extfieldsofstudy: Business Economics

s2fieldsofstudy:
Economics
Political Science

text:
The Commission’s internal conditions for social re-regulation: Market efficiency and wider social goals in setting the rules for financial services in Europe

The European Union is often considered as a prime example of a liberal regulatory state. We argue, however, that being limited to the regulatory policy does not prevent the European Commission from pursuing political aims going beyond market efficiency. We draw up two ideal-type perspectives of market regulation – being either efficiency or equality enhancing – that differ systematically in terms of rationale, degree of intervention, patterns of stakeholder access and conflict within the regulator. We trace these aspects in three financial services initiatives on the registration and supervision of reinsurers, equal treatment in financial services and the regulation of consumer credit. Our analyses suggest that there is scope for equality-enhancing re-regulation when proactive agents proceed decidedly on the basis of social-treaty concerns and frame regulatory beneficiaries as market participants as well as when they seek the redistribution of rights instead of resources.

Introduction
Limited possibilities for taxation and meagre budgets severely restrict the EU regarding the pursuit of wider social goals through governmental redistribution of existing resources. Rather than as a positive state, the EU has been characterized as the leading example of a liberal regulatory state that focuses mainly on the correction of various market failures (Majone, 1993(Majone, , 1997. The fact that EU regulation emerged increasingly as a response to the privatization of utilities (Grande, 2011) has added to the view that the Union's first and foremost regulatory aim is the efficiency of the Single European Market.
However, the traditional welfare state at the national level has also partly relied on regulatory components (Mabbett, 2009;Leisering, 2011a) while the definition of the regulatory does not necessarily imply particular political goals (Levi-Faur, 2012: esp. 14-16). Influential authors have identified a European social policy, although being of the regulatory type (Leibfried, 2010;Falkner et al., 2005). Clearly, EU regulation covers increasingly broad social themes, including, for example, consumers, the environment and energy (Knill et al., 2003;Vogel, 2003;Coen et al., 2008). Moreover, the demand for European regulation with a more social face has increasingly been on the agenda since the failed referendums in 2005 and since the financial crisis that put the spotlight on the downsides of unfettered market forces. So while EU decision-making has a strong regulatory thrust, regulation is not automatically tied to the (political) ambition for liberalization, but may also be a vehicle for attaining distributive goals. Consequently, it is relevant to ask for the conditions under which EU market regulation pursues social goals that go beyond mere market efficiency.
We tackle this question with regard to the European Commission. The long-term perspective of its regulatory staff, its unique access to broad policy expertise and, especially, its formal monopoly of legislative initiative all provide the Commission with substantial influence over the contents of European regulation (Princen, 2009;Tsebelis et al., 2000). However, against the background of external constraintsmost notably the provisions of EU treaties, as well as political preferences in the Council of Ministers and the European Parliament -the Commission must keep up the appearance of impartiality and thus has reason to mask the outright pursuit of social goals. Following an ideal-type distinction between efficiency-enhancing and equality-enhancing regulation, we argue in Section 2 that the Commission's ambitions can nevertheless be identified if we take the systematic differences between the regulatory processes into account. This claim is supported by comparing three short histories of drafting processes on financial services regulations presented in Section 3. Initially, the European financial services market could have been realized through liberalization and the simple application of the freedoms of movement enshrined in the EU treaties. However, especially since the publication of the EU Green Book on the financial services market in the late 1990s, European regulation of the sector abounds (Posner et al., 2010: 400). In shifting from the coverage of purely commercial activities to retail financial services for private consumers, 'the FSAP [Financial Service Action Plan] represented a change in EU strategy away from market opening measures towards common regulatory measures' (Quaglia, 2010(Quaglia, : 1007. These efforts may only be a consequence of further market integration, but they still make financial services a promising policy area for studying regulatory capacities that go beyond market efficiency and the regulatory processes that push the Commission towards the pursuit of wider social concerns. We trace the theoretically developed characteristics with respect to three regulatory processes on financial intermediaries, equal treatment in service markets and consumer credit. The article generates two expectations regarding the conditions under which the European Commission places a more social European market on the legislative agenda. While its capacity remains limited to the regulatory section of the overall social policy spectrum, the Commission can and does proactively redistribute rights 1) where this is pushed by specific internal actors who are in turn able to draw on a decidedly social-treaty base and frame the societal beneficiaries as market participants, and 2) where rights rather than resources are at stake, typically to the benefit of diffuse stakeholder interests. Under these conditions, it becomes possible to propose equality-enhancing regulation that goes beyond the aim of more efficient competition.

Conceptualizing regulation: Market efficiency and wider social equality
This article seeks to contribute to the debate on whether and under which conditions Europe's markets can be embedded 'within a broader set of social and political rules and cultural understandings that make them work not only more efficiently but also more equitably, with greater security for market and nonmarket participants and in tune with a variety of other social purposes' (Caporaso et al., 2009: 579). Existing research on this question differs along two perspectives. Firstly, it focuses on the implications of EU decisions for national welfare-state models. EU integration's lack of social embeddedness is ascribed to the systemic features of supranational decision-making, which prevent the reproduction of welfare states at the supranational level (Scharpf, 1999(Scharpf, , 2010. Our interest here is more circumscribed, focusing on the re-regulatory capacity of day-to-day policymaking in Brussels. In other words, we accept the premise that the EU is a regulatory state with very limited capabilities for redistributing resources directly and that its regulatory instruments are constrained by the nature and interests of the inter-institutional process. However, any European market regulation must start with a policy proposal by the Commission, which in turn defines the issues as well as the strategic preferences that govern inter-institutional negotiations (Princen, 2009;Tsebelis et al., 2000). So if Commission proposals are not a priori equality enhancing when adopted by the Commission, the chances are low that such a policy orientation will be produced later in the policy cycle. Focusing on Europe's central agenda-setter is thus crucial for understanding the scope for and the degree to which EU regulation can be embedded in a social dimension.
A second dividing line in the literature runs between arguments about the natural or spontaneous emergence of market embeddedness and contributions highlighting the necessity for political construction in the pursuit of distributive goals. The former perspective is often evoked in relation to ECJ rulings in the area of antidiscrimination policies or cross-border health care (Hervey et al., 2007;more critical Höpner et al., 2012), but here we are concerned with intentional political construction. We start from the premise that market embedding is not an autonomous process, rather depends on specific conditions linked to decisive regulatory actors such as the European Commission.
These decisions already highlight the fact that the Commission only occupies the middle section of a larger social policy continuum that spans from completely unfettered markets, on the one side, to an autocratic distribution of resources along social justice criteria, on the other. Strictly speaking, only a section somewhere in between these extremes is filled with regulatory policy where authoritarian intervention is based on rules stated in general terms. But also within this section, the regulator will explicitly or implicitly decide on 'who will be indulged and who deprived' (Lowi, 1964: 690-1). In this view, regulation makes a socially relevant difference (Braithwaite et al., 2007) and the narrow section should already provide crucial insights concerning the conditions under which a policy outcome will be situated more towards one or the other end of the entire social policy continuum. Even when we only look at regulatory means, the outcome can be markets of very different social quality. We start from a rough distinction between two ideal-type perspectives on the organization and purpose of markets as a mode of social interaction: they may be either seen to produce the optimal allocation of resources in the equilibrium of economic supply and demand or as a socially constructed mechanism that balances competing values and demands.
The classical perspective of welfare economics starts from the utility of individual actors in the marketplace. It assumes that the socially optimal allocation of resources is attained when no individual can be made better off without disadvantaging others. Such a Pareto-efficient outcome is reached through the equilibrium of supply and demand, which in turn can only be achieved in a perfectly competitive market where the individual market participants try to maximize their individual welfare (Stiglitz, 1986). But even from this perspective, some regulation is required to preserve the market (Weingast, 1995), for instance with respect to property rights or contract certainty. In practice, markets are hardly commensurate with a model of perfect competition, for example where information is asymmetrically distributed among the market participants (Akerlof, 1970). Thus, classical welfare economics considers market regulation to be warranted where markets fail, externalities accrue or other imperfections prevent optimal decisions on the part of individual market participants (see, e.g., Majone, 1996: esp. 28-30). In this perspective, regulation is seen only as a second-best solution to the outcomes a fully competitive and undistorted market would produce.
A second perspective on market regulation criticizes this purely economic view from normative points of departure (see Prosser, 2006 or;McVea, 2005). Approaches in this vein argue that market efficiency is qualitatively different from other, equally legitimate purposes of social interaction, such as social justice. Here, regulation does not target perfect competition in markets, but rather aims to alter their very quality by changing the rules under which individual transactions will be carried out. Instead of guaranteeing the mere pursuit of individual welfare, regulation addresses misallocation from a moral point of view, and is thus seen as a 'balancing of competing values setting out the sort of society we wish to live in' (Prosser, 2006: 375). This is consistent with the distinction between economic and social regulation that has figured prominently in the US debate on regulatory market interventions (cf. Reagan, 1987: esp. ch. 2). In the context of the EU, this approach has also been described as 'market cushioning policies' (Sbragia et al., 2008: 133;Sbragia, 2000; similarly, 'market shaping ' Quaglia, 2010; 'extended regulation ' Leisering, 2011b: 6). While economic regulation aims at freeing competitive forces, social regulation is primarily motivated by problems that often cannot be solved by an increase in economic competition alone. Rather than targeting the individual pursuit of welfare, social regulation aims at protecting broader societal groups from systematic disadvantages they may encounter in competitive markets (Ramsay, 1995).
We argue that the two perspectives should result in different models of regulation. Of course, in practice regulators often simultaneously face demand to create efficient markets that minimise burdens on industry and to create societies that are more just, equal and socially cohesive -and thus differ from the outcomes produced by market forces alone. In practice, thus we expect to see various mixtures "of interconnected and interdependent social and economic values, and that the distinction […] lies in the primacy of the values it is designed to advance and the purpose it is designed to achieve" (Windholz et al., 2013: 25), Yet, while analytical boundaries might be very fuzzy at times deriving ideal-types allows us to point out more specific empirical implications which ultimately lead us to a finergrained perspective on the European Commission's choices and options in embedding European markets in wider social concerns. The discussion so far yields the following two ideal types of regulation: In the first perspective -termed 'efficiency enhancing' -regulation is perceived as necessary to increase the immediate functioning of markets by countering market failures or imperfections understood as non-optimal transactions. Regulation should be expected where markets face a lack of competition due to monopolies, negative externalities or informational asymmetries. The beneficiaries of regulation are individual market participants.
In the second perspective -termed 'equality enhancing' -market regulation is perceived as necessary to deliver broader societal goals and operates with a more long-term perspective on market gains. Such regulation is motivated by other goals than purely increasing competition. Structurally weaker parties in market transactions are the beneficiaries of regulation.
Most importantly, distinguishing these two ideal-types is at odds with the seminal perspective on the regulatory state, which is most often portrayed as the polar opposite of the positive (welfare) state (Majone, 1997: 149;Levi-Faur, 2012: 12-15). In this account, the main function of the positive state is Keynesian macroeconomic stabilization and redistribution. Majone's regulatory state by contrast puts administrative and economic performance first -it is 'efficiency enhancing'. In between these extremes, the 'equality-enhancing' perspective we develop here implies that there is a socially and analytically relevant middle ground. And this middle ground should differ from both the liberal regulatory state and the positive state not only with respect to long-term outcomes 'but with regard to access, procedures and organization' (Leisering, 2011b: 258, original emphasis). However, what follows from Majone's original distinction for a 'state' model that sits in between, and that starts with a clear (re)distributive function operates through regulation only? We argue that if the goals of regulation differ, as the original distinction of the ideal types suggests, then also the process of rulemaking at the EU level should deviate from the typical regulatory state model in at least four respects.
First, in his seminal conceptualization of the regulatory state, Majone argues that redistributive consequences result from 'potential policy constraints rather than policy objectives' for regulators (Majone, 1997: 162). In this efficiency-enhancing world, policy-makers try to avoid costs for any market participant by striving for a solution that is beneficial to all. In contrast, the equality-enhancing perspective implies that actors will focus on the creation of benefits for particular market participants that may or may not imply costs for others -this is in line with taxing and redistribution under the positive state. Rather than a constraint, (re)distributive consequences should become the rationale of regulation in an equality-enhancing model.
Secondly, the envisaged structure of the regulatory interventions should differ. In an efficiency-enhancing world, and in line with the goal of individual choice, as many decisions as possible are delegated to the market participants themselves, so that 'indirect government' best describes the mode of regulatory production as well as the regulatory contents (Majone, 1997: 146-8). By contrast, the equalityenhancing perspective implies that markets produce skewed outcomes so that the regulators will take as many decisions as possible themselves and try to set conclusive rules for the issue in question.
Thirdly, the two ideal types should differ with respect to the underlying conflict dimension and the related integration of external stakeholders into the regulatory process. Especially in the EU multilevel system, stakeholder integration is best understood as support from allies. The positive state is typically associated with corporatist patterns structured around class conflict. Capital and labour are granted access at the level of organized interests (Cioffi, 2009: 244). Under such representational monopolies, access to the regulator is biased (Schmitter et al., 1979). Majone's regulatory state, by contrast, moves away from this central political dynamic and implies competition among single-issue interest groups. Following pluralist theory (Truman, 1951), regulators grant equal access to the different sides of the debate and act as neutral arbiters searching for the equilibrium solution. The influence of external stakeholders might be biased, but the bias should not result from prefixed access patterns, rather from competition between the interests. Under the equality-enhancing model, stakeholder access should cease to be neutral without, however, focusing only on the conflicts underlying the capitalistlabour divide. Where normative imbalances among pluralist contending interests are the starting point of regulation, regulators should grant privileged access for those groups whose interest they see specifically at stake. The equality-enhancing model is thus more consistent with the neo-pluralist view on interest-group integration (cf. Dunleavy et al., 1987).
Lastly, the two possible worlds of regulation should differ concerning the way actors within the Commission interact. As the EU's agenda-setter, the Commission is not a unitary actor when proposing regulation, rather different interests prevail. The position finally proposed is the outcome of a long internal process characterized by multiple and often conflictual interactions (Hartlapp et al., 2013). In the seminal conception of the regulatory state, governmental actors 'usually cast their arguments in the language of 'regulatory science' rather than in the more traditional language of interest or class policies' (Majone, 1997: 157). Arguments should focus on technically 'best' solutions, and actors should adhere to 'a problem-solving rather than a bargaining style of decision-making' (ibid: 162). By contrast, the equality-enhancing perspective emphasizes normative differences in the quality of markets and should thus be much more prone to political conflict and more fundamental political exchange among actors. Summarized in Table 1, we can now identify equality-enhancing regulation as a distinct option for rule-makers. It is located in between unfettered markets and the idealized positive welfare state and, more importantly, it distinguishes itself from the efficiency-enhancing regulation usually associated with the regulatory state not only in terms of policies proposed, but also with regard to the regulatory processes at work. On the basis of these procedural differences, we can analyse whether the European Commission actually pursues equality-enhancing regulation and what scope there is for the social embeddedness of Europe's internal market.

Tracing equality-enhancing regulation in the European Commission
The outcome we are interested in is the extent to which a policy is equality enhancing, whereas the market-failure perspective in the classical regulatory state model of the Commission serves as our baseline expectation. In other words, our empirical starting point is the Commission's regulatory output and, more specifically, the degree to which a Commission proposal deviates from the regulatory status quo in the name of efficiency or social equality.
In order to highlight how the regulatory processes leading to these outcomes differ systematically we selected three cases from a broader project that studies decision-making within the Commission on the basis of semi-structured interviews conducted in Brussels between 2006 and 2009 with involved Commission officials from different Directorates-General and hierarchical levels. 1 Firstly, the proposed directive on financial intermediaries, by regulating registration and consumer rights, seeks to establish an efficient single market for insurers distributing financial products on their own account. Secondly, the proposed directive on equal treatment in the access to and supply of goods and services for men and women and for all persons irrespective of religion or belief, disability, age or sexual orientation addresses the disadvantages facing specific groups in financial markets, for example with respect to insurance contributions and benefits or the conditions for loans. And, thirdly, the final act seeks to protect vulnerable consumers by regulating creditor obligations concerning the advertisement, conclusion and termination of consumer credit agreements. Whereas the first proposal follows an efficiency-enhancing logic, the second two cases lean towards the equality-enhancing side. Tracing the three regulatory processes, we work our way through the four dimensions of the two ideal types derived above. While the very notion of ideal-types excludes the possibility of finding them empirically in pure form, we should find evidence that decision-making in the Commission deviates from the classical regulatory-state model by following the logic of distributing political benefits, neo-pluralist and intentionally skewed interest group access, as well as substantial internal conflict instead of efficiency-enhancing problem-solving.

The models in practice: Drafting European regulations on financial services
A first case that provides insights on the Commission's internal conditions for reregulation is the regulation of reinsurers and financial intermediaries as institutions that redistribute financial products on their own account -such as travel agencies or car dealers. 2 Although a forerunner directive with a narrower scope as well as a non-binding recommendation already existed, the regulatory logic was one of interface management rather than common-market regulation. The size of national markets differed, with in some cases more than 50 percent of insurance being distributed via intermediaries (the figures are especially high in Belgium, the Netherlands, the UK and Ireland, but are low in Denmark). National rules varied 'with regard to the access and purchase of the activity, in particular with regard to financial capacities [and] professional relations' (COM109), de facto confining intermediaries to their national markets. Especially with respect to consumer interests, member states typically had national rules in place. Accordingly, commercial actors faced substantial uncertainty regarding which rules to apply in cross-border markets (Dassesse, 1997:108).
Commissioner Monti first announced the new regulation in February 1997 (AE, 1997a) and the proposal was adopted in 2000. For DG MARKT, the rationale was to boost the retail market in insurance for private individuals through further liberalization, much along the lines this had been achieved for the wholesale market of large industrial and commercial risks. To this end, one part of the proposed directive established a single registration system in the member state of origin which harmonized the recognition of insurance agents and brokers throughout Europe (a regulatory logic also applied to other directives, Quaglia, 2010Quaglia, : 1030 and went hand in hand with professional requirements and supervision through the country of origin. A drafting official summarized this rationale as follows: 'we did it, because it was worth for the industry and also to enforce the internal market. It was a very heavy case, where not a lot of work was necessary from the internal market viewpoint' (COM109). The other group of provisions considered a necessary ingredient for making retail markets for intermediaries function efficiently was to strengthen the position of consumers by introducing information and disclosure rights as well as facilities for complaints: '[I]t will increase the choice of insurance products available to customers and help ensure they can trust the advice they are getting from intermediaries' (Commissioner Bolkestein, cited in: AE, 2002, likewise COM109). Clearly, in this case, consumer protection was not sought as a right per se, but to prevent the failure of a common market for financial mediation and thus qualifies as efficiency enhancing.
Different sources provide evidence that drafting did not take place behind closed doors and that external stakeholders were relevant in explaining both the choice of a binding instrument with a broad scope that included small intermediaries, as well as the other substantial provisions of the proposal (AE, 1997a, COM109). Following the efficiency-enhancing model, DG MARKT consulted broadly not only across several member states and the EP, but also across stakeholders from the financial services industry, notably the European Insurance Committee (EIC), the Association of European Cooperative Insurers (AECI), intermediaries such as the International Association of Insurance and Reinsurance Intermediaries (BIPAR) and customers represented by the European Bureau of Consumers' Unions (BEUC; COM109). These single-issue interest groups finally all found their positions represented in the proposal. The binding provisions clearly open the market for cross-country provision of services and thereby foster competition between professional insurance intermediaries more strongly than earlier, nonbinding recommendations. And they do so 'within a clear framework of common rule to protect the consumer' (BEUC Director Jim Murray, cited in AE, 1997a; similarly BIPAR in AE, 1997b).
Drafting inside the Commission stretched over a period of five to six years and different portfolios were involved (COM109). Although DG SANCO, especially, was reported to have argued for consumer interests to be reflected, the actors involved did not perceive the interaction as being conflictual and reported that all internal comment was taken on board (COM109). In combination with the obviously negligible interest at the political level for this rather technical directive, the process followed a problem-solving logic.
The second case is the promotion of equality of opportunity for disadvantaged groups in services, especially financial services. Traditionally, at the EU level antidiscrimination regulation had been developed as part of labour law. Legislation on actuarial factors differed starkly across member states, with the unisex principle mainly in place in the Scandinavian countries, France and Slovenia. The UK also had unisex tariffs for private health insurance, whereas car coverage showed sharp differences (AE, 2004). After the Amsterdam Treaty had introduced a new basis for legal action with article 13, the Commission proactively sought to extend equivalent principles to other areas, including the financial services market.
The Commission proposal 3 is equality enhancing in that it does away with practices of unequal treatment in terms of access to the insurance cover provided or in the level of benefits paid out. Such practices -most pronounced in motor, health and life insurance -are efficient from a purely market point of view, but they disadvantage societal groups on the basis of factors that lie beyond personal control. Against this background, in 2003 the Commission had proposed a directive implementing the principle of equal treatment between women and men regarding access to and supply of goods and services that was adopted as directive 2004/113/EC. In 2008, a more far-reaching proposal was drafted by DG EMPL. If it becomes law, this proposal would extend the quest for equal treatment between the sexes in services to all persons, irrespective of religion or belief, disability, age or sexual orientation. 4 Taking the effect of the directive already adopted and the pending proposal together would mean that, in principle, insurers and banks will no longer be able to use age, sex or disability in a way that discriminates against groups with these particular characteristics. This will increase costs both for insurers and for those consumers who have hitherto benefitted from the unequal market relationship. Moreover, the very fact that the Commission has intervened to explicitly extend the market to those who were previously excluded by law or in practice underlines the goal of creating more equal markets above and beyond what could be considered economically efficient.
With respect to the rationale characterizing the regulatory process in this second case, we find that while market inefficiencies were invoked in the debate, the core ambition of the drafting DG was clearly to alter the quality of this market in accordance with a political reasoning targeting greater equality. Enhancing the efficiency of markets by responding to changing environments and countering unfair practices was certainly on the agenda of the Commission, especially in view of persisting implementation difficulties (COM125, AE, 2007). However, we also note clearly voiced criticism that under the envisaged regulations the opposite outcome would be the case, because premiums would ultimately be forced upward (Minder, 2003b;Parker, 2003). Besides, the goal of increasing equality in markets was also explicitly voiced by the Commission. The proposal is described in official documents as a necessary 'signal of solidarity between women and men in European society' (Secrétariat Général, 2003:21) and as the 'result of a real moral and legal obligation' (Proposal, p.4). Commissioner Diamantopoulou stressed that there were 'political reasons for not accepting gender as an element justifying a differentiation of the treatment of individuals in the access to the supply of goods and services', as well as the signalling function of the act for European society (Secrétariat Général, 2003:21).
As suggested by our ideal-type model, this case was characterized by biased stakeholder access to favour presumably weaker parts in the regulated market relationships. In the run-up to the negotiation process in 2003, DG EMPL had very actively used and encouraged changes in interest-group organizations to create a broad front against opposition from those who wanted to keep more expensive discrimination grounds out of the discussed acts. Already prior to concrete drafting, the Commission had incentivized existing and scattered NGOs representing minorities to join forces in an umbrella organization which was not only consulted but also substantially influenced the legislative proposal. 'It was certainly strategic to involve civil society groups more than social partners […] We saw the potential victims of discrimination as being people who were at risk of some sort of social marginalization as a result of discrimination, and they are the ones that tended not to have a voice in social partnership' (COM151). Industry, by contrast, was consulted only to provide information that would allow the Commission to follow its goal of greater market equality. To gain a better understanding of where the technical requirements of risk-based assessments for capital adequacy end and individual business interests begin, the drafting DG EMPL consulted industry early on in the drafting process for the 2008 proposal (COM42 and COM19). On the basis of the acquired knowledge, the Commission was able to argue that the question as to whether existing rules and practices had to be changed depended on the product and on whether justification could be provided 'on the basis of accurate and up-to-date data' (Commission of the European Communities, 2008: 42). The proactive and selective distribution of access for interests from different societal segments by DG EMPL clearly contradicts the presumed neutrality of the regulator that a purely efficiency-enhancing model would imply.
Finally, with respect to the conflict structure inside the Commission, both the decision to have equal treatment legislation on financial services and the more concrete implications this would have for insurers in their actuarial practices were debated among central actors inside and outside the Commission, reflecting substantial differences in the underlying understandings of markets. Inside the Commission, DG MARKT, especially, defended the positions of industry -'various financial sector federations' (COM42) and those member states where 'the practice of differentiating insurance costs [...] were very, very strong. UK, Ireland, but also Germany' (COM21) -by trying to carry out 'a damage limitation exercise' (COM19). DG ENTR was fundamentally opposed to having a binding act at all, and especially the broad 2008 directive, because they did not consider this a regulatory issue to be tackled at community level and feared costs for enterprises through the intervention (COM81). For these actors inside the Commission, the ideal regulatory position was for 'insurance to be [kept] completely out' (COM21). In contrast, DG EMPL, holding the pen and largely acting on normative grounds, was strongly in favour of the acts (COM21, Commissioner Diamantopoulou in Minder, 2003a), but had to overcome opposition from the critical portfolios inside the Commission as well as from opposing member states and organized interests.
The third case is the initiation of consumer credit regulation proposed by the Commission in 2002. 5 Supranational regulation had existed beforehand, but it had left member states with the possibility of protecting their consumers unilaterally, so that myriad different regulations on consumer credit persisted in Europe. These barriers became increasingly relevant as consumer credit became the 'lubricant of economic life' (COM89), amounting to 7 percent of the EU-15 GDP and exhibiting an upward trend (COM119, Commission of the European Communities, 2002: 3). For the Commission's internal market Directorate-General (DG MARKT), differing national regimes hampered growth and thus needed to be removed (COM119, COM89). However, unlike most other regulations on financial services, it was not DG MARKT but the newly established consumer-policy DG SANCO, that was assigned responsibility for the regulation, and the latter DG pursued a rationale that went way beyond the facilitation of cross-border trade.
In fact, DG SANCO officials aimed at protecting the 'weak consumer' (COM89). Drafting was based on the assumption that the consumer was not able to fully grasp the implications of credit agreements and thus needed to be protected against the exploitation of that very fact by the banking or retail industry. This position was informed by observing individual cases of immensely over-indebted consumers, but also by the hype surrounding 'behavioural economics' at the time (COM89). This resulted in the redistributive 'ambition […] to create a very comprehensive, very exhaustive consumer credit regulation which would be burdensome for industry', and DG SANCO officials transferred the most stringent national regulations they could find to the proposal (COM89), thereby increasing consumer protection rather than harmonizing at the level of the lowest common denominator.
DG SANCO's equality-enhancing approach can be highlighted by means of some of the key regulatory provisions, in which especially the principle of responsible lending was contested. The proposal fully obliged creditors to assess before the agreement whether the consumer was actually able to honour the credit obligations (Commission of the European Communities, 2002: article 9). The banking industry was shocked (ESBG, 2003: 7-9), whereas consumer associations were highly satisfied. In addition, the responsible lending principle stirred up significant internal conflict with DG MARKT (COM89; COM90). Another heatedly contested issue was the way in which the total costs of credit should be communicated to the consumer, encapsulated in the annual percentage rate (APR). Compared to the regulatory status quo, DG SANCO's proposal included a range of additional cost elements such as notary fees or even insurance premiums, meaning that creditors would have to disclose costs that went beyond their control. This rule, in particular, highlights the fact that DG SANCO was pursuing a regulatory approach involving direct and conclusive rules governing the market. The banking industry defied this as 'unworkable' (ESBG, 2003: 10), and was internally supported by DG MARKT (COM89). Finally, for credits linked to particular consumer goods, the proposed regulation would render creditors liable for defective products purchased on credit (Commission of the European Communities, 2002: article 19) -a regulatory approach borrowed from the UK Consumer Credit Act which was considered 'totally outlandish' in other EU states (COM89). The banking industry 'strongly opposed' it (ESBG, 2003: 12), again backed internally by DG MARKT (COM119). But DG SANCO stuck once more with the most stringent rule it could find amongst the member state regimes, did not try to balance competing interests and was applauded by the consumer side (EURO COOP, 2003;also BEUC, 2002: 14-5).
In sum, DG SANCO proposed a regulation that was geared towards equal protection for consumers in the European market. However, this regulation could only come about because inefficiencies had persisted in that market beforehand. These provided the basic justification for regulation, but the intentional focus on weak market participants and DG SANCO's need to demarcate its competences from those of the internal market DG resulted in a proposal that clearly aimed at redistributing rights in line with an equality-enhancing motivation. Not least, the high level of protection entailed in the regulation was driven by the political aim of protecting consumers in credit markets. Merely enhancing efficiency by removing regulatory barriers would 'deliver a bad message' politically and would be 'a very difficult position for the Commission' to be in (COM119).
The clear focus on only one societal segment was also mirrored in interest-group access. It is important to note that stakeholder meetings on the issue indicated a 'clear desire' from the consumer associations (COM111) as well as 'unanimous' opposition from the industry side. In response to this outright industry disagreement, 'further consultation was entirely avoided' by DG SANCO (ESBG, 2003: 2-3), so that 'the industry felt that it had been very much disregarded in the process' (COM119).
This intentionally skewed interest-group involvement also affected internal interaction with DG MARKT, which 'had more the ear […] to the banking sector' (COM111). DG MARKT officials felt that banking interests were 'not being properly consulted' (COM119), which pushed the Directors-General of both DGs to the drafting table early on and resulted in a blockade of the formal inter-service consultation. As in the earlier case, these more fundamental questions were subject to exchange among Commissioners about the quality of markets, so that 'in the end it [was] a political decision' (COM89).
As predicted by the equality-enhancing model of regulation, redistribution of rights among producers and consumers was an aim rather than a constraint, detailed rules rather than self-regulation along predefined objectives were prescribed, and a neo-pluralist style of communicating with external stakeholder contacts coincided with a political bargaining process among the relevant actors within the Commission.

Conclusion: Social equality through the market?
The case studies emphasize that regulatory decision-making in the EU can follow an equality-enhancing type of regulation and thereby take an identifiable middle ground between traditional welfare-state models and purely efficiency-focused regulation. Against this view, the social embeddedness of Europe's internal market cannot be considered as automatic as contemporary writing often seems to imply. Instead, our analysis suggests that market retrenchment at the supranational level depends on the outcome of political struggles across, and especially within the regulatory institutions of the EU. Where the regulator pursues social equality rather than solely focusing on market efficiency, the regulatory processes differ strongly in terms of their basic rationale, the chosen intervention, the type of stakeholder access and the internal conflict during rule formulation.
Initially, prevailing inefficiencies in the European Single Market were the starting point of rule-making in all three cases examined. And in the two equality-enhancing processes, the favoured societal group had to be framed as active participants in the regulated market. But rather than addressing such market failures by means of the least intrusive rule-making geared towards self-regulation, the regulators responded with encompassing and conclusive rules. Furthermore, the respective leading Commission DGs fostered and then counted on the support of consumer associations or a broad coalition of anti-discrimination NGOs and intentionally disregarded the input of other affected market stakeholders. Stakeholder access followed a neo-pluralist pattern and deviated from unbiased access in the classical conception of the regulatory state, but also from the corporatist pattern ascribed to the positive state. And, finally, the equality-enhancing processes led to strong internal opposition from other Commission DGs that would have preferred less intrusive regulatory measures.
Several implications follow for the broader question as to under which conditions market regulation in the EU pursues wider social goals. Initially, we noted that both the equal-treatment and the consumer-policy cases were driven by the agency of actors within the Commission. Equality or efficiency enhancement is always pursued by some and not by others. To answer the question as to whether we can expect Brussels to intervene only where markets fail or also where social interests demand regulation, we need a disaggregated view on Europe's central regulator.
On the one hand, this refers to entrepreneurial actors in the drafting DGs. But for both cases it was also important that the Directorates-General held a decidedly social mandate due to their portfolios and could rely on treaty bases justified along equality-enhancing lines. Delineating their own competences from traditionally efficiency-oriented DGs, political entrepreneurs within the lead departments explain the push for more equality, while the available institutional mandates explain how the aim of social equality could be pursued. This finding suggests that a politicization of the selection of key personnel (Hartlapp, 2014, forthcoming) and the layout of portfolios within the Commission may significantly advance the social embeddedness of European integration.
But against this hope, it has to be noted that the apparent need to frame beneficiaries of equality-enhancing regulation as market participants still limits the scope for European re-regulation. Compared to the positive state at the national level, where social policy often targets those not able to participate in the marketplace, doubts remain as to whether the regulatory processes at the supranational level can really work to the benefit of market outsiders. Related to this, one has to keep in mind that the equality-enhancing model is also limited to the regulatory section of the overall social policy spectrum. In other words, equality enhancement in the Commission context means a redistribution of individual rights rather than resources. In a positive account, many see the fundamental rights agenda as completing the political union (Caporaso et al., 2009). Given the EU's institutional difficulties in enacting actual resource transfers, aggravated by the increasing heterogeneity of political preferences in the Council, individual rights provide much better opportunities for social embeddedness because they cut across traditional conflict lines. What is more, the proactive redistribution of rights can help to make the EU 'loveable' (to allude to the famous phrase by Delors that 'you cannot love a market'). However, from a more critical perspective, the shift towards regulating individual rights calls into question the traditional logic of welfare provision in the 'positive' state through social security, corporatist actors and macroeconomic stabilization. Individual rights increase the autonomy of citizens in a market setting, but they may endanger the notion of solidarity across social groups. In times when most western societies are characterized by ever-growing disparities, this might be a fatal development -even more so where the emphasis on individual rights undermines solidarity institutions that have evolved historically, for example in national labour or tax law (Scharpf, 2010;Höpner et al., 2012).
In sum, this article highlights the fact that political actors can exploit European regulation as a means to achieve broader societal goals, but are forced to take a detour via market efficiency. Put differently, while social policy in the positive state sought to decommodify individuals from the market, EU regulatory policy reflects actor interests in order to change the process and outcome of existing market transactions. In conclusion, there is scope for a social embedding of the European internal market in principle, but it is constrained by its institutional foundation, the existence of proactive agents within the Commission, a focus on market participants and the mere redistribution of rights rather than resources.


extfieldsofstudy:
Geography
Computer Science
Physics
Biology
Medicine

text:
Dynamical Structure of a Traditional Amazonian Social Network

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane': shocdye') drinking events in a Tsimane' village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.

Introduction
The evolutionary social and life sciences seek to understand both the ultimate evolutionary logic that motivates the formation of valuable social relationships, as well as the proximate mechanisms that support the stability of these relationships over time [2][3][4]. Longitudinal social network data provide an arena for evaluating the predictions of evolutionary models for relationship formation and maintenance through time. These data also inform us of the time dimension of network activity, a topic of both empirical and theoretical interest in the network sciences [5,6]. More generally, these data can shed light on the time scale of the processes that are in motion within social and biological systems [7][8][9].
Among the indigenous farming populations of the Amazon, social gatherings centered around drinking home-brewed manioc beer (Spanish: chicha; Tsimane': shocdye') provide a context for the development and exercise of valuable cooperative relationships [10]. In addition to the food value of the beer, conviviality and intoxication create a social venue for conversation, exchange and friendship. Investment in these social bonds may yield benefits in other domains of social life, such as cooperative labor, food sharing, politics, mobility and mate search.
Here, we analyze time-series data on hosting and attending manioc beer events collected across a sixteen-week period in a Tsimane' village in Bolivian Amazonia. We use multilevel modeling methods to analyze the stationary patterns of social events across the sample period. We evaluate the extent to which these patterns are consistent with predictions from the evolutionary theories of kin selection-that individuals behave more altruistically toward closer kin [11]-and reciprocity-that favors given are repaid by favors received in return [12]. For our analysis of the nature of the underlying dynamical processes that create and sustain these stationary patterns, we formulate an account of reciprocity in terms of conditional probabilities and test for statistical significance using a hierarchy of null models. These models evaluate the extent to which family i hosting family j is predictive of j hosting i in return within a specified period of time.

Evolutionary Background and Motivation
Evolutionary theory suggests a number of pathways by which selection can be expected to favor the emergence and stability of altruistic and cooperative social relationships [13,14]. Hamilton's theory of kin selection [11] (which predicts that altruism is favored when the cost to donors is less than the benefit to recipients devalued by the coefficient of relatedness, r) provides a fundamental anchor-point for understanding the stability of many observed cases of altruism in the natural world [15][16][17]. Anthropologists have similarly recognized the central organizing role of kinship in structuring patterns of social interaction in human societies [18]; and a growing number of quantitative studies have demonstrated the explanatory power of relatedness in traditional field settings [13,[19][20][21].
The theory of dyadic reciprocity, articulated in sociology by Mauss [22] and developed in evolutionary terms by Trivers [12] and Axelrod and Hamilton [23], provides an additional pathway to cooperation, which may operate independently of or in interaction with genetic kinship. The stability of cooperation through reciprocity is established by virtue of the fact that favors given are repaid by favors received in return. In the context of the repeated prisoner's dilemma, the tit-for-tat strategy provides a simple and direct instantiation of the principle of reciprocal support [23].
Empirical studies in humans and other primates have generally evaluated the consistency of observed patterns of interaction with the theory of reciprocity by measuring the degree to which help given is correlated with help received across pairs of individuals or families within a community (often controlling for other factors, such as kinship); this metric is typically termed "contingency" in the anthropological literature [24] or "reciprocity" in the social network literature [25][26][27]. Field studies of human and non-human primates have tended to yield positive estimates of contingency in networks of directed sharing, grooming and support [28,29]; high levels of reciprocity are also a common feature of networks analyzed in the social network literature [26,30]. In either case, levels of contingency/ reciprocity have shown considerable variation depending on local circumstance and the type of interaction examined [24,[26][27][28][29][30].
The time scales over which reciprocity may operate in natural settings remain an open area of research. On the one hand, if a simple tit-for-tat-like rule takes into account only one or a few recent interactions, feedback between an individual's actions and their partners' responses should be observable within a relatively short span of time (sufficient to capture multiple opportunities for interaction). On the other hand, decisions to lend or withhold support may take into account a pair's history of interaction over relatively longer spans of time, making their conditionality hard to detect within the space of a single observational study. Alternatively and complementarily, long-term dynamics of partner choice and selective retention may establish the conditions for reciprocity without short-term accounting of goods and services exchanged [29,31,32].
The time scales of reciprocity have proven difficult to characterize empirically. Given constraints on data collection in ethnographic settings, most tests of contingency in humans compare interaction rates summarized at a single point in time [33] or rates that are averaged or summed over the period of sampling [13,34]. In one approach, Gurven's analysis of Ache food sharing showed that rates of giving to a particular family in one period correlated positively with rates of receiving from that family in the subsequent period [24]. While studies of captive primates have produced some evidence for short-term contingent reciprocity [35][36][37][38], results have tended to be weak, leading researchers to suggest that the mechanisms supporting cooperative dyadic relationships may tend to operate over durations of time that exceed the span of most longitudinal studies [29,[38][39][40][41][42][43].
Additionally, there is an open debate about the expected relationship between kinship and reciprocity in naturalistic settings. On the one hand, Axelrod and Hamilton [23] and subsequent theoretical models [44][45][46] have predicted a positive synergy between kinship and reciprocity. Relatedness may facilitate the establishment of reciprocal exchange by increasing the effective benefits of initial altruistic acts, or increasing the probability that cooperative strategies find each other [23,44,47]. Compared to non-kin, kin may often face lower transaction costs in forming reciprocal relationships, due to greater proximity, familiarity or the likelihood of repeated interaction [47].
If, on the other hand, kinship and reciprocity act as strategic substitutes, cooperation may be maintained by mainly altruistic motives among kin, but reciprocity among non-kin. When statistically modeling cooperative relationships in field settings, a negative interaction between kinship and contingency may also capture inequality/asymmetries within reciprocal relationships driven by the directionality of kin-based altruism (e.g., due to age or differential productivity). Empirical results on this topic ( [20,33,47,48], current study) have been mixed, which may reflect true cross-case variation in the degree to which kinship and reciprocity are complementary or substitutable means for stabilizing social bonds.
While generosity motivated by kinship or dyadic reciprocity implies that interactions should be patterned according to dyadic characteristics (i.e., degree of relatedness, rates of favors returned), other evolutionary processes may support patterns of behavior that are not so clearly resolved at the dyadic level. In systems of indirect reciprocity, competitive altruism or generosity signaling [49][50][51], individuals extend help to others with the expectation of the return of benefits, not from the recipient per se, but from other members of the community who become aware of the act and adjust their behavior toward the donor accordingly (see, also, Trivers' discussion of generalized altruism in [12]). Accounts of "strong reciprocity" and cultural group selection have suggested that unconditional altruism and behaviors reinforcing prosociality (such as peer monitoring and punishment) may be favored by group or multi-level selection [52,53].
In such situations where giving is diffuse and generalized, one would not expect strong correlations between the rates of giving and receiving within dyads, nor systematic heterogeneity in the quality of dyadic relationships (except that which is produced as a function of spatial proximity or other exogenous factors affecting rates of association). Similarly, prosocial behavior motivated by the benefits of signaling one's wealth, condition or quality [54,55] generally predicts stronger patterning of behavior according to donor characteristics, rather than dyadic characteristics, such as kinship or return generosity.

Current Aims and Approach
The current study explores the stationary and dynamical patterns of interaction within one social network over a sixteen-week window with respect to kinship and reciprocity. Motivated by the theoretical and empirical research outlined above, we ask: to what degree is there stable heterogeneity in the intensity of dyadic social relationships (as opposed to equal mixing or generalized giving within the sample), controlling for spatial proximity? To test predictions from the theories of kin selection and dyadic reciprocity, we evaluate the degree to which this heterogeneity is associated with the degree of relatedness and patterns of reciprocation across dyads. We further ask whether there is an observable signal of direct, event-dependent reciprocation of hosting between dyads within the time scale of the study. In order to shed light on the empirical relationship between kinship and dyadic reciprocity, we ask whether and how the stationary (long-term) or dynamic (short-term) signals of reciprocity differ between related and unrelated dyads.
For the statistical evaluation of these questions, we employ a mix of quantitative methods. Multi-level regression models are utilized to analyze the structure of stationary patterns of interaction over the sample period. We also introduce a new method for the evaluation of conditional, non-stationary or time-specific reciprocity effects, drawing on the formalism of probability calculus. This analysis builds on methods for the analysis of network dynamics developed in [8,9].

Population and Field Setting
The Tsimane' are an indigenous population of forager-horticulturalists living in the Beni region of northeastern Bolivia. There are currently roughly 11,000 Tsimane' residing in 90 villages in the forested region between the foothills of the Andes and the wetland-savannas of the Llanos de Moxos. The Tsimane' practice a subsistence economy of hunting, fishing, collecting and horticulture; some families in villages with access to the modern Bolivian market also engage in wage labor or cash crop rice. It is typical for multiple, often closelyrelated, nuclear families to reside together in residential clusters, within which food and labor are regularly shared. Individuals and families commonly cooperate in hunting, fishing and horticultural field labor (e.g., clearing and harvesting fields). Villagers also rely on each other for support during periods of illness, injury or loss [20,56].
Among the Tsimane', manioc beer is made by cooking and fermenting sweet manioc (sp. Manihot esculenta) over a period of several days. While men and women both contribute to cultivating manioc, preparing the beer is almost exclusively the activity of women. After harvesting, the manioc is peeled, sliced and boiled in water for several hours in large metal pots over open fire, with intermittent stirring. The viscous mixture is then poured into wooden bins to ferment. Pieces of boiled manioc are masticated and reintroduced to the brew, which is fermented for a period of 1-6 days. Ground maize is sometimes added to increase alcohol content, which is likely to fall in the range of 0%-4% [10]. The home-brew is consumed from a hollowed gourd within the household and at social gatherings and fiestas: "when offering chicha, all those present are served from the same gourd in a rotating fashion, to refresh them and to satiate their stomachs, until the last drop is finished" [1]. Sweeter, usually less fermented chicha is also made from maize or bananas.

Data Collection and Preparation
Manioc beer network data were collected in 2007 in one Tsimane' village (population198 individuals; 35 nuclear families) located on the Río Maniqui, downriver from the town of San Borja, Bolivia. Over a sample period of 16 weeks, interviews were conducted in the Tsimane' language by P.L.H. or one of two Tsimane' research assistants with each family roughly twice per week. These interviews recorded the family's reports of preparing, drinking and serving manioc beer during the preceding two days.
For analysis of the manioc beer dataset as a network, nuclear families constitute nodes, while the daily frequencies of hosting constitute the weight of directed edges that connect them. The sample of 54,218 possible directed dyadic interactions includes one observation for each day that a given family was at risk of being observed hosting another given family (because either the potentially hosting or hosted family completed an interview covering that day). Given an observed tendency for under-reporting, observation days for which two families' reports disagree on attendance were reconciled in favor of the positive report. Eleven instances in which hosts and guests reported divergent dates for what was likely to be the same event were randomly reconciled in favor of one of the two reports.
Kinship was estimated on the basis of genealogical data (census and demographic interviews) collected by the Tsimane' Health and Life History Project throughout 2006-2007 [57]. The genetic relatedness between a given pair of families, i and j, was calculated as the mean relatedness of each member of family i with each member of family j on the basis of ≥3 generations of genealogical data using the kinship2 package in R [58,59]. Distances between families were calculated according to the spherical law of cosines using household locations recorded with a handheld GPS unit. The hosting and kinship networks were visualized using the igraph package in R, with node layouts optimized by the Fruchterman-Reingold algorithm [60].

Multi-Level Regression
For the stationary analysis of hosting patterns between families, multi-level logistic regression models predicting the probability that one family hosted another on a given risk day, were estimated using the glmer function in the lme4 package in R [61,62]. These models take the form: (1) Stationary heterogeneity in the patterning of interactions across families and dyads was modeled with the inclusion of random effects, α i , α j and α ij , for the identities of hosts, guests and host-guest dyads, respectively. The use of random effects allows for the partitioning of variance between multiple levels of aggregation within the data. The variance of the random effects in these models indicates the share of variance unexplained by the model's predictors (Xβ), which is attributable to higher-order groupings, in this case, host, guest and host-guest dyad identity [63,64].
To account for differential under-reporting by guests relative to hosts, a fixed effect was included that flagged potential dyadic events that were covered in the interviews of potential guests, but not hosts. Changes in the frequency of hosting over time (see Figure 1) were modeled with the inclusion of fixed effects for each week of observation. The ability of family-level characteristics of hosts and guests-the mean age of household heads and number of family members-to explain variation in rates of hosting and attendance was evaluated on the basis of minimization of Akaike's Information Criterion (AIC).
The ability of spatial proximity, kinship and stationary reciprocity (i.e., contingency) to explain variation in the probability of family i hosting family j was evaluated by inclusion of predictor variables representing: (1) the natural logarithm of distance between households (km); (2) the mean genetic relatedness of each dyad; and (3) the mean rate that family j hosted i over the sample period. The potential for different patterns of reciprocation depending on kinship status was evaluated with inclusion of an interaction between relatedness and reciprocal hosting.
We recognize that correlations between the rates that i hosts j and j hosts i may arise for a number of reasons. For example, i may visit j and vice versa simply because i and j live in close proximity or because i and j are closely related. Correlations due entirely to such factors would not provide strong evidence for the kind of contingent reciprocity described in the evolutionary literature; the presence, however, of a significant relationship controlling for "confounding" third factors, such as distance or kinship, provides stronger support for the hypothesis that relationships are maintained in part by the logic of reciprocity (i.e., that the generosity of i to j is encouraged by the generosity of j to i, and vice versa). It is also worth recognizing that the use of spatial proximity as an independent control in tests of the effects of kinship and reciprocity is conservative, in the sense that household locations are endogenous choices, which are made, in part, with respect to the location of one's kin and close social partners; this is especially true among the Tsimane', who exhibit high residential mobility and are unconstrained by private ownership of land. Twenty-two percent of the variance in distance between families is explained by genetic relatedness in the current dataset.
While random effects account for some aspects of the non-independence of outcomes across observations, conventional significance tests for models of network data may be biased, due to other aspects of non-independence in the data structure. Significance values for estimates of fixed and random effects were therefore bootstrapped by comparing observed values against the distribution of values produced from resampled data in which hosting outcomes (0 or 1) were randomly shuffled across observations.

Time-Series Analysis
In addition to our analysis of stationary patterns, we also look for dynamical signatures of reciprocity. We look for evidence, at all timescales-days to months-available in our data, for causal and non-stationary relationships between families mediated by hosting. To do this, we construct a general model for pairwise interactions, which we refer to as "conditional-action" reciprocity.
Informally, conditional-action reciprocity is when family j "returns the favor" of being hosted by family i, by themselves hosting a member of family i at a later date. Formally, we consider the ratio of the conditional probability to the base rate: (2) or, in other words, the fraction by which the probability of j hosting i is increased given a prior hosting of j by i.
The definition of Equation (2) has intuitive appeal as a way to track the underlying mechanisms by which reciprocity might develop and be maintained over time. Care, however, must be taken in systems where patterns of pro-social behavior are connected to long-standing social expectations. Imagine, for example, that families i and j have established a practice where, on average, each hosts the other five times a month. Each family has a constant probability of hosting (≈1/6 per day), which is unchanged by the occurrence of a hosting event by the other. Such a pattern would thus be invisible to the measure above, despite the fact that it might well be part of a social practice maintained by the norms of reciprocity.
Interruptions of the pattern established by i and j may lead to a detectable signal, as might the (reciprocated) decision by one family to increase the frequency with which they host the other. Such essentially non-stationary effects are necessary to lead to a detectable change in the conditional probabilities underlying the definition in Equation (2). Conditional-action definitions of reciprocation have the paradoxical effect that they lead to zero signal, not only in non-reciprocal populations, but also in highly pro-social systems, where patterns of reciprocation are stable and unchanging.
In analogous cases in the non-human world, interventions are possible, in which variables are altered by external manipulation (the "do" operation of [65]). This allows an experimenter to interrupt stable patterns of reciprocity-by, say, preventing the commission, or undoing the omission, of a favor-to detect an underlying conditional law. For reasons of both ethics and practicality, however, data in human systems are often observational, as opposed to interventional, in nature. In our case, we only observe what people have done, rather than asking them to behave differently. This makes dynamical and causal relationships more difficult, though not in all cases impossible, to detect. In particular, it requires more sophisticated attention to both estimation methods and to null model design, which we address further in Section 2.4.2.

Estimating Conditional-Action Reciprocity-
The data consist of samples taken on discrete days, concerning the events of the two previous days. Sampling was incomplete; we account for all sampling effects by means of an observation window, o ij (t). Here, o ij (t) is unity if the hosting event "i hosts j at time t" was potentially observable (if either the host or the guest were interviewed). The actual hosting events we represent by h ij (t), where h ij (t) is unity if i was indeed observed to host j at time t, and zero otherwise. By definition, if h ij (t) is non-zero for a particular pair and time, so is o ij (t).
Given these definitions, any particular R ij pair can be estimated, correcting for observational windowing effects: (4) and the maximum-likelihood estimator: (5) where N is the number of observations and N(A) the number of observations where event type A was observed to obtain. As the amount of data becomes large, R̂ becomes an increasingly good estimator of R; in particular, as: (6) then: (7) In the limit of infinite data, and arbitrarily long time-streams, a system without dynamical, conditional-action reciprocity will measure R ij for each i-j pair at its expectation value of one. Because the measurement of reciprocity in any particular i-j pair is expected to be extremely noisy, we consider R ij estimates averaged over pairs drawn from sets of interest. We focus on three groups: all pairs with observed reciprocity, all close kin pairs with observed reciprocity and all distant and non-kin pairs with observed reciprocity. We renormalize the observed values of R ij by reference to the expectation for that particular pair under the null model of interest, so that we report: (8) where averaging over realizations of a particular null model is written 〈 … 〉 null and averaging over sets of i-j pairs is written 〈 … 〉 ij∈C ; the class, C, might be "all pairs", "close kin pairs" or "not close kin pairs".

A Three-Level Hierarchy of Null Models for Conditional-Action
Reciprocity-Null models are a standard tool for determining to what information an underlying mechanism needs to have access. Use of a hierarchy of null models (with increasingly strict nulls forcing the allowed behavior closer and closer to the observed data) allows us to gather information for or against different kinds of underlying effective mechanisms. Previous studies of behavioral systems in non-human primates have used null hierarchies to determine the role of pairwise vs. higher-order relationships in conflict [8] and to determine the relative role of exogenously vs. endogenously-driven decision-making in conflict management [9].
Here, we use nulls to compare measurements of R̂i j to those measured on simulated datasets that match (on average) various stationary properties of the data while selectively destroying correlations. This allows us to determine both effect size (how much larger R̂i j is than unity, for example-a direct measure of the level of conditional-action reciprocity in the system) and effect significance (whether or not an effect of a particular size could have occurred by chance in a model without an underlying condition-action mechanism). These nulls amount to large collections of simulated datasets. We measure the same quantities (such as average R̂i j ) on this simulated data and determine whether or not we can establish evidence for mechanisms "over and above" those allowed for by the nulls.
We consider three null models of increasing strictness. The mechanisms underlying all three nulls are time-independent: i.e., they contain no conditional-action mechanisms. Each observation period is an independent and identically distributed (i.i.d.) sampling from mechanisms defined by what features of the original data they hold constant.
The first null we call the homogeneous null. In this case, we preserve, on average, each family's probability of hosting a party and the distribution of the sizes of the parties they do host. In particular, we preserve: (9) and (by means of a sufficient statistic for the binomial distribution): (10) This null preserves many features of the data. Families that host many parties do so (on average) in this null and with the same size distribution. However, the preferences that a host might have to host one family over another are erased. In this model, parties are "pure sociability"-they reflect only an individual family's desire to share resources with the larger community.
The second, stricter, null is the kin-heterogeneous null. In this case, we allow hosting preferences to vary, but require them, for each host, to be identical for all families with the same mean r. In this model, parties are reflections of long-standing, stationary preference that particular hosts have for kin groups. These preferences are allowed to be asymmetric, meaning that P ("i hosts j") and P ("j hosts i") can differ.
The third, and most strict, null is the full-heterogeneous null. In this case, we preserve, on average, all host-guest pairwise probabilities; in particular, the probability of observing a particular host-guest pair, P ("i hosts j") is preserved on average. In this model, parties are reflections of long-standing, stationary preferences between particular host-guest pairs. As in the kin-heterogeneous null, these preferences are allowed to be asymmetric.
These three nulls specify how to construct a set of probabilities for hosting, P ("i hosts j"), which can then be sampled, in an i.i.d. fashion, to produce simulated data, which is then windowed by the actual observation structure and from which the conditional-action reciprocity term, R̂i j (Δt), can be estimated. We make, for the sake of accuracy, two minor corrections to this account-a non-stationary correction for seasonal effects and a stationary correction for differences in how guests vs. hosts report information to the observers; these corrections are described in detail in the Appendix, Section A.

Results
We now summarize a number of characteristics of the network and report our results for kinship and stationary and dynamical reciprocity.

Summary Statistics
The mean daily probability of one family hosting another given family measured over the sample period is 0.926% (502/54,218 host-guest risk days). The distributions of the frequency of hosting and being hosted across families are given in Figure 2a,b, respectively. It is clear that while most families attend chicha events, only some families host them frequently; as a result, the variance in the rate of hosting is roughly five times that of the rate of being hosted.
The distribution of the weights of host-guest relationships over the sample period is given in Figure 3a. At least one hosting event was observed for 17% (208/1190) of possible hostguest dyads. The distribution of mean genetic relatedness between families is given in Figure 3b. For the genealogical data available (which may omit more distant ancestry), the mean relatedness of families in this community is 0.0293. Roughly 4% (23/595) of family dyads are related with mean r ≥ 0.25 (principally parent-offspring and sibling dyads); 21% (124/595) are related with 0 < r < 0.25 (secondary and more distant kin); while 75% (448/595) are unrelated. Figure 4 plots the hosting and kinship networks with two different layouts: the layout of the first row of networks is set according to the kinship network, while that of the second row is set according to the hosting network. For either layout, comparison of the hosting network with the kinship network shows that while hosting tends to occur most frequently between close kin, many hosting relationships also occur between non-kin; there is also clear variation in the relative magnitude of relationships within both related and unrelated interacting dyads. Table 1 gives the results of the multi-level regression predicting the likelihood of host-guest interactions across the sample period. Baseline parameter estimates with the inclusion of controls for the week of observation and reporting bias are given in Model A. The variance of the random effects in this baseline model quantifies the degree to which hosting is idiosyncratically distributed across hosts, guests and host-guest relationships. Confirming the impression given in Figure 2, there is substantially greater systematic variation in the propensity to host than in the propensity to attend (2.581 vs. 0.046). The variance in hosting that is attributable to the identity of specific host-guest dyads (2.733) is also significant, indicating substantial heterogeneity in the intensity of relationships between families within the community. The reporting control confirms that events covered only by guests' interviews were reported at significantly lower rates than those covered by hosts'.

Multi-Level Regression
Model B shows the best-fit model for hosting frequency with the inclusion of family-level predictors. The rate of hosting and attendance varies significantly with the mean age of household heads, with AIC minimization favoring quadratic and linear fits for host and guest ages, respectively. The frequency of hosting first increases with age, peaks around age 50 and declines thereafter; the frequency of attendance, on the other hand, decreases weakly with age. There is also a positive association between the frequency of hosting and family size (β = 0.224, p = 0.024), but the effect is not significant and does not yield improvements in AIC when age terms are also included in the model. Rates of attendance are not significantly predicted by the family size of guests (β = 0.028, p = 0.416) [66].
Models C, D, E and F assess the extent to which proximity, kinship and the stationary rate of reciprocal hosting can account for idiosyncratic variation in the quality of host-guest relationships. Models C and D show that on their own, household distance and kinship are each able to explain 59% (1-1.166/2.817) and 60% (1-1.120/2.817) of the variance attributed to dyad identity in Model B, respectively. Included together in model E, each of these variables independently predicts hosting frequency and together explain a total of 71% (1-0.824/2.817) of the variance attributed to dyad identity in model B.
The full model including terms for distance, stationary reciprocity, kinship and their interaction is given in Model F. This model shows that the mean rate at which family j hosts family i is a substantially positive predictor of the likelihood of family i hosting j on any given day, controlling for both spatial proximity and kinship. The coefficient on the reciprocity term indicates that a 0.01 increase in the daily rate that j hosts i is associated with a 22% increase in the likelihood that i hosts j on a given sample day. The combination of the three dyadic predictors together account for roughly 78% (1-0.619/2.817) of the total variance attributable to dyad identity in Model B. Model F also estimates a negative interaction between kinship and reciprocal hosting, which may suggest the following interpretation: while the baseline level of interaction is higher among kin than non-kin, the estimated marginal effect of j hosting i on the likelihood of i hosting j is greater for non-kin than kin. The interaction, however, is not statistically significant when compared against resampled null models.
In sum, the regression results confirm the existence of stationary heterogeneity in the quality of relationships across the sample period, which is patterned according to and well explained on the basis of both kinship and stationary reciprocity, above and beyond the effects of spatial proximity.

Time-Series Analysis
We present our results for conditional-action reciprocity in Figure 5. These panels show the average reciprocity, R, over all pairs in the system with hosting in both directions (i.e., R(Δt) is the average of all R ij (Δt), such that N ("i hosts j") and N ("j hosts i") are non-zero and observable at some separation).
Some apparent signatures of conditional-reciprocity can be seen, particularly on the very shortest (≤3 day) scales. Evaluated against the null expectations of no heterogeneity in interaction rates between specific host-guest pairs (Figure 5a) or of heterogeneity conditioned only on the degree of relatedness (Figure 5b), the effect is large, with a roughly eight-fold increase in the conditional probability that j hosts i within three days after i hosts j. Figure 5c shows the signal of time-dependent reciprocity compared against a null allowing for idiosyncratic stationary (i.e., i.i.d. process) preferences for interaction between host-dyad pairs. Under this stricter null, what appeared to be a large effect reduces in size, but does not disappear entirely: the fully heterogeneous preference model estimates an approximately 2.4-fold increase in the conditional probability that j reciprocates within three days of i hosting j, an effect which is significant at p ≈ 0.01, or approximately 2σ.
In Figure 6, we examine the evidence for short-term (≤3 day) reciprocity among three hostdyad groupings: All (all possible hosting pairs), Close kin (all pairs with average kinship greater than or equal to 0.25) and Not close kin (all pairs with kinship strictly less than 0.25). We plot the distributions for each of these categories, compared to the null expectation, and compute the p-score. The positive result for ≤3 day conditional-action reciprocity across all pairs (mean R ij = 2.4, p ≈ 0.01) is clearly driven by interactions between distant and non-kin (mean R ij = 3.0, p = 0.008) rather than close kin pairs (mean R ij = 0.63, p ≈ 0.6; no detectable deviation from null expectation).
The bottom panel of Figure 5 also shows a positive R value exceeding null expectations at the two-sigma level at around 50 days. As there is no specific reason to expect a positive signal of time-dependent reciprocity at this temporal distance (rather than, say, at 45 or 55 days), we consider this most likely a fluctuation due to chance alone. Care must be taken not to hunt for signals at an arbitrary time without either good justification or careful adjustment of p-value significance, since, in the words of [67], "look long enough, find anything."

Discussion
We have probed the structure of the Tsimane' manioc beer hosting network for statistical signatures expected from the theories of kin selection and dyadic reciprocity. With respect to kinship, the current results are unambiguous. Kinship independently predicts rates of hosting and attendance between dyads. In addition to the independent effect of kinship, a portion of the variance in hosting attributed to spatial distance can also be attributed to kinship, since kinship also partially determines the location of households.
With respect to reciprocity, the results allow some conclusions, but not others. The stationary analysis of Section 3.2 shows that across the sample period, families clearly host others that host them in turn at a level above and beyond that expected on the basis of kinship and spatial proximity alone. In the dynamic analysis of Section 3.3, we searched for the signature of time-sensitive, conditional-action reciprocity that might account for the observed rates of stationary reciprocation within the network. This analysis revealed a significant increase in the probability that j hosts i within three days of i hosting j, above and beyond the general (time-independent) tendency for j to host i over the sample period. The relative size and time-frame of the detected conditional-action reciprocity effect are modest, suggesting that an alternative model-one which allows for dyad-specific preferences for hosting and attendance, but which has no dynamic properties-is in fact nearly sufficient to explain the observed rates of bilateral hosting. These results obtain in the context of the four-month baseline of behavior established across the study, a period that included significant changes in task (e.g., a shift from harvesting rice in May and June to clearing new fields in August and September).
There are a number of reasons why strong signals of conditional-action reciprocity have remained difficult to detect, even if reciprocity does act as an important mechanism maintaining cooperation in this and other social systems. First, it may be that many of the relevant causal events occur outside the confines of the current network. Chicha, for example, may be offered as a way of eliciting or rewarding cooperative labor in horticultural work, food sharing or political support. Second, the decision rules underlying conditional social behavior are likely to be substantially more subtle and complex, depending on the specific context and history of interaction, than the simple time-dependent mechanism posited here. Third, the mechanisms supporting reciprocation within long-term relationships -such as selective partner choice and retention or emotional commitment-may occur across relatively longer time scales than those investigated in the current study, creating the conditions for reciprocity without explicit or perceptible score-keeping at shorter time scales [32,38].
Additionally, the threat of conditional-action reciprocity effects may remain relevant to the construction of highly reciprocal and pro-social societies, yet prove undetectable in the absence of perturbation. A perfectly law-abiding society has no record of arrests, but this does not indicate that the laws themselves do not exist. This apparent paradox-that more fully-cooperative societies would show less evidence for dynamical reciprocity-may be accessible to empirical study. While direct-intervention experiments are problematic due to considerations of ethics or expense, naturally occurring perturbations could give a window onto the problem.
It may be the case, for example, that a new arrival to a highly-cooperative society must establish their bona fides with the community by means of conditional-reciprocity effects. In particular, preferences for the new arrival must be established by the other community members, and vice versa. In the formal language of the present time-series analysis, a newly arrived family, i, could be expected to show evidence for above-null R-either R i★ or R ★ias they raise their probability of hosting another family, j, in response to signals from j that this is desired. The signal that j might send includes the hosting of i in return, so that first P ("i hosts j") rises as i experiments with a new relationship, then P ("j hosts i") rises. If the prior probability of j hosting i is small (as might be expected for the new-comer, i), this effect would lead to an R ij much greater than unity.
It is worth recognizing that other motivations and processes are likely to underlie behavior in the chicha hosting network beyond dyadic reciprocity and kinship-based altruism. Some degree of generosity in this network, for instance, may be motivated by concerns for reputation management, which supersede the specific dyad in question [49]. Generalized altruism might also be reinforced through mechanisms of reward and punishment not captured here, as suggested by the literature on peer monitoring and strong reciprocity [52,53]. The fact that one may fairly easily join chicha gatherings without explicit invitation may also limit hosts' control of attendance in ways that create a scope for scrounging (or tolerated theft) and disrupt clear patterns of directed reciprocation [13,68]. One may note, however, that none of these processes predicts the substantial within-community heterogeneity in dyad-specific relationship quality, which we have observed in the current study.
The substantial variance in the rates of hosting unexplained by age or family size also begs investigation and is consistent with the hypothesis that hosting may be motivated in part by the benefits of signaling wealth, productivity or social status [54]. Such heterogeneity would also be consistent with a degree of specialization and division of labor between families (with some families focusing, for example, on production of meat, and others on the production of horticultural goods, including chicha, as described in [20,69]).
The current results also bear on the intersection of kin selection and dyadic reciprocity. In addition to the negative (though not significant) interaction between reciprocity and kinship in the stationary analysis, the significant conditional-action reciprocity effect of the dynamic analysis held among distant and non-kin, but not among close kin. These patterns suggest that kinship and reciprocity in this network act as strategic substitutes, rather than complements, in contrast to the results of several evolutionary models and empirical studies [23,44,47,48]. This may prompt the refinement of the theory to better understand the social and ecological conditions under which different mechanisms supporting altruism and cooperation may reinforce each other or crowd each other out [70].

Summary and Conclusion
In this paper, we have presented new results on the nature of cooperation and the maintenance of social ties and have introduced new methods to detect signals of dynamical processes within social networks. Using detailed information gathered from a sixteen-week ethnographic study of manioc beer drinking events, we have looked for signatures of genetic kinship and stationary reciprocity in patterns of directed social behavior. While spatial distance is a major predictor of the rates of hosting and attendance, families host other families (a) that are more closely related and (b) that host them in turn significantly more frequently than expected based on distance or family-level characteristics alone.
We have sought a potential dynamical signature for the emergence of these long-lasting stationary ties. Using the formalism of probability calculus, we have searched for the simplest possible signature of conditional-action reciprocity at different time-scales. We have shown how to estimate conditional-action reciprocity and determine its statistical significance using a hierarchy of null behavioral models. The results show a significant signal of conditional-action reciprocity at very short (≤3 day) time scales among distantly and unrelated families, but not among close kin. Apart from this effect, much of the variation in interaction rates is well accounted for by assuming stable preferences for hosting between specific family pairs, which are stationary and unchanging across the time period of observation. Figure 1 shows that the overall level of hosting changes throughout the dataset. While these shifts could in principle be caused by multiple conditional-reciprocity effects, a more parsimonious explanation might associate them with general shifts in activity over the course of the year. A truly i.i.d. sampling from stationary preferences would not allow for these exogenous events and would lead to the anomalous detection of signals of conditional reciprocity.

A.1. Correction for Seasonality
As an example, consider the lull in party activity seen in the data in late June. This occurs between a high-activity period in May and a medium-activity period in July and August. If these seasonal effects were not corrected for, this large-scale pattern would lead to a detection of anomalously low conditional reciprocity at separations of roughly thirty days. We thus, in all three nulls, allow for overall, family-independent, shifts in the stationary probabilities in the data. We smooth these corrections on one-week scales, which we consider sufficient to capture exogenously-driven changes in party hosting.

A.2. Correction for Reporting
There are two ways in which an observer can come to know about a party: by interviewing the host and asking them to list the guests or by interviewing someone who turns out to have been a guest. The two questions ("who drank at your house?" and "at whose house did you drink?"), though formally symmetric (if i hosted j, then j was hosted by i) may be answered differently, due to memory, shyness or other social factors. In many cases, both host and guest have been interviewed, and differences in the data can be reconciled.
In other cases, however, only one of the pairs is contacted at that time period, and so, we are unable to perform reconciliation. In order to properly compare simulated data to actual data, however, we need to simulate not only "the truth"-i.e., the actual hosting patterns-but also the reporting of that truth. Since we do not have directly-observed evidence for party attendance and our data rely upon participant report, we model the host reports as truth and deviations from host reports as due to additional reporting mechanisms.
To a first approximation, we model the guest reporting mechanism with a single parameter, p omit : the probability that a guest fails to report a party that the host reports. We emphasize that this refers not to the forgetting of an actual event (as we do not have access to this information), but rather to the probability that a guest omits describing an event that a host describes.
We can determine p omit by requiring that our null models reproduce, on average, the same level of realized guest-host pairs (502) as seen in the data. We find that the correction necessary is quite high, p omit , the probability that a guest "forgets" to mention a party that the host indicates has taken place, to be 0.687 ± 0.001 for the full-heterogeneous case, 0.720 ± 0.002 for the kin-heterogeneous case and 0.727 ± 0.001 for the homogeneous case.
In our model, this probability is independent of both guest and guest-host. Indirect evidence that this is only an approximation to the true underlying dynamics is given by the slight differences, over and above statistical error, in p omit within the null hierarchy, which should coincide if (1) the reporting mechanisms are independent of the party-attending mechanisms and (2) all relevant features of the reporting mechanisms are captured.
An additional effect is the possibility of non-random sampling: in particular, a correlation between i hosting j, j hosting i, i being observed and j being observed. Simulations suggest that this effect, if it exists, is below the level of noise due to small-number fluctuations. In particular, it is not possible to reject the hypothesis that "correlations do not exist" by reference to the stationary probabilities observed in the data versus those measured in the nulls. Seasonal effects in hosting. Shown here, for the discrete sampling points, is the probability that an observed pair is a hosting pair. A lull in late June separates two more active periods; the May period is overall the most active, followed by the July and August season. Frequency distributions of mean daily probability of (a) hosting and (b) being hosted for any given family each sample day for 35 families.   Networks of kinship and manioc beer hosting. Panels (a) and (c) represent the kinship network, with the edge width indicating mean genetic relatedness. Panels (b) and (d) represent the hosting network; edge widths indicate the mean hosting frequency between the two families, while arrows indicate the direction of attendance. The layouts of panels (a) and (b) are optimized based on the kinship network, while those of (c) and (d) are optimized based on the hosting network. Node color indicates mean age of household heads (ascending in age from green to red), while node size indicates family size.   Multi-level logistic regression models predicting the likelihood that family i hosts family j on a given sample day (the models include intercept and time controls, not shown).