www.pnas.org
Open in
urlscan Pro
104.18.2.247
Public Scan
Submitted URL: https://doi.org/10.1073/pnas.1507151112
Effective URL: https://www.pnas.org/doi/full/10.1073/pnas.1507151112
Submission: On July 29 via manual from US — Scanned from DE
Effective URL: https://www.pnas.org/doi/full/10.1073/pnas.1507151112
Submission: On July 29 via manual from US — Scanned from DE
Form analysis 11 forms found in the DOM
Name: defaultQuickSearch — GET /action/doSearch
<form action="/action/doSearch" name="defaultQuickSearch" method="get" id="header-quick-search">
<fieldset>
<legend class="sr-only">Quick Search anywhere</legend>
<div class="input-group option-0 animation-underline"><label for="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc620" class="sr-only">Enter text / DOI / article type / keywords / authors / etc</label><input type="search"
id="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc620" name="AllField" autocomplete="off" placeholder="Enter text / DOI / article type / keywords / authors / etc" data-auto-complete-max-words="7" data-auto-complete-max-chars="32"
data-contributors-conf="3" data-topics-conf="3" data-publication-titles-conf="3" data-history-items-conf="3" value="" class="quick-search__input form-control autocomplete" role="combobox" aria-haspopup="true" aria-expanded="false"
aria-controls="autoComplete-0" aria-autocomplete="both">
<div class="input-group-append"></div>
</div>
</fieldset>
</form>Name: citationQuickSearch — GET /action/quickLink
<form action="/action/quickLink" name="citationQuickSearch" method="get">
<fieldset class="row no-gutters">
<legend class="sr-only">Quick search in Citations</legend>
<div class="input-group option-citation option-1"><label for="citationJournalDropdown" class="sr-only">Journal*</label><select name="quickLinkJournal" id="citationJournalDropdown" required="required" class="form-control animation-underline">
<option disabled="disabled" selected="selected" hidden="hidden" value="">Journal*</option>
<option value="nexus">PNAS Nexus</option>
<option value="pnas">Proceedings of the National Academy of Sciences</option>
</select>
<div class="citationSearch-wrapper"><label for="quickLinkYearba20dcbf-b159-41b6-a89e-49552f0ecc621" class="sr-only">Year</label><input type="search" id="quickLinkYearba20dcbf-b159-41b6-a89e-49552f0ecc621" name="quickLinkYear"
autocomplete="false" placeholder="Year" class="form-control"><label for="quickLinkVolumeba20dcbf-b159-41b6-a89e-49552f0ecc621" class="sr-only">Volume*</label><input type="search" id="quickLinkVolumeba20dcbf-b159-41b6-a89e-49552f0ecc621"
name="quickLinkVolume" autocomplete="false" placeholder="Volume*" class="form-control"><label for="quickLinkIssueba20dcbf-b159-41b6-a89e-49552f0ecc621" class="sr-only">Issue*</label><input type="search"
id="quickLinkIssueba20dcbf-b159-41b6-a89e-49552f0ecc621" name="quickLinkIssue" autocomplete="false" placeholder="Issue*" class="form-control"><label for="quickLinkPageba20dcbf-b159-41b6-a89e-49552f0ecc621" class="sr-only">Page/eLocation
ID*</label><input type="search" id="quickLinkPageba20dcbf-b159-41b6-a89e-49552f0ecc621" name="quickLinkPage" autocomplete="false" placeholder="Page/eLocation ID*" class="form-control"><input type="hidden" name="quickLink" value="true">
<div class="input-group-append"></div>
</div>
<div class="citation-annotation">*Required field</div>
</div>
</fieldset>
</form>Name: publicationQuickSearch — GET /action/doSearch
<form action="/action/doSearch" name="publicationQuickSearch" method="get" id="header-quick-search-2">
<fieldset>
<legend class="sr-only">Quick Search in Journals</legend>
<div class="input-group option-2 option-journal animation-underline"><label for="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc622" class="sr-only">Enter text / DOI / article type / keywords / authors / etc</label><input type="search"
id="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc622" name="AllField" autocomplete="off" placeholder="Enter text / DOI / article type / keywords / authors / etc" data-auto-complete-max-words="7" data-auto-complete-max-chars="32"
data-contributors-conf="3" data-topics-conf="3" data-publication-titles-conf="3" data-history-items-conf="3" value="" class="quick-search__input form-control autocomplete" role="combobox" aria-haspopup="true" aria-expanded="false"
aria-controls="autoComplete-1" aria-autocomplete="both"><input type="hidden" name="SeriesKey" value="nexus">
<div class="input-group-append"></div>
</div>
</fieldset>
</form>Name: publicationQuickSearch — GET /action/doSearch
<form action="/action/doSearch" name="publicationQuickSearch" method="get" id="header-quick-search-3">
<fieldset>
<legend class="sr-only">Quick Search in Journals</legend>
<div class="input-group option-3 option-journal animation-underline"><label for="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc623" class="sr-only">Enter text / DOI / article type / keywords / authors / etc</label><input type="search"
id="AllFieldba20dcbf-b159-41b6-a89e-49552f0ecc623" name="AllField" autocomplete="off" placeholder="Enter text / DOI / article type / keywords / authors / etc" data-auto-complete-max-words="7" data-auto-complete-max-chars="32"
data-contributors-conf="3" data-topics-conf="3" data-publication-titles-conf="3" data-history-items-conf="3" value="" class="quick-search__input form-control autocomplete" role="combobox" aria-haspopup="true" aria-expanded="false"
aria-controls="autoComplete-2" aria-autocomplete="both"><input type="hidden" name="SeriesKey" value="pnas">
<div class="input-group-append"></div>
</div>
</fieldset>
</form>Name: defaultQuickSearch — GET /action/doSearch
<form action="/action/doSearch" name="defaultQuickSearch" method="get" id="quick-search-from-85eb734f-3d8c-4802-b914-d40de047cf3c0" role="search">
<fieldset>
<legend class="sr-only">Quick Search anywhere</legend>
<div class="input-group option-0 align-items-center animation-underline animation-underline--primary"><label for="AllField85eb734f-3d8c-4802-b914-d40de047cf3c0" class="sr-only">Enter words / phrases / DOI / ISBN / keywords / authors /
etc</label><input type="search" id="AllField85eb734f-3d8c-4802-b914-d40de047cf3c0" name="AllField" autocomplete="off" placeholder="Enter words / phrases / DOI / ISBN / keywords / authors / etc" data-auto-complete-max-words="7"
data-auto-complete-max-chars="32" data-contributors-conf="3" data-topics-conf="3" data-publication-titles-conf="3" data-history-items-conf="3" value="" class="quick-search__input form-control autocomplete" role="combobox" aria-haspopup="true"
aria-expanded="false" aria-controls="autoComplete-3" aria-autocomplete="both">
<div class="input-group-append"><button type="submit" title="Search" class="btn quick-search__btn p-0"><i class="icon-search align-middle"></i></button></div>
</div>
</fieldset>
</form>Name: frmCitmgr — POST /action/downloadCitation
<form action="/action/downloadCitation" name="frmCitmgr" class="citation-form" method="post" target="_self"><input type="hidden" name="doi" value="10.1073/pnas.1507151112"><input type="hidden" name="downloadFileName" value="csp_112_"><input
type="hidden" name="include" value="abs">
<fieldset class="format-select">
<label for="slct_format">Format</label>
<div class="select-container">
<select id="slct_format" name="format" class="js__slcInclude">
<option value="" selected="selected">Please select one from the list</option>
<option value="ris">RIS (ProCite, Reference Manager)</option>
<option value="endnote">EndNote</option>
<option value="bibtex">BibTex</option>
<option value="medlars">Medlars</option>
<option value="refworks">RefWorks</option>
</select>
</div>
<div class="form-check custom-control custom-checkbox py-2"><input id="direct-import" type="checkbox" name="direct" value="true" checked="checked" class="custom-control-input"><label for="direct-import"
class="custom-control-label text-uppercase font-weight-bold">Direct Import</label></div>
</fieldset>
<footer class="form-footer">
<input onclick="onCitMgrSubmit()" class="btn btn-primary" type="submit" name="submit" value="Download">
</footer>
</form>Name: mc-embedded-subscribe-form — POST https://pnas.us15.list-manage.com/subscribe/post?u=371d96023d24fb109b778621f&id=b053c2ccce
<form action="https://pnas.us15.list-manage.com/subscribe/post?u=371d96023d24fb109b778621f&id=b053c2ccce" method="post" id="mc-embedded-subscribe-form" name="mc-embedded-subscribe-form" class="validate signup-ad__form" target="_blank"
novalidate="">
<input type="email" value="" name="EMAIL" class="signup-ad__input" id="mce-EMAIL" placeholder="name@example.com" required="">
<!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->
<input style="display:none;" type="text" name="b_371d96023d24fb109b778621f_b053c2ccce" tabindex="-1" value="" aria-hidden="true">
<button type="submit" name="subscribe" id="mc-embedded-subscribe" class="signup-ad__submit">Subscribe<i aria-hidden="true" class="icon-arrow-right"></i></button>
</form>POST /action/requestUsername
<form action="/action/requestUsername" class="raa-form" method="post"><input type="hidden" name="requestUsername" value="requestUsername">
<p>Can't sign in? Forgot your username? <br> Enter your email address below and we will send you your username</p>
<div class="alert alert-danger d-none"></div>
<div class="form-row">
<div class="col">
<div class="form-group">
<label for="6eccc267-4bd8-4882-88e8-d9b06094352c.email">Email</label>
<input id="6eccc267-4bd8-4882-88e8-d9b06094352c.email" class="form-control" type="text" name="email" value="" size="15">
</div>
</div>
</div>
<div class="form-row">
<div class="col">
<div class="form-group">
<div class="username-recaptcha-ajax"></div>
</div>
</div>
</div>
<div class="form-row">
<div class="col text-right">
<button type="submit" data-dismiss="modal" class="btn btn-submit">
<span>Submit</span>
<span aria-hidden="true" class="icon-arrow-right ml-3"></span>
</button>
</div>
</div>
</form>POST /action/registration
<form action="/action/registration" class="registration-form raa-form" method="post"><input type="hidden" name="redirectUri" value="/doi/full/10.1073/pnas.1507151112">
<div class="form-row">
<div class="col">
<div class="form-group">
<label for="f96c9a0d-f4a9-4ca3-a52b-99fb097c56d6.email">Email</label>
<input id="f96c9a0d-f4a9-4ca3-a52b-99fb097c56d6.email" class="email form-control" type="email" name="email" value="" required="required">
</div>
</div>
</div>
<div class="form-row">
<div class="col-12 col-sm-6">
<a href="/action/showLogin" class="show-login">
Returning user
</a>
</div>
<div class="col text-right">
<button type="submit" data-dismiss="modal" class="btn btn-submit">
<span>Submit</span>
<span aria-hidden="true" class="icon-arrow-right ml-3"></span>
</button>
</div>
</div>
</form>POST /action/doLogin
<form action="/action/doLogin" id="loginForm" class="raa-form login-form " method="post"><input type="hidden" name="id" value="465aa1e9-3090-4f74-8ca8-f0d260f07eeb">
<input type="hidden" name="redirectUri" value="/doi/full/10.1073/pnas.1507151112">
<input type="hidden" name="loginUri" value="/doi/full/10.1073/pnas.1507151112">
<input type="hidden" name="popup" value="true">
<div class="form-row">
<div class="col-12 col-sm-6">
<div class="form-group">
<div class="form-group">
<div class="label ">
<label for="login">Email<span class="required-marker" aria-hidden="true">*</span></label>
</div>
<input id="login" class="form-control" type="text" name="login" value="" size="15" placeholder="Enter your email" aria-required="true">
</div>
</div>
<div class="actions raa-form__reset-password text-right mt-n4">
</div>
</div>
<div class="col">
<div class="form-group">
<div class="form-group">
<div class="label ">
<label for="password">Password<span class="required-marker" aria-hidden="true">*</span></label>
</div>
<input id="password" class="form-control" type="password" name="password" value="" autocomplete="off" placeholder="Enter your password" aria-required="true">
<span class="password-eye-icon icon-eye hidden"></span>
</div>
</div>
<div class="raa-form__reset-password text-right">
<span>Forgot your password? Reset it </span>
<a href="/action/requestResetPassword" type="button" data-target="#modal--resetPassword" data-toggle="modal" data-dismiss="modal">here</a>
</div>
</div>
</div>
<div class="form-row mt-4">
<div class="col-12 col-sm-6 text-left">
<div class="custom-control custom-checkbox">
<input id="465aa1e9-3090-4f74-8ca8-f0d260f07eeb-remember" class="custom-control-input" type="checkbox" name="remember" value="true">
<label for="465aa1e9-3090-4f74-8ca8-f0d260f07eeb-remember" class="custom-control-label"> Keep me logged in </label>
</div>
</div>
<div class="col text-right">
<button type="submit" class="btn btn-submit">
<span>Login</span>
<span aria-hidden="true" class="icon-arrow-right ml-3"></span>
</button>
</div>
</div>
<div class="form-row border-top mt-3 pt-3">
<div class="col raa-form__register">
<span>Don’t have an account? Register</span>
<a href="/action/registration">
here
</a>
<a href="/action/ssostart?redirectUri=%2Fdoi%2Ffull%2F10.1073%2Fpnas.1507151112" class="float-right">
<div class="institution-login">
Institutional Login
</div>
</a>
</div>
</div>
</form>POST /action/verifyPhoneNumber
<form action="/action/verifyPhoneNumber" method="post"><input type="hidden" name="submit" value="submit">
<div class="alert alert-danger d-none"></div>
<div class="form-row">
<div class="col">
<div class="form-group">
<label for="946b33a7-d695-4878-8fb4-546789ae57b3-code">Enter the verification code</label>
<input id="946b33a7-d695-4878-8fb4-546789ae57b3-code" class="form-control" type="text" name="verificationCode" value="" size="15" pattern="^[0-9]{6}$" required="required">
</div>
</div>
</div>
<div class="form-row">
<div class="col text-right">
<button type="submit" class="btn btn-submit">
<span>Submit</span>
<span aria-hidden="true" class="icon-arrow-right ml-3"></span>
</button>
</div>
</div>
</form>Text Content
* ARTICLES
Current IssueLatest ArticlesSpecial FeaturesList of IssuesPNAS Nexus
* Front Matter
* AUTHORS
Information for AuthorsEditorial and Journal PoliciesSubmission
ProceduresPublication Charges
* Topics
* Physical Sciences
* Social Sciences
* Biological Sciences
FEATURED TOPICS
* Physics
* Chemistry
* Sustainability Science
ARTICLES BY TOPIC
* Applied Mathematics
* Applied Physical Sciences
* Astronomy
* Biophysics and Computational Biology
* Computer Sciences
* Earth, Atmospheric, and Planetary Sciences
* Engineering
* Environmental Sciences
* Mathematics
* Statistics
FEATURED TOPICS
* Anthropology
* Sustainability Science
ARTICLES BY TOPIC
* Demography
* Economic Sciences
* Environmental Sciences
* Political Sciences
* Psychological and Cognitive Sciences
FEATURED TOPIC
* Anthropology
* Sustainability Science
ARTICLES BY TOPIC
* Agricultural Sciences
* Applied Biological Sciences
* Biochemistry
* Biophysics and Computational Biology
* Cell Biology
* Developmental Biology
* Ecology
* Environmental Sciences
* Evolution
* Genetics
* Immunology and Inflammation
* Medical Sciences
* Microbiology
* Neuroscience
* Pharmacology
* Physiology
* Plant Biology
* Population Biology
* Psychological and Cognitive Sciences
* Systems Biology
*
0 Cart
Sign in Register
Individual Login Institutional Login
Submit
Quick Search anywhere
Enter text / DOI / article type / keywords / authors / etc
Quick search in Citations
Journal*Journal*PNAS NexusProceedings of the National Academy of Sciences
YearVolume*Issue*Page/eLocation ID*
*Required field
Quick Search in Journals
Enter text / DOI / article type / keywords / authors / etc
Quick Search in Journals
Enter text / DOI / article type / keywords / authors / etc
Searching:
Anywhere
AnywhereCitationPNAS NexusProceedings of the National Academy of Sciences
Advanced Search Search
SUGGESTED SEARCHES:
Covid-19 Demography Mpox Climate Change Gun Violence
Sign in Register
Individual Login Institutional Login
Quick Search anywhere
Enter words / phrases / DOI / ISBN / keywords / authors / etc
* ARTICLES
* Current Issue
* Latest Articles
* Special Features
* List of Issues
* PNAS Nexus
* Front Matter
* AUTHORS
* Information for Authors
* Editorial and Journal Policies
* Submission Procedures
* Publication Charges
* Topics
*
*
*
*
*
Terms of Service Privacy Policy Accessibility
back
Physical Sciences
FEATURED TOPICS
* Physics
* Chemistry
* Sustainability Science
ARTICLES BY TOPIC
* Applied Mathematics
* Applied Physical Sciences
* Astronomy
* Biophysics and Computational Biology
* Computer Sciences
* Earth, Atmospheric, and Planetary Sciences
* Engineering
* Environmental Sciences
* Mathematics
* Statistics
back
Social Sciences
FEATURED TOPICS
* Anthropology
* Sustainability Science
ARTICLES BY TOPIC
* Demography
* Economic Sciences
* Environmental Sciences
* Political Sciences
* Psychological and Cognitive Sciences
back
Biological Sciences
FEATURED TOPIC
* Anthropology
* Sustainability Science
ARTICLES BY TOPIC
* Agricultural Sciences
* Applied Biological Sciences
* Biochemistry
* Biophysics and Computational Biology
* Cell Biology
* Developmental Biology
* Ecology
* Environmental Sciences
* Evolution
* Genetics
* Immunology and Inflammation
* Medical Sciences
* Microbiology
* Neuroscience
* Pharmacology
* Physiology
* Plant Biology
* Population Biology
* Psychological and Cognitive Sciences
* Systems Biology
back
Topics
Physical Sciences Social Sciences Biological Sciences
Research Article
Anthropology
Open access
Share on
*
*
*
*
NO EVIDENCE THAT POLYGYNOUS MARRIAGE IS A HARMFUL CULTURAL PRACTICE IN NORTHERN
TANZANIA
David W. Lawson david.lawson@lshtm.ac.uk, Susan James, Esther Ngadaya, +2 ,
Bernard Ngowi, Sayoki G. M. Mfinanga, and Monique Borgerhoff Mulder -2Authors
Info & Affiliations
Edited by James Holland Jones, Stanford University, Stanford, CA, and accepted
by the Editorial Board September 23, 2015 (received for review May 14, 2015)
October 26, 2015
112 (45) 13827-13832
https://doi.org/10.1073/pnas.1507151112
View related content
Letter
March 02, 2016
Polygyny and child health revisited
Matthias Rieger, Natascha Wagner
This article has a reply
Letter
March 02, 2016
Reply to Rieger and Wagner: Context matters when studying purportedly harmful
cultural practices
David W. Lawson, Susan James [...] Monique Borgerhoff Mulder
28,45147
METRICS
TOTAL VIEWS28,451
* Last 12 Months4,797
TOTAL CITATIONS47
* Last 12 Months9
View all metrics
* Contents
Vol. 112 | No. 45
* Significance
* Abstract
* Results
* Discussion
* Materials and Methods
* SI Text
* Other Supporting Information Files
* Acknowledgments
* Supporting Information
* References
* * Information & Authors
* Metrics & Citations
* View Options
* References
* Media
* Share
SIGNIFICANCE
Polygynous marriage is commonly regarded as a harmful cultural practice,
detrimental to women and children at the individual and group level. We present
counterevidence that polygyny is often positively associated with food security
and child health within communities and that, although polygyny and health are
negatively associated at the group level, such differences are accounted for by
alternative socioecological factors. These results support models of polygyny
based on female choice and suggest that, in some contexts, prohibiting polygyny
could be costly for women and children by restricting marital options. Our study
highlights the dangers of naive analyses of aggregated population data and the
importance of considering locally realizable alternatives and context dependency
when considering the health implications of cultural practices.
ABSTRACT
Polygyny is cross-culturally common and a topic of considerable academic and
policy interest, often deemed a harmful cultural practice serving the interests
of men contrary to those of women and children. Supporting this view,
large-scale studies of national African demographic surveys consistently
demonstrate that poor child health outcomes are concentrated in polygynous
households. Negative population-level associations between polygyny and
well-being have also been reported, consistent with the hypothesis that modern
transitions to socially imposed monogamy are driven by cultural group selection.
We challenge the consensus view that polygyny is harmful, drawing on multilevel
data from 56 ethnically diverse Tanzanian villages. We first demonstrate the
vulnerability of aggregated data to confounding between ecological and
individual determinants of health; while across villages polygyny is associated
with poor child health and low food security, such relationships are absent or
reversed within villages, particularly when children and fathers are coresident.
We then provide data indicating that the costs of sharing a husband are offset
by greater wealth (land and livestock) of polygynous households. These results
are consistent with models of polygyny based on female choice. Finally, we show
that village-level negative associations between polygyny prevalence, food
security, and child health are fully accounted for by underlying differences in
ecological vulnerability (rainfall) and socioeconomic marginalization (access to
education). We highlight the need for improved, culturally sensitive measurement
tools and appropriate scales of analysis in studies of polygyny and other
purportedly harmful practices and discuss the relevance of our results to
theoretical accounts of marriage and contemporary population policy.
SIGN UP FOR PNAS ALERTS.
Get alerts for new articles, or get an alert when an article is cited.
Manage alerts
Recent years have witnessed growing recognition of the importance of gender in
all aspects of international development (1). This shift includes domestic and
international efforts to abolish so-called “harmful cultural practices,” a term
used to describe practices of, typically nonwestern, cultures deemed detrimental
to well-being, most often with regard to women and children (SI Text). Most
attention has focused on female genital cutting and on child and forced marriage
(2, 3). In many policy-orientated texts, this label is also given to polygynous
marriage (hereafter polygyny). For example, the United Nations Convention on the
Elimination of All Forms of Discrimination Against Women states that polygyny
“contravene[s] a woman’s right to equality with men and can have such serious
emotional and financial consequences for her and her dependents that such
marriages ought to be discouraged and prohibited” (2). Such statements are
frequently presented as stylized facts and made without discussion of supporting
evidence. However, a recent spate of articles, mostly based on large-scale
African Demographic and Health Surveys (DHS), conclude that polygyny is indeed
harmful, reporting that children in polygynous households are consistently more
likely to be of ill health or die in early childhood than children in monogamous
households (4–8). Reviews of the literature have also informed policy in
developed countries, including via the presentation of expert evidence in a
recent retrial of the legal prohibition of polygyny in Canada (9).
Historically, more than 80% of preindustrial societies permitted polygyny (10).
Today it is most prevalent in sub-Saharan Africa (11). If women and children do
not benefit from polygyny then why is it so common? Evolutionary anthropologists
have long puzzled the costs and benefits of polygyny (12). This literature,
drawing on small-scale field studies of specific cultural contexts, reaches a
consensus on the benefits of polygyny to men; polygynous men generally have
higher reproductive success than their monogamous counterparts (13–16). The
potential benefits of, and motivation for, polygyny for women are less clear.
The “polygyny-threshold model” posits that polygyny occurs when the costs of
sharing a husband are offset by equal or greater resource access than could
otherwise be obtained via monogamy (17, 18). Supporting this model, polygynous
men are typically wealthier than monogamous men (19, 20), and several studies
show no apparent deficit in reproductive success or child health for
polygynously married women (19, 21). However, in other cases, polygyny is
associated with relatively poor child health (20, 22–24). Poor outcomes for
women and/or children do not necessarily imply a rejection of the polygyny
threshold model (12, 19). However, these findings have been interpreted as
evidence of sexual conflict, with polygyny maximizing total reproductive success
for men at the cost of suboptimal outcomes for individual wives and children
(25). Drawing generalizable conclusions regarding the potential costs of
polygyny from the anthropological literature alone is difficult (25, 26).
Findings are mixed, study sites are rarely regionally or nationally
representative, and small sample sizes raise issues of statistical power. Given
these problems, the consistency of findings presented in recent large-scale,
representatively sampled demographic studies of polygyny and child health is
seductive (4–8). However, as we will argue, studies relying on highly aggregated
data bring their own, often overlooked, methodological problems (27), problems
that are acute when contrasting polygynous and monogamous households, in part
because the former tend to be most common in remote and/or marginalized groups
facing numerous socioecological barriers to health (SI Text).
Not only policy, but also grand theory, is built on the view that polygyny is
harmful. It has been argued that cultural shifts to “socially imposed monogamy”
in modern stratified societies can be accounted for by detrimental effects of
polygyny at the group level, including costs to child health (28, 29). Most
recently, Henrich et al. (28) assert that monogamy evolves by cultural group
selection, with normative polygyny (i) incentivizing strategies of reduced
paternal care, so that male effort is diverted into accumulating wives rather
than raising offspring, and (ii) increasing the propensity for social unrest
driven by a larger pool of unmarried men. To support the specific claim that
polygyny has negative group-wide consequences for children, Henrich et al. (28)
rely on data from large-scale demographic studies, as well as on selected
population-specific contrasts where children in polygynous households experience
relatively poor well-being. Consistent with the claim of greater social unrest
in polygynous groups, the authors review evidence that the proportion of
unmarried men positively predicts national rates of rape, murder, assault,
theft, and fraud. However, such crude comparisons have limited inferential value
in the face of many potential confounds. A recent review reveals no clear
association between adult sex ratio, a likely correlate of the proportion of
unmarried men, and violent crime (30).
Given the significance of the purported harmful effects of polygyny for both
policy and our understanding of marriage systems, we conducted an innovative
study addressing both individual and group-level relationships between polygyny,
food security, and child health. We draw on multilevel data from 56 villages in
northern Tanzania (Fig. S1). Tanzania experiences a high burden of food
insecurity and malnutrition; 45% of children are stunted by World Health
Organization (WHO) standards (31), a measure of developmental potential
predictive of both later physical and cognitive functioning (32). One in four
married women in rural Tanzania have at least one cowife (31), and female status
is poor; internationally Tanzania scores 124/152 on the Gender Inequality Index
(33). In many respects, our study combines the relative strengths of prior
large-scale demographic and small-scale anthropological studies (SI Text). We
sampled more households (n = 3,584) than the Tanzanian DHS for the same regions
(34). However, unlike DHS studies, we incorporate data on ethnicity and
livelihood-specific measures of household wealth (i.e., land cultivated and
livestock owned), and, crucially, sufficient village-level data to enable a
statistically robust consideration of within and between-village variation. Four
main ethnic groups reside in the area, including the highly polygynous Maasai
and Sukuma, the moderately polygynous Rangi and the predominantly monogamous
Meru (34) (Tables S1 and S2). This setup provides a unique opportunity to
consider relationships between polygyny and health in a context of varied and
transitioning marital norms.
Fig. S1.
Location of the 56 study villages included in the Whole Village Project.
Ethnicity is coded as the most common ethnicity in each village. Red circle,
Maasai village; orange triangle, Rangi village; green diamond, Sukuma village;
blue square, Meru village; white diamond, other ethnicity village. Reproduced
from ref. 34.Open in viewer
Table S1.
Demographic characteristics by household type and ethnicity for working sample
(n = 1764 households, 2833 children)
Ethnic groupMale-headed monogamousMale-headed polygynousFemale-headed
polygynousFemale-headed monogamousSukuma No. of households (no. of children <5
y)289 (577)109 (223)23 (47)5 (8) Mean head age in years (SD)44 (13)48 (13)40
(11)33 (8) Mean household size (SD)8.2 (3.8)9.2 (4.3)8.7 (3.0)6.4 (1.1) Mean no.
<5 y (SD)2.0 (1.0)2.3 (1.3)2.0 (0.9)1.6 (0.9) Mean no. 5-<15 y (SD)2.6 (1.8)2.7
(2.2)2.6 (1.4)1.8 (0.8) Mean no. 15–64 y (SD)3.4 (2.0)4.0 (2.3)4.0 (1.9)3.0
(1.0) Mean no. 65+ y (SD)0.2 (0.5)0.2 (0.5)0.1 (0.3)0.0 (0.0)Maasai No. of
households (no. of children <5 y)143 (207)82 (127)92 (145)42 (59) Mean head age
in years (SD)39 (13)47 (12)34 (11)30 (14) Mean household size (SD)5.4 (1.9)6.1
(2.3)6.5 (3.0)5.5 (1.6) Mean no. <5 y (SD)1.4 (0.7)1.6 (0.9)1.6 (0.8)1.4
(0.6) Mean no. 5-<15 y (SD)1.5 (1.2)1.7 (1.3)2.3 (2.0)1.8 (1.2) Mean no. 15–64 y
(SD)2.4 (1.0)2.5 (1.0)2.5 (1.7)2.1 (0.9) Mean no. 65+ y (SD)0.1 (0.3)0.2
(0.4)0.2 (0.6)0.2 (0.6)Rangi No. of households (no. of children <5 y)149 (219)33
(51)4 (4)6 (9) Mean head age in years (SD)40 (11)52 (14)32 (8)38 (13) Mean
household size (SD)6.2 (2.0)7.4 (2.3)4.0 (0.8)5.8 (1.0) Mean no. <5 y (SD)1.4
(0.7)1.4 (0.8)1.0 (0.0)1.5 (0.8) Mean no. 5-<15 y (SD)1.9 (1.2)2.8 (1.6)0.8
(1.0)2.0 (0.9) Mean no. 15–64 y (SD)2.8 (1.1)2.9 (1.3)2.3 (1.3)2.3 (1.4) Mean
no. 65+ y (SD)0.1 (0.3)0.2 (0.5)0.0 (0.0)0.0 (0.0)Meru No. of households (no. of
children <5 y)135 (170)3 (4)0 (0)9 (13) Mean head age in years (SD)39 (9.6)56
(13)—38 (8) Mean household size (SD)5.6 1.8)5.3 (1.5)—5.9 (0.9) Mean no. <5 y
(SD)1.1 (0.5)1.0 (1.0)—1.3 (0.7) Mean no. 5-<15 y (SD)1.7 (1.3)0.7 (0.6)—2.3
(0.7) Mean no. 15–64 y (SD)2.6 (1.1)3.3 (1.5)—2.2 (0.8) Mean no. 65+ y (SD)0.1
(0.4)0.3 (0.6)—0.0 (0.0)Other ethnicity No. of households (no. of children <5
y)500 (746)79 (136)35 (47)26 (41) Mean head age in years (SD)40 (12)49 (14)33
(7.4)37 (13) Mean household size (SD)6.3 (2.3)7.1 (2.3)6.0 (1.8)6.3 (2.5) Mean
no. <5 y (SD)1.5 (0.7)1.8 (1.0)1.3 (0.5)1.4 (0.6) Mean no. 5-<15 y (SD)1.9
(1.4)1.9 (1.3)2.0 (0.9)2.2 (1.3) Mean no. 15–64 y (SD)2.7 (1.3)3.2 (1.5)2.5
(1.1)2.6 (1.1) Mean no. 65+ y (SD)0.1 (0.4)0.3 (0.5)0.1 (0.3)0.1 (0.4)
EXPAND FOR MORE
Open in viewer
Table S2.
Household and village characteristics for working sample by ethnicity
Household characteristics (n = 1,764 households)Ethnicity of household
headSukumaMaasaiRangiMeruOtherNumber of households426359192147640Main livelihood
of household
headFarming92%25%94%71%83%Livestock0%67%1%3%4%Business4%3%4%12%7%Other/none4%5%2%14%6%Wealth
indexMean log(x + 1) (SD)1.4 (0.4)1.0 (0.5)1.4 (0.4)1.8 (0.4)1.4 (0.4)Cultivates
land?% yes99%66%97%99%95%Acres of land cultivated (for cultivators only)Mean
log(x + 1) (SD)1.9 (0.7)1.4 (0.7)1.7 (0.6)1.2 (0.5)1.5 (0.7)Owns livestock?%
yes70%94%52%88%68%Tropical livestock units (for livestock owners only)Mean log(x
+ 1) (SD)1.8 (1.1)1.6 (0.9)1.4 (0.9)0.9 (0.4)1.4 (0.9) Majority ethnicity of
villageVillage characteristics (n = 56 villages)SukumaMaasaiRangiMeruOtherNumber
of villages where ethnicity is in the majority12117620Polygyny prevalence (% of
household heads polygynously married) (SD)25% (8)38% (9)14% (4)7% (4)16%
(10)Mean annual rainfall in mm3 (SD)847 (69)626 (87)683 (27)973 (126)762
(166)Mean distance to District capital in km (SD)33 (19)35 (20)35 (16)20 (19)33
(16)Percent household heads with nonzero education (SD)70% (8)32% (11)68% (5)78%
(7)75% (14)
EXPAND FOR MORE
Open in viewer
RESULTS
CONTRASTING MONOGAMOUS AND POLYGYNOUS HOUSEHOLDS.
We first estimate relationships between polygyny, food security and the heights
and weights of children under 5 y using linear regression aggregating data
across all villages (Table S3). This method is analytically equivalent to
existing studies of large-scale demographic surveys, which routinely ignore both
ethnic variation and village-level spatial clustering of health (SI Text).
Consistent with such studies, polygynous households have lower food security
than monogamous households (β = −1.56, 95% confidence intervals (95%CI) =
−2.31;-0.81, P < 0.001) and, using WHO standardized z-scores, lower child
height-for-age (HAZ, β = −0.21, 95%CI = −0.34;−0.08, P < 0.01). Child
weight-for-height (WHZ) did not significantly differ between polygynous and
monogamous households (β = −0.06, 95% CI = −0.16; 0.05, P > 0.1).
Table S3.
Linear and multilevel regressions predicting household food security, HAZ, and
WHZ
Model set A: Monogamous vs. polygynous binary codingFood security (n = 1,745) [β
(95% CIs)]Height-for-age Z-score (n = 2,704) [β (95% CIs)]Weight-for-height
Z-score (n = 2,711) [β (95%
CIs)]LinearMultilevelLinearMultilevelLinearMultilevelHousehold type (reference:
monogamous)Polygynous−1.56*** (−2.31; −0.81)0.26 (−0.47; 0.98)−0.21** (−0.34;
−0.08)−0.07 (−0.20; 0.06)−0.06 (−0.16; 0.05)0.00 (−0.12; 0.11)Child age (mo)
(centered at 30 mo)——−0.09*** (−0.10; −0.08)−0.09*** (−0.10; −0.07)−0.03***
(−0.04; −0.01)−0.02*** (−0.04; −0.01)Child age squared (mo2) (centered at 30
mo2)——0.001*** (0.001; 0.002)0.001*** (0.001; 0.002)0.000 (0.000; 0.000)0.000
(0.000; 0.000)Child sex (reference: boy)Girl——0.12* (0.01; 0.24)0.13* (0.02;
0.24)0.06 (−0.04; 0.15)0.05 (−0.05; 0.14)Age of household head (y) (centered at
43 y)0.00 ns (−0.02; 0.03)−0.02† (−0.05; 0.00)0.01** (0.00; 0.01)0.00 (0.00;
0.01)0.01** (0.00; 0.01)0.00† (0.00; 0.01)Season (reference: not
hunger)Hunger—−2.66** (−4.32; −0.99)—−0.37** (−0.62; −0.12)—−0.05 (−0.23;
0.14)Intercept17.33*** (16.94; 17.71)20.62*** (18.07; 23.17)−2.02*** (−2.13;
−1.91)−1.54*** (−1.93; −1.15)0.11* (0.02; 0.21)0.17 (−0.13; 0.46)Random effects
varianceCons—8.65—0.17—0.09Residual—39.58—2.09—1.56Model set B: Monogamous vs.
polygynous four-category coding Household type (reference: monogamous
male-headed)Polygynous male-headed−0.38 (−1.28; 0.51)0.86* (0.01; 1.70)−0.16*
(−0.31; −0.01)−0.09 (−0.24; 0.06)−0.00 (−0.13; 0.13)0.01 (−0.12; 0.14)Polygynous
female-headed−4.20*** (−5.38; −3.02)−1.16† (−2.34; 0.01)−0.37*** (−0.58;
−0.16)−0.07 (−0.30; 0.15)−0.17† (−0.36; 0.01)0.01 (−0.18; 0.20)Monogamous
female-headed−2.72*** (−4.25; −1.19)−0.34 (−1.78; 1.10)−0.32* (−0.59;
−0.04)−0.10 (−0.38; 0.17)0.04 (−0.19; 0.27)0.18 (−0.05; 0.41)Child age (mo)
(centered at 30 mo)——−0.09*** (−0.10; −0.08)−0.09*** (−0.10; −0.07)−0.03***
(−0.04; −0.01)−0.02*** (−0.04; −0.01)Child age squared (mo2) (centered at 30
mo2)——0.001*** (0.001; 0.002)0.001*** (0.001; 0.002)0.000 (0.000; 0.000)0.000
(0.000; 0.000)Child sex (reference: boy)Girl——0.12* (0.01; 0.24)0.13* (0.02;
0.24)0.06 (−0.04; 0.15)0.05 (−0.05; 0.14)Age of household head (y) (centered at
43 y)−0.02 (−0.05; 0.01)−0.03* (−0.06; −0.01)0.01* (0.00; 0.01)0.00 (0.00;
0.01)0.01** (0.00; 0.01)0.00* (0.00; 0.01)Season (reference: not
hunger)Hunger—−2.59** (−4.22; −0.97)—−0.37** (−0.61; −0.12)—−0.05 (−0.24;
0.14)Intercept17.45*** (17.05; 17.84)20.54*** (18.05; 23.03)−2.00*** (−2.11;
−1.89)−1.54*** (−1.92; −1.15)0.11* (0.02; 0.20)0.16 (−0.13; 0.45)Random effects
varianceCons—8.18—0.17—0.09Residual—39.43—2.09—1.56
EXPAND FOR MORE
†
P < 0.1, *P < 0.05, **P < 0.01, and **P < 0.001. Statistically significant
estimates at P < 0.1 are in bold.
Open in viewer
However, there is a clear tendency for relatively polygynous villages and ethnic
groups (particularly the Maasai) to have poor food security and child health
(Fig. 1) (see ref. 34 for a comphensive analysis of ethnic differences in food
security and child health). Accounting for this variance by including a random
effect for village demonstrates that neither food security nor child health are
significantly associated with polygyny when contrasted within villages (food
security: β = 0.26, 95% CI = −0.47; 0.98, P > 0.1; HAZ: β = −0.07, 95% CI =
−0.20; 0.06, P > 0.1; WHZ: β = 0.00, 95% CI = −0.12; 0.11, P > 0.1; Table S3).
As such, multilevel analysis reveals a Simpson’s paradox (27), i.e.,
village-level differences obscure underlying relationships between polygyny,
food security, and child health within villages.
Fig. 1.
Child height-for-age by village sorted by polygyny prevalence. There is strong
ethnic and village-level variation in child health. Relatively monogamous Meru
villages tend to have relatively good child health, whereas relatively
polygynous Maasai villages tend to have relatively poor child health. The dashed
line represents the WHO cutoff for chronic malnutrition. Ethnicity is coded as
the majority ethnic group residing in each village. Error bars represent 95%
CIs. Red circle, Maasai; green diamond, Sukuma; orange triangle, Rangi; blue
square, Meru; white diamond, other ethnicity.Open in viewer
Polygynous men generally resided with their first wife (SI Text), and in only
10% of male-headed polygynous households did multiple wives coreside (most
commonly among the Sukuma, where 17% of polygynously married male household
heads lived with multiple wives). Second or later cowives and their children
typically lived in separate, but often adjacent, dwellings to their husbands.
Distinguishing between these household types reveals that male-headed polygynous
households have significantly higher food security than monogamous households
within villages (β = 0.86, 95% CI = 0.01; 1.70, P < 0.05). Stratified analysis
confirms that a trend toward higher food security for male-headed polygynous
households is present in all three ethnic groups with a substantial prevalence
of polygyny (Fig. 2), although this is only statistically significant in the
Sukuma (β = 2.00, 95% CI = 0.68; 3.32, P < 0.01). Furthermore, in both the
Sukuma and Rangi, children in male-headed polygynous households also had higher
WHZ (Sukuma: β = 0.21, 95% CI = 0.03; 0.39, P < 0.05, Rangi: β = 0.33, 95% CI =
−0.01; 0.67, P = 0.06). Overall, female-headed polygynous households had lower
food security than monogamous households within the same village (β = −1.16, 95%
CI = −2.34; 0.01, P = 0.05), although this pattern did not approach statistical
significance in stratified analyses (Fig. 2 and Tables S3 and S4).
Fig. 2.
Food security and child health by household type. Within villages polygyny is
associated with relatively high food security when households are headed by a
male and relatively low food security when headed by a female (typically later
wife households). Stratified analysis confirms higher food security in the
Sukuma and relatively improved child weight-for-height in both the Sukuma and
Rangi, for male-headed polygynous households. The reference category (dashed
line) is male-headed monogamous households (Table S4 for full model output). +P
< 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.Open in viewer
Table S4.
Multilevel regressions predicting household food insecurity, HAZ, and WHZ
stratified by ethnic group
Independent variableFood security [β (95% CIs)]Height-for-age Z-score [β (95%
CIs)]Weight-for-height Z-score [β (95% CIs)]Sukuma (n = 426)Maasai (n =
358)Rangi (n = 191)Sukuma (n = 829)Maasai (n = 500)Rangi (n = 272)Sukuma (n =
832)Maasai (n = 506)Rangi (n = 269)Household type (reference: monogamous
male-headed)Polygynous male-headed2.00** (0.68; 3.32)1.05 (−1.12; 3.21)0.43
(−1.86; 2.73)−0.01 (−0.22; 0.20)−0.11 (−0.53; 0.30)−0.13 (−0.54; 0.28)0.21*
(0.03; 0.39)−0.15 (−0.51; 0.20)0.33† (−0.01; 0.67)Polygynous female-headed0.38
(−2.16; 2.92)−0.58 (−2.67; 1.51)2.07 (−3.56; 7.71)−0.16 (−0.57; 0.24)0.11
(−0.29; 0.50)−0.67 (−2.15; 0.81)0.15 (−0.20; 0.50)−0.03 (−0.38; 0.32)0.04
(−1.15; 1.23)Monogamous female-headed−2.37 (−7.64; 2.90)−0.39 (−3.17; 2.39)0.07
(−4.53; 4.67)−0.51 (−1.45; 0.43)0.32 (−0.22; 0.86)−0.40 (−1.26; 0.45)−0.09
(−0.90; 0.72)0.26 (−0.20; 0.73)0.10 (−0.59; 0.79)Child age (mo) (centered at 30
mo)———−0.08*** (−0.10; −0.06)−0.06** (−0.09; −0.02)−0.11*** (−0.15; −0.08)−0.01
(−0.03; 0.01)−0.07*** (−0.10; −0.03)−0.01 (−0.04; 0.01)Child age squared (mo2)
(centered at 30 mo2)———0.001*** (0.001; 0.002)0.001* (0.000; 0.001)0.002***
(0.001; 0.002)0.000 (0.000; 0.000)0.001* (0.000; 0.001)0.000 (−0.001;
0.000)Child sex (reference: boy)————0.09 (−0.09; 0.27)−0.03 (−0.34; 0.27)0.21
(0.10; 0.51)−0.01 (−0.17; 0.15)0.17 (−0.09; 0.43)−0.17 (−0.42; 0.08)Age of
household head (y) (centered at 43 y)0.00 (−0.05; 0.04)−0.05 (−0.12; 0.02)0.02
(−0.05; 0.09)0.00 (−0.01; 0.00)0.02** (0.01; 0.03)0.00 (−0.02; 0.01)0.00* (0.00;
0.01)0.01† (0.00; 0.02)−0.01† (−0.02; 0.00)Season (reference: not
hunger)Hunger−1.96** (−3.40; −0.51)−1.37 (−4.47; 1.74)−1.16 (−3.26; 0.95)0.00
(−0.23; 0.22)−0.44* (−0.85; −0.04)−0.20 (−0.60; 0.19)0.01 (−0.15; 0.17)−0.08
(−0.55; 0.39)−0.09 (−0.51; 0.32)Intercept20.42*** (18.17; 22.66)14.45*** (9.02;
19.88)17.95*** (15.21; 20.70)−1.73*** (−2.10; −1.35)−1.58*** (−2.32;
−0.84)−2.13*** (−2.68; −1.58)0.29* (0.02; 0.57)−0.15 (−0.99; 0.69)0.18 (−0.42;
0.77)Random effects
varianceCons0.746.110.260.010.050.000.000.130.05Residual34.6657.3831.561.762.991.611.332.241.02
EXPAND FOR MORE
†
P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001. Statistically significant
estimates at P < 0.1 are in bold.
Open in viewer
POLYGYNY AND WEALTH.
Wealth was measured by an asset-based household wealth index (SI Text), a
generic measure favored by large-scale surveys and used across rural and urban
contexts (35). This measure indicates minimal differences in wealth between
monogamous and polygynous households. However, livelihood-specific measures of
wealth reveal that polygynous households, particularly when male-headed, both
cultivate more land (β = 0.22, 95% CI = 0.14; 0.31, P < 0.001) and own more
livestock (β = 0.49, 95% CI = 0.36; 0.62, P < 0.001) than monogamous households
(Fig. 3). These differences are apparent in all major ethnic groups in
stratified analyses and are robust to statistical adjustment for the number of
adults and young dependents in the household (Tables S5 and S6). Thus,
consistent with the polygyny threshold model, higher wealth presents a strong
candidate mechanism for superior food security and child nutrition in
male-headed polygynous households.
Fig. 3.
Wealth index, land cultivated, and livestock owned by household type. Within
villages polygynous households, particularly when headed by males, cultivate
more land and own more livestock than monogamous households. The reference
category (dashed line) is male-headed monogamous households (Table S6 for full
model output). +P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.Open in viewer
Table S5.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned
Model set A: Without adjustment for number of adults and dependents in
householdWealth index logged (n = 1,721) [β (95% CIs)]Acres cultivated logged (n
= 1,546) [β (95% CIs)]Tropical livestock units logged (n = 1,292) [β (95%
CIs)]Household type (reference: monogamous male-headed)Polygynous
male-headed0.02 (−0.03; 0.07)0.22*** (0.14; 0.31)0.49*** (0.36; 0.62)Polygynous
female-headed−0.06 (−0.13; 0.03)0.15* (0.03; 0.28)0.26** (0.09; 0.43)Monogamous
female-headed−0.05 (−0.14; 0.03)0.00 (−0.15; 0.16)−0.01 (−0.23; 0.21)Age of
household head (y) (centered at 43 y)0.002** (0.001; 0.004)0.01*** (0.01;
0.01)0.01*** (0.01; 0.01)Season (reference: not hunger)Hunger−0.12† (−0.25;
0.01)−0.15 (−0.34; 0.05)0.08 (−0.12; 0.29)Intercept1.54*** (1.33; 1.74)1.73***
(1.43; 2.03)1.25*** (0.94; 1.57)Random effects
varianceCons0.060.120.12Residual0.140.340.70Model Set B: With adjustment for
number adults and dependents in householdWealth index logged (n = 1,657) [β (95%
CIs)]Acres cultivated logged (n = 1,494) [β (95% CIs)]Tropical livestock units
logged (n = 1,239) [β (95% CIs)]Household type (reference: monogamous
male-headed)Polygynous male-headed0.01 (−0.04; 0.06)0.21*** (0.13; 0.29)0.47***
(0.34; 0.59)Polygynous female-headed−0.07* (−0.14; 0.00)0.10 (−0.03; 0.22)0.21*
(0.04; 0.37)Monogamous female-headed−0.07† (−0.16; 0.01)−0.02 (−0.16; 0.13)0.00
(−0.22; 0.22)Age of household head (y) (centered at 43 y)−0.002 (−0.002;
0.001)0.002 (−0.001; 0.004)0.005* (0.001; 0.009)Number of adults in household
(15+ y) (centered on three persons)0.06*** (0.04; 0.07)0.11*** (0.09;
0.13)0.08*** (0.05; 0.12)Number of dependents in household (<15 y) (centered on
four persons)0.01 (0.00; 0.02)0.05*** (0.03; 0.07)0.06*** (0.03; 0.09)Season
(reference: not hunger)Hunger−0.11 (−0.24; 0.02)−0.13 (−0.31; 0.05)0.12 (−0.09;
0.32)Intercept1.53*** (1.33; 1.73)1.73*** (1.46; 2.00)1.22*** (0.90; 1.53)Random
effects varianceCons0.060.100.12Residual0.130.300.66
EXPAND FOR MORE
†
P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001. Statistically significant
estimates at P < 0.1 are in bold.
Open in viewer
Table S6.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned stratified by ethnic group
Model Set A: Without adjustment for number of adults and dependents in
householdWealth index logged [β (95% CIs)]Acres cultivated logged [β (95%
CIs)]Tropical livestock units logged [β (95% CIs)]Sukuma (n = 422)Maasai (n =
340)Rangi (n = 190)Sukuma (n = 420)Maasai (n = 216)Rangi (n = 185)Sukuma (n =
296)Maasai (n = 338)Rangi (n = 99)Household type (reference: monogamous
male-headed)Polygynous male-headed0.07† (0.00; 0.15)0.06 (−0.07; 0.18)0.05
(−0.07; 0.17)0.21** (0.06; 0.35)0.31* (0.07; 0.55)0.12 (−0.10; 0.35)0.39**
(0.12; 0.65)0.65*** (0.40; 0.90)0.40* (0.02; 0.78)Polygynous female-headed−0.02
(−0.17; 0.13)−0.13* (−0.25; 0.00)−0.01 (−0.30; 0.29)0.27† (0.00; 0.54)0.09
(−0.13; 0.31)0.03 (−0.51; 0.56)0.33 (−0.18; 0.83)0.15 (−0.09; 0.39)0.17 (−0.57;
0.92)Monogamous female-headed−0.13 (−0.44; 0.17)−0.16† (−0.32; 0.00)0.04 (−0.20;
0.29)−0.27 (−0.84; 0.30)−0.15 (−0.46; 0.16)0.03 (−0.42; 0.47)−0.53 (−1.95;
0.89)−0.11 (−0.43; 0.21)−0.39 (−1.42; 0.64)Age of household head (y) (centered
at 43 y)0.00 (0.00; 0.00)0.03 (−0.00; 0.01)0.005** (0.001; 0.009)0.01*** (0.01;
0.02)0.00 (0.00; 0.01)0.01*** (0.01; 0.02)0.02*** (0.01; 0.03)0.00 (−0.01;
0.00)0.02** (0.01; 0.03)Season (reference: not hunger)Hunger0.07 (−0.04;
0.18)−0.08 (−0.28; 0.12)−0.03 (−0.14; 0.08)0.03 (−0.28; 0.33)−0.31† (−0.67;
0.05)−0.12 (−0.48; 0.24)0.24 (−0.08; 0.56)0.06 (−0.29; 0.41)−0.24 (−0.85;
0.37)Intercept1.23*** (1.06; 1.40)1.23*** (0.88; 1.57)1.46*** (1.32;
1.60)1.73*** (1.25; 2.20)1.91*** (1.27; 2.54)1.87*** (1.37; 2.38)1.24*** (0.75;
1.72)1.28*** (0.67; 1.89)1.49*** (0.68; 2.30)Random effects
varianceCons0.010.030.000.080.080.060.040.070.12Residual0.110.180.090.390.380.281.010.730.50Model
set B: With adjustment for number adults and dependents in householdSukuma (n =
417)Maasai (n = 315)Rangi (n = 186)Sukuma (n = 415)Maasai (n = 202)Rangi (n =
181)Sukuma (n = 292)Maasai (n = 313)Rangi (n = 98)Household type (reference:
monogamous male-headed)Polygynous male-headed0.05 (−0.02; 0.13)0.04 (−0.09;
0.17)0.06 (−0.06; 0.17)0.17** (0.04; 0.29)0.28* (0.04; 0.52)0.07 (−0.15;
0.28)0.35** (0.10; 0.61)0.58*** (0.33; 0.83)0.33† (0.05; 0.72)Polygynous
female-headed−0.07 (−0.21; 0.07)−0.13* (−0.27; 0.00)−0.05 (−0.24; 0.33)0.13
(0.10; 0.37)0.02 (−0.22; 0.25)0.22 (−0.29; 0.72)0.19 (−0.28; 0.67)0.14 (−0.12;
0.40)0.46 (−0.31; 1.23)Monogamous female-headed−0.16 (−0.44; 0.13)−0.19* (−0.36;
0.03)0.07 (−0.16; 0.30)−0.31 (−0.80; 0.18)−0.18 (−0.49; 0.12)0.05 (−0.36;
0.46)−0.53 (−1.86; 0.80)−0.12 (−0.45; 0.21)−0.39 (−1.41; 0.64)Age of household
head (y) (centered at 43 y)0.00 (0.00; 0.00)0.00 (0.00; 0.00)0.00 (0.00;
0.01)0.00 (0.00; 0.00)0.00 (−0.01; 0.01)0.01* (0.00; 0.01)0.01* (0.00;
0.02)−0.01 (−0.01; 0.00)0.02** (0.01; 0.03)Number of adults in household (15+ y)
(centered on three persons)0.06*** (0.04; 0.08)0.06** (0.02; 0.11)0.08*** (0.05;
0.12)0.16*** (0.13; 0.19)0.08* (0.01; 0.15)0.11*** (0.05; 0.16)0.14*** (0.08;
0.20)0.03 (−0.06; 0.12)0.05 (−0.07; 0.17)Number of dependents in household (<15
y) (centered on four persons)0.00 (−0.01; 0.02)0.02 (−0.01; 0.05)0.02 (−0.01;
0.05)0.04 ** (0.01; 0.07)0.06* (0.00; 0.11)0.10*** (0.05; 0.16)0.06* (0.01;
0.11)0.04 (−0.02; 0.10)0.11* (0.00; 0.22)Season (reference: not
hunger)Hunger0.09 (−0.03; 0.21)−0.05 (−0.22; 0.13)−0.01 (−0.11; 0.10)0.05
(−0.20; 0.29)−0.29 (−0.65; 0.07)−0.12 (−0.50; 0.25)0.22 (−0.11; 0.56)0.07
(−0.29; 0.43)−0.14 (−0.74; 0.45)Intercept1.19*** (1.01; 1.37)1.20*** (0.91;
1.50)1.46*** (1.33; 1.60)1.62*** (1.24; 2.00)1.92*** (1.29; 2.54)1.94*** (1.40;
2.47)1.10*** (0.60; 1.61)1.29*** (0.68; 1.90)1.40*** (0.62; 2.18)Random effects
varianceCons0.010.020.000.050.080.080.050.080.10Residual0.100.180.080.290.350.240.880.700.49
EXPAND FOR MORE
†
P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001. Statistically significant
estimates at P < 0.1 are in bold.
Open in viewer
CONTRASTING MONOGAMOUS AND POLYGYNOUS VILLAGES.
We next consider how village characteristics predict individual measures of food
security and child health using multilevel regression including village-level
random and fixed effects (SI Text and Table S7). Independently of individual
marital status, each 10% increase in the proportion of polygynous households
sampled per village is associated with an estimated −1.52 unit decrease in food
security (β = −1.52, 95% CI = −2.09; −0.95, P < 0.001), a −0.15 reduction in
child HAZ (β = −0.15, 95% CI = −0.25; −0.05, P < 0.001), and a −0.07 reduction
in child WHZ (β = −0.07, 95% CI = −0.15; 0.01, P < 0.1). However, once we adjust
analyses for village-level proxies for ecological vulnerability (annual
rainfall) and socioeconomic marginalization (distance to district capital and
the proportion of household heads with nonzero education), these associations
dramatically attenuate and become statistically nonsignificant in the case of
food security and child HAZ, whereas the proportion of polygynous households in
a village becomes positively associated with child WHZ (β = 0.08, 95% CI =
−0.01; 0.18, P = 0.09; Fig. 4). As such, our analyses do not support the idea
that polygyny has negative group-level consequences on well-being.
Fig. 4.
Village differences in food security and child health by polygyny prevalence.
Predicted village intercepts before (A) and after (B) adjustment for
village-level differences in ecological vulnerability (annual rainfall) and
socioeconomic marginalization (distance to district capital and proportion of
household heads with nonzero education). After adjustment, polygyny prevalence
is unrelated to food security and HAZ, and positively predicts WHZ. Intercepts
are mean/mode centered for household characteristics. See text for estimated
coefficients and Table S7 for corresponding model output. Red circle, Maasai
village; green diamond, Sukuma village; orange triangle, Rangi village; blue
square, Meru village; white diamond, other ethnicity village.Open in viewer
Table S7.
Multilevel regressions predicting household food security, HAZ, and WHZ
Independent variableFood security (n = 1,745) [β (95% CIs)]Height-for-age
Z-score (n = 2,704) [β (95% CIs)]Weight-for-height Z-score (n = 2,711) [β (95%
CIs)]Model 1Model 2Model 1Model 2Model 1Model 2Household type (reference:
monogamous male-headed)Polygynous male-headed1.05* (0.20; 1.90)1.04* (0.19;
1.89)−0.06 (−0.21; 0.10)−0.05 (−0.20; 0.10)0.03 (−0.10; 0.16)0.03 (−0.10;
0.16)Polygynous female-headed−0.85 (−2.03; 0.33)−0.74 (−1.92; 0.44)−0.02 (−0.25;
0.20)−0.01 (−0.24; 0.21)0.05 (−0.15; 0.24)0.06 (−0.14; 0.25)Monogamous
female-headed−0.16 (−1.60; 1.29)−0.10 (−1.54; 1.34)−0.07 (−0.35; 0.20)−0.07
(−0.34; 0.21)0.20† (−0.03; 0.43)0.22+ (−0.02; 0.45)Child age (mo) (centered at
30 mo)——−0.09*** (−0.10; −0.07)−0.09*** (−0.10; −0.07)−0.02*** (−0.03;
−0.01)−0.02*** (−0.03; −0.01)Child age squared (mo2) (centered at 30
mo2)——0.001*** (0.001; 0.002)0.001*** (0.001; 0.002)0.000 (0.000; 0.000)0.000
(0.000; 0.000)Child sex (reference: boy)Girl——0.13* (0.02; 0.24)0.13* (0.02;
0.24)0.05 (−0.05; 0.14)0.04 (−0.05; 0.14)Age of household head (y) (centered at
43 y)−0.03* (−0.06; −0.01)−0.03* (−0.06; −0.01)0.00 (0.00; 0.01)0.00 (0.00;
0.01)0.00* (0.00; 0.01)0.00† (0.00; 0.01)Season (reference: not
hunger)Hunger−1.39† (−2.80; 0.02)−1.75** (−2.81; −0.69)−0.25* (−0.50;
−0.01)−0.32** (−0.53; −0.11)0.00 (−0.19; 0.19)−0.03 (−0.18; 0.13)Polygyny
prevalence (per 10%) (centered at 22%)−1.52*** (−2.09; −0.95)−0.02 (−0.68;
0.64)−0.15** (−0.25; −0.05)0.04 (−0.09; 0.17)−0.07† (−0.15; 0.01)0.08† (−0.01;
0.18)Annual rainfall (per 100 mm3) (centered at 780 mm3)—0.58** (0.24;
0.91)—0.07* (0.01; 0.14)—0.09*** (0.04; 0.14)Percent nonzero education (per 10%)
(centered at 64%)—1.08*** (0.66; 1.49)—0.13** (0.05; 0.21)—0.11*** (0.05;
0.17)Distance to capital (per 10 km) (centered at 33 km)—0.03 (−0.26;
0.32)—−0.06† (−0.11; 0.00)—0.04† (0.00; 0.08)Intercept18.57*** (16.39;
20.75)19.26*** (17.61; 20.91)−1.73*** (−2.11; −1.34)−1.61*** (−1.94; −1.28)0.07
(−0.23; 0.38)0.13 (−0.12; −0.38)Random effects
varianceCons5.122.230.150.090.090.04Residual39.4039.402.082.081.561.56
EXPAND FOR MORE
†
P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001. Statistically significant
estimates at P < 0.1 are in bold.
Open in viewer
DISCUSSION
We challenge the widespread notion that polygyny is harmful to children.
Consistent with prior studies (4–8), polygyny is predictive of relatively low
food security and poor child health in aggregated data. However, such
associations are driven entirely by the tendency of polygyny to be more common
in marginalized and ecologically vulnerable villages and ethnic groups. Within
villages, polygynous households, at least those headed by males, often had
higher food security and better child outcomes than monogamous households.
Polygynous households were also wealthier in terms of livelihood-specific forms
of wealth (land and livestock), although not in asset ownership, which is the
foundation of wealth indices favored by national demographic surveys (35). These
findings are consistent with classic evolutionary and economic models suggesting
that sharing a husband can be in a woman’s strategic interest, at least in
contexts where women depend on men for resources, by enabling access to equal or
greater wealth than could be achieved by opting for monogamy (17, 18). Our
results also highlight the inherent weaknesses of highly aggregated samples such
as the DHS, the primary data source for population scientists studying family
structure and health in sub-Saharan Africa (36).
That polygyny is associated with better outcomes for specifically male-headed
households indicates that cowives resident with their husband are most likely to
benefit from polygyny. Female-headed polygynous households in contrast may often
lose cowife conflicts over shared resources. We found that female-headed
polygynous households had lower food security than monogamous households when
considering the sample as a whole, although child health did not differ (Fig.
2). In this context, first wives are most often coresident with their husband.
Advantages to first wives have been reported elsewhere (13, 24). In rural
Ethiopia, Gibson and Mace (13) found first wives were in better physical health
and had more surviving offspring than monogamously married women and that
relatively poor child health was only associated with polygyny for second or
later cowives. This result may reflect selection effects, i.e., women of good
health/social standing are less likely to enter polygynous marriages as later
wives, such that differences in child outcomes, or indeed food security, cannot
be seen as consequences of polygyny itself (13, 16). Alternatively, first wives
may benefit from exclusivity before sharing their husband and subsequent
seniority over later wives. Thus, to the extent that deficits in child health or
food security are unequally portioned among wives, we note that polygyny may, in
some instances, be considered harmful.
We demonstrate ethnic variation in the relationship between polygyny and health.
Findings from prior small-scale studies suggest such variation, but comparing
results across studies is hampered by methodological differences (25).
Specifically, we detect an advantage of being raised in male-headed polygynous
households for the Sukuma (the largest ethnic group in Tanzania) and the Rangi,
but not for the Maasai. Although our stratified analyses here have relatively
low statistical power, at least two factors may account for these differences:
low status of Maasai women and the relative poverty of this ethnic group.
Previous studies emphasize low female status in the Maasai (37), restricting
women’s control over their marital arrangements (including divorce and the
addition of cowives) and/or preventing women from effectively allocating
household resources to children (38). The Maasai also suffered the greatest
burden of food insecurity and poor health in our study (34). Borgerhoff Mulder
(39, 40) found that polygyny was negatively associated with child survival only
in the poorest households in Kenyan Kipsigis. Strassmann (22) observed negative
associations between polygyny and child health in the Dogon of Mali in all but
one “exceptionally large and wealthy village” (p. 10,897). Thus, it might be
that polygyny fails to provide better circumstances in conditions of relative
resource scarcity where children are most vulnerable to biased intrahousehold
resource allocation, accounting for the differences between the Maasai and
neighboring ethnic groups.
Our analyses do not support the assertion that polygyny has group-wide costs on
child health (28). Instead, it seems parsimonious that highly polygynous,
predominantly Maasai, villages do poorly not because of polygyny, but because of
vulnerability to drought, low service provision, and broader sociopolitical
disadvantages. Highly monogamous, predominantly Meru, villages on the other hand
occupy the relatively high rainfall, fertile slopes of Mount Meru close to
Arusha city, benefiting from improved health care and education infrastructure
(34). It is possible that polygyny has negative group-level consequences on
unmeasured aspects of well-being. However, we are skeptical of the theoretical
foundation of such arguments. Recent reformulations of sexual selection theory
emphasize facultative responses to partner availability, predicting that the
more common sex will cater to the preferences of the rarer sex to acquire and
retain mates. As such when polygyny leads unmarried women to be in relatively
short supply we might expect higher not lower levels of paternal investment (30,
41). Consistent with this perspective, our adjusted analyses found that child
WHZ was marginally higher in the most polygynous villages (Fig. 4).
If polygyny does not bestow group-level costs on women and children, as
suggested by Henrich et al. (28), how can we account for observed transitions to
socially imposed monogamy with economic development? In Tanzania, the spread of
both Islam and Christianity have clearly influenced marital norms. Missionary
influence may be partially responsible for the ubiquity of monogamy among the
Meru (42). However, explanations based solely on religion are unsatisfactory
because religious prescriptions and marriage patterns most likely coevolve,
constrained to some extent by systems of production (43). Fortunato and Archetti
(44) propose that monogamy evolves via the maximization of individual, not group
benefits, and is best understood as an inheritance strategy favored when
intergenerational resource transfers are critical to descendant success.
Monogamy may thus be beneficial to both men and women when returns to parental
investment favor offspring quality over quantity. In line with this account, the
Meru were early adopters of relatively intensified agriculture (42), where
productivity is limited by land inheritance, as opposed to low intensity
agriculture and pastoralism, which may be relatively labor limited. The Meru
also have the highest educational attainment (34), which is associated with
transitions to low fertility. Once individuals opt for smaller family sizes, a
pattern best understood as motivated by economic rather than reproductive
success (45), the reproductive advantages of polygyny are likely outweighed by
novel opportunities to invest more per child, e.g., via formal education.
Although we make important methodological advancements, our study shares several
limitations with prior studies of polygyny. Our use of the standard demographic
household definition (SI Text) often cleaves polygynous families into distinct
survey units, preventing direct contrasts of children of first and later wives
sharing the same husband. Cross-sectional data also limit our ability to infer
causality, preventing explicit consideration of the impact of additional wives
on previously monogamous women and their children. A recent retrospective study
in Bolivia reports that, although women in polygynous marriages had lower
fertility than women in monogamous marriages overall, the addition of a second
wife did not impact on the fertility of the first wife in intraindividual
analyses (16). Self-selection may thus be responsible for reported effects of
polygyny in some cross-sectional studies (13). We also caution that the
relatively small number of female-headed polygynous households (at least for the
Sukuma and Rangi; Table S1) in our study may have resulted from disagreement
between the village register sampling frame and household definition used by
enumerators on the ground. If unsampled and sampled households systematically
differ this may bias our estimates. The common use of rigid household
definitions is coming under increasing criticism for obscuring the measurement
of complex demographic phenomena, and we support recent calls (46) for
experimentation with alternative survey methodologies that more accurately cater
to the reality of African family structure.
Our study concerns food security and child health and cannot tell us about the
wider potential of polygyny to cause harm. Other aspects of physical and mental
well-being may be influenced by polygyny (47). Recent studies counter simple
intuition. Polygyny is associated with lower HIV prevalence at both national and
regional levels across Africa. Reniers and colleagues (48) suggest polygyny
increases individual exposure, but selective recruitment of HIV-positive women
into polygynous marriages where coital frequency is lower isolates transmission
risks from the wider population. A recent study in Tanzania also found no
evidence for an association between polygyny and maternal anxiety and depression
(49). Whatever the outcome, we do not anticipate universal relationships between
polygyny and well-being. We have demonstrated variation in the estimated
consequences of polygyny both between women (by coresidence with husband) and
between ethnic groups. Moreover, the vital insight of both economic and
anthropological theory is that cultural diversity in marriage practices stems in
large part from context-dependency in the pay-offs to alternative behavioral
strategies (50). As anthropologists have long emphasized, polygyny itself is
also a diverse institution with considerable cultural variation in associated
norms of spousal recruitment and residence (51).
We particularly advocate that policy makers distinguish low female autonomy from
polygyny rather than treat the latter as a definitive indicator of the former.
Where women have control over marital placements, we do not anticipate costs to
polygyny. Indeed, if there are large differences in male wealth, prohibiting
polygyny may be disadvantageous to women by restricting marital options.
Levirate marriage or widow inheritance, whereby a women marries the close male
relative of her deceased spouse as a polygynous bride, is also likely to offer
women and their children substantially better prospects than living as a single
widow in many contexts (52). On the other hand, if female autonomy is low,
and/or when polygyny is not associated with differences in male wealth, marital
placements may logically be prone to negative impacts of male coercion. We also
recommend future research prioritizes data analysis at the level of social
groups (i.e., villages, neighborhoods). Institutions for marriage and
child-raising are rapidly changing across the globe, and their gendered impacts
are increasingly taking center stage in discussions of international development
(1). Policy analysts concerned with these transformations need to consider
appropriate comparison groups, selection effects, and broader community
confounds. Only by making meaningful contrasts, which capture alternatives
readily available to individuals, and by taking into account the distribution of
specific traditions across different communities and ecologies, can we expect to
achieve a true understanding of the health implications of cultural practices.
MATERIALS AND METHODS
Data (Dataset S1) were collected between 2009 and 2011 as part of the Whole
Village Project (WVP), coordinated by Savannas Forever Tanzania, the University
of Minnesota (UM), and the Tanzanian National Institute of Medical Research
(NIMR). The WVP received ethical approval from the UM Institutional Review Board
(code 0905S65241) and NIMR. Between 60–75 households were randomly selected from
56 villages (Fig. S1), leading to a sample of 3,584 households, 2,268 of which
contained children under 5 y of age. Nearly half (45%) provided anthropometric
data on more than one child (two children, 35%; three or more children, 10%).
Four ethnicities, the Maasai, Sukuma, Rangi, and Meru, make up 65% of
households. Maasai are traditionally seminomadic pastoralists but have recently
diversified into cultivation. Sukuma, Rangi, and Meru are all characterized as
agro-pastoralists. Rangi and Meru primarily identify as Muslims and Protestants,
respectively. Sukuma and Maasai identify with either Christian or indigenous
religions (34). Our analysis is limited to households with a married head of at
least 16 y, bringing our working sample to 1,764 households, containing 2,833
children (averaging 32 households and 51 children per village). The Household
Food Insecurity Access Scale assesses food insecurity during the last month on a
27-point scale. We reversed this measure so that a higher score indicates
greater food security (mean: 16.9; SD: 7.0). Anthropometrics were WHO
standardized. HAZ assesses chronic malnourishment (mean: −1.6; SD: 1.6) and WHZ
assesses acute malnourishment (mean: 0.2; SD:1.3). Z-scores less than −2.0
indicate stunting and wasting, respectively. Relatedness data are available for
villages 15–56 only: 80% of children were biological children of the head, 14%
were grandchildren, 6% were other relatives. A wealth index was calculated by
principal component analysis applied to the ownership of 37 assets. Acres
cultivated and livestock units were recorded separately. Wealth measures were
transformed (log x + 1) to approximate normal distributions. Village mapping was
used to compute distance to district capital and estimated annual rainfall. SI
Text provides further information on child, household, and village data.
Regressions were fit using maximum likelihood estimation and include controls
for child age and sex, age of household head, and hunger season (for details,
see SI Text and Tables S3–S7).
SI TEXT
HARMFUL CULTURAL PRACTICES.
The terms “harmful cultural practice” and “harmful traditional practice” are
used interchangeably by the international development and human rights community
to refer to nonwestern cultural practices deemed detrimental to individual
well-being, most often with regard to women and children. The concept was
initially developed by the United Nations (UN) to name and combat ostensibly
blatant forms of male domination of women, culminating in a 1995 UN Fact sheet
devoted to the issue (53). We avoid the more commonly used term harmful
traditional practice in our text following concern that “traditional” falsely
implies that modern/western practices are exempt from potential to cause harm
and that cultural subordination of women is limited to traditional populations
(53). There is no single universally agreed list of harmful cultural practices,
but the concept is most frequently used in reference to female genital cutting,
gendered violence, child, early, and forced marriage, and polygynous marriage.
Attention on harmful cultural practices has grown in recent years, in line with
an increased focus on gender in all spheres of international development (1).
For further discussion of current and historical negative characterizations of
polygynous marriage from both human rights and theological perspectives, see
refs. 2, 54, and 55.
METHODOLOGICAL LIMITATIONS OF PRIOR RESEARCH ON POLYGYNY AND CHILD HEALTH.
Our study overcomes important limitations of prior research, combining
methodological strengths of prior small-scale anthropological and large-scale
demographic studies. The main limitations of small-scale anthropological
studies, particularly from the perspective of public health, is that sample
sizes are generally extremely small (often n < 100). Furthermore, we can’t
directly contrast results across small-scale studies to compare specific
cultural and ecological contexts because of idiosyncratic variation in
statistical methodology and study design (e.g., differences in sampling,
definition, and use of independent and dependent variables, inclusion of
controls for potential confounders). Our study provides a large sample more
characteristic of large-scale demographic studies. Indeed, our initial sample (n
= 3,584 households) surpasses the Tanzania DHS for the same regions (31, 34).
Using parallel sampling and analysis methods across multiple ethnic groups, our
study also enables effective estimation of context dependency in relationships
between polygyny, food security, and child health. However, we caution that,
although our study site encompasses a large area of northern Tanzania, our
results cannot be taken as nationally representative. Rather, our findings
should only be treated as representative of our specific study villages (Fig.
S1). Most notably we sampled a high proportion of Maasai households. Our study
site contains 22% Maasai households compared with the 1996 Tanzanian DHS, which
included only 2% Maasai households (34). The Maasai are exceptional for
primarily relying on pastoralism as opposed to agriculture, high levels of
polygyny relative to other Tanzanian ethnic groups, and for experiencing high
levels of socioeconomic marginalization (34).
In contrast to small-scale anthropological studies, the primary concern with
large-scale demographic studies is their inherent vulnerability to confounding
between ecological and individual determinants of health (i.e., vulnerability to
the “ecological fallacy”). Previous studies of DHS data have attempted to deal
with this problem in various ways we believe are largely unsatisfactory. First,
several studies have included random effects to adjust for hierarchical
clustering at the national level only (e.g., ref. 4). Second, other studies have
incorporated random effects at the subnational regional level (e.g., ref. 6).
National or subnational clusters are likely to only crudely map spatial
covariance in marriage and health outcomes. Subnational regions are an
improvement but still aggregate data across much structured diversity in both
health and cultural practices. For example, in our case, Maasai and Meru
villages are often directly adjacent but offer the most extreme comparisons in
terms of both polygyny prevalence and health outcomes (Fig. S1) (34). Finally,
Wagner and Rieger (5) incorporate random effects at the level of primary
sampling units (PSUs). Although PSUs offer higher resolution, their value is
questionable. We question the value of including random effects for PSU for two
reasons. The first reason is that very few households are surveyed per PSU by
most DHS [e.g., only 16–22 households in Tanzania (ref. 31, p. 10)], and among
those sampled, sample size per PSU cluster is further reduced by data
restrictions (most obviously in this case many households will not contain
children < 5 y old). Small cluster size can lead to estimation problems,
particularly when analysis rests on the estimation of many parameters that may
vary to differing degrees within each cluster. The second reason is that PSUs
are usually based on census enumeration areas, which do not necessarily
correspond with specific villages or cohesive communities (including, for
example, adjacent urban zones within towns and cities), which means they are not
ideal for contextual analysis. For this reason previous studies have avoided the
incorporation of PSUs as a random effect (ref. 6, p. 347). Our study has the
advantage of using clearly defined village units as random effects with
relatively high-density sampling per cluster (averaging 32 households and 51
children per village included in our final analyses).
Interestingly, Wagner and Rieger (5), who adjust for PSUs as a random effect in
a mixed urban and rural sample of 26 African DHS surveys, estimate that, whereas
in the majority of countries there was a negative relationship between polygyny
and child anthropometrics, a positive and nonsignificant relationship was
estimated for Tanzania (ref. 5, p. 17). As we have noted, our study site is not
representative of Tanzania as a whole and therefore direct comparisons of effect
estimates should be avoided. However, this finding could be seen as consistent
with our conclusion that, once low-level spatial clusters are adjusted for,
polygyny is no longer predictive of child health for Tanzanian families.
Unfortunately, Wagner and Reiger (5) do not report country-specific estimates
both with and without adjustment for PSUs, so we cannot infer from this study
whether or not adjusting for PSU specifically modifies their effect estimates
compared with more aggregated analyses. We are skeptical of the use of PSUs as
random effects for the reasons outlined above, but advocate future researchers
explore alternative methods for dealing with spatial clustering with DHS data.
Ultimately the adequacy of using PSUs as clusters will depend on the specific
number of cases per cluster, the nature of the sample (e.g., rural vs. urban),
and the specific analysis methodology implemented.
The final major advantage of our study is the utilization of additional
household-level data generally not incorporated into large-scale demographic
studies of polygyny and child health. Previous studies of the DHS have used a
standardized household wealth index to measure wealth. We use an equivalent
measure based on the distribution of asset-based wealth in our population, but
also livelihood specific forms of wealth in the form of the acres of land
cultivated and amount of livestock owned (see below). As our results
demonstrate, these measures reveal wealth differences between households
obscured when relying on generic wealth indices alone. We also incorporate data
on ethnicity. With the exception of Gyimah (8), no DHS study of polygyny and
child health has adjusted or stratified estimates by ethnicity. However,
ethnicity covaries strongly with both culturally shared marital norms and
broader social and ecological determinants of child health. As such there is
considerable margin for error in the interpretation of large-scale DHS analyses
neglecting ethnicity. This issue is particularly salient to Tanzania, where
ethnicity data have not been made available for DHS data for almost two decades.
Five DHSs have thus far been conducted in Tanzania (1991/2, 1996, 1999, 2004/5,
and 2010), and to our knowledge, only the 1991/2 and 1996 DHS provide ethnicity
data.
SAMPLING AND ETHNICITY.
Overall, 56 villages were sampled by the WVP, between mid-2009 and mid-2011,
across the northern and central Tanzanian regions of Arusha (19 villages),
Manyara (11 villages), Dodoma (7 villages), Singida (5 villages), Shinyanga (8
villages), Mwanza (3 villages), and Mara (3 villages). The sampling of villages
was based in part on the priorities of development agency partners and the
permission of government leaders, although effort was made to randomize village
sampling where possible and to ensure a wide geographic spread. Fig. S1 shows
the location of each village, color coded by the majority ethnic group (recorded
at the household level), in relation to major settlements, main roads, national
parks, and game reserves. Lawson et al. (34) provide information on the exact
number of households and children sampled by village, district, and region.
Maasai, Sukuma, Meru, and Rangi were the most common ethnic groups sampled,
collectively accounting for 60.4% of households sampled. Other ethnic groups
were also sampled at relatively low frequency. These groups include the Arusha
(6.4%), the Wanda (4.7%), the Iraqw (4.3%), the Turu (3.0%), the Mbugwe (2.9%),
the Gogo (2.0%), and a large number of ethnic groups each accounting for <2% of
the sampled households.
Within each village, between 60 and 75 households were randomly selected for
participation from a list provided by village administrators, leading to a total
of 3,584 surveyed households. Household head marital status is used to contrast
monogamous and polygynous family settings. Heads were identified as the person
responsible for household upkeep and households defined as “a group of persons
who live together in the same house or compound, share the same house-keeping
arrangments, and eat together as one unit.” Informed oral consent was obtained
from participants, and all individual data were anonymized before analysis.
Anthropometric measurements were taken for all resident children under 5 y of
age. Of 3,584 sampled households, 2,268 (63%) contributed child anthropometric
data, and just under half of those households provided data on more than one
child (two children: 35%; three or more children: 10%), leading to a total of
3,586 surveyed children. The sample for our current analysis is limited to
households with a verified currently married head of at least 16 y, bringing our
working sample to 1,764 households, containing 2,833 children.
CHILD, HOUSEHOLD, AND VILLAGE DATA.
CHILD ANTHROPOMETRICS.
The mean age of sampled children was 28.9 mo, with roughly even sampling across
the age range of zero to 60 mo and evenly split by sex (34). Child weight was
measured to the nearest 100 g using a Salter-type spring hanging scale for
infants and electronic scales for children able to stand. Child height was
measured to the nearest millimeter using a measuring board for young children
and using a stadiometer for children of 2 y or older. All measurements were made
once and immediately entered into a database. Children were measured by
different field staff depending on the village sampled, but training of
enumerators by United Nations Children’s Fund staff and oversight of
anthropometric sessions by the Tanzanian National Institute of Medical Research
personnel ensured high levels of interrater reliability before data collection.
Three anthropometric indicators were derived using WHO age- and sex-specific
growth standards (56). HAZ serves as an indicator of long-term effects of
malnutrition. A child with a HAZ of <−2 SDs from the WHO reference is considered
stunted, i.e., chronically malnourished, which reflects failure to receive
adequate nutrition over a long period and is influenced by recurrent and chronic
illness. Weight-for-height Z-scores (WHZ) measure body mass in relation to body
height/length and describes current nutritional status. A child with a WHZ <−2
SDs is considered acutely malnourished (i.e., wasted), which represents the
failure to achieve adequate nutrition in the period immediately preceding
measurement and may result from inadequate food intake or illness. HAZ and WHZ
scores were derived in IBM SPSS v.20 using WHO-supplied syntax, which
automatically removes extreme cases, including those likely to have resulted
from measurement error, i.e., incorrect recorded child age, height, or weight.
HAZ scores of <−6 or >6 are removed, and WHZ scores of <−5 or >5 are removed.
Applying these criteria reduces our sample of child anthropometric data from
2,833 to 2,704 and 2,711 valid HAZ and WHZ scores, respectively. The mean HAZ
score is −1.61 with an SD of 1.56, with 40.5% categorized as stunted. The mean
WHZ score is 0.17 with a SD of 1.33, with 4.1% categorized as wasted.
The use of WHO standardized growth scores is ubiquitous in both the demographic
and anthropological literature on polygyny and child health, and we have chosen
to use these measurement standards to enhance comparability with prior research.
We caution that the application of WHO reference standards to an ethnically and
socio-ecologically diverse sample cannot take into account differing genetic
capacity for growth and the potential for environmental variation to modify the
local health significance of growth indicators. However, we have previously (34)
confirmed that differences in child anthropometric indicators between ethnic
groups closely map onto differences in subjective ratings of child health,
recorded illnesses, food insecurity, and recent food consumption at this site.
In all cases, Maasai households, particularly when primarily reliant on
pastoralism, appear substantially disadvantaged. This result suggests that
differences in child anthropometry between ethnic groups make appropriate
proxies for health. The alternative of making Z-scores specific to each ethnic
group is not feasible because (i) the “Other” ethnic group category contains
many ethnic groups with no clear point of internal reference, and (ii) our
sample is composed of children from 0 to 60 mo with relatively few cases for
each age and sex combination to make robust age- and sex-specific estimates.
HOUSEHOLD FOOD SECURITY.
The Household Food Insecurity Access Scale (HFIAS) was used to measure food
security (57). It is a brief survey instrument developed by the US Agency for
International Development (USAID) funded Food and Nutrition Technical Assistance
(FANTA) Project to improve the measurement of food security and ultimately
better target interventions to the most vulnerable households. The scale is
based on a household’s reported experience of problems regarding three domains
of food insecurity argued to be universal across cultures: (i) feelings of
uncertainty or anxiety about household food supplies; (ii) perceptions that
household food is of insufficient quality (including variety and food type
preference); and (iii) insufficient food intake and its physical consequences.
The HFIAS is composed of nine questions recording the occurrence and frequency
of specific problems along these domains. Responses were scored so that “never”
received a score of 0, “rarely” scored 1, “sometimes” scored 2 and “often”
scored 3, so that when summed, the lowest possible score was 0 and the highest
27. This measure was then reversed for our study so that a higher value
represents higher food security rather than food insecurity. For our sample of
1,764 households, complete responses were available for all but 19 households.
For the 1,745 households with complete data, the mean food security score was
16.91 with an SD of 7.04. A categorical measure can also be computed on the
basis of the HFIAS questions. By this measure, 46% of all sampled households can
be categorized as severely food insecure (34), meaning they cut back on meal
size or number of meals often and/or experience any of the three most severe
conditions (running out of food, going to bed hungry, or going a whole day and
night without eating) at least once a month (57).
HOUSEHOLD TYPE.
We categorized households by the sex and marital status of the household head.
Out of our working sample of 1,764 households, 69% of households were headed by
a monogamously married male, 17% were headed by a polygynously married male, 9%
by a polygynously married female, and 5% by a monogamously married female. Table
S1 provides household descriptive data by household type and ethnicity for the
working sample. Formal data on wife rank were not collected, but field
observations confirmed that male-headed polygynous households typically
consisted of a husband, his first wife, and their shared children, whereas
female-headed polygynous households typically consisted of later cowives and her
children living separately. In only a small proportion of cases was more than
one wife coresident in a male-headed polygynous household (17% in the Sukuma, 7%
in Maasai, 6% in the Rangi, 0% in the Meru, and 8% in the other ethnicity
group.) We did not collect data linking households containing cowives for the
same husband. This limitation prevents us from making contrasts between children
of different mothers sharing the same father.
HOUSEHOLD WEALTH.
Table S2 provides supporting data on household soioeconomic characteristics. A
Household Wealth Index was calculated on the basis of a principal components
analysis (PCA). The PCA was applied to a total of 37 dichotomous variables
representing ownership of assets and characteristics of assets at the household
level. Owning a particular asset or a better asset increases the value of the
index by different amounts determined by the household’s score for the first
principal component. The index is scaled so that its minimum is zero, i.e., by
construction the poorest household has a score of zero. The following assets
were included: drinking water source, household flooring type, household roofing
type, type of toilet, owns land, house, cart, hoe, motorcycle, bicycle, plow,
sewing machine, lantern, wheelbarrow, computer, radio, water tank, video, chair,
sofa, bed, cupboard, chest, dining set, car, cell phone, solar panel, watch or
clock, and drum. Note the index does include land ownership (yes/no) and items
relating to farming such as a plow or hoe but does not include livestock
ownership, despite cattle having a clear economic value. Therefore, it should be
interpreted as a non-livestock wealth index. Based on household surveys, we were
able to complete a wealth index for 3,480/3,584 (97.1%) of surveyed households.
Livestock ownership was measured separately in tropical livestock units with the
following conversion rates: cattle (0.7 units); goats and sheep (0.1 units);
pigs (0.2 units); and donkey/horses/mules (0.5 units).
VILLAGE-LEVEL DATA.
Table S2 shows supporting data on the village-level indicators by the majority
ethnic group. Polygyny prevalence and the percentage of heads with nonzero
education are measured as the percentage of sampled households within each
village (using the complete sample of 3,584 households). Annual rainfall data
for each village were derived from the WorldClim climatic data resource as the
mean annual total precipitation over the period covering 1950–2000 at a
resolution of 1 km2 mapped to a central point of each village (34). Distance to
the district capital was calculated for each village using the straight-line
distance between the mean coordinates of all sampled households within each
village and the central point of the district capital in kilometers. Villages
were sampled across an extensive geographic area that straddles two climatic
zones experiencing either bimodal or unimodal rainy seasons, influencing the
timing of so-called hunger or lean seasons. Although annual rainfall patterns
are erratic, the hunger season generally occurs from October to December in the
bimodal zone; whereas in the unimodal zone, an overlapping but longer hunger
season generally falls from November to February. Based on this monthly
categorization, we coded whether or not each village was sampled during in the
hunger season, with all villages from the regions of Arusha, Mara, and Mwanza
considered to be in the bimodal zone and the regions of Shinyanga, Dodoma,
Manyara, and Singida in the unimodal zone. A binary coding of not hunger season
(31 villages) vs. hunger season (25 villages) was included in all models.
ANALYTICAL STRATEGY AND FULL MODEL OUTPUT.
MODEL ESTIMATION.
All models were fit using maximum likelihood estimation in Stata version 13
using the “regress” command for standard linear regression and the “xtmixed’’
command for multilevel linear regression. Models predicting household-level
variables (food security, wealth index, land cultivated, and total livestock
units) adjust for the age of the household head in years (centered at 43 y).
Models predicting child-level outcomes (i.e., HAZ and WHZ) adjust for age of the
household head in years (centered at 43 y), child age, and child age squared in
months (centered at 30 mo). Standard linear regression models aggregate all data
across villages, effectively estimating relationships across the full study area
without consideration for the hierarchical spatial clustering of data.
Multilevel linear regression models include a random intercept for village. Note
that, although child-level outcomes are also clustered within households
(because some households contribute data on more than one child), we do not
include a random effect for household when predicting child anthropometrics.
This strategy is followed because when clusters (i.e., households) are
unbalanced and sparsely populated (i.e., ≤2 cases per level), both fixed and
random effects may be overestimated (58). All multilevel regression models for
both household- and child-level outcome variables also include a village-level
fixed effect for whether or not the village was sampled during a hunger season.
CONTRASTING MONOGAMOUS AND POLYGYNOUS HOUSEHOLDS.
Table S3 shows the results of a set of standard linear vs. multilevel regression
models predicting household food security, child HAZ, and child WHZ, using a
dichotomous coding of polygynous vs. monogamous households. Table S3 also shows
the results of a parallel set of models using a four-category variable for
household type (i.e., male-headed monogamous household, female-headed monogamous
household, male-headed polygynous household, and female-headed polygynous
household). Comparing the results from standard linear and multilevel regression
analyses in these tables demonstrates that adjustment for village-level
differences in food security and child health substantially modifies the
statistical significance and magnitude of effect estimates. Estimates from Table
S3 further demonstrate that the estimated effect of polygyny differs depending
on whether households are male or female headed. Table S4 reports the results of
a stratified analysis with models run for the three main ethnic groups with a
substantial proportion of polygynous households (the Sukuma, the Maasai, and the
Rangi). Contrasts in this analysis have relatively low statistical power due to
reduced sample size. However, they strongly imply context dependency by ethnic
group in the estimated effects of polygyny on food security and child health.
Effect estimates from this stratified analysis are graphically represented in
Fig. 2. Note that very few female-headed polygynous households were sampled in
the Rangi (only four households). Therefore, estimates for the difference
between male-headed monogamous households and female-headed polygynous
households for the Rangi are deemed unreliable and not graphically represented
in Fig. 2.
POLYGYNY AND WEALTH.
Table S5 reports the results of multilevel regression analyses predicting
household wealth index, the number of acres cultivated, and tropical livestock
units. Note for both acres cultivated and tropical livestock units, analyses are
restricted to cases that own at least some land and some cattle, respectively.
Table S6 reports the results of stratified analyses for the same outcomes by
ethnic group. Effect estimates from this stratified analysis are graphically
represented in Fig. 3. Tables S5 and S6 also show estimates adjusted for the
number of adults (i.e., age 15 y and over) and number of dependents (i.e., aged
<15 y) in the household. Data are incomplete for these variables on a small
fraction of cases, leading to a slight change in sample size between models.
CONTRASTING MONOGAMOUS AND POLYGYNOUS VILLAGES.
Finally, Table S7 reports the results of multilevel linear regressions
predicting individual household food insecurity, child HAZ, and child WHZ that
further incorporate village-level fixed effects. These analyses enable us to
determine what influence the proportion of polygynous households in a village
has over and above the marital status of an individual household. Model 1 for
each outcome in Table S7 is identical to the multilevel regression models shown
in Table S3 with the four-category coding of household type, except that they
also include a village-level fixed effect for polygyny prevalence, with each
unit increase representing an additional 10% of sampled households being
polygynous rather than monogamous (centered at 22% prevalence). Model 2 for each
outcome in Table S7 also includes additional village-level fixed effects for
annual rainfall in 100s of cubic millimeters (centered at 780 mm3), the
proportion of sampled household heads with nonzero educational attainment
(centered at 64%), and distance to the district capital in 10s of kilometers
(centered at 33 km). Comparing the results of model 1 and model 2 for each
outcome demonstrates that the effects of polygyny prevalence are substantially
modified by adjusting estimates for independent determinants of village
differences in food security and child health. Fig. 4 graphically represents
this comparison by plotting polygyny prevalence against predicted village
intercepts for each outcome before and after adjustment for annual rainfall,
proportion of household heads with nonzero education, and village distance from
the district capital.
OTHER SUPPORTING INFORMATION FILES
Dataset S1 (XLSX)
ACKNOWLEDGMENTS
We thank the village residents, C. Packer, D. Levison, K. Hartwig, M. Kaziya, F.
Rabison Msangi, J. Felix, and E. Sandet for contributions to the Whole Village
Project (WVP) and L. Fortunato, M. Grote, C. Moya, R. Sear, G. Stulp, C. Uggla,
and the Human Behavioral Ecology and Cultural Evolution Group at University of
California, Davis, for comments. The WVP was funded by the US Agency for
International Development, Partners for Development, the University of
Minnesota, and the Canadian Foodgrains Bank. The Wellcome Trust supported E.N.,
B.N., and S.G.M.M. during project implementation. D.W.L. is funded by the UK
Medical Research Council and Department for International Development (Grant
MR/K021672/1).
SUPPORTING INFORMATION
Supporting Information (PDF)
Supporting Information
* Download
* 756.46 KB
pnas.1507151112.sd01.xlsx
* Download
* 375.83 KB
REFERENCES
1
A Coles, L Gray, J Momsen The Routledge Handbook of Gender and Development
(Routledge, New York, 2015).
Crossref
Google Scholar
* a [...] of gender in all aspects of international development
* b [...] stage in discussions of international development
* c [...] on gender in all spheres of international development
2
R Gaffney-Rhys, A comparison of child marriage and polygamy from a human rights
perspective: Are the arguments equally cogent? J Soc Welf Fam 34, 49–61 (2012).
Crossref
Google Scholar
* a [...] genital cutting and on child and forced marriage
* b [...] such marriages ought to be discouraged and prohibited”
* c [...] human rights and theological perspectives, see refs.
3
N Wadesango, S Rembe, O Chabaya, Violation of women’s rights by harmful
traditional practices. Anthropologist 13, 121–129 (2011).
Go to reference
Crossref
Google Scholar
4
D Omariba, M Boyle, Family structure and child mortality in sub‐Saharan Africa:
Cross‐national effects of polygyny. J Marriage Fam 69, 528–543 (2007).
Crossref
Google Scholar
* a [...] early childhood than children in monogamous households
* b [...] studies of polygyny and child health is seductive
* c [...] is harmful to children. Consistent with prior studies
* d [...] clustering at the national level only (e.g., ref.
5
N Wagner, M Rieger, Polygyny and child growth: Evidence from twenty-six African
countries. Fem Econ 21, 1–26 (2014).
Google Scholar
* a [...] ). Finally, Wagner and Rieger
* b [...] Interestingly, Wagner and Rieger
* c [...] relationship was estimated for Tanzania (ref.
* d [...] Tanzanian families. Unfortunately, Wagner and Reiger
6
E Smith-Greenaway, J Trinitapoli, Polygynous contexts, family structure, and
infant mortality in sub-saharan Africa. Demography 51, 341–366 (2014).
Crossref
PubMed
Google Scholar
* a [...] effects at the subnational regional level (e.g., ref.
* b [...] the incorporation of PSUs as a random effect (ref.
7
FK Amey, Polygyny and child survival in West Africa. Soc Biol 49, 74–89 (2002).
PubMed
Google Scholar
8
SO Gyimah, Polygynous marital structure and child survivorship in sub-Saharan
Africa: some empirical evidence from Ghana. Soc Sci Med 68, 334–342 (2009).
Crossref
PubMed
Google Scholar
* a [...] early childhood than children in monogamous households
* b [...] studies of polygyny and child health is seductive
* c [...] is harmful to children. Consistent with prior studies
* d [...] data on ethnicity. With the exception of Gyimah
9
J Henrich, Polygyny in Cross-Cultural Perspective: Theory and Implications.
Affidavit submitted to the Supreme Court of British Columbia in the matter of
the constitutionality of s. 293 of the Criminal Code of Canada, R.S.C. 1985, c.
C-46, July 15, 2010. Available at www.alliancealert.org/2010/2010071902.pdf.
Accessed October 8, 2015. (2010).
Go to reference
Google Scholar
10
GP Murdock, DW White, Standard cross-cultural sample. Ethnology 8, 329–369
(1969).
Go to reference
Crossref
Google Scholar
11
C Westoff, Trends in marriage and early childbearing in developing countries.
DHS Comparative Reports No. 5 (ORC Macro, Calverton, MD). (2003).
Go to reference
Google Scholar
12
L Fortunato, Marriage systems, evolution of. International Encylopedia of Social
and Behavioural Sciences, 2nd Ed, ed Wright J (Elsevier, Oxford, UK), pp
611–619. (2015).
Google Scholar
* a [...] have long puzzled the costs and benefits of polygyny
* b [...] imply a rejection of the polygyny threshold model
13
MA Gibson, R Mace, Polygyny, reproductive success and child health in rural
Ethiopia: why marry a married man? J Biosoc Sci 39, 287–300 (2007).
Crossref
PubMed
Google Scholar
* a [...] success than their monogamous counterparts
* b [...] Advantages to first wives have been reported elsewhere
* c [...] ). In rural Ethiopia, Gibson and Mace
* d [...] cannot be seen as consequences of polygyny itself
* e [...] effects of polygyny in some cross-sectional studies
14
L Cronk, Wealth, status, and reproductive success among the Mukogodo of Kenya.
Am Anthropol 93, 345–360 (1991).
Crossref
Google Scholar
15
B Mulder, On cultural and reproductive success : Kipsigis evidence. Am Anthropol
89, 617–634 (1987).
Crossref
Google Scholar
16
J Winking, J Stieglitz, J Kurten, H Kaplan, M Gurven, Polygyny among the Tsimane
of Bolivia: An improved method for testing the polygyny-fertility hypothesis.
Proc Roy Soc B Biol Sci 280(1756):20123078. (2013).
Google Scholar
* a [...] success than their monogamous counterparts
* b [...] cannot be seen as consequences of polygyny itself
* c [...] of the first wife in intraindividual analyses
17
GH Orians, On the evolution of mating systems in birds and mammals. Am Nat 103,
589–603 (1969).
Crossref
Google Scholar
* a [...] access than could otherwise be obtained via monogamy
* b [...] wealth than could be achieved by opting for monogamy
18
GS Becker A Treatise on the Family (Harvard Univ Press, Cambridge, UK, 1981).
Google Scholar
* a [...] access than could otherwise be obtained via monogamy
* b [...] wealth than could be achieved by opting for monogamy
19
M Borgerhoff Mulder, Kipsigis women’s preferences for wealthy men: Evidence for
female choice in mammals? Behav Ecol Sociobiol 27, 255–264 (1990).
PubMed
Google Scholar
* a [...] men are typically wealthier than monogamous men
* b [...] success or child health for polygynously married women
* c [...] imply a rejection of the polygyny threshold model
20
BI Strassmann, Polygyny as a risk factor for child mortality among the Dogon.
Curr Anthropol 28, 688–695 (1997).
Crossref
Google Scholar
* a [...] men are typically wealthier than monogamous men
* b [...] is associated with relatively poor child health
21
R Sear, F Steele, IA McGregor, R Mace, The effects of kin on child mortality in
rural Gambia. Demography 39, 43–63 (2002).
Go to reference
Crossref
PubMed
Google Scholar
22
BI Strassmann, Cooperation and competition in a cliff-dwelling people. Proc Natl
Acad Sci USA 108(Suppl 2):10894–10901. (2011).
Google Scholar
* a [...] is associated with relatively poor child health
* b [...] the poorest households in Kenyan Kipsigis. Strassmann
23
C Hadley, Is polygyny a risk factor for poor growth performance among Tanzanian
agropastoralists? Am J Phys Anthropol 126, 471–480 (2005).
Crossref
PubMed
Google Scholar
24
DW Sellen, Polygyny and child growth in a traditional pastoral society: The case
of the Datoga of Tanzania. Hum Nat 10, 329–371 (1999).
Crossref
PubMed
Google Scholar
* a [...] is associated with relatively poor child health
* b [...] Advantages to first wives have been reported elsewhere
25
D Lawson, C Uggla, Family Structure and Health in the Developing World : What
Can Evolutionary Anthropology Contribute to Population Health Science? Applied
Evolutionary Anthropology: Darwinian Approaches to Contemporary World Issues,
eds Gibson MA, Lawson DW (Springer, New York), pp 85–118. (2014).
Google Scholar
* a [...] suboptimal outcomes for individual wives and children
* b [...] from the anthropological literature alone is difficult
* c [...] studies is hampered by methodological differences
26
M Borgerhoff Mulder, Women’s strategies in polygynous marriage : Kipsigis,
Datoga, and other East African cases. Hum Nat 3, 45–70 (1992).
Go to reference
Crossref
PubMed
Google Scholar
27
TV Pollet, JM Tybur, WE Frankenhuis, IJ Rickard, What can cross-cultural
correlations teach us about human nature? Hum Nat 25, 410–429 (2014).
Crossref
PubMed
Google Scholar
* a [...] their own, often overlooked, methodological problems
* b [...] such, multilevel analysis reveals a Simpson’s paradox
28
J Henrich, R Boyd, PJ Richerson, The puzzle of monogamous marriage. Philos Trans
R Soc Lond B Biol Sci 367, 657–669 (2012).
Crossref
PubMed
Google Scholar
* a [...] at the group level, including costs to child health
* b [...] ). Most recently, Henrich et al.
* c [...] group-wide consequences for children, Henrich et al.
* d [...] that polygyny has group-wide costs on child health
* e [...] on women and children, as suggested by Henrich et al.
29
RD Alexander, JL Hoogland, RD Howard, KM Noonan, PW Sherman, Sexual dimorphisms
and breeding systems in pinnipeds, ungulates, primates and humans. Evolutionary
Biology and Human Social Behaviour: An Anthropological Perspective, eds NA
Chagnon, W Irons (Duxbiry Press, North Scituate, MA), pp. 402–435 (1979).
Go to reference
Google Scholar
30
R Schacht, KL Rauch, M Borgerhoff Mulder, Too many men: The violence problem?
Trends Ecol Evol 29, 214–222 (2014).
Crossref
PubMed
Google Scholar
* a [...] of the proportion of unmarried men, and violent crime
* b [...] expect higher not lower levels of paternal investment
31
; National Bureau of Statistics Tanzania and ICF Macro, Tanzania Demographic and
Health Survey 2010. (NBS and ICF Macro, Dar es Salaam, Tanzania). (2011).
Google Scholar
* a [...] stunted by World Health Organization (WHO) standards
* b [...] women in rural Tanzania have at least one cowife
* c [...] surpasses the Tanzania DHS for the same regions
* d [...] DHS [e.g., only 16–22 households in Tanzania (ref.
32
S Grantham-McGregor, et al., Developmental potential in the first 5 years for
children in developing countries. Lancet; International Child Development
Steering Group 369, 60–70 (2007).
Go to reference
Crossref
PubMed
Google Scholar
33
; UNDP, Human Development Report 2014. Sustaining Human Progress: Reducing
Vulnerabilities and Building Resilience (United Nations, New York). (2014).
Go to reference
Google Scholar
34
DW Lawson, et al., Ethnicity and child health in northern Tanzania: Maasai
pastoralists are disadvantaged compared to neighbouring ethnic groups. PLoS One
9, e110447 (2014).
Crossref
PubMed
Google Scholar
* a [...] = 3,584) than the Tanzanian DHS for the same regions
* b [...] polygynous Rangi and the predominantly monogamous Meru
* c [...] diamond, other ethnicity village. Reproduced from ref.
* d [...] ) (see ref.
* e [...] burden of food insecurity and poor health in our study
* f [...] from improved health care and education infrastructure
* g [...] The Meru also have the highest educational attainment
* h [...] identify with either Christian or indigenous religions
* i [...] surpasses the Tanzania DHS for the same regions
* j [...] DHS, which included only 2% Maasai households
* k [...] high levels of socioeconomic marginalization
* l [...] terms of both polygyny prevalence and health outcomes
* m [...] national parks, and game reserves. Lawson et al.
* n [...] the age range of zero to 60 mo and evenly split by sex
* o [...] of growth indicators. However, we have previously
* p [...] can be categorized as severely food insecure
* q [...] mapped to a central point of each village
35
SO Rutstein, The DHS Wealth Index : Approaches for Rural and Urban Areas (Macro
International, Calverton, MD). (2008).
Google Scholar
* a [...] surveys and used across rural and urban contexts
* b [...] wealth indices favored by national demographic surveys
36
P David, S Haberlen, 10 best resources for...measuring population health. Health
Policy Plan 20, 260–263 (2005).
Go to reference
Crossref
PubMed
Google Scholar
37
DL Hodgson, Pastoralism, patriarchy and history: Changing gender relations among
Maasai in Tanganyika, 1890-1940. J Afr Hist 40, 41–65 (1999).
Go to reference
Crossref
PubMed
Google Scholar
38
GJ Carlson, K Kordas, LE Murray-Kolb, Associations between women’s autonomy and
child nutritional status: A review of the literature. Matern Child Nutr 11,
452–482 (2014).
Go to reference
Crossref
PubMed
Google Scholar
39
M Borgerhoff Mulder, Hamilton’s rule and kin competition: The Kipsigis case.
Evol Hum Behav 28, 299–312 (2007).
Go to reference
Crossref
Google Scholar
40
M Borgerhoff Mulder, Marrying a Married Man: A Postscript. Human Nature: A
Critical Reader, ed L Betzig (Oxford University Press, New York), pp. 115–117
(1997).
Go to reference
Google Scholar
41
H Kokko, MD Jennions, Parental investment, sexual selection and sex ratios. J
Evol Biol 21, 919–948 (2008).
Go to reference
Crossref
PubMed
Google Scholar
42
T Spear Mountain Farmers: Moral Economies of Land and Agricultural Development
in Arusha and Meru (Univ of California Press, Oakland, CA, 1997).
Google Scholar
* a [...] for the ubiquity of monogamy among the Meru
* b [...] early adopters of relatively intensified agriculture
43
J Goody, Polygyny, economy, and the role of women. The Character of Kinship, ed
J Goody (Cambridge Univ Press, London), pp. 175–190 (1973).
Go to reference
Google Scholar
44
L Fortunato, M Archetti, Evolution of monogamous marriage by maximization of
inclusive fitness. J Evol Biol 23, 149–156 (2010).
Go to reference
Crossref
PubMed
Google Scholar
45
A Goodman, I Koupil, DW Lawson, Low fertility increases descendant socioeconomic
position but reduces long-term fitness in a modern post-industrial society. Proc
Roy Soc B Biol Sci 279(1746):4342–4351. (2012).
Go to reference
Google Scholar
46
S Randall, E Coast, Poverty in African households: The limits of survey and
census representations. J Dev Stud 51, 162–177 (2014).
Go to reference
Crossref
Google Scholar
47
R Bove, C Valeggia, Polygyny and women’s health in sub-Saharan Africa. Soc Sci
Med 68, 21–29 (2009).
Go to reference
Crossref
PubMed
Google Scholar
48
G Reniers, R Tfaily, Polygyny, partnership concurrency, and HIV transmission in
Sub-Saharan Africa. Demography 49, 1075–1101 (2012).
Go to reference
Crossref
PubMed
Google Scholar
49
C Patil, C Hadley, Symptoms of anxiety and depression and mother’s marital
status: An exploratory analysis of polygyny and psychosocial stress. Am J Hum
Biol 20, 475–477 (2008).
Go to reference
Crossref
PubMed
Google Scholar
50
MA Gibson, DW Lawson, Applying evolutionary anthropology. Evol Anthropol Issues.
Rev 24, 3–14 (2015).
Go to reference
Google Scholar
51
D White, Rethinking polygyny: Co-wives, codes and cultural systems. Curr
Anthropol 19, 529–572 (1988).
Go to reference
Crossref
Google Scholar
52
E Palmore, Cross-cultural perspectives on widowhood. J Cross Cult Gerontol 2,
93–105 (1987).
Go to reference
Crossref
PubMed
Google Scholar
53
B Winter, D Thompson, S Jeffreys, The UN approach to harmful traditional
practices. Int Fem J Polit 4, 72–94 (2002).
Crossref
Google Scholar
* a [...] in a 1995 UN Fact sheet devoted to the issue
* b [...] of women is limited to traditional populations
54
O Jonas, The practice of polygamy under the scheme of the Protocol to the
African Charter on Human and Peoples’ Rights on the Rights of Women in Africa: A
critical appraisal. J Afr Stud Dev 4, 142–149 (2012).
Go to reference
Google Scholar
55
Jr J Witte The Western Case for Monogamy Over Polygamy (Cambridge Univ Press,
Cambridge, UK, 2015).
Go to reference
Crossref
Google Scholar
56
M de Onis, et al., Worldwide implementation of the WHO Child Growth Standards.
Public Health Nutr; WHO Multicentre Growth Reference Study Group 15, 1603–1610
(2012).
Go to reference
Crossref
PubMed
Google Scholar
57
J Coates, A Swindale, P Bilinsky, Household Food Insecurity Access Scale (HFIAS)
for Measurement of Food Access: Indicator Guide (v.3) (Food and Nutrition
Technical Assistance Project, Academy for Educational Development, Washington
DC). (2007).
Google Scholar
* a [...] Access Scale (HFIAS) was used to measure food security
* b [...] day and night without eating) at least once a month
58
P Clarke, When can group level clustering be ignored? Multilevel models versus
single-level models with sparse data. J Epidemiol Community Health 62, 752–758
(2008).
Go to reference
Crossref
PubMed
Google Scholar
Show all references
INFORMATION & AUTHORS
InformationAuthors
INFORMATION
PUBLISHED IN
Proceedings of the National Academy of Sciences
Vol. 112 | No. 45
November 10, 2015
PubMed: 26504213
CLASSIFICATIONS
1. Biological Sciences
2. Anthropology
3.
4. Social Sciences
5. Social Sciences
6.
COPYRIGHT
Freely available online through the PNAS open access option.
SUBMISSION HISTORY
Published online: October 26, 2015
Published in issue: November 10, 2015
KEYWORDS
1. evolutionary anthropology
2. public health
3. family structure
4. child health
5. food security
ACKNOWLEDGMENTS
We thank the village residents, C. Packer, D. Levison, K. Hartwig, M. Kaziya, F.
Rabison Msangi, J. Felix, and E. Sandet for contributions to the Whole Village
Project (WVP) and L. Fortunato, M. Grote, C. Moya, R. Sear, G. Stulp, C. Uggla,
and the Human Behavioral Ecology and Cultural Evolution Group at University of
California, Davis, for comments. The WVP was funded by the US Agency for
International Development, Partners for Development, the University of
Minnesota, and the Canadian Foodgrains Bank. The Wellcome Trust supported E.N.,
B.N., and S.G.M.M. during project implementation. D.W.L. is funded by the UK
Medical Research Council and Department for International Development (Grant
MR/K021672/1).
NOTES
This article is a PNAS Direct Submission. J.H.J. is a Guest Editor invited by
the Editorial Board.
AUTHORS
AFFILIATIONSEXPAND ALL
DAVID W. LAWSON1 DAVID.LAWSON@LSHTM.AC.UK
Department of Population Health, London School of Hygiene and Tropical Medicine,
London, WC1E 7HT, United Kingdom;
View all articles by this author
SUSAN JAMES
Savannas Forever Tanzania, Arusha, Tanzania;
View all articles by this author
ESTHER NGADAYA
National Institute for Medical Research, Muhimbili Medical Research Centre, Dar
es Salaam, 11101, Tanzania;
View all articles by this author
BERNARD NGOWI
National Institute for Medical Research, Muhimbili Medical Research Centre, Dar
es Salaam, 11101, Tanzania;
View all articles by this author
SAYOKI G. M. MFINANGA
National Institute for Medical Research, Muhimbili Medical Research Centre, Dar
es Salaam, 11101, Tanzania;
View all articles by this author
MONIQUE BORGERHOFF MULDER
Savannas Forever Tanzania, Arusha, Tanzania;
Department of Anthropology, University of California, Davis, CA 95616
View all articles by this author
NOTES
1
To whom correspondence should be addressed. Email: david.lawson@lshtm.ac.uk.
Author contributions: D.W.L., S.J., E.N., B.N., S.G.M.M., and M.B.M. designed
research; D.W.L. analyzed data; D.W.L. and M.B.M. wrote the paper; and S.J.,
E.N., B.N., and S.G.M.M. collected data.
COMPETING INTERESTS
The authors declare no conflict of interest.
METRICS & CITATIONS
MetricsCitations
METRICS
Article usage
* Views
* Citations
No data available.
0100200300400500FebMarAprMayJunJul
28,451
47
* Total
* 6 Months
* 12 Months
Total number of views and citations for the most recent 6 whole calendar months.
Note: The article usage is presented with a three- to four-day delay and will
update daily once available. Due to ths delay, usage data will not appear
immediately following publication. Citation information is sourced from Crossref
Cited-by service.
CITATION STATEMENTS
61
4
65
1
Smart Citations
61
4
65
1
Citing PublicationsSupportingMentioningContrasting
View Citations
See how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of
the citation, a classification describing whether it supports, mentions, or
contrasts the cited claim, and a label indicating in which section the citation
was made.
ALTMETRICS
See more details
Picked up by 28 news outlets
Blogged by 2
Tweeted by 61
Referenced in 1 Wikipedia pages
Mentioned in 3 Google+ posts
Reddited by 2
193 readers on Mendeley
CITATIONS
If you have the appropriate software installed, you can download article
citation data to the citation manager of your choice. Simply select your manager
software from the list below and click Download.
Format
Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex
Medlars RefWorks
Direct Import
CITED BY
Loading...
VIEW OPTIONS
VIEW OPTIONS
PDF FORMAT
Download this article as a PDF file
DOWNLOAD PDF
MEDIA
FiguresTablesOther
FIGURES
Fig. S1.
Location of the 56 study villages included in the Whole Village Project.
Ethnicity is coded as the most common ethnicity in each village. Red circle,
Maasai village; orange triangle, Rangi village; green diamond, Sukuma village;
blue square, Meru village; white diamond, other ethnicity village. Reproduced
from ref. 34.
Go to FigureOpen in Viewer
Fig. 1.
Child height-for-age by village sorted by polygyny prevalence. There is strong
ethnic and village-level variation in child health. Relatively monogamous Meru
villages tend to have relatively good child health, whereas relatively
polygynous Maasai villages tend to have relatively poor child health. The dashed
line represents the WHO cutoff for chronic malnutrition. Ethnicity is coded as
the majority ethnic group residing in each village. Error bars represent 95%
CIs. Red circle, Maasai; green diamond, Sukuma; orange triangle, Rangi; blue
square, Meru; white diamond, other ethnicity.
Go to FigureOpen in Viewer
Fig. 2.
Food security and child health by household type. Within villages polygyny is
associated with relatively high food security when households are headed by a
male and relatively low food security when headed by a female (typically later
wife households). Stratified analysis confirms higher food security in the
Sukuma and relatively improved child weight-for-height in both the Sukuma and
Rangi, for male-headed polygynous households. The reference category (dashed
line) is male-headed monogamous households (Table S4 for full model output). +P
< 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.
Go to FigureOpen in Viewer
Fig. 3.
Wealth index, land cultivated, and livestock owned by household type. Within
villages polygynous households, particularly when headed by males, cultivate
more land and own more livestock than monogamous households. The reference
category (dashed line) is male-headed monogamous households (Table S6 for full
model output). +P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.
Go to FigureOpen in Viewer
Fig. 4.
Village differences in food security and child health by polygyny prevalence.
Predicted village intercepts before (A) and after (B) adjustment for
village-level differences in ecological vulnerability (annual rainfall) and
socioeconomic marginalization (distance to district capital and proportion of
household heads with nonzero education). After adjustment, polygyny prevalence
is unrelated to food security and HAZ, and positively predicts WHZ. Intercepts
are mean/mode centered for household characteristics. See text for estimated
coefficients and Table S7 for corresponding model output. Red circle, Maasai
village; green diamond, Sukuma village; orange triangle, Rangi village; blue
square, Meru village; white diamond, other ethnicity village.
Go to FigureOpen in Viewer
TABLES
Table S1.
Demographic characteristics by household type and ethnicity for working sample
(n = 1764 households, 2833 children)
Go to TableOpen in Viewer
Table S2.
Household and village characteristics for working sample by ethnicity
Go to TableOpen in Viewer
Table S3.
Linear and multilevel regressions predicting household food security, HAZ, and
WHZ
Go to TableOpen in Viewer
Table S4.
Multilevel regressions predicting household food insecurity, HAZ, and WHZ
stratified by ethnic group
Go to TableOpen in Viewer
Table S5.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned
Go to TableOpen in Viewer
Table S6.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned stratified by ethnic group
Go to TableOpen in Viewer
Table S7.
Multilevel regressions predicting household food security, HAZ, and WHZ
Go to TableOpen in Viewer
OTHER
SHARE
SHARE
SHARE ARTICLE LINK
https://www.pnas.org/doi/full/10.1073/pnas.1507151112
COPY LINK
Copied!
Copying failed.
SHARE ON SOCIAL MEDIA
FacebookTwitterLinkedinGmailemail
REFERENCES
REFERENCES
1
A Coles, L Gray, J Momsen The Routledge Handbook of Gender and Development
(Routledge, New York, 2015).
Crossref
Google Scholar
* a [...] of gender in all aspects of international development
* b [...] stage in discussions of international development
* c [...] on gender in all spheres of international development
2
R Gaffney-Rhys, A comparison of child marriage and polygamy from a human rights
perspective: Are the arguments equally cogent? J Soc Welf Fam 34, 49–61 (2012).
Crossref
Google Scholar
* a [...] genital cutting and on child and forced marriage
* b [...] such marriages ought to be discouraged and prohibited”
* c [...] human rights and theological perspectives, see refs.
3
N Wadesango, S Rembe, O Chabaya, Violation of women’s rights by harmful
traditional practices. Anthropologist 13, 121–129 (2011).
Go to reference
Crossref
Google Scholar
4
D Omariba, M Boyle, Family structure and child mortality in sub‐Saharan Africa:
Cross‐national effects of polygyny. J Marriage Fam 69, 528–543 (2007).
Crossref
Google Scholar
* a [...] early childhood than children in monogamous households
* b [...] studies of polygyny and child health is seductive
* c [...] is harmful to children. Consistent with prior studies
* d [...] clustering at the national level only (e.g., ref.
5
N Wagner, M Rieger, Polygyny and child growth: Evidence from twenty-six African
countries. Fem Econ 21, 1–26 (2014).
Google Scholar
* a [...] ). Finally, Wagner and Rieger
* b [...] Interestingly, Wagner and Rieger
* c [...] relationship was estimated for Tanzania (ref.
* d [...] Tanzanian families. Unfortunately, Wagner and Reiger
6
E Smith-Greenaway, J Trinitapoli, Polygynous contexts, family structure, and
infant mortality in sub-saharan Africa. Demography 51, 341–366 (2014).
Crossref
PubMed
Google Scholar
* a [...] effects at the subnational regional level (e.g., ref.
* b [...] the incorporation of PSUs as a random effect (ref.
7
FK Amey, Polygyny and child survival in West Africa. Soc Biol 49, 74–89 (2002).
PubMed
Google Scholar
8
SO Gyimah, Polygynous marital structure and child survivorship in sub-Saharan
Africa: some empirical evidence from Ghana. Soc Sci Med 68, 334–342 (2009).
Crossref
PubMed
Google Scholar
* a [...] early childhood than children in monogamous households
* b [...] studies of polygyny and child health is seductive
* c [...] is harmful to children. Consistent with prior studies
* d [...] data on ethnicity. With the exception of Gyimah
9
J Henrich, Polygyny in Cross-Cultural Perspective: Theory and Implications.
Affidavit submitted to the Supreme Court of British Columbia in the matter of
the constitutionality of s. 293 of the Criminal Code of Canada, R.S.C. 1985, c.
C-46, July 15, 2010. Available at www.alliancealert.org/2010/2010071902.pdf.
Accessed October 8, 2015. (2010).
Go to reference
Google Scholar
10
GP Murdock, DW White, Standard cross-cultural sample. Ethnology 8, 329–369
(1969).
Go to reference
Crossref
Google Scholar
11
C Westoff, Trends in marriage and early childbearing in developing countries.
DHS Comparative Reports No. 5 (ORC Macro, Calverton, MD). (2003).
Go to reference
Google Scholar
12
L Fortunato, Marriage systems, evolution of. International Encylopedia of Social
and Behavioural Sciences, 2nd Ed, ed Wright J (Elsevier, Oxford, UK), pp
611–619. (2015).
Google Scholar
* a [...] have long puzzled the costs and benefits of polygyny
* b [...] imply a rejection of the polygyny threshold model
13
MA Gibson, R Mace, Polygyny, reproductive success and child health in rural
Ethiopia: why marry a married man? J Biosoc Sci 39, 287–300 (2007).
Crossref
PubMed
Google Scholar
* a [...] success than their monogamous counterparts
* b [...] Advantages to first wives have been reported elsewhere
* c [...] ). In rural Ethiopia, Gibson and Mace
* d [...] cannot be seen as consequences of polygyny itself
* e [...] effects of polygyny in some cross-sectional studies
14
L Cronk, Wealth, status, and reproductive success among the Mukogodo of Kenya.
Am Anthropol 93, 345–360 (1991).
Crossref
Google Scholar
15
B Mulder, On cultural and reproductive success : Kipsigis evidence. Am Anthropol
89, 617–634 (1987).
Crossref
Google Scholar
16
J Winking, J Stieglitz, J Kurten, H Kaplan, M Gurven, Polygyny among the Tsimane
of Bolivia: An improved method for testing the polygyny-fertility hypothesis.
Proc Roy Soc B Biol Sci 280(1756):20123078. (2013).
Google Scholar
* a [...] success than their monogamous counterparts
* b [...] cannot be seen as consequences of polygyny itself
* c [...] of the first wife in intraindividual analyses
17
GH Orians, On the evolution of mating systems in birds and mammals. Am Nat 103,
589–603 (1969).
Crossref
Google Scholar
* a [...] access than could otherwise be obtained via monogamy
* b [...] wealth than could be achieved by opting for monogamy
18
GS Becker A Treatise on the Family (Harvard Univ Press, Cambridge, UK, 1981).
Google Scholar
* a [...] access than could otherwise be obtained via monogamy
* b [...] wealth than could be achieved by opting for monogamy
19
M Borgerhoff Mulder, Kipsigis women’s preferences for wealthy men: Evidence for
female choice in mammals? Behav Ecol Sociobiol 27, 255–264 (1990).
PubMed
Google Scholar
* a [...] men are typically wealthier than monogamous men
* b [...] success or child health for polygynously married women
* c [...] imply a rejection of the polygyny threshold model
20
BI Strassmann, Polygyny as a risk factor for child mortality among the Dogon.
Curr Anthropol 28, 688–695 (1997).
Crossref
Google Scholar
* a [...] men are typically wealthier than monogamous men
* b [...] is associated with relatively poor child health
21
R Sear, F Steele, IA McGregor, R Mace, The effects of kin on child mortality in
rural Gambia. Demography 39, 43–63 (2002).
Go to reference
Crossref
PubMed
Google Scholar
22
BI Strassmann, Cooperation and competition in a cliff-dwelling people. Proc Natl
Acad Sci USA 108(Suppl 2):10894–10901. (2011).
Google Scholar
* a [...] is associated with relatively poor child health
* b [...] the poorest households in Kenyan Kipsigis. Strassmann
23
C Hadley, Is polygyny a risk factor for poor growth performance among Tanzanian
agropastoralists? Am J Phys Anthropol 126, 471–480 (2005).
Crossref
PubMed
Google Scholar
24
DW Sellen, Polygyny and child growth in a traditional pastoral society: The case
of the Datoga of Tanzania. Hum Nat 10, 329–371 (1999).
Crossref
PubMed
Google Scholar
* a [...] is associated with relatively poor child health
* b [...] Advantages to first wives have been reported elsewhere
25
D Lawson, C Uggla, Family Structure and Health in the Developing World : What
Can Evolutionary Anthropology Contribute to Population Health Science? Applied
Evolutionary Anthropology: Darwinian Approaches to Contemporary World Issues,
eds Gibson MA, Lawson DW (Springer, New York), pp 85–118. (2014).
Google Scholar
* a [...] suboptimal outcomes for individual wives and children
* b [...] from the anthropological literature alone is difficult
* c [...] studies is hampered by methodological differences
26
M Borgerhoff Mulder, Women’s strategies in polygynous marriage : Kipsigis,
Datoga, and other East African cases. Hum Nat 3, 45–70 (1992).
Go to reference
Crossref
PubMed
Google Scholar
27
TV Pollet, JM Tybur, WE Frankenhuis, IJ Rickard, What can cross-cultural
correlations teach us about human nature? Hum Nat 25, 410–429 (2014).
Crossref
PubMed
Google Scholar
* a [...] their own, often overlooked, methodological problems
* b [...] such, multilevel analysis reveals a Simpson’s paradox
28
J Henrich, R Boyd, PJ Richerson, The puzzle of monogamous marriage. Philos Trans
R Soc Lond B Biol Sci 367, 657–669 (2012).
Crossref
PubMed
Google Scholar
* a [...] at the group level, including costs to child health
* b [...] ). Most recently, Henrich et al.
* c [...] group-wide consequences for children, Henrich et al.
* d [...] that polygyny has group-wide costs on child health
* e [...] on women and children, as suggested by Henrich et al.
29
RD Alexander, JL Hoogland, RD Howard, KM Noonan, PW Sherman, Sexual dimorphisms
and breeding systems in pinnipeds, ungulates, primates and humans. Evolutionary
Biology and Human Social Behaviour: An Anthropological Perspective, eds NA
Chagnon, W Irons (Duxbiry Press, North Scituate, MA), pp. 402–435 (1979).
Go to reference
Google Scholar
30
R Schacht, KL Rauch, M Borgerhoff Mulder, Too many men: The violence problem?
Trends Ecol Evol 29, 214–222 (2014).
Crossref
PubMed
Google Scholar
* a [...] of the proportion of unmarried men, and violent crime
* b [...] expect higher not lower levels of paternal investment
31
; National Bureau of Statistics Tanzania and ICF Macro, Tanzania Demographic and
Health Survey 2010. (NBS and ICF Macro, Dar es Salaam, Tanzania). (2011).
Google Scholar
* a [...] stunted by World Health Organization (WHO) standards
* b [...] women in rural Tanzania have at least one cowife
* c [...] surpasses the Tanzania DHS for the same regions
* d [...] DHS [e.g., only 16–22 households in Tanzania (ref.
32
S Grantham-McGregor, et al., Developmental potential in the first 5 years for
children in developing countries. Lancet; International Child Development
Steering Group 369, 60–70 (2007).
Go to reference
Crossref
PubMed
Google Scholar
33
; UNDP, Human Development Report 2014. Sustaining Human Progress: Reducing
Vulnerabilities and Building Resilience (United Nations, New York). (2014).
Go to reference
Google Scholar
34
DW Lawson, et al., Ethnicity and child health in northern Tanzania: Maasai
pastoralists are disadvantaged compared to neighbouring ethnic groups. PLoS One
9, e110447 (2014).
Crossref
PubMed
Google Scholar
* a [...] = 3,584) than the Tanzanian DHS for the same regions
* b [...] polygynous Rangi and the predominantly monogamous Meru
* c [...] diamond, other ethnicity village. Reproduced from ref.
* d [...] ) (see ref.
* e [...] burden of food insecurity and poor health in our study
* f [...] from improved health care and education infrastructure
* g [...] The Meru also have the highest educational attainment
* h [...] identify with either Christian or indigenous religions
* i [...] surpasses the Tanzania DHS for the same regions
* j [...] DHS, which included only 2% Maasai households
* k [...] high levels of socioeconomic marginalization
* l [...] terms of both polygyny prevalence and health outcomes
* m [...] national parks, and game reserves. Lawson et al.
* n [...] the age range of zero to 60 mo and evenly split by sex
* o [...] of growth indicators. However, we have previously
* p [...] can be categorized as severely food insecure
* q [...] mapped to a central point of each village
35
SO Rutstein, The DHS Wealth Index : Approaches for Rural and Urban Areas (Macro
International, Calverton, MD). (2008).
Google Scholar
* a [...] surveys and used across rural and urban contexts
* b [...] wealth indices favored by national demographic surveys
36
P David, S Haberlen, 10 best resources for...measuring population health. Health
Policy Plan 20, 260–263 (2005).
Go to reference
Crossref
PubMed
Google Scholar
37
DL Hodgson, Pastoralism, patriarchy and history: Changing gender relations among
Maasai in Tanganyika, 1890-1940. J Afr Hist 40, 41–65 (1999).
Go to reference
Crossref
PubMed
Google Scholar
38
GJ Carlson, K Kordas, LE Murray-Kolb, Associations between women’s autonomy and
child nutritional status: A review of the literature. Matern Child Nutr 11,
452–482 (2014).
Go to reference
Crossref
PubMed
Google Scholar
39
M Borgerhoff Mulder, Hamilton’s rule and kin competition: The Kipsigis case.
Evol Hum Behav 28, 299–312 (2007).
Go to reference
Crossref
Google Scholar
40
M Borgerhoff Mulder, Marrying a Married Man: A Postscript. Human Nature: A
Critical Reader, ed L Betzig (Oxford University Press, New York), pp. 115–117
(1997).
Go to reference
Google Scholar
41
H Kokko, MD Jennions, Parental investment, sexual selection and sex ratios. J
Evol Biol 21, 919–948 (2008).
Go to reference
Crossref
PubMed
Google Scholar
42
T Spear Mountain Farmers: Moral Economies of Land and Agricultural Development
in Arusha and Meru (Univ of California Press, Oakland, CA, 1997).
Google Scholar
* a [...] for the ubiquity of monogamy among the Meru
* b [...] early adopters of relatively intensified agriculture
43
J Goody, Polygyny, economy, and the role of women. The Character of Kinship, ed
J Goody (Cambridge Univ Press, London), pp. 175–190 (1973).
Go to reference
Google Scholar
44
L Fortunato, M Archetti, Evolution of monogamous marriage by maximization of
inclusive fitness. J Evol Biol 23, 149–156 (2010).
Go to reference
Crossref
PubMed
Google Scholar
45
A Goodman, I Koupil, DW Lawson, Low fertility increases descendant socioeconomic
position but reduces long-term fitness in a modern post-industrial society. Proc
Roy Soc B Biol Sci 279(1746):4342–4351. (2012).
Go to reference
Google Scholar
46
S Randall, E Coast, Poverty in African households: The limits of survey and
census representations. J Dev Stud 51, 162–177 (2014).
Go to reference
Crossref
Google Scholar
47
R Bove, C Valeggia, Polygyny and women’s health in sub-Saharan Africa. Soc Sci
Med 68, 21–29 (2009).
Go to reference
Crossref
PubMed
Google Scholar
48
G Reniers, R Tfaily, Polygyny, partnership concurrency, and HIV transmission in
Sub-Saharan Africa. Demography 49, 1075–1101 (2012).
Go to reference
Crossref
PubMed
Google Scholar
49
C Patil, C Hadley, Symptoms of anxiety and depression and mother’s marital
status: An exploratory analysis of polygyny and psychosocial stress. Am J Hum
Biol 20, 475–477 (2008).
Go to reference
Crossref
PubMed
Google Scholar
50
MA Gibson, DW Lawson, Applying evolutionary anthropology. Evol Anthropol Issues.
Rev 24, 3–14 (2015).
Go to reference
Google Scholar
51
D White, Rethinking polygyny: Co-wives, codes and cultural systems. Curr
Anthropol 19, 529–572 (1988).
Go to reference
Crossref
Google Scholar
52
E Palmore, Cross-cultural perspectives on widowhood. J Cross Cult Gerontol 2,
93–105 (1987).
Go to reference
Crossref
PubMed
Google Scholar
53
B Winter, D Thompson, S Jeffreys, The UN approach to harmful traditional
practices. Int Fem J Polit 4, 72–94 (2002).
Crossref
Google Scholar
* a [...] in a 1995 UN Fact sheet devoted to the issue
* b [...] of women is limited to traditional populations
54
O Jonas, The practice of polygamy under the scheme of the Protocol to the
African Charter on Human and Peoples’ Rights on the Rights of Women in Africa: A
critical appraisal. J Afr Stud Dev 4, 142–149 (2012).
Go to reference
Google Scholar
55
Jr J Witte The Western Case for Monogamy Over Polygamy (Cambridge Univ Press,
Cambridge, UK, 2015).
Go to reference
Crossref
Google Scholar
56
M de Onis, et al., Worldwide implementation of the WHO Child Growth Standards.
Public Health Nutr; WHO Multicentre Growth Reference Study Group 15, 1603–1610
(2012).
Go to reference
Crossref
PubMed
Google Scholar
57
J Coates, A Swindale, P Bilinsky, Household Food Insecurity Access Scale (HFIAS)
for Measurement of Food Access: Indicator Guide (v.3) (Food and Nutrition
Technical Assistance Project, Academy for Educational Development, Washington
DC). (2007).
Google Scholar
* a [...] Access Scale (HFIAS) was used to measure food security
* b [...] day and night without eating) at least once a month
58
P Clarke, When can group level clustering be ignored? Multilevel models versus
single-level models with sparse data. J Epidemiol Community Health 62, 752–758
(2008).
Go to reference
Crossref
PubMed
Google Scholar
View full textDownload PDF
Open in viewer
Go to
Go to
Show all references
Request permissionsExpand All
Collapse
EXPAND FOR MORE
Authors Info & Affiliations
FURTHER READING IN THIS ISSUE
* Research ArticleOctober 26, 2015
ACCURATE MARKET PRICE FORMATION MODEL WITH BOTH SUPPLY-DEMAND AND
TREND-FOLLOWING FOR GLOBAL FOOD PRICES PROVIDING POLICY RECOMMENDATIONS
* Marco Lagi,
* Yavni Bar-Yam,
* Karla Z. Bertrand,
* [...]
* Yaneer Bar-Yam and
* +1 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
* Research ArticleOctober 26, 2015
DISCOVERY AND FUNCTIONAL CHARACTERIZATION OF A NEOMORPHIC PTEN MUTATION
* Helio A. Costa,
* Michael G. Leitner,
* Martin L. Sos,
* Angeliki Mavrantoni,
* Anna Rychkova,
* Jeffrey R. Johnson,
* Billy W. Newton,
* Muh-Ching Yee,
* Francisco M. De La Vega,
* James M. Ford,
* Nevan J. Krogan,
* Kevan M. Shokat,
* Dominik Oliver,
* Christian R. Halaszovich,
* [...]
* Carlos D. Bustamante,
* +12 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
* Research ArticleOctober 26, 2015
DETERMINING HYDRODYNAMIC FORCES IN BURSTING BUBBLES USING DNA NANOTUBE
MECHANICS
* Rizal F. Hariadi,
* Erik Winfree,
* [...]
* Bernard Yurke,
* +0 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
Trending
* PerspectiveAugust 1, 2022
CLIMATE ENDGAME: EXPLORING CATASTROPHIC CLIMATE CHANGE SCENARIOS
Prudent risk management requires consideration of bad-to-worst-case
scenarios. Yet, for climate change, such potential futures are poorly
understood. Could anthropogenic climate change result in worldwide societal
collapse or even eventual human ...
* Luke Kemp,
* Chi Xu,
* Joanna Depledge,
* Kristie L. Ebi,
* Goodwin Gibbins,
* Timothy A. Kohler,
* Johan Rockström,
* Marten Scheffer,
* Hans Joachim Schellnhuber,
* Will Steffen,
* [...]
* Timothy M. Lenton,
* +8 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
* OpinionJuly 19, 2023
MINK FARMING POSES RISKS FOR FUTURE VIRAL PANDEMICS
* Thomas P. Peacock,
* [...]
* Wendy S. Barclay and
* +0 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
* Research ArticleJuly 11, 2023
SUPERMASSIVE DARK STAR CANDIDATES SEEN BY JWST
In 2007, we proposed the idea of Dark Stars. The first phase of stellar
evolution in the history of the universe may be Dark Stars (DS), powered by
dark matter (DM) heating rather than by nuclear fusion. Although made almost
entirely of ...The first generation of stars in the universe is yet to be
observed. There are two leading theories for those objects that mark the
beginning of the cosmic dawn: hydrogen burning Population III stars and Dark
Stars, made of hydrogen and helium but powered ...
* Cosmin Ilie,
* Jillian Paulin,
* [...]
* Katherine Freese,
* +0 authors
This is an addendum toComplex viscosity of helical and doubly helical
polymeric liquids from general rigid bead-rod theory
RECOMMENDED ARTICLES
Research ArticleAugust 5, 2019
RISK OF BEING KILLED BY POLICE USE OF FORCE IN THE UNITED STATES BY AGE,
RACE–ETHNICITY, AND SEX
This is an addendum toComplex viscosity of helical and doubly helical polymeric
liquids from general rigid bead-rod theory
Research ArticleMay 15, 2017
IMPACT OF INTERNATIONAL MONETARY FUND PROGRAMS ON CHILD HEALTH
This is an addendum toComplex viscosity of helical and doubly helical polymeric
liquids from general rigid bead-rod theory
LetterMarch 2, 2016
REPLY TO RIEGER AND WAGNER: CONTEXT MATTERS WHEN STUDYING PURPORTEDLY HARMFUL
CULTURAL PRACTICES
This is an addendum toComplex viscosity of helical and doubly helical polymeric
liquids from general rigid bead-rod theory
SIGN UP FOR THEPNAS HIGHLIGHTS NEWSLETTER
Get in-depth science stories sent to your inbox twice a month.
Subscribe
*
*
*
*
*
*
*
Browse
* Current Issue
* PNAS Nexus
* Special Features
* Colloquia
* List of Issues
* Collected Papers
* PNAS In The News
* Front Matter
* Journal Club
* Podcasts
Information
* About
* Diversity and Inclusion
* Editorial Board
* Authors
* Reviewers
* Subscribers
* Librarians
* Press
* Cozzarelli Prize
* PNAS Updates
Back to top
Copyright © 2023 National Academy of Science. All rights reserved. | Online ISSN
1091-6490
PNAS is a partner of CHORUS, CLOCKSS, COPE, CrossRef, ORCID, and Research4Life.
* Contact
* Site Map
* Terms & Privacy Policy
* Accessibility
REQUEST USERNAME
x Close
Can't sign in? Forgot your username?
Enter your email address below and we will send you your username
Email
Submit
If the address matches an existing account you will receive an email with
instructions to retrieve your username
CREATE A NEW ACCOUNT
xClose
Email
Returning user
Submit
We use cookies on this site to enhance your user experience. By using this
website, you are giving your consent for us to set cookies. Find out more
CONTINUE
LOGIN
xClose
Email*
Password*
Forgot your password? Reset it here
Keep me logged in
Login
Don’t have an account? Register here
Institutional Login
CHANGE PASSWORD
xClose
Enter the verification code
Submit
CONGRATS!
Your Phone has been verified
1800
1801
1802
1803
1804
Now Reading:
No evidence that polygynous marriage is a harmful cultural practice in northern
Tanzania
Share
*
*
*
*
FiguresTables
View figure
Fig. S1.
Location of the 56 study villages included in the Whole Village Project.
Ethnicity is coded as the most common ethnicity in each village. Red circle,
Maasai village; orange triangle, Rangi village; green diamond, Sukuma village;
blue square, Meru village; white diamond, other ethnicity village. Reproduced
from ref. 34.
View figure
Fig. 1.
Child height-for-age by village sorted by polygyny prevalence. There is strong
ethnic and village-level variation in child health. Relatively monogamous Meru
villages tend to have relatively good child health, whereas relatively
polygynous Maasai villages tend to have relatively poor child health. The dashed
line represents the WHO cutoff for chronic malnutrition. Ethnicity is coded as
the majority ethnic group residing in each village. Error bars represent 95%
CIs. Red circle, Maasai; green diamond, Sukuma; orange triangle, Rangi; blue
square, Meru; white diamond, other ethnicity.
View figure
Fig. 2.
Food security and child health by household type. Within villages polygyny is
associated with relatively high food security when households are headed by a
male and relatively low food security when headed by a female (typically later
wife households). Stratified analysis confirms higher food security in the
Sukuma and relatively improved child weight-for-height in both the Sukuma and
Rangi, for male-headed polygynous households. The reference category (dashed
line) is male-headed monogamous households (Table S4 for full model output). +P
< 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.
View figure
Fig. 3.
Wealth index, land cultivated, and livestock owned by household type. Within
villages polygynous households, particularly when headed by males, cultivate
more land and own more livestock than monogamous households. The reference
category (dashed line) is male-headed monogamous households (Table S6 for full
model output). +P < 0.1, *P < 0.05, **P < 0.01, and ***P < 0.001.
View figure
Fig. 4.
Village differences in food security and child health by polygyny prevalence.
Predicted village intercepts before (A) and after (B) adjustment for
village-level differences in ecological vulnerability (annual rainfall) and
socioeconomic marginalization (distance to district capital and proportion of
household heads with nonzero education). After adjustment, polygyny prevalence
is unrelated to food security and HAZ, and positively predicts WHZ. Intercepts
are mean/mode centered for household characteristics. See text for estimated
coefficients and Table S7 for corresponding model output. Red circle, Maasai
village; green diamond, Sukuma village; orange triangle, Rangi village; blue
square, Meru village; white diamond, other ethnicity village.
Table S1.
Demographic characteristics by household type and ethnicity for working sample
(n = 1764 households, 2833 children)
Table S2.
Household and village characteristics for working sample by ethnicity
Table S3.
Linear and multilevel regressions predicting household food security, HAZ, and
WHZ
Table S4.
Multilevel regressions predicting household food insecurity, HAZ, and WHZ
stratified by ethnic group
Table S5.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned
Table S6.
Multilevel regressions predicting household wealth index, land cultivated, and
livestock owned stratified by ethnic group
Table S7.
Multilevel regressions predicting household food security, HAZ, and WHZ
Reference #1
__("articleCrossmark.closePopup")
✓
Thanks for sharing!
AddToAny
More…