygyny is economic, that is, as the result of income inequality and female subsistence contributions (see White and Burton 1988, 872, for an overview). 3.

Dalton and Leung (2014) arrive at their results by analyzing forty-five DHS surveys in twenty-five African countries between 1990 and 2010. They use a question which asks married female respondents whether they have a co-wife to calculate current polygyny rates at the ethnic group level specified by Nunn’s and Wantchekon’s (2011) EA map. 4.

This does not mean that these marriages are voluntary unions. For a discussion on female choice in polygynous societies, see the third and fourth chapter of Barash (2016). 5.

For a comparative assessment of socioeconomic inequality, hierarchy, and polygyny, see Betzig (1986). 6.

For the Kisama and Bomvana, there are two entries (name=KISAMA and v107=BOMVANA). 7.

For a description of the coding of the variable, see Online Appendix Section 2. 8.

We provide robustness checks using the three-scale variable (monogamy, limited polygyny, and general polygyny) in Table A4 in the Online Appendix. The results are robust. 9.

To produce the maps, we used the georeferenced group borders used in Nunn and Wantchekon (2011). These are available at http://scholar.harvard.edu/files/nunn/files/murdock_shapefile.zip. 10.

If a group borders the sea, a lake, or uninhabited territory (i.e., West Saharan Desert, Libyan Desert), we exclude these border segments from the denominator. 11.

One favorable feature of ACLED is that its categories—in particular communal militia activity—resonate well with our hypothesized effect on local intergroup violence. Relatedly, ACLED also includes nonlethal violent events, which also speaks to our theory. Adversely, ACLED has been criticized to incorporate an urban bias (Eck 2012, 132), a problem we can address as we exclude events in urban areas. Conversely, UCDP-GED is argued to be superior to ACLED because the media sources of UCDP-GED are more consistent by focusing on major international media outlets. 12.

Specifically, for the ACLED sample, we use the INTERACTION variable, which classifies each event according to prespecified actor interactions. We are interested only in those events which involve ethnic militias but exclude any political or rebel-based organizations. We use only the following interaction values: 40—sole communal militia action, 44—communal militia versus communal militia, 47—communal militia versus civilians. Furthermore, we restrict our sample to events where (1) the spatial location quality (GEO_PRECIS) is exact or (2) part of a region. We exclude (3) less precise events. From the UCDP-GED, we include only events which have been categorized as nonstate conflict (type_of_violence = 2) and events whose location (where_precise) was either (1) exactly identified or (2) identified within a twenty-five kilometer radius [“region” = “Africa” AND “type_of_violence” = 2 AND (“where_precise” = 1 OR “where_precise” = 2)]. 13.

All tables are produced using estout (Jann 2005, 2007). 14.

Unfortunately, these Afrobarometer surveys do not include questions on whether a respondent is married, but we believe that having no children is a reasonable indicator of being married or not in many African societies. 15.

Figure A1 shows the first differences between members of polygynous and monogamous groups, and the results are robust to those in Figure 6. 16.

We use a linear model with robust standard errors and control for age, age2, education, assets, and a dummy for urban/rural residence. 17.

Afrobarometer Merged Round 3 Codebook, 2005, http://www.afrobarometer.org/data/merged-round-3-codebook-18-countries-2005.