I put together this reading list as part of a guided reading with Professor Matthew Hall. We intend it to function as an overview of the field and as a syllabus for a graduate seminar. The list broadly covers the use of new data sources for demographic estimation, tools for accessing hard to reach populations, and some specific examples of agent-based modelling.
Hard to reach populations
Residuals, logic, and imputation
(Summary) Fazel-Zarandi, M. M., Feinstein, J. S., & Kaplan, E. H. (2018). The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990 to 2016. PLoS ONE, 13(9), 1–11. https://doi.org/10.1371/journal.pone.0201193
(Summary) Capps, R., Gelatt, J., Van Hook, J., & Fix, M. (2018). Commentary on “The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990-2016.” PLoS ONE, 13(9), 1–10. https://doi.org/10.1371/journal.pone.0204199
(Summary) Capps, R., Bachmeier, J. D., & Van Hook, J. (2018). Estimating the Characteristics of Unauthorized Immigrants Using U.S. Census Data: Combined Sample Multiple Imputation. Annals of the American Academy of Political and Social Science, 677(1), 165–179. https://doi.org/10.1177/0002716218767383
(Summary) Merli, M. G., Moody, J., Smith, J., Li, J., Weir, S., & Chen, X. (2015). Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling. Social Science and Medicine, 125, 79–93. https://doi.org/10.1016/j.socscimed.2014.04.022
(Summary) Crawford, F. W., Wu, J., & Heimer, R. (2018). Hidden population size estimation from respondent-driven sampling: a network approach. Journal of the American Statistical Association, 113(522), 755–766. https://doi.org/10.1080/01621459.2017.1285775.Hidden
Crawford, F. W. (2016). The graphical structure of respondent-driven sampling. Sociological Methodology, 46(1), 187–211. https://doi.org/10.1177/0081175016641713
(Summary) Helms, Y. B., Hamdiui, N., Kretzschmar, M. E. E., Rocha, L. E. C., Van Steenbergen, J. E., Bengtsson, L., Thorson, A., Timen, A., & Stein, M. L. (2021). Applications and recruitment performance of web-based respondent-driven sampling: Scoping review. Journal of Medical Internet Research, 23(1). https://doi.org/10.2196/17564
Li, J., Valente, T. W., Shin, H. S., Weeks, M., Zelenev, A., Moothi, G., Mosher, H., Heimer, R., Robles, E., Palmer, G., & Obidoa, C. (2018). Overlooked threats to respondent driven sampling estimators: Peer recruitment reality, degree measures, and random selection assumption. AIDS and Behavior, 22(7), :2340-2359. https://doi.org/10.1007/s10461-017-1827-1
Mouw, T., & Verdery, A. M. (2012). Network Sampling with Memory: A Proposal for More Efficient Sampling from Social Networks. Sociological Methodology, 42(1), 206–256. https://doi.org/10.1177/0081175012461248
(Summary) Goel, S., & Salganik, M. J. (2010). Assessing respondent-driven sampling. Proceedings of the National Academy of Sciences of the United States of America, 107(15), 6743–6747. https://doi.org/10.1073/pnas.1000261107
Regions with sparse data
- Blumenstock, J.E., Cadamuro, G and On, R. (2015) Predicting Poverty and Wealth from Mobile Phone Metadata. Science, 350:1073-1076
(Summary) Turrell, A., Speigner, B. J., Djumalieva, J., Copple, D., & Thurgood, J. (2019). Transforming Naturally Occurring Text Data Into Economic Statistics: The Case of Online Job Vacancy Postings. NBER Working Paper Series, 34. http://www.nber.org/papers/w25837
(Summary) Baker, R. S., Bloom, N., & Davis, S. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(November), 1593–1636. https://doi.org/https://doi.org/10.1093/qje/qjw024
Brand, J. E., Xu, J., Koch, B., & Geraldo, P. (2021). Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning. In Sociological Methodology. https://doi.org/10.1177/0081175021993503
Cengiz, D., Dube, A., Lindner, A., & Zentler-Munro, D. (2021). Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes. http://www.nber.org/papers/w28399.pdf
Lukac, M., & Grow, A. (2020). Reputation systems and recruitment in online labor markets: insights from an agent-based model. Journal of Computational Social Science, 0123456789. https://doi.org/10.1007/s42001-020-00072-x
(Summary). Subbotin, A., & Aref, S. (2020). Brain drain and brain gain in Russia: Analyzing international migration of researchers by discipline using scopus bibliometric data 1996-2020. ArXiv, 49(May), 0–26.
(Summary). Fiorio, L., Zagheni, E., Abel, G., Hill, J., Pestre, G., Letouzé, E., & Cai, J. (2020). Analyzing the Effect of Time in Migration Measurement Using Geo-referenced Digital Trace Data. 49(May), 0–41. https://doi.org/10.4054/MPIDR-WP-2020-024
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D. and Tatem, A.J. (2014) Dynamic Population Mapping Using Mobile Phone Data. Proceedings of the National Academy of Sciences 111(45):15888-15893.
Phillips, D. C. (2020). Measuring Housing Stability With Consumer Reference Data. Demography, 57(4), 1323–1344. https://doi.org/10.1007/s13524-020-00893-5
Palmer, J.R.B., Espenshade, T.J., Bartumeus, F., Chung, C.Y., Ozgencil, N.E., and Li K. (2012). New Approaches to Human Mobility: Using Mobile Phones for Demographic Research. Demography(50):1105-1128.
Marquez, N., Garimella, K., Toomet, O., Weber, I. G., & Zagheni, E. (2019). Segregation and Sentiment: Estimating Refugee Segregation and Its Effects Using Digital Trace Data. Guide to Mobile Data Analytics in Refugee Scenarios, 49(0), 265–282. https://doi.org/10.1007/978-3-030-12554-7_14
(Summary) Muggleton, N., Parpart, P., Newall, P., Leake, D., Gathergood, J., & Stewart, N. (2021). The association between gambling and financial, social and health outcomes in big financial data. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-01045-w
(Summary) Stewart, I., Flores, R. D., Riffe, T., Weber, I., & Zagheni, E. (2019). Rock, rap, or reggaeton?: Assessing Mexican immigrants’ cultural assimilation using Facebook data. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 3258–3264. https://doi.org/10.1145/3308558.3313409
(Summary). Herdaǧdelen, A., State, B., Adamic, L., & Mason, W. (2016). The social ties of immigrant communities in the United States. WebSci 2016 - Proceedings of the 2016 ACM Web Science Conference, 78–84. https://doi.org/10.1145/2908131.2908163
Gil-Clavel, S., Zagheni, E., & Bardone, V. (2020). Close Social Networks among Older Adults : The Online and Offline Perspectives. 49(October), 0–25.
Dimaggio, P., & Garip, F. (2012). Network effects and social inequality. Annual Review of Sociology, 38, 93–118. https://doi.org/10.1146/annurev.soc.012809.102545
Simulating population processes
Billari, F.C., Fent, T., Prskawetz, A., & Scheffran, J. (2006). Agent-based computational modelling: An introduction. In eds. Billari, Fent, Prskawetz, Scheffran, Agent-based computational modelling: Applications in demography, social, economic and environmental sciences.
Hilton, J. and Bijak, J. (2016). Design and analysis of demographic simulations. In eds. Bavel, Grow, Agent-based modelling in population studies.
Williams, N.E., O’Brien, M.L. (2016). Using survey data for agent-based modelling: design and challenges in a model of armed conflict and population change. In eds. Bavel, Grow, Agent-based modelling in population studies.
Grow, A. (2016). Regression metamodels for sensitivity analysis in agent-based computational demography. In eds. Bavel, Grow, Agent-based modelling in population studies.
Wolfson M., Gribble, S., & Beall, R. (2016). Exploring contingent inequalities: Building the theoretical health inequality model. In eds. Bavel, Grow, Agent-based modelling in population studies.
Kashyap, R., & Villavicencio, F. (2016). The Dynamics of Son Preference, Technology Diffusion, and Fertility Decline Underlying Distorted Sex Ratios at Birth: A Simulation Approach. Demography, 53(5), 1261–1281. https://doi.org/10.1007/s13524-016-0500-z
Agent-based modelling and neighborhood choice
Bruch, E. E., & Mare, R. D. (2006). Neighborhood choice and neighborhood change. American Journal of Sociology, 112(3), 667–709. https://doi.org/10.1086/507856
Van de Rijt, A., Siegel, A., & Macy, M. W. (2009). Neighborhood chance and neighborhood change: A comment on Bruch and Mare. American Journal of Sociology, 114(4). https://doi.org/https://doi.org/10.1086/588795
Bruch, E. E., & Mare, R. D. (2009). Preferences and pathways to segregation: Reply to Van de Rijt, Siegel, and Macy. American Journal of Sociology, 114(4). https://doi.org/https://doi.org/10.1086/597599
Clark, W. A. V., & Fossett, M. (2008). Understanding the social context of the Schelling segregation model. Proceedings of the National Academy of Sciences of the United States of America, 105(11), 4109–4114. https://doi.org/10.1073/pnas.0708155105
Bruch, E. E., & Mare, R. D. (2012). Methodological Issues in the Analysis of Residential Preferences, Residential Mobility, and Neighborhood Change. Sociological Methodology, 42(1), 103–154.https://doi.org/10.1177/0081175012444105