Computational Demography Reading List

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.

Computational social science and demography

  • (Summary) Billari, F., and Zagheni, E. (2017). Big data and population processes: a revolution? Proceedings of the Italian Statistical Society 2017

  • (Summary) Bavel, J.V., and Grow, A. (2016). Introduction: Agent-based modelling as a tool to advance evolutionary population theory. In eds. Bavel, Grow, Agent-based modelling in population studies

  • Mario Molina and Filiz Garip. (2019). “Machine Learning for Sociology.” Annual Review of Sociology 45: 27-45.

Social media and demographic processes

  • (Summary) Zagheni E., Garimella, K., Weber, I., and State, B. (2014). Inferring international and internal migration patterns from Twitter data. Proceedings of ACM WWW (Companion): 439-444

  • (Summary) Zagheni, E., Weber, I., & Gummadi, K. (2017). Leveraging Facebook’s Advertising Platform to Monitor Stocks of Migrants. Population and Development Review, 43(4), 721-734.

  • (Summary) Bruch, Elizabeth and Mark Newman. (2018). “Aspirational Pursuit of Mates in Online Dating Markets.” Science Advances, 4

  • Yildiz, D., Munson, J., Vitali, A., Tinati, R., & Holland, J. A. (2017). Using Twitter data for demographic research. Demographic Research, 37(1), 1477–1514.

  • Rampazzo, F., Zagheni, E., Weber, I., Testa, M. R., & Billari, F. (2018). Mater certa est, pater numquam: What can facebook advertising data tell us about male fertility rates? 12th International AAAI Conference on Web and Social Media, ICWSM 2018, 672–675.

Representativeness of social media data

  • Zagheni, E., & Weber, I. (2015). Demographic research with non-representative internet data. International Journal of Manpower, 36(1), 13–25.

  • (Summary) Wang, W., Rothschild, D., Goel, S., & Gelman, A. (2015). Forecasting elections with non-representative polls. International Journal of Forecasting, 31(3), 980–991.

  • Gil-Clavel, S., & Zagheni, E. (2019). Demographic differentials in facebook usage around the world. Proceedings of the 13th International Conference on Web and Social Media, ICWSM 2019, Icwsm, 647–650.

  • Feehan, D. M., & Cobb, C. (2019). Using an online sample to estimate the size of an offline population. Demography, 2377–2392. 00840-z)

  • Alexander, M., Polimis, K., & Zagheni, E. (2020). Combining social media and survey data to nowcast migrant stocks in the United States. Population Research and Policy Review, 0123456789.

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.

  • (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.

  • (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.

Respondent-driven sampling

  • (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.

  • (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.

  • Crawford, F. W. (2016). The graphical structure of respondent-driven sampling. Sociological Methodology, 46(1), 187–211.

  • (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).

  • 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.

  • 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.

  • (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.

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

Labor markets

  • (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.

  • (Summary) Baker, R. S., Bloom, N., & Davis, S. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(November), 1593–1636.

  • Brand, J. E., Xu, J., Koch, B., & Geraldo, P. (2021). Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning. In Sociological Methodology.

  • 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.

  • 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.

  • (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.

  • 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.

  • 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.

Sociocultural processes

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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.

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.

  • 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).

  • 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).

  • 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.

  • 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.