Economics 5740 2019 Summer Session II
Summer Session I ECON 5740 Syllabus Summer 2019
GIS Mapping for Sustainable Development (using Stata, R, QGIS, ArcGis, MatLab, Eviews or SAS) Vox Article on Chetty’s new course note the last 4 lectures of Chetty’s Harvard course ECON 1152 are exercises with both R and Stata code. Here is Raj Chetty’s course as well as Marcel La Fleur’s talk on Big Data and the UN SDGs
GIS for Economists
- 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 GIS 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 GIS Data in Stata
- Spatial Data Analysis in Stata
- Stata in space: Econometric analysis of spatially explicit raster data
Dealing with GIS Data in R
- For a list R spatial packages see the Analysis of Spatial Data library
- Common R packages for GIS are:
- Basic GIS in R tutorials
- Advanced GIS in R tutorials
Economics Papers in this Field
- Chen, X and W D Nordhaus (2011) “Using luminosity data as a proxy for economic statistics”, Proceedings of the National Academy of Sciences.
- 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 Table”, 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
13. Kudamatsu, Masayuki. “GIS for credible identification strategies in economics research.” CESifo Economic Studies 64, no. 2 (2018): 327-338.
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. The course content is updated on 27 July 2018.