The Evolution of Human Post-Marital Residence

The evolution of human post-marital residence

A thesis submitted to the University of Manchester for the degree of

Master of Philosophy in Bioarchaeology

in the Faculty of Science and Engineering

2016

Edith Invernizzi

School of Earth and Environmental Sciences

List of contents List of content p. 2 List of tables p. 4 List of figures p. 5 Abstract p. 6 Declaration p. 7 Copyright statement p. 7 Acknowledgements p. 8 CHAPTER I p. 9 1.1 A brief overview of human residence strategies p. 10 1.2 Factors in the emergence of human residence strategies p. 12 1.2.1 Resources p. 12 1.2.2 Productivity p. 14 1.2.3 Warfare p. 14 1.2.4 Individual investment in offspring versus kin p. 16 1.2.5 Inter- and intra-generational conflict within the family p. 18 1.3 Dispersal, life history traits and cooperative breeding p. 20 1.4 Foreword to a model of dispersal p. 22 CHAPTER II p. 24 Summary p. 24 2.1 Methods p. 24 2.1.1 AGENT-BASED MODEL p. 24 2.1.2 EXPERIMENTS p. 30 2.2 Results p. 33 2.3 Discussion p. 38 2.4 Conclusion p. 40 2.5 Supplementary Information: cultural mutation p. 41 CHAPTER III p. 43 3.1 The Mosuo p. 43 3.1.1 Human behavioural ecology of the Mosuo p. 45 3.2 The Han p. 47 3.3 Male investment towards household subsistence: a history of conflict p. 48 CHAPTER IV p. 52 4.1 Methods p. 52 4.2 Results p. 59 4.3 Discussion p. 64

4.4 Conclusion p. 66 CHAPTER V p. 67 5.2 A general discussion p. 67 5.2 Conclusion p. 70 References p. 71 Appendix 1 p. 75 Appendix 2 p. 90

Final word count: 22,982 Final word count with references and appendices: 31,943

List of tables Table 1.1: PMRS key terminology p. 10 Table 2.1: Strategy classification based on dispersal decisions made by couple’s members p. 28 Table 4.1.1: Descriptive statistics of collected data for the response and each predictor variable used p. 55 Table 4.1.2: Summary of data for categorical variables in the used predictor subset (marriage status and partner co-residence status) p. 57 Table 4.1.3: List of predictors of total number of hours worked for each analysed data set p. 58 Table 4.2.1: Effect size and summed weight of tested predictors of total hours worked for the full data-set. p. 60 Table 4.2.2: Effect size and summed weight of tested predictors of total hours worked for the Mosuo data-set. p. 61 Table 4.2.3: Effect size and summed weight of tested predictors of total hours worked for the Han data-set. p. 61 Table 4.2.4: Effect size and summed weight of tested predictors of total hours worked for the female Mosuo data-set. p. 62 Table 4.2.5: Effect size and summed weight of tested predictors of total hours worked for the male Mosuo data-set. p. 62 Table 4.2.6: Effect size and summed weight of tested predictors of total hours worked for the married male Mosuo data-set. p. 63 Appendix table 1: Collected household data for each individual participant. p. 90 Appendix table 2: Data on worked hours by individual participants p. 97

List of figures Figure 2.1.1: Flowchart of the model, from the agent’s perspective p. 31 Figure 2.1.2: Flowchart of the model, from a household perspective p. 32 Figure 2.2.1: Trajectories over time of the population-average sex-specific dispersal trait values and relative PMRS frequencies, in the full life-history model p. 33 Figure 2.2.2: Boxplots showing variability in final dispersal trait values and PMRS frequencies in the full life-history model. p. 34 Figure 2.2.3: Trajectories over time of the population-average sex-specific dispersal trait values and relative PMRS frequencies, in the basic model p. 36 Figure 2.2.4: Representative trajectories of population average dispersal traits under the female- biased cost and variability plots of the final trait values and PMRS frequencies, when duolocality is absent p. 37 Supplementary Figure 2.5.1: Trajectories of the mean population dispersal trait values and PMRS frequencies shown under both cost conditions in the mutation model p. 41 Figure 3.1: Map of the fieldwork site, Lugu Lake, within the People’s Republic of China p. 43 Figure 4.1.1. Bar graph showing number of individuals in each age category in the collected data, divided by sex and ethnicity. p. 57

Abstract

This thesis was presented at the University of Manchester by Edith Invernizzi, candidate for the degree level Master of Philosophy in Bioarchaeology, in date 27th September 2016, with the title The evolution of human post-marital residence. Post-marital residence is an aspect of human cultures strongly connected to ecology. It influences the dynamics of fitness conflict within the family nucleus, but it also interacts with environment’s ecology through its dependence on resources. The problem of why this trait should take different forms within the same species has been the subject of a longstanding debate. Here, I will present a theoretical simulation study modelling the emergence of post-marital residence strategies (PMRS), whose results show how reproductive cost and offspring investment are drivers of sex-biased dispersal. The mechanism described represents an underlying factor to strategy evolution, in-built in human life-history, with which other ecological aspects are likely to interact. This outcome places the two factors mentioned at the centre of the discussion on strategy emergence. To attempt an empirical investigation of sex differences in offspring investment, I will present a fieldwork study conducted among a Chinese ethnic minority, the Mosuo, in which members of a married couple reside separately, each with their matrilineal kin. This study consists in a series of exploratory analyses of labour effort allocation and is aimed at addressing the problem of male contribution to household subsistence (here seen as a form of family investment). My perspective, as formed from the results, is of the emergence of different strategies as (also) resulting from the unstable balances reached in the conflict for reproductive investment between the sexes.

Declaration

The candidate Edith Invernizzi hereby declares that portions of Chapter I in the present thesis have been submitted as part of a literature review for the present degree course in December 2015, under the title “Agent-based modelling and the evolution of residence strategies”. No other portion of the work used or referred to in the following thesis has previously been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

Copyright statement i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and she has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property University IP Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses

Acknowledgments

This Master of Philosophy was made possible only by the generous contribution of Mr James Davidson to the KNH Centre for Egyptology, University of Manchester. I thank Prof Andrew Chamberlain for taking the risk of awarding me the funding.

The project and research work was created and put into effect only thanks to the collaboration of the Human Evolutionary Ecology research group at the University College of London (UCL) and of the Zoology department at the Chinese Academy of Sciences (CAS), Beijing branch. I thank in particular:

 Prof Ruth Mace (UCL)

 Prof Yi Tao (CAS)

 Dr Ji Ting (UCL and CAS)

 Dr Qiaoqiao He (CAS)

 Dr Matthew Thomas (UCL, Norwegian Institute for Cultural Heritage Research (NIKU) and Norwegian Institute for Nature Research (NINA))

I thank Prof Andrew Chamberlain and Dr Tucker Gilman for the excellent supervision and personal support.

CHAPTER I Introduction

Human dispersal in relation to mating shows considerable within-species variation in its sex bias. When looking at the movement of the two members of a couple after marriage, opposite PMRSs such as patrilocality and matrilocality are found in populations living in relatively close geographical proximity. In contrast, in other primate species, only one direction of bias, or a lack of bias, is predominant across all populations (Pusey, 1992). Several different ecological factors have been found to influence patterns of dispersal in animals (see e.g. the reviews by Bowler and Benton, 2005, Handley and Perrin, 2007). Equally, a series of factors has been identified and described in the anthropological and behavioural ecology literature, which may explain the variety and distribution of PMRSs in humans. In this chapter, I will present a brief overview of the distribution of PMRSs across human populations and briefly review the factors suggested to underlie their emergence. I will also succinctly touch upon the interaction of life history and cooperative breeding behaviour with dispersal, which is important when trying to define the evolutionary background of these strategies.

Different residence strategies seem to characterise different world areas. While patrilocality has been historically predominant in the Euroasiatic region, in Africa both matrilocality and patrilocality are present (Murdock, 1949, Goody, 1976). In the Austronesian region, patterns exhibit more flexibility: bilocality (a newly married couple being equally likely to reside in proximity of the wife’s or the husband’s kin) is more common than clearly sex-biased strategies and usually includes cases of ambilocality (in which the initial choice is also unbiased but unlikely to change later in life; Burton et al., 1996). While historical events do affect the extent to which this type of culturally influenced traits spread and survive, there is support for shifts from one strategy to the other occurring along the same ethnic lineage without the influence of external political or cultural change. This is supported by analyses that combine the use of phylogenetics and linguistics with anthropological data, to reconstruct shifts in residence strategy within an ethnic group (see, for example, Holden and Mace, 2003). The change of residence system due to internal, ecologically linked circumstances, as well as the vast literature on dispersal in other animals, suggests that, in humans as well, intrinsic factors might be responsible for the occurrence of a particular strategy. They might, at least, have contributed to create biases towards one residence type over the others in the first human groups, in conditions of early social organisation. The putative ability of some characteristics of residence systems to promote strategy success can be interpreted as an evolutionary adaptive value, explained not -just- in terms of cultural evolution, but through a strictly ecological and biological fitness approach.

Residence with relatives from the maternal line, usually Matrilocality coupled with uxorilocality (i.e. the husband moving to the wife’s family, where her maternal relatives reside) Residence with the relatives from the paternal line, usually coupled with virilocality (i.e. the wife moving to Patrilocality the husband’s family, where his paternal relatives reside) Establishment of an independent family nucleus Neolocality focused around the couple and their children Duolocality Separate residence of the two partners Avunculocality Residence with the wife’s maternal uncle Bilocality Residence with each spouse’s kin is equally likely Residence with each spouse’s kin is equally likely, but Ambilocality is usually maintained throughout the length of the marriage once established Virilocality Wife’s dispersal to the husband’s household Uxorilocality Husband’s dispersal to the wife’s household Table 1.1. PMRS key terminology

1.1 A brief overview of human residence strategies

PMRSs have historically been present at radically different frequencies across the world, at least since we have historical and anthropological records (an overview of the key terminology used in the classification of PMRSs is given in Table 1.1). The patrilocal strategy, characterised by female-only dispersal to the husband’s and in-laws’ family group after marriage, is reported as found in 68.5% of societies in the Ethnographic Atlas (Murdock, 1967). Patrilocality is often accompanied by a patrilineal bias in inheritance and patriarchy as type of hierarchy (Pasternak, 1976). Historically, a typical reference to this type of patrifocal societies was that to pastoralists (for example, the Nuer of Sudan; Evans-Pritchard, 1940), which are predominantly patrilocal and patrilineal. The opposite strategy sex-wise, matrilocality, where males relocate to their wife’s family, is strikingly not nearly as common among societies on record: only 13.1% of tribal societies are reported as matrilocal. Moreover, rarely matrilocal societies come with a purely matriarchal social organisation; on the contrary, men are almost always the sex retaining authority, an absence of coincidence between residence and power that constitutes a substantial difference between the matrilocal and the patrilocal system (Schneider, 1961). For example, in the Khasi of Northeast India, matrilocal and matrilineal, with a society structure organised in matriclans and where women have major economic roles (Leonetti et al., 2007), local governing assemblies, or durbar, are yet composed of men only (Gurdon, 1914). In terms of inheritance, matrilocality is most often

10 associated with matriliny, but it is, again, more flexible than patrilocality and matrilocal-patrilineal systems are also common. Patrilocality and matrilocality are the most common and probably clear-cut forms of residence among those classified. Rarer forms of post-marital residence are found in a few societies around the globe. Duolocality is the one reported as least common (0.9%), characterised by neither husband nor wife leaving the native household. A few duolocal populations are found in the same area of Southwestern China, such as the Mosuo (Ji et al., 2013) and a minority of Pumi villages (Wu et al., 2015), both showing the association of duolocality with matriliny. Bilocality (8.5%) is present in a few hunter-gatherer populations, such as the !Kung bushmen (ambilocal; Ember, 1978). Avunculocality (4.3%) is also associated with matriliny (Bodley, 2011) and refers to a particular form of residence where the couple lives with the husband’s maternal uncle, from whom he can acquire useful knowledge and skills. Avunculocal are the Trobriand Islanders of Melanesia. Finally, a type of residence currently becoming predominant across societies with a developing economy is neolocality. In this residence form, a newly-married couple leaves the parental family nucleus to establish a new household. Despite being now in expansion, in the cross-cultural comparison done in 1967, and thus before the start of the current economic and cultural globalisation phase, neolocality accounted only for 4.7% of the 858 analysed societies. This suggests its emergence is linked to modern economic conditions or/and is being influenced by cultural transmission. Other forms of classification of post-marital residence reflect a different focus in analysis. The division virilocality (females residing with their husbands) versus uxorilocality (males residing with their wives) emphasises dispersal with respect to the member of the marital pair, rather than the underlying family structure. A particular useful classification has been formulated by Ember and Ember (1971), regrouping residence systems on the basis of which sex’s kinship members are found living together. With this system, strategies such as avunculocality, which have a strong matrilineal focus, nevertheless emerge as bringing males of the same matrilineage together, therefore shifting the focal point from females to males when considering the possible causes of its appearance. Particularly in hunter-gatherer societies, however, residence can vary from phase to phase of a couple’s life. For example, in the Hadza of Tanzania, men spend the first few years after marriage working for their wife’s family, a practice that is regarded as a form of bride service. Only later, usually when children are added to the original family nucleus, the couple may move to the husband’s native household (Wood and Marlowe, 2011). Moreover, still among hunter-gatherers, a statistical analysis done by Hill et al. (2011) on data from both literature and unpublished records shows that in less than half of the 32 societies studied one pattern of residence is predominant over the others, and, even in these cases, other strategies are used by a non-negligible number of society members. It is therefore possible that, whatever factors favour one strategy over the

11 other, the balance may shift within the life cycle of a couple, and that these factors might by themselves be unable to determine the predominance of a single strategy over the other, requiring instead a combination of factors.

1.2 Factors in the emergence of human residence strategies

The factors I list and describe below have been discussed by researchers in the framework of anthropology and behavioural ecology. They incorporate observations on human populations as well as hypotheses formulated in the wider context of animal behavioural ecology.

1.2.1 Resources

The ability of individuals to disperse is constrained by resource distribution. Individuals are dependent on resources to survive and reproduce and, while resources within an area are limited, so might be their ability to disperse further away, by geographical, ecological factors, costs implicit to leaving a larger group and those of obtaining and exploiting a new territory. This is known, particularly in the context of the evolution of cooperative breeding, as the ecological constraints hypothesis (Emlen, 1982, Alexander, 1974). Thus, dispersal and the creation of an independent breeding nucleus might not always been a viable option, or not the higher fitness one. When dispersal is constrained, resources may be more efficiently exploited through a cooperative effort. While this potentially allows for expansion in population size, it at the same time creates conflict for access to resources themselves. Cooperation for resource exploitation can occur at different levels. It is hypothesised that the division of labour between the two sexes might be profitable to exploit the full potential of the environmental niche and avoid constraints inherent to differences in life history between the sexes (pregnancy and lactation; Washburn and Lancaster, 1968). Cooperation can occur among kin (following an inclusive fitness evolutionary factor), but cooperation within larger groups where relatedness is limited is also a puzzling feature of human behaviour. In particular, the relationship between environment and changes in ecology and life history can clearly be seen in our species, where the ability to control and exploit resources is unparalleled and is a key feature in our evolution (Foley and Gamble, 2009). Focusing on sex-biased dispersal, it is hypothesised that females are driven by the distribution of resources, which they need to support their higher investment in offspring, and males by the distribution of females (Wrangham, 1980). Therefore, if one of the two sexes can acquire the ability to control resources in a territory, it can then drive the dispersal of the other towards that area and also gain indirect control over the reproductive partners. This has been known in animals as resource defence mating strategy and has been used in humans to draw a connection between post-marital residence and another aspect of human organisation which differs from culture to culture in its sex bias: inheritance (Hartung, 1982). In terms of resource quality, patrilineal inheritance, and thus usually patrilocality, has been associated with highly controllable, high-yield

12 types of resources, while matriliny in all its forms of residence is found in poorer areas where resources are limited (Aberle, 1961). The association between patrilineal inheritance and female- biased dispersal (and often polygyny) has been explained as preferential investment towards sons with the aim of maximising their reproductive chances (Hartung, 1982). Male-biased inheritance theoretically allows maximisation of reproductive success over generations, due to the higher number of offspring males can potentially produce (hence the link to polygyny). In favour of the analysis by Aberle focusing on resource type and its association with different residence types, Holy (1996) reports how matrilineal societies, including matrilocal, duolocal and avunculocal, are generally susceptible to changes from a “production for subsistence to production for exchange”, often leading to the emergence of neolocality. The same pattern has been observed by Mattison (Mattison, 2010) in the Mosuo, as tourism becomes a common income source in the local area. Holden and Mace (2003) checked the association of matriliny with lack of highly valuable resources given by Aberle (1961) by looking at the influence of cattle- herding over inheritance systems in Bantu-speaking populations and by controlling for common descent. They found that matriliny is negatively associated with cattle: matrilineal societies who acquire it tend to change descent strategy in favour of a patrilineal focus. On the other hand, patriliny and mixed descent populations seem more stable against economic change. Ember and Ember (1971) equally find a significant positive association between mobile forms of wealth (domestic animals or slaves) and residence strategies that regroup males of the same lineage. With regards to changes in the residence system when new, profitable economic niches appear, the apparent emergence of neolocality can potentially be explained in terms of the relaxation of resource constraints. When resources are limited, a husband-wife pair may not, without support, generate enough for reproduction while guaranteeing survival of all offspring to adulthood. In general, inheritance of the same resource across generations might be more profitable than the establishment of a new household with an independent resource pool, despite inherent costs. Moreover, kin proximity is beneficial both in terms of cooperative labour and help in breeding (the grandmother effect or simply a cooperative breeding system). However, the same multigenerational family structure will generate inter and intra-generational competition for reproduction and thus, indirectly, for resources (see section below, Inter and intra-generational conflict within family group). When the pool of available resources finally expands, constraints to dispersal away from the natal family nucleus are relaxed. This produces a shift in the balance between intra-household conflict and benefits of cooperation, which promotes the abandoning of the natal household by children, especially those not favoured by inheritance. On the other hand, resources might be so limited that acquisition of a new member who is not blood-related or the loss of labour force weighs too heavily on the household. In these circumstances, duolocality might be expected to arise.

In terms of sex-biases in resource control, this can be interpreted, in one possible way, as differential productivity (where one of the sexes contributes more to the family income or its caloric intake). A more literal interpretation of the term control might refer to the bias in the ability to take hold of resources which favours males, simply due to the biological differences between the two sexes. This would, as explained above, lead to control over females. However, this cause is also connected to a male bias in the hierarchical system (patriarchy) and, once this system is in place, it is likely to reinforce the pattern of residence through an additional, cultural type of influence. Another way in which resource control, this time by the family group, might influence the residence system is in the case of fighting for control among different groups. This will be discussed below in the Warfare section.

1.2.2 Productivity

The extent of female contribution to family subsistence was seen for a long time as the predominant explanation for patterns of patrilocality versus matrilocality (Murdock, 1949). Matrilocality would be predicted by dominance in female contribution, while a bias towards male contribution would be expected to be associated with patrilocality and an equal contribution would be found with equal probability in each of the two systems or in societies where there is no predominant one. Ember and Ember (1971) contested this hypothesis when their analysis of this association across societies, as recorded in Murdock’s Ethnographic Atlas, found no consistent relationship between the two patterns. The only exception was North American aborigines, where dominance in female contribution to foraging had already been associated to matrilocality (Driver and Massey, 1957). None of the two studies just mentioned, however, phylogenetically controlled for common descent among the analysed populations – thus checking for inherited cultural patterns - , an issue which undermines the reliability of the results. However, the suggestion by Ember and Ember (1971) that another factor might instead underlie the same pattern of data by intertwining with changes in sex-biased productivity deserves attention. This factor is warfare.

1.2.3 Warfare

Two factors have been hypothesised to moderate the link between warfare and residence: one is migration and the second, mentioned above, is productivity. Divale found a significant association between matrilocality and the presence of migration events in the recent history of a population (Divale, 1974). He explained this association by noticing that migration to an already inhabited territory leads, inevitably, to conflict with pre-existing populations. Newly migrated groups would better respond to warring pressures by reducing internal warfare and providing cohesion against external offenders. In this context, matrilocality (in the form of uxorilocality) would offer the means to reduce rivalry among internal groups, by dispersing males of the same kin. As males find themselves separated from their closer same-sex relatives, they lack the

14 support to engage in aggressive behaviour focused on the kinship group, while joining the wife’s kinship group against others might result in warfare against their own blood relatives. On the other hand, male dispersal does by no means hinder the cohesion needed for external warfare and the overall group would thus benefit by the lack of internal conflict. Therefore, groups which have migrated recently and find themselves at war with external enemies would benefit from a shift in their pattern of residence as this then produces a change in the predominant type of warfare. Although Ember and Ember agree with the association between residence and warfare (1971), they however reverse the underlying causal relationship: they hypothesise that the type of warfare determines the modality of residence and not vice versa (Ember, 1974). Internal warfare promotes patrilocal residence, as males are required to be close to their native households to provide defence. In this case, cohesion among males of the same family is the best strategy to defend resources against members of another family. External warfare is, other factors not be taken into account, irrelevant to the type of residence system. However, different types of warfare have different effects on sustenance: in particular, any form of warfare which prevented men from taking care of productive activities would require women to take their place. In the case of internal warfare, though, this does not affect the residence system, as the presence of men within the household is necessary against internal enemies. When only external warfare is present, on the other hand, and interferes with male sustenance activities, the shift in the productivity balance will favour matrilocality. The authors find support for this analysis in the fact that only matrilocal societies are associated with purely external warfare, while, when internal warfare is present or the economic activity is compatible with warring, then patrilocality dominates. Carol Ember (1974) also draws attention to the fact that, were residence the main determinant of warfare type, then the positive correlation between small society size and absence of internal warfare would be unaccounted for. While agreeing that the role of residence is unlikely to be the only factor underlying the type of warfare and that other demographic elements, such as internal tensions due to population size, probably hold a major causal role, I think the effect of matrilocality on diminishing internal conflict should not be overlooked. The dispersal of closely related males within a society does indeed have a major effect on within group dynamics and should be taken into consideration when weighing adaptive benefits of the single strategies, in the context of productivity as well as intra-family conflict. Importantly, the correlations presented by Ember and Ember (1974) are, again, not phylogenetically controlled and a re-test might indeed not find any association. In any case, their argument suggests interesting potential causes or influencing factors of strategy shifts. The emergence of internal warfare might potentially be sufficient to promote patrilocality as a strategy in a population without a clear dispersal pattern. However, the presence of warfare presupposes the presence of that type of resources which, we saw above, are put into relationship with a patrifocal system before the emergence of group conflict, through the inheritance system. Conflict

15 for resources could also independently increase the imbalance between the power held by men and that held by women, because of the precious defence role played by the former within the family group. Therefore, the presence of a patrilocal system is likely to precede warfare in a context such as this. The emergence of the first system is a separate event from the one discussed by Ember and Ember, whose hypothesis addresses a secondary shift in the residence system and not the initial emergence event.

1.2.4 Individual investment in offspring versus kin

Proximity to kin is also associated with the investment an individual makes in different relatives, with the evolutionary purpose of maximising inclusive fitness. The first level of investment should be from a parent towards their offspring, as their relatedness is highest. However, because females biologically bear the greater load of parental investment and males have higher reproductive potential in the number of offspring they can have, male investment is expected to be lower. Evolutionarily, a higher reproductive fitness might be obtained by directing their effort towards increasing their chances to mate with other females, while at least some benefit could derive from contributing to the reproductive fitness of their kin, as long as this does not lower their own inclusive fitness. In humans, where prolonged ties between a male-female reproductive pair have evolved and are culturally reinforced (marriage), men’s contribution to partner and children’s survival is higher than in most species. However, its extent is still the subject of intense debate (see Chapter III). Still, their investment is likely to be lower than the one made by females, who would then benefit from a strategy which increases the contribution of their partner towards their own reproduction and the survival of their joint offspring. Separating the husband from his own kin after marriage (uxorilocality) reduces the amount of labour effort he is able to allocate to his native household. Moreover, the wife can obtain additional benefit from the proximity of her own relatives, who can contribute to her own reproductive effort. When the number of children born to the couple increases, the man benefits more from investing in direct (children) rather than indirect (siblings and their offspring) fitness, so that even in close proximity to his kin is investment in offspring is enough for his strategy to match his wife’s interests. With reference to the Hadza hunter-gatherers, Wood and Marlowe (2011) produced a model to reproduce this shift and discussed how this is likely to reduce female costs of becoming virilocal and may justify the change in residence that is common in this society later in the life of a couple. The uxorilocal strategy in the Hadza is interpreted as bride price (the gift of goods or labour from a man to a woman’s family in exchange for the right to take her as a wife), given in the form of labour. Wood and Marlowe ask which out of the two factors, bride price and female gain from husband’s labour, is likely to give rise to early uxorilocal residence. However, they do not address the question of why males would agree to abandon their own kin and move uxorilocally if the wife’s interest were the determinant factor, as the husband effectively loses potential inclusive fitness. The problem

16 is non-trivial because, although the woman gains direct reproductive fitness through this strategy and so do her daughters, her sons are, conversely, likely to lose direct fitness (if uxorilocality is the only factor limiting male out-of-marriage reproductive rates) and generally the whole kin group might lose inclusive fitness by missing out on the support of younger males. Therefore, an uxorilocal strategy is not necessarily fitness-optimal for females over generations, as their sons might obtain less reproductive fitness if using uxorilocality than if adopting a different strategy. The existence of bride price, on the other hand, is dependent on other causes, such as the scarcity of available females and the paternal investment directed towards maximising daughters‘ reproductive fitness. It is more likely, then, that the tactic of bride price is the first determinant of uxorilocality, in this context, while the decrease in female costs occurring later in life might be involved, to some extent, in defining when uxorilocal residence terminates. The balance between these two factors is probably dependent on the relative strength of the various underlying forces, so that a heavy skew in the number of males versus females will favour an extended period of bride service, while an intermediate skew coupled with, for example, the effect of intra-generational conflict in the woman’s family will shorten the duration of uxorilocal residence.

While effort or wealth given to offspring is always a safe investment in females, in males paternity might not be certain and husbands incur in the risk of directing resources towards another man’s children. The level of paternity uncertainty has been linked to matrilineal inheritance (Alexander, 1974, Hartung, 1985, Greene, 1978). This hypothesis is based on a comparison of the degree of relatedness a man has to the children born from his wife versus his relatedness to the nephews born from his sister, given a certain paternity value (the paternity value is the proportion of children actually born from the putative father over the total number children), where the nephew should be favoured when relatedness is higher. A paternity threshold of 0.268 has been calculated (Greene, 1978), below which a male is more related to his sister’s offspring than to his own and should thus invest consequently. This value is very low and unlikely to be found in most matrilineal societies. However, the fact that men are still to some degree related to their sister’s offspring is still enough to justify some investment along the maternal family line, an investment that should only increase the more likely his wife is to be unfaithful (Hartung, 1985). This benefit of brother- to-sister’s-son input is likely to be correlated to avunculocality, although a direct comparison attempted by Greene could not yield interpretable results due to the lack of large enough datasets (Greene, 1978). The same link has been hypothesised for duolocality, although Wu et al. (2013) have proposed an alternative model. In particular, their study pointed at the fact that, if the number of breeding sisters that co-reside is higher than two, it is more advantageous for the male to allocate his effort to the sororal household rather than to his wife’s, independently from the level of paternity (assuming that, for every child born to the wife, one is born to every sister). Moreover, the amount of effort that should be given to either one of the two households quickly reaches a

17 value of zero when the man has the possibility to invest the same amount of time in activities favouring extra-pair paternity. The authors show that, among Mosuo, the degree of relatedness of a male to his sisters’ household decreases as he ages and the proportion of grandnephew and grandnieces grows, while relatedness to his wife’s household increases as his daughters reproduce (it is assumed that paternity is certain). This is consistent with observed data, which record a portion of elder males leaving their natal household at already advanced age and switching to neolocal or uxorilocal living.

When discussing the benefits of group living, some are specific to a species’ life history traits, infant mortality and length of period to adulthood in particular. The task of feeding and protecting juveniles can be better achieved by a group of individuals rather than by mothers (or parents) alone, especially if this collaboration is reciprocal. Besides enhancing juvenile survival, time saved from parenting can be better allocated to foraging, with a net benefit for adults as well as young. This type of behaviour in which more than one individual contributes to a single reproductive effort, with reciprocity, is termed as cooperative breeding (see Hatchwell and Komdeur, 2000, for a discussion). Cooperative breeding is a key feature of human kinship groups. The role of maternal grandmothers in grandchildren survival has also been proposed as investment of elderly individuals into their offspring’s reproductive success (Hawkes et al., 1998) rather than their own. Despite its extent is debated, (see, for example, Peccei, 2001, Strassmann and Kurapati, 2010, Strassmann and Garrard, 2011), the positive survival effect provided by maternal grandmothers to their grandchildren emerges in many of the societies investigated (see, for example, the review of kin effects by Sear and Mace, 2005). With the premise that other effects such as genetics, the contemporary loss of other relatives which might account for the result, etc., are difficult if not impossible to separate in these studies, human parental investment has been interpreted as a three-generation model (Leonetti et al., 2007). Therefore, co-residence of kin and particularly female kin constitutes a possible advantage that might weigh on the choice of residence. However, co-residence also determines dynamics of conflict within a group.

1.2.5 Inter- and intra-generational conflict within the family

Within-kin conflict is one form of the conflict for resources that naturally arises between any two individuals residing in the same territory. Therefore, it is built within a kinship system and co- residence strategies do, by definition, emerge in spite of these conflicts. However, conflict between two specific members of a family may have a different weight in different systems of post-marital residence. Moreover, these dynamics also play an important role in the stability of a system, because they contribute to the probability of its dissolution when other cohesive factors go amiss. In the case of neolocality mentioned above, for example, this residence type is likely to have emerged from the pre-existing matrilocal system when constraints in territory resources were released.

Reproductive conflict arises as a consequence of conflict for resources, as resources are needed to support offspring and also to gain access to reproductive partners (Ji et al., 2013; Ji et al., 2014). This has been observed when looking at inheritance, where the inheriting brother or sister often has higher reproductive fitness than his or her siblings (Ji et al., 2013). Culturally introduced biases in inheritance determine the allocation of resources and therefore the number of offspring and partners (if the system is polygynous) each member of family in the same generation may have. Moreover, the number of co-residing individuals affects reproductive rates, often with a sex and relatedness bias: in Mosuo, the number of sisters and female cousins has been shown to negatively correlate to the number of children a woman has, while having no effects on males. More distant female relatives might have had an impact on female fertility in older generations (Ji et al., 2013). While there is no evidence of conflict among brothers in this case, younger-versus- older-brother conflict has been shown for patrilocal, patrilineal systems. In these latter systems, father-to-son conflict is also present and is bound to delay the son’s age at reproduction, as this latter indefinitely gains inclusive fitness from his father’s reproduction. Again, this conflict is due to the skew in access to resource among males in the same family, since the elder member (the father) starts off with exclusive access. The degree of conflict is also determined by the marriage system (monogamy versus polygamy or serial monogamy) due to the different degree of inclusive fitness which derives (Ji et al., 2014). Although mother-versus-daughter conflict should seem naturally present when reversing the system to matriliny, the effect is in fact muffled by cessation of female reproduction at menopause. Moreover, mothers seem generally more inclined to help their daughters in their reproductive effort and reproductive overlap between mother and daughter is rare and with little cost (Lahdenpera et al., 2012, Mace and Alvergne, 2012). On the other hand, conflict between mother-in-law and daughter-in-law has been hypothesised to negatively impact on grandchildren survival, as some cases might confirm (Sear and Mace, 2008). Cant and Johnstone, however, argued in their model that mothers-in-law are always bound to lose, as they still gain inclusive fitness from the reproduction of their sons’ wives, while the latter gain none from theirs (Cant and Johnstone, 2008).

As we have seen in the last few paragraphs, causes and consequences of sex-biased dispersal interact with life history aspects of human evolution, such as menopause and extended childhood. The same interplay can be observed in other animals and it is in all cases often difficult to disentangle cause-effect relationships among these elements, especially when other ecological circumstances come into the picture. In the paragraph below, I will focus on two specific interactions, one with menopause and one, more generally presented, of life history traits with cooperative breeding behaviour. These two aspects, as mentioned, play a role in determining benefit-conflict dynamics and are likely to have influenced the emergence of PMRSs, if they arose

19 before biases in dispersal. It is thus important to have an overview of our knowledge of the relative timing of their appearance, to set the background to the evolution of residence.

1.3 Dispersal, life history traits and cooperative breeding

Female post-reproductive life span (PRLs) has recently been put into relationship to species dispersal system. In several animal species, particularly among mammals, females have now been recognised to have a PRLS, that is, early reproductive senescence or early reproductive cessation, with respect to somatic senescence (Cohen, 2004). However, only in humans and whales PRLS extends to and over 20 years (e.g. Hill and Hurtado, 1996, Kasuya and Marsh, 1984, Olesiuk et al., 1990). Whether the origin of this trait lies in an evolutionarily adaptive advantage has been the subject of a discussion centred around two ideas: the mother hypothesis (Peccei, 1995) and the grandmother hypothesis (Hawkes et al., 1998), both focusing on the higher fitness benefit putatively gained by elderly women when contributing to their descendants’ reproductive success rather than to their own. The first of the two hypotheses maintains that the high mortality risk faced in giving birth and the high altriciality (dependence on others for food and survival) of human offspring, both a consequence of the increase in brain size, would make pregnancies later in life a high-cost strategy for women with dependent children, when the survival of their already born offspring is highly dependent on their own. The grandmother hypothesis, on the other hand, argues that, when a woman’s offspring has already reached reproductive maturity, she may gain higher reproductive fitness by caring after her grandchildren rather than giving birth to other direct descendants. Although these two conjectures have received mixed support (e.g. Peccei, 2001, Strassmann and Kurapati, 2010, Strassmann and Garrard, 2011), two studies have recently considered life-time changes in inclusive fitness in the framework of dispersal. Johnstone and Cant (2010) analyse the change in local relatedness under different dispersal and mating (local or outside-of-group) systems and put this in relation with the benefit of helping in older versus younger females. It has to be noted that, in their model, helping is not positively biased towards kin, so their analytical formulation of fitness advantages is conservative. They find that, under male philopatry in both mating systems or under out-of-group mating with no sex bias in dispersal, the fitness benefit of helping increases with age, due to slowly building relatedness as a female moves to a new group before starting reproduction. In systems with male biased dispersal, conversely, the benefit decreases with age, because females’ older kin die and sons and brothers emigrate. Since humans exhibit a tendency towards male philopatry while, in cetaceans, both sexes are philopatric with out-of-group mating, the results from this analytical formulation have been interpreted as providing a favourable demographic background to the evolution of PRLS. However, Nichols et al. (2016) tested the ability of this model to explain real data, using a phylogenetic analysis of PRLS and dispersal co-evolution on observations from wild populations, and argued this latter interpretation. Although their results show a significant

20 association between male philopatry and the presence of a PRLS - and females of species in which males disperse or philopatry is female-biased have a shorter average PRLS -, there is large variation in PRLS length for species with male philopatry. Moreover, half of the species with male dispersal also exhibit PRLS. Importantly, the phylogenetic reconstruction in strongly male- philopatric species (interestingly, for the sake of my discussion, only primates in this study) suggests an earlier origin of PRLS relative to male-biased philopatry. Given the widespread presence of PRLS independently from dispersal biases and the association of PRLS with longer total life span, the authors interpret their data as supporting an initial non-adaptive origin of PRLS, followed by later emergence of an adaptive advantage thanks to the later-evolved dispersal pattern.

Cooperative breeding is one of the aspects potentially most influenced by dispersal patterns and group composition and is an important element to take into consideration when weighing benefits and costs of dispersal, for its immediate link with reproductive success (in terms of offspring survival to adulthood). Conversely, the time of its emergence in relation to that of sex-biased dispersal might influence the evolution of the latter, if cooperative breeding appeared first. The most credited hypotheses for the evolution of cooperative breeding look at the influence of ecology - the above mentioned ecological constraints hypothesis – or at that of life history traits - the life history hypothesis (Brown, 1974) as key causal factors. The influence of life history traits on cooperative breeding behaviour lies in breeder turnover rates (i.e. the rate at which breeding individuals die), determining the availability of mates as well as breeding territories. Thus, species with evolved greater longevity, low adult mortality and larger clutch size may be more likely to develop cooperative breeding behaviour. Recently, ecology and life history traits have been proposed to jointly determine the emergence of cooperative behaviour: either life history traits might act as a necessary background factor, from which ecological circumstances push cooperation to emerge (Arnold and Owens, 1998) or the two mechanisms provide separate influences, neither being a single determiner, but whose effects may act in concert to promote cooperative breeding (Hatchwell and Komdeur, 2000). In tests of the life history hypothesis, the relationship of cooperative breeding with longevity and clutch size has not been supported by early studies (Yomtov et al., 1992, Poiani and Jermiin, 1994), but an analysis conducted by Arnold and Owen (1998) across bird phylogenies found an association between low adult mortality rates and the presence of the behaviour. Generally, the distribution of cooperative breeding behaviour across the species they analyse is non-random, suggesting that evolutionarily conserved traits preceding the emergence of cooperation are likely to play a role in its evolution. Importantly, moreover, adult mortality is significantly lower in families in which cooperative breeding species are found, including in species which are non-cooperative. This would indicate that low adult mortality is a cause rather than a consequence of cooperative breeding, directly supporting the

21 life history hypothesis. In a model of PMRS emergence incorporating life history traits, it is thus reasonable to suppose support at least by kin members in raising the offspring. In the context of dispersal, population viscosity (i.e. limited or short-distance dispersal) rather than sex biases has been proposed to promote cooperative breeding, by creating close co-residence of several breeders. In particular, viscosity can promote high relatedness in the area and a kinship structure favourable to cooperation, through an inclusive fitness model. Differences in dispersal patterns between cooperative and non-cooperative breeding species have yet to be found (Hatchwell, 2009). However, applying the model of Johnstone and Cant (2010), described for PRLS, to the evolution of breeding helpers, helping should be favoured in young individuals in male-biased dispersal species, while it is expected to increase with age (as seen above) in male- philopatric species or in conditions of no sex bias and out-of-group mating. This suggests a complex picture in which evolutionary patterns are hard to identify. The effects of sex-biased dispersal on this behaviour, whether independently or through dynamics of within group relatedness, are still to be clarified.

1.4 Foreword to a model of dispersal

Of the factors presented above, some require a pre-existent relatively complex social system or culturally enforced traits. Particularly, residence intertwines with two other key aspects of a kinship system: inheritance and sex biases in dominance (patriarchy and matriarchy). Since the question here is whether ecological factors may alone determine the emergence of strategies, I have only discussed their influence in the framework of inclusive fitness (investment towards offspring) and conflict for resources. However, not only there are likely to be common influences behind the emergence of the three, but if one of these three factors did emerge and develop a predominant bias before the others, this direction probably exerted a non-negligible influence on the other two. Another aspect I have not discussed is the type of marriage (mating) system, namely monogamy or polygamy. Since this determines the direction of intra-family conflicts, the degree of inclusive fitness and other factors, its presence in the system constitutes another source of marked influence. Moreover, since which one of these two systems is present in a society is also determined by factors such as resource distribution, sex biases in parent-to- offspring investment and sex biases in resource access and inheritance, their evolution is also likely to accompany that of residence. The evolution of these factors can thus not be easily separated from the evolution of residence as two distinctive non-mutually influencing processes.

To analyse the influences of the factors described in this chapter and above, simulation modelling provides a very informative approach in which the theoretical outcome of each factor, alone or in combination with others, can be tested under several background conditions representing population and environment’s structure. In the next chapter, I will describe a basic model testing

22 the evolution of PMRS from basic human life-history traits and under a very simple model of social organisation. This represents a test of whether preliminary conditions exist, before the introduction of complex cultural traits, of complex conflict dynamics or of the establishment of sex biases in the factors listed above, which alone provide a direction to the evolution of sex-biased dispersal. As well as not featuring the cultural trait of resource inheritance, we exclude from the model also the need for resource defence (internal or external warfare), sex biases in resource control or production and differential offspring investment. Conflict for resources needed for reproduction is also not explicitly modelled and thus features no directionality (e.g. younger versus older generations, mother-in-law versus daughter-in-law), nor is conflict for reproductive opportunities; instead, these derive from the collective use of a resource pool and from a stochastically determined order of reproductive opportunities among individuals. In practice, this model represents a very simple study case, in which no directionality in conflict and no form of resource control is present. The model assumes a starting condition in which dispersal is random; therefore, its results can be interpreted as the effect of the factors investigated in the absence of pre-existing dispersal skews. As characterising life-history traits, I model extended childhood, with complete dependence on adults for survival, and female post-reproductive life-span. As form of basic social organisation, I choose grouping into households based on kin and marriage links which provide a form of collective resource exploitation. As social behaviour, I model resource sharing and communal offspring rearing within these units. The choice of introducing female post-reproductive life span relies on the assumption that this arose before any species-level or population-level dispersal patterns. The link between post-reproductive life-span and sex-biased dispersal has been analysed in the paragraphs above; the assumption that its emergence was not accompanied by concomitant changes in dispersal patterns is a non-negligible simplification whose alternatives should be addressed in further modelling studies. However, this representation allows to identify any effects of a simplified human fertility curve on the reproductive fitness dynamics leading to dispersal. With regard to the mating system, in this model we focus on the case study of monogamy. This system was chosen as first case study for its simplicity to model and because it represents the only mating system of 15.1% of human cultures (Murdock, 1967) and is widespread also in those that allow polygamy (Murdock, 1949). Future modelling studies should analyse the cases of polygyny, polygamy and the parallel evolution of dispersal and mating systems. Giving the basic conditions offered by this model, its results represent factors underlying strategy emergence, onto which the more complex mechanisms described in this first chapter may insert.

CHAPTER II The following chapter is written in the form of a journal article (without an introductory section, as the background was set in Chapter I). Supplementary information containing additional analyses which do not add major details to the main results are presented at the end of the chapter.

Summary

Post-marital dispersal in humans follows different patterns across populations, with patrilocality showing historical predominance and matrilocality, neolocality and duolocality as alternative observed outcomes (Murdock, 1967). Causes leading to the emergence of distinct strategies are debated. We build an agent-based model (ABM) representing a population with a human-like life history, monogamy and basic social organisation based on household-level units. We track the emergence of residence strategies over time in the population following fitness-selection driven change in a culturally inherited, sex-specific post-marital dispersal trait, on a life-history background complete of fixed values for the timing of extended childhood, menopause and inter- birth interval (in our full model). We compare multiple versions of the model to isolate the dynamics leading to dispersal strategy evolution. Our main results show that i) reproductive cost allocation to one sex is the main driver of which strategy emerges at higher frequency and ii) the presence of cheaters in within-couple sharing of the reproductive cost is responsible for the evolutionary success of a sex-biased residence strategy. The outcome provides an understanding of basic dynamics leading to the emergence of this cultural trait and offers a potential explanation for the historical frequency of patrilocality across geographically distant human populations.

2.1 Methods

2.1.1 AGENT-BASED MODEL

We present the model introducing elements from the Overview, Design Concept and details (ODD) protocol proposed by Grimm et al. (Grimm et al., 2006, Grimm et al., 2010). We choose this formulation to favour reproducibility and because it has the advantage of highlighting model built- in effects, such as stochasticity. All model versions were implemented in MATLAB software, version R2015a. The full code for the Full model version is attached to the Appendix 1 to this thesis.

Overview 1. Purpose Our model simulates a population of male and female agents with a human-like life history, that is, including an extended childhood with delayed sexual maturity and a late age at which

24 independent provisioning is achieved. In the full life-history model, a threshold of post- reproductive lifespan (menopause) and a minimum inter-birth interval (IBI) are also present, to accurately represent the human reproductive cycle. The population is organised into multiple households, representing basic independent units in which we hypothesise resources can be collectively exploited. The model’s aim is to track the evolution of an inherited trait for individual propensity to disperse after marriage, in the population, over a period of 10,000 human-like years. Trait evolution should emerge from the dynamics inherent to life history and individual movement between households.

2. Entities, state variables and scales

Agents. Agents are characterised by a series of attributes, namely: ID, sex, age, parents’ identity, marriage status, household membership and dispersal trait. An agent’s sex (male or female) determines the presence of menopause and of a minimum IBI (females only, both) in individual life history - when these traits are included in the model. Menopause determines the onset of females’ inability to give birth, after which they can be considered, in the context of the household, as producers only (the grandmother effect). We set menopause onset at age 45, corresponding to the hunter-gatherer average as calculated by Marlowe and Berbesque (Marlowe and Berbesque, 2012). The minimum IBI remains constant throughout a woman’s lifetime and is set to 3 years after birth, as from the same source. Minimum IBI presence affects both marriage and birth, implying that female agents cannot give birth nor marry –if widowed- if their last born is less than 3 years of age (unweaned) and still living. Age determines the properties of an agent and corresponds to different life cycle phases, namely: weaning (<3 year-old), childhood (<15), fertile adulthood (15-45 for females, >15 for males) and post-reproductive life-span (>45 for females only). Age at maturity has been set at 15 as a between-sex average (Marlowe and Berbesque, 2012) and corresponds both to sexual maturity (ability to marry and give birth) and to the transition from dependent to producer in household economy. Household membership is assigned at birth, on the basis of which sex’s parent is assigned children until maturity (the reproductive cost described below; i.e. whether children are members of the mother’s or father’s household). Membership can change through an agent’s lifetime, depending on the dispersal decision made at marriage. An agent’s tendency to disperse is defined by the individual dispersal trait locus, a pseudo-genetic trait which is vertically transmitted from the same-sex parent. The trait value, comprised between 0 and 1, where 0 represents a probability of dispersing equal to 0 or a stay trait, corresponds to the individual’s probability to take a decision to disperse at the Dispersal step (see Process overview). This formulation models the trait as an inherited cultural element, with transmission along the same-sex line, while also maintaining a certain degree of flexibility in the dispersal decision through a probability form -a realistic assumption for a behavioural trait.

Collectives. Households are the group-level unit in the model and the basic unit for resource collection and sharing. The household resource pool gathers all contributions deriving from the productivity of its members, counted in resource units. The total amount of resources available in a household is limited by the per-household cap (10 resource units), representing a limit to the cooperative exploitation potential of resources associated with a household (e.g. a farming field).

Spatial and temporal scales. The model is not spatially explicit, but nevertheless represents a finite environment limited by a fixed maximum number of supported household (200). The ability to disperse to a new household is thus restricted. Each simulation cycle is built to correspond to events occurring during one human-like year. Total number of cycles (i.e.years) in every simulation is 10,000.

3. Initialisation

The model is initialised with a starting population of 500 individuals, with a 50:50 sex ratio. Individuals are randomly distributed across households and their age is drawn from a uniform distribution [0,69]. Initial dispersal traits are assigned from a uniform distribution [0,1] .

4. Processes and scheduling

Processes are discrete and individuals enter each process either in a fixed order, according to their ID number (Ageing, Production and Survival) or randomly when order influences the dynamics (Marriage, Dispersal and Birth). Whether or not an individual takes part in a process in any given cycle depends on age and marriage status, as described. Individuals undergo model processes in the order below. A graphic overview of process flow is given in figures 2.1.1 and 2.1.2, showing events from an agent- and a household-level perspective.

Ageing Every surviving individual ages one year at the start of every cycle. At the end of the Ageing process, all individuals enter the Production phase.

Production For all adult individuals (i.e. aged 15 or over) in a household, net production is 3 units of resources each, which go contribute to the shared household resource pool unless the per-household resource cap has already been reached, in which case there is no additional contribution. The per-household cap is set at 10 in all simulations. Children (i.e. aged below 15) do not produce but consume 1 resource unit per year. Because adults produce but do not consume, the amount of resources available can be regarded as a surplus used for reproduction. It has to be noted that productivity is independent from all agent properties except for whether an individual is an adult or a child, that is, there are no sex biases in productivity and no age-related productivity curve. In addition, resource allocation to children is independent from whether two, one or any (if orphans)

26 of the parents have contributed to the pool, so that children, including orphans, are raised through cooperative breeding. At the end of the Production step, adult unmarried individuals enter the Marriage process, while married couples skip all processes until Birth.

Marriage Unmarried individuals who have reached sexual maturity or widowed individuals can (re-)marry, if a suitable partner is found. Suitability consists in whether the individual is of the opposite sex, within a 10 year (± 10) age range and not a sibling or a parent. The age-range assumption was made to avoid unrealistic cases of very young males marrying very old (post-reproductive span) females and thus failing to reproduce. We do not believe it to significantly affect model outcome. Individuals who can potentially marry are randomly selected to assess all potential partners, in a random order, until they find a suitable match or until all available individuals of the opposite sex have been assessed; another individual is then randomly selected and enters the process, until all single agents have had a chance to marry. In the menopause-IBI simulation, women must await the end of the IBI before re-marring (if widowed). The model only considers monogamous marriage, i.e. individuals can re-marry only if their previous partner has died. All newly married individuals go through the Dispersal process.

Dispersal Individuals who have just married take a dispersal decision, based on their dispersal trait. Following the combination of decisions taken by the two members, the residence strategy of a couple emerges (Table 2.1.1). The dispersal decision is made by comparing the individual’s dispersal trait value to a randomly extracted value from the same uniform distribution [0,1]: a dispersal decision is made if the trait value is higher and a stay decision if the value is lower. That is, an individual with a dispersal trait value of 0.8 has a 80% probability of taking a disperse decision every time he or she goes through the process (i.e. after every marriage). In the case where both individuals make the decision to disperse, the couple can become neolocal only if empty households are available, that is, if the environment is not saturated. If there are no empty households, the couple dissolves and individuals have to wait the next cycle for the possibility to marry again. Therefore, neolocality is penalised when habitat is saturated. Constraints to neolocality are in agreement with the ecological constraints hypothesis (Emlen, 1982) and with current hypotheses on the emergence of this strategy (Holy, 1996, Mattison, 2010). Children of parents who re-marry follow the parent’s dispersal decision if they are yet to reach maturity, that is, move to the new household if their living parent re-disperses.

Males Stay Disperse

s Stay Duolocality Matrilocality

Disperse Patrilocality Neolocality Female

Table 2.1.1 Strategy classification based on dispersal decisions made by couple’s members

Birth Couples who have been married for more than one year can give birth to a new individual. The only restriction to giving birth is a threshold of minimum resource units present in the household pool (3 units). Therefore, fertility does not follow an age-related curve, but models an effective IBI (in addition to the fixed minimum one in the model) which is dependent on the availability of resources in the household environment. The household pool considered is that of the current year (birth year), rather than the preceding one (pregnancy year), for easiness of tracking. At birth, the new individual is given an ID, sex, household membership and a dispersal trait value identical to the same-sex parent’s. In the menopause-IBI model, couples in which the woman is aged over 45 or still within her last IBI do not enter this process.

Survival All individuals in the population enter the Survival process. The model has three sources of mortality. A yearly mortality rate of 0.04 is applied to the whole population, independently of age or other model- or agent-level variables, and is kept constant throughout the simulation (extrinsic mortality). Agents reach their maximum life-span at the age of 70, after which they are eliminated from the simulation. The second source of mortality is resource-dependent. Households whose resource pool is negative after production (i.e. households who are unable to support all residing children) are identified. For any negative resource unit in the pool, one child within the household is randomly chosen to be eliminated from the simulation. For example, for a household with a production-consumption balance of -2 , two children are selected to die among members. Which children die is randomly determined, independently from whether both, one or no parents (i.e. orphans) reside in the household and have contributed to the pool. Again, this is an assumption deriving from cooperative breeding. Finally, unweaned children whose parents have both died are assumed to also die. This last condition represents the assumption of a limited investment by other household members towards indirectly related and very young (high-cost) children.

Design concepts

1. Emergence Population-wide frequency and distribution of each PMRS emerge from the change over time in population frequencies of the sex-specific dispersal trait values. Therefore, the emergence of a population-wide PMRS is a consequence of the selection acting at the dispersal trait level. Post- marital residence, as modelled here, is thus not a culturally transmitted trait per se, but a classification of the outcome arising from the independent transmission along separate sex lineages of the dispersal cultural trait.

2. Adaptation

Selection is not explicitly modelled, but derives from within-household resource competition and its effect on individual reproductive success, so that dispersal traits present in households who are able to support a larger offspring are likely to be found at higher population frequency in the next generation. Resource competition is not biased (e.g. by birth order), but a simple effect of household size, children-to-adult ratio and stochasticity determining which couple or individual member reproduces and who dies in a given year. Thus, within-household competition is ultimately dependent on the dispersal strategy of its members, which determines household composition.

3. Stochasticity

The stochastic processes in the model are the following: agent-agent interaction; dispersal choice outcome; extrinsic mortality; within-household child mortality as an effect of lack of resources (i.e. which child dies is stochastically determined). PMRS frequencies emerge from the random interaction between agents to form married couples and the outcome of their dispersal choices. Population-wide trait value distribution and agent interactions are influenced by population demography. Household composition is determined by the life histories of its members (marriage status, dispersal choice, reproductive history) and in turn affects implicit within-household competition for resources.

4. Observation

Data are collected at 100-cycle intervals across the simulation, after 300 years burn-in time to allow the population’s demography to stabilise. The following data are collected: dispersal trait value data: population mean dispersal trait value for each sex; PMRS frequencies: relative PMRS frequency calculated as the proportion of couples using a strategy at a given time point over total number of couples.

2.1.2 EXPERIMENTS

We compared three versions of the model, each under two opposite reproductive cost conditions. Each version contained changes aimed at observing the effect of specific factors on results and dynamics. We first ran a model with complex life history, comprehensive of menopause and IBI (we will call this the full life-history model). In the first run of simulations, we implemented a female-household-biased reproductive cost, assigning children to the maternal household after birth (first condition or female-household-biased cost). We made this choice based on the assumption that females are biologically imposed an investment in their offspring, while males have the choice of a limited or null investment after reproduction. We then tested the effect of reproductive costs with a reversed model in which children were assigned to the paternal household (second condition or male-household-biased cost). To assess the effect of the presence of producers-only (i.e. couples or individuals in their post- reproductive life stage) and of reduced fertility on the outcome, we ran a second model where menopause and IBI are absent (we will call this the basic model). In this version, there is no actual differentiation between males and female agents, apart from reproductive cost allocation. Finally, we tested dynamics underlying strategy emergence with a third model in which the option of duolocality was denied. Here, couples whose members both took a stay decision were forced to re-take their choice, until a different strategy was obtained (the no-duolocality model).

For the first, full life-history model, 100 simulations were run. This was aimed at assessing how much of the variation in the results is due to drift effects on dispersal trait values in the population and to stochasticity-driven dynamics –i.e. how robust the outcome is to stochasticity. Since results proved to be consistent, 5 simulations were run for all other models.

Figure 2.1.1 Flowchart of the model, from the agent’s perspective. The figure shows the flow through model processes of each agent in the simulation. age = agent’s age; hh resources = total resources in the household pool; TSLB = time since last birth (reset to zero if child dies before weaning); f_age = age of the female agent in the couple; IBI = minimum IBI (3 years); menopause = 45 years.

Figure 2.1.2 Flowchart of the model, from a household perspective. The figure (next page) shows events at household level as the simulation proceeds through each process. rc = reproductive cost.

Changes in household age structure cause changes in household productivity and consumption.

The dispersal traits present in a household determine the probability of its members to disperse or stay. A household has no influence on influx from other households and only dispersal traits at the level of other households determine the probability of an incoming producer.

Household age composition determines the number of resident fertile females and so do household’s female dispersal traits relative to female dispersal traits in other households.

Extrinsic death might shift household producer-consumption balance.

2.2 Results

Figure 2.2.1. Trajectories over time of the population-average sex-specific dispersal trait values and relative PMRS frequencies, in the full life-history model. The figure shows results for the full life-history model version: dispersal trait values (a and c) and PMRS frequency (b and d) over time, averaged across 100 simulations. Top panels show values and frequencies for the female-household-biased cost condition, bottom panels for the male-household-biased cost condition. The x axis represents time points of data collection across simulation time (every 100 years after burn-in, for a total of 97 time points) in all panels. In panels b and d, the y axis represents relative strategy frequency at a given time point (i.e. proportion of couples using each strategy in the population). Female (a) and male (c) dispersal traits reach a higher value when the cost is female-household- and male- household-biased, respectively. Correspondently, patrilocality is the highest-frequency strategy in the first reproductive cost allocation (b) and matrilocality in the second (d). Due to the effect of menopause and IBI reducing the fertility rate, females still show a tendency towards dispersing even when reproductive costs are born by males, while males evolve a stay trait in the opposite condition.

Figure 2.2.2. Boxplots showing variability in final dispersal trait values and PMRS frequencies in the full life-history model. Top panels (a and b) represent variability within 100 simulations for the female-household-biased cost condition, bottom panels (c and d) for the male-household-biased condition. Median dispersal trait values for the two sexes and mean strategy frequencies neatly differ, under both conditions.

Full life-history and basic models When the reproductive cost is assigned to females, they show a tendency to disperse (mean trait at end of simulation: 0.72), while males are selected for a stay strategy (0.47; Fig. 2.2.1a), resulting in predominant patrilocality (2.2.1b). Plots of the variability in the final trait values and final PMRS frequencies show clearly distinct trends in the two sexes’ dispersal (Fig. 2.2.2a and c) and in frequency (2.2.2b and d) of the two main strategies. A reversed cost assignment produces the opposite effect (2.2.1c), but not a mirroring one. Males are the sex with the highest dispersal tendency, but females still show a disperse rather than a stay trend. This produces a prevalence of matrilocal couples, but a still high-frequency patrilocality (2.2d). We hypothesised this lack of overlap to come from life-history differences (menopause and IBI) and ran a basic

34 model version in which these two traits are eliminated (Fig. 2.2.3). Here, loosening of reproductive constraints results in a tendency to disperse also in the sex not paying the reproductive cost (2.2.3c), indicating that menopause and IBI lower the strain put on households by non-dispersing reproducing females by reducing their reproductive rate and adding post-reproductive producers to the household.

Duolocality has equal low-intermediate frequency under both versions of the cost in all models, with a frequency (0.20) consistent with a) mean dispersal trait values and b) the influx of duolocals from originally neolocal-choosers who could not find an empty household (i.e. the frequency of duolocality is slightly higher than what expected from raw dispersal probabilities). Neolocality is constrained across model versions by limited empty household availability, but also by the small number of producers in the household. In fact, despite productivity by parents only - combined with the minimum IBI - is in principle enough to support several children, the relatively high extrinsic mortality rate means both parents are likely to die before reproductive cessation is reached. Parental death has a catastrophic effect on offspring survival in neolocal couples: while the effect of the death of one parent is the halving of the household’s ability to support children, the death of both produces the death of all their dependent children within one-year. In fact, the presence of adult offspring (producers) in the house is unlikely, since they probably carry a high dispersal trait value and thus leave the natal family after marriage.

Results are qualitatively identical when mutation is introduced in the model (Supplementary Figure 2.5.1), showing that the effect of drift on trait values is limited.

We think that the emergency of either patrilocality or matrilocality in the model is driven by the irreversible evolutionary direction taken by the dispersal trait as a consequence of the reproductive costs. As an example, when the reproductive cost is female-household-biased, households in which females leave and males stay are favoured early in population history, because benefitting from male producers while paying less reproductive costs. When high female dispersal propensity is common within the population, households gain less from staying males, as their wives are likely to come residing with them. Therefore, the payoff is now identical for males or females staying, with females having already evolved a tendency to disperse. On the other hand, neolocality is constrained by the lack of available households and is in any case associated with less resources and thus with lower fertility. In this context, a male dispersal propensity cannot also evolve without being penalised and patrilocality becomes more common, while matrilocality and duolocality are also maintained in the population as minority outcomes.

Basic model

Figure 2.2.3. Trajectories over time of the population-average sex-specific dispersal trait values and relative PMRS frequencies, in the basic model. Results are qualitatively identical to the full life-history model, but there is a tendency towards dispersal in both the sex paying and the one not paying reproductive costs, similar to the male-household-biased cost condition in the first model. Panels and axes as in Figure 2.2.1. Results are averaged across 5 simulations.

Effect of duolocality

Figure 2.2.4. Two representative trajectories of population average dispersal traits under the female-household-biased cost and variability plots of the final trait values and PMRS frequencies across simulations, when duolocality is absent. Panels a and b show two representative trajectories of the average population dispersal traits obtained in different simulation repetitions under identical conditions, for the female-household-biased cost. They show opposite evolutionary patterns of sex-specific dispersal and great fluctuation of the dispersal trait values over time. Drift effects reduce population trait distribution, reducing the frequency of trajectory shifts towards the end of the simulation. Panels c-f contain the boxplots

37 of variability in final trait values and PMRS frequencies across simulations. Medians of the two trait value distributions and of matrilocality and patrilocality frequency distributions are not significantly different, both under the female-household-biased (c and d) and male-household-biased (e and f) cost condition, indicating that both outcomes are equally likely to emerge in the absence of duolocality.

Imposed monogamous marriage for both sexes implies a shared reproductive cost within the couple. Therefore, it is not immediately intuitive where the evolutionary advantage of preferentially promoting dispersal in one specific sex lies. We hypothesised that the presence of couples residing separately –duolocality- is at the root of the emergence of one particular strategy out of the patrilocality-matrilocality duo. In fact, a duolocal male and a duolocal female have an opposite effect on household reproductive fitness: when cost is female-household-biased, females weigh on the reproductive success of all co-residing sisters (in agreement with Ji et al., 2013) and of brothers who are patrilocal, while duolocal males’ productivity, conversely, is put to the benefit of all resident married siblings without any added consumption. Because, in the model, traits are shared by direct descent within same-sex siblings only, staying females weigh more often on their co-residing sisters than they benefit from a resident duolocal brother. This only in the absence of other influencing factors, such as inheritance, and direction of within-household reproductive conflict. We implemented a no-duolocality model, in which duolocality was eliminated from possible dispersal process outcomes, to test this idea. Results (Figure 2.2.4) show no evolutionarily consistent trajectory, with large fluctuations in population mean trait values over time which are limited only by the absence of mutation re-introducing lost values in the population. In the model, the presence of duolocal individuals is thus a necessary condition for the emergence of a predominant strategy.

2.3 Discussion

PMRS frequencies as emerging from our model derive from several factors. Neolocality is here constrained by a limited environment modelling a finite resource pool, in accordance with current hypotheses on neolocality emergence and on the on-going shift from traditional residence systems (matrilocal, patrilocal, duolocal, avunculocal, etc.) to a modern Western-like neolocal model in economically developing areas (Holy, 1996, Mattison, 2010). Duolocality is constrained by household costs of raising children when the reproductive cost is sex-biased. Matrilocality and patrilocality frequencies derive from the interaction of duolocality with this sex bias. We believe that the dynamics identified by the model can be a potential mechanism pushing the direction of evolution of post-marital residence towards a sex-biased dispersal, with patrilocality emerging under most cases of reproductive investment distribution.

From the point of view of realistic evolutionary dynamics, the model represents the evolution from a population without defined post-reproductive strategies, in which the sex bias in reproductive burden allocation triggers the initial divergent direction of selection in the two sexes. Initial conditions lead to the building up of a dominant dispersal pattern, unless – we assume – other factors do not intervene in changing the reproductive resource balance (discussed below). The influence of initial condition on final dispersal strategies is consistent with what found by Ji et al. (2016), when looking at the effect of frequency-dependency on strategy evolution. The difference in the distribution of post-marital dispersal strategies that we observe in our model compared to hunter-gatherers might be explained with the absence, in the latter, of household private property, with most anthropologists believing that the reproductive burden is shared across the whole community via food sharing. In the context of household property and their dynamics of reproductive investment, we can regard duolocal members of the sex which does not bear the reproductive cost (which is, under the biological distribution of reproductive investment in humans, males) as cheaters. The presence of cheaters and of a reproductive burden weighing heavily on one sex offers a potential explanation for the enforcement of cultural norms limiting reproduction outside strictly regulated marriage ties (Cook, 2007). Factors that might deviate strategy evolution from the direction determined by initial conditions include the introduction of biases in the ability to acquire and maintain resources, which may be expressed by inheritance rules. Duolocality in the Mosuo, for example, might be sustainable only thanks to the imposition of a matrilineal inheritance system, which might have overcome female disadvantage in reproductive investment. Violent competition for resources (leading to warfare) might also accelerate or consolidate the emergence of patrilocality by promoting patrilineal inheritance. Finally, dispersal strategies might also be heavily influenced by the direction and weight of kin competition dynamics within a household, which are here not modelled.

A key element in the interpretation of results obtained from this model is the per-household resource cap, whose size modulates the intensity of the selective pressure to disperse. It is important to stress that, given the value assigned to individual production versus consumption by children here, in PMRS where partners co-reside the cap affects within-household reproductive competition and children’ survival only after the death of one parent and before offspring reaches maturity. In fact, the three-year IBI effectively spaces reproduction so that the eldest child reaches maturity well before too many younger siblings are born (a couple can support up to six children per year, but gives birth only to a child every three years, so that the two eldest will have turned 15 by the time a sixth sibling is born). This is evident in the increased dispersal rate in the basic model, where the lack of an IBI intensifies resource competition. Therefore, the cap importantly influences the disadvantage of duolocality compared to the other strategies. It also implies that

39 the role of cooperative breeding in the model is limited to cooperation in rearing children born from a duolocal strategy or orphan of at least one parent. Competition for resources limited to one-resident-parent children is also an effect of the fact that reproduction in the model is cheap, with a relatively low cost of raising children and no reproductive cost paid by women in terms of reduced productivity during weaning. The introduction of reduced female productivity combined with an increased cost of child rearing is likely to influence the outcome of population PMRS frequency, particularly by diminishing the affordability of neolocality and duolocality. Given that the cost of raising children is, in the majority of circumstances, higher and not lower that what modelled, we believe that, when considering the range of household resource cap that might be imposed, selection should promote dispersal under every realistic condition.

Finally, within the context of household resource limitation, differential ability to access resources among members (e.g. mother versus daughter, older versus younger sibling) might affect the relative advantage of sex-biased dispersal strategies. However, in the full model, competition between elder and younger females in the family is annulled with only little effect. It is still likely, however, that skews acting in different directions (e.g. birth order) may produce more substantial changes. Inserting a sex skew in reproductive investment might also change the dynamics of within-household competition and the benefits of dispersing. Reproductive investment skews in combination with explicit and directed kin competition biases would be a primary test for the validity of our model across multiple contexts.

2.4 Conclusion

We have presented here a simple simulation of sex-biased dispersal in a population with human life-history characteristics and in the absence of other culturally transmitted traits such as inheritance. We have shown that the sex bias in reproductive cost allocation and the possibility by the opposite sex to defect is sufficient to create a corresponding sex bias in the dispersal strategy, with the sex bearing the reproductive cost - in humans, females - dispersing to benefit from their partner’s contribution. This effect, arising from basic assumptions of human biological features, is a potential underlying bias with which more complex factors, both ecologically and culturally influenced, may interact. A potential next step in investigating the importance of this mechanism is to ask how the extent of the between sex imbalance in contribution to the reproductive cost influences the effect on dispersal. This test would evaluate the applicability of this study to humans, or in different human populations, in the light of the literature on male contribution to family subsistence.

2.5 Supplementary Information Cultural mutation

Supplementary Figure 2.5.1. Trajectories of the mean population dispersal trait values and PMRS frequencies shown under both cost conditions in the mutation model. The figure shows model results when pseudo-genetic mutation in the dispersal trait value is allowed in the full life-history model. The outcome is qualitatively identical to the no-mutation version. Averaged across 5 simulations.

Since new traits are not created in the model, variability in final dispersal trait values is likely to be strongly affected by drift-driven trait value loss from the population pool. We ran a mutation version of the simulation, in which deviation of the inherited dispersal trait from the parental value was introduced, to observe trait trajectory when free from this influence. Mutation modelled here can best be interpreted as a copying error (pseudo-genetic) and is bias-free (i.e. free from potential biases affecting cultural transmission). We consider two potential forms of mutation. The first represents a small deviation from the original trait and is drawn from a normal distribution

41 with mean 0 and standard deviation 0.05, occurring with probability 10-2. The second corresponds to a large cultural mutation, occurring more rarely with probability 10-4 and changing the value to any value from the full trait range (a uniform distribution [0,1]). These two forms of mutation aim at representing both common intergenerational variation in a culturally inherited trait, including copying error or small individual variation created by the new generation, and large-scale change from the inherited trait, as a consequence of copying error or of other, non-explicitly modelled influences on cultural transmission. The two probabilities of mutation are mutually exclusive, so that if large mutation occurrs, it cannot be substituted or changed furtherly by a small one. The mutation model is otherwise identical to the full life-history model.

As shown in the Supplementary Figure 2.1, mutation does not influence model results, which are qualitatively identical to those obtained in its absence. Trait value is susceptible to small changes within nearby generations, but trajectories are stable.

CHAPTER III

Introduction to fieldwork data analysis: Mosuo, Han and the debate on male contribution to household subsistence

The role of reproductive investment in determining sex-biased dispersal patterns is at the centre of the conclusions drawn from the model. The relative labour effort allocated by each sex towards their household of residence and the impact of co-resident children and of household structure on this effort are all elements worth investigating in relation to this topic. I will approach these three factors by presenting, in the next chapter (Chapter IV), the results of anthropological fieldwork I conducted in three villages on the Southern border of the Sichuan province, China. Of these villages, while one is nearly exclusively Han, the other two have a population of predominant Mosuo ethnicity. In this chapter, I will give a short introduction to the two ethnicities and then discuss the problem of male contribution to family subsistence as interpreted in the current literature.

Figure 3.1. Map of the fieldwork site, Lugu Lake, within the People’s Republic of China. Retrieved and edited from: http://www.pbs.org/frontlineworld/rough/2005/07/introduction_tolinks.html .

3.1 The Mosuo

The Mosuo people are classified by the People’s Republic of China (PRC) ’s central government as members of the Naxi ethnic minority, representing a subgroup rather than a separate ethnicity. This assignment is contested at least by those Mosuo residing on the Sichuan side of Lake Lugu –a lake on the border between Sichuan and Yunnan, around which most of the 40,000 members of this group live. Mosuo from this area refer to their ethnicity as Mongolian, although this latter

43 origin is so far not supported (Shih, 2001, Hua, 2001) and references to people with similar customs in the area predate the arrival of Mongols and Genghis Khan’s conquests. The Mosuo of the Yunnan lake side, on the other hand, are more prone to accepting the government’s official nomenclature. The most accredited hypothesis for the origin of the Mosuo refers back to a second century AD’s migration of the ancient Qiang population from the Tibetan plateau. Accounts of a matrilineal inheritance system in use among the populations of the nearby area date back to the second century AD (Hua, 2001). What is commonly referred to as the traditional marriage form, the visiting marriage or tisèsè (“to walk back and forth”), is described by the anthropologist Chuan-Kang Shih as “noncontractual, nonobligatory, and nonexclusive” (Shih, 2000). The type of relationship indicated with this name is in fact a form of sexual visit in which partners are not bound to fidelity and which may be short- lived but equally extend to the long term, according to the free will of both parties (Shih, 2000, Hua, 2001). Shih himself considers this tradition as different from what is, in the anthropological field, defined as marriage, because of the lack of obligations between the two partners (Shih, 2000). Co-habitation of two partners has traditionally only occurred when one of the two households lacked individuals of one of the two sexes and acquisition of a woman from another household when the family female line was extinct is reported as a formal event (Hua, 2001). Hua, in particular, stresses households’ unwillingness to give up one of their daughters, in fear that they would be ill-treated when entering an unrelated family. At present, marriage, or a strictly enforced monogamous interpretation of visiting marriage, is most common on the Sichuan side of the lake and reported extra-pair paternity is extremely low (Wu et al., 2013). Mosuo people from this area currently reject the accounts of tisèsè in the form described above and stress the interpretation of their traditional marriage customs as equivalent to monogamy in all but duolocal residence. These personal observations are in agreement with past reports (see Shih, 2000) and are likely to be a consequence of the regulations and campaigns promoted by the Han government from the late 1950s (Shih, 2000, Hua, 2001). Duolocality in the residence form is, however, still very common (48% of married Mosuo individuals in the censed households live duolocally, compared to 43% who co-reside with their partner). Since the fieldwork focused on the Sichuan Mosuo population, I do not have a clear account of current customs of residence and monogamy on the Yunnan side of the lake.

Traditionally, the economy around Lugu Lake relies on the farming of corn, mainly, but also potatoes and local vegetables. Typically, each household owns two or three pigs and poultry, with richer households raising in addition one or two heads of cattle and a horse used for ploughing. Although tourism is a major source of income in the villages closer to the lake shore and many households there run a family hotel, shop or restaurant, the isolation provided to Lake

Lugu by the surrounding mountain chains means that food is still primarily obtained through domestic production. The economy relies more heavily on farming in backcountry villages, although many individuals travel to other villages to work as itinerant sellers, drivers or daily labourers. Migration of young individuals to the closest largest cities to work as labourers is also frequent.

To-day, there are two types of family structures common among the Sichuan Mosuo. The traditional household hosts up to four generations, all matrilineally related. The head of the household usually cohabits with her brothers and one or more sisters, although these latter might leave the native household to set up an independent nucleus, with the help of some of the brothers or of their children. Husbands do, rarely, go live uxorilocally, while I have observed only a few cases of Mosuo women living virilocally in a matrilineal household. In most, the woman was effectively the head of the household, although it is unclear whether they had moved to the house when their husbands’ sisters and/or mother were still resident (all were of advanced age). Virilocal and uxorilocal residence in this context has been reported to occur only when the household is in lack of individuals of that sex (Shih, 2000, Hua, 2001). In these matrilineal households, one of the eldest women within the household holds the head role (lao zumu) and runs the household administration as well as having a major influence on family decisions. A male household head is also traditionally present, with equal status although with a minor role, following the matrifocal tradition, and was, in the past, in charge of the selling and administration of land and livestock, as well as of entertaining household relationships. Thus, his role was primarily social. Among the censed matrilocal households, size is typically comprised within 6-10 individuals, with the largest household interviewed counting 16 members registered there. Resources are shared among family members and, typically, the bigger a household, the less the workload on each member, so that not all males (the sex usually seeking employment outside the household) have a job.

The second common structure is that of neolocal households, consisting of a married couple with co-residing children. The distinction between a neolocal and a matrilocal household can be blurred, with children of a neolocal duo sometimes being in a duolocal marriage.

3.1.1 Human behavioural ecology of the Mosuo

In recent years, a number of behavioural ecological studies have focused on Mosuo traditions, particularly on their unique residence form. In 2007, the UCL Human Evolutionary Ecology group created a database of demographic data of all Mosuo households in five villages around Lugu Lake Town (the main town on the shores of Lugu Lake), collected by means of interview and then updated in 2012. The collected data (for each household member: name, ethnic group, gender, year of birth, animal sign, education, parents' name, marriage status, spouses’ name, children's

45 name, children's year of birth, children's gender and place of residence; for each household: GPS location, land size, number of livestock and number of hotels and businesses; Ji et al., 2013) were used in two subsequent studies, focusing on dynamics of reproductive fitness within the structure of communal households. Ji et al. (2013) obtained a statistical model of female reproductive fitness (number of children) dependent on number of co-resident kin and with a stronger negative effect of sisters in older age categories. In addition, elder sisters as observed in their fieldwork study worked more in the household fields during the planting and harvest seasons, thus undertaking heavier labouring effort than their younger female siblings and cousins. Following the framework set by Johnstone and Cant (2008), Ji et al. explained this pattern through a game theoretical analysis of reproductive fitness and competition between elder and younger sisters. They showed that, if a competitive advantage is granted to the older sister, the optimal strategy for the younger sister is to allocate more time to competition for reproductive fitness and less to household productivity, thus resulting in a heavier workload for her elder counterpart. This latter does however benefit from higher reproductive success, because of her advantage in access to resources. The second study, Wu et al. (2013), aimed at studying the optimal allocation of labouring effort by males as divided among signalling activities, contribution to the wife’s household and contribution to the sisters’ household and across the full range of extra-pair paternity values, thus considering both historical accounts of promiscuity among the Mosuo and the traditional communal household structure to explain patterns of work allocation by Mosuo men. Using an inclusive fitness model, they showed that the presence of multiple co-breeding sisters makes investment towards the sororal household more advantageous than investment towards the partner’s household (as described in Chapter I), fairly independently from extra-pair paternity levels: the effect persists up to high values of paternity certainty when two sisters co-breed and always when the number of co-breeding sisters is higher. Importantly, this model can be interpreted as being conservative in the relative advantage obtained, as it assumes that a man’s (potential) children all reside in a single household, thus increasing the fitness benefits of contributing towards his partner’s house. Activities aimed at increasing the chances of extra-pair mating, moreover, should be preferable to any form of household investment till intermediate values of paternity certainty and take up a considerable proportion of available effort up to high certainty values (assuming effort allocated to extra-pair mating activities and to household contribution are mutually exclusive). In the authors’ analysis of fieldwork data, moreover, female relatedness within the household increases with age, under no-dispersal and out-of-group (out- of-household) mating, conditions that in Johnstone and Cant’s model promote help by elder individuals. An additional outcome is also the later age at first reproduction in duolocal versus neolocal households in the villages surveyed, a fact that is interpreted as the result of resource constraints in communal households.

A more recent paper by the same research group (Ji et al., 2016) focuses on the shifts in residence patterns in the Mosuo and neighbouring Pumi communities. Here, a theoretical evolutionary game model is used to look at the evolutionary stable strategy (ESS) under different relative PMRS payoffs. According to this study, two basins of evolutionary attraction may co-exist at one time, which ones depending on the relative advantage given by each PMRS to each sex. Only two alternative co-attraction scenarios are possible: the duos duolocality-neolocality (adopting your partner’s same dispersal strategy) and patrilocality-matrilocality (adopting your partner’s opposite dispersal strategy). In each of these evolutionary scenarios, convergence towards one of the two poles is dependent on population’s initial strategy frequency. Although the fundamental question of what determines a PMRS’s payoff over the others, and thus which alternative strategies are ESSs, is not addressed in this model, it nevertheless assesses the role of frequency in PMRS shifts and offers an explanation for the shift to duolocality of patrilocal Pumi villages located within the Mosuo area. Overall, these studies have given a fairly detailed insight into inclusive fitness and conflict dynamics of Mosuo households and offer a good background for further behavioural ecology studies.

3.2 The Han

The most conspicuous Han migration to Lugu Lake area occurred after the 1930s (Hua, 2001). The villages formed from this flow retained their original customs and culture, so that the residence system and family structure is almost exclusively neolocal and patrifocal. Dispersal is mainly of daughters and neolocality has a form close to patrilocality: women from nearby villages move to their new husband’s village where the house of residence is very close to the husband’s natal one. Economy consists of farming, breeding and small commercial activities such as itinerant selling.

3.3 Male investment towards household subsistence: a history of conflict

The issue of male investment into family subsistence has been studied mainly in the context of hunter-gatherer foragers, as models of early or basic social organisation. It more broadly inserts into the discussion on human sexual division of labour. Division as such, as Robert Brightman (1996) states, follows a precise pattern only in a single foraging activity: large game hunting. Despite fishing, gathering of resources and other subsistence activities most commonly being distributed sex-wise, which sex is in charge of which activity shows variability across populations. Moreover, men display greater flexibility in the range of labour activities they may take care of, being in charge of occupations normally attributed to females when household composition is sex-biased or more labour force is required for that task. Reversed cases of women being responsible for male-biased occupations are rarer (Brightman, 1996).

Attempts to explain this difference in the allocation of subsistence activities have resulted in two distinct and partially conflicting hypotheses: a classic model of cooperative provisioning, within the unit of monogamous long-term pairs (originally proposed by Lovejoy, 1981), and a costly signalling theory interpretation of male foraging behaviour (Hawkes, 1991, Bird, 1999). While the cooperative provisioning hypothesis sees different contributions from the two sexes as complementary and jointly aiming towards resource maximisation, the signalling model considers male foraging specialisations as aimed at display, in the context of extra-pair mating.

The weakness of hypotheses of men as provisioning mainly for their families lies in the evolutionary pay-off of paternal care relative to that of seeking to increase mating opportunities. In this light, effective male contribution to their spouse and children’s subsistence has been the object of controversy. Kristen Hawkes’s studies have argued the case that male foraging is not as optimal as it could be were men to follow a female or mixed foraging strategy. For example, Ache men would gain a higher daily caloric return through the extraction of palm starch compared to hunting (Hawkes, 1991, based on Hill et al., 1987). This study has however been disproved (Gurven and Hill, 2009) and others of similar outcome are not supported by later work: large game hunting indeed has higher daily caloric return than the chase of small game or than gathering activities (Hawkes, 1991 and 2001; Hurtado and Hill, 1990) and males do take part in female activities such as small game hunting and honey gathering, although these are also highly shareable resources (see below; see Gurven and Hill, 2009, for a review of male provisioning in caloric intake). It seems thus that men do provide for their families, especially if taking into account the fact that any evaluation of the importance of hunting for child growth and partner’s reproductive fitness needs to also consider the value of proteins and lipids (Gurven and Hill, 2009). This is important particularly in the context of large brain evolution and of the development of an

48 extended phase of dependent childhood (Kaplan et al., 2000): even if returns from hunting were so unreliable that gathering provided a net higher daily caloric intake, any large animal protein and fat intake, no matter how sporadic, might nevertheless enhance brain development in the child and his or her long-term fitness. Despite small game hunting also guarantees meat intake, large game hunting provides bigger portions at once.

Regarding the use of hunting or, more generally, the foraging of nutrient-rich resources for signalling purposes by men, support for this thesis comes from the nature of the resources on which males focus, as well as from patterns of sharing towards non-family members. In the original paper on signalling theory, Hawkes (1991) reports and builds on data by Kaplan (Kaplan et al., 1984; Kaplan and Hill, 1985) to show that a) males focus on resources providing larger packages (package size is defined as return in both weight and calories, Kaplan et al., 1984) and for which acquisition is unpredictable and b) these two characteristics strongly predict the probability of a resource to be shared outside the acquirer’s family. The researcher interprets these data as supporting the existence of a signalling goal behind men’s foraging activity, a hypothesis additionally backed by the fact that, in this Ache case study, the male sex of the acquirer predicts 69-74 % of food sharing, compared to only 15% predicted by package size. However, here how much of the listed resource types is acquired by each sex is not weighted. Indeed, the importance of sex in predicting the amount of sharing decreases as male-acquired resources are progressively eliminated from the count and resources collected by both sexes remain. As Hawkes herself states, resource types collected by both and which are widely shared by men are also widely shared by women, indicating that either sharing is not limited to a signalling goal or women are signalling as well. The existence in men of a greater propensity to share is itself controversial: Gurven and Hill (2009) argue that skews disappear once accounting for package size. However, Hawkes, despite not controlling for weight, does control for value in calories.

In Hawkes’s original hypothesis, sharing by men is interpreted as tolerated theft and is consistent with the idea that the main benefits of acquiring the resource do not lie in the resource itself but in the signalling conveyed. Following this premise, defending a resource after acquisition becomes unprofitable relative to later pay-offs (Hawkes, 1991; Bird, 1999). Signalling is here interpreted as behavioural display to “attract (both) the attention of potential future mates and potential allies” in mating competition (Hawkes, 1991). In support for this hypothesis, there is a large body of evidence, reviewed by Smith (2004), which shows that skilled hunting is indeed a favoured trait for mate selection; Kaplan and Hill, in an ethnographic study of Ache data (Kaplan and Hill, 1985), find that good hunters have more putative illegitimate children than poor hunters. This latter study, however, also shows that the overall difference in total (recognised and putative) number of children between the two categories is only significant not when born, but when

49 surviving children are considered. This fact can potentially be framed within the male offspring provisioning hypothesis, but also offers support to the case that interpreting sharing only in the light of mate attraction provides a very limited understanding. First of all, the cost of a decision to share rather than keep the bulk of the resources just acquired must be put into the context of a) ability to preserve highly degradable food, which we can grant as acquired, and b) package size relative to family consumption needs, which might markedly decrease the cost of granting others access to these resources. Giving up a share of resources, be it at a given cost, when the benefit for the receiver is much greater than what the initial acquirer may obtain diminishes the cost of theft, but might also provide an important pay-off in the long-term. This is true in a context of group cooperation and reciprocity (defined in the literature cited here as contingency, i.e. contingent to the fact that the receiver is also a sharer; Gurven and Hill, 2009): Gurven (2004) reports that resources are preferentially allocated to those who have shared in the past and distribution of meat is dependent on active participation to the hunting activity among Ache, even for teenagers (Gurven and Hill, 2009). Hawkes herself highlights how non-synchrony in the acquisition of a resource type (that is, when a resource is not obtained at the same time by the whole group, but is subject to unpredictability; this is the case, for example, of hunting) is an important predictor of sharing. Unpredictability and non-synchrony of the return are two characteristics which suit a sharing strategy rather than an independent provisioning mode. The interpretation of sharing in the context of promoting long-term fitness offers an explanation to why women, as well as men, should share. Moreover, although Hawkes does not explicitly clarify, signalling in her theory seems to better be interpreted as signalling for good genes and female preference for the best signallers as selection of the best genotype. However, given the value of hunting in nutritional return and especially if we grant meat a higher value in child development and human survival compared to other foods, selection for good hunters might also stand for selection for good producers: signalling for parenting value, thus, rather than signalling for good genes. Therefore, men seem to specialise in resources which have a higher sharing value, when sharing may be interpreted as both signalling and as a strategy with long-term payoffs for offspring fitness. The type of resources acquired by men, in addition to their potential signalling value, might also be essential for the healthy development of their children.

These two perspectives on male behaviour in family subsistence can be merged into a more inclusive model. Bird (1999) proposes that different parenting activities have different trade-offs with other strategies of reproductive fitness maximisation. Parenting can include costly behaviours such as direct provisioning, which exploits time and energies that could otherwise be allocated by the male to finding new partners. However, other forms of provisioning, such as hunting, can serve the double purpose of feeding the young (and the partner, to increase her

50 fertility and willingness to mate) and attract new mates, effectively falling into the category of costly signalling. Consequently, more similar provisioning strategies in the two parents or an equal investment should be expected when the cost of feeding the offspring is higher and (or) chances of extra-pair mating or other means of enhancing reproductive success (e.g. relying on preferential treatment derived from social prestige, also a consequence of signalling) are scarce. A partial investment in the offspring by the father should on average always be expected in humans, in accordance with the evolutionary trajectory connected to extended childhood. Gurven and Hill (2009) provide a working framework in which parenting (provisioning) and selfish activities are both part of the male strategy and relative time allocation depends on the ease of extracting resources from the environment as well as a threshold of minimum expectations from the partner before the pair dissolves (in the context of monogamy’s benefit-cost balance). In particular, when household production is cheaper, males have an optimal point of allocation where both time for production for the household and for activities targeted at enhancing individual fitness can be easily fitted. When production is more expensive, however, males need to allocate more effort to the household than is optimal for their own gain, to maintain the pair- bond. In this context, the conflict between the two sexes is greater and different outcomes of the pair-bond should be seen.

A duplex value can thus be given to male reproductive investment. I will use this inclusive framework to collectively interpret the results from the fieldwork and model analyses in the last chapter of this thesis.

CHAPTER IV

Analysis of the allocation of a daily time-budget among rural Mosuo and Han

Several approaches to quantifying the contribution of an individual towards family subsistence have been used throughout the anthropological literature, from the caloric value of foraging returns (see Chapter III) to the calculation of overall physical activity levels (PAL). An alternative way is to assess individual time investment into activities which directly or indirectly contribute to the maintenance of a household and its members, compared to selfish activities. Here, I collect data on the daily budget spent by individual members of Mosuo and Han households and explore the relationship to household composition through exploratory analyses based on linear modelling. In addition to the main analysis, I will also look for patterns in smaller subsets of the data, trying to isolate effects on single ethnicities and on each sex. In particular, I will exploit the traditional Mosuo duolocal system – combined with the recent diffusion of neolocality - for a series of exploratory analyses comparing male contribution with and without partner and offspring co- residence.

4.1 Methods

Data were collected during two months of fieldwork, for a total of 19 days of observations. 108 households were visited in total and time budget allocations from 157 individuals were collected. The interview consisted of two parts, the first regarding household demography and aimed at updating the 2012 database (see Chapter III for details; for all resident individuals: name, age, ethnicity, marriage status, partner’s co-residence status if married, stable residence, kin relationship to other household members; collected once per household in this study) and the second relative to daily time budget allocation (collected from any household member willing to participate). Interviews were conducted in the interviewee’s house. All present household members contributed to household demographics, which I then cross-checked with the 2012’s pre-existing data to verify their validity. When more than one household member was present, I tried to limit the influence of others on the reported time-budget by directly asking the interviewee to only report their recollections and by repeating the question with this emphasis when another household member had replied first. Collection of time-budget data was planned as follows: a working day was defined as from waking up to the last evening meal and divided into sub-sections, namely waking up to breakfast, breakfast to lunch, early afternoon to dinner. Individuals were asked to report the activities done the day before in each time section and were listed the following categories: housework (cooking, cleaning, washing, sewing and weaving), farming, breeding, childcare, building, remunerated

52 activities (e.g. running a shop, driver, employed by the village’s administration), religious and leisure. Example categories were specified to make sure the interviewees considered the full range of possible activities they could have undertaken. This approach was chosen after noticing some activities, especially those household-related, such as house chores and childcare, tended to be under-reported and were mentioned only when specifically asked. An estimate in hours or minutes of the time allocated to each activity was asked. When “the whole morning” or “the whole afternoon” were given as estimated time, I assigned an approximate value of 5 and 6 hours respectively. Interviews were conducted in Mandarin Chinese, with the help of an interpreter for Mosuo language (spoken by elderly Mosuo) and Sichuan dialect. Interviews were restricted to individuals older than 18 years of age and conducted according to UCL and the University of Manchester’s ethics guidelines. The research was covered by the approval document granted by UCL as part of the project “Kinship and cooperation in the Mosuo” (ethics certificate n. 0449/002). From the raw data, I excluded data points from individuals of ethnicity different than Han or Mosuo (married into a Han or a Mosuo household; 5 data points) or of one of these two ethnicities but who had married into a household from the other (2 data points), to exclude the effect of inherited cultural influences when inserted into a different family structure. Of the days of observations in the Han village, two coincided with a wedding feast, in which, traditionally, the whole village participates. Of observations collected in these two days, I only used those in which less than 7 hours were spent at the feast (counted as leisure). Of the included data points (6), only 3 individuals had participated in the feast, for 6, 4 and 2 hours respectively. The final data set includes 152 observations and is included in the Appendix 2 to this thesis. Descriptive statistics are given for each data (sub)set in Tables 4.1.1 and 4.1.2 and a graphic summary of data distribution by age and sex is presented in Figure 4.1.1. In this study, I used an exploratory analysis rather than a hypothesis testing approach, that is, I did not make a priori hypotheses including one or more potential predictors and then tested the probability of their fit against a null model. Rather, I defined a set of potential predictors and quantified the relative importance of each based on the relative likelihood of all models in which it appears, using the Akaike Information Criterion (AICc) model comparison approach (Akaike, 1973) as a ranking method. This exploratory approach, although not performing a direct test for specific hypotheses, yields yet an indication of potential directions for future research. As a dependent variable in linear regression modelling, I excluded time allocated to leisure and childcare from the data and calculated the total hours allocated to all remaining activities (productive activities). I created a list of p potential predictors based on collected household demographics and fitted all possible models with a number of predictors between 1 and p, included. Interactions were not included in this analysis: considering all possible interactions would have made the use of this exploratory approach statistically unreliable, by creating spurious predictor-response associations (due to the total number of predictors and interactions relative to

53 the number of observations; Anderson and Burnham, 2002) and the use of a candidate interaction subset approach was not attempted due to time constraints. For each model, I calculated the relative weight as obtained from AICc. I then summed model weights for each predictor, across all models in which that predictor appears, thus obtaining a predictor-specific summed weight value. This index provides a relative estimate of the importance of the predictor’s effect on the value of the dependent variable compared to all other predictors tested and is commonly used in exploratory analysis (Burnham and Anderson, 2004). In addition to the full data sets, I also used five data subsets for exploratory analyses: Mosuo, Han, male Mosuo, female Mosuo and married Mosuo men. These subsets can be interpreted as a part of the larger data set for which an interaction can be supposed to exist between listed predictors and the features characterising the subset, e.g. ethnicity potentially interacting with each predictor, in the Mosuo and Han set. A list of parameters used for each data (sub)set analysis is reported in Table 4.1.3. Statistical analysis was conducted in R software, version 3.3.1 .

In the following pages: Table 4.1.1. Descriptive statistics of collected data for the response and each predictor variable used. Statistics are given for the whole of data set and for each data subset. Legend: tot n of hours worked = tot n of reported hours spent in working activities, excluding childcare; co-resident offspring = n of co-resident children and grand-children under the age of 18 or in higher education; co-resident indirectly related children = n of co-resident nephews, nieces and other indirectly related or unrelated children under the age of 18 or in higher education; co-resident sisters = n of co-resident sisters over the age of 18; co- resident brothers = n of co-resident brothers over 18; resident adult males = tot n of co-resident males over 18; resident adult females = tot n of co-resident females over 18; household size = tot n of permanently resident individuals, including children but excluding dependent children studying away from home.

Full data set Mean SD Median Min Max Mode Tot n of hours 8.42 4.50 9 0 24.5 9 worked N of co-resident 1.39 1.41 1 0 6 0 offspring N of co-resident indirectly related 0.38 1.00 0 1 6 0 children N of co-resident 0.20 0.43 0 0 2 0 sisters N of co-resident 0.40 0.83 0 0 4 0 brothers N of co-resident 2.11 0.96 2 0 5 2 adult females N of co-resident 2.30 1.42 2 0 7 1 adult males Household size 5.82 2.74 5 1 13 5 Mosuo data subset Tot n of hours 8.56 4.39 9 0 24.5 9 worked N of co-resident 1.52 1.56 1 0 6 0 offspring N of co-resident indirectly related 0.6 1.22 0 0 6 0 children N of co-resident 0.32 0.51 0 0 2 0 sisters N of co-resident 0.63 0.98 0 0 4 0 brothers N of co-resident 2.35 1.02 2 0 5 2 adult females N of co-resident 2.67 1.61 3 0 7 3 adult males Household size 6.63 2.89 6 1 13 4 Han data subset Tot n of hours 8.18 4.39 8.8 0 19.3 11 worked N of co-resident 1.18 1.07 1 0 4 2 offspring N of co-resident indirectly related 1.72 0.70 2 1 4 2 children N of co-resident 1.68 0.69 2 1 3 1 sisters N of co-resident 4.47 1.81 4 2 10 5 brothers N of co-resident 8.18 4.39 8.8 0 19.3 11 adult females N of co-resident 1.18 1.07 1 0 4 2 adult males Household size 1.72 0.70 2 1 4 2

Female Mosuo subset Mean SD Median Min Max Mode Tot n of hours 9.79 4.03 9.6 2.83 24.5 8.5 worked N of co-resident 1.76 1.54 1 0 6 1 offspring N of co-resident indirectly related 0.43 0.99 0 0 4 0 children N of co-resident 0.22 0.42 0 0 1 0 sisters N of co-resident 0.69 1.05 0 0 4 0 brothers N of co-resident 2.47 0.99 2 1 5 2 adult females N of co-resident 2.72 1.68 2 0 7 2 adult males Household size 6.79 3.07 6.5 2 13 4 Male Mosuo subset Tot n of hours 6.65 4.23 7.83 0 15.30 9 worked N of co-resident 1.14 1.55 0 0 6 0 offspring N of co-resident indirectly related 0.86 1.48 0 0 6 0 children N of co-resident 0.46 0.61 0 0 2 0 sisters N of co-resident 0.54 0.87 0 0 3 0 brothers N of co-resident 2.16 1.04 2 0 4 3 adult females N of co-resident 2.60 1.52 3 0 6 3 adult males Household size 6.38 2.61 6 1 11 6 Married male Mosuo subset Tot n of hours 7.51 4.40 8.5 1 15.3 1.5 worked N of co-resident 1.68 1.63 1 0 6 0 offspring N of co-resident indirectly related 0.56 1.33 0 0 6 0 children N of co-resident 0.36 0.57 0 0 2 0 sisters N of co-resident 0.44 0.71 0 0 2 0 brothers N of co-resident 2.00 1.16 2 0 4 2 adult females N of co-resident 2.24 1.48 2 0 5 1 adult males Household size 5.96 2.72 6 1 11 6

Partner co- Partner non- Married Single resident coresident Mosuo (all) 80 15 42 38 Female Mosuo 55 3 26 29 Male Mosuo 25 12 16 9 Han (all) 55 2 48 7 Female Han 28 2 23 5 Male Han 27 0 25 2 Table 4.1.2. Summary of data for categorical variables in the used predictor subset (marriage status and partner co-residence status). The Married category includes both married and widowed individuals. The latter result as Married with Partner non-coresident. Partner non-coresident Han are thus widowed individual only (duolocality is not used in this ethnicity). Data on partner status here refer to married individuals only (single individuals are coded as Partner non-coresident in the variable matrix).

Figure 4.1.1. Bar graph showing number of individuals in each age category in the collected data, divided by sex and ethnicity.

Data set Predictors of total number of hours worked

sex, ethnicity, age, marriage, partner’s co-residence, n of co-resident offspring, n of All data co-resident indirectly related children, n of co-resident sisters , n of co-resident brothers, n of resident adult males, n of resident adult females, household size

Mosuo As for all data, excluding ethnicity

sex, age, partner’s co-residence, n of co-resident offspring, n of resident adult Han males, n of resident adult females, household size

Mosuo males As for all data, excluding sex and ethnicity

Mosuo As for all data, excluding sex and ethnicity females

Married As for all data, excluding sex, ethnicity and marriage Mosuo males

Table 4.1.3 List of predictors of total hours worked for each analysed data set. For the Han data subset, predictors including co-resident siblings and un-related children were excluded, because the strict neolocal system used by this ethnicity means that no co-residence is found. Partner’s co-residence was included rather than marriage because more informative: all married individuals being co-resident, co-residence status allows to distinguish between widowed individuals (co-residence coded as 0) and individuals whose partner is still living. Legend as for Table 4.1.1 ; in addition: marriage status = logical (currently married; currently without partner); partner’s co-residence = logical (partner currently resident in the household; no partner present in household).

4.2 Results

Exploratory analyses

In interpreting the value of summed weights as proposed by Burnham and Anderson (2004), it is informative to choose values which not only associate with low-weight models, but for which the association is also unlikely to be due to chance. Gilman and Hebets used a bootstrapping approach to calculate the probability of obtaining identical summed weights by chance alone in a recent study (Gilman and Hebets, in preparation). The probabilities calculated are consistent for each summed weight values, meaning they are a property of that summed weight range for that data set. Their results, although specific to their data set, can more generally be used an indicative measure of which summed weight values are reliably informative. The approximate threshold for a likelihood of obtaining the same summed weight value by chance above 95% confidence intervals is 0.73. Here, I use a conservative value of 0.75 to identify potentially informative predictors and a value of 0.8 to define highly associated predictors (a summed weight of 0.87- 0.88 obtains a probability of being due to chance of 0.012-0.021 in Gilman and Hebets’ analysis). Due to the limited number of data points, the outcomes from the analyses described below are indicative only and their interpretation is limited to pointing towards potential directions for further studies on larger data sets (and for analyses with interaction models).

Full data set

Predictor of Effect size Summed tot n of hours weight worked

intercept 9.345407 -

sex: male -3.07399 0.999141 n of co-resident 1.968703 0.687402 sisters partner’s co- 1.352232 0.537383 residence n of co-resident -0.61012 0.411107 adult females

age -0.02378 0.329928 n of co-resident 0.921281 0.307302 adult males marriage: 0.214885 0.307079 married

household size 0.089682 0.300547 n of co-resident

indirectly 0.151746 0.275572 related children Table 4.2.1 Effect size and summed weight of n of co-resident tested predictors of total hours worked for the 0.04786 0.262912 full data-set. offspring Summed weight is calculated as the sum of AICc n of co-resident weights in all models containing the predictor. -0.05905 0.256546 brothers Effect size is calculated as the sum across models of the predictor’s coefficient, multiplied by each ethnicity: 0.047519 0.255396 model’s AICc weight scaled by predictor’s summed Mosuo weight. Data set size: 152 data points.

Full data set: Mosuo and Han

In the full data set analysis, the only predictor obtaining a summed weight value which can be considered informative is sex, responsible for a large proportion of total model weight. Specifically, male individuals report on average fewer worked hours compared to females.

Since the approach I used includes no interactions, I will break down the data set into smaller subsets, to identify predictor effects which might be hidden by a main effect analysis in the full data set.

Mosuo

Predictor of Han Summed tot n of hours Effect size weight Predictor of worked tot n of Summed Effect size intercept 10.3782 - hours weight worked sex: male -3.49913 0.996031 n of co- intercept 8.472618 - resident 2.184978 0.752019 sex: male -2.55314 0.727697 sisters n of co- n of co- resident -1.24613 0.587428 resident adult -1.03132 0.564759 offspring females partner’s co- n of co- 2.419126 0.45018 residence resident 0.564571 0.540084 n of co- offspring residence 1.221706 0.371212 age -0.04393 0.414849 adult males household household 0.278406 0.375499 0.093102 0.341171 size size partner’s co- n of co- 0.716108 0.297834 residence residence -0.24188 0.256503 n of co- adult females resident adult -0.02813 0.28184 age -0.02124 0.252036 males

n of co- Table 4.2.3. Effect size and summed weight of resident tested predictors of total hours worked for the Han data set. indirectly 0.177356 0.269212 Indexes calculated as in Table 4.2.1. Size of data related set: 57 data points. children marriage : 0.329406 0.251627 married n of co-

resident -0.10604 0.247593 brothers

Table 4.2.2. Effect size and summed weight of tested predictors of total hours worked for the Mosuo data set. Indexes calculated as in Table 4.2.1. Size of data set: 95 data points.

Female Mosuo Male Mosuo

Predictor of Predictor of Summed tot n of Summed tot n of hours Effect size Effect size weight hours weight worked worked Intercept 7.451252 - Intercept 16.82688 - n of co- marriage: resident 1.799378 0.892475 -6.95521 0.94637 married offspring n of co- Age -0.05066 0.377745 resident adult -2.1844 0.829841 n of co- females resident -0.53505 0.321662 n of co- brothers resident 2.633473 0.712249 partner’s co- 0.857511 0.286515 sisters residence partner’s co- household -3.4771 0.450074 0.161023 0.271176 residence size n of co- n of co- resident resident adult -0.44435 0.267255 indirectly 0.869916 0.417283 females related n of co- children resident marriage: indirectly 0.384313 0.26628 2.276411 0.316924 married related household children 0.400353 0.289232 size n of co- resident 0.151959 0.246061 Age -0.03879 0.231908 offspring n of co- n of co- resident adult -0.0094 0.206213 resident adult 0.069651 0.243565 males males n of co- n of co- resident 0.166566 0.181963 resident 0.486004 0.241812 brothers sisters Table 4.2.5. Effect size and summed Table 4.2.4. Effect size and summed weight weight of tested predictors of total hours of tested predictors of total hours worked for worked for the male Mosuo data set. the female Mosuo data set. Indexes calculated as in Table 4.2.1. Size of Indexes calculated as in Table 4.2.1. Size of data set: 37 data points. data set: 58 data points.

Married male Mosuo

Predictor Effect size Summed weight

intercept 6.935957 - n of co-resident 1.933848 0.913723 offspring n of co-resident 4.652173 0.896002 sisters n of co-resident -2.05069 0.767508 adult females partner’s co- -3.48643 0.362664 residence

household size -0.19476 0.211849 n of co-resident indirectly -0.19667 0.177451 related children n of co-resident 0.41712 0.172095 adult males Table 4.2.6. Effect size and summed weight of n of co-resident tested predictors of total hours worked for the 0.597339 0.157473 brothers married male Mosuo data set. Indexes calculated as in Table 4.2.1. Size of age 0.015551 0.150192 data set: 25 data points.

In the Mosuo, there is very strong evidence for an effect of sex on total number of worked hours, consistent with what found in the main data set and with the same bias (Table 4.2.2). In the Han, the summed weight value for sex is below the set cut-off threshold, but suggests a potential effect (Table 4.2.3). No indications of other potential predictors are identifiable in this latter ethnic group from a no-interaction model. Despite the difference between males’ and females’ worked hours which emerges from the analysis, once the effect of sex is made implicit by a further break down, marriage seems to have a negative effect on female workload (Table 4.2.4). This effect is independent from partner’s co- residence, which is uninformative. The contradiction between these two results might be tentatively explained by the fact that the variable marriage as coded regroups both individuals in a marriage at the time of the interview and those widowed. Since age in the sample is biased towards older individuals, married but widowed females could be associated with a larger household and thus a diminished workload. However, the summed weight for the presence adult members of both sexes in the house does not support this interpretation, nor does that of co-

63 residence. It seems that married females do in general work less than unmarried females, possibly an effect of the number of hours dedicated to childcare only, which here is not included in the worked hour count. Work effort in Mosuo men seems to be positively affected by the number of children and grandchildren resident in the house, with a relatively high summed weight value (Table 4.2.5), an effect which is even greater in the married Mosuo male subset (Table 4.2.6). Co-residence with one’s partner is irrelevant. Because many co-residing couples without co-resident dependent children are individuals in their middle or elderly age, whose children are now adults without offspring, this might mean that male investment focuses within one’s partner’s reproductive life span and the period of altriciality of the offspring. While the presence of adult women in the house apparently decreases the input for males in general, in accordance with the sex-bias in labour distribution, the married male data set also suggests a tendency to increase labour effort for each co-resident sister. This potential effect, if interpreted in the frame of duolocality, may indicate that men tend to contribute towards their sisters’ household when not residing with their wife (Mosuo men who reside with their sisters are rarely joined by their partner).

4.3 Discussion

Labouring effort has been measured in previous studies in terms of PAL, calculated as the total energy expenditure over 24 hours divided by the basal metabolic rate (the first of which is obtained for activities measured from oxygen volume and gas components of emitted outbreaths, e.g. Panterbrick, 1993; FAO/WHO/UNU, 1985). For comparison between the sexes, the male/female PAL ratio is used (Panter-Brick, 2002). Several studies have measured sex-specific daily PAL of agriculturalist or agro-pastoralist populations, with opposite outcomes (see Panter- Brick and Pollard, 1998, for a listing), suggesting ecological context effects (Panter-Brick, 2002). In particular, the Tamang agro-pastoralists of Nepal, of putative Tibetan origin and living at the foot of the Himalayan mountain chain, are the group geographically and ethnically closer to the Mosuo among those studied and whose ecological context is most likely to resemble that of the Yanyuan plateau where Lugu Lake is situated. For this group, male and female PAL values are close, although male PAL is significantly higher (PAL ratio: 1.1; PanterBrick, 1996; Panter-Brick, 2002). Tamang economy is based on self-subsistence and Panter-Brick reports their labour division between sexes as “flexible and egalitarian” (Panter-Brick, 1996), meaning that most activities are equally taken care of by men and women and the workload is not openly biased towards any of the two sexes. However, when quantifying workload through direct measurements of time allocation - exclusive of rest -, the authors report equal engagement in outdoor subsistence activities by both sexes but significantly higher engagement in domestic work by females. In studies of Burkina Faso farmers (Brun et al., 1981, Bleiberg et al., 1980), female workload is, on the other hand, significantly higher (PAL ratio: 0.8; Panter-Brick, 2002). While at

64 times of intense agricultural workload males engage in farming more heavily than women, the time they spend resting during periods of low farming workload is significantly higher. Time measurements for these studies refer to time allocated to the activity as generally defined and thus include rest and collateral engagements. It is significant here that, for time allocated to the market, equal time was employed for the actual selling activity by men and women (45 min), but men’s total expenditure reached 151 min because inclusive of other, non-related activities which would almost all be classified as selfish –“loitering, chatting with friends and purchasing miscellaneous products for home consumption or handicraft” (Brun et al., 1981). Thus, if females also allocate time to socialising and other not directly productive (from a household perspective) activities, their allocation must coincide with other productive engagements to explain the overlap. In the Mosuo, male and female activities are strongly diversified. Although during harvesting men are required to work in the family (or extended family, or, rarely, partner’s family) field, farming as well as animal breeding and domestic work are mostly the care of women throughout the rest of the year (Hua, 2001; Ji et al., 2013). It is thus not surprising that sex is a strong predictor of effort allocation, nor that the number of adult females resident in the house decreases male workload. Male effort is subjected to a positive effect of offspring co-residence –but not of partner’s co- residence – and of co-resident sisters. This points to a role of reproductive pay-offs in determining male labour allocation, consistent with the inclusive fitness framework, also proposed by Ji (Ji et al., 2013). Allocation of effort to sister versus wife’s household could be studied more in detail in a larger data set of married duolocal and neolocal Mosuo. Mosuo males do however offer only limited contribution to their family. In the past, male Mosuo were engaged in land and livestock trade and the male head of the household had a primarily social role (see Chapter III; Hua, 2011). From the measure of labour effort used in the study, the total number of self-reported worked hours, sex differences do not derive from the type of activity undertaken (some potentially more time-consuming than others), but from overall daily labouring effort. Time spent in entertaining relationships was not collected and individuals tended not to include it in their own reports when mentioning working activities (e.g. one individual who supposedly worked as a driver admitted to having spent only two hours driving tourists and five eating barbecue with his friends; these five hours were not included in his driving time estimate). Thus, what was used here was a measure of time spent in direct productivity (in food, building or salary).

How useful is the use of a time budget in comparison to PAL when measuring labour effort? Time measurements benefit from much greater ease of collection, but they also provide an alternative measure of contribution: rather than energy invested, time invested, which might reflect willingness to invest at a more conscious level. Although time here was self-measured and self- reported, I am unaware of the existence of sex biases in estimating passed time or in reporting

65 time spent working. Therefore, I believe the data provide a good relative estimate of sex biases in work done. Does seasonality have an impact on the workload sex skew in the Mosuo? The studies mentioned above provided measures for all farming seasons and the wet season was characterised by significantly higher male input in Burkina Faso (Brun et al., 1981). As mentioned, males do participate in agricultural activities during the harvest period (about two-month long: July to end of August); I do not exclude that during this period male and female contributions might be at least equal, particularly if measured in energy levels. However, I regard this as consistent with the presence of some contribution to family subsistence by males, when this cannot be met by females alone, especially in periods key to guaranteeing household survival through the rest of the farming year. However, when basic family need can be met by female work alone, male contribution is limited, a fact consistent with reports and evidence of domestic work as a female activity throughout the literature.

4.4 Conclusion

I have described a study of sex-specific labour effort in matrilineal Mosuo and patrilineal Han farming populations, measured in time investment. While data are not sufficient to draw any meaningful conclusion for the Han, I have shown in the Mosuo a potential indication of males contributing fewer labour hours, a contribution that yet seems increased by the presence of their children or sisters. I interpret this in an inclusive fitness versus reproductive investment framework, by which males offer a limited investment towards their children and relatives, the remaining time being employed in activities which offer a potential reproductive or prestige signalling value.

CHAPTER V Reproductive investment and post-marital residence: a general discussion and conclusion

I have presented here a theoretical model analysing the dynamics behind human sex-biased dispersal, showing that sex differences in reproductive investment are a factor in the emergence of PMRSs. Interpreting reproductive investment as contribution to household subsistence, I have then used the data from an anthropological survey to explore the association between sex- specific differences in investment and household kinship structure.

5.1 A general discussion The results from our model suggest the existence of an inherent sex bias in human dispersal, deriving from the skew in reproductive investment between the sexes. This can be put in relationship with the related between-sex conflict towards maximising partner’s investment effort relative to own. While reproductive skews and mating strategies have been proposed as underlying sex-biased dispersal in both animals (Greenwood, 1980) and humans (the association between patrilineal inheritance, polygyny and patrilocality made by Hartung, 1982, for example), only one element among those proposed in Chapter I reflects, to a certain extent, investment in contribution to offspring survival: productivity. The model features the simplifying assumption of identifying reproductive investment with productivity only and the same was used for interpreting my data analysis. Although reproductive investment includes many other forms of investment (as discussed in Chapter III), productivity towards household subsistence can, reversely, safely be interpreted as one of these forms. Despite the direction of sex biases in productivity being one of the earliest proposed explanations of post-marital residence distribution across societies, I reported in Chapter I how there are no conclusive results obtained so far from cross-cultural analyses of this association. However, the relationship between the two patterns might not be as straightforward as suggested by this early hypothesis. The traditional idea that sex biases in productivity might determine residence system proposes patrilocality as a consequence of male- biased dispersal, matrilocality as that of a female bias and ambi-, bi- and neolocality as following from equal contribution by the two sexes. This hypothesis suffers from a key fallacy: the assumption that optimal reproductive success derives from the maximisation of collective productivity, that is, the grouping together of the highest number of producers. The problem here lies in two points. First, this assumption does not take into account reproductive conflict and its detrimental outcomes for one’s offspring, which might counterbalance the benefits of higher production. Second, and accounting for within-household conflict as well as problems with investment in inclusive rather than direct fitness, the optimal strategy for women might be to enforce some level of contribution from their partner -sharing reproductive interest in the same

67 offspring- rather than to obtain it from kin with only a limited reproductive payoff from her versus their own descendants. This latter point is particularly true when other activities, which males may be more likely to provide (e.g. defence, teaching of key skills to sons, etc.) are included in the definition of reproductive investment. As already mentioned, given the altriciality of human children, men should nearly always allocate some effort towards their own offspring, especially if this effort does not come at the expenses of other mating opportunities and if the probability of paternity is high. Where productivity is male- biased, be it a cause or a consequence of residence, females benefit more from co-residing with the father of their children, because of the resource gain (residence is a consequence), and males benefit more from allocating these resources towards their offspring (residence may also be a cause). Thus, observed male-biased productivity should most often be associated with patrilocality, other factors not taken into account. On the other hand, as some productivity investment or other types of investment from their partner might provide a higher benefit to women than co-residence with kin, or when resources are controlled by males, patrilocality may be found also when productivity is female-biased. As seen in Chapter IV, Burkina Faso farmers, patrilocal and patrilineal, do show a female-biased allocation of labour effort. My analysis of time budget allocation within the Han dataset seems to point at a female skew in labour effort, despite Han culture being strongly patrifocal and their residence system strictly neolocal. I will discuss below how duolocality may also be an outcome of female-biased productivity. Labour effort and biases in productivity can thus not be read as straightforward predictors of residence, as their relationships passes through several other factors; not last, the difference between labour effort and reproductive investment. Nevertheless, studying patterns of labour allocation in relation to residence might help identifying associations which support the role of reproductive investment in determining PMRS. They might also help elucidating the interaction with and influence of other elements, such as promiscuity, presence of kin and investment in inclusive fitness.

Results from fieldwork data analysis of Mosuo labour contribution suggest that having their offspring as dependents in their own household has a positive effect on the labour contribution given by a man. Whether this is a consequence of the residence system (partner’s co-residence is likely to decrease offspring extra-pair paternity and thus favour higher paternal investment) or a cause (females choose a strategy which involves partner’s co-residence, thus increasing his contribution) cannot be established from the present study. In both cases, whatever the mechanism leading to the effect, it is conceivable that the increase in male contribution given by children co-residence has had an impact on sex-biased dispersal. Promiscuity in the Mosuo is likely to have been historically high, until the change in customs enforced by the PRC government in the ‘60s. Setting a paternal investment- related interpretation of residence systems, there are

68 two possible hypotheses. If accounts of promiscuity are true, Mosuo males would receive little gain from supporting their partner, exception made for the occasional gifts falling within the framework of signalling theory. High gain would instead come from investing in multiple or high visibility signalling activities, with the aim of attracting other partners. I have seen no historical accounts describing additional male activities in the Mosuo, although males had the primary role of dealing with household social relationships and exchanges. Moreover, the Mosuo do have a rich music and dancing repertoire, most of which focused around courtship. All of these activities fall well under the definition of signalling. Males would also gain additional fitness by helping the survival of their sisters’ daughters and sons, in agreement with the model proposed by Ji et al. (2013) and especially under a threshold of investment that does not compromise time and effort spent signalling. The social role played by males can itself be seen as an important form of parental investment, potentially increasing survival and reproductive opportunities for the offspring of the household, while also serving as signalling. This joint effect might derive from the conflict between parental investment and mating success as reproductive strategies in the two sexes, whereby males might win by obtaining higher reproductive success with display traits that also provide a form of parenting, without the need to engage in more expensive investment. Thus, signalling for parenting skills as well as for good genes. Successful social relationships might indeed be an honest signal of a male’s value in contributing towards his offspring’s survival and reproductive success, under sexual selection for parenting. Hunting of big game, as mentioned in Chapter III, may also have a similar duplex value. Independently from whether accounts of promiscuity in the Mosuo are accurate, the level of relatedness provided by more than one sister co-breeding in a single household might in theory also explain male preferential contribution to sisters’ offspring, according to Ji et al. as above. In the light of this latter model, lower effort allocation by males compared to females might be explained by differential reproductive investment combined with household producers’ saturation: no additional benefit is gained by increasing the number of producers after household maximum productive capability is reached, so that, if females invest more in productivity, males can contribute less. This is also in agreement with the limited resource availability associated with duolocality by Wu et al. (2013). In this framework, residence is a cause and not a consequence of the direction of investment by males. Reasons why sisters should start breeding communally are not clearly supported in Ji et al.’s paper. In fact, dispersal constraints resulting from lack of resources and consequent cooperative breeding more generally explain the formation of pluri-nucleal households and thus may also be associated with patri- or matrilocality. On the other hand, co-residence with kin is, as we have seen, costly from the point of view of reproductive competition, although less costly than co- residence with breeding non-kin.

Female-biased productivity might have made cooperative breeding by sisters more advantageous. When comparing with the dynamics suggested by our simulation, we have to take into account that results arise under the assumption of equal reproductive investment by the two sexes (i.e. equal productivity). Introducing a female bias in the amount of resources produced by female agents might instead produce duolocality, or of matrilocality, as a main strategy. However, since multiple co-breeding females give rise to reproductive conflict, also in the Mosuo (Ji et al., 2013), other factors are needed to shift the balance in favour of the helping received by sisters for their co-residence to be promoted. A pre-existing low investment by males is a potential one and high promiscuity one of the underlying causes. Non-ecological factors at other levels of social complexity might also be involved. Shih (2000) suggests that the emergence of a matrifocal culture with lack of dispersal was promoted by the poor, strictly local nature of the farming economy and by the social system in place, which made association at the level of kin the only form of social grouping. In ecological terms, this might translate to a resource-poor environment where cooperation is necessary for resource exploitation and where no other form of association other than that provided by the birth group has emerged.

5.2 Conclusion Based on the work described in this thesis, I conclude that reproductive provides an underlying bias in the emergence of a population’s post-marital residence system. The relative investment offered by the two sexes is a leading skew on which dynamics of inclusive fitness and conflict within a family group interact, determining costs and benefits of dispersal for reproductive individuals of both sexes. I believe that future studies looking more in detail at this interaction will provide a more detailed insight on how post-marital residence strategies emerge and change on the basis of these factors –when these factors only can be considering as primarily affecting dispersal. Among the influences here not considered, but related, I consider differential investment in the offspring on the basis of sex or age (inheritance rules) to be a major mechanism interacting with these dynamics.

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APPENDIX 1 Model’s code. Annotations are given in green, working code in black. function Main(ex,rep) rng(rep) if (ischar(ex)) ex=str2double(ex); end if (ischar(rep)) rep=str2double(rep); end

%% random mating, no division of labour, no new households (hh) created if newly-married neolocal couple cannot find an empty one (hh number is fixed)

IBI=3; menopause=45; n_households=200; startn_individuals=500; %% resource cap per household cap=10; %% adult productivity productivity=3; %% resources min for survival not_enough=0; %% years=10000; burn_in=300; collection_interval=100; last_collection_point=burn_in; collection_point=1; %% PMR data matrix(saves couples currently using each strategy) %Columns: 1=wife ID; 2=husband ID; 3=residence strategy(1=patrilocal, 2=matrilocal, % 3=neolocal, 4=duolocal) pmr_mat=[]; %% dispersal trait, PMR freq and demographic matrices collecting data across whole simulation % (every 100 years from burn_in) % pmr_stat sums data in pmr_mat on tot number of couples using each strategy % disp_trait saves average dispersal trait for each sex (col1=females,col2=males) % demomat saves demographics (col1=tot population, 2=births, 3=deaths, 4=deaths<15, % 5=deaths>60, 6= tot unmarried adult f over tot adult f, 7=f mean age first marriage, % 8=f mean age first birth, 9=mean tot n children for women>45 who

% have been married, 10=tot unmarried adult m over tot adult m, 11=m mean age first marriage, % 12==m mean age first birth, 13=mean tot n children for men>55 who have been married) rowdown=(years-burn_in)/collection_interval; pmr_stat=zeros(rowdown,4); dispersal=zeros(rowdown,2); demomat=zeros(rowdown,13); dead_children=0; dead_elderly=0; %% create individuals %% % Columns: 1= ID; 2= sex (0=f, 1=m); 3=age; 4=household; 5=spouse ID (0= not married or % widowed); 6= mother ID; % 7= father ID; 8= n of children alive; 9= alive unweaned children; 10= dispersal trait; 11= age % at first dispersal; % 12= age first birth; 13= age last birth; 14= time since last birth; 15= n children ever; % 16= dispersal decision (0= never dispersed; 1=stayed; 2=gone); 17= newly wed (0= no, % 1=yes) individuals=zeros(startn_individuals,17); % initialise first population for row=1:startn_individuals individuals(row,1)=row; end last_ID=startn_individuals; % sex ratio=1:1 individuals(1:(startn_individuals/2),2)=0; individuals(((startn_individuals/2)+1):startn_individuals,2)=1; age=randi([0,69],startn_individuals,1); individuals(:,3)=age; % assign each individual to a household hh=randi([1,n_households],startn_individuals,1); individuals(:,4)=hh; individuals(:,5)=0; individuals(:,6)=0; individuals(:,7)=0; individuals(:,8)=0; individuals(:,9)=0; % assign dispersal traits from a uniform distribution [0;1] disp_trait=rand(startn_individuals,1); individuals(:,10)=disp_trait; individuals(:,11)=0; individuals(:,12)=0; individuals(:,13)=0; % "time since last birth" initialised as 0 for everyone. Individuals can have a child even if

% another was born in previous year (fertility rate includes IBI) individuals(:,14)=0; individuals(:,15)=0; individuals(:,16)=0; individuals(:,17)=0; n_people=size(individuals,1); ID_list=individuals(:,1); %% create households %% % Columns: 1= empty (0) or inhabited (1); 2= tot resources % Households (hh) have no numerical identity saved in the matrix,the row number is their ID households=zeros(n_households,2); % identify inhabited hh for estate=1:n_households if sum(individuals(:,4)==estate)>0 households(estate,1)=1; end end %% run through simulation %% for y=1:years; %% ageing %% individuals(:,3)=individuals(:,3)+1; % updates n unweaned children/mother (but only if they were actually born, not in initial % population) for n=1:n_people if individuals(n,3)==4 && individuals(n,6)~=0 free_mother_n=individuals(n,6); free_mother=find(ID_list==free_mother_n); individuals(free_mother,9)=individuals(free_mother,9)-1; end % updates time since last birth if individuals(n,15)>0 individuals(n,14)=individuals(n,14)+1; end end % updates newly wed individuals(:,17)=0;

%% production %% % adults produce +3, children subtract 1 for h=1:n_households subtractres=0; inputres=0; inhab=find(individuals(:,4)==h);

for in=1:size(inhab,1) inh_row=inhab(in,1); age1=individuals(inh_row,3); if age1=maturity && age1<70 inputres=inputres+productivity; end end households(h,2)=households(h,2)+inputres; if households(h,2)>cap households(h,2)=cap; end households(h,2)=households(h,2)-subtractres; end

%% marriage %% % Choice is made by females (no assortative mating). List of all available females and all % available % males. Female: adult,unmarried, no unweaned children. Males: adult, unmarried. blondies=find(individuals(:,2)==0 & individuals(:,3)>=maturity & individuals(:,5)==0 & individuals(:,9)==0); bachelors=find(individuals(:,2)==1 & individuals(:,3)>=maturity & individuals(:,5)==0); mate_finder=blondies; mate_finder_n=size(mate_finder,1);

for fem_suitors=1:size(blondies,1) bride_to_be=mate_finder(randperm(mate_finder_n,1),1); if sum(sum(bachelors))>0 bride_to_be_name=ID_list(bride_to_be); bachelor_n=size(bachelors,1); mate_to_be_found=bachelors; mate_to_be_found_n=size(mate_to_be_found,1); % keeps looking till it finds a match match=0; % avoids going into a loop if there are no matches available considered=0; while match==0 && considered<=bachelor_n considered=considered+1; if sum(mate_to_be_found,1)>0 bethroted=mate_to_be_found(randperm(mate_to_be_found_n,1),1); bethroted_name=ID_list(bethroted); % check for age: +-10 from partner if individuals(bethroted,3)>=individuals(bride_to_be,3)-10 && individuals(bethroted,3)<=individuals(bride_to_be,3)+10 % check for relatedness: not a sibling or a parent (can potentially marry uncles and aunts)

if (individuals(bethroted,6)~=individuals(bride_to_be,6) || individuals(bethroted,6)==0) && (individuals(bethroted,7)~=individuals(bride_to_be,7) || individuals(bethroted,7)==0) && individuals(bethroted,6)~=bride_to_be_name && individuals(bride_to_be,7)~=bethroted_name % signing wedding certificate individuals(bride_to_be,5)=bethroted_name; individuals(bethroted,5)=bride_to_be_name; % set as newly married individuals(bride_to_be,17)=1; individuals(bethroted,17)=1; match=1; bachelors=setdiff(bachelors,bethroted); else match=0; % ricalculates n of available males excluding those who have already been % assessed - avoids some % females remaining unmarried because not going through all males in the % list mate_to_be_found=setdiff(mate_to_be_found,bethroted); mate_to_be_found_n=size(mate_to_be_found,1); end end else mate_to_be_found=setdiff(mate_to_be_found,bethroted); mate_to_be_found_n=size(mate_to_be_found,1); end end mate_finder=setdiff(mate_finder,bride_to_be); mate_finder_n=size(mate_finder,1); end end

%% dispersal %% % Individuals can disperse after every marriage, based on an independent dispersal % decision. Children who have not yet reached maturity follow the living parent. % looks at female members of a newly-married couple (not to go through the list twice) deciders=find(individuals(:,2)==0 & individuals(:,17)==1); hesitants=deciders; hesitants_n=size(hesitants,1); for x=1:size(deciders,1) decision_maker=hesitants(randperm(hesitants_n,1),1); % put the couple in the pmr registry pmr_mat=[pmr_mat;[0,0,0]]; pmr_n=size(pmr_mat,1); husband=individuals(decision_maker,5); wife=individuals(decision_maker,1); pmr_mat(pmr_n,1)=wife; pmr_mat(pmr_n,2)=husband;

79 h_index=find(ID_list==husband); my_disp_trait=individuals(decision_maker,10); myhusb_disp_trait=individuals(h_index,10); if my_disp_trait<=rand my_dispersal=1; else my_dispersal=2; end if myhusb_disp_trait<=rand husb_dispersal=1; else husb_dispersal=2; end if my_dispersal==1 && husb_dispersal==1 %avoid duolocal pmr_mat(pmr_n,3)=4; elseif my_dispersal==1 && husb_dispersal==2 %matrilocal pmr_mat(pmr_n,3)=2; % husband is registered as a member of the wife's hh individuals(h_index,4)=individuals(decision_maker,4); % check if husband has alive children if individuals(h_index,8)>0 phalfsibs_list=find(father_list==husband); % check if they are <15 to see if they have to follow father for k=1:size(phalfsibs_list,1) psib_index=phalfsibs_list(k); if individuals(psib_index,3)<15 individuals(psib_index,4)=individuals(decision_maker,4); end end end elseif my_dispersal==2 && husb_dispersal==1 %patrilocal pmr_mat(pmr_n,3)=1; individuals(decision_maker,4)=individuals(h_index,4); % check if wife has alive children if individuals(decision_maker,8)>0 mhalfsibs_list=find(mother_list==wife); % check if they are <15 to see if they have to follow mother for k=1:size(mhalfsibs_list,1) msib_index=mhalfsibs_list(k); if individuals(msib_index,3)<15 individuals(msib_index,4)=individuals(decision_maker,4); end end end elseif my_dispersal==2 && husb_dispersal==2 %neolocal --> need a new hh % take random empty hh in the hh list... property_list=find(households(:,1)==0);

n_property=size(property_list,1); %...if there is any if n_property>0 home=property_list(randperm(n_property,1)); individuals(decision_maker,4)=home; individuals(h_index,4)=home; pmr_mat(pmr_n,3)=3; households(home,1)=1; % give new hh some resources, so that couple does not get killed off in "survival" households(home,2)=1; % transfer any children not yet of age: wife's if individuals(decision_maker,8)>0 mhalfsibs_list=find(mother_list==wife); for k=1:size(mhalfsibs_list,1) msib_index=mhalfsibs_list(k); if individuals(msib_index,3)<15 individuals(msib_index,4)=home; end end end % husband's if individuals(h_index,8)>0 phalfsibs_list=find(father_list==husband); % check if they are <15 to see if they have to follow father for k=1:size(phalfsibs_list,1) psib_index=phalfsibs_list(k); if individuals(psib_index,3)<15 individuals(psib_index,4)=home; end end end else %annul wedding vows if no house is available individuals(decision_maker,5)=0; individuals(h_index,5)=0; individuals(decision_maker,17)=0; individuals(h_index,17)=0; pmr_mat(pmr_n,:)=[]; end

end % record last dispersal decision and age at first dispersal if marriage has not % been annulled if individuals(decision_maker,17)==1; individuals(decision_maker,16)=my_dispersal; individuals(h_index,16)=husb_dispersal; if individuals(decision_maker,11)==0

individuals(decision_maker,11)=individuals(decision_maker,3); end if individuals(h_index,11)==0 individuals(h_index,11)=individuals(h_index,3); end end hesitants=setdiff(hesitants,decision_maker); hesitants_n=size(hesitants,1); end

%% give_birth (new IDs created) %% t0pop=size(individuals,1); maternities=find(individuals(:,2)==0 & individuals(:,3)<=menopause & individuals(:,5)>0 & individuals(:,17)==0 & individuals(:,9)==0); pregnancies=maternities; pregnancies_n=size(pregnancies,1); for mater=1:size(maternities,1) materrow=pregnancies(randperm(pregnancies_n,1),1); home_n=individuals(materrow,4); if households(home_n,2)>=3 last_ID=last_ID+1; individuals=[individuals;zeros(1,17)]; row_n=size(individuals,1); individuals(row_n,1)=last_ID; if 0.5<=rand individuals(row_n,2)=0; else individuals(row_n,2)=1; end individuals(row_n,3)=0; % assign hh individuals(row_n,4)=home_n; individuals(row_n,5)=0; individuals(row_n,6)=individuals(materrow,1); individuals(row_n,7)=individuals(materrow,5); individuals(row_n,8)=0; individuals(row_n,9)=0; % dispersal trait fatherrow=find(ID_list==individuals(row_n,7)); if individuals(row_n,2)==0 individuals(row_n,10)=individuals(materrow,10); else individuals(row_n,10)=individuals(fatherrow(1,1),10); end individuals(row_n,11:17)=0; % update mother's details individuals(materrow,8)=individuals(materrow,8)+1; individuals(materrow,9)=individuals(materrow,9)+1;

if individuals(materrow,15)==0 %age at first birth individuals(materrow,12)=individuals(materrow,3); end individuals(materrow,13)=individuals(materrow,3); individuals(materrow,14)=0; individuals(materrow,15)=individuals(materrow,15)+1; % update father's details. Does not record unweaned/weaned individuals(fatherrow,8)=individuals(fatherrow,8)+1; if individuals(fatherrow,15)==0 %age at first birth individuals(fatherrow,12)=individuals(fatherrow,3); end individuals(fatherrow,13)=individuals(fatherrow,3); individuals(fatherrow,14)=0; individuals(fatherrow,15)=individuals(fatherrow,15)+1; end pregnancies=setdiff(pregnancies,materrow); pregnancies_n=size(pregnancies,1); end

%% survival (takes out couples where a member has died from pmr; removes newly-wed % mark where one member has died) starve=find(households(:,2)

% register of individuals who died that year death_record=[]; % hh which ran out of resources for carelesshh=1:sz_starve hhname=starve(carelesshh,1); st_childr=find(individuals(:,4)==hhname & individuals(:,3)<15); n_ch=size(st_childr,1); xtrach_n=abs(0-households(hhname,2)); if xtrach_n>n_ch xtrach_n=n_ch; end kill_ch=randperm(n_ch,xtrach_n); for kk=1:size(kill_ch,2) chn=kill_ch(1,kk); kill_index=st_childr(chn,1); death_record=[death_record;individuals(kill_index,1)]; dead_children=dead_children+1; end households(hhname,2)=not_enough; end % removes dead 1 ndeaths=size(death_record,1); if ndeaths>0

% eliminate dead individuals from n children alive and n unweaned children alive, before % data on parents go lost and % count children and individuals over 60 who have died for oo=1:ndeaths offs=death_record(oo,1); row_index=find(individuals(:,1)==offs);

mother=individuals(row_index,6); father=individuals(row_index,7); index_mother=find(individuals(:,1)==mother); % check if on population list, because she could have died before if sum(index_mother)>0 individuals(index_mother,8)=individuals(index_mother,8)-1; if individuals(row_index,3)<4 individuals(index_mother,9)=individuals(index_mother,9)-1; end end index_father=find(individuals(:,1)==father); if sum(index_father)>0 individuals(index_father,8)=individuals(index_father,8)-1; end if individuals(row_index,3)<15 dead_children=dead_children+1; end end for dd=1:ndeaths deceasedname=death_record(dd,1); deaddata=find(individuals(:,1)==deceasedname); individuals(deaddata,:)=[]; end end n_people=size(individuals,1); elderly=[]; dyind=[]; % natural mortality for nat_death=1:n_people age2=individuals(nat_death,3); if age2>70 death_record=[death_record;individuals(nat_death,1)]; elderly=[elderly;individuals(nat_death,1)]; dead_elderly=dead_elderly+1; elseif rand<0.04 death_record=[death_record;individuals(nat_death,1)]; dyind=[dyind;individuals(nat_death,1)]; if individuals(nat_death,3)<15 dead_children=dead_children+1;

end % eliminate dead individuals from n children alive and n unweaned children alive, % before data on parents go lost % and count children and individuals over 60 who have died mother=individuals(nat_death,6); father=individuals(nat_death,7); if sum(death_record(:,1)==mother)==0 index_mother=find(individuals(:,1)==mother); % check if on population list, because she could have died before if sum(index_mother)>0 individuals(index_mother,8)=individuals(index_mother,8)-1; if individuals(nat_death,3)<4 individuals(index_mother,9)=individuals(index_mother,9)-1; end end end if sum(death_record(:)==father)==0 index_father=find(individuals(:,1)==father); if sum(index_father)>0 individuals(index_father,8)=individuals(index_father,8)-1; end end end end % removes dead 2 for ee=1:size(elderly,1) eldn=elderly(ee,1); eldrow=find(individuals(:,1)==eldn); individuals(eldrow,:)=[]; end for ad=1:size(dyind,1) idn=dyind(ad,1); indrow=find(individuals(:,1)==idn); individuals(indrow,:)=[]; end n_people=size(individuals,1); ndeaths=size(death_record,1); % set individuals whose partner has died as widowed, remove couple from PMR list if ndeaths>0 for pp=1:n_people if sum(find(individuals(pp,5)==death_record(:,1)))>0 individuals(pp,5)=0; % reset newly-wed if individuals(pp,17)==1 individuals(pp,17)=0; end

if individuals(pp,2)==0 pmr_row=find(pmr_mat(:,1)==individuals(pp,1)); elseif individuals(pp,2)==1 pmr_row=find(pmr_mat(:,2)==individuals(pp,1)); end % check row has not been eliminated yet (both partners died in same round) if sum(pmr_row)>0 pmr_mat(pmr_row,:)=[]; end end end end % unweaned children whose parents have both died if ndeaths>0 orphans=[]; for ch=1:n_people if individuals(ch,3)<=IBI && sum(individuals(:,1)==individuals(ch,6))==0 && sum(individuals(:,1)==individuals(ch,7))==0 orphans=[orphans;individuals(ch,1)]; end end death_record=[death_record;orphans]; orphann=size(orphans,1); dead_children=dead_children+orphann; % remove orphans if orphann>0 for dc=1:orphann orphanname=orphans(dc,1); ddchdata=find(individuals(:,1)==orphanname); individuals(ddchdata,:)=[]; end end n_people=size(individuals,1); end ndeaths=size(death_record,1); % update household data if ndeaths>0 for census=1:n_households if sum(individuals(:,4)==census)==0 households(census,:)=0; end end ID_list=individuals(:,1); else dead_children=0; end father_list=individuals(:,7);

mother_list=individuals(:,6); people=size(individuals,1)

%% collect data %% if y>burn_in && y==last_collection_point+collection_interval %build mat of data that need to be saved at each data collection point % PMRS count patri=sum(pmr_mat(:,3)==1); matri=sum(pmr_mat(:,3)==2); neo=sum(pmr_mat(:,3)==3); duo=sum(pmr_mat(:,3)==4); n_couples=size(pmr_mat,1); pmr_stat(collection_point,1)=patri./n_couples; pmr_stat(collection_point,2)=matri./n_couples; pmr_stat(collection_point,3)=neo./n_couples; pmr_stat(collection_point,4)=duo./n_couples; % dispersal traits female_list=find(individuals(:,2)==0); male_list=find(individuals(:,2)==1); fl=size(female_list,1); ml=size(male_list,1); tot_fdisp=0; for fn=1:fl fem_r=female_list(fn); tot_fdisp=tot_fdisp+individuals(fem_r,10); end av_fdisp=tot_fdisp/fl; tot_mdisp=0; for mn=1:ml male_r=male_list(mn); tot_mdisp=tot_mdisp+individuals(male_r,10); end av_mdisp=tot_mdisp/ml; dispersal(collection_point,1)=av_fdisp; dispersal(collection_point,2)=av_mdisp; % demographics demomat(collection_point,1)=people; demomat(collection_point,2)=sum(individuals(:,3)==0); demomat(collection_point,3)=ndeaths; demomat(collection_point,4)=dead_children; demomat(collection_point,5)=dead_elderly; women=find(individuals(:,2)==0 & individuals(:,3)>15); men=find(individuals(:,2)==1 & individuals(:,3)>15); % if individual has never dispersed, then he/she has never been married wildfree_w=find(individuals(:,2)==0 & individuals(:,3)>15 & individuals(:,16)==0); wildfree_m=find(individuals(:,2)==1 & individuals(:,3)>15 & individuals(:,16)==0);

n_women=size(women,1); n_men=size(men,1); johns=0; wedding_ring=0; f_age_1birth=0; f_age_1marrg=0; m_age_1birth=0; m_age_1marrg=0; grannies=0; granpas=0; children_w=0; children_m=0; % proportion umarried women out of total adult women demomat(collection_point,6)=size(wildfree_w,1)/n_women; % age at first marriage and first birth for women who have been married and mean n % children for women over 45 for wm=1:n_women woman_row=women(wm); if individuals(woman_row,16)>0 wedding_ring=wedding_ring+1; f_age_1marrg=f_age_1marrg+individuals(woman_row,11); f_age_1birth=f_age_1birth+individuals(woman_row,12); if individuals(woman_row,3)>45 grannies=grannies+1; children_w=children_w+individuals(woman_row,15); end end end demomat(collection_point,7)=f_age_1marrg/wedding_ring; demomat(collection_point,8)=f_age_1birth/wedding_ring; demomat(collection_point,9)=children_w/grannies; demomat(collection_point,10)=size(wildfree_m,1)/n_men; % age at first marriage and first birth for men who have been married and mean n children % for men >55 who have been married for mn=1:n_men man_row=men(mn); if individuals(man_row,16)>0 johns=johns+1; m_age_1marrg=m_age_1marrg+individuals(man_row,11); m_age_1birth=m_age_1birth+individuals(man_row,12); if individuals(man_row,3)>55 granpas=granpas+1; children_m=children_m+individuals(man_row,15); end end end demomat(collection_point,11)=m_age_1marrg/johns; demomat(collection_point,12)=m_age_1birth/johns;

demomat(collection_point,13)=children_m/granpas; demomat(isnan(demomat))=0; last_collection_point=y; collection_point=collection_point+1; end if y==years % data saved at final step only hhtxtname=['households',num2str(ex),'-',num2str(rep),'.txt']; hhmatname=['households',num2str(ex),'-',num2str(rep)]; save (hhtxtname,'households','-ascii','-double') save (hhmatname,'households') idtxtname=['individuals',num2str(ex),'-',num2str(rep),'.txt']; idmatname=['individuals',num2str(ex),'-',num2str(rep)]; save (idtxtname,'individuals','-ascii','-double') save (idmatname,'individuals') pmrlist_matname=['pmr_last',num2str(ex),'-',num2str(rep)]; save (pmrlist_matname,'pmr_mat') % save data matrices built throughout simulation pmrtxtname=['PMR',num2str(ex),'-',num2str(rep),'.txt']; pmrmatname=['PMR',num2str(ex),'-',num2str(rep)]; save (pmrtxtname,'pmr_stat','-ascii','-double') save (pmrmatname,'pmr_stat') disptxtname=['dispersal',num2str(ex),'-',num2str(rep),'.txt']; dispmatname=['dispersal',num2str(ex),'-',num2str(rep)]; save (disptxtname,'dispersal','-ascii','-double') save (dispmatname,'dispersal') demotxtname=['demography',num2str(ex),'-',num2str(rep),'.txt']; demomatname=['demography',num2str(ex),'-',num2str(rep)]; save (demotxtname,'demomat','-ascii','-double') save (demomatname,'demomat') end dead_children=0; dead_elderly=0; end end

APPENDIX 2

Appendix table 1. Collected household data for each individual participant. ID = individual ID; hh = household ID; vlg = village (A = A’wa, Q = Qidui, M = Muzhi); day = date of collection. Observation for which the date of collection has not been recorded are indicated with NA; age; sex (M = male, F = female); ethn = ethnicity (Mo = Mosuo, H = Han); marr = marriage status (1 = married or elderly widowed individual; 0 = single); cor_part = partner co-residence status (1 = co-resident, 0 = non-coresident); cor_dir_off = n of co-resident or dependent children and grand-children; cor_ind_ch = n of co-resident or dependent indirectly related children; cor_sis = n of co-resident sisters; cor_br = n of co-resident brothers; cor_adm = n of co-resident adult males in the household; cor_adf = n of co-resident adult females in the household; hh_size = tot n of co-resident individuals in household, including young children and excluding dependent children studying away from home. Individuals for which more than one observation was recorded appear more than once.

ID hh vlg day age sex ethn marr cor_part cor_dir_off cor_ind_ch cor_sis cor_br cor_adm cor_adf hh_size 102202 1 A 205 69 M Mo 0 0 0 1 0 0 3 3 7 102202 1 A 2805 69 M Mo 0 0 0 1 0 0 3 3 7 101102 2 A 205 72 M Mo 1 1 1 0 0 0 1 3 5 102103 3 A 205 45 M Mo 0 0 0 4 1 0 3 3 10 102103 3 A NA 45 M Mo 0 0 0 4 1 0 3 3 10 102103 3 A 1111 45 M Mo 0 0 0 4 1 0 3 3 10 101301 7 A 405 62 M Mo 1 0 0 0 0 0 0 0 1 101301 7 A 2205 62 M Mo 1 0 0 0 0 0 0 0 1 101405 8 A 405 21 M Mo 0 0 0 2 1 1 3 3 8 101403 8 A 405 27 M Mo 1 1 1 1 1 1 3 3 8 102101 9 A 405 48 M Mo 1 1 1 0 0 0 1 1 3 102803 14 A 605 55 M Mo 1 1 3 0 0 0 3 4 10 102602 16 A 605 65 M Mo 1 1 3 0 0 0 1 2 6 102907 22 A 805 25 M Mo 0 0 0 0 0 3 5 1 6 102902 22 A NA 62 M Mo 1 0 0 0 1 0 5 1 6

102902 22 A NA 62 M Mo 1 0 0 0 1 0 5 1 6 102001 23 A 805 53 M Mo 1 1 2 0 0 0 1 1 4 101504 24 A 2805 35 M Mo 1 0 2 2 1 0 1 2 4 101005 25 A NA 43 M Mo 1 0 0 0 1 1 3 2 5 101002 25 A NA 48 M Mo 0 0 0 0 1 1 3 2 5 100101 28 A 805 60 M Mo 1 1 2 0 0 0 4 2 8 101605 30 A NA 36 M Mo 0 0 0 1 1 1 6 2 9 100403 33 A NA 51 M Mo 1 1 1 0 0 0 1 2 3 109405 Q2 Q 2205 38 M H 1 1 2 0 0 0 1 2 5 109301 Q3 Q 2205 49 M H 1 1 1 0 0 0 1 1 2 108801 Q5 Q 2205 35 M H 1 1 2 0 0 0 2 1 5 108805 Q5 Q 2205 62 M H 1 0 2 0 0 0 2 1 5 108501 Q7 Q 2405 61 M H 1 1 2 0 0 0 2 2 6 107302 Q15 Q 2405 51 M H 1 1 1 0 0 0 3 2 6 107101 Q19 Q 2505 59 M H 1 1 2 0 0 0 1 2 5 107901 Q22 Q 2505 72 M H 1 1 0 0 0 0 2 2 4 107602 Q25 Q 2705 40 M H 1 1 1 0 0 0 1 1 2 N0201 Q28 Q 2705 46 M H 1 1 2 0 0 0 1 1 4 104501 Q29 Q 2705 45 M H 1 1 0 0 0 0 1 1 2 106701 Q30 Q 2705 48 M H 1 1 0 0 0 0 2 1 3 104701 Q31 Q 2705 46 M H 1 1 0 0 0 0 2 1 3 103901 Q34 Q 2805 71 M H 1 1 0 0 0 0 2 1 3 105002 Q36 Q 2805 43 M H 1 1 1 0 0 0 1 2 4 109101 Q37 Q 2805 76 M H 1 1 0 0 0 0 1 1 2 105101 Q40 Q 3105 68 M H 1 0 2 0 0 0 3 4 9

106301 Q41 Q 3105 67 M H 1 1 0 0 0 0 2 1 3 106501 Q42 Q 3105 66 M H 1 1 2 0 0 0 2 2 6 104204 Q44 Q 3105 33 M H 1 1 2 0 0 0 2 2 6 104201 Q44 Q 3105 51 M H 1 1 2 0 0 0 2 2 6 101103 2 A 205 35 F Mo 1 0 1 0 1 0 1 3 5 101201 3 A 205 59 F Mo 1 1 4 0 0 1 3 3 10 101204 3 A 2805 34 F Mo 1 0 2 2 0 1 3 3 10 101902 4 A 205 46 F Mo 1 0 2 0 0 2 3 1 4 101802 5 A 205 72 F Mo 1 0 6 0 0 0 5 3 13 101802 5 A 1111 72 F Mo 1 0 6 0 0 0 5 3 13 101402 8 A 405 51 F Mo 1 1 2 0 0 0 3 3 8 127701 10 A 605 54 F Mo 1 1 1 0 0 0 1 1 2 102301 11 A 605 69 F Mo 1 0 3 0 0 0 4 4 10 100604 12 A 605 27 F Mo 1 0 1 0 0 1 1 2 4 100603 12 A 605 50 F Mo 1 0 1 0 0 0 1 2 4 102502 13 A 605 52 F Mo 0 0 0 2 1 4 7 4 13 102504 13 A 605 44 F Mo 1 0 0 2 1 4 7 4 13 102804 14 A NA 54 F Mo 1 1 2 0 0 0 4 4 10 102706 15 A 605 35 F Mo 0 0 1 1 1 1 4 4 10 102601 16 A 605 59 F Mo 1 1 3 0 0 0 1 2 6 103401 17 A 605 52 F Mo 1 1 1 0 0 0 2 3 5 103404 17 A 2205 32 F Mo 0 0 0 1 1 1 2 3 5 100706 19 A 605 39 F Mo 1 0 2 2 1 1 1 2 4 102002 23 A 805 44 F Mo 1 1 2 0 0 0 1 1 4 101502 24 A 805 60 F Mo 1 0 4 0 0 0 1 2 4

101001 25 A NA 74 F Mo 1 0 0 0 0 0 3 2 5 101003 25 A 805 45 F Mo 1 1 0 0 0 2 3 2 5 101003 25 A NA 45 F Mo 1 1 0 0 0 2 3 2 5 100201 26 A 805 59 F Mo 1 1 5 0 0 0 2 3 10 100201 26 A NA 59 F Mo 1 1 5 0 0 0 2 3 10 100105 28 A NA 28 F Mo 1 1 2 0 0 2 4 2 8 100102 28 A 805 61 F Mo 1 1 2 0 0 0 4 2 8 100903 29 A 805 59 F Mo 1 0 0 0 0 0 2 2 4 101702 30 A NA 26 F Mo 1 1 1 0 0 2 6 2 9 101602 30 A 805 50 F Mo 1 1 1 0 0 0 6 2 9 101602 30 A NA 50 F Mo 1 1 1 0 0 0 6 2 9 N0301 31 A 1205 43 F Mo 1 1 2 0 0 1 2 1 3 100402 33 A NA 43 F Mo 1 1 1 0 0 0 2 1 3 100401 33 A 1205 66 F Mo 1 0 1 0 0 0 2 1 3 100504 34 A 1205 42 F Mo 1 0 2 0 0 4 4 1 5 100802 35 A 1205 49 F Mo 1 1 0 0 0 0 2 1 3 102405 36 A 1205 46 F Mo 1 1 2 0 0 0 1 1 4 103701 Q39 Q 2805 65 F Mo 1 0 1 0 0 0 1 2 3 103702 Q39 Q 2805 42 F Mo 1 0 1 0 0 1 1 2 3 205602 M1 M 406 46 M Mo 1 1 3 0 0 0 1 2 6 205305 M2 M 406 36 M Mo 1 0 0 1 1 0 1 1 3 205303 M2 M 406 41 F Mo 1 0 1 0 0 1 1 1 3 205202 M3 M 406 67 F Mo 1 0 4 0 0 2 3 4 8 204908 M4 M 406 26 M Mo 0 0 0 0 0 3 5 2 7 204904 M4 M 406 54 F Mo 1 0 0 0 1 1 5 2 7

204903 M4 M 406 59 F Mo 1 0 0 0 1 1 5 2 7 205101 M5 M 406 64 M Mo 1 1 6 0 0 1 3 4 11 205103 M5 M 406 57 M Mo 1 0 0 6 0 1 3 4 11 205104 M5 M 406 39 F Mo 1 0 2 4 1 1 3 4 11 205008 M6 M 506 33 F Mo 1 0 2 2 1 1 2 5 11 204802 M7 M 506 59 M Mo 1 1 4 0 0 0 2 3 9 204803 M7 M 506 59 F Mo 1 1 4 0 0 0 2 3 9 204706 M8 M 506 54 F Mo 1 0 1 0 1 1 2 3 6 203606 M9 M 506 28 F Mo 1 0 1 3 1 0 2 3 9 203601 M9 M 506 74 F Mo 1 0 4 0 0 0 2 3 9 203704 M10 M 506 47 F Mo 1 1 2 2 0 0 2 3 7 205502 M11 M 706 64 F Mo 1 1 1 0 0 0 4 2 7 205506 M11 M 706 35 M Mo 1 1 1 0 0 2 4 2 7 205402 M12 M 706 52 M Mo 1 1 3 0 0 2 4 1 6 230001 M13 M 706 49 M Mo 1 1 3 0 0 0 1 1 5 204607 M14 M 706 24 M Mo 0 0 0 0 0 0 2 2 4 204501 M15 M 706 71 M Mo 1 1 4 0 0 0 3 3 8 204002 M16 M 706 57 F Mo 1 1 3 0 0 0 2 2 6 204305 M17 M 706 46 F Mo 1 0 0 4 1 2 2 4 7 204404 M18 M 706 61 M Mo 0 0 0 1 2 0 1 3 4 204201 M19 M 1006 70 F Mo 1 0 1 0 0 0 0 3 4 203402 M20 M 1006 50 M Mo 1 0 0 2 2 1 2 2 6 203002 M21 M 1006 74 F Mo 1 1 1 0 0 0 2 3 5 226304 M22 M 1006 64 F Mo 1 1 3 0 0 0 3 3 9 200902 M23 M 1006 35 F Mo 1 0 1 0 0 0 0 2 3

200802 M24 M 1006 47 M Mo 1 1 2 2 1 2 3 3 7 200701 M26 M 1006 55 M H 1 1 1 0 0 0 3 1 4 109302 Q3 Q 2205 44 F H 1 1 1 0 0 0 1 1 3 108703 Q4 Q 2205 37 M H 1 1 2 0 0 0 1 2 5 108702 Q4 Q 2205 64 F H 1 0 2 0 0 0 1 2 5 108802 Q5 Q 2205 34 F H 1 1 2 0 0 0 2 1 5 108603 Q6 Q 2205 44 F H 1 1 0 0 0 0 1 2 3 108603 Q6 Q 2705 44 F H 1 1 0 0 0 0 1 2 3 108604 Q6 Q 2205 23 F H 0 0 0 0 0 0 1 2 3 108604 Q6 Q 2705 23 F H 0 0 0 0 0 0 1 2 3 109702 Q8 Q 2405 59 F H 1 0 1 0 0 0 1 2 4 108002 Q11 Q 2405 64 F H 1 0 3 0 0 0 1 2 5 107202 Q13 Q 2405 40 F H 1 1 1 0 0 0 2 1 3 127102 Q17 Q 2505 53 F H 1 1 4 0 0 0 3 3 10 127102 Q17 Q 2705 53 F H 1 1 4 0 0 0 3 3 10 104602 Q21 Q 2505 55 F H 1 1 1 0 0 0 2 2 5 107902 Q22 Q 2505 70 F H 1 1 0 0 0 0 2 2 4 N0101 Q23 Q 2705 44 F H 1 1 1 0 0 0 2 2 5 107501 Q24 Q 2705 63 F H 1 0 2 0 0 0 1 2 5 107503 Q24 Q 2705 30 F H 1 1 2 0 0 0 1 2 5 104102 Q26 Q 2705 62 M H 1 1 0 0 0 0 2 2 4 104401 Q27 Q 2705 53 M H 1 1 0 0 0 0 1 1 2 104402 Q27 Q 2705 51 F H 1 1 0 0 0 0 1 1 2 N0202 Q28 Q 2705 36 F H 1 1 2 0 0 0 1 1 4 104702 Q31 Q 2705 43 F H 1 1 0 0 0 0 2 1 3

103802 Q32 Q 2705 67 F H 1 1 2 0 0 0 2 2 6 103804 Q32 Q 2705 31 F H 1 1 2 0 0 0 2 2 6 104802 Q33 Q 2805 33 F H 1 1 2 0 0 0 1 1 4 103901 Q34 Q 2805 67 F H 1 1 0 0 0 0 2 1 3 108303 Q35 Q 2805 47 F H 1 1 2 0 0 0 2 3 6 109102 Q37 Q 2805 71 F H 1 1 0 0 0 0 1 1 2 106503 Q42 Q 3105 36 M H 1 1 2 0 0 0 2 2 6 104202 Q44 Q 3105 55 F H 1 1 2 0 0 0 2 2 6 103301 18 A 605 39 F H 1 1 0 0 0 0 2 2 4 103003 20 A 805 47 F H 1 1 0 0 0 0 3 3 6 103001 20 A 805 76 F H 1 0 0 0 0 0 3 3 6 103101 21 A 805 41 M H 1 1 2 0 0 0 1 1 4

Appendix table 2. Data on worked hours by individual participants. ID = individual ID; house = tot n of hours spent in housework; farm_re = tot n of hours spent farming or in animal rearing; religious = tot n of hours spent in religious activities; empl = tot n of hours spent in business or employment; assist = n of hours spent assisting another adult household member (in work or due to illness); chcare = tot n of hours spent in childcare; leisure = tot n of hours spent in leisure activities; celebrative = n of hours spent at wedding feast; tot_nochc = tot n of hours worked, excluding childcare; totwh = tot n of hours worked, including childcare.

ID house farm_re religious building empl assist chcare leisure celebrative tot_nochc totwh 102202 0,3 1 0,3 0 0 NA 3 0 NA 1,6 4,6 102202 0,09 1 2 0 0 0,5 5 2 NA 3,59 8,59 101102 0 4 0,17 0 0 NA 0 0 NA 4,17 4,17 102103 0 0 0 7 0 NA 0 0 NA 7 7 102103 0 0 0 9 0 NA 6 0 NA 9 15 102103 0 0 0 9 0 NA 0 0 NA 9 9 101301 2,3 5 1 0 0 NA 0 2 NA 8,3 8,3 101301 1,83 5 1 0 0 NA 0 1 NA 7,83 7,83 101405 0 0 0 9 0 NA 0 0 NA 9 9 101403 0 0,7 0 9 0 NA 0 0 NA 9,7 9,7 102101 0,5 1 1,25 0 0 NA 1 2 NA 2,75 3,75 102803 0,3 2 1 0 0 NA 0 0 NA 3,3 3,3 102602 0,5 0,5 0,5 0 0 NA 11 0 NA 1,5 12,5 102907 0 0 0 9 0 NA 0 0 NA 9 9 102902 0 0 0 10 0 NA 0 0 NA 10 10 102902 0 9 1,7 0 0 NA 0 0 NA 10,7 10,7 102001 0 2 0,5 6 0 NA 0 1 NA 8,5 8,5 101504 0 0 0 0 0 11 0 0 NA 11 11 101005 0 0 0 0 11 NA 0 0 NA 11 11 101002 0 0 0 0 0 NA 0 2 NA 0 0

100101 0 0 1,5 0 0 NA 11 0 NA 1,5 12,5 101605 0 0 0,17 0 0 NA 0 2 NA 0,17 0,17 100403 0 0,5 0,7 0 0 1 3 0 NA 2,2 5,2 109405 0 0 0,25 7 0 NA 2 0 NA 7,25 9,25 109301 0 0 0,17 0 0 NA 4 1 NA 0,17 4,17 108801 0 0 0,17 9 0 NA 0 0 NA 9,17 9,17 108805 0 0 0 0 0 NA 2 0 NA 0 2 108501 1,3 0 0 0 0 NA 6 1 2 1,3 7,3 107302 1,17 5 0 0 0 4 0 0 6 10,17 10,17 107101 1,7 0 0 8 0 NA 3,5 0 NA 9,7 13,2 107901 1 0 0 0 0 NA 0 0 4 1 1 107602 0,7 0 0 0 1,5 NA 4 2 NA 2,2 6,2 N0201 2 0 0 0 9 NA 0 0 NA 11 11 104501 0 7 0 0 4 NA 0 0 NA 11 11 106701 3,7 7,3 0 0 0 NA 0 0 NA 11 11 104701 0 0 0 10 0 NA 0 0,5 NA 10 10 103901 0 14,5 0 0 1 NA 0 0,5 NA 15,5 15,5 105002 0 0,5 0 0 0,17 NA 0,5 3 NA 0,67 1,17 109101 9 0 0 0 0 NA 0 0 NA 9 9 105101 3,5 3 0 0 0 NA 8 1 NA 6,5 14,5 106301 2 8,5 0 0 0 NA 0 0 NA 10,5 10,5 106501 0 11 0 0 0 NA 0 2 NA 11 11 104204 0 0 0 0 13 NA 0 0 NA 13 13 104201 0 0 0 8 0 NA 5 0 NA 8 13 101103 1,5 5,5 0,17 0 0 NA 0 0 NA 7,17 7,17

101201 3,17 4 0,3 0 0 NA 1 0 NA 7,47 8,47 101204 0,5 8,5 0 0 0 NA 0 0 NA 9 9 101902 7,47 1,7 0,17 0 0 NA 0 0 NA 9,34 9,34 101802 9,5 1,83 1,5 0 0 NA 0 0 NA 12,83 12,83 101802 10 0 3,17 0 0 NA 11 0 NA 13,17 24,17 101402 0 0 0,75 11 0 NA 11 0 NA 11,75 22,75 127701 3,17 4,5 2,7 0 0 NA 0 0 NA 10,37 10,37 102301 4 2 2,5 0 0 NA 11 0 NA 8,5 19,5 100604 4,5 2,5 0 0 0 NA 5 0 NA 7 12 100603 4,17 5 0,5 0 0 NA 8 2 NA 9,67 17,67 102502 3,5 8 1 0 0 NA 0 0 NA 12,5 12,5 102504 2 9,5 0 0 0 NA 0 0 NA 11,5 11,5 102804 1,75 3 0,5 0 0 NA 9 0 NA 5,25 14,25 102706 4,5 10 1,5 0 0 8,5 0 0 NA 24,5 24,5 102601 3 4 0,5 0 0 NA 9 0 NA 7,5 16,5 103401 6 5 2,5 0 0 NA 0 0 NA 13,5 13,5 103404 4 8 0 0 0 NA 0 0 NA 12 12 100706 1,5 2,3 0,3 0 8 NA 0 0 NA 12,1 12,1 102002 7,5 6,5 1 0 0 NA 0 0 NA 15 15 101502 8 1 1 0 0 NA 0 0 NA 10 10 101001 2 4 1,3 0 0 NA 0 0 NA 7,3 7,3 101003 3 6 0,3 0 0 NA 0 0 NA 9,3 9,3 101003 3,3 9,5 0,17 0 0 NA 0 0 NA 12,97 12,97 100201 3,7 1,7 1 0 0 NA 0 0 NA 6,4 6,4 100201 2,17 8 2,83 0 0 NA 11 0 NA 13 24

100105 7,5 2 0,3 0 0 NA 0,7 0 NA 9,8 10,5 100102 0 4 0 0 0 NA 6 0 NA 4 10 100903 6,75 8 0,5 0 0 NA 0 0 NA 15,25 15,25 101702 4,5 2,5 0 0 0 NA 11 0 NA 7 18 101602 5 3 0,5 0 0 NA 6 0 NA 8,5 14,5 101602 3,5 7 2 0 0 NA 3 0 NA 12,5 15,5 N0301 4 4,7 0,83 0 0 NA 0 0 NA 9,53 9,53 100402 0 0 0 0 10 NA 0 0 NA 10 10 100401 3 4,5 1 0 0 NA 0,5 0 NA 8,5 9 100504 2,5 1,3 0,5 0 0 NA 0 0 NA 4,3 4,3 100802 5 3,5 3 0 0 NA 0 0 NA 11,5 11,5 102405 3,7 4 1,5 0 0 NA 0 0 NA 9,2 9,2 103701 0 5,5 0,83 0 0 NA 0 0 NA 6,33 6,33 103702 2,3 0 0,5 0 9 NA 0 0 NA 11,8 11,8 205602 0 2,83 0 0 0 NA 0 0 NA 2,83 2,83 205305 0 0 0 9 0 NA 0 0 NA 9 9 205303 3,3 11,5 1,3 0 0 NA 0 0 NA 16,1 16,1 205202 1 0,7 1,75 0 0 NA 0 2 NA 3,45 3,45 204908 0 0 0 0 3 NA 0 2 NA 3 3 204904 3 8,17 0 0 0 NA 0 2 NA 11,17 11,17 204903 0,17 7,5 0,6 0 0 NA 0 2 NA 8,27 8,27 205101 0 0 0 0 9 NA 1 0 NA 9 10 205103 0 1 0 0 0 NA 1 5 NA 1 2 205104 0,17 11,5 0 0 0 NA 0 0 NA 11,67 11,67 205008 0 6 0 0 4 NA 0 0 NA 10 10

100

204802 0 0 0 0 12 NA 0 0 NA 12 12 204803 3 5 0,83 0 0 11 0 0 NA 19,83 19,83 204706 2,83 3 1,3 0 0 NA 0 1,5 NA 7,13 7,13 203606 0 3 0 0 3 NA 7 1 NA 6 13 203601 1 1 0,83 0 0 NA 11 0 NA 2,83 13,83 203704 2 8 0,83 0 0 NA 0 1,5 NA 10,83 10,83 205502 0,17 0 1,83 0 6 NA 0 0,75 NA 8 8 205506 1 0 0,5 0 0 NA 1 5 NA 1,5 2,5 205402 2 1 0,5 11 0 NA 5,5 0 NA 14,5 20 230001 0,3 0 0 0 15 NA 0 0 NA 15,3 15,3 204607 4 0,7 0 0 0 NA 0 3 NA 4,7 4,7 204501 0 5 0,83 0 6 NA 0 0 NA 11,83 11,83 204002 2,7 1 0 0 0 NA 0 5 NA 3,7 3,7 204305 2,75 7,5 1,3 0 0 NA 0 0 NA 11,55 11,55 204404 0 2 0,08 0 0 NA 0 4 NA 2,08 2,08 204201 0 3 0 0 0 NA 11 0 NA 3 14 203402 0 0 0 0 11 NA 3 0 NA 11 14 203002 1 4 0 0 0 NA 11 0 NA 5 16 226304 4 9 1,83 0 0 NA 0 0 NA 14,83 14,83 200902 3 4 0 0 0 NA 11 0 NA 7 18 200802 3,3 4 0 0 0 NA 0 0 NA 7,3 7,3 200701 0 5 0 0 0 NA 0 2 NA 5 5 109302 5,7 0,5 0 0 0 NA 0 0 NA 6,2 6,2 108703 0 0,5 0 0 0 NA 11 1 NA 0,5 11,5 108702 4,3 4,5 0 0 0 NA 3 4 NA 8,8 11,8

101

108802 1,5 0,7 0 9 0 NA 0 0 NA 11,2 11,2 108603 0,5 9,5 0 0 0 NA 11 0 NA 10 21 108603 0,5 10,3 0 0 0 NA 0 0 NA 10,8 10,8 108604 3,17 3 0 0 0 NA 5 0 NA 6,17 11,17 108604 6,3 2 0 0 0 NA 5 0 NA 8,3 13,3 109702 2,5 2 0 0 0 NA 0 0 NA 4,5 4,5 108002 1,5 0,5 0 0 6 NA 0 0 NA 10 8 107202 6,3 4 0 0 0 NA 0 0 NA 10,3 10,3 127102 0,83 3,7 0 0 0 NA 11 3,5 NA 4,53 15,53 127102 0,83 1,17 0 0 0 NA 11 0 NA 2 13 104602 3 10,5 0 0 0 NA 0 0 NA 13,5 13,5 107902 2,17 1 0 0 0 NA 0 0 NA 3,17 3,17 N0101 0,75 0 0 0 0 NA 8 1,5 NA 0,75 8,75 107501 0 0,3 0 0 8 NA 4 0 NA 8,3 12,3 107503 1,17 0,25 0 0 7 NA 2 0 NA 8,42 10,42 104102 0 0 0 7 4 NA 0 0 NA 11 11 104401 0 0 0 0 5 NA 0 0 NA 5 5 104402 2,83 7 0 0 2 NA 0 0 NA 11,83 11,83 N0202 1,17 9,3 0 0 0 NA 0 0 NA 10,47 10,47 104702 1,75 2,5 0 0 0 NA 0 0 NA 4,25 4,25 103802 0 5 0 0 0 NA 0 0 NA 5 5 103804 3 3 0 0 7 NA 0 0 NA 13 13 104802 2,17 8 0 0 0 NA 0 0,5 NA 10,17 10,17 103901 7 11 0 0 0 NA 0 4 NA 18 18 108303 2,7 10,5 0 0 0 NA 0 0,17 NA 13,2 13,2

102

109102 4,08 4,3 0 0 0 NA 0 0 NA 8,38 8,38 106503 2,5 5 0 0 0 NA 3 3 NA 7,5 10,5 104202 3 4 0 6 0 NA 0 2 NA 13 13 103301 4,5 2,5 0 0 0 11 0 0 NA 18 18 103003 11 8 0,3 0 0 NA 0 0 NA 19,3 19,3 103001 2,3 4 0 0 0 NA 0 0 NA 6,3 6,3 103101 1 0 0,17 0 0 NA 0 2 NA 1,17 1,17

103