Calendar & Readings

Readings must be done before the date listed, so that you arrive prepared to discuss them. Required readings are marked in red with three stars (***) – students should be prepared to discuss the minutiae of these papers, and to provide critical commentary on the design and execution of the study. Recommended readings are marked by a single star (*) – students should skim these articles, to the point that they could summarize the data, methods, and key results. Other readings are optional.


Jan 25: Introduction and Overview

Required for those who are less familiar with Python:
Optional (skim a few!)

February 1: Methodological primer: Econometrics & Machine Learning

Required readings
Recommended reading for those less familiar with machine learning:
Recommended reading for those less familiar with development economics:
Optional readings

February 8: Data Sources: Traditional Data & Satellite Imagery

Required readings
Recommended reading
Optional readings

February 15: No Class (President’s Day)

February 22: Data Sources: Mobile phones

Required readings
Recommended readings
Optional readings

March 1: Data Sources: Internet, social media, and alternative forms of instrumentation

Required readings
Recommended readings
Optional readings

March 8: Catch-up day

March 15: Applications: Targeting

Required readings
Recommended readings
  • Chi, G., Fang, H., Chatterjee, S., Blumenstock, J.E., 2021. Micro-Estimates of Wealth for all Low- and Middle-Income Countries. Working paper (on bCourses).
  • Smythe, I., and Blumenstock, J.E., 2021. Geographic Micro-Targeting of Social Assistance with High-Resolution Poverty Maps. Working paper (on bCourses)
  • Noriega-Campero, A., Garcia-Bulle, B., Cantu, L.F., Bakker, M.A., Tejerina, L., Pentland, A., 2020. Algorithmic targeting of social policies: fairness, accuracy, and distributed governance, in: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20. Association for Computing Machinery, New York, NY, USA, pp. 241–251. https://doi.org/10.1145/3351095.3375784
Optional readings
  • Abelson, B., Varshney, K.R., Sun, J., 2014. Targeting Direct Cash Transfers to the Extremely Poor, in: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14. ACM, New York, NY, USA, pp. 1563–1572. https://doi.org/10.1145/2623330.2623335
  • Aiken, E., Bedoya, G., Coville, A., Blumenstock, J.E., 2020. Targeting Development Aid with Machine Learning and Mobile Phone Data. Working Paper. http://jblumenstock.com/files/papers/jblumenstock_ultra-poor.pdf
  • Alatas, V., Banerjee, A., Hanna, R., Olken, B.A., Tobias, J., 2012. Targeting the Poor: Evidence from a Field Experiment in Indonesia. American Economic Review 102, 1206–40. https://doi.org/10.1257/aer.102.4.1206
  • Alatas, V., Banerjee, A., Chandrasekhar, A.G., Hanna, R., Olken, B.A., 2016. Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia. American Economic Review 106, 1663–1704. https://doi.org/10.1257/aer.20140705
  • Björkegren, D., Blumenstock, J.E., and Knight, S. 2021. (Machine) Learning what Policymakers Value.  Working Paper.
  • Brown, C., Ravallion, M., van de Walle, D., 2018. A poor means test? Econometric targeting in Africa. Journal of Development Economics 134, 109–124. https://doi.org/10.1016/j.jdeveco.2018.05.004
  • Coady, D., Grosh, M., Hoddinott, J., 2004a. Targeting of transfers in developing countries: Review of lessons and experience. The World Bank.
  • Coady, D., Grosh, M., Hoddinott, J., 2004b. Targeting outcomes redux. The World Bank Research Observer 19, 61–85.
  • Cole, S., Fernando, A.N., 2012. The value of advice: Evidence from mobile phone-based agricultural extension.
  • Elbers, C., Fujii, T., Lanjouw, P., Özler, B., Yin, W., 2007. Poverty alleviation through geographic targeting: How much does disaggregation help? Journal of Development Economics 83, 198–213. https://doi.org/10.1016/j.jdeveco.2006.02.001
  • Gentilini, U., Almenfi, M., Orton, I., Dale, P., 2020. Social Protection and Jobs Responses to COVID-19 (No. 33635), World Bank Other Operational Studies, World Bank Other Operational Studies. The World Bank.
  • Hanna, R., Olken, B.A., 2018. Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries. Journal of Economic Perspectives 32, 201–226. https://doi.org/10.1257/jep.32.4.201
  • Lindert, K., Karippacheril, T.G., Caillava, I.R., Chávez, K.N., 2020. Sourcebook on the Foundations of Social Protection Delivery Systems. World Bank Publications.
  • Noriega-Campero, A., Garcia-Bulle, B., Cantu, L.F., Bakker, M.A., Tejerina, L., Pentland, A., 2020. Algorithmic targeting of social policies: fairness, accuracy, and distributed governance, in: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20. Association for Computing Machinery, New York, NY, USA, pp. 241–251. https://doi.org/10.1145/3351095.3375784
  • Varshney, K.R., Chen, G.H., Abelson, B., Nowocin, K., Sakhrani, V., Xu, L., Spatocco, B.L., 2015. Targeting Villages for Rural Development Using Satellite Image Analysis. Big Data 3, 41–53. https://doi.org/10.1089/big.2014.0061
  • Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., Burke, M., 2020. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications 11, 2583. https://doi.org/10.1038/s41467-020-16185-w

March 22: No Class (Spring Break)

March 29: Targeting, part 2

  • Refer to readings assigned for March 15

April 5: Deep Dive on Ethics and Privacy

Required readings
Recommended readings
Optional readings

April 12: Mobility, Disasters, Displacement, Crime, and Civil Unrest

Required readings
Recommended readings
  • Tai, Mehra, Blumenstock, 2021. Estimating the Effect of Violence on Internal Displacement using Mobile Phone Data (on bCourses).
  • Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Hentenryck, P.V., Fowler, J., Cebrian, M., 2016. Rapid assessment of disaster damage using social media activity. Science Advances 2, e1500779. doi:10.1126/sciadv.1500779
Optional readings

April 19: Applications: Financial Inclusion

Required readings
Recommended readings
Optional readings

April 26: Final presentations


Optional: Public Health and Epidemiology

Required readings
Recommended readings
Recent work on Covid-19
  • Jeffrey, B., Walters, C.E., Ainslie, K.E., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N.F., 2020. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research 5.
  • Kraemer, M.U., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D.M., Du Plessis, L., Faria, N.R., Li, R., Hanage, W.P., 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497.
  • Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K.E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N.F., Cattarino, L., Cooper, L.V., 2021. Reduction in mobility and COVID-19 transmission. Nature communications 12, 1–9.
  • Warren, M.S., Skillman, S.W., 2020. Mobility changes in response to COVID-19. arXiv preprint arXiv:2003.14228.
  • Wellenius, G.A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T.C., Hennessy, J., Williams, B., Gadepalli, K., Boulange, A., Pearce, A., 2020. Impacts of state-level policies on social distancing in the United States using aggregated mobility data during the COVID-19 pandemic. arXiv preprint arXiv:2004.10172.
Optional readings

Optional: Crime and Civil Unrest

Optional: Social networks

Optional: Agriculture, Environment & Sustainability