DOI: 10.5281/zenodo.8030398
title: Replication data for Temperature variability and long-run economic development

description: <strong>Description</strong> This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2023): Temperature variability and long-term economic development, published in the Journal of Environmental Economics and Management. The main data on nightlights stem from the satellites of the Visible Infrared Imaging Radiometer Suite (VIIRS). The data are downloaded as annual composites (vcm) of version V1 (Elvidge et al., 2017). For robustness tests, also annual composites of version V2 are used (Elvidge et al., 2021). Additional data on nightlights are taken from the U.S. Air Force Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of Version 4. Data on the global distribution of crop land and pasture land (Ramankutty et al., 2008) are taken from NASA (Ramankutty et al., 2010). Data on population are from the Gridded Population of the World (GPW) dataset version 4.0 (CIESIN, 2018). Data on elevation are from the Global Land One-kilometer Base Elevation (GLOBE) dataset in version 1 provided by the National Oceanic and Atmospheric Administration (NOAA) (Hastings et al., 1999). Data on terrain ruggedness are from a global dataset with a resolution of 1 km (Shaver et al., 2018). Data on distances from the nearest coast are from NASA. Distance from inland water bodies are from the GloboLakes dataset (Carrea et al., 2015). Finally, data on weather are from ERA5 reanalysis (Hersbach et al. 2018). All datasets are spatially aggregated to the grid cells of the ERA5 reanalysis. <strong>Acknowledgements</strong> The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Image and data processing by Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines. DMSP data collected by US Air Force Weather Agency. <strong>Bibliography</strong> Carrea, L., Embury, O., andMerchant, C.(2015). GloboLakes: High-resolution global limnology data. Center for Environmental Data Analysis. http://catalogue.ceda.ac.uk/uuid/06cef537c5b14a2e871a333b9bc0b482. CIESIN (2018). Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11. Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing, 38(21):5860–5879. Elvidge, C.D, Zhizhin, M., Ghosh T., Hsu FC, Taneja J. Annual time series of global VIIRS nighttime lights derived from monthly averages:2012 to 2019. Remote Sensing 2021, 13(5), p.922, doi:10.3390/rs13050922. Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., Bootz, M., Murakami, H., Maruyama, H., Masaharu, H., Holland, P., Payne, J., Bryant, N. A., et al. (1999). The global land one-kilometer base elevation (GLOBE) digital elevation model, version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47 Ramankutty, N., Evan, A., Monfreda, C., and Foley, J. (2010). Global Agricultural Lands: Pastures, 2000. Global Agricultural Lands Dataset. Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A. (2008). Farming the Planet: 1. Geographic Distribution of Global Agricultural Lands in the Year 2000: Global Agricultural Lands in 2000. Global Biogeochemical Cycles, 22(1). Shaver, A., Carter, D. B., and Shawa, T. W. (2018). Terrain ruggedness and land cover: Improved data for most research designs. Terrain Ruggedness and Land Cover Dataset.