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Measuring reciprocity: Double sampling, concordance, and network construction

Published online by Cambridge University Press:  12 September 2021

Elspeth Ready Open the ORCID record for Elspeth Ready [Opens in a new window]  and
Eleanor A. Power Open the ORCID record for Eleanor A. Power [Opens in a new window]
Elspeth Ready*
Affiliation:
Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Department of Anthropology, University of Florida, Gainesville, FL 32611, USA
Eleanor A. Power
Affiliation:
Department of Methodology, London School of Economics and Political Science, London WC2A 2AE, UK
*
*Corresponding author. Email: elspeth_ready@eva.mpg.de
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Abstract

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Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Directed networks provide a way to examine the structure of reciprocity in a community. However, measuring social networks involves assumptions about what relationships matter and how to elicit them, which may impact observed reciprocity. In particular, the practice of aggregating multiple sources of data on the same relationship (e.g., “double-sampled” data, where both the “giver” and “receiver” are asked to report on their relationship) may have pronounced impacts on network structure. To investigate these issues, we examine concordance (ties reported by both parties) and reciprocity in a set of directed, double-sampled social support networks. We find low concordance in people’s responses. Taking either the union (including any reported ties) or the intersection (including only concordant ties) of double-sampled relationships results in dramatically higher levels of reciprocity. Using multilevel exponential random graph models of social support networks from 75 villages in India, we show that these changes cannot be fully explained by the increase in the number of ties produced by layer aggregation. Respondents’ tendency to name the same people as both givers and receivers of support plays an important role, but this tendency varies across contexts and relationships type. We argue that no single method should necessarily be seen as the “correct” choice for aggregation of multiple sources of data on a single relationship type. Methods of aggregation should depend on the research question, the context, and the relationship in question.

Keywords

social networksreciprocityconcordancedouble samplinginformant accuracy
Type
Research Article
Information
Network Science , Volume 9 , Issue 4 , December 2021 , pp. 387 - 402
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

Action Editor: Ulrik Brandes

References

adams, j., & Moody, J. (2007). To tell the truth: Measuring concordance in multiply reported network data. Social Networks, 29(1), 44–58.CrossRefGoogle Scholar
An, W., & Schramski, S. (2015). Analysis of contested reports in exchange networks based on actors credibility. Social Networks, 40, 25–33.CrossRefGoogle Scholar
Appadurai, A. (1985). Gratitude as a social mode in South India. Ethos, 13(3), 236–245.CrossRefGoogle Scholar
Baldassarri, D. (2015). Cooperative networks: Altruism, group solidarity, reciprocity, and sanctioning in Ugandan producer organizations. American Journal of Sociology, 121(2), 355–395.CrossRefGoogle ScholarPubMed
Ball, B., & Newman, M. E. J. (2013). Friendship networks and social status. Network Science, 1(1), 16–30.CrossRefGoogle Scholar
Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144), 1236498–1–7.CrossRefGoogle ScholarPubMed
Bell, D. C., Belli-McQueen, B., & Haider, Ali. (2007). Partner naming and forgetting: Recall of network members. Social Networks, 29(2), 279–299.CrossRefGoogle ScholarPubMed
Bernard, H. R., & Killworth, P. D. (1977). Informant accuracy in social network data II. Human Communication Research, 4(1), 3–18.CrossRefGoogle Scholar
Bernard, H. R., Killworth, P. D., Kronenfeld, D., & Sailer, L. (1984). The problem of informant accuracy: The validity of retrospective data. Annual Review of Anthropology, 13, 495–517.CrossRefGoogle Scholar
Bernard, H. R., Killworth, P. D., & Sailer, L. (1979). Informant accuracy in social network data IV: A comparison of clique-level structure in behavioral and cognitive network data. Social Networks, 2(3), 191–218.CrossRefGoogle Scholar
Bernard, H. R., Killworth, P. D., & Sailer, L. (1982). Informant accuracy in social-network data V. An experimental attempt to predict actual communication from recall data. Social Science Research, 11(1), 30–66.CrossRefGoogle Scholar
Blau, P. M. (1964). Exchange and power in social life. New York: J. Wiley.Google Scholar
Brashears, M. E. (2013). Humans use compression heuristics to improve the recall of social networks. Scientific Reports, 3, 1513.CrossRefGoogle ScholarPubMed
Butts, C. T. (2003). Network inference, error, and informant (in)accuracy: A Bayesian approach. Social Networks, 25(2), 103–140.CrossRefGoogle Scholar
Carley, K. M., & Krackhardt, D. (1996). Cognitive inconsistencies and non-symmetric friendship. Social Networks, 18(1), 1–27.CrossRefGoogle Scholar
Comola, M., & Fafchamps, M. (2014). Testing unilateral and bilateral link formation. The Economic Journal, 124(579), 954–976. Publisher: Wiley.CrossRefGoogle Scholar
De Soto, C. B. (1960). Learning a social structure. The Journal of Abnormal and Social Psychology, 60(3), 417.CrossRefGoogle ScholarPubMed
Faust, K., & Skvoretz, J. (2002). Comparing networks across space and time, size and species. Sociological Methodology, 32(1), 267–299.CrossRefGoogle Scholar
Feld, S. L., & Carter, W. C. (2002). Detecting measurement bias in respondent reports of personal networks. Social Networks, 24(4), 365–383.CrossRefGoogle Scholar
Freeman, L. C. (1992). Filling in the blanks: A theory of cognitive categories and the structure of social affiliation. Social Psychology Quarterly, 55(2), 118–127.CrossRefGoogle Scholar
Freeman, L. C., Romney, A. K., & Freeman, S. C. (1987). Cognitive structure and informant accuracy. American Anthropologist, 89(2), 310–325.CrossRefGoogle Scholar
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
Gouldner, A. W. (1960). The norm of reciprocity: A preliminary statement. American Sociological Review, 25(2), 161–178.CrossRefGoogle Scholar
Grippa, F., & Gloor, P. A. (2009). You are who remembers you. Detecting leadership through accuracy of recall. Social Networks, 31(4), 255–261.CrossRefGoogle Scholar
Gurven, M. (2004). Reciprocal altruism and food sharing decisions among Hiwi and Ache hunter-gatherers. Behavioral Ecology and Sociobiology, 56(4), 366–380.CrossRefGoogle Scholar
Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.CrossRefGoogle Scholar
Jaeggi, A. V., & Gurven, M. (2013). Reciprocity explains food sharing in humans and other primates independent of kin selection and tolerated scrounging: A phylogenetic meta-analysis. Proceedings of the Royal Society B: Biological Sciences, 280(1768), 20131615.CrossRefGoogle ScholarPubMed
Kasper, C., & Borgerhoff Mulder, M. (2015). Who helps and why?: Cooperative networks in Mpimbwe. Current Anthropology, 56(5), 701–732.CrossRefGoogle Scholar
Killworth, P. D., & Bernard, H. R. (1976). Informant accuracy in social network data. Human Organization, 35(3), 269–286.CrossRefGoogle Scholar
Killworth, P. D., & Bernard, H. R. (1979). Informant accuracy in social network data III: A comparison of triadic structure in behavioral and cognitive data. Social Networks, 2(1), 19–46.CrossRefGoogle Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P, Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.CrossRefGoogle Scholar
Koster, J. (2018). Family ties: The multilevel effects of households and kinship on the networks of individuals. Open Science, 5(4), 172159.Google ScholarPubMed
Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.CrossRefGoogle Scholar
Krackhardt, D., & Kilduff, M. (1990). Friendship patterns and culture: The control of organizational diversity. American Anthropologist, 92(1), 142–154.CrossRefGoogle Scholar
Krackhardt, D., & Kilduff, M. (1999). Whether close or far: Social distance effects on perceived balance in friendship networks. Journal of Personality and Social Psychology, 76(5), 770–782.CrossRefGoogle Scholar
Kranton, R. E. (1996). Reciprocal exchange: A self-sustaining system. The American Economic Review, 86(4), 830–851.Google Scholar
Lee, F., & Butts, C. T. (2018). Mutual assent or unilateral nomination? A performance comparison of intersection and union rules for integrating self-reports of social relationships. Social Networks, 55, 55–62.CrossRefGoogle Scholar
Lee, F., & Butts, C. T. (2020). On the validity of perceived social structure. Journal of Mathematical Psychology, 98, 102384.CrossRefGoogle Scholar
Malinowski, B. (1922). Argonauts of the western Pacific: An account of native enterprise and adventure in the archipelagoes of Melanesian New Guinea. London: G. Routledge & Sons, Ltd.Google Scholar
Malinowski, B. (1926). Crime and custom in savage society. London: K. Paul, Trench, Trubner & Co, Ltd.Google Scholar
Marin, A. (2004). Are respondents more likely to list alters with certain characteristics?: Implications for name generator data. Social Networks, 26(4), 289–307.CrossRefGoogle Scholar
Marineau, J. E., Labianca, G., Brass, D. J., Borgatti, S. P., & Vecchi, P. (2018). Individuals’ power and their social network accuracy: A situated cognition perspective. Social Networks, 54, 145–161.CrossRefGoogle Scholar
Marsden, P. V. (1990). Network data and measurement. Annual Review of Sociology, 16(1), 435–463.CrossRefGoogle Scholar
Mauss, M. (1954). The gift: Forms and functions of exchange in archaic societies. Glencoe, IL: Free Press.Google Scholar
Newman, M. E. J. (2018). Network structure from rich but noisy data. Nature Physics, 14(6), 542–545.CrossRefGoogle Scholar
Nolin, D. A. (2008). Food-sharing networks in Lamalera, Indonesia: Tests of adaptive hypotheses. Ph.D. dissertation, University of Washington, Seattle.Google Scholar
Nolin, D. A. (2010). Food-sharing networks in Lamalera, Indonesia: Reciprocity, kinship, and distance. Human Nature, 21(3), 243–268.CrossRefGoogle ScholarPubMed
Polanyi, K. (1957). The economy as instituted process. In Polanyi, K., Arensberg, C., & Pearson, H. (Eds.), Trade and market in the early empires: Economies in history and theory (pp. 243–270). New York: Free Press.Google Scholar
Power, E. A., & Ready, E. (2018). Building bigness: Reputation, prominence, and social capital in rural South India. American Anthropologist, 120(3), 444–459.CrossRefGoogle Scholar
Power, E. A., & Ready, E. (2019). Cooperation beyond consanguinity: Post-marital residence, delineations of kin, and social support among South Indian Tamils. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1780), 20180070.CrossRefGoogle ScholarPubMed
Ready, E. (2016). Food, sharing, and social structure in an Arctic mixed economy. Ph.D. dissertation, Stanford University, Stanford.Google Scholar
Ready, E., Habecker, P., Abadie, R., Davila, C. A., Rivera Villegas, A., Khan, B., & Dombrowski, K. (2020a). Comparing social network structures generated through sociometric and ethnographic methods. Field Methods, 32(4), 416–432.CrossRefGoogle Scholar
Ready, E., Habecker, P., Abadie, R., Khan, B., & Dombrowski, K. (2020b). Competing forces of withdrawal and disease avoidance in the risk networks of people who inject drugs. PLoS ONE, 15(6), e0235124.CrossRefGoogle ScholarPubMed
Ready, E., & Power, E. A. (2018). Why wage earners hunt: Food sharing, social structure, and influence in an Arctic mixed economy. Current Anthropology, 59(1), 74–97.CrossRefGoogle Scholar
Redhead, D., McElreath, R., & Ross, C. T. (2021). Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND. Psyarxiv, 10.31234/osf.io/mkp2y.CrossRefGoogle Scholar
Romney, A. K., & Weller, S. C. (1984). Predicting informant accuracy from patterns of recall among individuals. Social Networks, 6(1), 59–77.CrossRefGoogle Scholar
Safdari, H., Contisciani, M., & De Bacco, C. (2021). A generative model for reciprocity and community detection in networks. arXiv, 2012.08215.CrossRefGoogle Scholar
Sahlins, M. D. (1972). Stone Age economics. Chicago: Aldine-Atherton.Google Scholar
Shakya, H. B., Christakis, N. A., & Fowler, J. H. (2017). An exploratory comparison of name generator content: Data from rural India. Social Networks, 48, 157–168.CrossRefGoogle ScholarPubMed
Simpson, C. R. (in press). On the structural equivalence of coresidents and the measurement of village social structure. Social Networks.Google Scholar
Snijders, T. A. B. (2002). Markov chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3(2), 1–40.Google Scholar
Stewart, J., & Schweinberger, M. (2018). mlergm: Multilevel exponential-family random graph models. R package version 0.1.Google Scholar
Szell, M., Lambiotte, R., & Thurner, S. (2010). Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31), 13636–13641.CrossRefGoogle Scholar
Trivers, R. L. (1971). The evolution of reciprocal altruism. The Quarterly Review of Biology, 46(1), 35–57.CrossRefGoogle Scholar
Vaquera, E., & Kao, G. (2008). Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Social Science Research, 37(1), 55–72.CrossRefGoogle Scholar
Wang, D. J., Shi, X., McFarland, D. A., & Leskovec, J. (2012). Measurement error in network data: A re-classification. Social Networks, 34(4), 396–409.CrossRefGoogle Scholar
Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2021). Bayesian inference of network structure from unreliable data. Journal of Complex Networks, 8, cnaa046.CrossRefGoogle Scholar
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