Big Data for Sustainable Development
This course introduces students to the key methods and concepts behind new AI and Big Data methods, this course does not require econometrics but some knowledge of R and/or Stata and at least two undergraduate Statistics courses are required to do the case studies. Students who do not have this background are invited to enroll in a graduate tutorial that reflects their particular interests and to complete a paper on this topic inside the Summer grading window. Big Data has many applications in economics, see Rajj Chetty’s Vox Article on ECON 1152 new course note the last 4 lectures of Chetty’s Harvard course ECON 1152 includes exercises with both R and Stata code. Here is Raj Chetty’s course as well as Marcel La Fleur’s talk on Fordham Presentation on Big Data and the UN SDGs
Guest speakers: (dates TBA) Emmanuel Letouzé Founder, Data-Pop Alliance; Marcelo La Fleur, UN DESA; Frank Hsu, CIS, Mustapha Akinkunmi, American University of Nigeria; Erick Rengifo, CIPS Founder
Related Big Data, GIS, and AI courses
- UCB, Professor Joshua Blumenstock, Info 288: Big Data & Development,
- Harvard & Stanford, Raj Chetty Using Big Data to Solve Social and Economic Problems
- Melissa Dell, MIT Economics Department, 2009 GIS Analysis for Applied Economists
- Kudamatsu’s Course: ArcGIS 10 for Economics Research
- CIESIN Thematic Guide to Night-time Light Remote Sensing and its Applications
General Data Resources
- MIT geospatial library
- Harvard Center for Geographic Analysis Newsletter Geospatial library
- Tufts GIS Tutorials
- GIS Training Manual for Historians
- Environmental Systems Research Institute (ESRI) ArcGIS tutorials
- GIS Programming and Automation (Open Access Online Class, PennState )
- Python Scripting for ArcGIS
Dealing with Spatial Data in R and Stata
- Spatial Data Analysis in Stata
- Stata in space: Econometric analysis of spatially explicit raster data
- For a list R spatial packages see the Analysis of Spatial Data library
- Common R packages for Big Data and GIS are:
Relevant CIS and Economics Papers
- Jones, Charles I., and Charles Tonetti (2020) “Nonrivalry and the Economics of Data” American Economic Review 110, no. 9 (2020): 2819-58.
- Furman, Jason, & Robert Seamans (2019) “AI and the Economy” Innovation policy and the economy 19,1:161-191.
- Varian, Hal (2018) Artificial intelligence, economics, and industrial organization. Working Paper w24839. National Bureau of Economic Research.
- Chen, X and W D Nordhaus (2011) “Using luminosity data as a proxy for economic statistics“, Proceedings of the National Academy of Sciences. Cited by 815
- Elvidge, C D, K E Baugh, E A Kihn, H W Kroehl and E R Davis (1997) “Mapping city lights with night-time data from the DMSP operational linescan system”, Photogrammetric Engineering & Remote Sensing, 63(6): 727-734.
- Feenstra, R C, R Inklaar and M P Timmer (2015) “The next generation of the Penn World Tables”, American Economic Review, 105(10): 3150-3182.
- Henderson, J. V., A. Storeygard and D. Weil (2012) “Measuring Growth from Outer Space”,
American Economic Review, 102(2), pp.994-1028.
- Pinkovskiy, M. (2013) “Economic Discontinuities at Borders: Evidence from Satellite Data on
Lights at Night”, Working Paper.
- Pinkovskiy, M L and X Sala-i-Martin (2016a) “Lights, camera, … income! Illuminating the national accounts-household surveys debate”, Quarterly Journal of Economics, 131(2): 579-631.
- Pinkovskiy, M L and X Sala-i-Martin (2016b) “Newer need not be better: Evaluating the Penn World Tables and the World Development Indicators using night-time lights”, NBER, Working Paper no 22216.
- Harttgen, K., Klasen, S., & Vollmer, S. (2013). An African growth miracle? Or: what do asset indices tell us about trends in economic performance?.Review of income and Wealth, 59(S1), S37-S61.
- Young, Alwyn. “The African Growth Miracle.” Journal of Political Economy 120.4 (2012): 696-739.
- Andy Schmitz, 2012, Geographic Information System Basics, v. 1.0 Creative Commons (homepage)
- Gibson, J., & McKenzie, D. (2007). Using global positioning systems in household surveys for better economics and better policy. The World Bank Research Observer, 22(2), 217-241.
- Travelling the Distance: A GPS-Based Study of the Access to Birth Registration Services in Latin America and the Caribbean
- Kudamatsu, Masayuki. “GIS for credible identification strategies in economics research.” CESifo Economic Studies 64, no. 2 (2018): 327-338. Masayuki Kudamatsu’s website
- Kudamatsu, Masayuk (2018) Causal Inference with Spatial Data:ArcGIS 10 for Economics Research This course introduces economists to ArcGIS 10 and Python programming to handle spatial datasets for causal inference in economics research. Content updated 27 July 2018.
- Kogure, Katsuo, and Yoshito Takasaki. “GIS for empirical research design: An illustration with georeferenced point data.” PloS one 14, no. 3 (2019): e0212316.