Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem

References

• Abel, Guy J., and Nikola Sander. 2014. Quantifying global international migration flows, Science 343(6178): 1520–1522. doi:https://doi.org/10.1126/science.1248676
   PubMed Web of Science ®Google Scholar
• Adjiwanou, Vissého, Nurul Alam, Leontine Alkema, Gershim Asiki, Ayaga Bawah, Donatien Béguy, Valeria Cetorelli et al. 2020. Measuring excess mortality during the COVID-19 pandemic in low- and lower-middle income countries: The need for mobile phone surveys, SocArXiv. doi:https://doi.org/10.31235/osf.io/4bu3q
   Google Scholar
• Alburez-Gutierrez, Diego, Emilio Zagheni, Samin Aref, Sofia Gil-Clavel, André Grow, and Daniela Veronica Negraia. 2019. Demography in the digital era: New data sources for population research, Preprint. SocArXiv. doi:https://doi.org/10.31235/osf.io/24jp7
   Google Scholar
• Alderman, Harold, Jere R. Behrman, Hans-Peter Kohler, John A. Maluccio, and Susan Cotts Watkins. 2001. Attrition in longitudinal household survey data: Some tests for three developing-country samples, Demographic Research 5: 79–124. doi:https://doi.org/10.4054/DemRes.2001.5.4
   Google Scholar
• Alexander, Monica, and Leontine Alkema. 2018. Global estimation of neonatal mortality using a Bayesian hierarchical splines regression model, Demographic Research 38: 335–372. doi:https://doi.org/10.4054/DemRes.2018.38.15
   Web of Science ®Google Scholar
• Alexander, Monica, Kivan Polimis, and Emilio Zagheni. 2019. The impact of hurricane Maria on Out-migration from Puerto Rico: Evidence from Facebook data, Population and Development Review 45(3): 617–630. doi:https://doi.org/10.1111/padr.12289
   Web of Science ®Google Scholar
• Alexander, Monica, Kivan Polimis, and Emilio Zagheni. 2020. Combining social media and survey data to nowcast migrant stocks in the United States, Population Research and Policy Review. doi:https://doi.org/10.1007/s11113-020-09599-3
   Web of Science ®Google Scholar
• Alkema, Leontine, Doris Chou, Daniel Hogan, Sanqian Zhang, Ann-Beth Moller, Alison Gemmill, Doris Ma Fat et al. 2016. Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: A systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group, The Lancet 387(10017): 462–474. doi:https://doi.org/10.1016/S0140-6736(15)00838-7
   PubMed Web of Science ®Google Scholar
• Alkema, Leontine, Jin Rou New, Jon Pedersen, Danzhen You, and all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group. 2014. Child mortality estimation 2013: An overview of updates in estimation methods by the United Nations Inter-Agency Group for Child Mortality Estimation, PLOS ONE 9(7): e101112. doi:https://doi.org/10.1371/journal.pone.0101112
   PubMed Web of Science ®Google Scholar
• Alkema, Leontine, Adrian E. Raftery, Patrick Gerland, Samuel J. Clark, and Francois Pelletier. 2012. Estimating trends in the total fertility rate with uncertainty using imperfect data: Examples from West Africa, Demographic Research 26(15): 331–362. doi:https://doi.org/10.4054/DemRes.2012.26.15
   Web of Science ®Google Scholar
• Andriano, Liliana, and Julia Behrman. 2020. The effects of growing-season drought on young women’s life course transitions in a Sub-Saharan context, Population Studies 74(3): 331–350. doi:https://doi.org/10.1080/00324728.2020.1819551
   PubMed Web of Science ®Google Scholar
• Andriano, Liliana, and Christiaan W. S. Monden. 2019. The causal effect of maternal education on child mortality: Evidence from a quasi-experiment in Malawi and Uganda, Demography 56(5): 1765–1790. doi:https://doi.org/10.1007/s13524-019-00812-3
   PubMed Web of Science ®Google Scholar
• Aref Samin, Emilio Zagheni, and Jevin West. 2019. The demography of the peripatetic researcher: Evidence on highly mobile scholars from the Web of Science, in Ingmar Weber, Kareem M. Darwish, Claudia Wagner, Emilio Zagheni, Laura Nelson, Samin Aref, and Fabian Flöck (eds), Social Informatics. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 50–65. doi:https://doi.org/10.1007/978-3-030-34971-4_4
   Google Scholar
• Bakker, Bart F. M., Johan van Rooijen, and Leo van Toor. 2014. The system of social statistical datasets of Statistics Netherlands: An integral approach to the production of register-based social statistics, Statistical Journal of the IAOS 30(4): 411–424. doi:https://doi.org/10.3233/SJI-140803
   Google Scholar
• Barban, Nicola, Rick Jansen, Ronald de Vlaming, Ahmad Vaez, Jornt J. Mandemakers, Felix C. Tropf, Xia Shen et al. 2016. Genome-wide analysis identifies 12 loci influencing human reproductive behavior, Nature Genetics 48(12): 1462–1472. doi:https://doi.org/10.1038/ng.3698
   PubMed Web of Science ®Google Scholar
• Barbieri, Magali, John R. Wilmoth, Vladimir M. Shkolnikov, Dana Glei, Domantas Jasilionis, Dmitri Jdanov, Carl Boe et al. 2015. Data resource profile: The Human Mortality Database (HMD), International Journal of Epidemiology 44(5): 1549–1556. doi:https://doi.org/10.1093/ije/dyv105
   PubMed Web of Science ®Google Scholar
• Barclay, Kieron, Anna Baranowska-Rataj, Martin Kolk, and Anneli Ivarsson. 2020. Interpregnancy intervals and perinatal and child health in Sweden: A comparison within families and across social groups, Population Studies 74(3): 363–378. doi:https://doi.org/10.1080/00324728.2020.1714701
   PubMed Web of Science ®Google Scholar
• Barclay, Kieron, and Mikko Myrskylä. 2016. Advanced maternal age and offspring outcomes: Reproductive aging and counterbalancing period trends, Population and Development Review 42(1): 69–94. doi:https://doi.org/10.1111/j.1728-4457.2016.00105.x
   Web of Science ®Google Scholar
• Battiston, Pietro, Ridhi Kashyap, and Valentina Rotondi. 2021. Reliance on scientists and experts during an epidemic: Evidence from the COVID-19 outbreak in Italy, SSM – Population Health 13(March): 100721. doi:https://doi.org/10.1016/j.ssmph.2020.100721
   PubMedGoogle Scholar
• Bell, Martin, Elin Charles-Edwards, Dorota Kupiszewska, Marek Kupiszewski, John Stillwell, and Yu Zhu. 2015. Internal migration data around the world: Assessing contemporary practice, Population, Space and Place 21(1): 1–17. doi:https://doi.org/10.1002/psp.1848
   Web of Science ®Google Scholar
• Bell, Martin, Elin Charles-Edwards, Philipp Ueffing, John Stillwell, Marek Kupiszewski, and Dorota Kupiszewska. 2015. Internal migration and development: Comparing migration intensities around the world, Population and Development Review 41(1): 33–58. doi:https://doi.org/10.1111/j.1728-4457.2015.00025.x
   Web of Science ®Google Scholar
• Bernardi, Laura, and Andreas Klärner. 2014. Social networks and fertility, Demographic Research 30: 641–670. doi:https://doi.org/10.4054/DemRes.2014.30.22
   Web of Science ®Google Scholar
• Berry, Brent. 2006. Friends for better or for worse: Interracial friendship in the United States as seen through wedding party photos, Demography 43(3): 491–510. doi:https://doi.org/10.1353/dem.2006.0020
   PubMed Web of Science ®Google Scholar
• Bhrolcháin, Máire Ní, and Tim Dyson. 2007. On causation in demography: Issues and illustrations, Population and Development Review 33(1): 1–36. doi:https://doi.org/10.1111/j.1728-4457.2007.00157.x
   Web of Science ®Google Scholar
• Bicego, George. 1997. Estimating adult mortality rates in the context of the AIDS epidemic in Sub-Saharan Africa: Analysis of DHS sibling histories, Health Transition Review 7: 7–22. Available: https://www.jstor.org/stable/40652323
   PubMedGoogle Scholar
• Bijak, Jakub. 2019. Editorial: P-values, theory, replicability, and rigour, Demographic Research 41(32): 949–952. doi:https://doi.org/10.4054/DemRes.2019.41.32
   Web of Science ®Google Scholar
• Bijak, Jakub, and John Bryant. 2016. Bayesian demography 250 years after Bayes, Population Studies 70(1): 1–19. doi:https://doi.org/10.1080/00324728.2015.1122826
   PubMed Web of Science ®Google Scholar
• Bijak, Jakub, Jason Hilton, Eric Silverman, and Viet Dung Cao. 2013. Reforging the wedding ring: Exploring a semi-artificial model of population for the United Kingdom with Gaussian process emulators, Demographic Research 29(27): 729–766. doi:https://doi.org/10.4054/DemRes.2013.29.27
   Web of Science ®Google Scholar
• Billari, Francesco C. 2015. Integrating macro- and micro-level approaches in the explanation of population change, Population Studies 69(S1): S11–S20. doi:https://doi.org/10.1080/00324728.2015.1009712
   PubMed Web of Science ®Google Scholar
• Billari Francesco C., Francesco D’Amuri, and Juri Marcucci. 2016. Forecasting births using Google, in CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Valencia: Editorial Universitat Politècnica de València, pp. 119–119. doi:https://doi.org/10.4995/CARMA2016.2015.4301
   Google Scholar
• Billari, Francesco C., Alexia Prskawetz, Belinda Aparicio Diaz, and Thomas Fent. 2007. The “wedding-ring”: An agent-based marriage model based on social interaction, Demographic Research 17: 59–82. doi:https://doi.org/10.4054/DemRes.2007.17.3
   Web of Science ®Google Scholar
• Billari, Francesco C., and Emilio Zagheni. 2017. Big Data and population processes: A revolution? doi:https://doi.org/10.31235/osf.io/f9vzp.
   Google Scholar
• Blumenstock, Joshua E. 2012. Inferring patterns of internal migration from mobile phone call records: Evidence from Rwanda, Information Technology for Development 18(2): 107–125. doi:https://doi.org/10.1080/02681102.2011.643209
   Web of Science ®Google Scholar
• Bohon, Stephanie A. 2018. Demography in the big data revolution: Changing the culture to forge new frontiers, Population Research and Policy Review 37(3): 323–341. doi:https://doi.org/10.1007/s11113-018-9464-6
   Web of Science ®Google Scholar
• Bongaarts, John. 2007. Late marriage and the HIV epidemic in Sub-Saharan Africa, Population Studies 61(1): 73–83. doi:https://doi.org/10.1080/00324720601048343
   PubMed Web of Science ®Google Scholar
• Bongaarts, John, and Susan Cotts Watkins. 1996. Social interactions and contemporary fertility transitions, Population and Development Review 22(4): 639–682. doi:https://doi.org/10.2307/2137804
   Web of Science ®Google Scholar
• Brass, William. 1996. Demographic data analysis in less developed countries: 1946–1996, Population Studies 50(3): 451–467. doi:https://doi.org/10.1080/0032472031000149566
   Web of Science ®Google Scholar
• Bruch, Elizabeth E., and M. E. J. Newman. 2018. Aspirational pursuit of mates in online dating markets, Science Advances 4(8): eaap9815. doi:https://doi.org/10.1126/sciadv.aap9815
   PubMed Web of Science ®Google Scholar
• Bruns, Axel. 2019. After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research, Information, Communication & Society 22(11): 1544–1566. doi:https://doi.org/10.1080/1369118X.2019.1637447
   Web of Science ®Google Scholar
• Buckee, Caroline O., Satchit Balsari, Jennifer Chan, Mercè Crosas, Francesca Dominici, Urs Gasser, Yonatan H. Grad et al. 2020. Aggregated mobility data could help fight COVID-19, Science 368(6487): 145–146. doi:https://doi.org/10.1126/science.abb8021
   PubMed Web of Science ®Google Scholar
• Cattuto, Ciro, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza, Jean-François Pinton, and Alessandro Vespignani. 2010. Dynamics of person-to-person interactions from distributed RFID sensor networks, PLOS ONE 5(7): e11596. doi:https://doi.org/10.1371/journal.pone.0011596
   PubMed Web of Science ®Google Scholar
• Cesare, Nina, Hedwig Lee, Tyler McCormick, Emma Spiro, and Emilio Zagheni. 2018. Promises and pitfalls of using digital traces for demographic research, Demography 55(5): 1979–1999. doi:https://doi.org/10.1007/s13524-018-0715-2
   PubMed Web of Science ®Google Scholar
• Cleland, John. 1996. Demographic data collection in less developed countries 1946–1996, Population Studies 50(3): 433–450. doi:https://doi.org/10.1080/0032472031000149556
   PubMed Web of Science ®Google Scholar
• Coale, Ansley, and James Trussell. 1996. The development and use of demographic models, Population Studies 50(3): 469–484. doi:https://doi.org/10.1080/0032472031000149576
   PubMed Web of Science ®Google Scholar
• Coffey, Diane, Payal Hathi, Nazar Khalid, and Amit Thorat. 2021. Measurement of population mental health: Evidence from a mobile phone survey in India, Health Policy and Planning 36(5): 606–619. doi:https://doi.org/10.1093/heapol/czab023
   Web of Science ®Google Scholar
• Coleman, David. 2013. The twilight of the census, Population and Development Review 38: 334–351. doi:https://doi.org/10.1111/j.1728-4457.2013.00568.x
   Web of Science ®Google Scholar
• Connelly, Roxanne, Christopher J. Playford, Vernon Gayle, and Chris Dibben. 2016. The role of administrative data in the big data revolution in social science research, Social Science Research 59(September): 1–12. doi:https://doi.org/10.1016/j.ssresearch.2016.04.015
   PubMed Web of Science ®Google Scholar
• Corsi, Daniel J., Melissa Neuman, Jocelyn E. Finlay, and S. V. Subramanian. 2012. Demographic and Health Surveys: A profile, International Journal of Epidemiology 41(6): 1602–1613. doi:https://doi.org/10.1093/ije/dys184
   PubMed Web of Science ®Google Scholar
• Courgeau Daniel, Jakub Bijak, Robert Franck, and Eric Silverman. 2017. Model-based demography: Towards a research agenda, in André Grow and Jan Van Bavel (eds), Agent-based Modelling in Population Studies: Concepts, Methods, and Applications. The Springer Series on Demographic Methods and Population Analysis. Cham: Springer International Publishing, pp. 29–51. doi:https://doi.org/10.1007/978-3-319-32283-4_2
   Google Scholar
• Courgeau, Daniel, Robert Franck, and Julia Key. 2007. Demography, a fully formed science or a science in the making? An outline programme, Population 62(1): 39–45. doi:https://doi.org/10.3917/popu.701.0039
   Web of Science ®Google Scholar
• COVID-19 Mobility Data Network. 2021. Available: https://www.covid19mobility.org/partners/ (accessed: 30 July 2021).
   Google Scholar
• Crimmins, Eileen M. 1993. Demography: The past 30 years, the present, and the future, Demography 30(4): 579–591. doi:https://doi.org/10.2307/2061807
   PubMed Web of Science ®Google Scholar
• Crimmins, Eileen, Jung Ki, and Sarinnapha Vasunilashorn. 2010. Biodemography: New approaches to understanding trends and differences in population health and mortality, Demography 47(1): S41–S64. doi:https://doi.org/10.1353/dem.2010.0005
   PubMed Web of Science ®Google Scholar
• De Maria, Pier Francesco, Leonardo Tomazeli Duarte, Álvaro de Oliveira D’Antona, and Cristiano Torezzan. 2019. Digital humanities and big microdata: New approaches for demographic research, in Leonardo Bacelar Lima Santos, Rogério Galante Negri, and Tiago José de Carvalho (eds), Towards Mathematics, Computers and Environment: A Disasters Perspective. Cham: Springer International Publishing, pp. 217–231. doi:https://doi.org/10.1007/978-3-030-21205-6_11
   Google Scholar
• Deville, Pierre, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R. Stevens, Andrea E. Gaughan, Vincent D. Blondel et al. 2014. Dynamic population mapping using mobile phone data, Proceedings of the National Academy of Sciences 111(45): 15888–15893. doi:https://doi.org/10.1073/pnas.1408439111
   PubMed Web of Science ®Google Scholar
• Diamond-Smith, Nadia, Avery E. Holton, Sarah Francis, and Drew Bernard. 2020. Addressing anemia among women in India—an informed intervention using Facebook Ad manager, MHealth 6: 39. doi:https://doi.org/10.21037/mhealth-19-237a
   PubMedGoogle Scholar
• Diaz, Belinda Aparicio, Thomas Fent, Alexia Prskawetz, and Laura Bernardi. 2011. Transition to parenthood: The role of social interaction and endogenous networks, Demography 48(2): 559–579. doi:https://doi.org/10.1007/s13524-011-0023-6
   PubMed Web of Science ®Google Scholar
• Dodoo, F. N. 1998. Men matter: Additive and interactive gendered preferences and reproductive behavior in Kenya, Demography 35(2): 229–242. doi:https://doi.org/10.2307/3004054
   PubMed Web of Science ®Google Scholar
• Dorélien, Audrey, Deborah Balk, and Megan Todd. 2013. What Is urban? Comparing a satellite view with the Demographic and Health Surveys, Population and Development Review 39(3): 413–439. doi:https://doi.org/10.1111/j.1728-4457.2013.00610.x
   Web of Science ®Google Scholar
• Dribe, Martin, J. David Hacker, and Francesco Scalone. 2014. The impact of socio-economic status on net fertility during the historical fertility decline: A comparative analysis of Canada, Iceland, Sweden, Norway, and the USA, Population Studies 68(2): 135–149. doi:https://doi.org/10.1080/00324728.2014.889741
   PubMed Web of Science ®Google Scholar
• Dykstra, Pearl, Matthijs Kalmijn, Trudie Knijn, Aafke Komter, Aart Liefbroer, and Clara Mulder. 2006. Family Solidarity in the Netherlands. Available: https://repub.eur.nl/pub/18191/.
   Google Scholar
• Einav, Liran, and Jonathan Levin. 2014. The data revolution and economic analysis, Innovation Policy and the Economy 14(January): 1–24. doi:https://doi.org/10.1086/674019
   Google Scholar
• Entwisle, Barbara, Nathalie E. Williams, Ashton M. Verdery, Ronald R. Rindfuss, Stephen J. Walsh, George P. Malanson, Peter J. Mucha et al. 2016. Climate shocks and migration: An agent-based modeling approach, Population and Environment 38(1): 47–71. doi:https://doi.org/10.1007/s11111-016-0254-y
   PubMed Web of Science ®Google Scholar
• Ernsten, Annemarie, David McCollum, Zhiqiang Feng, Dawn Everington, and Zengyi Huang. 2018. Using linked administrative and census data for migration research, Population Studies 72(3): 357–367. doi:https://doi.org/10.1080/00324728.2018.1502463
   PubMed Web of Science ®Google Scholar
• Esteve, Albert, Ron Lesthaeghe, and Antonio López-Gay. 2012. The Latin American cohabitation boom, 1970–2007, Population and Development Review 38(1): 55–81. doi:https://doi.org/10.1111/j.1728-4457.2012.00472.x
   PubMed Web of Science ®Google Scholar
• Esteve, Albert, Christine R. Schwartz, Jan Van Bavel, Iñaki Permanyer, Martin Klesment, and Joan Garcia. 2016. The end of hypergamy: Global trends and implications, Population and Development Review 42(4): 615–625. doi:https://doi.org/10.1111/padr.12012
   PubMed Web of Science ®Google Scholar
• Facebook. 2021. Available: https://dataforgood.fb.com/tools/symptomsurvey/ (accessed: 30 July 2021).
   Google Scholar
• Fatehkia, Masoomali, Ridhi Kashyap, and Ingmar Weber. 2018. Using Facebook ad data to track the global digital gender gap, World Development 107(July): 189–209. doi:https://doi.org/10.1016/j.worlddev.2018.03.007
   Web of Science ®Google Scholar
• Feehan, Dennis M., and Curtiss Cobb. 2019. Using an online sample to estimate the size of an offline population, Demography 56(6): 2377–2392. doi:https://doi.org/10.1007/s13524-019-00840-z
   PubMed Web of Science ®Google Scholar
• Feehan, Dennis M., and Ayesha S. Mahmud. 2021. Quantifying population contact patterns in the United States during the COVID-19 pandemic, Nature Communications 12(1): 893. doi:https://doi.org/10.1038/s41467-021-20990-2
   PubMed Web of Science ®Google Scholar
• Ferrari, Giulia, and Ross Macmillan. 2019. Until work do us part: Labour migration and occupational stratification in non-cohabiting marriage, Population Studies 73(2): 197–216. doi:https://doi.org/10.1080/00324728.2019.1583359
   PubMed Web of Science ®Google Scholar
• Festy, Patrick. 2007. Numbering same-sex couples in censuses and population registers, Demographic Research 17: 339–368. doi:https://doi.org/10.4054/DemRes.2007.17.12
   Web of Science ®Google Scholar
• Fiorio, Lee, Emilio Zagheni, Guy Abel, Johnathan Hill, Gabriel Pestre, Emmanuel Letouzé, and Jixuan Cai. 2021. Analyzing the effect of time in migration measurement using georeferenced digital trace data. Demography 58(1): 51–74. doi:https://doi.org/10.1215/00703370-8917630
   PubMed Web of Science ®Google Scholar
• Fiorio, Lee, Guy Abel, Jixuan Cai, Emilio Zagheni, Ingmar Weber, and Guillermo Vinué. 2017. Using Twitter data to estimate the relationship between short-term mobility and long-term migration, in Proceedings of the 2017 ACM on Web Science Conference. WebSci'17. Troy, NY: Association for Computing Machinery, pp. 103–110. doi:https://doi.org/10.1145/3091478.3091496
   Google Scholar
• Fire, Michael, and Yuval Elovici. 2015. Data mining of online genealogy datasets for revealing lifespan patterns in human population, ACM Transactions on Intelligent Systems and Technology 6(2): 28:1–28:22. doi:https://doi.org/10.1145/2700464
   Web of Science ®Google Scholar
• Fletcher, Jason M. 2019. Environmental bottlenecks in children’s genetic potential for adult socio-economic attainments: Evidence from a health shock, Population Studies 73(1): 139–148. doi:https://doi.org/10.1080/00324728.2018.1498533
   PubMed Web of Science ®Google Scholar
• Frankenberg, Elizabeth, Wayan Suriastini, and Duncan Thomas. 2005. Can expanding access to basic healthcare improve children’s health status? Lessons from Indonesia’s “Midwife in the Village” programme, Population Studies 59(1): 5–19. doi:https://doi.org/10.1080/0032472052000332674
   PubMed Web of Science ®Google Scholar
• Freelon, Deen. 2018. Computational research in the post-API age, Political Communication 35(4): 665–668. doi:https://doi.org/10.1080/10584609.2018.1477506
   Web of Science ®Google Scholar
• Gabrielli, Lorenzo, Emanuel Deutschmann, Fabrizio Natale, Ettore Recchi, and Michele Vespe. 2019. Dissecting global air traffic data to discern different types and trends of transnational human mobility, EPJ Data Science 8(1): 26. doi:https://doi.org/10.1140/epjds/s13688-019-0204-x
   Web of Science ®Google Scholar
• Gakidou, Emmanuela, and Gary King. 2006. Death by survey: Estimating adult mortality without selection bias from sibling survival data, Demography 43(3): 569–585. doi:https://doi.org/10.1353/dem.2006.0024
   PubMed Web of Science ®Google Scholar
• Gauthier, Anne H., Susana Laia Farinha Cabaço, and Tom Emery. 2018. Generations and Gender Survey study profile, Longitudinal and Life Course Studies 9(4): 456–465. doi:https://doi.org/10.14301/llcs.v9i4.500
   Web of Science ®Google Scholar
• Gaydosh, Lauren, Daniel W. Belsky, Benjamin W. Domingue, Jason D. Boardman, and Kathleen Mullan Harris. 2018. Father absence and accelerated reproductive development in non-Hispanic white women in the United States, Demography 55(4): 1245–1267. doi:https://doi.org/10.1007/s13524-018-0696-1
   PubMed Web of Science ®Google Scholar
• Gebru, Timnit, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, and Li Fei-Fei. 2017. Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States, Proceedings of the National Academy of Sciences 114(50): 13108–13113. doi:https://doi.org/10.1073/pnas.1700035114
   PubMed Web of Science ®Google Scholar
• Gil-Clavel, Sofia, and Emilio Zagheni. 2019. Demographic differentials in Facebook usage around the world, Proceedings of the International AAAI Conference on Web and Social Media 13(July): 647–650. https://ojs.aaai.org/index.php/ICWSM/article/view/3263
   Google Scholar
• Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant. 2009. Detecting influenza epidemics using search engine query data, Nature 457(7232): 1012–1014. doi:https://doi.org/10.1038/nature07634
   PubMed Web of Science ®Google Scholar
• González-Bailón, Sandra, and Tommy E. Murphy. 2013. The effects of social interactions on fertility decline in nineteenth-century France: An agent-based simulation experiment, Population Studies 67(2): 135–155. doi:https://doi.org/10.1080/00324728.2013.774435
   PubMed Web of Science ®Google Scholar
• Google Ngram viewer. 2021. Available: https://books.google.com/ngrams (accessed: 30 July 2021).
   Google Scholar
• Grace, Kathryn, Andrew Verdin, Audrey Dorélien, Frank Davenport, Chris Funk, and Greg Husak. 2021. Exploring strategies for investigating the mechanisms linking climate and individual-level child health outcomes: An analysis of birth weight in Mali, Demography 58(2): 499–526. doi:https://doi.org/10.1215/00703370-8977484
   PubMed Web of Science ®Google Scholar
• Grantz, Kyra H., Hannah R. Meredith, Derek A. T. Cummings, C. Jessica E. Metcalf, Bryan T. Grenfell, John R. Giles, Shruti Mehta et al. 2020. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology, Nature Communications 11(1): 4961. doi:https://doi.org/10.1038/s41467-020-18190-5
   PubMed Web of Science ®Google Scholar
• Greene, Margaret E., and Ann E. Biddlecom. 2000. Absent and problematic men: Demographic accounts of male reproductive roles, Population and Development Review 26(1): 81–115. doi:https://doi.org/10.1111/j.1728-4457.2000.00081.x
   Web of Science ®Google Scholar
• Groves, Robert M. 2006. Nonresponse rates and nonresponse bias in household surveys, Public Opinion Quarterly 70(5): 646–675. doi:https://doi.org/10.1093/poq/nfl033
   Web of Science ®Google Scholar
• Groves, Robert M. 2011. Three eras of survey research, Public Opinion Quarterly 75(5): 861–871. doi:https://doi.org/10.1093/poq/nfr057
   Web of Science ®Google Scholar
• Grow, André, and Jan Van Bavel. 2016. Agent-Based Modelling in Population Studies: Concepts, Methods, and Applications. Cham: Springer.
   Google Scholar
• Grow, André, Daniela Perrotta, Emanuele Del Fava, Jorge Cimentada, Francesco Rampazzo, Sofia Gil-Clavel, and Emilio Zagheni. 2020. Addressing public health emergencies via Facebook surveys: Advantages, challenges, and practical considerations, Journal of Medical Internet Research 22(12): e20653. doi:https://doi.org/10.2196/20653
   PubMed Web of Science ®Google Scholar
• Guilmoto, Christophe Z. 2017. Gender bias in reproductive behaviour in Georgia, Indonesia, and Vietnam: An application of the own-children method, Population Studies 71(3): 265–279. doi:https://doi.org/10.1080/00324728.2017.1330489
   PubMed Web of Science ®Google Scholar
• Hathi, Payal, Sabrina Haque, Lovey Pant, Diane Coffey, and Dean Spears. 2017. Place and child health: The interaction of population density and sanitation in developing countries, Demography 54(1): 337–360. doi:https://doi.org/10.1007/s13524-016-0538-y
   PubMed Web of Science ®Google Scholar
• Hauer, Mathew, and Stephanie Bohon. 2020. Causal inference in population trends: Searching for demographic anomalies in big data, SocArXiv. doi:https://doi.org/10.31235/osf.io/xn2v9
   Google Scholar
• Hauer, Mathew E., and Alexis R. Santos-Lozada. 2021. Differential privacy in the 2020 census will distort COVID-19 rates, Socius: Sociological Research for a Dynamic World 7(January): 237802312199401. doi:https://doi.org/10.1177/2378023121994014
   Google Scholar
• Hauser, Robert M., and David Weir. 2010. Recent developments in longitudinal studies of aging in the United States, Demography 47(1): S111–S130. doi:https://doi.org/10.1353/dem.2010.0012
   PubMed Web of Science ®Google Scholar
• Helleringer, Stephane, Hans-Peter Kohler, Agnes Chimbiri, Praise Chatonda, and James Mkandawire. 2009. The Likoma Network Study: Context, data collection and initial results, Demographic Research 21(15): 427–468. doi:https://doi.org/10.4054/DemRes.2009.21.15
   PubMed Web of Science ®Google Scholar
• Helleringer, Stephane, Chong You, Laurence Fleury, Laetitia Douillot, Insa Diouf, Cheikh Tidiane Ndiaye, Valerie Delaunay et al. 2019. Improving age measurement in low- and middle-income countries through computer vision: A test in Senegal, Demographic Research 40(9): 219–260. doi:https://doi.org/10.4054/DemRes.2019.40.9
   Web of Science ®Google Scholar
• Henry, Louis. 1967. Manuel de Démographie Historique.
   Google Scholar
• Hertrich, Véronique, Pascaline Feuillet, Olivia Samuel, Assa Doumbia Gakou, and Aurélien Dasré. 2020. Can we study the family environment through census data? A comparison of households, dwellings, and domestic units in rural Mali, Population Studies 74(1): 119–138. doi:https://doi.org/10.1080/00324728.2019.1694166
   PubMed Web of Science ®Google Scholar
• HFD. 2021. Available: www.humanfertility.org (accessed: 30 July 2021).
   Google Scholar
• Hilbert, Martin, and Priscila López. 2011. The world’s technological capacity to store, communicate, and compute information, Science 332(6025): 60–65. doi:https://doi.org/10.1126/science.1200970
   PubMed Web of Science ®Google Scholar
• Hill, Kenneth, Peter Johnson, Kavita Singh, Anthony Amuzu-Pharin, and Yagya Kharki. 2018. Using census data to measure maternal mortality: A review of recent experience, Demographic Research 39(December): 337–364. doi:https://doi.org/10.4054/DemRes.2018.39.11
   PubMed Web of Science ®Google Scholar
• Hill, Kenneth, Alan D. Lopez, Kenji Shibuya, and Prabhat Jha. 2007. Interim measures for meeting needs for health sector data: Births, deaths, and causes of death, The Lancet 370(9600): 1726–1735. doi:https://doi.org/10.1016/S0140-6736(07)61309-9
   PubMed Web of Science ®Google Scholar
• HMD. 2021. Available: www.mortality.org (accessed: 30 July 2021).
   Google Scholar
• Holmlund, Helena, Helmut Rainer, and Thomas Siedler. 2013. Meet the parents? Family size and the geographic proximity between adult children and older mothers in Sweden, Demography 50(3): 903–931. doi:https://doi.org/10.1007/s13524-012-0181-1
   PubMed Web of Science ®Google Scholar
• Hosegood, Victoria, and Ian Timæus. 2006. Household composition and dynamics in KwaZulu Natal, South Africa: Mirroring social reality in longitudinal data collection, in Etienne Van De Walle (ed), African Households: Censuses and Surveys. New York: M.E. Sharpe, pp. 98–117.
   Google Scholar
• IAEG. 2014. A World That Counts: Mobilising the Data Revolution for Sustainable Development. Available: https://www.undatarevolution.org/report/.
   Google Scholar
• ITU. 2020. Measuring Digital Development: Facts and Figures 2020. Available: https://www.itu.int/en/ITU-D/Statistics/Dashboards/Pages/IFF.aspx.
   Google Scholar
• IUSSP. 2015. The IUSSP on a data revolution for development, Population and Development Review 41(1): 172–177. doi:https://doi.org/10.1111/j.1728-4457.2015.00041.x
   Web of Science ®Google Scholar
• IUSSP. 2021a. Available: https://iussp.org/en/panel/digital-demography (accessed: 30 July 2021).
   Google Scholar
• IUSSP. 2021b. Available: https://iussp.org/fr/data-collection-during-pandemic-global-south-challenges-and-opportunities (accessed: 30 July 2021).
   Google Scholar
• Jasilioniene, Aiva, Tomáš Sobotka, Dmitri A. Jdanov, Kryštof Zeman, Dora Kostova, Evgeny M. Andreev, Pavel Grigoriev et al. 2016. Data resource profile: The Human Fertility Database, International Journal of Epidemiology 45(4): 1077–1078. doi:https://doi.org/10.1093/ije/dyw135
   PubMed Web of Science ®Google Scholar
• Kalimeri, Kyriaki, Mariano G. Beiro, Andrea Bonanomi, Alessandro Rosina, and Ciro Cattuto. 2020. Traditional versus Facebook-based surveys: Evaluation of biases in self-reported demographic and psychometric information, Demographic Research 42: 133–148. doi:https://doi.org/10.4054/DemRes.2020.42.5
   Web of Science ®Google Scholar
• Kaplanis, Joanna, Assaf Gordon, Tal Shor, Omer Weissbrod, Dan Geiger, Mary Wahl, Michael Gershovits et al. 2018. Quantitative analysis of population-scale family trees with millions of relatives, Science 360(6385): 171–175. doi:https://doi.org/10.1126/science.aam9309
   PubMed Web of Science ®Google Scholar
• Kashyap, Ridhi, Francesco C. Billari, Nicolo Cavalli, Eric Qian, and Ingmar Weber. 2017. Ultrasound Technology and “Missing Women” in India: Analyses and now-casts based on Google searches. IUSSP Conference Paper, Cape Town. Available: https://iussp.confex.com/iussp/ipc2017/mediafile/Presentation/Paper4760/IUSSP.pdf.
   Google Scholar
• Kashyap, Ridhi, Masoomali Fatehkia, Reham Al Tamime, and Ingmar Weber. 2020. Monitoring global digital gender inequality using the online populations of Facebook and Google, Demographic Research 43: 779–816. doi:https://doi.org/10.4054/DemRes.2020.43.27
   Web of Science ®Google Scholar
• Kashyap, Ridhi, and Francisco Villavicencio. 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. doi:https://doi.org/10.1007/s13524-016-0500-z
   PubMed Web of Science ®Google Scholar
• Kishor, Sunita, and Kiersten Johnson. 2006. Reproductive health and domestic violence: Are the poorest women uniquely disadvantaged? Demography 43(2): 293–307. doi:https://doi.org/10.1353/dem.2006.0014
   PubMed Web of Science ®Google Scholar
• Kitchin, Rob. 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. Thousand Oaks, CA: Sage.
   Google Scholar
• Kiti, Moses C., Michele Tizzoni, Timothy M. Kinyanjui, Dorothy C. Koech, Patrick K. Munywoki, Milosch Meriac, Luca Cappa et al. 2016. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors, EPJ Data Science 5(1): 1–21. doi:https://doi.org/10.1140/epjds/s13688-015-0062-0
   Web of Science ®Google Scholar
• Klabunde, Anna, Sabine Zinn, Frans Willekens, and Matthias Leuchter. 2017. Multistate modelling extended by behavioural rules: An application to migration, Population Studies 71(S1): 51–67. doi:https://doi.org/10.1080/00324728.2017.1350281
   PubMed Web of Science ®Google Scholar
• Klüsener, Sebastian, Martin Dribe, and Francesco Scalone. 2019. Spatial and social distance at the onset of the fertility transition: Sweden, 1880–1900, Demography 56(1): 169–199. doi:https://doi.org/10.1007/s13524-018-0737-9
   PubMed Web of Science ®Google Scholar
• Kohler, Hans-Peter, Jere R. Behrman, and Susan C. Watkins. 2001. The density of social networks and fertility decisions: Evidence from South Nyanza District, Kenya, Demography 38(1): 43–58. doi:https://doi.org/10.1353/dem.2001.0005
   PubMed Web of Science ®Google Scholar
• Kolk, Martin. 2014. Multigenerational transmission of family size in contemporary Sweden, Population Studies 68(1): 111–129. doi:https://doi.org/10.1080/00324728.2013.819112
   PubMed Web of Science ®Google Scholar
• Kowal, Paul, Somnath Chatterji, Nirmala Naidoo, Richard Biritwum, Wu Fan, Ruy Lopez Ridaura, Tamara Maximova et al. 2012. Data resource profile: The World Health Organization Study on Global AGEing and Adult Health (SAGE), International Journal of Epidemiology 41(6): 1639–1649. doi:https://doi.org/10.1093/ije/dys210
   PubMed Web of Science ®Google Scholar
• Kreuter, Frauke, Georg-Christoph Haas, Florian Keusch, Sebastian Bähr, and Mark Trappmann. 2020. Collecting survey and smartphone sensor data with an app: Opportunities and challenges around privacy and informed consent, Social Science Computer Review 38(5): 533–549. doi:https://doi.org/10.1177/0894439318816389
   Web of Science ®Google Scholar
• Kukutai, Tahu, Victor Thompson, and Rachael McMillan. 2015. Whither the census? Continuity and change in census methodologies worldwide, 1985–2014, Journal of Population Research 32(1): 3–22. doi:https://doi.org/10.1007/s12546-014-9139-z
   Web of Science ®Google Scholar
• Lai, Shengjie, Elisabeth zu Erbach-Schoenberg, Carla Pezzulo, Nick W. Ruktanonchai, Alessandro Sorichetta, Jessica Steele, Tracey Li et al. 2019. Exploring the use of mobile phone data for national migration statistics, Palgrave Communications 5(1): 1–10. doi:https://doi.org/10.1057/s41599-019-0242-9
   Web of Science ®Google Scholar
• Laney, Douglas. 2001. 3D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note 6.
   Google Scholar
• Lansley, Guy, Wen Li, and Paul A. Longley. 2019. Creating a linked consumer register for granular demographic analysis, Journal of the Royal Statistical Society: Series A (Statistics in Society) 182(4): 1587–1605. doi:https://doi.org/10.1111/rssa.12476
   Web of Science ®Google Scholar
• Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The parable of Google Flu: Traps in big data analysis, Science 343(6176): 1203–1205. doi:https://doi.org/10.1126/science.1248506
   PubMed Web of Science ®Google Scholar
• Lazer, David, Alex (Sandy) Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis et al. 2009. Life in the network: The coming age of computational social science, Science (New York, N.Y.) 323(5915): 721–723. doi:https://doi.org/10.1126/science.1167742
   PubMed Web of Science ®Google Scholar
• Lazer, David, and Jason Radford. 2017. Data ex machina: Introduction to big data, Annual Review of Sociology 43(1): 19–39. doi:https://doi.org/10.1146/annurev-soc-060116-053457
   Web of Science ®Google Scholar
• Leasure, Douglas R., Warren C. Jochem, Eric M. Weber, Vincent Seaman, and Andrew J. Tatem. 2020. National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty, Proceedings of the National Academy of Sciences 117(39): 24173–24179. doi:https://doi.org/10.1073/pnas.1913050117
   PubMed Web of Science ®Google Scholar
• Leone, Tiziana, Ernestina Coast, Sonia Correa, and Clare Wenham. 2021. Web-based searching for abortion information during health emergencies: A case study of Brazil during the 2015/2016 Zika outbreak, Sexual and Reproductive Health Matters 29(1): 1883804. doi:https://doi.org/10.1080/26410397.2021.1883804
   PubMed Web of Science ®Google Scholar
• Letouzé, Emmanuel, and Johannes Jutting. 2015. Official statistics, Big Data and Human Development. Data-Pop Alliance White Paper Series. New York.
   Google Scholar
• Lloyd, Christopher T., Alessandro Sorichetta, and Andrew J. Tatem. 2017. High resolution global gridded data for use in population studies, Scientific Data 4(1): 170001. doi:https://doi.org/10.1038/sdata.2017.1
   PubMed Web of Science ®Google Scholar
• Longley, Paul, James Cheshire, and Alexander Singleton. 2018. Consumer Data Research. UCL Press.
   Google Scholar
• Lyngstad, Torkild Hovde. 2011. Does community context have an important impact on divorce risk? A fixed-effects study of twenty Norwegian first-marriage cohorts, European Journal of Population / Revue Européenne de Démographie 27(1): 57–77. doi:https://doi.org/10.1007/s10680-010-9226-6
   PubMed Web of Science ®Google Scholar
• Magadi, Monica Akinyi, Eliya Msiyaphazi Zulu, and Martin Brockerhoff. 2003. The inequality of maternal health care in urban sub-Saharan Africa in the 1990s, Population Studies 57(3): 347–366. doi:https://doi.org/10.1080/0032472032000137853
   PubMed Web of Science ®Google Scholar
• Mahapatra, Prasanta, Kenji Shibuya, Alan D. Lopez, Francesca Coullare, Francis C. Notzon, Chalapati Rao, and Simon Szreter. 2007. Civil registration systems and vital statistics: Successes and missed opportunities, The Lancet 370(9599): 1653–1663. doi:https://doi.org/10.1016/S0140-6736(07)61308-7
   PubMed Web of Science ®Google Scholar
• Masquelier, Bruno. 2013. Adult mortality from sibling survival data: A reappraisal of selection biases, Demography 50(1): 207–228. doi:https://doi.org/10.1007/s13524-012-0149-1
   PubMed Web of Science ®Google Scholar
• Mencarini, Letizia, Delia Irazú Hernández-Farías, Mirko Lai, Viviana Patti, Emilio Sulis, and Daniele Vignoli. 2019. Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis, Demographic Research 40: 693–724. doi:https://doi.org/10.4054/DemRes.2019.40.25
   Web of Science ®Google Scholar
• Mikkelsen, Lene, David E. Phillips, Carla AbouZahr, Philip W. Setel, Don de Savigny, Rafael Lozano, and Alan D. Lopez. 2015. A global assessment of civil registration and vital statistics systems: Monitoring data quality and progress, The Lancet 386(10001): 1395–1406. doi:https://doi.org/10.1016/S0140-6736(15)60171-4
   PubMed Web of Science ®Google Scholar
• Mills, Melinda C., and Charles Rahal. 2019. A scientometric review of genome-wide association studies, Communications Biology 2(1): 1–11. doi:https://doi.org/10.1038/s42003-018-0242-0
   PubMed Web of Science ®Google Scholar
• Mills, Melinda C., and Felix C. Tropf. 2020. Sociology, genetics, and the coming of age of sociogenomics, Annual Review of Sociology 46(1): 553–581. doi:https://doi.org/10.1146/annurev-soc-110619-031647
   Web of Science ®Google Scholar
• Monti, Andrea, Sven Drefahl, Eleonora Mussino, and Juho Härkönen. 2019. Over-coverage in population registers leads to bias in demographic estimates, Population Studies 74(3): 451–469. doi:https://doi.org/10.1080/00324728.2019.1683219
   PubMed Web of Science ®Google Scholar
• Mostafa, Tarek. 2016. Variation within households in consent to link survey data to administrative records: Evidence from the UK Millennium Cohort Study, International Journal of Social Research Methodology 19(3): 355–375. doi:https://doi.org/10.1080/13645579.2015.1019264
   Web of Science ®Google Scholar
• Mostafa, Tarek, and Richard Wiggins. 2015. The impact of attrition and non-response in birth cohort studies: A need to incorporate missingness strategies, Longitudinal and Life Course Studies 6(2): 131–146. doi:https://doi.org/10.14301/llcs.v6i2.312
   Web of Science ®Google Scholar
• Moultrie, Thomas A. 2016. Demography, demographers and the “data revolution” in Africa, Afrique Contemporaine No 258 2: 25–239. doi:https://doi.org/10.3917/afco.258.0025
   Google Scholar
• Moultrie, Tom A., Takudzwa S. Sayi, and Ian M. Timæus. 2012. Birth intervals, postponement, and fertility decline in Africa: A new type of transition? Population Studies 66(3): 241–258. doi:https://doi.org/10.1080/00324728.2012.701660
   PubMed Web of Science ®Google Scholar
• Obermeyer, Ziad, Julie Knoll Rajaratnam, Chang H. Park, Emmanuela Gakidou, Margaret C. Hogan, Alan D. Lopez, and Christopher J. L. Murray. 2010. Measuring adult mortality using sibling survival: A new analytical method and new results for 44 countries, 1974–2006, PLOS Medicine 7(4): e1000260. doi:https://doi.org/10.1371/journal.pmed.1000260
   PubMed Web of Science ®Google Scholar
• Oberski, Daniel L., and Frauke Kreuter. 2020. Differential privacy and social science: An urgent puzzle, Harvard Data Science Review 2: 1. doi:https://doi.org/10.1162/99608f92.63a22079
   Google Scholar
• Odimegwu, Clifford, Vesper H. Chisumpa, and Oluwaseyi Dolapo Somefun. 2018. Adult mortality in sub-Saharan Africa using 2001—2009 census data: Does estimation method matter? Genus 74(1): 10. doi:https://doi.org/10.1186/s41118-017-0025-3
   PubMedGoogle Scholar
• OECD. 2013. New Data for Understanding the Human Condition – OECD. OECD Global Science Forum Report on Data and Research Infrastructure for the Social Sciences. Available: https://www.oecd.org/sti/inno/new-data-for-understanding-the-human-condition.pdf.
   Google Scholar
• Oliver, Nuria, Bruno Lepri, Harald Sterly, Renaud Lambiotte, Sébastien Deletaille, Marco De Nadai, Emmanuel Letouzé et al. 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle, Science Advances 6(23): eabc0764. doi:https://doi.org/10.1126/sciadv.abc0764.
   PubMed Web of Science ®Google Scholar
• O’Neill, Dara, Michaela Benzeval, Andy Boyd, Lisa Calderwood, Cyrus Cooper, Louise Corti, Elaine Dennison et al. 2019. Data resource profile: Cohort and Longitudinal Studies Enhancement Resources (CLOSER), International Journal of Epidemiology 48(3): 675–676. doi:https://doi.org/10.1093/ije/dyz004
   PubMed Web of Science ®Google Scholar
• OPAL. 2021. Available: https://www.opalproject.org/home-en (accessed: 30 July 2021).
   Google Scholar
• Østby, Gudrun, Henrik Urdal, Andreas Forø Tollefsen, Andreas Kotsadam, Ragnhild Belbo, and Christin Ormhaug. 2018. Organized violence and institutional child delivery: Micro-level evidence from Sub-Saharan Africa, 1989–2014, Demography 55(4): 1295–1316. doi:https://doi.org/10.1007/s13524-018-0685-4.
   PubMed Web of Science ®Google Scholar
• Palmer, John R. B., Thomas J. Espenshade, Frederic Bartumeus, Chang Y. Chung, Necati Ercan Ozgencil, and Kathleen Li. 2013. New approaches to human mobility: Using mobile phones for demographic research, Demography 50(3): 1105–1128. doi:https://doi.org/10.1007/s13524-012-0175-z
   PubMed Web of Science ®Google Scholar
• Palotti, Joao, Natalia Adler, Alfredo Morales-Guzman, Jeffrey Villaveces, Vedran Sekara, Manuel Garcia Herranz, Musa Al-Asad et al. 2020. Monitoring of the Venezuelan exodus through Facebook’s advertising platform, PLOS ONE 15(2): e0229175. doi:https://doi.org/10.1371/journal.pone.0229175
   PubMed Web of Science ®Google Scholar
• Parsons, Christopher R., Ronald Skeldon, Terrie L. Walmsley, and L. Alan Winters. 2007. Quantifying international migration, a database of bilateral stocks, in Çağlar Özden and Maurice Schiff (eds), International Migration, Economic Development and Policy, Washington, The World Bank, pp. 17–58.
   Google Scholar
• Permanyer, Iñaki. 2013. Using census data to explore the spatial distribution of human development, World Development 46(June): 1–13. doi:https://doi.org/10.1016/j.worlddev.2012.11.015
   Web of Science ®Google Scholar
• Pesando, Luca Maria. 2019. Global family change: Persistent diversity with development, Population and Development Review 45(1): 133–168. doi:https://doi.org/10.1111/padr.12209
   PubMed Web of Science ®Google Scholar
• Polos, Jessica, and Jason Fletcher. 2019. Caesarean section and children’s health: A quasi-experimental design, Population Studies 73(3): 353–368. doi:https://doi.org/10.1080/00324728.2019.1624810
   PubMed Web of Science ®Google Scholar
• Potârcă, Gina, and Melinda Mills. 2015. Racial preferences in online dating across European countries, European Sociological Review 31(3): 326–341. doi:https://doi.org/10.1093/esr/jcu093
   Web of Science ®Google Scholar
• Preston, Samuel H. 1996. Population studies of mortality, Population Studies 50(3): 525–536. doi:https://doi.org/10.1080/0032472031000149596
   PubMed Web of Science ®Google Scholar
• Pullum, Thomas W. 2019. Strategies to assess the quality of DHS data, September. Available: https://dhsprogram.com/publications/publication-MR26-Methodological-Reports.cfm.
   Google Scholar
• Pullum, Thomas W., and Stan Becker. 2014. Evidence of omission and displacement in DHS birth histories, September. Available: https://www.dhsprogram.com/publications/publication-mr11-methodological-reports.cfm.
   Google Scholar
• Pullum, Thomas W., Bruno Schoumaker, Stan Becker, and Sarah E. K. Bradley. 2013. An assessment of DHS estimates of fertility and under-five mortality. IUSSP Conference Paper. Available: https://iussp.org/sites/default/files/event_call_for_papers/Pullum_Schoumaker_Becker_Bradley_IUSSP_2013.pdf.
   Google Scholar
• Pullum, Thomas W., and Sarah Staveteig. 2017. An assessment of the quality and consistency of age and date reporting in DHS surveys, 2000–2015, August. Available: https://www.dhsprogram.com/publications/publication-MR19-Methodological-Reports.cfm.
   Google Scholar
• Rampazzo, Francesco, Jakub Bijak, Agnese Vitali, Ingmar Weber, and Emilio Zagheni. 2021. A framework for estimating migrant stocks using digital traces and survey data: An application in the United Kingdom, Demography.
   Google Scholar
• Rampazzo, Francesco, Emilio Zagheni, Ingmar Weber, Maria Rita Testa, and Francesco Billari. 2018. Mater certa est, pater numquam: What can Facebook advertising data tell us about male fertility rates? Twelfth International AAAI Conference on Web and Social Media. Available: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17891.
   Google Scholar
• Rana, Md Juel, John Cleland, T. V. Sekher, and Sabu S. Padmadas. 2021. Disentangling the effects of reproductive behaviours and fertility preferences on child growth in India, Population Studies 75(1): 37–50. doi:https://doi.org/10.1080/00324728.2020.1826564
   PubMed Web of Science ®Google Scholar
• Randall, Sara, and Ernestina Coast. 2015. Poverty in African households: The limits of survey and census representations, The Journal of Development Studies 51(2): 162–177. doi:https://doi.org/10.1080/00220388.2014.968135
   Web of Science ®Google Scholar
• Randall, Sara, Ernestina Coast, and Tiziana Leone. 2011. Cultural constructions of the concept of household in sample surveys, Population Studies 65(2): 217–229. doi:https://doi.org/10.1080/00324728.2011.576768
   PubMed Web of Science ®Google Scholar
• Reid, Alice, Hannaliis Jaadla, Eilidh Garrett, and Kevin Schürer. 2020. Adapting the own children method to allow comparison of fertility between populations with different marriage regimes, Population Studies 74(2): 197–218. doi:https://doi.org/10.1080/00324728.2019.1630563
   PubMed Web of Science ®Google Scholar
• Reis, Ben Y., and John S. Brownstein. 2010. Measuring the impact of health policies using internet search patterns: The case of abortion, BMC Public Health 10(1): 514. doi:https://doi.org/10.1186/1471-2458-10-514
   PubMed Web of Science ®Google Scholar
• Rodríguez-Vignoli, Jorge, and Francisco Rowe. 2018. How is internal migration reshaping metropolitan populations in Latin America? A new method and new evidence, Population Studies 72(2): 253–273. doi:https://doi.org/10.1080/00324728.2017.1416155
   PubMed Web of Science ®Google Scholar
• Rosenfeld, Michael J. 2010. Nontraditional families and childhood progress through school, Demography 47(3): 755–775. doi:https://doi.org/10.1353/dem.0.0112
   PubMed Web of Science ®Google Scholar
• Rotondi, Valentina, Ridhi Kashyap, Luca Maria Pesando, Simone Spinelli, and Francesco C. Billari. 2020. Leveraging mobile phones to attain sustainable development, Proceedings of the National Academy of Sciences 117(24): 13413–13420. doi:https://doi.org/10.1073/pnas.1909326117
   PubMed Web of Science ®Google Scholar
• Ruggles, Steven. 2014. Big microdata for population research, Demography 51(1): 287–297. doi:https://doi.org/10.1007/s13524-013-0240-2
   PubMed Web of Science ®Google Scholar
• Ruggles, Steven, Catherine Fitch, Diana Magnuson, and Jonathan Schroeder. 2019. Differential privacy and census data: Implications for social and economic research, AEA Papers and Proceedings 109(May): 403–408. doi:https://doi.org/10.1257/pandp.20191107
   Google Scholar
• Ruggles, Steven, Catherine A. Fitch, and Evan Roberts. 2018. Historical census record linkage, Annual Review of Sociology 44(1): 19–37. doi:https://doi.org/10.1146/annurev-soc-073117-041447
   PubMed Web of Science ®Google Scholar
• Ruggles, Steven, Evan Roberts, Sula Sarkar, and Matthew Sobek. 2011. The North Atlantic Population Project: Progress and prospects, Historical Methods: A Journal of Quantitative and Interdisciplinary History 44(1): 1–6. doi:https://doi.org/10.1080/01615440.2010.515377
   Web of Science ®Google Scholar
• Sagiroglu, Seref, and Duygu Sinanc. 2013. Big data: A review, 2013 International Conference on Collaboration Technologies and Systems (CTS) 42–47. doi:https://doi.org/10.1109/CTS.2013.6567202
   Google Scholar
• Saha, Unnati Rani, and Arthur van Soest. 2011. Infant death clustering in families: Magnitude, causes, and the influence of better health services, Bangladesh: 1982–2005, Population Studies 65(3): 273–287. doi:https://doi.org/10.1080/00324728.2011.602100
   PubMed Web of Science ®Google Scholar
• Sala, Emanuela, Jonathan Burton, and Gundi Knies. 2012. Correlates of obtaining informed consent to data linkage: Respondent, interview, and interviewer characteristics, Sociological Methods & Research 41(3): 414–439. doi:https://doi.org/10.1177/0049124112457330
   Web of Science ®Google Scholar
• Salganik, Matthew J. 2019. Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press.
   Google Scholar
• Salganik, Matthew J., Ian Lundberg, Alexander T. Kindel, Caitlin E. Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq, Drew M. Altschul et al. 2020. Measuring the predictability of life outcomes with a scientific mass collaboration, Proceedings of the National Academy of Sciences 117(15): 8398–8403. doi:https://doi.org/10.1073/pnas.1915006117
   PubMed Web of Science ®Google Scholar
• Sankoh, Osman, and Peter Byass. 2012. The INDEPTH network: Filling vital gaps in global epidemiology, International Journal of Epidemiology 41(3): 579–588. doi:https://doi.org/10.1093/ije/dys081
   PubMed Web of Science ®Google Scholar
• Santos-Lozada, Alexis R., Jeffrey T. Howard, and Ashton M. Verdery. 2020. How differential privacy will affect our understanding of health disparities in the United States, Proceedings of the National Academy of Sciences 117(24): 13405–13412. doi:https://doi.org/10.1073/pnas.2003714117
   PubMed Web of Science ®Google Scholar
• Schmertmann, Carl P., Suzana M. Cavenaghi, Renato M. Assunção, and Joseph E. Potter. 2013. Bayes plus Brass: Estimating total fertility for many small areas from sparse census data, Population Studies 67(3): 255–273. doi:https://doi.org/10.1080/00324728.2013.795602
   PubMed Web of Science ®Google Scholar
• Schneider, Daniel, and Kristen Harknett. 2019. What’s to like? Facebook as a tool for survey data collection, Sociological Methods & Research. doi:https://doi.org/10.1177/0049124119882477
   PubMed Web of Science ®Google Scholar
• Schoumaker, Bruno. 2013. A Stata module for computing fertility rates and TFRs from birth histories: TFR2, Demographic Research 28: 1093–1144. doi:https://doi.org/10.4054/DemRes.2013.28.38
   Web of Science ®Google Scholar
• Schoumaker, Bruno. 2017. Measuring male fertility rates in developing countries with Demographic and Health Surveys: An assessment of three methods, Demographic Research 36: 803–850. doi:https://doi.org/10.4054/DemRes.2017.36.28
   Web of Science ®Google Scholar
• Schoumaker, Bruno, and Sarah R. Hayford. 2004. A person-period approach to analysing birth histories, Population (English Edition) 59(5): 689–702. doi:https://doi.org/10.3917/pope.405.0689
   Google Scholar
• Schürer, K., and E. Higgs. 2014. Integrated Census Microdata (I-CeM), 1851-1911. [Data Collection]. UK Data Service. SN: 7481.
   Google Scholar
• Short Fabic, Madeleine, YoonJoung Choi, and Sandra Bird. 2012. A systematic review of Demographic and Health Surveys: Data availability and utilization for research, Bulletin of the World Health Organization 90(8): 604–612. doi:https://doi.org/10.2471/BLT.11.095513
   PubMed Web of Science ®Google Scholar
• Skeldon, Ronald. 2018. International migration, internal migration, mobility and urbanization: Towards more integrated approaches, UN. doi:https://doi.org/10.18356/a11581d8-en
   Google Scholar
• Song, Shige, and Sarah A. Burgard. 2008. Does son preference influence children’s growth in height? A comparative study of Chinese and Filipino children, Population Studies 62(3): 305–320. doi:https://doi.org/10.1080/00324720802313553
   PubMed Web of Science ®Google Scholar
• State, Bogdan, Mario Rodriguez, Dirk Helbing, and Emilio Zagheni. 2014. Migration of professionals to the U.S., in Luca Maria Aiello and Daniel McFarland (eds), Social Informatics: 6th International Conference, SocInfo 2014, Barcelona, Spain, November 11–13, 2014. Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 531–543. doi:https://doi.org/10.1007/978-3-319-13734-6_37
   Google Scholar
• Stevens, Forrest R., Andrea E. Gaughan, Catherine Linard, and Andrew J. Tatem. 2015. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data, PLOS ONE 10(2): e0107042. doi:https://doi.org/10.1371/journal.pone.0107042
   PubMed Web of Science ®Google Scholar
• Stier, Sebastian, Johannes Breuer, Pascal Siegers, and Kjerstin Thorson. 2020. Integrating survey data and digital trace data: Key issues in developing an emerging field, Social Science Computer Review 38(5): 503–516. doi:https://doi.org/10.1177/0894439319843669
   Web of Science ®Google Scholar
• Tamgno, James K., Roger M. Faye, and Claude Lishou. 2013. Verbal autopsies, mobile data collection for monitoring and warning causes of deaths. 2013 15th International Conference on Advanced Communications Technology (ICACT), pp. 495–501.
   Google Scholar
• Tate, A. Rosemary, Lisa Calderwood, Carol Dezateux, and Heather Joshi. 2006. Mother’s consent to linkage of survey data with her child’s birth records in a multi-ethnic national cohort study, International Journal of Epidemiology 35(2): 294–298. doi:https://doi.org/10.1093/ije/dyi287
   PubMed Web of Science ®Google Scholar
• Timæus, Ian M., and Momodou Jasseh. 2004. Adult mortality in Sub-Saharan Africa: Evidence from Demographic and Health Surveys, Demography 41(4): 757–772. doi:https://doi.org/10.1353/dem.2004.0037
   PubMed Web of Science ®Google Scholar
• Tolonen, Hanna, Satu Helakorpi, Kirsi Talala, Ville Helasoja, Tuija Martelin, and Ritva Prättälä. 2006. 25-year trends and socio-demographic differences in response rates: Finnish Adult Health Behaviour Survey, European Journal of Epidemiology 21(6): 409–415. doi:https://doi.org/10.1007/s10654-006-9019-8
   PubMed Web of Science ®Google Scholar
• Torche, Florencia. 2011. The effect of maternal stress on birth outcomes: Exploiting a natural experiment, Demography 48(4): 1473–1491. doi:https://doi.org/10.1007/s13524-011-0054-z
   PubMed Web of Science ®Google Scholar
• Townsend, Nicholas, Sangeetha Madhavan, Stephen Tollman, Michel Garenne, and Kathleen Kahn. 2002. Children’s residence patterns and educational attainment in rural South Africa 1997, Population Studies 56(2): 215–225. doi:https://doi.org/10.1080/00324720215925
   Web of Science ®Google Scholar
• Tropf, Felix C., S. Hong Lee, Renske M. Verweij, Gert Stulp, Peter J. van der Most, Ronald de Vlaming, Andrew Bakshi et al. 2017. Hidden heritability due to heterogeneity across seven populations, Nature Human Behaviour 1(10): 757–765. doi:https://doi.org/10.1038/s41562-017-0195-1
   PubMed Web of Science ®Google Scholar
• Turra, Cassio M., Noreen Goldman, Christopher L. Seplaki, Dana A. Glei, Yu-Hsuan Lin, and Maxine Weinstein. 2005. Determinants of mortality at older ages: The role of biological markers of chronic disease, Population and Development Review 31(4): 675–698. doi:https://doi.org/10.1111/j.1728-4457.2005.00093.x
   Web of Science ®Google Scholar
• UN Department of Economic and Social Affairs. 2019. International Migrant Stock 2019. Available: https://www.un.org/en/development/desa/population/migration/data/estimates2/estimates19.asp.
   Google Scholar
• UNECE. 2021. Censuses of the 2020 Round. Available: https://statswiki.unece.org/display/censuses/Censuses+of+the+2020+round (accessed: 30 July 2021).
   Google Scholar
• United Nations Statistics Division. 2013. Overview of National Experiences for Population and Housing Censuses of the 2010 Round. Available: https://unstats.un.org/unsd/censuskb20/KnowledgebaseArticle10706.aspx.
   Google Scholar
• United Nations Statistics Division. 2020. Report on the Results of the UNSD Survey on 2020 Round Population and Housing Censuses.
   Google Scholar
• Vaupel, James W., Kenneth G. Manton, and Eric Stallard. 1979. The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16(3): 439–454. doi:https://doi.org/10.2307/2061224
   PubMed Web of Science ®Google Scholar
• Wardrop, N. A., W. C. Jochem, T. J. Bird, H. R. Chamberlain, D. Clarke, D. Kerr, L. Bengtsson et al. 2018. Spatially disaggregated population estimates in the absence of national population and housing census data, Proceedings of the National Academy of Sciences 115(14): 3529–3537. doi:https://doi.org/10.1073/pnas.1715305115
   PubMed Web of Science ®Google Scholar
• Watkins, Susan Cotts. 1993. If all we knew about women was what we read in Demography, what would we know? Demography 30(4): 551–577. doi:https://doi.org/10.2307/2061806
   PubMed Web of Science ®Google Scholar
• Wheldon, Mark C., Adrian E. Raftery, Samuel J. Clark, and Patrick Gerland. 2016. Bayesian population reconstruction of female populations for less developed and more developed countries, Population Studies 70(1): 21–37. doi:https://doi.org/10.1080/00324728.2016.1139164
   PubMed Web of Science ®Google Scholar
• Wilde, Joshua, Wei Chen, and Sophie Lohmann. 2020. COVID-19 and the future of US fertility: What can we learn from Google? Working Paper 13776. IZA Discussion Papers. Available: https://www.econstor.eu/handle/10419/227303.
   Google Scholar
• Willekens, Frans, Jakub Bijak, Anna Klabunde, and Alexia Prskawetz. 2017. The science of choice: An introduction, Population Studies 71(S1): 1–13. doi:https://doi.org/10.1080/00324728.2017.1376921
   PubMed Web of Science ®Google Scholar
• Wilson, Robin, Elisabeth zu Erbach-Schoenberg, Maximilian Albert, Daniel Power, Simon Tudge, Miguel Gonzalez, Sam Guthrie et al. 2016. Rapid and near real-time assessments of population displacement using mobile phone data following disasters: The 2015 Nepal earthquake, PLoS Currents. doi:https://doi.org/10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c
   PubMedGoogle Scholar
• Wrigley, E. A., and R. S. Schofield. 1983. English population history from family reconstitution: Summary results 1600–1799, Population Studies 37(2): 157–184. doi:https://doi.org/10.1080/00324728.1983.10408745
   Web of Science ®Google Scholar
• Yildiz, Dilek, Jo Munson, Agnese Vitali, Ramine Tinati, and Jennifer A. Holland. 2017. Using Twitter data for demographic research, Demographic Research 37(46): 1477–1514. doi:https://doi.org/10.4054/DemRes.2017.37.46
   Web of Science ®Google Scholar
• Zagheni Emilio, and Ingmar Weber. 2012. You are where you e-mail: Using e-mail data to estimate international migration rates, in Proceedings of the 4th Annual ACM Web Science Conference. WebSci'12. Evanston, Illinois: Association for Computing Machinery, pp. 348–351. doi:https://doi.org/10.1145/2380718.2380764
   Google Scholar
• Zagheni, Emilio, and Ingmar Weber. 2015. Demographic research with non-representative internet data, International Journal of Manpower 36(1): 13–25. doi:https://doi.org/10.1108/IJM-12-2014-0261
   Web of Science ®Google Scholar
• Zagheni, Emilio, Ingmar Weber, and Krishna Gummadi. 2017. Leveraging Facebook’s advertising platform to monitor stocks of migrants, Population and Development Review 43(4): 721–734. doi:https://doi.org/10.1111/padr.12102
   Web of Science ®Google Scholar