# Women's Position in Ancestral Societies and Female HIV: The Long-Term Effect of Matrilineality in Sub-Saharan Africa

Jordan Loper

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Jordan Loper. Women's Position in Ancestral Societies and Female HIV: The Long-Term Effect of Matrilineality in Sub-Saharan Africa. 2026. hal-05509052

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# Women's Position in Ancestral Societies and Female HIV: The Long-Term Effect of Matrilineality in Sub-Saharan Africa *

Jordan Loper†

February 13, 2026

## Abstract

Can contemporary female HIV rates be traced back to women's position in ancestral societies? In matrilineal kinship organizations, lineage and inheritance are traced through female members, granting women historically greater agency while reducing the structural stability of the marital bond. This paper tests the hypothesis that ancestral matrilineality, by shaping women's sexual and contraceptive behaviors, significantly impacts the prevalence of female HIV today. By exploiting individual-level variation in ethnic identity across 18 countries, and using granular region-survey fixed effects, I find that women from ancestrally matrilineal ethnic groups face a significantly higher probability of being HIV-positive. This "matrilineal penalty" is confirmed by a geographic regression discontinuity design (RDD) at ethnic boundaries, ensuring the results reflect persistent social structures rather than geographic confounders. I identify three primary behavioral mechanisms: (i) increased female agency leading to greater sexual mobility and marital turnover; (ii) a coordination failure within the couple regarding condom use due to the fragility of the marital bond; and (iii) biological evidence from couple sero-discordance showing that the virus primarily enters the household through the female partner. These results suggest that female agency, in the absence of formal diagnostic information, may increase vulnerability to health shocks. My findings call for public health policies that prioritize routine HIV testing to bridge the information gap and convert latent ancestral agency into active health protection.

**JEL codes:** D13, I15, J12, J16, Z13

**Keywords:** Kinship systems, Matrilineality, Female Agency, Coordination failure, HIV, Cultural persistence.

<footnote>*I thank Alberto Alesina, Isaiah Andrews, Duman Bahrami-Rad, Jean-Marie Baland, Anke Becker, Nicolas Berman, Mathieu Couttenier, Melissa Dell, Yannick Dupraz, Victor Gay, Pauline Grosjean, Catherine Guirkinger, Joseph Henrich, Leda Inga Charaja, Asim Khwaja, Masahiro Kubo, Jacob Moscona, Èric Roca Fernández, Lorenzo Rotunno, Mohamed Saleh, Marc Sangnier, Heather Sarsons, Jonathan Schulz, Paul Seabright, Liyang Sun, Michèle Tertilt, Edoardo Teso, Alessandra Voena, Romain Wacziarg and participants at various seminars and conferences for helpful comments and suggestions. I also especially thank Simone Bertoli, Nathan Nunn and Roberta Ziparo for their warm support and guidance. I warmly thank Lucas Fages for excellent research assistance. Part of this paper was written while I was visiting Harvard University and I thank them for their hospitality. This work was supported by the *Agence Nationale de la Recherche* of the French government "France 2030" (grant number ANR-16-IDEX-0001). The usual disclaimers apply.</footnote>

<footnote>†Université Clermont Auvergne, CNRS, IRD, CERDI. Email: jordan.loper@uca.fr</footnote>
# 1 Introduction

While Sub-Saharan Africa remains the epicenter of the global HIV epidemic, the most striking feature of the crisis in this region is the “feminization” of the disease. In 2018, nearly 18.8 million women were living with HIV worldwide, with Sub-Saharan Africa accounting for approximately 80% of this population (UNAIDS, 2018). Strikingly, it is the only region where women are significantly more affected than men, being 1.5 times more likely to be infected. While recent research has explored the influence of legal systems (Anderson, 2018) or historical shocks such as the slave trade (Bertocchi and Dimico, 2019) to explain this gender gap, the role of deep-seated cultural institutions in shaping the variation of female HIV rates remains largely overlooked.

This paper investigates whether contemporary female HIV prevalence can be traced back to women's position in ancestral societies. Following the literature on the long-term persistence of cultural traits (Giuliano and Nunn, 2021; Enke, 2019), I focus on ancestral matrilineal kinship organizations—a fundamental social institution in Africa that historically determines lineage, inheritance, and women's status within the household. In matrilineal structures, lineage is traced through female members, and women traditionally benefit from greater autonomy and stronger “outside options” compared to their patrilineal counterparts (Lowes, 2023). However, I document a “matrilineal penalty”: females originating from these groups are significantly more likely to be HIV-positive today. This finding presents a striking paradox: why does an ancestral institution that empowers women lead to worse health outcomes in the face of a modern epidemic?

To resolve this paradox, I conduct an empirical analysis leveraging georeferenced individual data from 32 *Demographic and Health Surveys* (DHS) conducted across 18 Sub-Saharan African countries. These surveys provide a critical advantage for identifying health outcomes: they include standardized blood tests to determine HIV status, rather than relying on self-reported information, which significantly minimizes measurement error in the dependent variable. Crucially, the DHS also provides granular data on sexual and contraceptive behaviors—including partnership history, and contraceptive use and decision-making,—which allows me to precisely map the behavioral channels through which ancestral norms operate. I link these contemporary individual records with ancestral ethnic traits from the *Ethnographic Atlas* (Murdock, 1967), yielding a final sample of about 150,000 women.

My identification strategy relies on a multi-pronged approach designed to isolate the “matrilineal penalty” from geographic or institutional confounders. First, I employ an OLS framework with subnational region-survey fixed effects. By comparing individuals from different ethnic backgrounds who reside within the same administrative unit, I effectively control for local infrastructure and regional development. Crucially, I link individuals to their ancestral kinship using self-reported ethnicity. I argue that self-identification is a superior measure of internalized cultural norms, particularly in the African context of high internal migration where individuals often carry their ancestral heritage across ethnic boundaries (Bertoli et al., 2025).

The robustness of this OLS framework is confirmed through a series of rigorous checks. The matrilineal penalty remains remarkably stable once accounting for region-survey fixed effects, even after saturating the model with exhaustive individual, geographic, and ancestral controls. To address selection on unobservables, I perform a bounding exercise (Oster, 2017) focusing on within-region variation, which confirms that the estimated effect is unlikely to be driven by omitted variable bias. Furthermore, I examine the potential for selection bias arising from non-
random participation in HIV testing. Finding that matrilineal individuals are slightly less likely to undergo testing, I argue that my OLS estimates likely constitute a conservative lower bound of the true institutional effect. Finally, placebo tests on health outcomes unrelated to sexual behavior—such as anemia and BMI—ensure that the penalty is specific to HIV-related risks rather than a general health disadvantage.

To further ensure that the observed penalty is not an artifact of unobserved geographic factors, I implement a geographic regression discontinuity design (RDD) at ancestral ethnic borders, which I saturate with ethnic-pair fixed effects (Moscona et al., 2020). By comparing households living in close proximity within the same ethnic-pair buffer but on opposite sides of the ancestral boundary, I effectively hold constant all unobserved geographic, environmental, and ecological factors specific to each local border area. Under the assumption that unobservables vary smoothly across space, this RDD approach ensures that the documented “matrilineal penalty” reflects the persistent influence of ancestral social structures rather than unobserved geographic artifacts. The robustness of these findings is further supported by a series of checks on the functional form of the RDD design, including the use of flexible quadratic polynomials and fuzzy RDD specifications to account for potential imperfect compliance at the boundaries. Finally, these results are corroborated by a non-parametric nearest-neighbor matching (NNM) exercise on village-level geographic observables.

While this study focuses primarily on the female population, I further explore the reach of this institutional legacy by contrasting results across genders, regions, and legal contexts. I find that the matrilineal penalty is present for both sexes, although it is more robustly and precisely estimated for females. Crucially, the effect exhibits significant regional heterogeneity: it is primarily driven by groups in East and Southern Africa, where the historical “Matrilineal Belt” coincides with higher baseline epidemiological pressure. Furthermore, I demonstrate that the penalty is a distinct driver of the epidemic that interacts with, but is not a proxy for, colonial legal legacies (Anderson, 2018). Notably, the effect is magnified in Common Law countries, suggesting a “double burden” where ancestral norms and modern legal disadvantages reinforce women’s vulnerability. Finally, I show that the penalty persists independently of other marriage market configurations, such as polygyny or the legacy of slave trade intensity (Bertocchi and Dimico, 2019; Teso, 2019).

The final part of the paper explores the behavioral channels through which ancestral kinship norms translate into contemporary health outcomes. Guided by a conceptual framework of household bargaining under asymmetric information, I first rule out several alternative explanations. I show that the matrilineal penalty is not driven by differences in HIV awareness, access to protection, sexual debuts, or fertility patterns (Oster, 2005; Case and Paxson, 2013). Instead, the results reveal that the penalty is rooted in the specific incentive structures and spousal bargaining dynamics inherent to matrilineal kinship.

First, I document how matrilineality shapes women’s “outside options” and their subsequent sexual mobility (Lowes, 2023). Consistent with the “matrilineal puzzle,” I find that matrilineal women have a higher likelihood of marital dissolution and reside more frequently with their own relative. Using the number of distinct biological fathers for a woman’s children as an objective proxy for sexual network density, I show that this autonomy translates into a higher turnover of sexual partners. Furthermore, I find that matrilineal women exhibit greater mobility in the
marriage market, with a higher probability of inter-ethnic and inter-norm unions, suggesting an
institutional leakage that spreads behavioral risks across traditional boundaries.

Second, I identify a striking “coordination failure” regarding contraceptive choices. While
matrilineal women exhibit higher perceived agency and are more likely to justify negotiating safe
sex, this does not translate into safer outcomes: they are actually less likely to use condoms
(Islam et al., 2009; Chimbiri, 2007). This coordination gap is fundamentally moderated by the
salience of health information rather than a lack of awareness (Oster, 2012a). I find that the
negative effect of matrilineality on condom use only reverses for women who are aware of their
own HIV-positive status. From a policy perspective, this underscores the critical role of routine
HIV testing (Thornton, 2008; Paula et al., 2014; Delavande and Kohler, 2015). By converting
uncertain risks into certain information, testing acts as a catalyst that allows matrilineal women
to transform their latent agency into active protection.

Finally, I use couple-level biological data to pinpoint the primary infection route. I find that the matrilineal penalty is uniquely driven by a higher probability of the “Wife positive / Husband negative” configuration. This “female-first” transmission pattern confirms that the virus enters the household primarily through the autonomous networks and increased mobility inherent to matrilineal structures, rather than through traditional channels of intra-marital vulnerability.

This paper makes a three-fold contribution to the literature. First, I add to the growing
research on the long-term persistence of ancestral institutions (Alesina et al., 2013; Giuliano and
Nunn, 2021). While previous studies have primarily explored the historical roots of trust (Nunn
and Wantchekon, 2011), conflict (Moscona et al., 2020), and economic development (Michalopou-
los and Papaioannou, 2013), I extend this framework to the field of public health (Lowes and
Montero, 2021). By demonstrating that deep-seated kinship structures remain a first-order de-
terminant of contemporary epidemic outcomes, I provide evidence that institutional heritage can
shape the trajectory of modern health crises.

Second, I contribute to the literature on the historical (Anderson, 2018; Bertocchi and Dimico,
2019; Cagé and Rueda, 2020) and behavioral (Oster, 2005, 2012b; Corno and De Walque, 2012;
Case and Paxson, 2013) determinants of the HIV epidemic in Sub-Saharan Africa. By identifying
ancestral matrilineality as a key factor, I offer a novel institutional explanation for the geographic
and gendered variation of the disease. This work complements existing theories focused on colonial
legal origins or the legacy of the slave trade, while showing that the “matrilineal penalty” persists
as a distinct and powerful force. Specifically, my finding of a “double burden” in Common
Law countries provides a more nuanced understanding of how ancestral and colonial institutions
interact to shape contemporary health vulnerabilities.

Third, I bridge these fields with the literature on the effect of ethnic norms on female agency
and household bargaining (Lowes and Nunn, 2018; Ashraf et al., 2020; Bargain et al., 2024; Becker,
2025). My work nuances the traditional view that women's empowerment is unconditionally ben-
eficial for health. I show that the increased autonomy historically granted to matrilineal women
can lead to coordination failures regarding joint health protection. By demonstrating that this
penalty only reverses when health information is certain, I highlight a critical trade-off: in the ab-
sence of formal diagnostic information, structural autonomy can paradoxically increase exposure
to health shocks (Thornton, 2008; Paula et al., 2014; Björkman Nyqvist et al., 2018). This finding
challenges conventional wisdom on the relationship between gender norms and health, suggesting
that the benefits of agency are conditional on the resolution of information asymmetries.

The rest of the paper is organized as follows. Section 2 provides the historical context regarding matrilineal kinship in Africa, describes the regional and gendered dimensions of the HIV epidemic, and develops the conceptual framework of household bargaining under asymmetric information. Section 3 details the data sources and the construction of the analytical sample through ethnic matching. Section 4 outlines the primary empirical strategy based on individual ethnic identity and describes the geographic RDD used for validation. Section 5 presents the main findings, while Section 6 investigates the underlying behavioral mechanisms. Finally, Section 7 concludes and discusses the policy implications of the results.

# 2 Context and Conceptual Framework

## 2.1 Ancestral Kinship and the Matrilineal Puzzle

Kinship systems in Sub-Saharan Africa are not merely ancestral relics; they remain the enduring institutional bedrock of social organization, fundamentally determining land tenure, access to communal safety nets, and the intra-household distribution of power (Platteau, 2015; Moscona et al., 2020). While the majority of the continent's ethnic groups are patrilineal—tracing descent and property through the male line—a significant minority belong to the “Matrilineal Belt.” As illustrated in Figure 1a, this geographic band stretches from the Atlantic coast of Angola through the interior of Zambia and Malawi to the Indian Ocean, and is characterized by descent and inheritance through female members.

In these matrilineal systems, a woman's identity and her children's lineage are rooted in her own natal kin group, the *matriclan*. This creates what is classically termed the “matrilineal puzzle”: the inherent structural tension between a woman's role as a wife and her primary loyalty to her maternal uncles and brothers (Fox, 1934; Fortunato, 2012). Unlike in patrilineal societies, where a husband's authority is consolidated by his control over the lineage's future, a matrilineal husband traditionally lacks ultimate authority over his children. Instead, that role is held by the mother's kin, as the children are integrated into her line rather than the father's. These kinship structures are not static but continue to shape modern economic behavior and the transmission of social norms (Bau, Bau; Lowes, 2023).

Because a matrilineal woman remains a core member of her natal kin group throughout her life, she retains a lifelong claim to her family's land and the communal protection of her relatives (La Ferrara, 2007). She enters marriage with a significantly higher “outside option” or fallback position; if a partnership becomes conflictual, she has a recognized path to return to her matriclan without forfeiting her status or her children. These ancestral rules have persistent effects on female empowerment today, as matrilineal women exhibit greater autonomy in household decision-making and have shifted preferences regarding domestic gender roles (Lowes, 2023; Tène, 2026).

However, the expression of these norms is not uniform across the continent. In West Africa, matrilineal systems have historically undergone longer periods of hybridization with patrilineal religious (notably Islamic) and colonial legal frameworks, potentially leading to a more “diluted” impact on modern bargaining power. In contrast, the matrilineal systems of East and Southern Africa have often retained more distinct institutional lineages, maintaining a sharper contrast with neighboring patrilineal structures. I argue that this ancestral endowment of agency, and
its varying degrees of persistence, constitutes the fundamental institutional link to contemporary
health outcomes.

## 2.2 The HIV Epidemic in Africa: Regional and Gendered Dimensions

The HIV/AIDS epidemic in Sub-Saharan Africa remains one of the most significant impediments to human capital development and generational welfare. However, the trajectory of the virus is remarkably heterogeneous across the continent, shaped by an intricate interplay of geography and social structure. Regional transmission dynamics often distinguish between “four epidemics,” noting that West and Central Africa have historically seen lower prevalence compared to the hyper-endemic, generalized epidemics of the East and South (UNAIDS, 2018). This regional variation, visualized in Figure 1b, provides a critical scope condition for our analysis: in environments where the virus is concentrated in high-risk groups, the background risk for the general population may remain below the threshold where marginal changes in household bargaining significantly alter infection probabilities. Conversely, in the generalized epidemics of the East and South, the virus circulates widely, making the nuances of intra-household coordination a matter of life and death.

Within these regional contexts, the epidemic is profoundly gendered. Sub-Saharan Africa remains the only region globally where women are significantly more affected than men, being 1.5 times more likely to be HIV-positive (UNAIDS, 2018). This disparity is not merely a biological artifact of transmission efficiency; it is a manifestation of a deeper vulnerability gap created by the intersection of poverty, biological risk, and social standing. In many traditional settings, women face a dual constraint: they are biologically more susceptible to infection during heterosexual contact, yet they often lack the socio-economic agency to negotiate safe sexual practices or to demand marital exclusivity (Jewkes et al., 2010).

This gendered landscape is where the matrilineal institution enters the narrative. While early public health interventions focused on individual knowledge and risk perception, recent economic research has shifted toward the structural drivers of the gender gap (Anderson, 2018). I argue that the matrilineal “endowment” of agency—the increased bargaining power and outside options described earlier—fundamentally alters how women navigate this landscape of risk. Paradoxically, the very autonomy that empowers matrilineal women in the domestic sphere may lead to a breakdown in protective coordination within the marriage, particularly in high-prevalence regions where the margin for error is non-existent. By investigating the overlap between the “Matrilineal Belt” (Figure 1a) and these hyper-endemic zones (Figure 1b), I seek to uncover whether the specific behavioral incentives of these female-centric social structures contribute to the persistent gendered dimensions of the African epidemic.
<figure><img src="image_2.png" /><figcaption>Figure 1: Matrilineal Belt and Female HIV Rates</figcaption></figure>

*Notes*: The left panel displays the Matrilineal Belt based on Murdock's map. The right panel displays contemporary female HIV rates for the final sample.

## 2.3 Conceptual Framework

I propose an economic framework based on household bargaining under asymmetric information to bridge the gap between ancestral kinship and modern infection rates.

**Marital Agency and the Asymmetry of Interests.** Matrilineality serves as a structural endowment that shifts the household *threat point*. While a stronger outside option allows women to negotiate greater autonomy, this increased agency can lead to a divergence of spousal interests (Chiappori and Mazzocco, 2017). In patrilineal settings, strict social norms often enforce a “co-operative” equilibrium focused on the husband’s lineage. In matrilineal settings, where children belong to the mother’s line, the marital bond is often structurally more fragile and the husband’s incentive to invest in marital exclusivity is lower. This reduced marital stability facilitates higher mobility and a potential diversification of sexual partnerships, acting as a primary vector for HIV transmission.

*Prediction 1: Matrilineality increases female agency, which, in the absence of strong marital co-ordination incentives, leads to a higher likelihood of extra-marital infection.*

**Joint-Production and Coordination Failures.** Barrier protection (condoms) is conceptualized as a *joint-production* technology that requires synchronization and bilateral consent between partners. In this context, kinship structure plays a decisive role in the efficiency of household coordination. Individuals from matrilineal cultures exhibit significantly lower levels of cooperation with their spouses compared to those from patrilineal cultures (Lowes, 2023). Further-
more, intra-household coordination may be fundamentally limited by the existence of “gendered spheres” of responsibility and specialized knowledge (Ashraf et al., 2026). Following the logic of non-cooperative household models, if matrilineality fosters an equilibrium where interests are less aligned, coordination on “joint” technologies may fail—even if women possess the individual agency to request them.

*Prediction 2: The matrilineal HIV penalty is driven by a coordination failure in cooperative protection within fragile marital contracts, rather than by differences in individual health knowledge or female agency.*

**Information Asymmetry and Strategic Testing Refusal.** The strategic value of health information is central to this mechanism. Within autonomous partnerships where marital bonds are structurally less binding, the disclosure of a health shock may lead to a reorganization of household resources that is detrimental to the infected party. Spouses may strategically withhold information or avoid testing to maintain bargaining power (Thornton, 2008; Buchmann et al., 2025). This can lead to “rational ignorance,” where individuals strategically avoid formal HIV testing to maintain their social and sexual mobility.

*Prediction 3: The disparity is exacerbated by a strategic avoidance of formal health status information, allowing for asymptomatic transmission within autonomous networks.*

**The Interaction of Institution and Environment.** Finally, the conceptual framework implies that the institutional effects of matrilineality are not constant but are conditional on the external environment. The behavioral shifts described above (Predictions 1-3) require a high background prevalence to translate into differential infection rates. Furthermore, the intensity of the matrilineal institution itself varies across regions.

*Prediction 4: The matrilineal HIV penalty is more pronounced in regions where the matrilineal institution is historically more intensive and the HIV epidemic has reached a generalized status (East and Southern Africa), compared to regions with hybridized kinship norms and concentrated epidemics (West and Central Africa).*

While these institutional dynamics define the overarching environment of a community, their impact on health outcomes exhibits a gendered gradient. In this paper, I focus the behavioral analysis primarily on females for two fundamental reasons. First, matrilineal institutions are inherently centered on the female lineage, making females the primary subjects of the kinship obligations and bargaining dynamics under study. Second, while these institutional shifts alter the broader epidemiological landscape for the entire community—potentially leading to increased HIV prevalence among males through shared sexual networks—the underlying behavioral mechanisms exert a more direct and robust influence on the risk profiles of females. By isolating the effects on the female population, I can more precisely identify the internalized behavioral channels through which these ancestral rules persist today.
# 3 Data

## 3.1 Contemporary Health and Behavioral Outcomes: The DHS

Modern outcomes and individual-level characteristics are drawn from the Demographic and Health Surveys (DHS).<fnref n="1" /> The strength of the DHS lies in its standardized, high-quality data collection, designed to be representative at both the national and sub-national levels. A significant improvement over self-reported health data is the provision of laboratory-confirmed HIV biomarker results, which provide an objective measure of infection free from social desirability or recall bias.

Beyond objective health status, I leverage the detailed behavioral modules of the DHS to examine the transmission channels of the epidemic. These modules provide information on sexual and contraceptive behavior—such as infidelity, number of extramarital affairs, condom use—as well as subjective dimensions of risk management and female agency. Crucially, the DHS also records the self-reported ethnic identity of each respondent. This information is the primary pillar of my identification strategy, as it allows me to link individuals directly to the specific kinship norms of their ancestral group, regardless of their current place of residence.

Furthermore, the DHS provides a rich set of contemporary control variables at both the individual and household levels. In my baseline specifications, I account for standard socio-economic factors—such as age, education, wealth, and urban-rural residence—as well as religious affiliation, which is a key determinant of both sexual norms and institutional life in Sub-Saharan Africa.

Finally, the surveys provide the GPS coordinates for each survey cluster. In the main analysis, these coordinates are used to incorporate the geographic controls provided by the DHS to account for broad spatial trends. In subsequent robustness checks, I further exploit this spatial granularity to construct additional environmental controls and to implement a geographic-based assignment of kinship norms. This allows for spatial identification strategies, such as the Geographic RDD, which ensure that the results are not driven by unobserved local characteristics or potential measurement errors in self-reported identity.

## 3.2 Ancestral Institutions: The Ethnographic Atlas (EA)

The historical foundation of the study is the *Ethnographic Atlas* (Murdock, 1967), which provides a snapshot of the “pre-contact” institutional configurations of 1,267 ancestral ethnic groups worldwide. The primary treatment variable is ancestral matrilineality, identified through variable v43 (*Descent: Major Type*). I classify an ancestral ethnic group as matrilineal if descent is traced exclusively through the female line (Value 3). Conversely, an ancestral ethnic group is defined as patrilineal if descent is traced through the male line (Value 1).

It is important to be transparent about the inherent limitations of these ethnographic data. First, the *Ethnographic Atlas* provides a cross-sectional view of institutions that were themselves subject to historical change; the “pre-contact” state is an ethnographic reconstruction that may overlook internal societal evolution. Second, there is a recognized risk of systematic misclassifi-

<footnote n="1">¹I also incorporate data from the 2011 *AIDS Indicator Survey* (AIS) for Uganda. Both the DHS and AIS follow identical sampling designs and standardized protocols for HIV blood testing, ensuring full comparability across the pooled sample.</footnote>
cation in the *Ethnographic Atlas*, as many societies historically governed by matrilineal descent may have been recorded as patrilineal or bilateral due to the transformative influence of world religions or colonial legal frameworks prior to the ethnographic recording. Furthermore, the categorization of a group as purely matrilineal can obscure bilateral nuances or regional variations within a single ethnic group.

Given these constraints, I follow a conservative identification strategy by focusing exclusively on the distinction between matrilineal and patrilineal descent systems. I exclude groups coded as duolateral (Value 2), quasi-lineage (Value 4), ambilineal (Value 5), bilateral (Value 6), or mixed (Value 7), as these categories are frequently the product of institutional erosion or measurement noise. In Section 5, I demonstrate that including these intermediate categories in the reference group leads to a significant attenuation bias, confirming that the institutional signal is strongest when comparing pure unilineal systems.

Beyond the primary treatment, I utilize the *Ethnographic Atlas* to construct a set of ancestral control variables aimed at isolating the effect of kinship from other historical confounders. Accounting for these traits is essential, as the evolution of descent rules is historically intertwined with specific modes of production and social organizations that independently influence long-run health trajectories. Specifically, by controlling for ancestral polygyny, pastoralism, and the ethnographic year of observation, I account for the social structures governing sexual networks, the economic endowments that shape the gendered division of labor, and potential temporal heterogeneity in the recording of ethnographic data.<fnref n="2" /> Despite the granular limitations of the *Ethnographic Atlas*, it remains the most authoritative source for mapping institutional “endowments”. Recent research has cross-validated the *Ethnographic Atlas’s* data using contemporary survey responses and genetic data, confirming that these historical traits remain remarkably robust markers of cultural identity today (Bahrami-Rad et al., 2021).

## 3.3 The Ethnographic Atlas-DHS Matching Procedure

The core of the empirical strategy lies in linking modern DHS respondents to their ancestral societies. This procedure follows a hierarchical matching logic to ensure the highest possible degree of ethnic concordance. I first follow the mapping established by (Michalopoulos et al., 2019), which provides the baseline concordance for the majority of individuals in the sample. I then supplement this with the matching procedure from (Teso, 2019). Finally, for the remaining unmatched individuals, I perform an original matching using alternative ethnic nomenclature and linguistic synonyms from the *Joshua Project* and regional historical records.<fnref n="3" />

<footnote n="2">²The indicator of Ancestral polygyny is identified through variable v8 (*Domestic Organization*). I classify an ancestral ethnic group as practicing polygyny if domestic organization is “independent nuclear family, polygyny” (Value 2), “polygynous: unusual co-wives” (Value 4), or “polygynous: usual co-wives” (Value 5). The index of ancestral pastoralism is computed following the formula in Becker (2025), interacting a categorical variable based on the intensity of a group’s dependence on animal husbandry (variable v4) with an indicator of the presence of herding of large animals (variable v40). Finally, I include the year of ethnographic observation (variable v102) to account for the temporal variation in when these societies were documented.</footnote>

<footnote n="3">³For instance, in the Democratic Republic of Congo (DRC), where some respondents report geographic regions as their ethnicity, I follow Shapiro and Tambashe (2003) to attribute the dominant ancestral group of the region (i.e. *Mongo* for Cuvette Centrale, and *Ngombe* for Ubangi and Itimbiri). However, I am unable to properly identify a unique *Ethnographic Atlas* group for the Uele-Albert region, as several societies (*Alur*, *Kakwa*, *Bari*, *Lugbara*, *and Logo*) are equally plausible candidates; respondents from this specific region are therefore omitted from the baseline matching. To ensure that the inclusion of the DRC—given these matching challenges—does not drive the main findings, I show in Appendix Table B10 that the main results are robust to the full exclusion of the country</footnote>
This mapping process requires a stringent filtering to ensure institutional clarity. First, I restrict the sample to respondents originating from ethnic groups strictly classified as either “matrilineal” or “patrilineal” in the Ethnographic Atlas. Second, I omit individuals whose modern ethnic labels cannot be reliably linked to an ancestral society. Appendix Table A1 provides a detailed breakdown of this filtering process, documenting the transition from the raw DHS data to the final analytical sample. While these restrictions reduce the sample size, they are necessary to ensure that the institutional assignment is based on unambiguous ancestral norms, thereby minimizing measurement error in the kinship treatment.

To complement this identity-based measure, I also leverage the GPS coordinates of DHS clusters to construct a geographic measure of matrilineality. By mapping cluster locations to the historical territories of ethnic homelands as defined by Murdock (1967), I define a location-based treatment status. The two measures are not perfectly aligned due to migration, ethnic mixing, and potential measurement error inherent in the historical mapping of ethnic homelands. This geographic assignment nevertheless provides the basis for the spatial identification strategy—including the Geographic RDD and Nearest Neighbor Matching analyses—which I detail in Section 4.

The final estimation sample is restricted to 32 survey rounds from 18 Sub-Saharan African countries that satisfy a “triple-overlap” of data requirements: (i) laboratory-confirmed HIV biomarker modules, (ii) detailed self-reported ethnic identifiers compatible with the *Ethnographic Atlas* concordance, and (iii) sub-national GPS coordinates for cluster-level spatial assignment. The exclusion of certain countries, most notably South Africa, is necessitated by these criteria. Although South Africa is a major epicenter of the HIV epidemic, the national DHS does not record the ethnic identity of respondents, which is central to identifying “portable” institutional norms. Furthermore, South Africa lacks significant ancestral matrilineal populations; relying purely on geographic location in this context would not only introduce substantial measurement error but also fail to provide the necessary institutional contrast required for this study.

The resulting dataset yields a final analytical sample of approximately 150,000 unique individual female observations, representing the intersection of objective health data and high-resolution institutional mapping. Appendix Table A2 lists the included surveys and details the regional distribution of the kinship norms. The institutional diversity of the sample is further documented in Appendix Table A3, which lists the specific ancestral ethnic groups matched from the *Ethnographic Atlas*. By restricting the comparison to groups with unambiguous matrilineal or patrilineal descent rules, the sample ensures a clean contrast in kinship organization. Finally, Appendix Table A4 presents the summary statistics for the main variables at the individual, village, and ethnicity levels. These raw comparisons provide a preliminary view of the disparities between matrilineal and patrilineal populations, particularly regarding HIV prevalence, which I analyze formally in the following sections.

<footnote>from the sample.</footnote>
# 4 Empirical Strategy

## 4.1 Baseline OLS: Sub-national Variation

Identifying the causal impact of ancestral institutions on contemporary health outcomes requires an empirical design that accounts for the complex, multi-layered environment of the African HIV epidemic. My primary strategy exploits variation in ancestral descent rules between individuals residing within the same sub-national administrative units. To isolate the long-term effect of matrilineal kinship on female HIV prevalence, I estimate the following baseline specification:

$$HIV_{ie} = \alpha + \beta Matrilineal_{e} + X'_{i}\Gamma + Z'_{c}\delta + A'_{e}\Omega + \mu_{rs} + \epsilon_{ie} \quad (1)$$

where $HIV_{ie}$ is a binary indicator for the laboratory-confirmed HIV status of woman i belonging to ancestral ethnic group e. The treatment of interest, $Matrilineal_{e}$, is an indicator for whether the respondent belongs to an ethnic group e characterized by an ancestral matrilineal descent system. I prioritize self-reported ethnicity as the primary measure of treatment, as the transmission of HIV is fundamentally mediated by internalized behavioral norms, kinship obligations, and gendered bargaining—forces that are more closely tied to identity and cultural institutions than to geographic location alone.

The cornerstone of this identification strategy is the inclusion of region-by-survey fixed effects ($\mu_{rs}$), where regions are defined as the sub-national administrative units (provinces) within each country as recorded by the DHS. By comparing matrilineal and patrilineal women within the same province and survey wave, I effectively neutralize an exhaustive set of localized confounders. This “epidemiological approach” (Fernández, 2011) ensures that $\beta$ is estimated by comparing individuals facing the same institutional and economic environment. These fixed effects absorb disparities in localized public health infrastructure, the rollout of Antiretroviral Therapy (ART) campaigns, and provincial-level geographic dynamics such as regional climatic shocks or distance to the coast.

To ensure that $\beta$ isolates the specific role of descent rules from other competing factors, I incorporate three vectors of controls. The vector $X'_{i}$ accounts for contemporary human capital and socio-economic status by including age and its square, number of years of education, an employment status indicator, and wealth quintile indicators. Furthermore, I include religious affiliation indicators to capture heterogeneous social norms regarding sexual behavior and institutional stances on contraception, ensuring that the matrilineal penalty is not driven by the uneven adoption of world religions across kinship systems.

The vector $Z'_{c}$ represents village-level geographic and development characteristics, including altitude, nightlight intensity, and population density (2010).<fnref n="4" /> While the regional fixed effects ($\mu_{rs}$) handle macro-geographic gradients, these village-level covariates are essential to account for granular geographic endowments that vary within regions and may independently influence both ancestral settlement patterns and contemporary disease ecology. Following the literature on the spatial drivers of the epidemic, $Z'_{c}$ also includes indices of ethnic fractionalization and polarization to account for information asymmetries and the efficacy of social sanctions within communities (Tequame, 2012), as well as an indicator for the presence of an active mine in the

<footnote n="4">⁴Throughout the paper, I use the term “village” to refer to the DHS clusters or Primary Sampling Units (PSU).</footnote>
village's 0.5° × 0.5° cell to capture the impact of male migratory labor (Corno and De Walque, 2012).

Finally, the vector $A'_e$ isolates the matrilineal institution from other deep-rooted cultural and economic legacies. I control for ancestral pastoralism, as the historical reliance on livestock is often associated with the emergence of patrilineal inheritance and lower female status due to the exclusion of women from high-value production (Becker, 2025). Similarly, I account for ancestral polygyny, which significantly shapes the contemporary marriage market and household bargaining power, potentially influencing sexual networks and HIV risk independently of the descent system. By including these controls alongside the year of observation in the *Ethnographic Atlas*, I ensure that $\beta$ captures the specific institutional impact of matrilineal kinship rather than broader historical development stages, the timing of cultural documentation, or competing patriarchal structures.

To account for the potential correlation of residuals ($\epsilon_{ie}$) within shared cultural and kinship structures, standard errors are clustered at the ancestral ethnic group level. Beyond these baseline controls, I formally assess the sensitivity of my results to unobserved selection using the (Oster, 2017) framework. By benchmarking the stability of the matrilineal penalty against a baseline that already accounts for region-survey fixed effects, I quantify the degree of selection on unobservables within these units required to nullify the effect. Finally, to bolster the specificity of the institutional channel, I complement the analysis with a series of placebo tests on alternative health outcomes (i.e., anemia, BMI, and Rohrer index). This ensures that the observed penalty is uniquely tied to HIV-related risks rather than reflecting generalized ethnic-level health disadvantages.

## 4.2 Geographic RDD: Boundary Design

To supplement the baseline analysis, I deploy a Geographic Regression Discontinuity (RDD) design with ethnic-pair fixed effects as a test of the internal validity of the HIV penalty. While the self-reported identity used in the OLS remains the conceptually superior proxy for internalized cultural norms, the Geographic RDD shifts the identification to an individual's physical residence within a historical matrilineal homeland as defined by the Murdock map. This strategy exploits the spatial discontinuity of ancestral borders to compare individuals living in nearly identical geographic, ecological, and institutional environments. By focusing on narrow boundary segments, I can evaluate whether the matrilineal penalty persists when local unobserved characteristics are held constant. Although this approach introduces greater measurement error—stemming from historical migrations and the inherent imprecision of ethnographic mapping—it serves as a rigorous check against the possibility that the baseline results are driven by localized environmental factors. To implement this design, I restrict the sample to individuals living in villages in close proximity to matrilineal-patrilineal boundaries and estimate the following specification:

$$HIV_{ic} = \alpha + \gamma Matrilineal_{c} + f(Distance_{c}) + X'_{i}\Gamma + Z'_{c}\delta + \eta_{ep} + \mu_{cs} + \epsilon_{ic} \quad (2)$$

where $HIV_{ic}$ is the HIV status of individual i in village c. The treatment of interest, $Matrilineal_{c}$, is a binary indicator for whether village c is located within the historical boundaries of a matrilineal group. This variable is constructed by geolocating each DHS cluster and
matching it to the corresponding ethnic group polygon in the Murdock map shapefile. The co-efficient $\gamma$ thus captures the discontinuous “jump” in HIV prevalence at the border, identifying the institutional effect of residing within a matrilineal heritage zone independently of an individual’s self-declared identity. The forcing variable, $f(Distance_c)$, is a flexible polynomial of the geodesic distance from village $c$ to the nearest matrilineal-patrilineal boundary $b$. The vectors of individual-level characteristics $X'_i$ and village-level geographic and economic fundamentals $Z'_c$ are identical to those previously employed in OLS specification. To isolate the effect of kinship norms from broader institutional and local confounders, I include $\eta_{ep}$, a set of ethnic-pair fixed effects that restrict the comparison to immediate neighbors across specific ancestral boundaries, and $\mu_{cs}$, which denotes country-survey fixed effects. In this geographic framework, the error term $\epsilon_{ie}$ is clustered at the Murdock ethnic group level. Unlike the baseline OLS, where inference follows the respondent’s reported ancestry to match the level of treatment variation, the RDD approach identifies the institutional effect through geographic location. Matching individuals through their DHS village location to a specific ethnic territory on the Murdock map ensures that the inference accounts for spatial correlation and shared local shocks inherent to these ancestral territories.

My baseline geographic RDD identification strategy integrates three methodological pillars into this single specification: a “doughnut” design, a flexible linear polynomial, and a restrictive set of fixed effects. First, the doughnut component excludes observations in the immediate vicinity of the border (i.e., within 25km). This choice is motivated by the inherent spatial noise shown in Figure 2 and the specific nature of HIV as a communicable disease. Intense social and sexual networks across ethnic borders likely generate significant epidemiological spillovers, where higher prevalence in matrilineal groups “contaminates” neighboring patrilineal clusters as suggested by Figure 3. By removing these immediate boundary clusters, I isolate the “core” institutional environments and remove the areas most prone to geographic dilution and cross-border spillovers. Second, the use of a flexible linear polynomial allows the slope of the spatial trend to differ independently on either side of the boundary, ensuring that the estimated jump is not biased by asymmetric geographic or epidemiological gradients. Third, following the design in Moscona et al. (2020), I include ethnic-pair fixed effects ($\eta_{ep}$) alongside country-survey fixed effects ($\mu_{rs}$) to ensure a highly controlled comparison. This framework compares individuals to their immediate neighbors across specific boundary segments defined by the adjacency of matrilineal and patrilineal territories.<fnref n="5" /> By construction, the RDD subsample is limited to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined. Consequently, these countries do not provide the matrilineal-patrilineal boundaries required to estimate the geographic RDD with ethnic-pair fixed effects. By holding both the national institutional environment and the localized cultural-geographic context constant, this approach isolates the kinship system as the primary source of variation between otherwise similar households.

To further ensure that the results are not sensitive to the specific functional form of the spatial trend, I provide additional estimates using flexible quadratic polynomials. I complement

<footnote n="5">⁵Since an ethnic group may be adjacent to multiple groups of the opposite lineage, some individuals located near multiple borders may appear in multiple pairs. I account for this duplicated structure by applying individual weights—defined as the inverse of the number of times a unique respondent appears in the paired dataset ($\frac{1}{N_i}$)—ensuring that each individual contributes exactly once to the overall estimation and that the sample remains representative.</footnote>
this framework with two additional safeguards: a Fuzzy RDD, where individual identity is in-
strumented by geographic residence, and a Nearest Neighbor Matching (NNM) estimator at the
village level to address parametric concerns altogether. For the NNM, I restrict the set of covari-
ates to the aforementioned geographic and economic endowments, deliberately excluding social
dynamics to avoid over-controlling for potential downstream outcomes of kinship systems.

While these geographic strategies provide a robust validation for the core HIV penalty, they
may offer less consistency when exploring the underlying behavioral mechanisms. This diver-
gence stems from the fact that self-reported ethnicity remains the most accurate proxy for the
internalized norms that drive HIV transmission, whereas geographic residence near borders is
a noisier measure of cultural treatment. Furthermore, the unique social environment of ethnic
boundaries—characterized by increased inter-ethnic contact and potential cultural blurring—may
alter traditional behavioral patterns compared to the stable institutional effects observed in the
full sample. Finally, the shift to the village level for the NNM or the reduced sample size inherent
in the RDD limits the statistical power required to identify the more granular individual-level
behavioral shifts that mediate the overall disease prevalence. I nevertheless report the mech-
anism estimates using the baseline geographic RDD and NNM specifications in Appendix for
completeness.

4.3 Conceptual Interpretation of Estimators

To ensure full transparency regarding the source of identification, it is necessary to distinguish
what each estimator captures. The baseline OLS identifies the effect of internalized cultural
capital; it captures the behavioral norms and kinship rules that individuals carry with them
regardless of their current location. In contrast, the Geographic RDD identifies the effect of
the institutional environment, capturing how historical rules associated with a territory influence
outcomes through localized social structures or peer effects.

I acknowledge that these different lenses may yield varying results, particularly when examin-
ing behavioral mechanisms. Beyond the differences in treatment definition, the two estimators rely
on distinct spatial supports: while the OLS captures the broad matrilineal experience across 18
countries, the geographic RDD is structurally restricted to the 14 countries hosting matrilineal-
patrilineal boundaries. Furthermore, the spatial signal of the RDD is inherently sensitive to
blurring at ethnic boundaries, which arises from two distinct channels. First, as documented
later in Appendix Table B1, matrilineal individuals exhibit a significantly higher likelihood of
inter-ethnic marriage. This leads to a cultural blurring where the lived institutional reality of
a household often deviates from the historical descent rule of the land due to marital sorting.
Second, the geographic proximity at boundaries facilitates epidemiological spillovers. Because
HIV is a communicable disease transmitted through local sexual networks, the prevalence in a
matrilineal homeland can leak into neighboring patrilineal clusters.

Both marital and epidemiological spillovers lead to attenuation bias, pushing the RDD esti-
mates toward zero and suggesting that the spatial results provide a conservative lower bound of
the true institutional effect. Consequently, while the RDD remains a vital exogenous check for
the overall HIV penalty, the OLS framework — by utilizing self-reported identity — remains the
most theoretically precise vector for isolating the internalized behavioral mechanisms discussed
in Section 6.
# 5 Empirical Results

## 5.1 Baseline Results: The Matrilineal Penalty

Table 1 presents the baseline OLS estimates of the relationship between matrilineal ancestry and HIV status for the female sample. Before diving into the regression analysis, Table A4 provides crucial context through an unconditional comparison of means. The raw data reveals a stark “matrilineal penalty”: HIV prevalence among matrilineal women stands at 11.6%, compared to just 3.5% for their patrilineal counterparts—a highly significant gap of 8 percentage points. This raw difference is particularly noteworthy given that matrilineal and patrilineal groups differ significantly across several dimensions that correlate with HIV prevalence. As shown in Table A4, matrilineal women are more educated and more likely to reside in urban, densely populated areas with higher nightlight intensity and a greater presence of mines. They also reside in more ethnically fractionalized and polarized regions. Furthermore, significant differences exist in religious affiliation and ancestral traditions, such as polygyny and pastoralism. These systematic differences underscore the importance of the regression analysis that follows, which ensures that the matrilineal penalty is not simply a proxy for these contemporary socio-economic, geographic or ancestral ethnographic traits.

Column 1 of Table 1 reports the preliminary OLS specification without sub-national fixed effects, which confirms the large unconditional correlation. However, once I introduce region-survey fixed effects in Column 2, the magnitude of the matrilineality coefficient decreases substantially. This shift indicates that the raw 8-percentage-point gap is largely driven by the concentration of matrilineal groups in geographic areas with higher baseline HIV prevalence. Nevertheless, even when comparing women living within the same subnational region and survey wave—thereby netting out broad national and regional epidemiological factors—the matrilineality penalty remains positive and statistically significant.

In Columns 3 through 5, I progressively saturate the model based on the specification in Equation 1 with contemporary individual-level characteristics (human capital and socio-economic status), village-level geographic controls, and ancestral ethnographic traits. The most striking finding is the remarkable stability of the matrilineal coefficient across these specifications. Despite the inclusion of exhaustive controls that significantly increase the explanatory power of the model, the magnitude of the matrilineal penalty remains virtually unchanged from Column 2 to Column 5. This stability suggests that the observed effect is not a proxy for modern socio-economic gradients or local geographic endowments, but rather reflects a persistent institutional legacy that survives the inclusion of major contemporary determinants. This interpretation is further supported by Appendix Table B2, which demonstrates that these results are robust to using a constant sample of individuals across all specifications, ensuring that the stability of the coefficient is not driven by changes in sample composition as controls are introduced.

Quantitatively, the impact is first-order. The estimate in Column 5 indicates that being matrilineal is associated with a 1.5 percentage point increase in the probability of a female being HIV-positive, representing a substantial 43% increase over the sample mean of patrilineal women. This suggests that ancestral kinship structures remain a primary determinant of female health risks.

To gain a comprehensive understanding of the institutional reach of these rules, I contrast
these baseline results with those obtained for the male sample, reported in Appendix Table B3. The analysis reveals that the matrilineal penalty is not strictly confined to females; a positive and statistically significant effect is also observable for males across most specifications, though it is generally less precisely estimated. As with the female sample, these findings are robust to using a constant sample of male individuals, as shown in Appendix Table B4. This gendered gradient in precision and magnitude is consistent with the paper's conceptual framework. While matrilineal institutions likely alter the broader epidemiological landscape for the entire community through shared sexual networks, the underlying behavioral mechanisms exert a more direct and robust influence on the risk profiles of females.

<table><caption>Table 1: Ancestral Matrilineality and Female HIV (OLS)</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.079***<br>(0.016)</td><td>0.014**<br>(0.007)</td><td>0.015**<br>(0.007)</td><td>0.014*<br>(0.007)</td><td>0.015**<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>151289</td><td>151289</td><td>149471</td><td>145838</td><td>145481</td></tr><tr><td>Adj. R-squared</td><td>0.018</td><td>0.055</td><td>0.071</td><td>0.072</td><td>0.072</td></tr><tr><td>Clusters</td><td>171</td><td>171</td><td>171</td><td>171</td><td>169</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.035</td><td>0.035</td><td>0.035</td><td>0.035</td><td>0.035</td></tr></tbody></table>

*Notes:* This table presents OLS estimates of the effect of ancestral matrilineality on female HIV. The unit of observation is a female respondent. The sample includes individuals originating from either traditionally matrilineal or patrilineal ethnic groups. The dependent variable is a binary indicator for HIV-positive status based on DHS HIV tests. “Matrilineality” is a dummy variable equal to 1 if the respondent belongs to a traditionally matrilineal ethnic group. Column 1 reports the baseline estimate without controls or fixed effects. Subsequent columns progressively add the blocks of controls and region-by-survey fixed effects as specified. *Ind. controls* include individual’s age and its square, an urban location dummy, years of education, an employment status dummy, wealth index quintile indicators, and religious affiliation indicators. *Village-Geographic controls* include altitude, nightlight intensity, population density (2010), ethnic fractionalization and polarization indices, and a dummy indicating the presence of an active mine within the village’s 0.5° × 0.5° cell. *Ethnic Group controls* include a dummy for ancestral polygyny, an index of ancestral pastoralism, and the year of observation in the *Ethnographic Atlas*. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among women from patrilineal ethnic groups for the estimation sample. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

## 5.2 Validity and Robustness of OLS Estimates

To ensure the credibility of the baseline results, I conduct a series of robustness checks addressing potential biases from unobservables, geographic confounding, and sample definitions.

**Selection on unobservables and additional controls.** To formally address the concern that the matrilineal penalty might be driven by omitted variable bias, I assess the sensitivity of the re-
results using the (Oster, 2017) bounding exercise. Following the logic of my identification strategy, I benchmark the stability of the matrilineal coefficient against a baseline specification that already accounts for region-survey fixed effects. This ensures that the test for unobserved selection is conducted within the relevant geographic and epidemiological units. As reported in Appendix Table B5, the coefficient remains remarkably stable despite the inclusion of exhaustive controls, yielding bias-adjusted estimates between 0.016 and 0.019 across various $R_{max}^2$ scenarios.<fnref n="6" /> This stability, with adjusted point estimates slightly exceeding the OLS baseline, suggests that the results are not driven by unobserved selection. To further validate this, Appendix Table B6 incorporates a series of ancestral historical traits, such as pre-colonial settlement patterns, colonial contact, religious missions and the intensity of slave trade, alongside contemporary village-level drivers of HIV, including the proportion of circumcised males and the local prevalence of commercial sex. Although the inclusion of these variables reduces the sample size due to matching across disparate data sources, the matrilineality penalty remains robust in both magnitude and significance. This confirms that ancestral kinship rules exert an influence that is independent of both deep-rooted historical structures and localized epidemiological risk factors.

**Selection into HIV testing and attenuation bias.** A potential concern in any study using biomarker data is the non-random nature of sample participation. In Appendix Table B7 and Table B8, I investigate whether ancestral matrilineality predicts the probability of consenting to the test (column 1) and actually completing it (column 2) for both females and males. The sample sizes in these tables are smaller than in the main analysis because data on the consent process are not uniformly reported across all DHS waves; however, this restriction primarily affects earlier survey rounds and is geographically dispersed across West and East Africa, mitigating concerns regarding systematic regional bias in this sub-sample. The results indicate that matrilineal individuals are slightly less likely to have undergone testing. However, the magnitude of this selection appears limited, as overall participation remains remarkably high, with sample means for both consent and test completion ranging from 94% to 96% across both genders. To the extent that individuals with higher risk profiles—who may opt out due to stigma or prior knowledge of their status—are under-represented in the tested sample, these findings suggest a potential attenuation bias. Consequently, the OLS estimates reported throughout this paper likely constitute a lower bound of the true matrilineal penalty, ensuring that the estimated institutional impact remains conservative.

**Placebo tests.** To further rule out the possibility that the matrilineal penalty captures a general health disadvantage rather than a specific behavioral mechanism, Appendix Table B9 reports a series of placebo tests using alternative health outcomes. I examine whether matrilineal ancestry predicts women's health indicators that are unrelated to sexual or contraceptive behavior, such as anemia, Body Mass Index (BMI), and the Rohrer index. The absence of a systematic relationship

<footnote n="6">⁶While $R_{Max}^2$ theoretically reaches unity in the absence of measurement error, empirical work typically assumes a lower threshold to account for idiosyncratic noise (González and Miguel, 2015). By construction, $R_{Max}^2 \in [\hat{R}_F^2; 1]$. Oster (2017) provides some insights on how $R_{Max}^2$ should be chosen. I follow her procedure and define $R_{Max}^2 = \min(1.3\hat{R}_F^2; 1)$ as the primary benchmark for stability, a threshold derived from the survival rate of experimental results in top-tier journals. To ensure transparency, I also report bias-adjusted coefficients for more conservative scenarios where $R_{Max}^2$ equals $1.5\hat{R}_F^2$ and $2\hat{R}_F^2$.</footnote>
between matrilineality and these nutrition-based health markers ensures that the kinship signal
is not picking up broad socio-economic or biological vulnerabilities. Instead, it confirms that the
observed penalty is specific to the HIV-related risks hypothesized in the conceptual framework.

**Definition of the control group and hybrid systems.** In Appendix Table B11, I investigate the sensitivity of the results to the classification of kinship systems by expanding the reference group beyond strictly patrilineal societies. Specifically, the reference group now additionally includes individuals originating from ethnic groups categorized as ambilineal, bilateral, duolateral, mixed, and quasi-lineage systems. When these groups are included, the matrilineal coefficient is no longer statistically significant. This likely reflects the heterogeneous nature of the "other" group, which includes bilateral or hybrid descent systems that share characteristics with both matrilineal and patrilineal organizations, thereby blurring the institutional contrast. Descriptive statistics support this interpretation: the mean HIV prevalence in the reference group rises from 3.5% among strictly patrilineal groups to 4.4% in the expanded sample. This suggests that societies with intermediate descent rules exhibit higher HIV prevalence than strictly patrilineal ones, narrowing the gap with matrilineal groups and indicating that the matrilineal penalty is most sharply identified against the strict patrilineal model.

**Regional heterogeneity and epidemiological pressure.** Appendix Table B12 explores the spatial heterogeneity of the matrilineal penalty across the African continent. The results show that the effect is primarily driven by groups located in East and Southern Africa, whereas the coefficient is smaller and loses statistical significance in West and Central Africa. This regional divergence provides strong empirical support for *Prediction 4*, confirming that the matrilineal penalty is conditional on the external environment and the intensity of the institutional legacy. In regions where the baseline HIV prevalence is historically higher, the institutional constraints on sexual bargaining imposed by matrilineal systems become more consequential for health outcomes. Furthermore, this regional contrast is reinforced by the distribution of kinship systems: East and Southern Africa encompass the “Matrilineal Belt”, where the higher prevalence of matrilineal groups provides the necessary statistical variation to identify the penalty more precisely. This concentration of matrilineal institutions within specific national borders allows for a robust identification of the effect, providing a natural transition to the analysis of within-country variation.

**Polygyny and marriage market structures.** To ensure that the matrilineal penalty is not driven by specific marriage market configurations, Appendix Table B13 reports results from a split-sample analysis based on the household's polygynous status. The matrilineal penalty is positive, robust, and statistically significant for monogamous couples. In contrast, the effect is non-existent and lacks precision for polygynous households. This divergence is empirically consistent with the regional patterns discussed earlier: polygyny is predominantly concentrated in West Africa, where the prevalence of matrilineal kinship is near zero in my sample and where the epidemiological pressure is lower. Consequently, the matrilineal penalty is identified through the variation existing within monogamous structures, which characterize the vast majority of the “Matrilineal Belt.” This result confirms that the institutional influence of kinship on HIV risk
operates independently of polygynous marriage systems.

**Identifying variability and sample.** Finally, Appendix Table B14 shows the robustness of the results to the source of identifying variation by restricting the sample to country-surveys with the highest degree of intra-regional kinship norm diversity. While the baseline specification relies on region-survey fixed effects, this analysis ensures that the estimates are not driven by country-surveys with near-uniform kinship norms where identifying variation is naturally limited. The degree of this intra-regional heterogeneity is detailed in Table A2, which reports the average regional proportion of matrilineal individuals for each country-survey in the final sample. Column 1 of Table B14 replicates the baseline estimate for comparison. Column 2 restricts the sample to country-surveys where the regional proportion of matrilineal individuals is between 5% and 95%, while Column 3 further narrows this threshold to a 10%-90% range. The matrilineal penalty remains remarkably consistent in magnitude and statistical significance across these subsamples. This stability confirms that the institutional effect is precisely identified within the most informative national contexts—particularly within the “Matrilineal Belt” of East and Southern Africa—where the coexistence of these institutions allows for a clear comparison. These results demonstrate that the institutional effect persists even when national and regional policies are held constant, laying the empirical groundwork for the complementary geographic regression discontinuity (RDD) analysis in the following subsection.

## 5.3 Geographic Regression Discontinuity Results

**Validity of the Treatment Definition.** I begin by validating the geographic definition of the treatment. Figure 2 illustrates the probability of self-reporting a matrilineal ethnicity relative to the distance to the nearest matrilineal boundary. As expected, a discontinuity exists at the border, yet the transition is not perfectly discrete. This “blurred” boundary is consistent with the conceptual framework: historical migrations, mapping inaccuracies in the ethnographic data (Murdock, 1967), and inter-ethnic marriages at the borders create a zone of cultural overlap. This initial check confirms that while ancestral borders still predict contemporary identity, they do so with a degree of spatial noise inherent to ethnographic geographic data.

**The Doughnut RDD as Baseline Specification.** Given this inherent noise and the nature of HIV as a communicable disease, my baseline RDD approach follows a “Doughnut” specification. As discussed in Section 4.2, intense social and sexual networks across ethnic borders likely generate significant epidemiological spillovers, where higher prevalence in matrilineal groups “contaminates” neighboring patrilineal clusters. Appendix Table C1 provides empirical support for this strategy: when the boundary clusters are included, the estimated effect is smaller and statistically insignificant, reflecting the smoothing of the HIV distribution at the border due to local interactions and geographic misclassification. By excluding observations in the immediate proximity of the boundary (within a 25 km radius), I isolate the “core” institutional environments and remove the areas most prone to these spillovers. This strategy ensures a cleaner identification of the institutional penalty by comparing individuals who are less likely to be affected by local geographic dilution.
<figure><img src="image_3.png" /><figcaption>Figure 2: Ancestral Matrilineal Territories and Individual Matrilineality: RDD Evidence</figcaption></figure>

This figure illustrates the spatial Regression Discontinuity Design (RDD) for the probability of an individual belonging to an ancestrally matrilineal ethnic group. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical boundaries of matrilineal ancestral homelands. The sample consists of women residing in matrilineal or patrilineal homelands within 200 km of a boundary. The solid lines represent linear polynomials estimated separately on each side of the threshold. The dots represent local means of individual matrilineal identity within 5 km distance bins. The horizontal axis measures the distance (in km) to the nearest boundary, where positive values denote distances into matrilineal territories and negative values denote distances into patrilineal territories. The vertical axis represents the fraction of individuals originating from an ancestrally matrilineal ethnic group.

**Main Results and Robustness to Functional Form.** Figure 3 displays the RDD plot for HIV prevalence using this spatial framework, showing a discontinuous increase when crossing into matrilineal territory. Table 2 presents the corresponding parametric estimates based on the geographic RDD specification in Equation 2, using bandwidths of 100, 150, and 200 km in Columns 1 through 3, respectively. The estimation is conducted on a subsample of 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined by the Murdock map and therefore do not provide the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. I find a positive and statistically significant matrilineal penalty for the female sample, which remains robust across all bandwidths. In contrast, Appendix Table C2 shows that this effect is virtually absent for males; the coefficients are near zero and statistically insignificant across all specifications. This gender-specific divergence suggests that the matrilineal penalty is driven by gender-targeted institutional norms rather than unobserved geographic traits that would affect both sexes equally.

Notably, the estimated effect is largest at the narrowest bandwidth and slightly decreases in
<figure><img src="image_4.png" /><figcaption>Figure 3: Ancestral Matrilineal Territories and Female HIV Rate: RDD Evidence</figcaption></figure>

This figure illustrates the spatial Regression Discontinuity Design (RDD) for female HIV prevalence at ancestral matrilineal ethnic boundaries. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical boundaries of matrilineal ancestral homelands. The sample consists of women residing in matrilineal or patrilineal homelands within 200 km of a boundary. The solid lines represent linear polynomials estimated separately on each side of the threshold. The dots represent local means of female HIV prevalence within 5 km distance bins. The horizontal axis measures the distance (in km) to the nearest boundary, where positive values denote distances into matrilineal territories and negative values denote distances into patrilineal territories. The vertical axis represents the female HIV prevalence.

magnitude as the window expands to 150 and 200 km. This pattern is consistent with the RDD's focus on local identification: the contrast is sharpest in the immediate vicinity of the ethnic boundaries. As the bandwidth increases, the mean HIV prevalence among patrilineal women also rises—from 5.3% at 100 km to 5.8% at 200 km—reflecting the inclusion of individuals residing further from the ethnic border, where the epidemiological environment may differ from the border regions. The persistence of the effect despite these spatial gradients, combined with the doughnut exclusion zone, reinforces the institutional interpretation. This is further supported by Appendix Table C1, which reports estimates without the doughnut exclusion zone. As expected, including observations in the immediate vicinity of the border yields smaller and non-significant coefficients. This attenuation confirms that the institutional contrast is partially obscured at the boundary by ethnic mixing and geographic spillovers, justifying the exclusion of the immediate border area to capture the true discontinuous shift in kinship norms.

The preference for a linear specification is supported by the visual evidence in Figure 3 and the standard recommendations to avoid over-fitting with high-order polynomials (Gelman and
Imbens, 2019). Furthermore, while Appendix Table C3 shows that quadratic estimates yield larger coefficients, they are less precisely estimated, consistent with the statistical cost of added flexibility in a spatial RDD framework.

Finally, to address the imperfect compliance between geographic location and ethnic identity, I report fuzzy RDD estimates in Appendix Table C4. By instrumenting individual matrilineal identity with geographic residence, these estimates account for the measurement error inherent in ethnographic maps and local migrations. The fuzzy RDD coefficients are larger in magnitude than the reduced-form estimates, confirming that the latter represent a conservative lower bound. At the 100 km bandwidth, the estimate is 0.227 (significant at the 10% level); however, the relatively lower first-stage F-statistic (5.0) suggests that this larger magnitude should be interpreted with caution, as it may partially reflect the reduced predictive power of the geographic instrument in areas of intense inter-ethnic mixing near the border. The instrument's strength is maximized at the 150 km bandwidth (F-stat of 11.4), where the matrilineal penalty remains significant at 11.6 percentage points. As the window expands further to 200 km, the first-stage strength naturally decays and the point estimates become less precisely estimated. Overall, these tests reinforce the interpretation of the matrilineal penalty as a robust institutional effect, with the IV framework suggesting that the true impact on "compliers"—those whose identity aligns with their ancestral territory—is likely larger than the reduced-form spatial average.

**Additional Identification Checks: NNM.** To further ensure that the results are not sensitive to the functional form of the spatial trend or the specific parametric assumptions of the RDD, I employ a Nearest Neighbor Matching (NNM) estimator at the village level. By non-parametrically matching matrilineal villages to their geographically closest patrilineal counterparts based on a Mahalanobis distance function, this approach provides a robustness check that is independent of bandwidth choice or distance polynomial specifications. Villages are matched strictly on exogenous geographic and economic fundamentals—including altitude, nightlight intensity, population density, and proximity to mining sites—while ethnic composition (i.e. fractionalization and polarization) variables are excluded as they may themselves be downstream outcomes of kinship systems. Notably, the matrilineal penalty remains remarkably stable and statistically significant when using 1, 5, or 10 nearest neighbors, as shown in Columns 1 through 3 of Appendix Table C5. This stability across varying numbers of matches reinforces the conclusion that the observed HIV disparity is a structural feature of the kinship systems rather than an artifact of the spatial empirical framework.

**Discussion: Geography vs. Portable Identity.** While the RDD analysis provides a powerful test of the causal nature of the matrilineal penalty, it identifies an effect anchored in space. In contrast, the individual-level OLS estimates capture the “portable” institutional identity that individuals carry with them, regardless of their current location. By relying on self-reported ethnicity, the OLS minimizes the attenuation bias inherent in geographic boundaries and provides a more precise measure of the deep-seated cultural norms that govern individual behavior (Bertoli et al., 2025). Furthermore, I replicate the analysis on the male sample (Appendix Table C2) as a falsification test for unobserved spatial shocks. If the matrilineal penalty were merely a
<table><caption>Table 2: Matrilineal Ancestral Homelands and Female HIV: Doughnut RDD Estimates</caption><thead><tr><th rowspan="2">Bandwidth</th><th colspan="3">HIV</th></tr><tr><th>100km<br>(1)</th><th>150km<br>(2)</th><th>200km<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.027**<br>(0.012)</td><td>0.017**<br>(0.008)</td><td>0.015**<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic-Pair FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>62912</td><td>70830</td><td>77910</td></tr><tr><td>Adj. R-squared</td><td>0.065</td><td>0.071</td><td>0.077</td></tr><tr><td>Clusters</td><td>103</td><td>104</td><td>106</td></tr><tr><td>Countries</td><td>14</td><td>14</td><td>14</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.053</td><td>0.055</td><td>0.058</td></tr></tbody></table>

Notes: This table presents the baseline spatial Regression Discontinuity Design (RDD) estimates of the matrilineal penalty on female HIV prevalence. Following a Doughnut specification, all observations within a 25 km radius of the ethnic boundary are excluded to mitigate local geographic spillovers and measurement error arising from historical boundary uncertainty. The sample includes all women residing within the matrilineal or patrilineal ancestral ethnic homelands as defined by historical maps. By construction, the analysis is restricted to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined and thus lack the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. Columns 1–3 report estimates for bandwidths of 100, 150, and 200 km, respectively. In each specification, a linear polynomial in distance to the boundary is estimated separately on each side of the threshold. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical matrilineal boundaries. All specifications include individual, village-geographic controls, as well as country-survey fixed effects and ethnic-pair fixed effects. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among women in patrilineal territories for the estimation sample. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

proxy for omitted geographic variables—such as proximity to trade routes or specific ecological conditions—one would expect to see a similar discontinuity in male HIV prevalence. The absence of a significant effect for men rules out these broad geographic explanations. Instead, it suggests that the penalty is rooted in gender-specific institutional dynamics. Therefore, having established these causal foundations, the subsequent analysis reverts to the full-sample OLS to explore the broader institutional landscape and the underlying behavioral mechanisms.

## 5.4 Matrilineality and Legal Origins: Beyond the Common Law Effect

A recent and influential literature has highlighted the enduring impact of colonial legal legacies on development and gender outcomes in Africa (La Porta et al., 2008). Most notably, (Anderson,
2018) argues that these legal origins play a decisive role in contemporary female HIV outcomes. According to this thesis, Common Law systems—inherited from British colonization—provide weaker marital property rights for women compared to Civil Law systems. This legal disadvantage is thought to reduce women's intra-household bargaining power, limiting their ability to negotiate safe sexual practices and increasing their exposure to HIV through intra-marital transmission. Given that many matrilineal groups in my sample reside in Common Law countries, a crucial question is whether the “matrilineal penalty” identified in the previous sections is merely a proxy for these colonial legal legacies. It is important to note, however, that my baseline results already account for the influence of legal origins through the inclusion of region-survey fixed effects, which capture all time-invariant national and sub-national institutional characteristics, including the legal system. Nevertheless, to further disentangle these two institutional layers, this section explores whether ancestral kinship norms and colonial legal legacies act as independent or reinforcing drivers of the epidemic.

To adjudicate between the role of ancestral ethnic institutions and colonial legal origins, I expand upon the empirical focus of Anderson (2018) by jointly estimating the impacts of matri-lineality and Common Law heritage at both the country and individual levels. Table 3 presents these results. In Column 1, at the country level, the intensity of matrilineality remains a powerful and independent predictor of female HIV prevalence (0.100***), even when controlling for Com- mon Law (0.029*). Notably, the matrilineal coefficient is more than three times the magnitude of the legal origin effect, suggesting that ancestral institutions constitute a primary institutional determinant of the epidemic that persists independently of colonial legacies. This is further con- firmed in the individual-level analysis (Column 2); the matrilineal dummy (0.048***) consistently carries a larger coefficient than the Common Law indicator (0.030***), indicating that the ances- tral lineage system exerts a more substantial pressure on the contemporary HIV landscape than the broader legal environment.

The interacted specification in Column 3 provides a more nuanced view of this relationship. Strikingly, the standalone coefficient for matrilineality drops to near zero and loses significance, while the interaction term is large and highly significant (0.063***). This suggests that the matrilineal penalty is specifically activated in Common Law environments. While this lack of significance in Civil Law settings may partly reflect the lower identifying variation of matrilineal groups within these countries, the magnitude of the interaction points toward a genuine institu- tional synergy. This finding provides strong support for Prediction 4, suggesting that the insti- tutional penalty of matrilineality is contingent upon the broader legal framework. Specifically, while Common Law may weaken women's position within marriage, the greater sexual mobility inherent to matrilineality creates a “double burden” in environments where legal protections are already thin. This “double burden” hypothesis is further rigorously tested in Column 4, where I introduce region-survey fixed effects to absorb country-level unobservables and local shocks. The interaction remains robust and significant (0.025**), confirming that the matrilineal penalty is a distinct driver that is exacerbated by, rather than subsumed by, the broader legal environment. The next section turns to the behavioral channels through which matrilineality operates.
<table><caption>Table 3: Ancestral Matrilineality and Colonial Common Law: The Double Burden of Institutions on Female HIV</caption><thead><tr><th></th><th colspan="4">HIV</th></tr><tr><th></th><th>Country-level</th><th colspan="3">Individual-level</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.100***<br>(0.026)</td><td>0.048***<br>(0.014)</td><td>-0.001<br>(0.005)</td><td>-0.002<br>(0.005)</td></tr><tr><td>Common Law</td><td>0.029*<br>(0.015)</td><td>0.030***<br>(0.007)</td><td>0.025***<br>(0.007)</td><td></td></tr><tr><td>Matrilineality × Common Law</td><td></td><td></td><td>0.063***<br>(0.018)</td><td>0.025**<br>(0.011)</td></tr><tr><td>Ind. Controls</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Observations</td><td>18</td><td>145481</td><td>145481</td><td>145481</td></tr><tr><td>Adj. R-squared</td><td>0.602</td><td>0.054</td><td>0.055</td><td>0.073</td></tr><tr><td>Clusters</td><td></td><td>169</td><td>169</td><td>169</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var.</td><td>0.045</td><td>0.048</td><td>0.048</td><td>0.048</td></tr></tbody></table>

*Notes:* This table compares the impact of ancestral matrilineality and colonial legal origins (Common Law) on female HIV prevalence. Column 1 presents a country-level analysis for 18 African countries, where “Matrilineality” is the share of matrilineal individuals calculated from DHS data within the sample of traditionally matrilineal and patrilineal women. In Column 1, the dependent variable is the country-level female HIV prevalence, computed from this same sample. Columns 2–4 use individual-level data. The sample is restricted to women originating from either traditionally matrilineal or patrilineal ethnic groups. “Matrilineality” is an indicator for whether the woman belongs to a traditionally matrilineal group. “Common Law” is an indicator for countries with a British legal heritage, assigned based on the individual’s country of residence. In Column 4, the Common Law dummy is absorbed by the inclusion of region-survey fixed effects, which capture all country-level unobservables. Individual, village-geographic, and ethnic group controls are as defined in Table 1. “Mean dep. var.” denotes the average female HIV prevalence for the estimation sample. Robust standard errors are reported in Column 1. In Columns 2–4, standard errors are clustered at the ethnicity level. *** p<0.01, ** p<0.05, * p<0.1.

# 6 Mechanisms

The previous sections established a robust and causal matrilineal penalty on female HIV prevalence. In this section, I explore the behavioral channels through which ancestral kinship norms translate into contemporary health outcomes. I build on the conceptual framework where matrilineality is associated with greater female sexual and contraceptive autonomy but reduced spousal cooperation. Unlike the “intra-marital” vulnerability channel emphasized by the legal origins literature (Anderson, 2018), I hypothesize that the matrilineal penalty operates through a combination of increased sexual mobility and a breakdown in joint-protection coordination.

Before delving into the primary mechanisms, I first rule out several alternative explanations. As shown in Appendix Table D1, the matrilineal penalty is not driven by differences in HIV awareness or access to protection: matrilineal women are not less likely to have heard of AIDS or
STIs, nor do they face significantly greater difficulty in obtaining condoms. Furthermore, results in Table D2 discard differences in sexual debuts, as there is no significant effect of matrilineality on age at first sex or age at first marriage. Similarly, fertility patterns do not appear to be a driving force; although matrilineal women have fewer children on average (Appendix Table D3), there is no significant difference in the desire for more children.

Instead, the results point toward three specific behavioral pillars that align with the structural fragility of marital contracts and the “matrilineal puzzle” discussed in Section 2. First, I show that matrilineality is associated with greater marital bond fragility, increased female autonomy, and higher sexual mobility. Second, I document a coordination failure in contraceptive choices, where increased bargaining power does not translate into higher condom use, except when women are aware of their HIV-positive status. Finally, I provide evidence of a distinct “female-first” transmission pattern through an analysis of couple sero-discordance.

## 6.1 Female Autonomy and Sexual Mobility

The first pillar of the mechanism analysis focuses on how matrilineality shapes women's outside options and their subsequent sexual mobility. According to the “matrilineal puzzle” (Section 2), the structural tension between the marital bond and the woman's natal kin group often results in a more fragile marital union. In line with *Prediction 1*, I find that this increased agency translates into higher marital turnover and sexual mobility, providing the behavioral basis for the observed HIV penalty.

I find strong empirical evidence for this increased female autonomy and its impact on marital stability in Table 4. First, matrilineal women are more likely to reside with their own relatives (Col 1), providing them with a persistent communal safety net. This stronger fallback position translates into a significantly higher likelihood of being divorced or separated (Col 2). This autonomy appears to extend to sexual behaviors outside the marital union, which acts as a primary vector for HIV transmission. Matrilineal women are more likely to report infidelity (Col 3) and have a higher number of extramarital partners (Col 4). While the latter coefficient is imprecisely estimated—likely due to the sensitivity of self-reporting—I use “father diversification” as a more objective proxy for sexual mobility. I find that matrilineal women have a higher number of children from different fathers (Col 5) and a higher father diversification index (Col 6).<fnref n="7" /> Interestingly, while these women have fewer children in total (Appendix Table D3), the diversity of their children’s paternity confirms a higher turnover of sexual partners.

In addition to household-level dynamics, matrilineality also influences broader patterns of social mobility. Table B1 shows that matrilineality is associated with a higher probability of inter-ethnic marriages and, notably, marriages with partners from different kinship norms. This suggests that matrilineal women are less constrained by traditional ethnic endogamy and exhibit greater mobility in the marriage market. This high frequency of inter-norm unions has important conceptual implications: while the matrilineal endowment of agency is internalized by the woman, these mixed marriages suggest that the associated behavioral risks may spill over across ethnic boundaries. Such institutional spillovers likely introduces substantial noise into the male subsample, providing a potential explanation for why the matrilineal penalty is more precisely

<footnote n="7">⁷The number of children’s fathers is defined as the count of different biological fathers for the respondent’s children. The father diversification index is calculated by dividing this count by the total number of children.</footnote>
identified and robust among the female population, who remain the primary subjects of these kinship obligations. While these behavioral patterns are clearly identified in the full OLS sample, geographic specifications (RDD and NNM) yield less consistent results for these specific outcomes (see Appendix Table D4 and Table D6). This divergence is expected for two reasons. First, unlike the biological HIV status—a stable, accumulated measure of risk—self-reported behaviors are intrinsically noisier. Second, while the RDD identifies an effect anchored in space, the OLS captures the “portable” institutional identity that individuals carry regardless of their location. Third, given the high prevalence of inter-norm marriages (Table B1), the behavioral signal at the ethnic border is naturally blurred by local social mixing. Finally, the NNM analysis is performed at the village level, which naturally limits the statistical power to detect granular behavioral shifts compared to individual-level OLS estimates.
<table><caption>Table 4: Ancestral Matrilineality, Marital Stability and Female Sexual Autonomy (OLS)</caption><thead><tr><th></th><th>Wife's Parents<br>in HH</th><th>Divorced</th><th>Infidelity</th><th>Nb. of<br>extramarital<br>partners</th><th>Women's children's<br>number of<br>different fathers</th><th>Women's children's<br>father diversification</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.007***<br>(0.003)</td><td>0.013***<br>(0.004)</td><td>0.025***<br>(0.009)</td><td>0.019<br>(0.013)</td><td>0.008***<br>(0.002)</td><td>0.016***<br>(0.006)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>96384</td><td>145481</td><td>144732</td><td>144732</td><td>96374</td><td>96374</td></tr><tr><td>Adj. R-squared</td><td>0.017</td><td>0.033</td><td>0.133</td><td>0.025</td><td>0.017</td><td>0.323</td></tr><tr><td>Clusters</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.022</td><td>0.020</td><td>0.115</td><td>0.139</td><td>0.078</td><td>0.078</td></tr></tbody></table>

*Notes:* This table presents OLS estimates of the effect of ancestral matrilineality on various proxies for female marital stability and sexual behavior. The unit of observation is a female respondent. The sample and the “Matrilineality” dummy are defined in Table 1. “Wife’s Parents in HH” is an indicator for the presence of at least one of the wife’s parents in the household. “Divorced” is a dummy for current divorce status. “Infidelity” is an indicator for reporting at least one extramarital partner in the last 12 months, while “Nb. of extramarital partners” reports the total count of such partners. “Women’s children’s number of different fathers” and “Women’s children’s ather diversification” measure, respectively, the number of different biological fathers of the respondent’s children (aged under 17 and residing in the household) and this count divided by the total number of children. All columns include the full set of individual, ancestral, and village-geographic controls, as well as region-by-survey fixed effects, as defined in Table 1. “Mean dep. var. (Patri.)” denotes the mean of the corresponding dependent variable among women from patrilineal ethnic groups for the estimation sample. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
## 6.2 Contraceptive Choices and the Coordination Failure

The second pillar of the mechanism analysis explores the “joint-production” of protection within the household. According to *Prediction 2*, while matrilineal women possess greater individual agency, the structural fragility of the marital bond may lead to a breakdown in spousal coordination regarding safe sexual practices.

Table 5 provides striking evidence of this coordination paradox. On one hand, matrilineal women exhibit higher levels of perceived agency: they are more likley to be primary decision-maker regarding contraception (Col 1, though imprecisely estimated), they are more likely to find it justified to request a condom if their husband has an STI (Col 2) or to refuse sex if he has other partners (Col 3). On the other hand, this perceived agency does not translate into safer outcomes: matrilineal women are actually less likely to use condoms (Col 4). This suggests that while matrilineality provides women with the right to negotiate, it simultaneously creates an environment where joint coordination on barrier protection is less efficient. Aligned with this coordination failure, Column 5 provides suggestive evidence that matrilineal women may substitute condoms with long-term contraceptive methods, which require a lower level of spousal cooperation, such as the pill, IUD, injections, sterilization, implants or LAM.<fnref n="8" />

This coordination failure is fundamentally moderated by the salience of health risks rather than a lack of information. As shown in Table 6 (Col 1), the negative effect of matrilineality on condom use persists even among women who believe that condoms reduce HIV risk. This result is crucial as it aligns with the discarded mechanisms in Appendix Table D1: the matrilineal penalty is not a matter of absolute risk awareness, but of relative negotiation within the partnership.

The impact of this structural fragility is, however, highly sensitive to the resolution of information asymmetries. As shown in Table 6, the sign of the effect flips when considering women who are aware of their own HIV-positive status. While the interaction between matrilineality and HIV positivity is positive in the full sample (Col 2), this effect is entirely driven by women who have been tested in the past (Col 3). For those who have never been tested (Col 4), the matrilineal interaction remains insignificant. Notably, women who have ever been tested also exhibit a higher mean condom usage (0.048) compared to those never tested (0.020).

These findings suggest that the health costs of ancestral autonomy are not inevitable but are exacerbated by uncertainty, providing empirical support for the mechanism of strategic information avoidance described in *Prediction 3*. From a policy perspective, standard awareness campaigns focusing on the benefits of condoms are likely to yield diminishing returns in matrilineal contexts, as these women already acknowledge the effectiveness of protection. Instead, these results point toward the critical importance of universal and routine HIV testing. By converting uncertain risks into certain health information, testing acts as a catalyst that transforms latent ancestral agency into active protection, effectively bridging the coordination gap inherent to more autonomous kinship structures.

<footnote n="8">⁸Consistent with the discussion in previous subsection, geographic specifications (RDD and NNM) yield less precise estimates for these behavioral outcomes (see Appendix Table D5 and Table D7). This discrepancy underscores the challenge of capturing granular, high-frequency coordination choices—which are highly sensitive to social mixing at the ethnic border—through location-based identification compared to internalized “portable” identity.</footnote>
<table><caption>Table 5: Ancestral Matrilineality and Female Contraceptive Norms and Behaviors (OLS)</caption><thead><tr><th></th><th>Decides Contraception</th><th>Wife justified ask condom if husband has STI</th><th>Wife justified refuse sex if husband has other women</th><th>Condom</th><th>Long-term contraceptive method</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.023<br>(0.014)</td><td>0.024**<br>(0.010)</td><td>0.035***<br>(0.012)</td><td>-0.011***<br>(0.004)</td><td>0.010<br>(0.009)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>21202</td><td>123674</td><td>129961</td><td>135253</td><td>134397</td></tr><tr><td>Adj. R-squared</td><td>0.049</td><td>0.176</td><td>0.129</td><td>0.068</td><td>0.136</td></tr><tr><td>Clusters</td><td>138</td><td>168</td><td>168</td><td>152</td><td>152</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.888</td><td>0.754</td><td>0.638</td><td>0.029</td><td>0.119</td></tr></tbody></table>

*Notes:* This table presents OLS estimates of the effect of ancestral matrilineality on female reproductive agency and contraceptive use. The unit of observation is a female respondent. The sample and the “Matrilineality” dummy are defined in Table 1. “Decides contraception” is an indicator for whether the respondent is the primary decision-maker regarding contraceptive use. “Wife justified ask condom if husband has STI” and “Wife justifier refuse sex if husband has other women” are dummies indicating whether the respondent finds it justified for a wife to, respectively, ask for condom use if the husband has an STI or refuse sex if he has other partners. “Condom” and “Long-term contraceptive method” indicate the respondent’s current contraceptive method, where the latter includes the pill, IUD, injections, sterilization, implants, or LAM. All columns include the full set of individual, ancestral, and village-geographic controls, as well as region-by-survey fixed effects, as defined in Table 1. “Mean dep. var. (Patri.)” denotes the mean of the corresponding dependent variable among women from patrilineal ethnic groups for the estimation sample. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

## 6.3 Extra-marital Infection Routes and Couples’ Sero-discordance

The final pillar of the behavioral analysis uses the biological status of both spouses to pinpoint the primary route of HIV transmission within matrilineal households. This approach allows for a direct validation of *Prediction 1* If the matrilineal penalty were driven by a husband’s increased risky behavior or a lack of investment in the marital bond, we would expect to see a higher prevalence of couples where either only the husband is positive or both spouses are positive. Conversely, if the penalty operates through the “extra-marital” route, it should manifest primarily as a higher likelihood of the wife being the sole infected partner.

Table 7 provides definitive support for this latter hypothesis. Crucially, I find that matrilineality is significantly associated with a higher probability of the “Wife positive / Husband negative” configuration (Col 3 and 4). In contrast, I find no significant difference in the likelihood of both spouses being HIV-negative (Col 1 and 2), nor in the likelihood of the husband being the sole infected partner (Col 5 and 6) or both spouses being positive (Col 7 and 8). These results are robust to the inclusion of the husband’s own socio-economic and ancestral controls.<fnref n="9" />

<footnote n="9">⁹Husband’s controls include the same set of individual and ancestral characteristics as the wife’s, with the exception of household’s wealth and urban location and village-geographic characteristics, as these are inherently</footnote>
<table><caption>Table 6: Ancestral Matrilineality, HIV Risk Perception, and Condom Use (OLS)</caption><thead><tr><th></th><th colspan="4">Condom</th></tr><tr><th></th><th colspan="2">Full sample</th><th>Ever<br>tested</th><th>Never<br>tested</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Matrilineality × Condom reduces HIV</td><td>-0.011**<br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>Matrilineality × Condom not reduces HIV</td><td>-0.022***<br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>Matrilineality × HIV</td><td></td><td>0.016***<br>(0.005)</td><td>0.023***<br>(0.007)</td><td>-0.005<br>(0.007)</td></tr><tr><td>Matrilineality × No HIV</td><td></td><td>-0.015***<br>(0.004)</td><td>-0.016***<br>(0.006)</td><td>-0.011**<br>(0.005)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>108852</td><td>135253</td><td>49653</td><td>81443</td></tr><tr><td>Adj. R-squared</td><td>0.068</td><td>0.068</td><td>0.070</td><td>0.064</td></tr><tr><td>Clusters</td><td>151</td><td>152</td><td>148</td><td>151</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.036</td><td>0.029</td><td>0.048</td><td>0.020</td></tr><tr><td>F-Test Equality of coeff. (p-value)</td><td>0.049</td><td>0.000</td><td>0.000</td><td>0.306</td></tr></tbody></table>

Notes: This table presents OLS estimates of the effect of ancestral matrilineality on condom use, explored through the lens of HIV risk perception and objective serostatus. The unit of observation is a female respondent. The sample and the “Matrilineality” dummy are defined in Table 1. “Condom (not) reduces HIV” is an indicator for whether the respondent believes that consistent condom use (does not) reduce the risk of transmission. “HIV” and “No HIV” are indicators for the respondent’s actual serostatus as determined by the DHS blood test. “Ever tested” and “Never tested” subsamples distinguish between respondents who had already undergone HIV testing prior to the DHS survey and those who had not. The dependent variable “Condom” indicates the current use of male condom as a contraceptive or protective method. All columns include the full set of individual, ancestral, and village-geographic controls, as well as region-by-survey fixed effects, as defined in Table 1. “Mean dep. var. (Patri.)” denotes the mean condom use among women from patrilineal ethnic groups for the estimation sample. “F-test Equality of coeff. (p-value)” reports the p-value from a Wald test of the null hypothesis that the coefficients for the two matrilineal interaction terms in each column are equal. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

The concentration of the matrilineal penalty in the “Wife positive / Husband negative” category indicates that the virus enters the household primarily through the female partner. This pattern is inconsistent with theories of female vulnerability within marriage and instead confirms that the greater sexual mobility and autonomous networks inherent to matrilineal kinship structures are the primary drivers of the epidemic’s gendered burden.

<footnote>shared by both spouses and already captured by the wife's controls. In these couple-level specifications, I further account for the husband's ancestral characteristics—specifically the presence of the plough and the practice of bride price—to ensure that the observed effect of the wife's matrilineal ancestry is not merely a reflection of selective assortative mating with spouses from specific cultural backgrounds.</footnote>
<table><caption>Table 7: Ancestral Matrilineality and Couples HIV Discordance (OLS)</caption><thead><tr><th rowspan="3"></th><th colspan="8">Dep. Var.: Couple's Serostatus</th></tr><tr><th colspan="2">Wife - / Husband -</th><th colspan="2">Wife + / Husband -</th><th colspan="2">Wife - / Husband +</th><th colspan="2">Wife + / Husband +</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>-0.012<br>(0.009)</td><td>-0.005<br>(0.012)</td><td>0.010**<br>(0.005)</td><td>0.009*<br>(0.005)</td><td>0.004<br>(0.006)</td><td>0.003<br>(0.007)</td><td>-0.002<br>(0.006)</td><td>-0.007<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Husband's Controls</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Observations</td><td>39218</td><td>39218</td><td>39218</td><td>39218</td><td>39218</td><td>39218</td><td>39218</td><td>39218</td></tr><tr><td>Adj. R-squared</td><td>0.086</td><td>0.088</td><td>0.020</td><td>0.021</td><td>0.021</td><td>0.022</td><td>0.048</td><td>0.050</td></tr><tr><td>Clusters</td><td>166</td><td>166</td><td>166</td><td>166</td><td>166</td><td>166</td><td>166</td><td>166</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.959</td><td>0.959</td><td>0.013</td><td>0.013</td><td>0.015</td><td>0.015</td><td>0.013</td><td>0.013</td></tr><tr><td>Prop. Mixed Ethnicity</td><td>0.178</td><td>0.178</td><td>0.178</td><td>0.178</td><td>0.178</td><td>0.178</td><td>0.178</td><td>0.178</td></tr><tr><td>Prop. Mixed Matrilineality</td><td>0.040</td><td>0.040</td><td>0.040</td><td>0.040</td><td>0.040</td><td>0.040</td><td>0.040</td><td>0.040</td></tr></tbody></table>

Notes: This table presents OLS estimates of the effect of ancestral matrilineality on the HIV status of non-polygynous formally married couples. The unit of observation is a couple where both spouses originate from either a traditionally matrilineal or patrilineal ethnic group. “Matrilineality” is an indicator for whether the wife belongs to a traditionally matrilineal group. The dependent variables are four mutually exclusive indicators for the couple’s HIV status: both negative (columns 1–2), wife positive and husband negative (columns 3–4), wife negative and husband positive (columns 5–6), and both positive (columns 7–8). All columns include the wife’s individual, ancestral, and village-geographic controls as defined in Table 1. Even-numbered columns additionally include “Husband’s Controls,” which consist of the husband’s individual and ancestral characteristics (including ancestral plough and bride price), as well as indicators for “Mixed ethnicity” (spouses from different ancestral ethnic groups) and “Mixed matrilineality” (spouses from different ancestral kinship organizations). Models include region-by-survey fixed effects. “Mean dep. var. (Patri.)” denotes the mean of the corresponding dependent variable for couples in which the woman originates from a patrilineal ethnic group for the estimation sample. “Prop. Mixed Ethnicity” denotes the share of couples where spouses belong to different ethnic groups. “Prop. Mixed Matrilineality” denotes the share of couples where one spouse originates from a matrilineal group and the other from a patrilineal group. Standard errors are clustered at the ethnicity level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
# 7 Conclusion

This study provides new insights into the enduring influence of ancestral institutions on contemporary health outcomes. By comparing individuals from different ethnic backgrounds within the same geographic areas—and demonstrating that these results remain consistent across several alternative estimation strategies, including a geographic regression discontinuity design at ethnic boundaries—I demonstrate that matrilineal kinship organization is a significant predictor of the current female HIV epidemic in Sub-Saharan Africa. While matrilineality is historically associated with greater female agency, this autonomy involves a complex trade-off in the context of a modern health crisis.

The mechanisms identified through the three pillars of this analysis clarify this institutional penalty. First, the matrilineal structure reinforces women's outside options and their mobility in the marriage market, which mechanically increases sexual partner turnover and father diversification. Second, the structural fragility of the marital bond in these societies leads to a coordination failure within the couple; even when women perceive they have the right to negotiate, they are less likely to implement effective barrier protection in the absence of certain health information. Finally, the biological signature of couple sero-discordance—characterized by a higher probability of “Wife positive / Husband negative” configurations—confirms that the virus enters the household primarily through the female partner, validating the hypothesis of an extra-marital transmission route facilitated by increased institutional autonomy.

These findings offer critical implications for development policy and the broader literature on gender. They suggest that the impact of female agency on human capital and health is not monotonic but is deeply embedded in the prevailing kinship system. In matrilineal regions, standard awareness campaigns may yield diminishing returns because the bottleneck is not a lack of knowledge, but a coordination failure under uncertainty. Consequently, universal and routine HIV testing emerges as the most effective policy lever to bridge this gap, converting “latent” ancestral agency into active health protection. More broadly, this work invites a reassessment of female agency not as a universal panacea, but as a complex institutional endowment that requires supportive formal institutions—specifically regarding diagnostic information and healthcare—to ensure that freedom of choice does not translate into increased vulnerability to modern health shocks.
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# Appendix

This Appendix provides additional empirical evidence and robustness checks supporting the findings presented in the main text. It is organized into four sections. Appendix A details the data sources, the step-by-step sample construction, descriptive statistics and the comprehensive list of ancestral ethnic groups and their kinship organization. Appendix B assesses the robustness of the baseline OLS estimates. Appendix C presents results from alternative identification strategies for the main HIV outcome. Finally, Appendix D provides a sensitivity analysis of the behavioral mechanisms using local identification strategies.

## A Data and Descriptive Statistics

This section provides a detailed overview of the data sources and the construction of the analytical sample. I first document the sample selection process and the specific exclusion criteria in Table A1, highlighting the attrition at each stage of the data cleaning process. Following this selection, Table A2 lists the 32 Demographic and Health Surveys (DHS) across 18 countries included in the final sample and provides evidence of the intra-regional heterogeneity in ancestral kinship norms. I then provide a comprehensive list of all ancestral ethnic groups identified in the *Ethnographic Atlas* that are present in the final sample in Table A3, detailing their respective kinship organization and the number of individual observations. Finally, Table A4 presents summary statistics for the main variables used in the analysis at the individual, village, and ethnicity levels. Together, these tables provide a transparent documentation of the sample characteristics and the institutional mapping used in the empirical analysis.

<table><caption>Table A1: Sample Construction and Selection Process</caption><thead><tr><td>Step</td><td>Selection Criteria</td><td>Observations</td><td>Δ Obs.</td></tr></thead><tbody><tr><td>1</td><td>Initial DHS Sample (32 surveys, 18 countries)</td><td>409,333</td><td>-</td></tr><tr><td>2</td><td>Successful match with Ethnographic Atlas</td><td>371,892</td><td>-37,441</td></tr><tr><td>3</td><td>Individuals with valid HIV test results</td><td>189,325</td><td>-182,567</td></tr><tr><td>4</td><td>Exclusion of non-matri/patrilineal individuals<sup>a</sup></td><td>151,289</td><td>-38,036</td></tr><tr><td>5</td><td>Final Estimation Sample (with non-missing controls)</td><td><strong>145,481</strong></td><td>-5,808</td></tr></tbody></table>

*Notes:* This table documents the construction of the final analytical sample through a filtering process necessitated by a "triple-overlap" of criteria. The initial sample includes DHS and AIS surveys that satisfy the simultaneous requirement for laboratory-confirmed HIV biomarker modules, detailed self-reported ethnic identifiers, and cluster-level GPS coordinates. Step 2 restricts the sample to observations with self-reported ethnicity that could be successfully matched to ethnic groups in the *Ethnographic Atlas* (Murdock, 1967). The reduction in sample size at the HIV testing stage (Step 3) is driven by the survey design, as biomarker data are only collected from a systematically selected sub-sample of households based on a random start. Step 4 excludes individuals from ethnic groups with alternative kinship organizations (e.g., ambilineal, bilinear, or duolineal) to ensure a clean comparison between traditionally matrilineal and patrilineal systems. Finally, Step 5 removes observations with missing information on individual and household-level controls.
<table><caption>Table A2: List of Surveys and Regional Variation in Matrilineal Norms (Final Sample)</caption><thead><tr><th>Country (survey)</th><th>Number of regions</th><th>Avg. proportion Matrilineal per region</th></tr></thead><tbody><tr><td>Burkina Faso (2003)</td><td>14</td><td>0.097</td></tr><tr><td>Burkina Faso (2010)</td><td>13</td><td>0.044</td></tr><tr><td>Cameroon (2004)</td><td>12</td><td>0.040</td></tr><tr><td>Cameroon (2011)</td><td>12</td><td>0.000</td></tr><tr><td>Chad (2014-15)</td><td>21</td><td>0.035</td></tr><tr><td>Côte d'Ivoire (2011-12)</td><td>11</td><td>0.406</td></tr><tr><td>DR Congo (2007)</td><td>11</td><td>0.206</td></tr><tr><td>DR Congo (2013-14)</td><td>11</td><td>0.127</td></tr><tr><td>Ethiopia (2005)</td><td>11</td><td>0.000</td></tr><tr><td>Ethiopia (2011)</td><td>11</td><td>0.000</td></tr><tr><td>Ethiopia (2016)</td><td>11</td><td>0.000</td></tr><tr><td>Gabon (2012)</td><td>10</td><td>0.186</td></tr><tr><td>Ghana (2003)</td><td>10</td><td>0.000</td></tr><tr><td>Ghana (2014)</td><td>10</td><td>0.000</td></tr><tr><td>Guinea (2005)</td><td>8</td><td>0.000</td></tr><tr><td>Guinea (2012)</td><td>5</td><td>0.000</td></tr><tr><td>Kenya (2003)</td><td>8</td><td>0.000</td></tr><tr><td>Kenya (2008-09)</td><td>8</td><td>0.000</td></tr><tr><td>Liberia (2013)</td><td>5</td><td>0.000</td></tr><tr><td>Malawi (2004)</td><td>3</td><td>0.862</td></tr><tr><td>Malawi (2010)</td><td>27</td><td>0.856</td></tr><tr><td>Malawi (2015-16)</td><td>3</td><td>0.876</td></tr><tr><td>Mali (2006)</td><td>9</td><td>0.000</td></tr><tr><td>Mali (2012-13)</td><td>6</td><td>0.008</td></tr><tr><td>Senegal (2005)</td><td>11</td><td>0.000</td></tr><tr><td>Senegal (2010-11)</td><td>14</td><td>0.000</td></tr><tr><td>Sierra Leone (2008)</td><td>4</td><td>0.023</td></tr><tr><td>Sierra Leone (2013)</td><td>14</td><td>0.028</td></tr><tr><td>Togo (2013-14)</td><td>6</td><td>0.000</td></tr><tr><td>Uganda (2011)</td><td>10</td><td>0.000</td></tr><tr><td>Zambia (2007)</td><td>9</td><td>0.592</td></tr><tr><td>Zambia (2013-14)</td><td>10</td><td>0.592</td></tr></tbody></table>

Notes: This table lists the 32 Demographic and Health Surveys (DHS) across 18 countries that constitute the final analytical sample, which is restricted to matrilineal and patrilineal women with valid HIV test results. Column (2) reports the number of distinct subnational regions (DHS regions) identified in each survey. Column (3) presents the average proportion of matrilineal individuals calculated at the regional level. The reported regional averages confirm the presence of substantial intra-regional heterogeneity in ancestral kinship norms, providing the necessary variation to implement the identification strategy based on region-by-survey fixed effects.
<table><caption>Table A3: List of Ancestral Ethnic Groups (EA) and Kinship Organization</caption><thead><tr><th>Ethnic group</th><th>Kinship</th><th>Obs.</th><th>Ethnic group</th><th>Kinship</th><th>Obs.</th></tr></thead><tbody><tr><td>acholi</td><td>Patrilineal</td><td>584</td><td>kpelle</td><td>Patrilineal</td><td>1402</td></tr><tr><td>afar</td><td>Patrilineal</td><td>2125</td><td>kran</td><td>Patrilineal</td><td>212</td></tr><tr><td>ahaggaren</td><td>Matrilineal</td><td>40</td><td>kru</td><td>Patrilineal</td><td>404</td></tr><tr><td>alagya</td><td>Matrilineal</td><td>4</td><td>kuku</td><td>Patrilineal</td><td>25</td></tr><tr><td>alur</td><td>Patrilineal</td><td>682</td><td>kulango</td><td>Matrilineal</td><td>184</td></tr><tr><td>ambo</td><td>Matrilineal</td><td>19</td><td>kunda</td><td>Matrilineal</td><td>1186</td></tr><tr><td>anuak</td><td>Patrilineal</td><td>499</td><td>kundu</td><td>Patrilineal</td><td>537</td></tr><tr><td>anyi</td><td>Matrilineal</td><td>302</td><td>labwor</td><td>Patrilineal</td><td>1</td></tr><tr><td>arusi</td><td>Patrilineal</td><td>8272</td><td>laketonga</td><td>Matrilineal</td><td>597</td></tr><tr><td>assini</td><td>Matrilineal</td><td>38</td><td>lala</td><td>Matrilineal</td><td>804</td></tr><tr><td>avikam</td><td>Matrilineal</td><td>2</td><td>lamba</td><td>Matrilineal</td><td>459</td></tr><tr><td>awuna</td><td>Patrilineal</td><td>157</td><td>lango</td><td>Patrilineal</td><td>732</td></tr><tr><td>baditu</td><td>Patrilineal</td><td>21</td><td>lendu</td><td>Patrilineal</td><td>7</td></tr><tr><td>fafia</td><td>Patrilineal</td><td>54</td><td>lenge</td><td>Patrilineal</td><td>407</td></tr><tr><td>bagirmi</td><td>Patrilineal</td><td>52</td><td>lobi</td><td>Matrilineal</td><td>737</td></tr><tr><td>bajun</td><td>Patrilineal</td><td>220</td><td>luapula</td><td>Matrilineal</td><td>157</td></tr><tr><td>bako</td><td>Patrilineal</td><td>70</td><td>luba</td><td>Patrilineal</td><td>5898</td></tr><tr><td>bakwe</td><td>Patrilineal</td><td>648</td><td>luchazi</td><td>Matrilineal</td><td>127</td></tr><tr><td>bambara</td><td>Patrilineal</td><td>2812</td><td>lugbara, madi</td><td>Patrilineal</td><td>877</td></tr><tr><td>bamileke</td><td>Patrilineal</td><td>2600</td><td>luimbe</td><td>Matrilineal</td><td>22</td></tr><tr><td>bamum</td><td>Patrilineal</td><td>178</td><td>luo</td><td>Patrilineal</td><td>910</td></tr><tr><td>banda</td><td>Patrilineal</td><td>199</td><td>luvale</td><td>Matrilineal</td><td>623</td></tr><tr><td>banen</td><td>Patrilineal</td><td>19</td><td>makua</td><td>Matrilineal</td><td>3155</td></tr><tr><td>banna</td><td>Patrilineal</td><td>35</td><td>male</td><td>Patrilineal</td><td>1454</td></tr><tr><td>banyang</td><td>Patrilineal</td><td>35</td><td>malinke</td><td>Patrilineal</td><td>4865</td></tr><tr><td>basa</td><td>Patrilineal</td><td>2106</td><td>mao</td><td>Patrilineal</td><td>24</td></tr><tr><td>basketo</td><td>Patrilineal</td><td>5</td><td>margi</td><td>Patrilineal</td><td>8</td></tr><tr><td>bata</td><td>Patrilineal</td><td>6</td><td>masa</td><td>Patrilineal</td><td>436</td></tr><tr><td>baule</td><td>Matrilineal</td><td>835</td><td>masai</td><td>Patrilineal</td><td>102</td></tr><tr><td>baya</td><td>Patrilineal</td><td>1332</td><td>matakam</td><td>Patrilineal</td><td>218</td></tr><tr><td>bete</td><td>Patrilineal</td><td>675</td><td>mbum</td><td>Patrilineal</td><td>407</td></tr><tr><td>bisa</td><td>Patrilineal</td><td>509</td><td>mende</td><td>Patrilineal</td><td>4670</td></tr><tr><td>bobo</td><td>Patrilineal</td><td>692</td><td>meru</td><td>Patrilineal</td><td>421</td></tr><tr><td>bodi</td><td>Patrilineal</td><td>20</td><td>minianka</td><td>Patrilineal</td><td>541</td></tr><tr><td>buem</td><td>Patrilineal</td><td>174</td><td>mongo</td><td>Patrilineal</td><td>1275</td></tr><tr><td>burji</td><td>Patrilineal</td><td>36</td><td>mossi</td><td>Patrilineal</td><td>6717</td></tr><tr><td>chewa</td><td>Matrilineal</td><td>6812</td><td>mundang</td><td>Patrilineal</td><td>155</td></tr><tr><td>chiga</td><td>Patrilineal</td><td>632</td><td>musgu</td><td>Patrilineal</td><td>3</td></tr><tr><td>chokwe</td><td>Matrilineal</td><td>993</td><td>ndoro</td><td>Matrilineal</td><td>22</td></tr><tr><td>dan</td><td>Patrilineal</td><td>430</td><td>ngere</td><td>Patrilineal</td><td>372</td></tr><tr><td>darasa</td><td>Patrilineal</td><td>350</td><td>ngombe</td><td>Patrilineal</td><td>1377</td></tr><tr><td>daza</td><td>Patrilineal</td><td>693</td><td>ngumba</td><td>Patrilineal</td><td>144</td></tr><tr><td>didinga</td><td>Patrilineal</td><td>14</td><td>ngumbi</td><td>Matrilineal</td><td>83</td></tr><tr><td>diola</td><td>Patrilineal</td><td>546</td><td>nuer</td><td>Patrilineal</td><td>660</td></tr><tr><td>diula</td><td>Patrilineal</td><td>451</td><td>nunuma</td><td>Patrilineal</td><td>589</td></tr><tr><td>dogon</td><td>Patrilineal</td><td>1018</td><td>nyanja</td><td>Matrilineal</td><td>729</td></tr><tr><td>efik</td><td>Patrilineal</td><td>2</td><td>nyoro</td><td>Patrilineal</td><td>447</td></tr><tr><td>ekoi</td><td>Patrilineal</td><td>7</td><td>pltonga</td><td>Matrilineal</td><td>14</td></tr><tr><td>ewe</td><td>Patrilineal</td><td>2746</td><td>ruanda</td><td>Patrilineal</td><td>422</td></tr><tr><td>fajulu</td><td>Patrilineal</td><td>58</td><td>rundi</td><td>Patrilineal</td><td>83</td></tr><tr><td>fali</td><td>Patrilineal</td><td>67</td><td>safwa</td><td>Patrilineal</td><td>115</td></tr><tr><td>fang</td><td>Patrilineal</td><td>1284</td><td>sapo</td><td>Patrilineal</td><td>106</td></tr><tr><td>fur</td><td>Matrilineal</td><td>129</td><td>sara</td><td>Patrilineal</td><td>1254</td></tr><tr><td>fut</td><td>Patrilineal</td><td>140</td><td>sena</td><td>Matrilineal</td><td>1031</td></tr><tr><td>futajalonke</td><td>Patrilineal</td><td>7983</td><td>senufo</td><td>Patrilineal</td><td>826</td></tr><tr><td>ga, adangme</td><td>Patrilineal</td><td>631</td><td>senufo, minianka</td><td>Patrilineal</td><td>886</td></tr><tr><td>gagu</td><td>Patrilineal</td><td>18</td><td>shako</td><td>Patrilineal</td><td>115</td></tr><tr><td>galab</td><td>Patrilineal</td><td>2</td><td>sherbro</td><td>Patrilineal</td><td>272</td></tr></tbody></table>
<table><thead><tr><th>Ethnic group</th><th>Kinship</th><th>Obs.</th><th>Ethnic group</th><th>Kinship</th><th>Obs.</th></tr></thead><tbody><tr><td>ganda</td><td>Patrilineal</td><td>2175</td><td>shila</td><td>Matrilineal</td><td>7</td></tr><tr><td>gbande</td><td>Patrilineal</td><td>100</td><td>shuwa</td><td>Patrilineal</td><td>1309</td></tr><tr><td>gisu</td><td>Patrilineal</td><td>729</td><td>sidamo</td><td>Patrilineal</td><td>1195</td></tr><tr><td>gola</td><td>Patrilineal</td><td>215</td><td>soga</td><td>Patrilineal</td><td>1078</td></tr><tr><td>gurage</td><td>Patrilineal</td><td>1967</td><td>somali</td><td>Patrilineal</td><td>2887</td></tr><tr><td>gurma</td><td>Patrilineal</td><td>2505</td><td>songhai</td><td>Patrilineal</td><td>701</td></tr><tr><td>guro</td><td>Patrilineal</td><td>118</td><td>soninke</td><td>Patrilineal</td><td>1541</td></tr><tr><td>gusii</td><td>Patrilineal</td><td>441</td><td>suri</td><td>Patrilineal</td><td>1</td></tr><tr><td>hammar</td><td>Patrilineal</td><td>91</td><td>susu</td><td>Patrilineal</td><td>1472</td></tr><tr><td>ife</td><td>Patrilineal</td><td>131</td><td>tazarawa</td><td>Patrilineal</td><td>117</td></tr><tr><td>iwa</td><td>Patrilineal</td><td>691</td><td>teita</td><td>Patrilineal</td><td>120</td></tr><tr><td>janjero</td><td>Patrilineal</td><td>70</td><td>teke</td><td>Matrilineal</td><td>369</td></tr><tr><td>jie</td><td>Patrilineal</td><td>261</td><td>tem</td><td>Patrilineal</td><td>1376</td></tr><tr><td>kafa</td><td>Patrilineal</td><td>637</td><td>temne</td><td>Patrilineal</td><td>3891</td></tr><tr><td>kamba</td><td>Patrilineal</td><td>611</td><td>teso</td><td>Patrilineal</td><td>964</td></tr><tr><td>kaonde</td><td>Matrilineal</td><td>769</td><td>thonga</td><td>Patrilineal</td><td>2508</td></tr><tr><td>kikuyu</td><td>Patrilineal</td><td>1428</td><td>tigon</td><td>Patrilineal</td><td>30</td></tr><tr><td>kipsigis</td><td>Patrilineal</td><td>650</td><td>tigre</td><td>Patrilineal</td><td>4204</td></tr><tr><td>kissi</td><td>Patrilineal</td><td>598</td><td>tiriki</td><td>Patrilineal</td><td>1115</td></tr><tr><td>koko</td><td>Patrilineal</td><td>273</td><td>toma</td><td>Patrilineal</td><td>332</td></tr><tr><td>kom, nsaw</td><td>Matrilineal</td><td>163</td><td>toro</td><td>Patrilineal</td><td>321</td></tr><tr><td>koma</td><td>Patrilineal</td><td>1085</td><td>tuburi</td><td>Patrilineal</td><td>100</td></tr><tr><td>kongo</td><td>Matrilineal</td><td>1390</td><td>tumbuka</td><td>Patrilineal</td><td>2829</td></tr><tr><td>konjo</td><td>Patrilineal</td><td>258</td><td>turkana</td><td>Patrilineal</td><td>42</td></tr><tr><td>konso</td><td>Patrilineal</td><td>46</td><td>vai</td><td>Patrilineal</td><td>214</td></tr><tr><td>koranko</td><td>Patrilineal</td><td>1086</td><td>yao</td><td>Matrilineal</td><td>2117</td></tr><tr><td>koro</td><td>Patrilineal</td><td>40</td><td>yombe</td><td>Matrilineal</td><td>2</td></tr><tr><td>kota</td><td>Patrilineal</td><td>550</td><td></td><td></td><td></td></tr></tbody></table>

*Notes:* This table provides the list of the ancestral ethnic groups included in the final analytical sample. The sample is restricted to female respondents from the DHS surveys for whom HIV test results and ancestral ethnic identity are available. "Ethnic group (EA)" refers to the ethnic group name as classified in the *Ethnographic Atlas* (Murdock, 1967). "Kinship" indicates the ancestral organization, categorized as either Matrilineal or Patrilineal—ethnic groups with alternative kinship structures (e.g., ambilineal, bilinear, or duolineal) are excluded to ensure a clean comparison. "Obs." represents the number of individual-level observations matched to each ancestral group and used in the baseline estimations. The list is presented in two side-by-side sets of three columns to facilitate readability.
<table><caption>Table A4: Summary Statistics (Final Regression Sample)</caption><thead><tr><th></th><th>Matrilineal</th><th>Patrilineal</th><th>Difference</th></tr></thead><tbody><tr><td colspan="4"><em>Individual-level variables</em></td></tr><tr><td>HIV</td><td>0.116<br>(0.320)</td><td>0.035<br>(0.184)</td><td>0.080***<br>(0.002)</td></tr><tr><td>Age</td><td>28.242<br>(9.354)</td><td>28.518<br>(9.628)</td><td>-0.276***<br>(0.062)</td></tr><tr><td>Age squared</td><td>885.119<br>(574.466)</td><td>905.987<br>(600.964)</td><td>-20.869***<br>(3.784)</td></tr><tr><td>Urban</td><td>0.281<br>(0.450)</td><td>0.336<br>(0.472)</td><td>-0.054***<br>(0.003)</td></tr><tr><td>Education</td><td>5.399<br>(3.935)</td><td>3.980<br>(4.364)</td><td>1.419***<br>(0.026)</td></tr><tr><td>Working</td><td>0.577<br>(0.494)</td><td>0.564<br>(0.496)</td><td>0.013***<br>(0.003)</td></tr><tr><td>Wealth</td><td>3.048<br>(1.406)</td><td>3.034<br>(1.467)</td><td>0.013<br>(0.009)</td></tr><tr><td>Christian</td><td>0.853<br>(0.354)</td><td>0.540<br>(0.498)</td><td>0.313***<br>(0.002)</td></tr><tr><td>Muslim</td><td>0.112<br>(0.316)</td><td>0.418<br>(0.493)</td><td>-0.306***<br>(0.002)</td></tr><tr><td>Other/No religion</td><td>0.035<br>(0.184)</td><td>0.042<br>(0.201)</td><td>-0.007***<br>(0.001)</td></tr><tr><td colspan="4"><em>Village-level variables</em></td></tr><tr><td>Altitude</td><td>825.431<br>(402.034)</td><td>769.267<br>(689.785)</td><td>56.165***<br>(2.649)</td></tr><tr><td>Nightlight composite</td><td>2.581<br>(6.904)</td><td>1.947<br>(5.324)</td><td>0.635***<br>(0.045)</td></tr><tr><td>Pop. density (2010)</td><td>1128.533<br>(4446.317)</td><td>1244.256<br>(3611.632)</td><td>-115.722***<br>(29.291)</td></tr><tr><td>Ethnic fractionalization</td><td>0.396<br>(0.278)</td><td>0.261<br>(0.264)</td><td>0.134***<br>(0.002)</td></tr><tr><td>Ethnic polarization</td><td>0.656<br>(0.355)</td><td>0.472<br>(0.397)</td><td>0.184***<br>(0.002)</td></tr><tr><td>Mine(s)</td><td>0.067<br>(0.250)</td><td>0.047<br>(0.211)</td><td>0.020***<br>(0.002)</td></tr><tr><td colspan="4"><em>Ethnicity-level variables</em></td></tr><tr><td>Pastoralism</td><td>0.071<br>(0.056)</td><td>0.279<br>(0.215)</td><td>-0.208***<br>(0.000)</td></tr><tr><td>Polygyny</td><td>0.666<br>(0.472)</td><td>0.516<br>(0.500)</td><td>0.150***<br>(0.003)</td></tr><tr><td>Year of observation in E.A.</td><td>1916.989<br>(14.863)</td><td>1914.724<br>(22.634)</td><td>2.265***<br>(0.098)</td></tr><tr><td><strong>Observations</strong></td><td>23042</td><td>122439</td><td>145481</td></tr></tbody></table>

Notes: This table presents descriptive statistics for the main variables used in the empirical analysis, calculated for the final estimation sample. *Individual-level variables* are sourced directly from survey records. Religion categories are presented as separate indicators in this table to show sample distribution. *Village-level variables* are derived from cluster GPS coordinates spatially joined with external geospatial datasets and author's own calculations. *Ethnicity-level variables* are obtained from the *Ethnographic Atlas* (Murdock, 1967). The table reports means and standard deviations (in parentheses).
## B Robustness and Stability of the Baseline OLS Results

This section reports a series of rigorous tests to ensure the validity and stability of the baseline OLS estimates. I first document the patterns of inter-ethnic unions, showing that matrilineal women are more likely to engage in inter-ethnic marriages (Table B1). I then ensure that the main results are not driven by specific sample compositions or selection within the household. To do so, I replicate the analysis on a constant female sample (Table B2) as well as on a male sample (Table B3) and a constant male sample (Table B4). The analysis reveals that the matrilineal penalty is not strictly confined to females; a positive and statistically significant effect is also observable for males across most specifications, though it is generally less precisely estimated. I then address potential omitted variable bias by assessing the importance of bias from unobservables through an Oster bounding exercise (Table B5) and by demonstrating robustness to an extensive set of historical and geographic controls (Table B6). I further explore sample selection by testing for differential selection into HIV testing for both women and men (Table B7 and Table B8) and provide placebo tests on health outcomes unrelated to sexual behavior (Table B9). Additionally, I verify that the results are not driven by the Democratic Republic of the Congo, where the ethnic matching process is potentially more subject to measurement noise (Table B10). Finally, I explore the institutional and geographic dimensions of the matrilineal penalty by testing alternative control group definitions (Table B11), conducting a regional analysis (Table B12), examining heterogeneity across polygynous status (Table B13), and verifying that the results hold when focusing on country-surveys with sufficient intra-regional variation in kinship norms (Table B14).

<table><caption>Table B1: Ancestral Matrilineality and Inter-Ethnic Marriages (OLS)</caption><thead><tr><th></th><th colspan="2">Husband from a different:</th></tr><tr><th></th><th>Ethnicity<br>(1)</th><th>Matrilineality<br>norm<br>(2)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.063<br>(0.040)</td><td>0.156**<br>(0.068)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>41095</td><td>41095</td></tr><tr><td>Adj. R-squared</td><td>0.254</td><td>0.217</td></tr><tr><td>Clusters</td><td>166</td><td>166</td></tr><tr><td>Countries</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.144</td><td>0.013</td></tr></tbody></table>

*Notes:* This table reports OLS estimates of the relationship between ancestral matrilineality and inter-ethnic marriage patterns. The sample is restricted to coresiding couples where both spouses' ethnic identities are identified and matched to the *Ethnographic Atlas*. In column (1), the dependent variable is a dummy equal to one if the husband belongs to a different ethnic group. In column (2), it is a dummy equal to one if the husband belongs to an ethnic group with a different kinship norm. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean of the corresponding dependent variable among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B2: Ancestral Matrilineality and Female HIV (OLS): Constant sample</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.080***<br>(0.016)</td><td>0.014*<br>(0.008)</td><td>0.015*<br>(0.008)</td><td>0.015**<br>(0.008)</td><td>0.015**<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>145481</td><td>145481</td><td>145481</td><td>145481</td><td>145481</td></tr><tr><td>Adj. R-squared</td><td>0.019</td><td>0.055</td><td>0.071</td><td>0.072</td><td>0.072</td></tr><tr><td>Clusters</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.035</td><td>0.035</td><td>0.035</td><td>0.035</td><td>0.035</td></tr></tbody></table>

*Notes*: This table reports OLS estimates of the relationship between ancestral matrilineality and female HIV status. The sample is restricted to a constant set of observations across all specifications to ensure that the stability of the estimated coefficients is not driven by changes in sample composition. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean HIV prevalence among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

<table><caption>Table B3: Ancestral Matrilineality and Male HIV (OLS)</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.055***<br>(0.011)</td><td>0.009**<br>(0.005)</td><td>0.009*<br>(0.005)</td><td>0.009*<br>(0.005)</td><td>0.009*<br>(0.005)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>128849</td><td>128849</td><td>126397</td><td>123297</td><td>122970</td></tr><tr><td>Adj. R-squared</td><td>0.013</td><td>0.041</td><td>0.057</td><td>0.058</td><td>0.058</td></tr><tr><td>Clusters</td><td>169</td><td>169</td><td>169</td><td>169</td><td>168</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.024</td><td>0.024</td><td>0.024</td><td>0.024</td><td>0.024</td></tr></tbody></table>

*Notes*: This table reports OLS estimates of the relationship between ancestral matrilineality and male HIV status. The sample consists of all men in the DHS male surveys with identified ethnic groups matched to the *Ethnographic Atlas*. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean HIV prevalence among men from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B4: Ancestral Matrilineality and Male HIV (OLS): Constant sample</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.056***<br>(0.011)</td><td>0.008*<br>(0.005)</td><td>0.008*<br>(0.005)</td><td>0.008<br>(0.005)</td><td>0.009*<br>(0.005)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>122970</td><td>122970</td><td>122970</td><td>122970</td><td>122970</td></tr><tr><td>Adj. R-squared</td><td>0.014</td><td>0.041</td><td>0.057</td><td>0.058</td><td>0.058</td></tr><tr><td>Clusters</td><td>168</td><td>168</td><td>168</td><td>168</td><td>168</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.024</td><td>0.024</td><td>0.024</td><td>0.024</td><td>0.024</td></tr></tbody></table>

*Notes*: This table reports OLS estimates of the relationship between ancestral matrilineality and male HIV status. The sample is restricted to a constant set of observations across all specifications to ensure that the stability of the estimated coefficients is not driven by changes in sample composition. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean HIV prevalence among men from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

<table><caption>Table B5: (Oster, 2017) Bounding Exercise: Within-Region Sensitivity to Unobserved Selection</caption><thead><tr><th rowspan="2">β̂<sub>F</sub> ± 1.96 s.e.</th><th colspan="3">Minimum Coeff. Lower Bound (Oster, 2017)</th></tr><tr><th>R<sup>2</sup><sub>Max</sub> =<br>min(1.3R̂<sup>2</sup><sub>F</sub>; 1)<br>(1)</th><th>R<sup>2</sup><sub>Max</sub> =<br>min(1.5R̂<sup>2</sup><sub>F</sub>; 1)<br>(2)</th><th>R<sup>2</sup><sub>Max</sub> =<br>min(2R̂<sup>2</sup><sub>F</sub>; 1)<br>(3)</th></tr></thead><tbody><tr><td>[0.0009; 0.0288]</td><td>0.016</td><td>0.017</td><td>0.019</td></tr></tbody></table>

*Notes*: Column 1 reports the 95% confidence interval of the matrilineality coefficient from the fully controlled specification. Columns 2–4 report the bias-adjusted coefficients based on (Oster, 2017). These estimates evaluate the potential role of unobserved selection by benchmarking the full specification against a restricted model that includes only region-survey fixed effects. Following the proportional selection logic, the bias-adjusted coefficient is defined as: $\beta^* = \hat{\beta}_F - (\hat{\beta}_R - \hat{\beta}_F) \times \frac{R_{max}^2 - R_F^2}{R_F^2 - R_R^2}$, where $\hat{\beta}_F$ and $R_F^2$ are the estimate and $R_F^2$ from the full specification, while $\hat{\beta}_R$ and $R_R^2$ are the values from the restricted specification. The results are reported for three scenarios of $R_{max}^2$. A stable or increasing coefficient relative to the OLS baseline indicates that the matrilineal penalty is robust to unobserved selection within regional units.
<table><caption>Table B6: Ancestral Matrilineality and Female HIV: Robustness to Additional Historical and Village-Level Controls</caption><thead><tr><th></th><th colspan="9">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.018**<br>(0.007)</td><td>0.016**<br>(0.007)</td><td>0.016**<br>(0.007)</td><td>0.016**<br>(0.007)</td><td>0.017**<br>(0.007)</td><td>0.017**<br>(0.008)</td><td>0.018**<br>(0.008)</td><td>0.017**<br>(0.007)</td><td>0.019***<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Murdock Ethnic Groups Historical Controls:</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ln(1 + Total slave exports/area)</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Contact explorer route</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Colonial railway</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Catholic misiones/area</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Protestant missions/area</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>BFBS mission/area</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Distance Saharan route</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Distance Saharan node</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Cities in 1940</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Precolonial conflicts (1400-1700)</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td><strong>DHS Villages Contemporary Controls:</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Village's prop. of males circumcised</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Village's prop. of males who paid for sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>123092</td><td>123092</td><td>123092</td><td>124322</td><td>124322</td><td>124322</td><td>108741</td><td>121772</td><td>91180</td></tr><tr><td>Adj. R-squared</td><td>0.074</td><td>0.073</td><td>0.074</td><td>0.073</td><td>0.073</td><td>0.073</td><td>0.076</td><td>0.075</td><td>0.077</td></tr><tr><td>Clusters</td><td>132</td><td>132</td><td>132</td><td>133</td><td>133</td><td>133</td><td>145</td><td>147</td><td>108</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td><td>16</td><td>18</td><td>16</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.039</td><td>0.039</td><td>0.039</td><td>0.039</td><td>0.039</td><td>0.039</td><td>0.037</td><td>0.035</td><td>0.041</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and female HIV status, including additional historical and contemporary controls. The sample is restricted to women originating from either traditionally matrilineal or patrilineal ethnic groups. All baseline controls and fixed effects are the same as defined in Table 1. *Murdock Ethnic Groups Historical Controls* are measured at the ancestral *Murdock* ethnic group level and include: "ln(1 + Slave exports/area)" (log of 1 + total slaves taken from the ethnic group per km²); "Explorer route" and "Colonial railway" (dummies for the presence of European routes or railways on ancestral land); "Missions" (number of Catholic, Protestant, and BFBS missions per km²); "Distance Saharan route/node" (minimum distance to Saharan trade routes or cities); "Cities in 1400" and "Precolonial conflicts" (historical intensity of urbanization and warfare). *DHS Villages Contemporary Controls* are measured at DHS cluster level and include the "Proportion of males circumcised" and the "Proportion of males who paid for sex". "Mean dep. var. (Patri.)" denotes the mean HIV prevalence among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B7: Ancestral Matrilineality and Selection into HIV Testing</caption><thead><tr><th></th><th>Consent HIV Test<br>(1)</th><th>Take HIV Test<br>(2)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>-0.020***<br>(0.006)</td><td>-0.021***<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>117933</td><td>117933</td></tr><tr><td>Adj. R-squared</td><td>0.048</td><td>0.049</td></tr><tr><td>Clusters</td><td>129</td><td>129</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.957</td><td>0.953</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and the probability of consenting to and completing an HIV test. In column (1), the dependent variable is a dummy for whether the respondent consented to the HIV test. In column (2), the dependent variable is a dummy for whether the respondent actually took the test (i.e., a blood sample was collected). The sample is restricted to women selected for the HIV testing protocol. The number of observations is smaller than in the main analysis because data on the consent process and HIV test take-up are not uniformly reported across all DHS waves, leading to the exclusion of the following surveys: Burkina Faso (2003), Cameroon (2004), Ethiopia (2005), Ghana (2003), Guinea (2012), Kenya (2003), Malawi (2004), Senegal (2005), and Uganda (2011). All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean of the corresponding dependent variable among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B8: Ancestral Matrilineality and Selection into HIV Testing: Male Sample</caption><thead><tr><th></th><th>Consent HIV Test<br>(1)</th><th>Take HIV Test<br>(2)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>-0.021***<br>(0.006)</td><td>-0.022***<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>102196</td><td>102196</td></tr><tr><td>Adj. R-squared</td><td>0.049</td><td>0.051</td></tr><tr><td>Clusters</td><td>128</td><td>128</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.940</td><td>0.935</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and the probability of consenting to and completing an HIV test for the male sample. In column (1), the dependent variable is a dummy for whether the respondent consented to the HIV test. In column (2), the dependent variable is a dummy for whether the respondent actually took the test (i.e., a blood sample was collected). The number of observations is smaller than in the main analysis because data on the consent process and HIV test take-up are not uniformly reported across all DHS waves, leading to the exclusion of the following surveys: Burkina Faso (2003), Cameroon (2004), Ethiopia (2005), Ghana (2003), Guinea (2012), Kenya (2003), Malawi (2004), Senegal (2005), and Uganda (2011). All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean of the corresponding dependent variable among men from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B9: Ancestral Matrilineality and Physical Health Placebo Outcomes</caption><thead><tr><th></th><th>Anemia<br>(1)</th><th>BMI<br>(2)</th><th>Rohrer<br>Index<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.010<br>(0.013)</td><td>-0.131<br>(0.128)</td><td>-0.046<br>(0.103)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>103983</td><td>125622</td><td>125622</td></tr><tr><td>Adj. R-squared</td><td>0.083</td><td>0.208</td><td>0.170</td></tr><tr><td>Clusters</td><td>115</td><td>146</td><td>146</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.426</td><td>22.024</td><td>13.909</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and physical health placebo outcomes. In column (1), *Anemia* is a dummy variable indicating whether the respondent has any level of anemia. In column (2), *BMI* is the Body Mass Index. In column (3), the *Rohrer Index* is a measure of body composition (*weight/height*³). These health indicators are available only for the female sample. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean of the corresponding dependent variable among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

<table><caption>Table B10: Ancestral Matrilineality and HIV: Excluding the Democratic Republic of the Congo</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.084***<br>(0.016)</td><td>0.016**<br>(0.008)</td><td>0.018**<br>(0.008)</td><td>0.016*<br>(0.008)</td><td>0.017**<br>(0.008)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>141306</td><td>141306</td><td>139542</td><td>136519</td><td>136162</td></tr><tr><td>Adj. R-squared</td><td>0.020</td><td>0.054</td><td>0.071</td><td>0.072</td><td>0.073</td></tr><tr><td>Clusters</td><td>168</td><td>168</td><td>168</td><td>168</td><td>166</td></tr><tr><td>Countries</td><td>17</td><td>17</td><td>17</td><td>17</td><td>17</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.037</td><td>0.037</td><td>0.037</td><td>0.037</td><td>0.037</td></tr></tbody></table>

Notes: This table reproduces the main estimates from Table 1 while excluding all observations from the Democratic Republic of the Congo (DRC). The dependent variable is a dummy for whether the respondent tested positive for HIV. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri.)" denotes the mean HIV prevalence among women from patrilineal ethnic groups for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B11: Ancestral Matrilineality and HIV: Sensitivity to Alternative Reference Groups</caption><thead><tr><th></th><th colspan="5">HIV</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.071***<br>(0.016)</td><td>-0.003<br>(0.008)</td><td>-0.000<br>(0.008)</td><td>-0.001<br>(0.008)</td><td>-0.002<br>(0.008)</td></tr><tr><td>Ind. Controls</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Region-survey FE</td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>189325</td><td>189325</td><td>187405</td><td>183131</td><td>182774</td></tr><tr><td>Adj. R-squared</td><td>0.011</td><td>0.060</td><td>0.079</td><td>0.080</td><td>0.080</td></tr><tr><td>Clusters</td><td>198</td><td>198</td><td>198</td><td>198</td><td>196</td></tr><tr><td>Countries</td><td>18</td><td>18</td><td>18</td><td>18</td><td>18</td></tr><tr><td>Mean dep. var. (Patri./Other)</td><td>0.043</td><td>0.043</td><td>0.043</td><td>0.044</td><td>0.044</td></tr></tbody></table>

*Notes:* This table reproduces the main analysis from Table 1 but expands the reference group. While the baseline specification restricts the control group to patrilineal individuals, this table includes individuals from ethnic groups with alternative inheritance and descent rules as defined in the *Ethnographic Atlas* (Murdock, 1967). Specifically, the reference group now additionally includes individuals originating from ethnic groups categorized as *ambilineal*, *bilateral*, *duolateral*, *mixed*, and *quasi-lineage* systems. The dependent variable is a dummy for whether the respondent tested positive for HIV. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean dep. var. (Patri./Other)" denotes the mean HIV prevalence among women in the reference group (i.e. patrilineal and other) for the estimation sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B12: Ancestral Matrilineality and Female HIV: Regional Heterogeneity</caption><thead><tr><th rowspan="2">Sample</th><th colspan="4">HIV</th></tr><tr><th>All<br>(1)</th><th>Western<br>Africa<br>(2)</th><th>Central<br>Africa<br>(3)</th><th>East & Southern<br>Africa<br>(4)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.015**<br>(0.007)</td><td>-0.003<br>(0.005)</td><td>-0.009<br>(0.008)</td><td>0.025**<br>(0.010)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>145481</td><td>54766</td><td>24139</td><td>66576</td></tr><tr><td>Adj. R-squared</td><td>0.072</td><td>0.012</td><td>0.036</td><td>0.083</td></tr><tr><td>Clusters</td><td>169</td><td>53</td><td>42</td><td>84</td></tr><tr><td>Countries</td><td>18</td><td>9</td><td>4</td><td>5</td></tr><tr><td>Mean HIV</td><td>0.048</td><td>0.020</td><td>0.037</td><td>0.075</td></tr><tr><td>Std. dev. HIV</td><td>0.214</td><td>0.139</td><td>0.190</td><td>0.263</td></tr><tr><td>Mean Matri.</td><td>0.158</td><td>0.039</td><td>0.073</td><td>0.287</td></tr><tr><td>Std. dev. Matri.</td><td>0.365</td><td>0.194</td><td>0.261</td><td>0.453</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and HIV prevalence across geographic regions. Column (1) reproduces the baseline estimation from Table 1, column (5). Columns (2) to (4) restrict the analysis to specific regional subsamples: *Western Africa* (Burkina Faso, Côte d'Ivoire, Ghana, Guinea, Liberia, Mali, Sierra Leone, Senegal and Togo); *Central Africa* (Cameroon, Chad, DR Congo and Gabon); and *Eastern & Southern Africa* (Ethiopia, Kenya, Malawi, Uganda and Zambia). The dependent variable is a dummy for whether the respondent tested positive for HIV. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean HIV" and "Std. dev. HIV" denote the mean and standard deviation of female HIV prevalence within each estimation sample. "Mean Matri." and "Std. dev. Matri." denote the mean and standard deviation of the ancestral matrilineality indicator for the same sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B13: Ancestral Matrilineality and Female HIV: Heterogeneity by Polygynous Union Status</caption><thead><tr><th rowspan="3">Sample</th><th colspan="2">HIV</th></tr><tr><th>Ind. not in<br>Polygynous<br>Union</th><th>Ind. in<br>Polygynous<br>Union</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.015**<br>(0.007)</td><td>0.004<br>(0.013)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>116978</td><td>27003</td></tr><tr><td>Adj. R-squared</td><td>0.074</td><td>0.074</td></tr><tr><td>Clusters</td><td>169</td><td>160</td></tr><tr><td>Mean HIV</td><td>0.051</td><td>0.034</td></tr><tr><td>Std. dev. HIV</td><td>0.220</td><td>0.180</td></tr><tr><td>Mean Matri.</td><td>0.176</td><td>0.084</td></tr><tr><td>Std. dev. Matri.</td><td>0.381</td><td>0.278</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and HIV prevalence, splitting the sample by the respondent's union type. Column (1) restricts the sample to women not in a polygynous union, while Column (2) includes only women currently in a polygynous union. The dependent variable is a dummy for whether the respondent tested positive for HIV. All individual, village-geographic, and ethnic group controls, as well as region-survey fixed effects, are the same as defined in Table 1. "Mean HIV" and "Std. dev. HIV" denote the mean and standard deviation of female HIV prevalence within each estimation sample. "Mean Matri." and "Std. dev. Matri." denote the mean and standard deviation of the ancestral matrilineality indicator for the same sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table B14: Ancestral Matrilineality and Female HIV (OLS): Robustness to Intra-Regional Variation</caption><thead><tr><th rowspan="3">Sample</th><th colspan="3">HIV</th></tr><tr><th>Baseline</th><th colspan="2">Country-surveys with avg.<br>proportion Matrilineal per region</th></tr><tr><th>(1)</th><th>[5%; 95%]<br>(2)</th><th>[10%; 90%]<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.015**<br>(0.007)</td><td>0.016***<br>(0.006)</td><td>0.017***<br>(0.006)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>145481</td><td>42485</td><td>39908</td></tr><tr><td>Adj. R-squared</td><td>0.072</td><td>0.088</td><td>0.087</td></tr><tr><td>Clusters</td><td>169</td><td>62</td><td>56</td></tr><tr><td>Surveys</td><td>32</td><td>10</td><td>9</td></tr><tr><td>Mean HIV (Patri.)</td><td>0.035</td><td>0.055</td><td>0.059</td></tr><tr><td>Std. dev. HIV (Patri.)</td><td>0.184</td><td>0.227</td><td>0.236</td></tr><tr><td>Mean matri.</td><td>0.158</td><td>0.522</td><td>0.549</td></tr><tr><td>Std. dev. Matri.</td><td>0.365</td><td>0.500</td><td>0.498</td></tr></tbody></table>

Notes: This table assesses the robustness of the main estimates by restricting the sample to country-surveys with sufficient intra-regional variation in kinship norms, as detailed in Table A2. Column (1) reproduces the baseline estimation from Table 1, column (5). Column (2) includes country-surveys where the average regional proportion of matrilineal individuals is between 5% and 95%: Burkina Faso (2003), Côte d'Ivoire (2011-12), DR Congo (2007, 2013-14), Gabon (2012), Malawi (2004, 2010, 2015-16), and Zambia (2007, 2013-14). Column (3) further restricts the sample to those with a proportion between 10% and 90%, which excludes Burkina Faso (2003). All specifications include the full set of individual, geographic, and ancestral controls, as well as region-survey fixed effects. "Mean HIV (Patri.)" and "Std. dev. HIV (Patri.)" report the mean and standard deviation of HIV prevalence among women from patrilineal ethnic groups for the estimation sample. "Mean Matri." and "Std. dev. Matri." report the mean and standard deviation of the ancestral matrilineality indicator for the same sample. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
C Sensitivity of Geographic RDD and Non-Parametric Robustness

This section delves deeper into the geographic identification strategy presented in the main text by testing the sensitivity of the “matrilineal penalty” observed among the female sample to various technical specifications and alternative populations. I first document the sensitivity of the results to the inclusion of observations located in close proximity to the ethnic boundary; the attenuation of coefficients in the non-doughnut specifications (Table C1) suggests measurement noise or localized cross-border behaviors, justifying the ”doughnut RDD” as the preferred specification. I then replicate the main geographic RDD analysis on the male sample (Table C2); the absence of a significant effect for men suggests that the matrilineal penalty is rooted in gender-specific institutional dynamics rather than unobserved geographic shocks that would affect both sexes. Furthermore, I show the robustness of the baseline results for the female sample using quadratic polynomials and a Fuzzy RDD approach (Table C3 and Table C4) to account for potential imperfect adherence to ancestral norms. Finally, Table C5 complements this analysis with a non-parametric Nearest Neighbor Matching (NNM) approach, confirming the convergence of the results across different functional forms on the restricted geographic sample.
<table><caption>Table C1: Matrilineal Ancestral Homelands and Female HIV: RDD Estimates without Doughnut Exclusion</caption><thead><tr><th rowspan="2">Bandwidth</th><th colspan="3">HIV</th></tr><tr><th>100km<br>(1)</th><th>150km<br>(2)</th><th>200km<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.010<br>(0.008)</td><td>0.006<br>(0.007)</td><td>0.008<br>(0.006)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic-Pair FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>72002</td><td>79920</td><td>87000</td></tr><tr><td>Adj. R-squared</td><td>0.078</td><td>0.082</td><td>0.085</td></tr><tr><td>Clusters</td><td>108</td><td>108</td><td>109</td></tr><tr><td>Countries</td><td>14</td><td>14</td><td>14</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.054</td><td>0.056</td><td>0.058</td></tr></tbody></table>

Notes: This table reports geographic Regression Discontinuity Design (RDD) estimates of the relationship between ancestral matrilineality and female HIV prevalence. Unlike the baseline specification, this table does not follow a doughnut approach and includes all observations located immediately adjacent to the ethnic boundaries. The sample includes all women residing within the matrilineal or patrilineal ancestral ethnic homelands as defined by historical maps. By construction, the analysis is restricted to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined and thus lack the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. Columns (1)–(3) report estimates for bandwidths of 100, 150, and 200 km, respectively. In each specification, a linear polynomial in distance to the boundary is estimated separately on each side of the threshold. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical matrilineal boundaries. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among women in patrilineal territories for the estimation sample. All specifications include individual, village-geographic controls, as well as country-survey and ethnic-pair fixed effects. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table C2: Matrilineal Ancestral Homelands and Male HIV: Doughnut RDD Estimates</caption><thead><tr><th rowspan="2">Bandwidth</th><th colspan="3">HIV</th></tr><tr><th>100km<br>(1)</th><th>150km<br>(2)</th><th>200km<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.011<br>(0.013)</td><td>0.001<br>(0.008)</td><td>0.002<br>(0.007)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic-Pair FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>55701</td><td>62708</td><td>68861</td></tr><tr><td>Adj. R-squared</td><td>0.063</td><td>0.064</td><td>0.067</td></tr><tr><td>Clusters</td><td>103</td><td>104</td><td>106</td></tr><tr><td>Countries</td><td>14</td><td>14</td><td>14</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.039</td><td>0.040</td><td>0.044</td></tr></tbody></table>

Notes: This table reports geographic Regression Discontinuity Design (RDD) estimates of the relationship between ancestral matrilineality and male HIV prevalence. Following the baseline “doughnut” specification, all observations within a 25 km radius of the ethnic boundary are excluded to mitigate local geographic spillovers and measurement error arising from historical boundary uncertainty. The sample includes all men residing within the matrilineal or patrilineal ancestral ethnic homelands as defined by historical maps. By construction, the analysis is restricted to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined and thus lack the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. Columns (1)–(3) report estimates for bandwidths of 100, 150, and 200 km, respectively. In each specification, a linear polynomial in distance to the boundary is estimated separately on each side of the threshold. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical matrilineal boundaries. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among men in patrilineal territories for the estimation sample. All specifications include individual and village-geographic controls, as well as country-survey and ethnic-pair fixed effects. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table C3: Matrilineal Ancestral Homelands and Female HIV: Doughnut RDD Estimates with Quadratic Polynomials</caption><thead><tr><th rowspan="2">Bandwidth</th><th colspan="3">HIV</th></tr><tr><th>100km<br>(1)</th><th>150km<br>(2)</th><th>200km<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.027<br>(0.027)</td><td>0.028*<br>(0.015)</td><td>0.028*<br>(0.017)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic-Pair FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>62912</td><td>70830</td><td>77910</td></tr><tr><td>Adj. R-squared</td><td>0.065</td><td>0.071</td><td>0.077</td></tr><tr><td>Clusters</td><td>103</td><td>104</td><td>106</td></tr><tr><td>Countries</td><td>14</td><td>14</td><td>14</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.053</td><td>0.055</td><td>0.058</td></tr></tbody></table>

*Notes:* This table reports geographic Regression Discontinuity Design (RDD) estimates of the relationship between ancestral matrilineality and female HIV prevalence, using a flexible quadratic polynomial in distance to the boundary. Following the baseline “doughnut” specification, all observations within a 25 km radius of the ethnic boundary are excluded to mitigate local geographic spillovers and measurement error arising from historical boundary uncertainty. The sample includes all women residing within the matrilineal or patrilineal ancestral ethnic homelands as defined by historical maps. By construction, the analysis is restricted to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined and thus lack the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. Columns (1)–(3) report estimates for bandwidths of 100, 150, and 200 km, respectively. In each specification, the quadratic polynomial is estimated separately on each side of the threshold. “Matrilineality” is a geographic treatment indicator assigned based on the village’s location relative to the historical matrilineal boundaries. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among women in patrilineal territories for the estimation sample. All specifications include individual and village-geographic controls, as well as country-survey and ethnic-pair fixed effects. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table C4: Matrilineal Ancestral Homelands and Female HIV: Doughnut Fuzzy RDD Estimates</caption><thead><tr><th rowspan="2">Bandwidth</th><th colspan="3">HIV</th></tr><tr><th>100km<br>(1)</th><th>150km<br>(2)</th><th>200km<br>(3)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>0.222*<br>(0.120)</td><td>0.116*<br>(0.070)</td><td>0.111<br>(0.071)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic-Pair FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Country-survey FE</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>62912</td><td>70830</td><td>77910</td></tr><tr><td>Adj. R-squared</td><td>-0.035</td><td>0.008</td><td>0.012</td></tr><tr><td>Clusters</td><td>103</td><td>104</td><td>106</td></tr><tr><td>Countries</td><td>14</td><td>14</td><td>14</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.053</td><td>0.055</td><td>0.058</td></tr><tr><td>Kleibergen-Paap F-stat</td><td>5.001</td><td>11.453</td><td>8.966</td></tr></tbody></table>

*Notes:* This table reports fuzzy geographic Regression Discontinuity Design (RDD) estimates of the relationship between ancestral matrilineality and female HIV prevalence. Following a 2SLS procedure, the ancestral matrilineality of the respondent's ethnic group is instrumented by the geographic treatment indicator assigned based on the village's location relative to the historical matrilineal boundaries. Following the baseline “doughnut” specification, all observations within a 25 km radius of the ethnic boundary are excluded to mitigate local geographic spillovers and measurement error arising from historical boundary uncertainty. The sample includes all women residing within the matrilineal or patrilineal ancestral ethnic homelands as defined by historical maps. By construction, the analysis is restricted to 14 countries, as Ethiopia, Guinea, Senegal, and Uganda do not host matrilineal ethnic homelands as geographically defined and thus lack the matrilineal-patrilineal boundaries required for the ethnic-pair fixed effect design. Columns (1)–(3) report second-stage estimates for bandwidths of 100, 150, and 200 km, respectively. In each specification, a linear polynomial in distance to the boundary is estimated separately on each side of the threshold. “Mean dep. var. (Patri.)” denotes the mean HIV prevalence among women in patrilineal territories for the estimation sample. The last row reports the Kleibergen-Paap rk Wald F-statistic for the first stage. All specifications include individual and village-geographic controls, as well as country-survey and ethnic-pair fixed effects. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table C5: Ancestral Matrilineality and Village HIV Prevalence: Nearest Neighbor Matching Estimates</caption><thead><tr><th rowspan="3">Nb. of nearest neighbors</th><th colspan="3">Village's proportion of HIV positive females</th></tr><tr><th>1</th><th>5</th><th>10</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Ancestrally matrilineal area</td><td>0.045***<br>(0.006)</td><td>0.053***<br>(0.004)</td><td>0.056***<br>(0.004)</td></tr><tr><td>Observations</td><td>11383</td><td>11383</td><td>11383</td></tr><tr><td>Mean Dep. Var. (Villages in patri. area)</td><td>0.038</td><td>0.038</td><td>0.038</td></tr></tbody></table>

*Notes*: This table reports the average treatment effect on the treated (ATT) of ancestral matrilineality on the proportion of HIV-positive females at the village level. Estimates are obtained using nearest neighbor matching based on the Mahalanobis distance function, where villages located in ancestrally matrilineal areas are matched with villages in patrilineal areas. Villages are matched based on exogenous geographic and economic fundamentals: altitude, nightlight intensity, population density, and proximity to mining sites. Ethnic composition variables (fractionalization and polarization) are excluded from the matching set to avoid over-controlling for social dynamics, such as inter-ethnic marriage patterns, that may be downstream outcomes of kinship systems. Columns (1), (2), and (3) report results using 1, 5, and 10 nearest neighbors, respectively. Following Abadie and Imbens (2006, 2011), estimates are corrected for the large-sample bias that arises when matching on multiple continuous covariates. “Mean Dep. Var. (Villages in patri. area)” denotes the average female HIV prevalence among villages located in patrilineal ancestral homelands. Robust standard errors are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
# D Alternative Explanations and Sensitivity of Behavioral Mechanisms

This section provides additional evidence regarding the transmission channels of the matrilineal penalty. I first report results for several alternative explanations discarded by the analysis (Table D1, Table D2 and Table D3), confirming that the penalty is not driven by differences in HIV awareness, access to protection, timing of sexual or marital debuts, or fertility desires. I then explore the behavioral mechanisms discussed in the main text using geographic RDD and NNM frameworks. As shown in Table D4, Table D5, Table D6 and Table D7, these estimates lack statistical power and, in some cases, exhibit inconsistent signs. This sensitivity likely stems from the specific nature of populations residing at ethnic boundaries, where increased inter-ethnic contact may alter traditional behavioral patterns, unlike the more stable institutional effect observed on the overall HIV prevalence.

<table><caption>Table D1: Ancestral Matrilineality, Acknowledgment of HIV Risks and Access to Condom (OLS)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Acknowledgment of Risks</th><th colspan="2">Access to condom</th></tr><tr><th>Heard of AIDS<br>(1)</th><th>Heard of STI<br>(2)</th><th>Think condom<br>reduces HIV<br>(3)</th><th>Think having<br>one partner<br>reduces HIV<br>(4)</th><th>Know a<br>source to<br>get condom<br>(5)</th><th>Can get<br>condom<br>(6)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>-0.003<br>(0.004)</td><td>-0.001<br>(0.004)</td><td>-0.007<br>(0.010)</td><td>-0.000<br>(0.007)</td><td>-0.003<br>(0.011)</td><td>0.015<br>(0.011)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>145448</td><td>145411</td><td>117394</td><td>127040</td><td>129010</td><td>69666</td></tr><tr><td>Adj. R-squared</td><td>0.169</td><td>0.153</td><td>0.071</td><td>0.083</td><td>0.356</td><td>0.126</td></tr><tr><td>Clusters</td><td>169</td><td>169</td><td>168</td><td>169</td><td>169</td><td>163</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>0.937</td><td>0.945</td><td>0.802</td><td>0.867</td><td>0.520</td><td>0.630</td></tr></tbody></table>

*Notes:* This table reports OLS estimates of the relationship between ancestral matrilineality and indicators of HIV/STI awareness and access to protection. The unit of observation is a female respondent. The sample includes individuals originating from either traditionally matrilineal or traditionally patrilineal ethnic groups. “Matrilineality” is a dummy variable indicating whether the respondent belongs to a traditionally matrilineal ethnic group. “Heard of AIDS” is a dummy indicating whether an individual has ever heard of AIDS. “Heard of STI” is a dummy indicating whether an individual has ever heard of any STI. “Think condom reduces HIV” is a dummy indicating whether an individual thinks that always using condoms during sex reduces chance of getting HIV. “Think having one partner reduces HIV” is a dummy indicating whether an individual thinks that having only one sexual partner reduces chance of getting HIV. “Know source to get condom” is a dummy indicating whether an individual knows a source to get male condoms. “Can get condom” is a dummy indicating whether an individual can get herself a male condom. All columns include the full set of individual, ancestral, and village-geographic controls, as well as region-by-survey fixed effects, as defined in Table 1. “Mean dep. var. (Patri.)” denotes the mean of the respective outcome among women from patrilineal ethnic groups. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table D2: Ancestral Matrilineality and Sexual Debuts (OLS)</caption><thead><tr><th></th><th>Age first sex<br>(1)</th><th>Age first marriage<br>(2)</th></tr></thead><tbody><tr><td>Matrilineality</td><td>-0.020<br>(0.066)</td><td>0.120<br>(0.096)</td></tr><tr><td>Ind. Controls</td><td>✓</td><td>✓</td></tr><tr><td>Ethnic Group Controls</td><td>✓</td><td>✓</td></tr><tr><td>Village-Geographic Controls</td><td>✓</td><td>✓</td></tr><tr><td>Region-survey FE</td><td>✓</td><td>✓</td></tr><tr><td>Observations</td><td>118431</td><td>111545</td></tr><tr><td>Adj. R-squared</td><td>0.135</td><td>0.153</td></tr><tr><td>Clusters</td><td>169</td><td>169</td></tr><tr><td>Mean dep. var. (Patri.)</td><td>16.388</td><td>17.574</td></tr></tbody></table>

Notes: This table reports OLS estimates of the relationship between ancestral matrilineality and indicators of sexual and marital debuts. The unit of observation is a female respondent. The sample includes individuals originating from either traditionally matrilineal or traditionally patrilineal ethnic groups. “Matrilineality” is a dummy variable indicating whether the respondent belongs to a traditionally matrilineal ethnic group. “Age first sex” is the age at first sexual intercourse. “Age first marriage” is the age at start of first marriage or union. All columns include the full set of individual, ancestral, and village-geographic controls, as well as region-by-survey fixed effects, as defined in Table 1. “Mean dep. var. (Patri.)” denotes the mean of the respective outcome among women from patrilineal ethnic groups. Standard errors clustered at the ancestral ethnic group level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.