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The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naïve learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent’s social influence in this generalized DeGroot model is essentially proportional to the degree-weighted share of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We show information aggregation preserves “wisdom” in the sense that initial signals are weighed approximately equally in a model of network formation that captures the sparsity, clustering, and small-world properties of real-world networks. We also identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. Simulating the modeled learning process on a set of real-world networks, we find that there is on average 22.4 percent information loss in these networks. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real-world network data show that with clustered seeding, information loss climbs to 34.4 percent. (JEL D83, D85, Z13)

Partisan segregation within the news audience buffers many Americans from countervailing political views, posing a risk to democracy. Empirical studies of the online media ecosystem suggest that only a small minority of Americans, driven by a mix of demand and algorithms, are siloed according to their political ideology. However, such research omits the comparatively larger television audience and often ignores temporal dynamics underlying news consumption. By analyzing billions of browsing and viewing events between 2016 and 2019, with a novel framework for measuring partisan audiences, we first estimate that 17% of Americans are partisan-segregated through television versus roughly 4% online. Second, television news consumers are several times more likely to maintain their partisan news diets month-over-month. Third, TV viewers’ news diets are far more concentrated on preferred sources. Last, partisan news channels’ audiences are growing even as the TV news audience is shrinking. Our results suggest that television is the top driver of partisan audience segregation among Americans.

Addendum to HKS Misinformation Review “Research note: Examining potential bias in large-scale censored data” (https://doi.org/10.37016/mr-2020-74), published on July 26, 2021.

Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, YouTube's scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical anti-woke channels, both of which have been claimed to direct attention to radical political content. Here we test this hypothesis using a representative panel of more than 300,000 Americans and their individual-level browsing behavior, on and off YouTube, from January 2016 through December 2019. Using a labeled set of political news channels, we find that news consumption on YouTube is dominated by mainstream and largely centrist sources. Consumers of far-right content, while more engaged than average, represent a small and stable percentage of news consumers. However, consumption of anti-woke content, defined in terms of its opposition to progressive intellectual and political agendas, grew steadily in popularity and is correlated with consumption of far-right content off-platform. We find no evidence that engagement with far-right content is caused by YouTube recommendations systematically, nor do we find clear evidence that anti-woke channels serve as a gateway to the far right. Rather, consumption of political content on YouTube appears to reflect individual preferences that extend across the web as a whole., Comment: 10 pages, 7 figures, 2 tables

Since the 2016 US presidential election, the deliberate spread of misinformation online, and on social media in particular, has generated extraordinary concern, in large part because of its potential effects on public opinion, political polarization, and ultimately democratic decision making. Recently, however, a handful of papers have argued that both the prevalence and consumption of “fake news” per se is extremely low compared with other types of news and news-relevant content. Although neither prevalence nor consumption is a direct measure of influence, this work suggests that proper understanding of misinformation and its effects requires a much broader view of the problem, encompassing biased and misleading—but not necessarily factually incorrect—information that is routinely produced or amplified by mainstream news organizations. In this paper, we propose an ambitious collective research agenda to measure the origins, nature, and prevalence of misinformation, broadly construed, as well as its impact on democracy. We also sketch out some illustrative examples of completed, ongoing, or planned research projects that contribute to this agenda.

A policy debate centers around the question whether news aggregators such as Google News decrease or increase traffic to online news sites. One side of the debate, typically espoused by publishers, views aggregators as substitutes for traditional news consumption because aggregators’ landing pages provide snippets of news stories and therefore reduce the incentive to click on the linked articles. Defendants of aggregators, on the other hand, view aggregators as complements because they make it easier to discover news and therefore drive traffic to publishers. This debate has received particular attention in the European Union where two countries, Germany and Spain, enacted copyright reforms that allow newspapers to charge aggregators for linking to news snippets. In this paper, we use Spain as a natural experiment because Google News shut down all together in response to the reform in December 2014. We compare the news consumption of a large number of Google News users with a synthetic control group of similar non-Google News users. We find that the shutdown of Google News reduces overall news consumption by about 20% for treatment users, and it reduces page views on publishers other than Google News by 10%. This decrease is concentrated around small publishers while large publishers do not see significant changes in their overall traffic. We further find that when Google News shuts down, its users are able to replace some but not all of the types of news they previously read. Post-shutdown, they read less breaking news, hard news, and news that is not well covered on their favorite news publishers. These news categories explain most of the overall reduction in news consumption, and shed light on the mechanisms through which aggregators interact with traditional publishers.

We study a model of social learning and communication using hard anecdotal evidence. There are two Bayesian agents (a sender and a receiver) who wish to communicate. The receiver must take an action whose payoff depends on their personal preferences and an unknown state of the world. The sender has access to a collection of n samples correlated with the state of the world, which we think of as specific anecdotes or pieces of evidence, and can send exactly one of these samples to the receiver in order to influence her choice of action. Importantly, the sender's personal preferences may differ from the receiver's, which affects the seller's strategic choice of which anecdote to send. We show that if the sender's communication scheme is observable to the receiver (that is, the choice of which anecdote to send given the set they receive), then they will choose an unbiased and maximally informative communication scheme, no matter the difference in preferences. Without observability, however, even a small difference in preferences can lead to a significant bias in the choice of anecdote, which the receiver must then account for. This can significantly reduce the informativeness of the signal, leading to substantial utility loss for both sides. One implication is informational homophily: a receiver can rationally prefer to obtain information from a poorly-informed sender with aligned preferences, rather than a knowledgeable expert whose preferences may differ from her own.

Surveys are a vital tool for understanding public opinion and knowledge, but they can also yield biased estimates of behavior. Here we explore a popular and important behavior that is frequently measured in public opinion surveys: news consumption. Previous studies have shown that television news consumption is consistently overreported in surveys relative to passively collected behavioral data. We validate these earlier findings, showing that they continue to hold despite large shifts in news consumption habits over time, while also adding some new nuance regarding question wording. We extend these findings to survey reports of online and social media news consumption, with respect both to levels and trends. Third, we demonstrate the usefulness of passively collected data for measuring a quantity such as “consuming news” for which different researchers might reasonably choose different definitions. Finally, recognizing that passively collected data suffers from its own limitations, we outline a framework for using a mix of passively collected behavioral and survey-generated attitudinal data to accurately estimate consumption of news and related effects on public opinion and knowledge, conditional on media consumption.

We study the role of ethnicity in experimental labor markets where “employers” determine wages of “workers” who perform a real effort task. This task requires a true skill which we show is not affected by minority status. In some treatments, we provide subtle priming to employers about minority status of workers as commonly depicted on Chinese “Hukou” identification system. We conduct our experiments at two sites located in provinces that differ by their historical shares of ethnic groups in the population. We find that: (1) Han and minority workers are equally productive in both provinces; (2) in the diverse province, there is no difference in the wages between Han and minority workers; (3) in the non-diverse province, minority workers receive 4–7% lower wages than Han workers.

“Fake news,” broadly defined as deliberately false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive on the web, and on social media in particular, with serious consequences for public opinion, political polarization, and ultimately democracy. Using a unique multimode data set that comprises a nationally representative sample of mobile, desktop, and television consumption across all categories of media content, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans’ daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online, while a supermajority of Americans consume little or no news online at all. Third, fake news comprises only about 1% of overall news consumption and 0.15% of Americans’ daily media diet. Although consumption data alone cannot determine that online misinformation in any dose is not dangerous to democracy, our results suggest that the origins of public mis-informedness and polarization are more likely to lie in the content of ordinary news--especially on television--or alternatively in the avoidance of news altogether as they are in overt fakery.

The application of modern NLP and ML techniques to large-scale datasets can generate implicit graphs that are so densely connected as to be unusable when rendered as node-link diagrams. We present a two-stage approach to extracting usable, map-like layouts from large, dense input graphs. This approach uses edge-cutting strategies based on node and edge metrics to reduce a graph to a skeletal structure showing only essential relationships, before filling in the resulting communities to create dense but usable layouts. Through a case study on a 145k-document adversarial health communication dataset, we show that each edge-cutting strategy has advantages and disadvantages, and that the appropriate choice of strategy depends on the data, user, and task.

There has been a steep increase in empirical research in economics in the past 20–30 years. This chapter brings together several actors and stakeholders in these developments to discuss their drivers and implications. All types of data are considered: official data, data collected by researchers, lab experiments, randomized control trials, and proprietary data from private and public sources. When relevant, emphasis is placed on developments specific to Europe. The basic message of the chapter is that there is no single type of data that is superior to all others. We need to promote diversity of data sources for economic research and ensure that researchers are equipped to take advantage of them. All stakeholders – researchers, research institutions, funders, statistical agencies, central banks, journals, data firms, and policy-makers – have a role to play in this. Introduction The past 20–30 years have witnessed a steady rise in empirical research in economics. In fact, a majority of articles published by leading journals these days are empirical, in stark contrast with the situation 40 or 50 years ago (Hamermesh, 2013). This change in the distribution of methodologies used in economic research was made possible by improved computing power but, more importantly, thanks to an increase in the quantity , quality and variety of data used in economics. This chapter brings together several actors and stakeholders in these changes to discuss their drivers and implications. All types of data are considered. When relevant, emphasis is placed on developments specific to Europe. Sections 13.2 and 13.3 deal with official microdata. Section 13.2 focuses on the level of access to microdata in Europe and its determinants. Section 13.3 focuses on cross-country data harmonization. Section 13.4 then switches gears entirely and discusses the benefits and costs of large-scale data collection efforts led by researchers, instead of statistical offices. Section 13.5 discusses data produced by researchers, either in the context of lab experiments or in the context of randomized control trials. Both types of data have led to major advances; for the first one in our understanding of human behaviour and the robustness of economic institutions; for the second in our understanding of the impact of policies and themechanisms underlying them.

Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new randomized sales mechanism for such environments. Bidders can “buy-it-now” at a posted price, or “take-a-chance” in an auction where the top d > 1 bidders are equally likely to win. The randomized allocation incentivizes high-valuation bidders to buy-it-now. We analyze equilibrium behavior and apply our analysis to advertiser bidding data from Microsoft Advertising Exchange. In counterfactual simulations, our mechanism increases revenue by 4.4% and consumer surplus by 14.5% compared to an optimal second-price auction. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Social learning is a rapidly growing field for empirical and theoretical research in economics. We encounter social learning in many economically important phenomena, such as the adoption of new products and technologies or job search in labor markets. We review the existing empirical and theoretical literatures and argue that they have evolved largely independently of each other. This suggests several directions for future research that can help bridge the gap between both literatures. For example, the theory literature has come up with several models of social learning, ranging from naïve DeGroot models to sophisticated Bayesian models whose assumptions and predictions need to be empirically tested. Alternatively, empiricists have often observed that social learning is more localized than existing theory models assume, and that information can decay along a transmission path. Incorporating these findings into our models might require theorists to look beyond asymptotic convergence in social learning.

We develop a model of informal risk-sharing in social networks, where relationships between individuals can be used as social collateral to enforce insurance payments. We characterize incentive compatible risk-sharing arrangements and obtain two results. (1) The degree of informal insurance is governed by the expansiveness of the network, measured by the number of connections that groups of agents have with the rest of the community, relative to group size. Two-dimensional networks, where people have connections in multiple directions, are sufficiently expansive to allow very good risk-sharing. We show that social networks in Peruvian villages satisfy this dimensionality property; thus, our model can explain Townsend's (1994) puzzling observation that village communities often exhibit close to full insurance. (2) In second-best arrangements, agents organize in endogenous "risk-sharing islands" in the network, where shocks are shared fully within, but imperfectly across islands. As a result, network based risk-sharing is local: socially closer agents insure each other more.

Social networks can facilitate informal lending and risk-sharing in situations where formal institutions such as banks and insurance companies do not exist. The social collateral approach provides an analytically tractable framework that can be used to analyze a wide range of informal transfers. Moreover, the approach is easily amenable to empirical analysis .

We conduct online field experiments in large real-world social networks in order to decompose prosocial giving into three components: (1) baseline altruism toward randomly selected strangers, (2) directed altruism that favors friends over random strangers, and (3) giving motivated by the prospect of future interaction. Directed altruism increases giving to friends by 52 percent relative to random strangers, while future interaction effects increase giving by an additional 24 percent when giving is socially efficient. This finding suggests that future interaction affects giving through a repeated game mechanism where agents can be rewarded for granting efficiency enhancing favors. We also find that subjects with higher baseline altruism have friends with higher baseline altruism.

This paper builds a theory of trust based on informal contract enforcement in social networks. In our model, network connections between individuals can be used as social collateral to secure informal borrowing. We define network-based trust as the largest amount one agent can borrow from another agent and derive a reduced-form expression for this quantity, which we then use in three applications. (1) We predict that dense networks generate bonding social capital that allows transacting valuable assets, whereas loose networks create bridging social capital that improves access to cheap favors such as information. (2) For job recommendation networks, we show that strong ties between employers and trusted recommenders reduce asymmetric information about the quality of job candidates. (3) Using data from Peru, we show empirically that network-based trust predicts informal borrowing, and we structurally estimate and test our model.

Large community size is often negatively correlated with prosocial behaviors such as formal volunteering, working on public projects, and informal help to friends and strangers (Robert D. Putnam 2000, 119, 206). This may be because people who reside in large communities simply spend less time socializing with each other. As a result, people living in large cities have on average fewer friends, and their social networks support less cooperation. A complementary channel, which has received less attention in the literature, is that community size may affect outcomes by changing other aspects of the network structure. Specifically, even holding fixed the number of friends, we expect social networks in small communities to exhibit greater network closure, i.e., be more interconnected. The intuition is straightforward. In small communities, the pool of potential friends is limited, which increases the extent to which the network neighborhoods of two friends are likely to overlap. James S. Coleman (1990) suggested that variation in network closure can affect outcomes. In particular, he argued that networks with higher closure generate high trust between friends, which facilitates cooperation and improves welfare. The logic is that networks with high closure allow for greater social sanctions between

We seed noisy information to members of a real-world social network to study how information diusion and information aggregation jointly shape social learning. Our environment features substantial social learning. We show that learning occurs via diusion which is highly imperfect: signals travel only up to two steps in the conversation network and indirect signals are transmitted noisily. We then compare two theories of information aggregation: a naive model in which people double-count signals that reach them through multiple paths, and a sophisticated model in which people avoid double-counting by tagging the source of information. We show that to distinguish between these models of aggregation, it is critical to explicitly account for imperfect diusion. When we do so, we nd that our data are most consistent with the sophisticated tagged model.

Increasingly sophisticated tracking technology offers publishers the ability to offer targeted advertisements to advertisers. Such targeting enhances advertising efficiency by improving the match quality between advertisers and users, but also thins the market of interested advertisers. Using bidding data from Microsoft's Ad Exchange (AdECN) platform, we show that there is often a substantial gap between the highest and second highest willingness to pay. This motivates our new BIN-TAC mechanism, which is effective in extracting revenue when such a gap exists. Bidders can ``buy-it-now'', or alternatively ``take-a-chance'' in an auction, where the top d > 1 bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of distributions, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve. For the AdECN data, we use structural methods to estimate counterfactual revenues, and find that our BIN-TAC mechanism improves revenue by 11% relative to an optimal second-price auction.

A large literature in social psychology suggests that agents process information about their own ability in a biased manner. While this finding has generated exciting research in behavioral economics, some of the fundamental evidence has also been criticized as being potentially consistent with Bayesian updating. We reevaluate the evidence from social psychology by conducting a large-scale experiment with 656 undergraduate students. Subjects perform an IQ-test and receive noisy feedback on their performance while we track the evolution of their beliefs. Our experimental design allows us to precisely measure the relevant belief distribution of subjects simply and repeatedly. Our experiment confirms that subjects are asymmetric and put greater weight on positive performance signals compared to negative signals. However, we also find that subjects are conservative and update too little in response to both positive and negative signals. Subjects appear much more Bayesian when receiving feedback about someone else’s performance. We show that these seemingly related phenomena naturally arise in a simple model of biased information processing inspired by the anticipatory utility model of Brunnermeier and Parker (2005). Our model is parsimonious and adds just a single parameter to the standard model. We also find that women are more conservative than men, but not more asymmetric. High-confidence women have a higher value for information than men.

We present a simple auction mechanism which extends the second-price auction with reserve and is truthful in expectation. This mechanism is particularly effective in private value environments where the distribution of valuations are irregular. Bidders can "buy-it-now", or alternatively "take-a-chance" where the top d bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of valuations, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve.In addition, we present an evaluation of bid data from Microsoft's AdECN platform. We find the valuations are irregular, and counterfactual experiments suggest our BIN-TAC mechanism would improve revenue by 11% relative to an optimal second-price mechanism with reserve.

The success of future P2P applications ultimately depends on whether users will contribute their bandwidth, CPU and storage resources to a larger community. In this paper, we propose a new incentive paradigm, Networked Asynchronous Bilateral Trading (NABT), which can be applied to a broad range of P2P applications. In NABT, peers belong to an underlying social network, and each pair of friends keeps track of a credit balance between them. When user Alice provides a service (a file, storage space, computation and so on) to her friend Bob, she charges Bob credits. Thus, in NABT, there is no global currency; instead, there are only credit balances maintained between pairs of friends. NABT allows peers to supply each other asynchronously and further allows peers to trade with remote peers through intermediaries. We theoretically show that NABT is perfectly efficient with balanced demands and supports "networked tit-for-tat". The efficiency of NABT with unbalanced demands is determined by the min-cut of credit limits of the underlying social network. Using simulations driven by MySpace traces, we demonstrate that a simple two-hop NABT design can have high trading efficiency, provide service differentiation, exploit trading intermediaries, and discourage free-riders.

double-counting.

We conduct a field experiment in a large real-world social network to examine how subjects expect to be treated by their friends and by strangers who make allocation decisions in modified dictator games. Although recipients' beliefs accurately account for the extent to which friends will choose more generous allocations than strangers (i.e., directed altruism), recipients are not able to anticipate individual differences in the baseline altruism of allocators (measured by giving to an unnamed recipient, which is predictive of generosity toward named recipients). Recipients who are direct friends with the allocator, or even recipients with many common friends, are no more accurate in recognizing intrinsically altruistic allocators. Recipient beliefs are significantly less accurate than the predictions of an econometrician who knows the allocator's demographic characteristics and social distance, suggesting recipients do not have information on unobservable characteristics of the allocator. (JEL: C73, C91, D64)

We conduct field experiments in a large real-world social network to examine why decision makers treat friends more generously than strangers. Subjects are asked to divide surplus between themselves and named partners at various social distances, where only one of the decisions is implemented. In order to separate altruistic and future interaction motives, we implement an anonymous treatment where neither player is told at the end of the experiment which decision was selected for payment and a non-anonymous treatment where both players are told. Moreover, we include both games where transfers increase and decrease social surplus to distinguish between different future interaction channels including signaling one's generosity and enforced reciprocity, where the decision maker treats the partner to a favor because she can expect it to be repaid in the future. We can decompose altruistic preferences into baseline altruism towards any partner and directed altruism towards friends. Decision makers vary widely in their baseline altruism, but pass at least 50 percent more surplus to friends compared to strangers when decision making is anonymous. Under non-anonymity, transfers to friends increase by an extra 24 percent relative to strangers, but only in games where transfers increase social surplus. This effect increases with density of the network structure between both players, but does not depend on the average amount of time spent together each week. Our findings are well explained by enforced reciprocity, but not by signaling or preference-based reciprocity. We also find that partners' expectations are well calibrated to directed altruism, but that they ignore decision makers' baseline altruism. Partners with high baseline altruism have friends with higher baseline altruism and are therefore treated better.

We combine information on social networks with medical records and survey data in order to examine how friends affect one's decision to get vaccinated against the flu. The random assignment of undergraduates to residential halls at a large private university allows us to estimate how peer effects influence health beliefs and vaccination choices. Our results indicate that social exposure to medical information raises people's perceptions of the benefits of immunization. The average student's belief about the vaccine's health value increases by $5.00 when an additional 10 percent of her friends are assigned to residences that host inoculation clinics. Among students with no recent flu experience, a 10 percent rise in the number of friends living in residences with clinics raises cumulative valuations of the vaccine by $10.92, with 85 percent of this increase attributable to heightened perceptions about the medical benefits of immunization. We also find evidence of positive peer effects on individuals' vaccination decisions. A student becomes up to 8.3 percentage points more likely to get immunized if an additional 10 percent of her friends receive flu shots. Furthermore, the excess clustering of friends at inoculation clinics suggests that students coordinate their vaccination decisions with their friends.

We conduct field experiments in a large real-world social network to examine why decision-makers treat their friends more generously than strangers. Subjects are asked to divide a surplus between themselves and named partners at varying social distances, but only one of these decisions is implemented. We decompose altruistic preferences into baseline altruism towards strangers, and directed altruism towards friends. In order to separate the motives that are altruistic from the ones that anticipate a future interaction or repayment, we implement an anonymous treatment in which neither player is told at the end of the experiment which decision was selected for payment, and a non-anonymous treatment where both players are told the outcome. Moreover, in order to distinguish between different future interaction channels - including signaling one's propensity to be generous and enforced reciprocity, where the decision-maker grants the partner a favor because she expects it to be repaid in the future - the experiments include games where transfers both increase and decrease social surplus. We find that decision-makers vary widely in their baseline altruism, but pass at least 50 percent more surplus to friends as opposed to strangers when decision-making is anonymous. Under non-anonymity, transfers to friends increase by an extra 24 percent relative to strangers, but only in games where transfers increase social surplus. This effect increases with the density of the social network structure between both players. Our findings are well explained by enforced reciprocity, but not by signaling or preference-based reciprocity. We also find that partners' expectations are well attuned to directed altruism, but that they completely ignore the decision-makers' baseline altruism. Partners with higher baseline altruism have friends with higher baseline altruism and, therefore, are treated better by their friends.

The division of labor first increased during industrialization, and then decreased again after 1970 as job roles have expanded. In this paper, we explain these trends in the organization of work through a simple model, making two minimal assumptions: (a) machines require standardization to exploit economies of scale and (b) more customized products are subject to trends and fashions which make production tasks less predictable and a strict division of labor impractical. The model predicts capital-skill substitutability during industrialization and capital-skill complementarity in the maturing industrial economy: At the onset of industrialization, the market supports only a small number of generic varieties which can be mass-produced under a strict division of labor. Then, thanks to productivity growth, niche markets gradually expand, producers eventually move into customized production, and the division of labor decreases again. We test our model by exploiting the time-lags in the introduction of bar-coding in three-digit SIC manufacturing industries in the U.S.. We find that both increases in investments in computers and bar-coding have led to skill-upgrading. However, consistent with our model bar-coding has affected mainly the center of the skill distribution by shifting demand away from the high-school educated to the less-than-college educated.

We show the existence of a pure strategy, symmetric, increasing equilibrium in double auction markets with correlated, conditionally independent private values and many participants. The equilibrium we find is arbitrarily close to fully revealing as the market size grows. Our results provide strategic foundations for price-taking behavior in large markets.

Advances in communication and transportation technologies have the potential to bring people closer together and create a "global village." However, they also allow heterogeneous agents to segregate along special interests, which gives rise to communities fragmented by type rather than by geography. We show that lower communication costs should always decrease separation between individual agents even as group-based separation increases. Each measure of separation is pertinent for distinct types of social interaction. A group-based measure captures the diversity of group preferences that can have an impact on the provision of public goods. While an individual measure correlates with the speed of information transmission through the social network that affects, for example, learning about job opportunities and new technologies. We test the model by looking at coauthoring between academic economists before and during the rise of the Internet in the 1990s.